Analysis of Essex County Procurement and Contracting: Final Report

Analysis of Essex County Procurement and Contracting: Final Report Submitted to: The County of Essex Disparity Study Commission Hall of Records, Room 325 Newark, NJ 07102 Prepared by: University of Minnesota Disparity Study Research Team Roy Wilkins Center for Human Relations & Social Justice Hubert H. Humphrey Institute of Public Affairs University of Minnesota Dr. Samuel L. Myers, Jr., Chair John J. Heldrich Center for Workforce Development School of Planning & Public Policy Rutgers, the State University of New Jersey Dr. William M. Rodgers, III, Chief Economist Co-Principal Investigator Co-Principal Investigator

TABLE OF CONTENTS INTRODUCTION... 4 METHODOLOGY... 6 DATA COLLECTION... 6 GEOGRAPHIC MARKETPLACE... 10 AVAILABILITY AND UTILIZATION ANALYSIS... 11 PRESENCE OF DISCRIMINATION ANALYSIS... 17 DISPARITY STUDY FINDINGS AND CONCLUSIONS... 20 DISPARITY STUDY RECOMMENDATIONS & POLICY RECOMMENDATIONS... 29 INSTITUTIONAL REDESIGN... 29 ACCOUNTABILITY PROTOCOLS... 30 RACE NEUTRAL AND RACE CONSCIOUS PROGRAMS... 32 APPENDICES... 36 APPENDIX A: GLOSSARY OF KEY TERMS... 37 APPENDIX B: LEGAL ANALYSIS... 43 I. U.S. SUPREME COURT PRECEDENTS... 43 II. CONTROLLING LOCAL PRECEDENTS... 46 III. OVERVIEW OF OTHER KEY LOWER COURT DECISIONS... 49 IV. GUIDANCE ON DISPARITY STUDY METHODOLOGY AND PROGRAM DEVELOPMENT FROM RELEVANT COURT DECISIONS... 57 APPENDIX C: ASSUMPTIONS... 97 APPENDIX D: DESCRIPTION OF CONTRACTS AND DATA SOURCES... 100 I. FILES FROM THE ORIGINAL 19 AGENCIES... 100 II. FILES FROM FOUR ADDITIONAL AGENCIES... 105 III. DBE LIST CREATION... 111 APPENDIX E: SUMMARY OF ALL CONTRACTS ANALYZED... 114 APPENDIX F: ANALYSIS OF MINORITY OR ETHNIC GROUPS... 115 APPENDIX G: GEOGRAPHIC MARKETPLACE... 117 APPENDIX H: AVAILABILITY ANALYSIS... 125 APPENDIX I: UTILIZATION ANALYSIS... 175 APPENDIX J: PASSIVE DISCRIMINATION... 186 I. EMPLOYMENT... 186 II. SIZE AND CAPACITY OF BUSINESS... 191 III. CREDIT MARKETS... 192 APPENDIX K: STATISTICAL DISPARITY ANALYSIS... 224 I. BIDS... 224

II. CONTRACTS... 227 III. MEASURES OF DISCRIMINATION... 228 APPENDIX L: POLICY SIMULATIONS... 258 I. POLICY OPTIONS... 258 II. SIMULATION RESULTS... 259 III. METHODOLOGY... 261 APPENDIX M: ANALYSIS OF PURCHASING PRACTICES AND PROCEDURES... 267 APPENDIX N: ANECDOTAL EVIDENCE ANALYSIS... 273 COMMENTS FROM PUBLIC FORUMS... 273 BUYER INTERVIEWS... 275 FEEDBACK AND RECOMMENDATIONS FROM DISPARITY STUDY COMMISSIONERS... 277 REFERENCES... 303

INTRODUCTION History of Disparity Study In December 2004, the County of Essex, New Jersey announced the appointment of a 21- member Disparity Study Commission whose purpose was to help the County select a qualified consultant to conduct the County s first disparity study. The County issued a Request for Proposals (RFP) to solicit proposals from qualified firms that could analyze the effectiveness of County agencies in securing the services of qualified minority and womenowned firms that provide construction-related, professional, and procurement goods and services. The University of Minnesota, in conjunction with Rutgers, the State University of New Jersey, and the Minority Business Enterprise Legal Defense and Education Fund, was one of six teams that responded to the County s RFP. In February 2005, the University of Minnesota Research Team, led by Dr. Samuel L. Myers, Jr., chair and director of the Roy Wilkins Center for Human Relations & Social Justice at Minnesota s Hubert H. Humphrey Institute of Public Affairs, and Dr. William M. Rodgers, III, chief economist at Rutgers Heldrich Center for Workforce Development, was selected as a finalist and invited to deliver a presentation before the Disparity Study Commission. Following the presentation, the Commission recommended that the Essex County Board of Chosen Freeholders approve a resolution awarding the disparity study contract to the University of Minnesota Research Team. The resolution was approved and a contract was signed in March 2005. As a part of the contract, the Research Team agreed to submit a detailed work plan for completing the project and to prepare regular written and oral progress reports to the Commission. Upon completion of the study, the team agreed to submit a report that outlined the team s key findings and recommendations. This report summarizes the team s findings and recommendations, and provides detailed results of the various quantitative and qualitative analyses performed. Disparity Study Design This study was designed around five basic questions: Are there racial, ethnic, or gender disparities between the availability and utilization of qualified business firms doing business with the County of Essex? What are the possible explanations for any disparities found? Is there discrimination in contract awards or the bidding process in Essex County? Does the County of Essex passively engage in practices that result in disparities? What range of remedies is legally defensible and economically justified in light of the report s findings?

Analysis of Essex County Procurement and Contracting: Final Report 5 To answer these questions, we had to accumulate data on the bidding and procurement practices of Essex County as well as demographic data on Essex County, the state of New Jersey, and the United States. We had to design appropriate statistical analysis protocols that would not only detect if disparities were present in the County s purchasing practices, but could help inform researchers and policy makers about the size and nature of any disparities found and if discrimination was a factor in these disparities. This analysis further required that we learn not only about the bidding and purchasing practices of the County of Essex but how they interacted with the State of New Jersey s purchasing laws and procedures. In addition to the quantitative statistical analyses, we also wanted to understand better the purchasing experience first hand. Therefore, we designed a number of methods to reach out to County buying agents, local vendors, and Disparity Study Commissioners. After collecting all necessary data, conducting and evaluating the statistical analyses, and gaining first hand insight into the purchasing process, we are able to outline our findings about procurement and contracting in Essex County, with specific focus on the minority and female experience. We are also able to provide guidance on how the County of Essex can strengthen its purchasing practices going forward. Outline of this Report This report provides an in-depth view of the design and implementation of this study as well as the findings and recommendations of our team. The three sections immediately following this introduction outline the methodology of we used, our findings and conclusions, and our study and policy recommendations for the County of Essex. The main report is followed by 14 appendices. Each appendix expands on one step taken to complete the study. Among the appendices are discussions of the assumptions made; the legal underpinnings of disparity studies and race neutral and race conscious remedies; sources and how they were gathered, organized, and prepared for analysis; and in-depth discussion of the availability, utilization, and disparity analyses.

METHODOLOGY In-depth statistical and anecdotal analysis was conducted to determine if there are any disparities in the County of Essex procurement process, particularly in the construction-related, professional services, and other procurement services industries. Our analysis involved the following ten steps: 1. Collecting contract and bid data from the 23 agencies included in the study to determine the types of contracts utilized by the County, as well as to understand the distribution of the number and size of contracts. 2. Obtaining local, state, and national demographic data. 3. Defining the geographic marketplace. 4. Performing an availability/utilization analysis. 5. Conducting a presence of discrimination analysis to determine the potential causes of any disparities found. 6. Interviewing representatives from each of the 23 agencies in order to understand each agency s procurement process and commitment to expanding opportunities for all contractors. 7. Holding public hearings to engage the community in the research process and to receive their input into how to improve the County s contract and procurement process. 8. Surveying County vendors to update and verify vendor ownership information. 9. Surveying former, potential, and current County contractors via a web and mail survey to solicit their feedback on their experiences trying to secure County contracts. 10. Engaging in informal and formal feedback sessions with research team members, County administrators and staff, and Commission members. Data Collection Contract and Bid Data In March 2005, we submitted a comprehensive list of questions, concerns, and data requests to the County of Essex Disparity Study Director. The purpose of this request was to learn more about:

Analysis of Essex County Procurement and Contracting: Final Report 7 1. The type of datasets readily available and accessible from the County (i.e. list of contracts awarded, DBE/MBE/WBE 1 lists, vendor list, pre-qualification list etc.) 2. The format of the datasets (electronic or hard copy) 3. The location and/or source of the datasets (centrally located at the County of Essex Hall of Records, at the respective agency, or offsite) 4. The contents and comprehensiveness of the datasets 5. The County s expenditures over the past few years 6. The process for awarding County of Essex contracts 7. The process for tracking awards to minority and women-owned firms The Disparity Study Director distributed the list to the 23 agencies included in the study. It was later determined that many of the requested files were available from the County s Chief Financial Officer (CFO). The CFO provided an electronic contract file of all the records in the County contract database from 19 of the 23 agencies, for the period 2002 to 2004. 2 The file included a number of fields, such as contract number, purchase order number, vendor name, vendor address, date of the contract, a short description of the contract, an account number, and a subcommodity code. Bidder information was also obtained on the contracts in the contract database. The bid information was obtained by reviewing paper files for bids received from 2002 to 2004. The information collected during this review process was input into a database and subsequently merged with the contract data. The bid files included information such as a bid application, state business certification, number of employees, years in business, and other firm background data. The construction contract files provided additional information such as a list of projects completed by the firms within the past five years and proof of surety bonding. The electronic contract file did not include information on four agencies included in the study. Contract and bid information had to be obtained directly from these four agencies: 1) Essex County College, 2) the Essex County Utilities Authority, 3) Essex County Vocational School, 1 Various sections of this report refer to racial/minority related classifications such as Minority Business Enterprise (MBE), Minority/Women Business Enterprises (M/WBE) and Disadvantaged Business Enterprises (DBE). These terms are used to define the different types of minority and women-owned firms. All three of these terms will be used throughout the report. Typically, MBE refers to a business that is at least 51 percent owned by a minority person. An M/WBE is a minority or woman-owned business, which means that the firm is at least 51 percent owned by a minority and/or female. A DBE is defined as a small firm that is owned and controlled by a socially and economically disadvantaged individual. Typically, a firm has to apply to be certified as a DBE. Nevertheless, in some sections of the report, the term DBE is used to refer to a firm that may or may not have gone through a formal DBE certification process but has been designated as an MBE, M/WBE, or WBE by one of several sources we studied. For a more detailed definitions and discussion of this issue, please refer to Appendis A: Glossary of Key Terms and Appendix F: Analyis of Minority or Ethnic Groups. 2 Examining three years of data is the standard for studying current practices by an entity. While studying a longer period may be useful if the goal of the study is to understand how an entity arrived at its current situation, it is not useful to the study of current practices. To understand current disparities, researchers want to focus on the most relevant period of time in order to recommend the most appropriate remedies.

Analysis of Essex County Procurement and Contracting: Final Report 8 and 4) the Essex County Improvement Authority. The contract and bid information from these four additional agencies was merged with the data from the other 19 agencies. There were 11,260 separate contracts from 2002 to 2004 in the electronic contract file for the 19 agencies and an additional 15,693 contracts for the same period from the remaining four agencies. After the contract data were merged, there were 26,953 contracts. Intergovernmental transfers were removed from the master contract file, leaving a total of 25,053 contracts. The following table summarizes the number of contracts and the monetary value of the contracts that were analyzed for the 23 agencies. Table 1. Contracts and Contract Values from 23 Agencies in the Study Number of Transactions Number Individual Contracts of Number of Separate Firms Total Number of Dollars Original 19 Agency Procurement File, 2002-2004 122,563 11,260 3,877 $723,763,341.20 Information available on Yes for 19. Yes for 19. product codes No for other 4 No for other 4 N/A N/A Essex County College 3,283 3,283 831 $25,831,538.46 Utilities Authority 209 209 120 $63,096,816.02 Vocational School 6,967 6,967 1,045 $17,523,663.51 Improvement Authority 8,525 5,234 497 $294,804,436.05 Subtotal 26,953 $1,125,019,795.24 Intergovernmental Transfers 897 $105,069,476.71 Non-Profits 1,003 $82,129,119.48 Subtotal excluding 25,053 $937,821,199.05 intergovernmental transfers Geographic Market Area of 13,116 $891,892,666 11 Counties Information secured on SIC or NAICS codes Construction 832 $377,636,068.63 Professional Services 5,050 $442,734,930.21 Supplies and Equipment 5,097 $39,630,352.77 All Other 2,137 $31,891,314.39 Total Used in Availability - 10,979 $860,001,351.61 Utilization Analysis DBEs 1,215 $85,143,682.33 MBEs 723 $30,790,771.79 Females 170 $16,875,286.26 African Americans 620 $2,266,323.72 Hispanics 144 $29,999,389.58 Asians 64 $13,661,962.82 Note: The term contract in the table carries a slightly different meaning for each source of data. Due to differences in the respective data systems, it is not precisely correct to infer that a contract from any of the four additional agencies represents the same purchasing process as a contract originating from the 19 agencies that are part of the County data system.

Analysis of Essex County Procurement and Contracting: Final Report 9 The table differentiates between transactions and contracts because the original electronic contract file contained over 120,000 separate transactions. The County CFO provided direction as to how the transactions should be aggregated into a smaller number of contracts. A detailed description of this process is provided in Appendix D. National Datasets In addition to the contract and bid data acquired from the 23 agencies, national data were obtained from various United States Census sources. Population data were obtained from the U.S. Census Bureau to get a better understanding of the county and state demographics in comparison to the nation. Essex County has a very large minority population in comparison to the state of New Jersey and to the nation. Forty-one percent of the population in Essex County is black compared to only 13.6 percent of the population in New Jersey and 12.3 percent of the national population. 3 In addition, even though the Hispanic and Asian populations in Essex County are more comparable to the national population than is the black population, the percent of Hispanics and Asians in the state of New Jersey is higher than it is nationwide. Hence, in light of the large concentration of minorities in Essex County and New Jersey, one might assume that the availability of minority firms would be relatively high in this region. This assumption was tested by conducting an availability and utilization analysis of the minority firms in the narrowly defined geographic market. The tables comparing the local, state, and national demographics by population, size, and capacity of business can be found in Appendix J. In addition to using the U.S. Census Bureau data for the purpose of looking at population and firm demographics, census data were also used to perform a labor market analysis to look at employment-population ratios, unemployment, wages and self-employment. From this analysis, it was determined that labor market discrimination probably contributes to the disparities in selfemployment, employment, unemployment, and wage levels of women and minorities as compared to whites. Moreover, women and minorities lower employment-population ratios, higher unemployment rates, and lower wages serve as key barriers to self-employment and the ability to compete for Essex County contracts. A more thorough analysis of the labor market is provided in Appendix J. Census Bureau data were also used to calculate the number of minority and women-owned businesses for the availability analysis. Both the Survey of Women and Minority-Owned Businesses and the Zip Code Business Pattern data were used to perform the availability analysis. In addition, demographic data on firms were obtained from Dun & Bradstreet (D&B), a leading provider of credit and marketing information that provides basic ownership information on millions of firms. 3 U.S. Census 2000

Analysis of Essex County Procurement and Contracting: Final Report 10 Geographic Marketplace The first step in conducting an availability and utilization analysis was to narrowly define the geographic market, which appropriately identified the region from which the County of Essex draws a significant share of its vendors. There is not one uniformly accepted or applied method for determining a geographic marketplace. Two broad methods that have been used are: a) political jurisdictional, based on jurisdictions in which vendors are located; and b) virtual, based on the location of contracts and/or contractors in the client s database. All methods yield different counts or estimates of the numbers of firms within relevant industry codes and accordingly yield alternative measures of availability. We considered six alternative markets: Political Jurisdictional Method (PJM) PJM-1 PJM-2 PJM-3 All zip codes in Essex County Essex County plus four adjacent counties and six Trenton-area counties All zip codes in New York, New Jersey, and Pennsylvania with contract awards that account for 1 percent or more of total dollars awarded within a contract year. Virtual Method (VM) VM-1 VM-2 VM-3 Zip codes representing the intersection of a. Contracts awarded b. Bid lists c. DBE lists Zip codes in Essex County and adjacent counties with at least 1 percent or more of total dollars awarded and at least one DBE or bidder between 2002 and 2004. Zip codes satisfying the following criteria: a. Contracts awarded of $500,000 or more from 2002 to 2004 and at least one bidder or DBE from the zip code b. Contracts awarded of $50,000 to $499,999 and representing at least 1 percent of all contracts awarded within the category c. Contracts of $17,500 to $49,999 and NAICS categories accounting for 5 percent or more of total spending from 2002 to 2004. All six geographic marketplace measures were used to determine where the largest share of dollars was spent and the largest number of contracts awarded. This breakdown is shown and discussed in Appendix G. Although all six geographic markets were considered, PJM-2 was selected as the primary geographic market since it accounts for nearly 90 percent of the contract dollars awarded during the study period.

Analysis of Essex County Procurement and Contracting: Final Report 11 Availability and Utilization Analysis Availability and utilization analysis identifies the number of willing and able firms available to do business with an entity and compares it to the number of those firms hired by the entity. To produce the best estimate of the available number of women and minority-owned firms in PJM-2, four methods were used to calculate the availability rate: 1) SWOBE/SMOBE, 2) Certified DBE List, 3) Composite DBE list, and 4) Dun & Bradstreet. The availability rates were calculated by comparing the number of ready, willing, and able minority and womenowned firms in the defined geographic marketplace to the total number of ready, willing, and able firms in the same geographic marketplace. This analysis was performed separately for women and each included racial/ethnic group, as well as a cumulative rate for women and all racial/ethnic groups combined. In addition to calculating availability rates by race and gender, the rates were also calculated separately for construction-related services, professional services, and procurement services. The formula used to calculate the County of Essex availability rates is based on the Federal Transit Authority s prescribed formula for calculating the availability rate for state and local transit authority DBE Programs. Base Figure DBEs in NAICS ZBPs in NAICS 1 2 3 = x Weight + x Weight + x Weight + L 1 DBEs in NAICS ZBPs in NAICS 2 DBEs in NAICS ZBPs in NAICS This formula 4 was adjusted to account for the type of dataset used. For instance, not all of the datasets use NAICS codes, which is the current industry classification system used by the U.S. Census Bureau. For those datasets that did not provide NAICS codes, the firms were categorized by SIC codes, which is an older classification system. Although most data sources that provide industry data are moving toward using NAICS codes, not all data sources have made this transition. The availability rate is a ratio between the number of women and minority-owned firms and the total number of firms. For all four availability methods, the numerator represents the total number of DBE/MBE/WBEs in a particular industry, whether that industry is defined by a NAICS code or SIC code. The denominator represents the total number of firms in that same industry. The specific steps performed for the four methods used are detailed on the following pages. Method 1: SWOBE/SMOBE 3 100 Step One: Obtained a copy of the latest available Survey of Women (SWOBE) and Minority-Owned Businesses (SMOBE) from the U.S. Census Bureau, 5 which includes the total number of women (WBE) and minority-owned (MBE) firms in the entire country, broken down by state and SIC code. 4 ZBP Zip Code Business Pattern Data 5 1997 is the latest available version of the SMOBE/SWOBE report.

Analysis of Essex County Procurement and Contracting: Final Report 12 Step Two: Compiled a list of two-digit SIC codes which represents the primary industries utilized by the County of Essex. These industries were identified by reviewing the County of Essex contract files for the types of contracts awarded between 2002 and 2004. Step Three: Defined the geographic market as the state of New Jersey. 6 Step Four: Step Five: Step Six: Step Seven: Step Eight: Step Nine: Step Ten: Step Eleven: Determined the number of women-owned firms in each of the primary industries within the geographic market. Calculated the numerator of the SWOBE or SMOBE availability measure, by summing the number of women-owned firms in each of the primary industries (SIC Codes) and the defined geographic market. Determined the total number of firms in each of the primary industries (SIC Codes) within the defined geographic market. Calculated the denominator of the availability measure by summing the total number of firms in each of the relevant industries and the defined geographic market. Calculated the share or the unweighted availability of women-owned businesses, which is the ratio of women-owned firms to the total firms in the designated industry and narrowly tailored geographic market. Constructed weights for each two-digit SIC code. The weights are calculated by computing the percentage of County dollars spent in each of the primary industries. Computed the weighted availability measure by multiplying the weights by the share of women-owned businesses. Calculated the overall weighted availability rate for women-owned firms by summing the weighted availability measures for each SIC code. Step Twelve: Categorized the SIC codes by type of service industry: 1) construction-related services, 2) professional services and 3) other goods and procurement-related services. Step Thirteen: Calculated the weighted availability rates for the three service areas. Step Fourteen: Repeated Steps 3 through 13 for blacks, Hispanics, Asians, and minorities. 7 6 The geographic market for the SMOBE/SWOBE analysis is the State of New Jersey because the SMOBE/SWOBE reports only provide SIC code information at the state level and not the county level. 7 Minority includes blacks, Hispanics, Asians, and other.

Analysis of Essex County Procurement and Contracting: Final Report 13 Step Fifteen: Calculated the availability rate for DBE 8 firms by adding the counts for womenand minority-owned firms, subtracting the estimated overlap in order to avoid double-counting and then repeating Steps 3 through 9. The SMOBE/SWOBE availability measure yielded an overall weighted availability rate of 26.72 percent for DBEs, 10.91 percent for MBEs, 20.24 percent for WBEs, 3.31 percent for blacks, 3.83 percent for Hispanics, and 3.92 percent for Asians. Thus, the different groups represent between 3.31 percent and 26.72 percent of all available firms. The DBE availability rates for the three service areas are: 11.46 percent for construction-related services, 40.51percent for professional services, and 27.64 percent for other goods and procurement-related services. The tables showing how these numbers were calculated are found in Appendix H. Method 2: Certified DBE List Step One: Step Two: Step Three: Step Four: Step Five: Obtained copies of all available certified DBE lists for DBE firms certified to work in the state of New Jersey. Lists were obtained from New Jersey Transit, the New Jersey Department of Transportation, and the Port Authority of New York and New Jersey. Narrowly tailored the geographic market by defining the market as PJM-2, which only includes the zip codes of firms located in Essex County, the four adjacent counties, and six Trenton-area counties. Compiled a list of NAICS codes that represents the primary industries utilized by the County of Essex. These industries were identified by reviewing the County contract files for the period 2002 to 2004 to determine the types of contracts awarded by the County of Essex. The NAICS codes were not included in the original contract file. A subcommodity or product code was provided for most of the contracts from the 19 agencies in the electronic contract file. A subcommodity or product code was not provided in the contract files received from the four additional agencies. 9 Determined the number of DBE firms in each of the primary industries (NAICS codes) within the narrowly tailored geographic market. Calculated the numerator of the certified DBE list availability measure, by summing the number of DBEs in each of the primary industries (NAICS codes) and the narrowly tailored geographic market. 8 DBE does not refer to certified DBEs. DBE refers to the total number of women and minority-owned firms. 9 The three-digit subcommodity codes were converted to three-digit NAICS codes. This list was then submitted to a private company that has a mechanism for converting government commodity codes to NAICS codes. The company produced a list of six-digit NAICS codes. However, it was later learned that many of the contracts had been improperly lumped into a common six-digit NAICS code instead of a more comparable code. This could have been due to the fact the conversion was done through computer matching and the fact that we were only able to provide a three-digit commodity code. Hence, although the three-digit NAICS categorization is accurate, in some instances, the six-digit NAICS categorization may not be accurate.

Analysis of Essex County Procurement and Contracting: Final Report 14 Step Six: Step Seven: Step Eight: Step Nine: Step Ten: Used the latest available version of the Zip Code Business Pattern Data (ZBP) to determine the total number of firms in the narrowly tailored geographic market for each NAICS code. Calculated the denominator of the availability measure by summing the total number of firms in the narrowly tailored geographic market for each NAICS code representing the County s primary industries. Constructed weights for each NAICS code, representing the percentage of County dollars spent in each of the primary industries. Multiplied the weights by the certified DBE share, which is the ratio of certified DBEs to the total firms in the designated industry and narrowly tailored geographic market. Calculated the weighted certified DBE availability rate by summing the weighted measures for each NAICS code. Since the certified DBE lists did not provide a breakdown by race and gender, this availability measure was just calculated for DBEs and by service area. The certified DBE availability measure yielded an overall weighted availability rate of 3.91 percent. The availability rates for the three service areas are 5.98 percent for construction, 2.44 percent for professional services, and 0.60 percent for other goods and procurement-related services. Method 3: Composite DBE Method Step One: Step Two: Step Three: Step Four: Obtained several lists of DBEs, including certified DBEs and DBE/MBE/WBEs that have been identified by various public sources. DBE lists were obtained from the Essex County Office of Purchasing, City of East Orange, and City of Newark. The process for obtaining these lists is further discussed in Appendix D. Verified the racial/ethnic status of the DBEs by submitting several lists to Dun & Bradstreet. All DBE firms that had received contracts from the County between 2002 and 2004, all firms that were awarded a contract over $17,500, and a random sample of firms that were awarded a contract under $17,500 between 2002 and 2004 were sent to D&B for verification of their minority classification. 10 Created a Composite DBE List by merging the certified DBE lists with the names of firms that were identified as a DBE, MBE, or WBE on any of the other lists received from the County or Dun & Bradstreet. Followed Steps 2 through 10 of the Certified DBE Method 10 From this process, some firms that had been designated as DBEs on one list did not show up as a minority firm through Dun & Bradstreet and some firms that showed up as a minority firm through the Dun & Bradstreet process had not been designated as a DBE or MBE on any of the other lists.

Analysis of Essex County Procurement and Contracting: Final Report 15 Since the Composite DBE List did not provide a race and gender breakdown for all of the firms, this availability measure was only calculated for DBEs and by service area. The Composite DBE availability measure yielded an overall weighted availability rate of 7.39 percent. The availability rates for the three service areas are 10.79 percent for construction, 4.96 percent for professional services, and 2.13 percent for other goods and procurement-related services. Method 4: Dun & Bradstreet Step One: Step Two: Step Three: Step Four: Step Five: Step Six: Submitted a list of all County of Essex Contracts to Periscope Holding, Inc. in order to get a six-digit NAICS code classification to correspond to the product classifications 11 provided by the County agencies. This list was used to compute the overall Dun & Bradstreet availability measure. Matched the six-digit NAICS codes to a corresponding SIC Code 12 in order to use the Dun & Bradstreet search engine. Submitted a list of all County of Essex contracts over $17,500 and a random sample of the contracts under $17,500 for the period 2002 to 2004 to Dun & Bradstreet in order to get an alternative measure of the primary industries utilized by the County. Dun & Bradstreet provided a primary industry classification (SIC code) for all of the firms it was able to match to its database. These two lists were used to compute alternative Dun & Bradstreet availability measures. Compiled three lists which represent the primary industries utilized by the County of Essex. List #1: all the SIC codes which corresponded to the six-digit NAICS codes provided by Periscope. List #2: the four-digit SIC codes identified by Dun & Bradstreet as the primary industries utilized by the County of Essex for contracts over $17,500. List #3: the two-digit SIC codes identified by Dun & Bradstreet as the primary industries utilized by the County of Essex for contracts under $17,500. Narrowly tailored the geographic market by defining the market as PJM-2, which only includes the zip codes of firms located in Essex County, the four adjacent counties, and six Trenton-area counties. For each list, used the Dun & Bradstreet Prospectus List search engine to determine the number of women-owned firms in each of the SIC codes within the narrowly tailored geographic market. 11 Not all contracts had a product classification so it was not possible to assign all of the contracts a corresponding NAICS code. Please see the Assumptions section of the report for more information on how the product codes were matched with NAICS codes. 12 Dun & Bradstreet provided firm data by SIC code and not by NAICS code.

Analysis of Essex County Procurement and Contracting: Final Report 16 Step Seven: Step Eight: Step Nine: Calculated the numerator of the Dun & Bradstreet availability measure by summing the number of women-owned firms in each of the relevant industries (SIC Codes) and the narrowly tailored geographic market. Used the Dun & Bradstreet Prospectus List search engine to determine the total number of firms in each of the relevant industries within the narrowly tailored geographic market. Calculated the denominator of the availability measure by summing the total number of firms in each of the relevant industries and the narrowly tailored geographic market. Step Ten: Repeated Steps 6 through 9 for minorities, blacks, Hispanics, and Asians. 13 Step Eleven: Calculated the availability rate for minority, women-owned businesses by using the D&B search engine to determine the number of firms that are both female and minority and repeating Steps 6 through 9. Step Twelve: Determined the number of DBEs 14 by adding the number of WBEs and MBEs and subtracting the number of minority, women-owned firms (MWBEs) in order to avoid double counting of some women and minorities. Step Thirteen: Calculated the DBE availability rate by repeating Steps 6 through 9 for DBEs. Step Fourteen: Constructed weights 15 for each code, representing the percentage of County dollars spent in each of the primary industries. Step Fifteen: Multiplied the weights by the D&B DBE share, which is the ratio of DBEs to the total firms in the designated industry and narrowly tailored geographic market. Step Sixteen: Calculated the weighted D&B availability rate by summing the weighted measures for each code. The Dun & Bradstreet calculations were performed three ways: 1) by six-digit NAICS codes based on the entire contract file, 2) by four-digit SIC codes identified from the contracts over $17,500, and 3) by two-digit SIC codes for the contracts under $17,500. The weighted Dun & Bradstreet available measure for all contracts yielded an overall DBE availability rate of 8.24 percent. The availability rate for the three service areas are 8.05 percent for construction, 13.35 percent for professional services and 9.42 percent for supplies & equipment. The weighted Dun & Bradstreet availability measure for contracts over $17,500 yielded an overall DBE availability rate of 9.93 percent. The availability rates for the three service areas are 8.11 percent for construction, 11.6 percent for professional services, and 13.19 percent for other goods and procurement-related services. The weighted Dun & Bradstreet availability rate for contracts 13 Minority includes blacks, Hispanics, Asians and other. 14 DBE does not refer to certified DBEs. DBE refers to the total number of women- and minority-owned firms. 15 The weights are based on the NAICS codes in the contract file.

Analysis of Essex County Procurement and Contracting: Final Report 17 under $17,500 yielded an overall DBE availability rate of 11.33 percent. The availability rate for the three service areas are 7 percent for construction, 11.95 percent for professional services, and 12.91 percent for supplies & equipment. The utilization rate refers to the percent of dollars spent and contracts awarded, as compared to the total dollars spent and contracts awarded. Utilization rates were calculated by race, gender, and service area, using the data from merged contract file, which provided information on the number and size of contracts awarded from 2002 to 2004. The utilization results are discussed in Appendix I. These results are compared to the availability rates to determine if there is a disparity between the percent of women and minority-owned firms available in the market and the County s actual utilization of these firms. This analysis is discussed later in the report and the statistical comparison is provided in a summary table in Appendix H. Presence of Discrimination Analysis The presence of disparities does not automatically mean that discrimination is present in purchasing. We performed additional analyses to understand the nature of the disparities found. Both passive and agency discrimination 16 analyses were performed to determine if discrimination exists in the County of Essex procurement and contracting process. The passive discrimination analysis involved looking at labor markets, credit markets, and the size and capacity of businesses. Passive Discrimination Labor Market For the labor market analysis, we used micro data from the New Jersey portion of the 2000 U.S. Census. The Essex County sample consisted of 18,401 men and women between 25 and 65 years of age who are not enrolled in school. African Americans, Hispanics, and Asians comprise 35.5, 15.2, and 4.4 percent of the sample respectively. The state-level sample was limited to 215,656 men and women. The employment-population ratio, unemployment rate, and hourly wages of Essex County s minorities and women were compared to Essex County s white men. 17 The labor market 16 See Glossary in Appendix A. 17 We construct dummy variables for self-employment, employment, unemployment, and the logarithm of hourly wages. Self-employment equals 1 if the individual identified him/herself as self-employed in either a unincorporated or incorporated business, professional practice, or farm, and 0 if they are otherwise employed. The employment-population ratio is defined as the ratio of the number of employed to the sum of the number looking for work, the number working, the number with a job but not working, and all those who are out of the labor force. The unemployment rate is the ratio of the number of unemployed to the sum of the number looking for work and the number working. The logarithm of real hourly earnings is constructed from the respondent s pay status. If the respondent reported that they are paid on an hourly basis, we took the logarithm of their hourly wage. If the respondent reported that they are paid on a weekly basis, we took the logarithm of the ratio of their usual weekly earnings and usual hours worked per week.

Analysis of Essex County Procurement and Contracting: Final Report 18 outcomes of Essex County s minorities and women were also compared to the outcomes of minorities and women in the rest of New Jersey. To identify the portion of the observed gaps in employment, unemployment, wages, and self-employment that can be explained by differences in educational attainment and other factors, estimate regression models that control for differences in racial and gender differences in educational attainment, potential experience, marital status, immigration status, English-language ability, veteran status, disability status, and industry characteristics of the groups were estimated. The labor market analysis was performed for construction and professional services. This analysis is discussed in detail in Appendix J. Credit Markets Another component of passive discrimination is related to differences in access to credit markets. Since the most prevalent source of income used to secure start-up capital for a small business is owner equity and home equity represents the largest component of the typical entrepreneur s net worth, we used the Home Mortgage Disclosure Act (HMDA) data to estimate refinancing probabilities and measure potential discrimination in credit markets. We tested the hypothesis that female and minority borrowers are less likely than others to be approved for the refinancing of their home loans. As part of this analysis, regressions were run to identify loans in which bad credit was given as the first, second, or third reason a loan was denied. We then attempted to determine how much of an impact bad credit had on the denials. In these regressions, the following variables were controlled for: amount of loan, type of borrower, type of lender, and the ratio between loan amount and applicant income. The behavior of financial institutions that accept loan applications from Essex County businesses was examined to determine if differences in denial rates emerged among institutions. We calculated disproportionality ratios in denial rates between whites and blacks (blacks/whites) and Hispanics (Hispanic/white) as well as between women and men (women/men). In addition, to understand what portion of the unexplained gap in loan denial could be attributed to discrimination, a residual difference analysis was conducted on all financial institutions. A negative value derived from these regressions indicates the possibility of discrimination and positive value indicates an absence of discrimination. The tables highlighting the results of this analysis can be found in Appendix J. Size and Capacity of Business In addition to looking at the labor and credit markets, the passive discrimination analysis also examined the size and capacity of businesses to better understand the possible effects passive discrimination may have on the County s contracting and procurement process. For this analysis, we looked at population data from the U.S. Census Bureau and from the Survey of Women and Minority-Owned Businesses to get information on the number of firms and sales volume in order to understand how Essex County compares to the state of New Jersey and the nation.

Analysis of Essex County Procurement and Contracting: Final Report 19 Agency Discrimination As a part of the agency discrimination analysis, we looked at Essex County s bidding and contracting processes. Bid files were analyzed to identify how much of the gap in win probabilities could be explained by differences in characteristics of the contract and firms. Probit models in which the dependent variable was whether the firm won or lost the bid were used. The predictor variables were the firm s DBE status, the type of bid, the format of the bid, the firm s bid amount, the firm s number of employees, its age, the bid was or was not advertised in alternative newspapers, the bid was on a construction contract, and month and year dummy variables. To isolate the impact of each variable, we started with a probit of the win dummy variable and DBE status and then incrementally added the characteristics of the contract and firm. The contract files were analyzed to determine if discrimination was a factor in the contracting and procurement process. The contract files were supplemented with data received from Dun & Bradstreet, such as the number of employees, years in business, and sales volume. Different regression models were run in order to determine if the difference in the size and amount of contracts received by DBEs and non-dbes could be explained by a firm s qualifications. These regression models were run for DBEs vs. non-dbes as well as for women and for each racial/ethnic group. We conducted several tests including a difference in mean and difference in median test. The difference in mean test was performed to find out if the difference in the mean value of a contract received by a DBE and a non-dbe was statistically significant. The difference in median test was performed because there was a wide range in the amounts of the contracts received by DBEs and a difference in means would not be able to account for this variance. Anecdotal Evidence In addition to the statistical analysis, anecdotal evidence was collected through interviews, public hearings, and a mail and web survey. Interviews were conducted with a representative from each of the 23 included agencies. Three public hearings were held during the month of June. In an effort to garner the participation of a wide range of Essex County residents, two sessions were held in the evening one in a suburb and the other in Newark. Another session was held during the day. Almost 150 people attended the hearings and over 60 testified. In an effort to obtain additional demographic and ownership information on Essex County vendors, surveys were mailed to over 9,000 firms. Nearly 1,000 surveys were e-mailed to vendors for whom the County had e-mail addresses. In addition to providing demographic information, vendors were asked to provide information about their experiences with securing and/or trying to secure contracts with the County. Nearly 300 businesses responded to the short survey via U.S. mail, and another 200 completed the online web survey.

DISPARITY STUDY FINDINGS AND CONCLUSIONS Two types of questions were explored in the detailed analysis of disparities in procurement and contracting in Essex County. They were: Are there disparities between availability and utilization of willing, able and qualified minority and female-owned business enterprises receiving contracts from the County of Essex? If so, can these disparities be explained by factors such as differences in access to loans, capacity and size of businesses, employment disparities, access to self-employment, or differences in bid success rates? Are they the result of active or passive discrimination on the part of the County? To answer these questions, we assembled an array of quantitative and qualitative evidence, as described in the methodology section of this report. In this section, we offer the main findings and conclusions of our investigation. Full details of each stage of analysis are discussed in the appendices. QUESTION: Are there disparities between availability and utilization? Availability. We examined a wide range of availability measures in the geographic market area. The most conservative measures we adopted use Dun & Bradstreet information. These measures are conservative estimates because they tend to focus on established firms, those that are registered with the State Attorney General s office as corporations, and/or those which do business with the federal government. The measure of availability is the percent of firms weighted by the share of dollars actually awarded in contracts across the various industries and products for which Essex County contracts`. Utilization. Three utilization measures were used. The first is the percent of contract dollars awarded. The second is the percent of contracts awarded. The third is the percent of firms receiving contracts. Ideally, all three measures of utilization would equal the availability measure when there is no underrepresentation by race or gender of firm. The three measures of utilization, however, have different meanings and disparities found between these different measures of utilization and availability raise different substantive concerns. a. Contract Dollars. When there are disparities between contract dollars awarded and firm availability, there are differences in the share of firms in the marketplace and the share of contract dollars received by those firms. Adverse disparities would imply that firms are receiving less than one would expect if their share of contract dollars were equal to their share of firms overall. The consequence of such adverse disparities would be less revenue that could support the firm s investment in buildings, equipment, tools, or supplies that could make these firms more productive. Higher productivity of these

Analysis of Essex County Procurement and Contracting: Final Report 21 firms would help them grow and expand and ultimately add to the tax base of the county and increase employment of county residents. One important reason for placing great weight on disparities between contract dollars awarded and availability in an assessment of public procurement and contracting disparities is that there are long-term adverse tax and employment consequences associated with women and minority-owned firms not receiving their expected share of contract dollars. b. Contracts Awarded. When there are disparities between the underlying availability of firms and the number of contracts awarded, there is an indication that affected firms are not being successful in competing for contracts. One reason for lack of success on contracts that have a low-bid rule is that the affected firms fail to submit bids that are competitive. Another reason may be that affected firms are unaware of bid opportunities or they are not able to take advantage of no-bid contracts such as those that are secured through State purchase orders or those that are not put out for bid at all. Nonetheless, equality between the availability rate and the utilization rate based on contracts received may be deceptive because otherwise underrepresented firms might receive large numbers of smaller contracts but not receive their expected share of larger contracts. c. Firms Receiving Contracts. More difficult to measure but closer to the basic concept of disparity between availability and utilization is the difference between the share of affected firms receiving contracts and the share of affected firms in the marketplace. The reason it is more difficult to measure utilization of firms receiving contracts is that in many instances firms receive multiple contracts over a span of years. Most record keeping is based on contracts awarded, information on the number of contracts, and the amount of each contract, and not based on the number of separate vendors receiving these awards. Some vendors, in particular those that are suppliers or those that receive purchase orders, have multiple small contracts. High measures of the utilization of these firms relative to their availability may simply signal that there are many willing and qualified firms doing business with the County. But, high numbers of firms receiving contracts but low measures of contract dollars going to those firms would signal that these firms are largely receiving small contracts. Table 2 provides a summary of the main results comparing availability to utilization along these three dimensions. We discuss each group separately. Non-DBEs do not experience any disadvantage. They represent 92.61 percent of available firms and about 91.21 percent of contract dollars awarded. The ratio of utilization to availability is 98 percent, meaning that there is no substantive difference between non-dbes share of contract dollars received and their share of firms in the marketplace. The ratio of the utilization to the availability rate is a measure of overall disparity. Using the rule of thumb that one would expect the utilization rate to be at least four-fifths of the availability rate, we conclude that there is no adverse impact affecting non-dbes. Similarly, non-dbes do not face a disadvantage using the other two measures of utilization.

Analysis of Essex County Procurement and Contracting: Final Report 22 DBEs do not face a disadvantage in contract dollars. Table 1 shows that DBEs share of firms in the geographic market area is 7.39 percent and its share of contract dollars is 8.79 percent. Thus, there is no disparity between DBE availability and DBE share of contract dollars awarded. Similarly, there is no disparity between DBE share of contracts and DBE share of available firms. However, the DBE share of firms receiving contracts is only 3.29 percent. The ratio of utilization to availability on this measure is 45 percent, meaning that there is a disparity between the share of DBEs receiving contracts and their share of firms in the marketplace. MBEs do not experience a disparity between availability and utilization when utilization is measured by contracts. There is a very slight disparity between contract dollars and availability. MBE availability is 4.35 percent and their contract dollar utilization is 3.42 percent. There is a larger gap between the availability of MBEs and their representation among firms receiving contracts. MBEs only represent 1.76 percent of contracts awarded whereas they represent 4.24 percent of firms in the marketplace. Wide and significant disparities exist between the availability of WBEs and their utilization as measured on all three indicators of utilization. They represent 6.61 percent of available firms but only 0.98 percent of contracts, 1.85 percent of contract dollars, and 0.75 percent of firms. African American firms experience a sizeable gap or difference between their share of contract dollars and their share of available firms. Using Dun & Bradstreet, we estimate an extremely low availability rate of only 0.46 percent. We find an even lower share of contract dollars of 0.23 percent. The result is a ratio of utilization to availability of only 49 percent, meaning that blacks share of contract dollars is only about half of their share of firms in the marketplace. On the other two measures, there does not appear to be an adverse disadvantage facing African American firms. Hispanic firms have utilization rates based on contract dollars that are 2.4 times larger than their availability rates. However, there is underutilization when measured by Hispanics share of contracts or their share of firms. Asian firms have utilization rates based on contract dollars that are 1.4 times their availability rates. They, like Hispanics, are underutilized when measured by their share of contracts or their share of firms. Table 3 takes this analysis a step further and explores availability and utilization by construction contracts and by professional service contracts. We find no overall disadvantage among DBE, Hispanic, or Asian firms within the construction contracts on any of the measures of underutilization. Female firms are underrepresented on all three measures of utilization. Black firms are underrepresented relative to their share of contract dollars and their share of contracts in construction.

Analysis of Essex County Procurement and Contracting: Final Report 23 Table 2. Final Results and Interpretations: Availability vs Utilization All Contracts Contracts Utilization Availability Utilization/Availability Adverse Disparity Found? Contract Dun & DBE Contract Contract Dollars Firms Bradstreet Method Contracts Dollars Firms Contracts Dollars Firms Non-DBE 92.81% 91.21% 96.71% 91.76% 92.61% 100% 98% 104% No No No DBE 7.19% 8.79% 3.29% 8.24% 7.39% 97% 119% 45% No No Yes MBE 4.35% 3.42% 1.76% 4.24% 100% 79% 40% Yes Yes Yes WBE 0.98% 1.85% 0.75% 6.61% 12% 23% 9% Yes Yes Yes Black 3.65% 0.23% 0.51% 0.46% 777% 49% 109% No Yes No Hispanic 0.89% 2.72% 0.70% 1.28% 78% 239% 61% Yes No Yes Asian 0.45% 1.53% 0.22% 0.86% 42% 142% 20% Yes No Yes Table 3 also shows that all DBEs, MBEs, WBEs, and racial minority groups face a disadvantage based on all three utilization measures within professional services. ANSWER: There are Disparities between Availability and Utilization for Blacks and Females. The short answer to the question of whether there are racial or ethnic disparities between availability and utilization is yes, but not for all groups and all industries. There are sizeable overall gaps between availability of female and black firms and their shares of contract dollars awarded. This is true overall, and particularly in the construction industry, where the ratio of utilization to availability is 14 percent for blacks and 74 percent for women. Further, blacks and women also face gaps in professional services, where Hispanics and Asians also experience sizeable disparities between availability and utilization. One important caveat about the data on contract dollars is that these dollars reflect prime contracts. There is no systematic information available on subcontracts to DBEs except for a subset of largely construction contracts found among the 19 agencies and departments that are part of the County s central contract files. However, for the small share of contract records that had subcontract information, we find that there were zero subcontracts to African American firms. Thus, we have not understated the share of contract dollars received by blacks. However, there were nine subcontracts to femaleowned firms totaling $677,900 or about 4 percent of the total of prime contracts to WBEs. Including these subcontracts among the total contracts awarded to WBE prime contractors increases their utilization rate by half of a percentage point -- and not enough to erase the sizeable gap between availability and utilization.

Analysis of Essex County Procurement and Contracting: Final Report 24 Table 3. Final Results and Interpretations: Availability vs Utilization by Industry Construction Contracts Utilization Contract Dollars Firms Availability Dunn- Bradstreet Method Utilization/Availability Adverse Disparity Found? DBE Contract Contract Method Contracts Dollars Firms Contracts Dollars Non-DBE 92.46% 86.55% 92.24% 91.95% 89.21% 101% 94% 100% No No No DBE 7.54% 13.45% 7.76% 8.05% 10.79% 94% 167% 96% No No No MBE 3.44% 6.74% 3.35% 3.87% 89% 174% 87% No No No WBE 1.94% 3.59% 1.89% 4.82% 40% 74% 39% Yes Yes Yes Black 0.22% 0.06% 0.42% 0.41% 55% 14% 102% Yes Yes No Hispanic 3.14% 7.08% 3.35% 1.43% 219% 495% 235% No No No Asian 1.64% 3.20% 1.05% 0.51% 322% 628% 206% No No No Firms Professional Services Non-DBE 95.43% 97.60% 95.53% 86.65% 95.04% 110% 113% 110% No No No DBE 4.57% 2.40% 4.47% 13.35% 4.96% 34% 18% 33% Yes Yes Yes MBE 0.51% 0.74% 0.51% 6.37% 8% 12% 8% Yes Yes Yes WBE 1.01% 0.36% 1.17% 7.80% 13% 5% 15% Yes Yes Yes Black 0.48% 0.57% 0.29% 0.74% 64% 77% 40% Yes Yes Yes Hispanic 0.67% 0.47% 0.81% 1.43% 47% 33% 56% Yes Yes Yes Asian 0.50% 0.32% 0.29% 1.66% 30% 19% 18% Yes Yes Yes Thus, we can state with confidence that at least for blacks and for women disparities exist between availability and utilization (as measured by the share of contract dollars awarded) overall across all industries, and in particular within construction and within professional services. 18 We can also state with confidence that there is a disparity between availability and utilization of Hispanic and Asian firms (as well as DBEs and MBEs as a group) within professional services. The tables in Appendix H provide additional information on these disparities as well as statistical tests of significance between the availability and utilization rates. 18 We exclude discussion of supplies and equipment and other purchases because this category included cases where we were unable to match product codes and because these purchases often are made through State purchase orders and not through a competitive bid process managed by Essex County.

Analysis of Essex County Procurement and Contracting: Final Report 25 QUESTION: Why are there disparities between availability and utilization? We examined five possible explanations for the disparity between availability and utilization: Discrimination in the award of contracts through the bid process Differential access to information about bid opportunities Differential access to loans and/or bonding Discriminatory barriers to self-employment Size and capacity of firms Table 4. Summary of Qualitative and Quantitative Results Quantitative Evidence DBEs African Americans Hispanics Asians Females Bid Award Rates No NA No No Yes Self-Employment Rates Yes Yes No No Yes Size and Capacity of Firms NA Yes No Yes Yes Qualitative Evidence Access to Bid Opportunities Yes NA NA NA NA Credit Availability and Bonding No NA NA NA NA Size and Capacity of Firms Yes NA NA NA NA Table 4 summarizes the results of qualitative and quantitative evidence presented in Appendices J, K, and N showing that there is significant evidence that females face disadvantages in bid award rates, self-employment rates, and in the size and capacity of firms. The disparities in bid success rates, however, are partly explained by the higher bid prices that female contracts displayed and partly by the differences in success rates within the construction industry. Thus, the evidence on discrimination against females in bid awards is not strong. Since there were so few black firms that bid on Essex County contracts and race was not uniformly provided in our web survey, the evidence is inconclusive about whether blacks face disadvantages in bid award rates or access to bids any more than do other DBEs. Blacks do

Analysis of Essex County Procurement and Contracting: Final Report 26 experience lower self-employment rates, however. Significantly they have lower ratios of sales to firms, meaning that they have lower capacity and their firms are smaller. About 89 percent of all of the black firms estimated from the 1997 Survey of Minority-Owned Firms did not have paid employees. Our web survey revealed little evidence that any of the respondents faced significant barriers to obtaining bonding and few sought to apply for loans to start or maintain their businesses. Although there were statistically significant differences in loan denial rates for refinancing of homes a major source of capital for the start-up of firms we did not find that the lenders accounting for the largest discriminatory gaps were also lenders with which the County of Essex does the bulk of its business. Thus, we rule out the possibility of passive discrimination on the part of the County in regard to credit availability. A more perplexing aspect of the disparity between the availability and the utilization arises from significant differences in contract sizes. Although DBEs as a group have contract sizes that are larger than average, among small contracts, DBEs -- and in particular black firms -- have lower than average contracts for contracts below $17,500. The average contract under $17,500 for non-dbes was $1,762.80. The average contract under $17,500 awarded to blacks was only $970. Indeed, the average contract award to blacks overall was $3,665 while the average awarded to non-dbes was $37,632. This discrepancy is due in part to the fact that there were no black contracts awarded for any amount over $250,000. Since 82 percent of all contract dollars 35 awarded are accounted for by contracts valued over $250,000, blacks were relegated to the lower 18 percent of contract dollars awarded. Of course, there could be legitimate explanations for these racial disparities in the amount of contracts awarded. Using regression models, we tested whether the racial gap in the amount of contracts awarded could be explained by factors such as the size and tenure of the firm, the industry of the firm, the location of the firm, and the year of the award. A small part of the gap can be explained by these factors. The remaining portion of the gap the unexplained portion is interpreted as being racially unequal treatment of black and non-dbe firms. We performed a similar regression analysis for DBEs as a whole among contracts under $17,500 and found that some of the gap could be explained by tenure of firms, size of firms, location of firms, industry, and year of award. But, the bulk of the gap remains unexplained suggesting discrimination against DBEs in very small contract awards. Table 5 reveals that there are few adverse differences in contract amounts between females and non-dbes, Hispanics and non-dbes, and Asians and non-dbes and that these differences are not statistically significant once one controls for relevant factors or the differences favor these non-black firms. In Appendix K, we report results based on construction contracts alone, professional service contracts alone, and supplies and purchasing alone. These results do not confirm the results found when looking at the aggregate of contracts. The regression results suggest that the race/gender effects are not statically significant when one partitions the sample among these different types of contracts. Thus, the evidence of discrimination is not uniform and is weakened when one looks at smaller and smaller subsets of the data.

Analysis of Essex County Procurement and Contracting: Final Report 27 ANSWER: Disparities are partly explained by discriminatory access to bid opportunities and contract awards. There are large and statistically significant gaps between contract award amounts between blacks and non-dbes. These gaps cannot be explained by relevant factors such as size of firm, location of firm, tenure of firm, industry, or year of award. In the case of females, bid success rates are lower, in part as a result of lower bid success rates in construction and the higher bid prices female contractors offer. Thus, the bulk of the disparity between the availability and the utilization of female firms can be explained by the difficulty that female firms have in competing in the construction industry and in winning awards that are competitively bid, due to the higher prices that they bid. Table 5. All Contracts Final Results and Interpretations: Discrimination in Contract Amounts Mean Non-DBE $37,632.00 Over $17,500 $361,770.00 Under $17,500 $1,762.80 Percentage Difference Significance Level Adverse Disparity Found? Percentage Disparity Due to Race/Gender/E thnicity Significance Level Adverse Discrimination Found? DBE $70,077.00 86.22% 0.0196 No -0.15% 0.9815 No Over $17,500 $539,204.00 49.05% 0.0886 No 41.05% 0.0001 No Under $17,500 $1,478.40-16.13% 0.0006 Yes -11.15% 0.0336 Yes WBE $99,266.00 163.78% 0.0700 No 81.28% <.0001 No Over $17,500 $460,581.00 27.31% 0.5122 No 43.19% 0.0444 No Under $17,500 $2,196.90 24.63% 0.1040 No 46.84% 0.0012 No Black $3,655.40-90.29% < 0.0001 Yes -64.17% <.0001 Yes Over $17,500 $60,429.00-83.30% < 0,0001 Yes -60.07% 0.0128 Yes Under $17,500 $970.12-44.97% < 0,0001 Yes -41.32% <.0001 Yes Hispanic $208,329.00 453.60% 0.0004 No 61.53% 0.0006 No Over $17,500 $782,869.00 116.40% 0.0191 No 74.79% 0.0005 No Under $17,500 $2,362.00 33.99% 0.0460 No 23.27% 0.151 No Asian $213,468.00 467.25% 0.0150 No 152.48% <.0001 No Over $17,500 $502,011.00 38.77% 0.2966 No 60.59% 0.0152 No Under $17,500 $2,909.60 65.06% 0.0238 No 51.52% 0.0596 No By way of contrast, the disparity between the availability and utilization of black firms seems to be largely a reflection of the fact that they are actively competing for very small contracts and are virtually excluded from bidding on large lucrative contracts. Moreover, for the contract

Analysis of Essex County Procurement and Contracting: Final Report 28 awards they receive, there is little to explain the sizeable disparities. If we have controlled for all relevant factors contributing to the determination of the contract award, we can interpret the unexplained gap as the result of racial discrimination. The evidence of discrimination, however, is not robust across alternative specifications of the regression model and when one looks at small subsets of the data. As a result, the compelling evidence from the aggregate data should be viewed with caution.

DISPARITY STUDY RECOMMENDATIONS & POLICY RECOMMENDATIONS We have found statistical evidence of disparities between the availability and utilization of female and African American firms in procurement and contracting with the County of Essex. In the case of African Americans, much of the gap can be explained in part by the extremely low availability rates, but for those firms that actually have contracts with the County of Essex, there are large or sizeable discriminatory gaps in contract amounts. Few of the 26,000 contracts and transactions we examined were the result of a competitive bid process administered by the County. It is impossible to tell from the data provided if the reason for this small share of competitively bid contracts is due to the widespread use of State purchasing orders or due to the use of alternative contracting vehicles within the County. In particular, the vast majority of the contracts and transactions that occurred and are documented in our databases are contracts and transactions for amounts that are less than $17,500. Blacks and DBEs generally have lower than average awards even among these small awards and we have discovered evidence of discrimination in these contract awards. However, the evidence of discrimination against blacks and females is not robust across various specifications and subsamples of the data. As a result, a remedy to redress the racial gaps must be very carefully constructed. This quantitative evidence coupled with widespread anecdotal evidence about the failure of the County to share contracting opportunities among small and disadvantaged businesses in the local market broadly and widely, suggests three complementary avenues that might be pursued in redressing the problems of underrepresentation and underutilization. These avenues are: Institutional redesign to improve communication and trust with local businesses. Accountability protocols that track, monitor, and keep records on any remedies designed and implemented to redress discriminatory disparities to ensure that the remedies actually achieve the desired goals effectively and efficiently. Race-neutral and race-conscious programs that offer the promise of improving the share of dollars going to underrepresented groups and fostering their growth and development while also meeting legal standards of strict scrutiny and narrow tailoring. Institutional Redesign 1. Open and Transparent Process and Practices All efforts and results should be documented and made available for public review. Small business M/WBE guidelines should be widely adopted and made public.

Analysis of Essex County Procurement and Contracting: Final Report 30 2. Outreach Training should be provided throughout the process -- internally for County buying representatives and administrators and externally for prospective prime vendors and sub-contractors. Workshops, seminars, and forums should be presented regularly. Establish clear paths of communication -- both internally among purchasing representatives and externally for vendors and sub-contractors. 3. Efficiency: Avoid Economic Distortions Ensure that proposed solutions reflect cost-effective practices over the long term, keeping in mind the good of the public at large. 4. Accountability and Consistency Ensure that accountability is present -- both internally and externally. Establish appropriate levels of responsibility and authority -- internally among purchasing representatives and externally among primes and sub-contractors. 5. Interdependency Acknowledge the linked consequences of economic, social, and environmental responses to expanding access to minority and women-owned businesses. 6 Equity: Equal Opportunities for M/WBEs to Participate in the Economy 7. Focus on the Achievable; Build in Quick, Early Wins 8. Encouragement of Private Sector Investment Banks and Major Corporate Vendors as Mentors and Joint Venture Partners to Smaller Firms 9. Successful Leadership Requires Focus, Drive, and Simplicity Do not take a shotgun approach. Rather, invest adequate and appropriate energy on a few, solid initiatives; build a track record, then do add-ons. 10. Build an Evaluation and Monitoring Process into the Program from the Beginning Accountability Protocols Problem 1. Difficulty determining which contracts are bid on State contracts. 2. Difficulty matching Essex County product codes to other systems. Remedy 1. Develop specific coding for State contracts to better understand the State s impact on Essex County purchasing. 2. Convert all County product codes to NAICS codes.

Analysis of Essex County Procurement and Contracting: Final Report 31 Problem 3. The Essex County College, Improvement Authority, Utilities Authority, and Vocational School do not have product codes. 4. Lack of clarity as to under which part of the contract law (public bid, professional services, extraordinary unspecified services, contracts under $17,500 handled directly by the Office of Purchasing) specific contracts were awarded. 5. Lack of clarify as to what constitutes a single contract, that is, the series of purchases authorized by a single decision to hand out an award. Currently, the County data system is organized by purchase order, and there is no formal way to combine groups of purchase orders into single contracts. 6. Data from the County College, Improvement Authority, Utilities Authority, and Vocational School are not compatible with the balance of County data. 7. No DBE information in contracting system. 8. Bid data are incomplete, disorganized, and not in an electronic format. 9. Lack of clarity as to which departments within the County are responsible for individual contracts. Remedy 3. Use NAICS codes for all County contracts. 4. Add variable to data system that tracks the method of contracting. 5. Add contract variable to data system. 6. Update these agencies data systems so that they collect the same information and are compatible with other County agencies going forward. 7. Collect and add information when contracts are awarded. 8. Convert bid files to electronic format and make them compatible with contracting system, so that the same information is available about unsuccessful bids as is available for awarded contracts. 9. Establish clear map between current department/division variable and unit of government awarding the contract.

Analysis of Essex County Procurement and Contracting: Final Report 32 Race Neutral and Race Conscious Programs The statistical evidence is sufficiently compelling to meet a legal justification for a race conscious remedy to resolve the disparity between female and black utilization (based on contract dollars) and availability. There is qualified support for a race-conscious program in professional services. However, the econometric evidence often sends mixed signals both about the causes of the disparities and also about the possible factors that might eliminate or reduce the disparities. For example, among females we find substantial gaps between availability and utilization, and we find that their bid success rates are lower than non-dbes. But, we also discover that much of the gender differential in bid success can be explained by differences in bid price and that the females who finally do obtain contracts receive contracts of a size that are higher on average than the contracts awarded to non-dbes. This asymmetry of results is often viewed as the outcome of selection bias, wherein discrimination in early stages of a process causes the remaining pool of qualified female firms to be better qualified than other firms not subjected to discrimination. Unfortunately, we are unable to prove this hypothesis and, thus, must assume that the disparate outcomes could be the result of non-discriminatory processes. From our descriptive evidence we see the following problems that must be addressed in any attempt to remedy the disparity between availability and utilization among underrepresented firms. Extremely few African American firms are certified to perform the type of work for which the County contracts, especially in construction-related activities. Although 12.8 percent of all firms in Essex County are African American, only handful of them that were found listed in Dun & Bradstreet registries under the NAICS codes that correspond to the work contracted by Essex County. This means that a concerted effort is needed to qualify, certify, and register African American firms and to increase the availability of firms qualified to perform related, but not identical, services. An e-commerce strategy focusing on registration and certification might disproportionately benefit African American firms. Many firms are unaware of contracting opportunities, even among firms that are qualified to perform services. Again, an e-commerce solution could disproportionately benefit black firms. There is a heavy representation of African American and female firms among small businesses in Essex County. Upwards of 89 percent of black firms in Essex County have no paid employees. A small business development program that provides local incentives to businesses to contract with Essex County might improve African American prospects for obtaining contracts with Essex County. Similarly, African American firms are heavily concentrated in Essex County, relative to other parts of the state. Due to residential segregation, one of the side effects of this locational concentration is that policies that require County agencies to purchase from approved state vendors have a disproportionate adverse impact on African American

Analysis of Essex County Procurement and Contracting: Final Report 33 firms. Opening up opportunities for Essex County firms to bid on all contracts and expanding the share of contracts that are bid via preferences to Essex County firms might improve the representation of African American contracts and their share of contract dollars. The underlying problem of differences in the share of contract dollars awarded to black firms and the virtual absence of black firms among subcontractors on the small set of contracts on which we had information about primes and subcontractors presents its own dilemmas. This problem is often addressed by minority or disadvantaged business subcontracting set-asides. But, since female, Asian, and Hispanic contract sizes are often larger than those of non-dbes, the effect of a race-conscious goal of say, 15 percent for blacks and females (as the designated DBE group covered by the goal), could be to reduce the share of dollars going to other DBEs. It is clear, then, that both race-conscious and race-neutral approaches can attack the underlying problems that African American firms face while also being attentive to the disparities found among other groups in the marketplace. Since Essex County is charting new territory by undertaking this important initiative, it should weigh the pros and cons of these five options that seem to be narrowly tailored to redress the underlying disparities: 1. E-Commerce Solutions 2. Local Small Business Development Program 3. Essex County Preference Program 4. Aspirational Goal Program 5. Targeted Business Development Program We discuss each of these programs below. We also ran policy simulations to estimate the effect each of these program would have on the participation of M/WBEs in Essex County contracting. Details of these simulations can be found in Appendix L. E-commerce solutions focus on expanding registration, notification, and certification of all firms. This is a low cost solution with few if any legal obstacles. It is a race-neutral program that has enjoyed widespread and enthusiastic support in our public hearings. In the Appendix L, we detail how we simulate the expected gains from the adoption of this type of program. We estimate that there would be an almost $3.5 million increase in contract dollars awarded to DBEs with the bulk of it going to African American firms. We estimate that African American firms would receive 73 more contracts from Essex County agencies and departments as a result of the expanded registration, certification, and notification of bid opportunities. We rate this improvement as having a modest or medium level of effectiveness in addressing the underlying problem of few dollars being awarded to African American contracts. Unfortunately, this strategy does not necessarily increase the number of black vendors or contractors substantially. When we make some very strong assumptions about the ability to actually create new businesses and transform existing businesses so that they are able to compete for County contracts, the local small business development initiative produces astonishing results. It increases dollars awarded to DBEs by nearly half a billion dollars. The African American share is modest at $14 million,

Analysis of Essex County Procurement and Contracting: Final Report 34 but it comes about by producing a staggering increase in small business firms. This is clearly a policy simulation that is on the outer limits of what can be done locally, but it gives a sense of both what the costs are and what the benefits are. The costs are substantial. It would take a massive effort to mobilize the expected 5,900 DBE contracts, of which half would go to blacks. This effort would need to confront the New Jersey statutes that prohibit discrimination against firms located outside of Essex County. Since an implicit preference is being provided to local SBEs, there is always a risk of lawsuits from firms outside of the benefit area. Nonetheless, the strategy holds promise to increase substantially contract dollars to DBEs, with African Americans receiving a share of the increase approximately equal to their share of the marketplace. Alternatively referred to as Local Small Business Enterprise (LSBE) Programs, they provide bidding preferences to small business firms based upon their principal business location. Such preferences typically include set asides of small contracts for LSBE firms, LSBE subcontracting goals, or evaluation preferences for LSBE professional services firms. As the race and gender of business owners are not considered as factors in the application of these preferences, these policies avoid the kind of "suspect" classifications that would subject M/WBE preferences to heightened judicial scrutiny under Fourteenth Amendment Equal Protection analysis. Rather, the legal standard of review for determining the constitutionality of such race and gender-neutral LSBE provisions is a "rational basis" test. This is a much lower standard than strict scrutiny, and can easily be satisfied with such justifications as broadened local economic development, local job creation, and enhanced competition through promotion of the participation of small businesses in government contracts. Beyond constitutional considerations, however, some states and localities have code or charter provisions that expressly prohibit certain types of preferences in public contracts. To the extent the State of New Jersey's Constitution or legislative code prohibit limitations on bidding on the basis of a bidder's location or size, then a recommendation for such a policy must be accompanied by a request for legislation at the State level to exempt Essex County from such prohibitions, or to expressly authorize the County's use of such small local business enterprise policies in lieu of race-conscious remedies. An Essex County Preference program would face the same (if not higher) legal challenges as the Local Small Business Development program in that it provides a preference to contractors who are Essex County-based. Since it is not technically a set-aside but a preference program Essex County first it is more difficult to implement. The downside of this simulation is that it only produces about a $960,000 increase in dollars going to African American firms, even though it produces more than 860 new contracts awarded to DBEs. The upshot is that there would be a lot of small contracts because the foundation of such a policy is to give existing Essex County-based firms contracts and not necessarily to increase the number of Essex County-based contractors. Another option, of course, would be to institute an aspirational goal program of 15 percent to African Americans and female business owners. The assumptions in the simulation are that the size of total contracting remains unchanged. If the pool of dollars is not growing, then one

Analysis of Essex County Procurement and Contracting: Final Report 35 impact of the goals would be to reduce the share of whichever group is currently receiving a larger share than their representation among firms implies. As we have shown previously, as a group, DBEs receive contracts of an average size that are larger than those received by non-dbes. Thus, goals for females and blacks which could be justified by their underrepresentation and the disparity between their availability and utilization would have the effect of increasing their prime contract dollars and their subcontract dollars by $16 million. 19 This large increase makes the program extremely effective in redressing the underlying problem of huge disparities in contract amounts and availability. But because it is simple, it would not be difficult to implement or monitor and thus the cost of implementation would be modest. The main drawback is that it requires strict scrutiny and must be narrowly tailored in order to meet existing legal standards. A targeted business development program is similar to a local small business program except that rather than being open to all SBEs, it is aimed at businesses owned by specific population groups. The goal is to increase the capacity of the targeted firms so that they can better compete for public contracts. Firms participating in a program such as this receive support and services from organizations focused on helping either all small businesses or certain types of small businesses. We estimate that a targeted business program would increase the number of black firms by one, the number of contracts secured by black-owned firms 19, and the number of contract dollars awarded to black firms would be over $1.2 million. Table 6 summarizes these findings from the policy simulations. There is no clear winner in this exercise and in some respects the burden now shifts to the policy makers and elected officials to weigh the alternatives that are before them. These five options, in our view, can be viewed as complementary and overlapping. Each offers a different contribution to the resolution of the problem of disparities we have found. Each has a different risk and a different cost. Table 6: Summary Table: Additional Contracts, Contracts dollars and Number of firms with contracts 1 Policy Options DBE Black # of # of # of Contracts Firms Contract $ # of Contracts Firms Contract $ E-Commerce (1) 89 n.a. 3,485,892 73 n.a. 2,976,427 Local small business development 5,300 1,142 465,334,616 2,941 195 13,663,814 Essex County preference program 860 232 79,000,285 34 13 958,313 Aspirational goal program (2) 457 76 32,034,610 629 60 15,881,703 Targeted business development (3) n.a. n.a. n.a. 19 1 1,292,019 (1) These results and the methods used are discussed in depth in Appendix L. 19 It is important to note that this $16 million includes the shared increase of blacks and females.

APPENDICES

APPENDIX A: GLOSSARY OF KEY TERMS Agency Discrimination Bid Bid Book Bonding Buyer Certification Situations where equally qualified DBEs and non-dbes are treated differently. An offer or proposal for a specific amount of money (price) that is submitted by a vendor in response to a Request for Quotations or an Invitation for Bids. Also refers to notices or announcements from public agencies seeking vendors who will offer a price for products and/or services. Vendors compete for bids with price or service or both. Small ledger-style notebooks in which the date of each bid opening, the names of the bidders, the amount bid, and a short phrase describing the service for which the bid would be performed was recorded. A bond is a contract between at least three parties: (i) the principal, (ii) the obligee, and (iii) the surety. Through this agreement, the surety agrees to make the obligee whole (usually by payment of money) if the principal defaults in its performance of its promise to the obligee. Bonding is the act of a principal acquiring a bond for the purpose of guaranteeing performance (surety bonds) or price (bid bonds). A surety bond is often required of contractors bidding on construction work to ensure that the successful bidder will accept the job and will also provide a performance bond. Buyer is the term used to describe any County employee that is responsible for implementing the purchasing activity for his/her department or agency. Buyers procurement roles may range in authority and responsibility from clerical to decisionmaking. The process undertaken by a public or private agency of verifying the authenticity of vendor claims of membership within a particular racial, ethnic, or gender group (e.g., black, Hispanic, Asian, minority, woman), or having status as member of a class receiving specified affirmative action, e.g., a Disadvantaged Business Enterprise (DBE) or Small Business Enterprise (SBE). The process typically involves examination of business formation and tax return documents, birth/marriage certificates, passports, stock certificates, etc.

Analysis of Essex County Procurement and Contracting: Final Report 38 Change Order Construction Contracts Contract Contract Amount Disadvantaged Business Enterprise (DBE) Dun & Bradstreet (D&B) E-Commerce Extraordinary Unspecifiable Services (EUS) Firms with Paid Employees A revision in tasks specified in the original contract that typically results in an increase in the contract price; usually occurs after the delivery of contract services are well underway. Any legal commitment made to render activity in the construction trades. A written agreement between two or more parties that is enforceable by law. Dollar value attached to a legally enforceable agreement between two or more parties, specifying payment for delivery of goods or services. A business whose owner(s) is female and/or black, Hispanic, Asian, Native American, or Pacific Islander, and who can show social and economic disadvantage as proven by not owning more than $750,000 in business assets, and by not earning more than a designated annual net profit, which depends upon the industry in which they operate. DBE status can be assigned to white males on a case-by-case basis, depending on the business owner s ability to prove disadvantage as defined above. Dun & Bradstreet is the world s leading provider of business-tobusiness credit and marketing information and receivable management services. The primary business of D&B is to formulate credit ratings for millions of companies by finding out how promptly they pay their bills. Pursuant to this objective, D&B collects basic ownership data on millions of firms. Presently, Dun & Bradstreet maintains records for some 21.5 million North American companies. Doing business online, typically via the web. E-commerce involves business-to-business activities, including the process by which goods and services can be purchased online, or by which vendors can be solicited and/or catalogued online. E-commerce may also refer to electronic data interchange (EDI), in which one company's computer queries and transmits purchase orders to another company's computer. Services that are specialized and qualitative in nature and require expertise, extensive training, and proven reputation in the field of endeavor. Firms with employees who receive a salary or wages, in addition to the founder/owner.

Analysis of Essex County Procurement and Contracting: Final Report 39 Geographic Marketplace The geographic region from which an entity draws a significant portion of vendors for the entity's purchase of goods and services. It is critical to understand the appropriate region for each government entity in order to conduct a statistically accurate availability/utilization analysis. Large Purchases Purchases valued at more than $17,500. Minority/Woman-Owned Business (M/WBE) Minority-Owned Business Enterprise (MBE) NAICS Code New Jersey Department of Commerce SAVI database (NJSAVI) Passive Discrimination Any business that is owned and operated by a female and/or person who is member of a racial group that is also a federally protected class (i.e., black, Hispanic, Asian, Pacific Islander, Native American). Any business that is owned and operated by a person who is a member of a racial group that is also a federally protected class (i.e., black, Hispanic, Asian, Pacific Islander, Native American). The North American Industrial Classification System (NAICS) is used by business and government to classify and measure economic activity. It was developed to replace the older Standard Industrial Classification (SIC) system. NJSAVI is an electronic database designed to assist business owners that wish to do business with the State of New Jersey and the private sector. NJSAVI matches buyers with vendors for public and private contracting opportunities. NJSAVI serves state departments, colleges, authorities, commissions, county and municipal government, boards of education and private corporations. NJSAVI provides them easy access to identify potential vendors for possible contracts (including set-asides) and delegated purchase orders. There are over 6,000 current vendors listed in the NJSAVI database. NJSAVI lists vendors by name, federal ID number, and offers a detail of its services. It lists contact information, address and telephone numbers, commodities and construction trades. It also identifies vendors as small, woman-owned or minority-owned enterprises. To have your business listed in the NJSAVI database, business owners must complete a State of New Jersey Small Business Vendor Registration Application Form. (From http://www.state.nj.us/commerce) A situation in which a government entity does business with firms in an industry that discriminates against people of a particular group(s). By virtue of supporting firms that may be engaging in discrimination, the government entity is indirectly - or passively - participating in a discriminatory environment.

Analysis of Essex County Procurement and Contracting: Final Report 40 Pay to Play Probit Model Procurement Contracts Professional Services Prompt Payment Provision Purchase Order Generally refers to the practice of giving contracts and/or purchase orders to vendors who agree to compensate the agency officials who make those opportunities available through some form of kick back, e.g., purchase of tickets or contributions for particular programs. A statistical method used to estimate equations when the dependent variable is a binary outcome. The coefficients capture the impact that a particular characteristic has on the probability of an outcome (e.g., probability of winning) occurring. Any agreement, including but not limited to a purchase order or a formal agreement, which is a legally binding relationship enforceable by law, between a vendor who agrees to provide goods or perform services and a contracting unit which agrees to compensate the vendor, as defined by and subject to the terms and conditions of the agreement. May also include an arrangement whereby a vendor compensates a contracting unit for the right to perform a service, such as, but not limited to, operating a concession. Services a) rendered or performed by a person legally authorized to practice a recognized profession; b) whose practice is regulated by law; and c) that require knowledge of an advanced nature or a field acquired by prolonged specialized instruction and study, as distinguished from general academic instruction or apprenticeship and training. Professional services may also mean services rendered in the provision of goods or performance of services that are original and creative in character in a recognized field of artistic endeavor. New Jersey State law requires public agencies to pay for goods and services within 60 days of the agency's receipt of a properly executed payment voucher or within 60 days of receipt and acceptance of goods and services, whichever is later. Properly executed performance security, when required, must be received by the State prior to processing any payments for goods and services accepted by State agencies. Interest will be paid on delinquent accounts at a rate established by the State Treasurer. Interest will not be paid until it exceeds $5.00 per properly executed invoice. A document issued by the contracting agent that authorizes a purchase transaction with a vendor to provide goods or perform services for the contracting unit. When the purchase order is fulfilled in accordance with the terms and conditions of the

Analysis of Essex County Procurement and Contracting: Final Report 41 contracting agent s request and other provisions and procedures that may be established by the contracting unit, it will result in payment by the contracting unit. Race-Conscious Remedies Race-Neutral Remedies Responsible Bidder Responsive Bidder SIC Code Efforts to address disparities between two population groups that will help improve the position of the less advantaged group in a manner that may give specific advantages to members of one or more race or ethnic groups. Efforts to address disparities between two population groups that will help improve the position of the less advantaged group. These efforts are designed to a) improve the position of those less advantaged, b) allow members of all groups to participate, and c) not give specific preference to people of a particular race or ethnic group. "Responsible" means able to complete the contract in accordance with its requirements, including but not limited to requirements pertaining to experience, moral integrity, operating capacity, financial capacity, credit, workforce, equipment, and facilities availability. "Responsive" means conforming in all material respects to the terms and conditions, specifications, legal requirements, and other provisions of the request. Standard Industrial Classification is an older system to classify and measure economic activity. It is being phased out and replaced by the North American Industrial Classification system (NAICS). Small Purchases Purchases valued under $17,500. State Contract Subcommodity Code Survey of Minority and Women-Owned Business Per NJ Statute, local and county governments are required to purchase certain commodities from State approved vendors. In addition, these government entities also have the option to purchase additional commodities from State approved vendors. Agencies that purchase through State-approved vendors do not need to go through the process of bidding and awarding a contract. Essex County s own system for identifying products and services. A report published by the U.S. Census Bureau that provides statistics that describe the composition of U.S. businesses by

Analysis of Essex County Procurement and Contracting: Final Report 42 Enterprises (SMOBE/SWOBE) Vendor race, ethnicity, and gender. Business owner seeking to sell goods or services. Vendor Selection Very Small Purchases Purchases valued below $2,625. Selection of business owner(s) who agree to sell goods or services. Women-Owned Business Enterprise (WBE) Zip Code Business Pattern Data (ZBP) A business certified as being owned by a female. A data set compiled by the U.S. Census Bureau that provides the total number of firms in the U.S., broken down by zip code and NAICS code.

APPENDIX B: LEGAL ANALYSIS A Review of Relevant Court Decisions Related to MBE/WBE/DBE Programs and Disparity Study Methodology I. U.S. Supreme Court Precedents A. Croson The controlling legal precedent that sets forth the guidelines for lawful minority and women business programs enacted by state agencies and local governments using local dollars is the U.S. Supreme Court decision in City of Richmond v. J.A. Croson, 488 U.S. 469 (1989). In the Croson decision, the U.S. Supreme Court struck down the City of Richmond s minority business enterprise (MBE) program that mandated that the City would attempt to require its prime contractors to subcontract at least 30 percent of their construction contract dollars to minority-owned firms. In analyzing this case under the Fourteenth Amendment, the Supreme Court, for the first time, adopted a strict scrutiny standard for testing the legality of raceconscious affirmative action programs. In applying the strict scrutiny standard in Croson, the U.S. Supreme Court employed a twoprong analysis. First, the City was required to demonstrate a compelling governmental interest for using race-conscious criteria in the awarding of contracts. This requirement would have been satisfied if the City had demonstrated that its MBE program was remedial in nature to correct the effects of identified discrimination in the public and/or private sector local marketplace. Second, the City was required to demonstrate that its MBE program was narrowly tailored to address the effects of that identified discrimination. In this regard, factors considered by the Court included whether there were ethnic groups benefiting from the program for which there was no evidence of discrimination; whether the size of the MBE participation goal was flexible and rationally related to a relevant disparity in the marketplace; whether consideration was given to less restrictive race-neutral remedies; and whether the program contained sunset provisions or other means for periodic review to assure that it would not outlive its intended remedial purpose. In Croson, the Supreme Court held that the City s program failed to satisfy either prong of the strict scrutiny test. Richmond failed to demonstrate that its program was necessary to remedy the effects of discrimination in the marketplace. Accordingly, its program also was not narrowly tailored. The Court reasoned that the City s showing of a mere statistical disparity between the overall minority population in Richmond (50 percent African American) and awards of prime contracts to minority-owned firms (0.67 percent to African American firms) was an irrelevant comparison and insufficient to raise an inference of discrimination. Justice O Connor stated that the relevant statistical comparison was one between the percentage of minority firms available and willing to participate in the construction industry (including prime contractors and subcontractors) and the percentage of prime and subcontract dollars awarded to those minority firms. In addition,

Analysis of Essex County Procurement and Contracting: Final Report 44 particularized anecdotal accounts of discrimination could establish a compelling interest for a local government to institute a race-conscious remedy. However, conclusory claims of discrimination by City officials would not suffice. As for the second prong of the strict scrutiny test, the Supreme Court held that Richmond s MBE program, on several grounds, was not narrowly tailored to redress the effects of discrimination. First, the program extended to a long list of ethnic minorities (e.g., Aleuts) for which the City had established no evidence of discrimination. Therefore, the scope of the program was overly broad. Second, the Court held that the 30 percent goal for MBE participation in the Richmond program was an inflexible, rigid quota and was an arbitrary figure not rationally related to relevant disparities. The Court also criticized the City s lack of inquiry into whether a particular MBE seeking racial preference had suffered from the effects of past discrimination. Third, the City of Richmond failed to consider race-neutral alternatives to remedy the underrepresentation of minorities in contract awards. The fourth and final flaw was that the Richmond MBE program contained no sunset provision or mechanism for periodic review to assess continued need. Accordingly, the Richmond program could have outlived the need for any remedy. B. Adarand Croson only addressed race-based remedies voluntarily enacted by state and local governments. On June 12, 1995, in Adarand Constructors, Inc. v. Peña, 115 S. Ct. 2097 (1995), the U.S. Supreme Court issued another decision that essentially extended the strict scrutiny standard of review to federally enacted race-based classifications. In the Adarand opinion, a narrow five to four majority decided that even a relatively modest voluntary remedy where a racial classification is used to create a rebuttable presumption of social and economic disadvantage can only pass constitutional muster if it serves a compelling interest and is narrowly tailored to achieve that objective. The racial preference at issue in Adarand was a Subcontractor Compensation Clause ( SCC ) imposed by the Central Federal Lands Highway Division (a part of the U.S. Department of Transportation). The SCC terms provided that the prime contractor, Mountain Gravel, would receive additional compensation if it hired disadvantaged business enterprise (DBE) subcontractors. Mountain Gravel sought subcontractor bids for guardrail work, and plaintiff Adarand was the low bidder. However, Adarand was not certified as a DBE. The Department of Transportation (DOT) defined DBEs as businesses that were at least 51 percent owned and controlled by socially and economically disadvantaged individuals. The racial preference that triggered the application of strict scrutiny was a rebuttable presumption in the law that the term socially and economically disadvantaged individuals includes black Americans, Hispanic Americans, Native Americans, Asian Pacific Americans, and other minorities, or any other individual found to be disadvantaged by the Small Business Administration pursuant to Section 8(a) of the Small Business Act. Adarand was not awarded the subcontract because Mountain Gravel decided to take advantage of this Subcontractor Compensation Clause by hiring a certified DBE firm instead.

Analysis of Essex County Procurement and Contracting: Final Report 45 In writing the majority opinion for the Court, Justice O Connor stated that the strict scrutiny standard of review as imposed on any federal program containing racial classifications was identical to that imposed in Croson. Nevertheless, Justice O Connor was careful to point out that this strict scrutiny was not intended to be strict in theory, but fatal in fact. It was a standard that had been met in the past and could be met in the future. She further observed, Government is not disqualified from acting in response to the unhappy persistence of both the practice and the lingering effects of racial discrimination against minority groups in this country. The implications of the Adarand I decision are that strict scrutiny will be applied in testing the legality of any government program (federal, state, or local) that contains a racial classification. In response to the Adarand I decision, the federal government is now required to conduct its own disparity study and/or develop its own factual predicate for any of its affirmative action programs. To the extent local governments are required and authorized by federal legislation to implement race-conscious affirmative action programs, they may rely upon the federal government s factual predicate as a defense to a constitutional challenge. However, to the extent a local government adopts race-conscious goals or remedies that exceed the requirements of a federal program or that go beyond that which it is required to do under the federal law, that local government will be responsible for developing its own factual predicate to establish that its actions are narrowly tailored to remedy identified discrimination. This case was subsequently remanded to lower courts for further proceedings to determine facts as to whether there was a compelling interest and whether this remedy was narrowly tailored. Upon remand, the District Court granted Adarand s motion for summary judgment. The District Court found a compelling governmental interest for the program, but ruled that the program was not narrowly tailored. (Adarand II) On March 4, 1999, the Tenth Circuit Court of Appeals vacated that most recent District Court decision on grounds of mootness as Plaintiff Adarand had been certified as a DBE and no longer had apparent standing to challenge the DBE program. The Tenth Circuit Court of Appeals denied Adarand s appeal on this issue on May 19, 1999. Adarand appealed to the U.S. Supreme Court. Then, on January 12, 2000, the U.S. Supreme Court vacated this Tenth Circuit ruling as to mootness and remanded the case back to the Tenth Circuit for the purpose of obtaining a ruling on the merits of the appeals of the trial court s decision. However, in the interim, the Secretary of the U. S. Department of Transportation had suspended the use of the Subcontractor Compensation Clause, and in 1999 had issued new regulations under the Transportation Equity Act for the 21 st Century (TEA 21) for the enforcement of the DBE program in light of the Supreme Court s 1995 decision in Adarand I. (See 49 CFR Part 26 (1999)). Those new regulations pertained almost exclusively to the application of the disadvantaged business enterprise program to procurements wherein federal funds are used for highway projects let by states and localities. On September 25, 2000, the 10 th Circuit Court of Appeals held that, by virtue of the new regulatory framework under which the DOT s state and local DBE program now operates, the DBE program satisfied the strict scrutiny standard as enunciated by the Supreme Court in Adarand I and was constitutional. Specifically, the 10 th Circuit Court of Appeals relied upon the extensive factual predicate compiled by the Congress and the federal government and represented in its record Appendix as the compelling interest. The Court cited from a voluminous record, in great detail, to establish a strong basis in evidence of ongoing discrimination affecting the highway construction industry. The Court held this record to be

Analysis of Essex County Procurement and Contracting: Final Report 46 sufficient to provide a compelling interest for the DBE program. Adarand Constructors, Inc. v. Slater, 228 F.3d 1147, at 1176-1187 (10 th Cir. 2000). (Adarand III) The 10 th Circuit also held that the new DBE regulations were narrowly tailored to remedy the discrimination identified in the factual predicate. However, the old Subcontractor Compensation Clause originally challenged by Adarand was not narrowly tailored and was therefore held to be unconstitutional under the strict scrutiny standard. Adarand petitioned the Supreme Court to review this latest decision by the 10 th Circuit Court of Appeals. The Supreme Court initially granted Adarand s petition for writ of certiorari to review this decision and to determine whether the Court of Appeals had misapplied the strict scrutiny standard announced in Adarand I. However, following the submission of briefs and oral arguments, on November 27, 2001, the Supreme Court noted a shift in the posture of the case due to the new regulations and the suspension of the SSC program that was originally at issue in Adarand I. The Supreme Court then dismissed Adarand s writ of certiorari as being improvidently granted. Adarand Constructors, Inc. v. Mineta, 534 U.S. 103 (2001) (Adarand IV) This meant that the Supreme Court had refused to rule on the merits of Adarand s appeal. Accordingly, the 10 th Circuit Court of Appeals decision upholding the constitutionality of the new DBE program regulations issued under TEA 21 was not disturbed and therefore remains valid law. II. Controlling Local Precedents While these Supreme Court decisions are controlling precedents for Essex County, there are a number of subtleties and complexities in this area of the law that have not yet been directly addressed by the Supreme Court. For these unresolved issues, it is necessary to sort through a thicket of federal and state lower court opinions to glean appropriate guidance. Those legal precedents that are controlling or most instructive for Essex County include those arising from the Third Circuit Court of Appeals, federal district courts within the Third Circuit, and New Jersey State courts. As there have been relatively few post-croson/adarand court decisions that have addressed the constitutionality of MBE/WBE programs or methodological issues arising from disparity studies within Essex County, New Jersey (or, for that matter, within neighboring jurisdictions within the ambit of the Third Circuit Court of Appeals), it is sometimes necessary to obtain guidance from court decisions that are not legally binding upon Essex County. However, as shown below, the decisions do not always give complete or consistent direction on standards of review, assessment of evidence, and study methodology. Where there are differences among court decisions regarding methodological issues, we have exercised our professional judgment in deciding which legal precedents to follow based upon several factors. These factors include the level of authority of the court issuing the ruling (i.e., trial court vs. appellate court), the dominant trend among all courts, the logic and persuasiveness of those court decisions, and the relative weight and proximity of those decisions to Essex County, New Jersey. A summary of controlling or influential local precedents follows.

Analysis of Essex County Procurement and Contracting: Final Report 47 A. Third Circuit Appellate Decisions Contractors Association of Eastern Pennsylvania, Inc. v. City of Philadelphia, 735 F. Supp. 1274 (E.D. Pa., April 5, 1990), 945 F.2d 1260 (3 rd Cir. 1991), 6 F.3d 990 (3d Cir. 1993), 893 F. Supp. 419 on remand (E.D. Pa., Jan. 11, 1995), 91 F.3d 586 (3 rd Cir. 1996), cert. denied 519 U.S. 1113 (1997). In this case, eight construction trade associations filed suit attacking the constitutionality of Philadelphia s MBE/WBE program. In Contractors I, the Federal District Court granted plaintiff s motion for summary judgment on April 5, 1990, citing defendant s insufficiency of a legislative record proving discrimination. (735 F.Supp. 1274) Defendants appealed. On September 30, 1991, the Third Circuit Court of Appeals reinstated Philadelphia s MBE/WBE program. The Third Circuit concluded that the Federal District Court had prematurely terminated the City s program on summary judgment without ample opportunity for discovery and for introduction of evidence at trial regarding the existence of marketplace discrimination. (945 F.2d 1260) The case was remanded to federal district court. On September 22, 1992, the Federal District Court ruled again in this case. In Contractors II, it held on summary judgment that the program was unconstitutional. The City appealed again. The Third Circuit Court of Appeals then vacated the Federal District Court decision with respect to severable and non-construction-oriented sections of the MBE/WBE ordinance because the plaintiffs had only a personal stake in construction issues. The Court of Appeals also vacated the summary judgment with respect to construction provisions of the ordinance as applied to businesses owned by African Americans and handicapped contractors because of statistical evidence establishing a prima facie case of discrimination. The case was then remanded for trial. (6 F.3d 990) In Contractors III (January 11, 1995), the Federal District Court held for the third time that the program was unconstitutional due to insufficient and inadequate evidence to establish discrimination. (893 F. Supp 419) Defendants appealed again. On July 31, 1996, the Third Circuit Court of Appeals upheld the district court opinion on the basis that Philadelphia s program was not narrowly tailored to serve a compelling interest. (91 F.3d 586) The Court of Appeals held that there was an absence of a strong basis in evidence reflecting discrimination against black subcontractors by prime contractors or trade associations. The Court of Appeals also held that it was a close call whether the record in that case provided a strong basis in evidence for an inference of discrimination by the City against black construction firms in the prime contract market. The Court, however, declined to make that call and held that it was not necessary to do so. As Philadelphia s program focused almost exclusively on preferences to black subcontractors, the Court concluded it clearly was not narrowly tailored to address discrimination by the City in the market for prime contracts. Furthermore, to the extent the ordinance authorized a 15 percent set-aside applicable to all prime City contracts for black contractors, there was no basis in the record for believing that such a setaside of that magnitude was necessary to remedy discrimination by the City in that market. On February 18, 1997, the U. S. Supreme Court denied the City s petition for certiorari. (519 U.S. 1113)

Analysis of Essex County Procurement and Contracting: Final Report 48 B. Third Circuit Federal District Court Decisions Aside from the Federal District Court decisions discussed above in the context of the Third Circuit Appellate opinions in Contractors I, II, and III, there is one other federal district court opinion from within the Third Circuit that may provide some guidance. In Association for Fairness in Business, Inc. v. New Jersey, 82 F.Supp.2d 353 (D. N.J., Feb. 8, 2000), a federal district court held that a non-profit association whose members contracted to provide goods and services to gambling casinos had standing to seek injunctive relief on behalf of its members to preclude enforcement of a state statute requiring casino owners to set aside 15 percent of the dollar value of their contracts for goods and services for minority- and women-owned business enterprises. Some members of the association had been discouraged from bidding on contracts, denied contracts after submitting the lowest bid to casino owners because projects were set aside for M/WBEs, and subjected to penalties for failing to comply with set-aside provisions. The Court applied a strict scrutiny standard pursuant to Croson and Contractors I, and held that the Association had demonstrated a reasonable probability that it would succeed in showing that the set-aside provisions of the Casino Control Act and the regulations promulgated pursuant to that Act were unconstitutional. The State of New Jersey and the New Jersey Casino Control Commission failed to make a showing of discrimination that would support a finding that New Jersey has a compelling interest in applying a set-aside program to contracts for goods and services in the casino industry. There was little evidence that the creation of the set-aside program in this case was predicated on findings of race-based or gender-based discrimination in the casino industry. The State sought to rely upon a disparity study conducted by the State Commission on Discrimination in Public Works, Procurement, and Construction Contracts ( Final Report ). This Final Report was issued in February 1993, well after the Casino Control Act s MBE Provisions were adopted in 1985. While the Third Circuit permits post-enactment evidence to be considered, the Court held that New Jersey s reliance upon this Final Report was misplaced because it focused upon discrimination in State contract awards, not in the contract practices of privately owned casinos. The State also failed to allege or demonstrate any passive participation by the State in any discrimination practiced by the casinos. Furthermore, there was little indication that much attention was paid in establishing the Casino Control Act s list of intended beneficiaries for the set-aside program as to whether their inclusion was justified by evidence of past or current discrimination. The minority set-aside program that the Casino Control Act established was vulnerable to constitutional attack because it was not narrowly tailored to the compelling interest that New Jersey asserted. Specifically, the definition of minorities included persons of Native American, Native Alaskan, Hawaiian, or Portuguese descent, without any evidence of discrimination against companies run by such individuals. The court held that the set-aside program at issue in this case was, at best, loosely tailored to the State's alleged interest in remedying discrimination in the casino industry. Furthermore, there was no evidence in the record that New Jersey attempted race-neutral measures before adopting the minority set-aside program. Although the court applied strict scrutiny in evaluating the legality of MBE preferences, it similarly found that the WBE gender-based preferences failed under an intermediate scrutiny standard of review. The 15 percent set aside goals could be met under the provisions of the program either entirely with MBE participation or entirely with WBE participation, and were therefore clearly not

Analysis of Essex County Procurement and Contracting: Final Report 49 narrowly tailored to remedy the relative discrimination experienced by either group. Instead, the court criticized the potential windfall of remedial benefit that one group might receive while depriving the other group of any such benefit. C. New Jersey State Court Decisions As of this date, there is no known New Jersey state court decision that addresses disparity study methodological issues or that addresses issues related to the narrow tailoring of MBE/WBE programs based upon disparity study evidence. III. Overview of Other Key Lower Court Decisions Subsequent to the Croson and Adarand decisions, there have been several lower court opinions that have given considerably more guidance in describing key components of the requisite factual predicate for satisfying this strict scrutiny standard. The balance of this section examines the legal guidance from several relevant court opinions with respect to particular methodological issues. (Those case summaries and references that appear in bold type are decisions that have been rendered by local courts that have jurisdiction over some portion of Essex County s market area). As many of these relevant court decisions have complicated procedural histories, a brief introduction and summary of each case is provided below in alphabetical order: Association for Fairness in Business, Inc. v. New Jersey, 82 F.Supp.2d 353 (D. N.J., Feb. 8, 2000). A federal district court held that a non-profit association whose members contracted to provide goods and services to gambling casinos had standing to seek injunctive relief on behalf of its members to preclude enforcement of a state statute requiring casino owners to set aside 15 percent of the dollar value of their contracts for goods and services for minorityand women-owned business enterprises. Some members of the association had been discouraged from bidding on contracts, denied contracts after submitting the lowest bid to casino owners because projects were set aside for M/WBEs, and subjected to penalties for failing to comply with set-aside provisions. The court held that the Plaintiff would likely prevail on the merits as the State would be unable to establish that it had a compelling interest for the set-aside, or that it was a narrowly tailored remedy for identified discrimination on the part of the casino owners. The court preliminarily enjoined the portion of the New Jersey Casino Control Act that established the 15 percent MBE/WBE set-aside for contracts. Associated General Contractors of America, et al. v. City of Columbus, et al., 936 F.Supp. 1363 (S.D. Oh., August 26, 1996), vacated 172 F.3d 411 (6 th Cir. 1999). This case is a continuation of an earlier action (Columbus I) by the Associated General Contractors of America, Central Ohio Division ( AGC ) which challenged the constitutionality of ordinances enacted by the City of Columbus, Ohio which required that firms owned by minorities and women receive a certain percentage of the dollar amount of subcontracts awarded on City construction projects each year. In January 1991, the City of Columbus agreed that its M/DBE program was unconstitutional and consented to an order which enjoined it from enacting any laws containing race or gender-based preferences in City

Analysis of Essex County Procurement and Contracting: Final Report 50 construction contracts without first obtaining the approval of the court. Subsequently, the City hired consultants to complete a predicate study and held public hearings to collect additional relevant evidence. In December 1993, the City enacted an Equal Business Opportunity Code that provided a variety of race and gender-based preferences for possible use in contracting (e.g., contract-specific goals; bonding, financing, and technical assistance programs; price preferences; and a limited sheltered market program). The City of Columbus subsequently petitioned the Federal District Court for dissolution of the injunction of January 25, 1991, and for approval to activate the Equal Business Opportunity Code for Construction Services. On August 26, 1996, following discovery and trial, the Federal District Court denied the City s motion and held that the new Equal Business Opportunity Code of 1993 was unconstitutional on the basis that it does not serve a compelling state interest and that it is not narrowly tailored to the achievement of its goal. In March 1999, this decision was vacated by the Sixth Circuit Court of Appeals and remanded to the District Court for dismissal on the basis of lack of jurisdiction as the City s ordinance was never put into effect and thus could not be challenged. Associated General Contractors of California, Inc. v. Coalition for Economic Equity, et al., 950 F.2d 1401 (9th Cir. 1991), cert. denied 112 S. Ct. 1670 (1992). This case was a constitutional challenge to the revised 1989 San Francisco MBE ordinance. The plaintiff s motion for preliminary injunction was denied on October 9, 1990, on the grounds that the AGC was unlikely to prevail on the merits and that the balance of the hardships did not tip in the AGC s favor. The AGC filed an expedited appeal to the Ninth Circuit Court of Appeals. The Ninth Circuit affirmed the lower court decision on the same grounds. The Court held that San Francisco s factual predicate, comprised of statistical disparities in the utilization of available MBE/WBEs and a record documenting vast numbers of individual accounts of discrimination, likely established a compelling interest for a race-conscious remedy. The U.S. Supreme Court denied the plaintiff s petition for writ of certiorari on April 20, 1992. Associated Utility Contractors v. City of Baltimore, (Civil No. AMD 98-4060), 83 F.Supp.2d 613 (D. Md., Feb. 16, 2000) and 218 F.Supp.2d 749 (D. Md., Sep.9, 2002) Plaintiff Associated Utility Contractors of Maryland ( AUC ) filed a complaint challenging Baltimore City Ordinance 610 as enacted in 1990 which established an annual numerical set-aside goal for MBEs and WBEs applicable to a wide range of public contracts, including construction subcontracts. The City had set these set-aside goals in 1990 at 20 percent and 3 percent for MBEs and WBEs respectively. After a limited period of discovery, AUC filed a motion for Summary Judgment. Partial Summary Judgment was granted enjoining enforcement of the Ordinance as to construction contracts entered into by the City. Summary Judgment was denied as to AUC s facial attack on the constitutionality of the Ordinance due to a dispute of material fact as to whether the enactment of the Ordinance was supported by a factual record of unlawful discrimination properly remediable through race- and gender- based affirmative action. The City filed an appeal to the 4 th Circuit Court of Appeals challenging the partial summary judgment and sought a stay of the Summary Judgment pending appeal. Although further discovery and proceedings were originally contemplated, Judge Davis decided on February 16, 2000 that no further proceedings were necessary or appropriate due to City s apparent inability to produce any pre-enactment evidence whatsoever to support the constitutional basis for its affirmative action program. Moreover, the City failed to offer any contemporaneous justification for the 1999 set-aside goals it had adopted pursuant to the

Analysis of Essex County Procurement and Contracting: Final Report 51 Ordinance. Judge Davis rejected the City s argument that Summary Judgment should be stayed until its disparity study was completed. Inasmuch as the injunction of the enforcement of the 1999 goals awarded complete relief to the plaintiff, and any effort to adjudicate the issue of whether the City will adopt revised set-aside goals on the authority of the Ordinance upon the conclusion of the disparity study would be speculative, Judge Davis dismissed the matter without prejudice. The City s Mayor subsequently issued an Executive Order setting an M/WBE goal of 35 percent. The City also adopted a new Ordinance based upon its recently completed disparity study. AUC then challenged the new ordinance and executive order. The Federal District Court denied the City s motion to dismiss and granted AUC s standing to file suit. This second lawsuit was subsequently settled by the parties. Bilbo Freight Lines, et al. v. Dan Morales, et al., C.A. No. H-93-3808 (S.D. Tex., Feb. 3, 1994). This case was a constitutional challenge to Section 4(f) of the Texas Motor Carrier Act that gave preferential treatment to minorities and women in the issuance of Certificates of Authority for providing trucking services. Although defendant Texas Railroad Commission produced evidence suggesting underutilization of available minority truckers, the Federal District Court granted an injunction against the preference because there was no evidence presented to demonstrate the existence of discrimination in the issuance of Certificates of Authority, which is what the challenged preference addressed. Concrete Works of Colorado, Inc. v. City and County of Denver, 823 F. Supp. 821 (D. Co., Feb. 26, 1993), 36 F.3d 1513 (10th Cir., Sep. 23, 1994), cert. denied 514 U.S. 1004 (1995), on remand 86 F. Supp.2d 1042 (D. Co., Mar. 7, 2000), rev d 321 F.3d 950 (10 th Cir., Feb. 10, 2003) cert. denied 540 U.S. 1027 (2003). Denver s MBE Program had been established in 1990 and set an annual goal of 16 percent for construction dollars to be spent with MBE subcontractors, and 12 percent to be spent with women business enterprise ( WBE ) subcontractors. 20 Specific contract spending goals varied according to the availability of MBEs and WBEs offering the relevant commodities and services. Concrete Works allegedly lost three contracts with Denver because, as a prime contractor, it had failed to comply with spending goals enforced under the ordinance. On February 26, 1993, Judge Finesilver of the Federal District Court ruled in Concrete Works I on cross-summary judgment motions that the ordinance was constitutional under the Croson analysis. He further concluded that any city council could reasonably rely upon the record consisting of an exhaustive compilation of federal studies, anecdotal evidence, independent analysis, council hearings, census data, and statistical studies to infer the presence of discrimination. The Plaintiff appealed to the Tenth Circuit Court of Appeals. On September 23, 1994, the Tenth Circuit reversed and remanded the case to trial to resolve material issues of fact regarding disparity in utilization of MBE/WBEs. The first part of the trial was held in February 1999. The trial was completed in June 1999. On March 7, 2000, Judge Richard Matsch of the Federal District Court ruled in Concrete Works II that Denver s three M/WBE programs (enacted in 1990, 1996, and 1998 respectively) were unconstitutional as their factual predicates were not sufficiently probative and failed to establish a compelling government interest to remedy discrimination. 20 Under the terms of the 1990 ordinance, Denver required prime contractors to make good faith efforts to attain the subcontracting goals. However, prime contractors were not required to use unqualified MBEs, WBEs, or, in the event that good faith efforts failed, to attain the goals. During the course of this litigation, this ordinance was repealed and revised in 1996 and 1998 based upon additional evidence of racial and gender discrimination in Denver s construction industry.

Analysis of Essex County Procurement and Contracting: Final Report 52 Moreover, Judge Matsch ruled that as the City failed to utilize available race neutral remedies, its M/WBE programs were not narrowly tailored. The City of Denver appealed this decision to the 10 th Circuit Court of Appeals. Following on the heels of Adarand III, the 10 th Circuit Court of Appeals then reversed Judge Matsch s decision and held that Denver had complied with the 14 th Amendment and offer tailored preferences to groups that, according to credible data, have been or are discriminated against in the private sector of the marketplace. (Concrete Works III) [Concrete Works of Colorado v. City and County of Denver, 321 F.3d 950 (10 th Cir. 2003), cert. denied 540 U.S. 1027 (2003).] This was the first appellate court decision to uphold the constitutionality of a local government minority business enterprise program under the strict scrutiny standard based upon the merits of trial record evidence. Cone Corporation v. Hillsborough County, 5 F.3d 1397 (11th Cir. 1993), 1994 WL 371 386 (M.D. Fla., July 8, 1994). This case was a constitutional challenge to a Hillsborough County Resolution granting racial and gender preferences in the awarding of construction contracts. On October 16, 1989, the Federal District Court granted plaintiff s motion for preliminary injunction against the MBE/WBE program. (723 F. Supp. 699) On March 1, 1990, the Federal District Court made the injunction permanent based on its February 13, 1990, order granting plaintiff s motion for summary judgment. (730 F. Supp. 1568) However, on August 13, 1990, the Eleventh Circuit Court of Appeals reversed and remanded the case on the basis that there was a material issue of fact regarding whether the County had adequate evidence of discrimination to justify its MBE/WBE program. (908 F.2d 908) Plaintiff then petitioned the U.S. Supreme Court for a writ of certiorari. The U.S. Supreme Court denied plaintiff s petition. On July 16, 1991, defendant filed a motion to dismiss for lack of standing. The Federal District Court then dismissed the lawsuit on the basis that mere alleged economic injury was not enough to confer standing to challenge the MBE/WBE program. (777 F. Supp. 1558) The Eleventh Circuit Court of Appeals affirmed this decision on February 8, 1993. (983 F.2d 784) On October 29, 1993, the Court of Appeals held that the plaintiffs failed to allege injury and therefore lacked standing to challenge the program. However, the Court remanded the case for reconsideration in light of the AGC v. Jacksonville decision. (5 F.3d 1397) On April 5, 1994, the Federal District Court held that plaintiff s amended complaint still did not adequately allege injury to confer standing, but granted them a second opportunity to amend their complaint. On July 8, 1994, the Court also dismissed plaintiff s second amended complaint for failure to allege injury with sufficient particularity. Rule 11 sanctions were imposed against plaintiff s counsel. (1994 WL 371386) Contractors Association of Eastern Pennsylvania, Inc. v. City of Philadelphia, 735 F. Supp. 1274 (E.D. Pa., April 5, 1990), 945 F.2d 1260 (3 rd Cir. 1991), 6 F.3d 990 (3d Cir. 1993), 893 F. Supp. 419 on remand (E.D. Pa., Jan. 11, 1995), 91 F.3d 586 (3 rd Cir., 1996), cert. denied 519 U.S. 1113 (1997). In this case, eight construction trade associations filed suit attacking the constitutionality of Philadelphia s MBE/WBE program. In Contractors I, the Federal District Court granted plaintiff s motion for summary judgment on April 6, 1990, citing defendant s insufficiency of a legislative record proving discrimination. Defendants appealed. On September 30, 1991, the Third Circuit Court of Appeals reinstated Philadelphia s MBE/WBE program. The Third Circuit concluded that the Federal District Court had prematurely terminated the City s program on summary judgment program without ample opportunity for discovery and for introduction of evidence at trial regarding

Analysis of Essex County Procurement and Contracting: Final Report 53 the existence of marketplace discrimination. (945 F.2d 1260) The case was remanded to federal district court. On September 22, 1992, the Federal District Court ruled again in this case. In Contractors II, it held on summary judgment that the program was unconstitutional. The City appealed again. The Third Circuit Court of Appeals then vacated the Federal District Court decision with respect to severable and non-construction-oriented sections of the MBE/WBE ordinance because the plaintiffs had only a personal stake in construction issues. The Court of Appeals also vacated the summary judgment with respect to construction provisions of the ordinance as applied to businesses owned by African Americans and handicapped contractors because of statistical evidence establishing a prima facie case of discrimination. The case was then remanded for trial. (6 F.3d 990) In Contractors III (January 11, 1995), the Federal District Court held for the third time that the program was unconstitutional due to insufficient and inadequate evidence to establish discrimination. (893 F.Supp.2d 419) Defendants appealed again. On July 31, 1996, the Third Circuit Court of Appeals upheld the district court opinion on the basis that Philadelphia s program was not narrowly tailored to serve a compelling interest. The Court of Appeals held that there was an absence of a strong basis in evidence reflecting discrimination against black subcontractors by prime contractors or trade associations. The Court of Appeals also held that it was a close call whether the record in that case provided a strong basis in evidence for an inference of discrimination by the City against black construction firms in the prime contract market. The Court, however, declined to make that call and held that it was not necessary to do so. As Philadelphia s program focused almost exclusively on preferences to black subcontractors, the Court concluded it clearly was not narrowly tailored to address discrimination by the City in the market for prime contracts. Furthermore, to the extent the ordinance authorized a 15 percent set-aside applicable to all prime City contracts for black contractors, there was no basis in the record for believing that a set-aside of such magnitude was necessary to remedy discrimination by the City in that market. (91 F.3d 586) Coral Construction Co. v. King County, 941 F.2d 910 (9th Cir. 1991), cert. denied, 112 S. Ct. 875 (1992). This case was a constitutional challenge to the King County, Washington MBE program. Based upon affidavits and statistical analysis, the Federal District Court held in 1989 that there was ample evidence of discrimination in the County s marketplace to establish a compelling interest for the MBE program. The Court also held that the program was narrowly tailored. The plaintiffs appealed. On August 20, 1991, the Ninth Circuit Court of Appeals reversed and remanded the case to the lower court to permit introduction of a completed disparity study into evidence. The case was settled prior to reaching trial. Engineering Contractors Association of South Florida, Inc. et al. v. Metropolitan Dade County, et al., (Case No. 94-1848-CIV-RYSKAMP) 943 F.Supp. 1546 (S.D. Fla., September 17, 1996), aff d 122 F.3d 895 (11 th Cir. 1997). This case was a constitutional challenge by six construction trade associations to Dade County, Florida s M/WBE program that provided for race and gender-conscious measures to benefit African American, Hispanic, and women business enterprises on construction contracts. Following a four-day non-jury trial held in

Analysis of Essex County Procurement and Contracting: Final Report 54 December 1995, the Federal District Court found the Dade County M/WBE program to be unconstitutional under the Equal Protection Clause of the U.S. Constitution. The Federal District Court held that there was not a strong basis in evidence to establish a compelling interest for Dade County s program, nor was its program narrowly tailored. [A similar program in Dade County had been previously upheld under a more lenient analytical approach most closely akin to that set forth in Chief Justice Burger s opinion in Fullilove. See South Florida Chapter of Associated General Contractors of America, Inc. v. Metropolitan Dade County, 723 F.2d 846 (11th Cir. 1984), cert. denied, 105 S.Ct. 220 (1984). Fullilove has been subsequently overruled to the extent that it applied a standard of review other than strict scrutiny as applied in Croson. Adarand Constructors, Inc. v. Peña, 515 U.S. 200, 115 S.Ct. 2097 (1995).] In a September 2, 1997 decision, the U.S. Court of Appeals for the Eleventh Circuit upheld the District Court s decision in Engineering Contractors of South Florida as it related to race-based programs. While the Court of Appeals did find the gender-based program to be sufficiently flexible to satisfy the substantial relationship prong of intermediate scrutiny, it agreed with the lower court that Dade County failed to provide sufficient evidence of discrimination against women in the local construction industry. Houston Contractors Association v. Metropolitan Transit Authority of Harris County, Case No. H-93-3651 (S.D. Tex., November 13, 1997). Houston Contractors Association challenged the constitutionality of Metro s DBE program that was enacted in part to comply with Federal Transit Authority requirements. Based apparently on the Court s belief that any race or gender-conscious measures by government are unconstitutional, the Court struck down Metro s program and prohibited Metro from even collecting race, ethnicity and gender information at time of bid. Maryland v. Taylor. This February 1990 decision arose out of a State of Maryland criminal matter in which a defendant (Taylor) sought to dismiss criminal charges of fraud regarding his false reporting of MBE participation on a BWI Airport roofing contract. Defendant Taylor sought dismissal of indictments on the grounds that the State of Maryland s MBE program was unconstitutional, and therefore the requirement for reporting MBE participation that formed the basis for the fraud was null and void. The MBE program mandated that State agencies were to try to achieve MBE participation of no less than 10 percent of procurement contract dollars. Under the statutory definition, MBEs included socially or economically disadvantaged individuals who were either Alaskan Natives, American Indians, Asians, African Americans, Hispanics, Pacific Islanders, women, or physically or mentally disabled individuals. The MBE legislation contained no sunset provision, but did include provisions for annual review and reporting. The Court upheld the constitutionality of Maryland s MBE program based on a strong basis in evidence of past discrimination in the Maryland construction industry. This evidence included extensive historical data on available MBE contractors in 1976 and the underutilization of such firms in the absence of affirmative action. The evidence also included firsthand testimony of discrimination against MBE subcontractors. Moreover, the MBE program was found to be narrowly tailored because race-neutral alternatives had been considered by the State and, in some instances, implemented. The 10 percent MBE goal was

Analysis of Essex County Procurement and Contracting: Final Report 55 tied to an analysis of the local availability of qualified MBEs. Furthermore, the goal was not a rigid quota as it provided for waivers and goal reductions for cause. The program also provided for an annual legislative review of agency performance which gave assurances that the MBE Program would be subject to scrutiny year after year and would not outlive its remedial purpose. Accordingly, defendant s motion to dismiss the indictments was denied. Monterey Mechanical Co., v. Wilson, et. al. (Case No. 96-16729) 125 F.3d 702 (9 th Cir. 1997). This decision arose from a prime contractor s challenge to the State of California s construction subcontracting program for minority, women and disabled-owned businesses. The District Court had denied plaintiff s request for a preliminary injunction based upon a lack of standing. The Ninth Circuit reversed the District Court and granted a preliminary injunction. O Donnell Construction Co. v. District of Columbia, 762 F. Supp. 354 (D.D.C. 1991), rev d, 295 U.S. App. D.C. 317, 963 F.2d 420 (D.C. App. 1992), on remand, 963 F. Supp. 420 (D.D.C., Dec. 22, 1992). This case was a constitutional challenge to the D.C. Minority Contracting Act and the D.C. Department of Public Works Disadvantaged Business Enterprise Program. Plaintiff s motion for preliminary injunction was initially denied. (762 F. Supp. 354) The plaintiff appealed. On May 5, 1992, the D.C. Court of Appeals reversed the lower court decision and granted the preliminary injunction on the basis that the plaintiff was likely to prevail at trial on the merits since the defendant s evidence consisted only of evidence of general societal discrimination. On remand, the Federal District Court ruled on cross-summary judgment motions that the challenged ordinances were unconstitutional, due primarily to weaknesses in the statistical evidence. Phillips & Jordan, Inc., v. Watts Case No. 4:96cv286-WS (N.D. Fla, April 24, 1998). Phillips & Jordan challenged a set-aside program for state-funded highway maintenance contracts administered by the Florida Department of Transportation. On summary judgment, the Court struck down this program, as it did not find a compelling governmental interest for the program. RGW Construction, Inc. v. San Francisco BART, F. Supp. (slip opinion) (N.D. Calif., Nov. 25, 1992). This case was a constitutional challenge to the San Francisco Bay Area Rapid Transit DBE program. On September 18, 1992, the Federal District Court issued a preliminary injunction against the race-conscious program because BART, at that time, had not undertaken any studies and had not made any findings in order to satisfy Croson requirements. BART subsequently applied to modify the injunction based upon new evidence of prior discrimination. The Court then lifted the injunction in two out of four counties within BART s service area based upon completed disparity studies for those two counties. Ritchey Produce Co., Inc., V. State Of Ohio, Dept. Of Admn. Serv., (Civ. Case No. 97-2435) 85 Ohio St.3d 194, 707 N.E.2d 871 (1999). This case challenged the constitutionality of Ohio s MBE program. The Plaintiff in this case was of Lebanese descent and had mistakenly been granted Ohio MBE status using the state s Oriental category. After being denied recertification in 1996, Plaintiff brought this challenge arguing, in pertinent part, that the program was over- and under-inclusive. After several appeals, the Ohio Supreme Court

Analysis of Essex County Procurement and Contracting: Final Report 56 found that the Ohio General Assembly had a strong basis in evidence and, thus, a compelling governmental interest, to support the adoption of its MBE program. The Court also determined that the Ohio program was narrowly tailored and neither impermissibly undernor over-inclusive, even though it defined minority business enterprises with specific reference to race. Rothe Development Corporation v. U.S. DOD, et al., Civil Action No. SA-98-CV-1011-EP (W.D. Texas, April 28, 1999). This case challenged the constitutionality of Section 1207 of the National Defense Authorization Act (10 U.S.C. 2323) and Section 8(d) of the Small Business Act 15 U.S.C. 637 (d). Section 1207 of the National Defense Authorization Act of 1987 sets a statutory goal of 5 percent participation by economically and socially disadvantaged businesses in Department of Defense (DoD) contracts. The 1207 Program makes specific reference to section 8(d) of the Small Business Act in order to define economically and socially disadvantaged businesses. The 1207 Program authorizes DoD to apply a price evaluation adjustment (PEA) of 10 percent in order to attain the overall 5 percent contracting goal. The Court denied Plaintiff s request for a temporary restraining order to stay the contract and granted summary judgment to the Defendant. The Court held that a thorough examination of the statutory scheme at issue and its application to the contract at issue, reveal[ed] no illegitimate purpose, no racial prejudice, and no racial stereotyping. Rather, the program is designed to address a societal ill, [discrimination] that has been identified by Congress on the basis of extensive evidence. The Court went on to find the program to be narrowly tailored. Sherbrooke Sodding v. Minnesota Department of Transportation and the USDOT. This challenge to the constitutionality of the Minnesota Department of Transportation s (MnDOT) current DBE program was brought by Sherbrooke Turf, Inc., a non-disadvantaged sodding contractor. The Federal District Court granted the government s motion for summary judgment in this challenge to the constitutionality of MnDOT s current DBE program on November 14, 2001. The court also denied plaintiff s cross-motion for summary judgment. Sherbrooke appealed to the U.S. Court of Appeals for the Eighth Circuit. The argument, which was consolidated with the Gross Seed case argument, was held on May 15, 2003. In the consolidated appeal, the Eighth Circuit upheld the trial courts decisions affirming the constitutionality of the DBE program. After consolidation with the Gross Seed case below, this decision was appealed to the U.S. Supreme Court. On May 17, 2004, the U.S. Supreme Court denied certiorari on the appeal. This means that the constitutionality of the federal Disadvantaged Business Enterprise program has been upheld on the basis that there is a sufficient compelling interest for the program, and that the disadvantaged business enterprise program is narrowly tailored. Under federal regulations, State DOT s are required to set any DBE subcontracting goals on a contract-by-contract basis. [See Gross Seed Co. v. Nebraska Dept. of Roads, 345 F.3d 964 (8 th Cir. 2003), cert. denied 124 S.Ct. 2158 (2004).]

Analysis of Essex County Procurement and Contracting: Final Report 57 IV. Guidance on Disparity Study Methodology and Program Development from Relevant Court Decisions A. Standards of Review, Burden of Production, and Burden of Proof The strict scrutiny standard of review is best understood within the context of the judicial application of the burden of production and the burden of proof when evaluating various types of evidence. The examples discussed below provide a summary and overall assessment of courts views on various forms of evidence that have been introduced by defendant jurisdictions in their efforts to satisfy the strict scrutiny standard. The judicial evaluation of such evidence based upon the somewhat lower intermediate standard of review for gender-based programs is examined in as well. 1. Burden of production and burden of proof Croson and subsequent cases (with one or two exceptions) have been clear that strict scrutiny does not require defendant jurisdictions to prove discrimination before they are allowed to enact race and gender-conscious programs. Rather, a jurisdiction is required to have a strong basis in evidence of discrimination approaching a prima facie case. In this instance, a prima facie case would be one in which the government presented sufficient evidence that, viewed in a light most favorable to the government s case, establishes, on its face, all of the requisite elements of a claim of marketplace discrimination. An understanding of the magnitude of the initial burden placed on local governments is essential to development of disparity study methodology. The locally controlling Third Circuit precedents in Contractors Association of Eastern Pennsylvania, Inc. v. City of Philadelphia directly addressed the issue of burden of proof. In Contractors II, the Third Circuit reversed the District Court s grant of summary judgment invalidating the portion of the City s ordinance directed toward African American-owned construction firms. The plaintiff contractors had argued that the disparity study consultant s analysis was flawed because (a) only prime contracts were examined, and (b) qualifications and interest of contractors in doing City work were not considered in determining availability. The Court held that merely stating these objections without further analysis by the contractors was not enough to invalidate the City s program. The Court made clear that the ultimate burden of proof was on the plaintiff contractors. Upon remand, the District Court considered the plaintiff s neutral explanations for the disparities presented by the City. The Court held that the City s expert never considered how many African American contractors actually sought to participate in City-financed prime construction contracting. The plaintiff presented additional data on the number of African American contractors that sought to become prequalified to bid on City-financed prime construction contracts. Another neutral explanation cited by the Court was evidence presented by the plaintiff that African American contractors were heavily participating in federally-assisted public works projects during the time period for which the City examined its own utilization of African American-owned construction firms. In part based upon the evidence submitted by the plaintiff, the District Court struck down the remaining race and gender-based elements of Philadelphia s program.

Analysis of Essex County Procurement and Contracting: Final Report 58 However, in Contractors III, the Third Circuit found fault in the District Court s analysis of each of the neutral explanations cited above. The Court concluded that Whether this record provides a strong basis in evidence for an inference of discrimination in the prime contract market is a close call, and ultimately ruled against this part of the City s program based upon the different grounds of the City s failure to narrowly tailor its remedy. The Third Circuit s analysis of these alleged neutral explanations shows one appellate court s consideration of the factual record in light of the relative burdens placed on the plaintiff and the defendant. When the municipality comes forward with evidence of facts alleged to justify its conclusions, the plaintiff has the burden of persuading the court that those facts are not accurate. The predominant trend in the case law from other jurisdictions is similar to that of the Third Circuit. One decision that clearly delineates a jurisdiction s burden of production and the plaintiff s burden of proof in which a full disparity study had been performed for the jurisdiction is the Tenth Circuit decision in Concrete Works of Colorado, Inc. v. City and County of Denver I. The Tenth Circuit stated that although Croson places the burden of production on the jurisdiction to demonstrate a strong basis in evidence that its race and gender-based programs aim to remedy specifically identified past or present discrimination, the Fourteenth Amendment does not require a court to make an ultimate judicial finding of discrimination before a municipality may take affirmative steps to eradicate discrimination. The Court stated, We do not read Croson to require, nor do we embrace, an attempt to craft a precise mathematical formula to assess the quantum of evidence that rises to the Croson strong basis in evidence benchmark. That must be evaluated on a case-by-case basis. Further, the adequacy of a municipality s showing of discrimination must be evaluated in the context of the breadth of the remedial program advanced by the municipality. Notwithstanding the burden of initial production of evidence that rests with the municipality, the Tenth Circuit opinions in Concrete Works I and III were clear that the ultimate burden of proof remains with the challenging party to demonstrate the unconstitutionality of a race-based remedy. Thus, the Court stated, once Denver presented adequate statistical evidence of precisely defined discrimination in the Denver area construction market, it became incumbent upon Concrete Works either to establish that Denver s evidence did not constitute strong evidence of such discrimination or that the remedial statute was not narrowly drawn. In other words, once the municipality met its initial burden of coming forward with strong evidence to establish a prima facie case of marketplace discrimination, the burden then shifted to the plaintiff (Concrete Works) to prove by a preponderance of the evidence that the municipality s evidence did not show marketplace discrimination and that the municipality s conclusions were therefore not reasonable. Alternatively, the plaintiff could prevail also by proving by a preponderance of the evidence that the remedial statute was not narrowly tailored to remedy the identified discrimination. In the remand trial of Concrete Works II, Federal District Judge Matsch articulated a somewhat differing view of the standards of review and burdens of proof. He stated that based on Croson and Adarand, the challenged Denver ordinances must be analyzed skeptically with strict scrutiny to determine whether there is a strong showing that the purposes said to be served by those race and ethnic preferences are sufficiently compelling to be legitimate government interests, and whether the means adopted are narrowly tailored to meet those purposes. Stated differently, Judge Matsch held that the duty of the court is to look at disparity studies skeptically

Analysis of Essex County Procurement and Contracting: Final Report 59 and under strict scrutiny to determine whether they provide strong evidence that non-minority contractors systematically exclude M/WBEs from business opportunities that would be available to them but for their race, ethnicity and gender. As for the City s gender-based preference, the court in Concrete Works II held that the City must show evidentiary support for the need for a remedial program to redress gender discrimination to satisfy the intermediate scrutiny standard. In its final analysis, the Concrete Works II court held that inconclusive evidence presented by the City was not sufficiently probative of discrimination that a gender-based goals program could be found to be substantially related to the stated goal of remedying past discrimination or avoiding the City s participation in existing discrimination. 21 One exception to the general trend on burdens of proof was the recent federal district court opinion in Hershell Gill Consulting Engineers, Inc. v. Miami-Dade County, 333 F. Supp.2d 1305 (S.D. Fla., Aug. 24, 2004). In this decision, the Court specifically rejected the notion advanced by the 10 th Circuit Court of Appeals that the plaintiff challenging the affirmative action program retains the ultimate burden of proving the program s constitutionality. Instead, Judge Jordan embraced Supreme Court Justice Scalia s dissent in the denial of certiorari in the Concrete Works v. City and County of Denver decision, and then cited the 11 th Circuit precedent of Johnson v. Bd. Of Regents of the University of Georgia, 263 F.3d 1234 at 1244 (11 th Cir. 2001) for the proposition that the burden of proof under a strict scrutiny standard is on the defendant. Under District Court Judge Jordan s interpretation, the 11 th Circuit would be the only Circuit in the country where a defendant has the ultimate burden of proof in defending against a constitutional challenge to an affirmative action program. 2. Strict scrutiny standard: race-based programs In applying the strict scrutiny standard in Croson, the U.S. Supreme Court employed a twoprong analysis. First, the City was required to demonstrate a compelling governmental interest for using race-conscious criteria in the awarding of contracts. This requirement would have been satisfied if the City had demonstrated that its MBE program was remedial in nature to correct the effects of identified discrimination in the public and/or private sector local marketplace. Second, the City was required to demonstrate that its MBE program was narrowly tailored to address the effects of that identified discrimination. In this regard, factors considered by the Court included whether there were ethnic groups benefiting from the program for which there was no evidence of discrimination; whether the size of the MBE participation goal was flexible and rationally related to a relevant disparity in the marketplace; whether consideration was given to less restrictive race-neutral remedies; and whether the program contained sunset provisions or other means for periodic review to assure that it would not outlive its intended remedial purpose. 21 It should be noted, however, that the Tenth Circuit declined to follow Judge Matsch s somewhat revised construction of the legal standard of review, and instead held that once Denver had met its burden of showing a strong basis in evidence that a remedy is required, plaintiff CWC had to introduce credible, particularized evidence to rebut Denver s initial showing of a compelling interest. According to the Tenth Circuit, such evidence may consist of a neutral explanation for the statistical disparities, or by showing Denver s statistics are flawed; that disparities shown by statistics are not significant or actionable; or by presenting contrasting statistical data. However, the Court emphatically stated that the burden of proof at all times remains with the plaintiff to demonstrate the unconstitutionality of the MBE program.

Analysis of Essex County Procurement and Contracting: Final Report 60 a. compelling interest Croson and subsequent cases (with one or two exceptions) have been clear that strict scrutiny does not require jurisdictions to prove discrimination before they are allowed to enact race and gender-conscious programs. Rather, a jurisdiction is required to have a strong basis in evidence of discrimination approaching a prima facie case. In this instance, a prima facie case would be one in which the government presented sufficient evidence that, viewed in a light most favorable to the government s case, establishes, on its face, all of the requisite elements of a claim of marketplace discrimination. The discrimination that provides the basis of the compelling interest for government remedial relief may be in one of two forms: (a) discrimination that the government itself has engaged in benavior that has resulted in the underutilization of ready, willing, and able M/WBEs in government contracts; or (b) passive participation by the government in rewarding, supporting, or contributing to the perpetuation of the effects of private discrimination. A significant statistical disparity between the availability of ready, willing, and able M/WBE firms seeking to obtain contracts with the government and the actual utilization of such M/WBE firms by the government may give rise to an inference of discrimination. That inference of discrimination may be buttressed by anecdotal or qualitative evidence that tends to show that racial or gender discrimination is, in part, the cause of such significant statistical disparities. The court decisions that have explained and refined the passive participant concept have further declared that if evidence exists that a governmental entity is infusing public funds into a discriminatory industry, the governmental entity has a compelling interest in remedying the effects of such discrimination. Accordingly, wherever passive participation in private discrimination forms the basis for a government s compelling interest, its factual predicate for remedial relief should demonstrate the linkage between local government procurement and any evidence of discrimination within the private marketplace. (A more extensive discussion of relevant case law addressing the passive participation rationale for establishing a compelling interest is addressed below in Section IV. B. 4.) The controlling local precedent in Contractors II also weighed in on the side of a combination of statistical and anecdotal evidence in establishing a prima facie case of discrimination, stating, Although anecdotal evidence alone may, in an exceptional case, be so dominant or pervasive that it passes muster under Croson, it is insufficient here. The Third Circuit recommended a combination of statistical and anecdotal evidence. Upon remand of this case, the District Court held that the statistical disparity reported by Philadelphia s expert, standing alone, could not be conclusive without it being linked to additional evidence (even if the Court had accepted the City s statistical evidence, which it did not). (In Contractors III, however, the Third Circuit appears to have relied on the statistical evidence in concluding that it was a close call whether the City s evidence was adequate to provide a strong basis in evidence for an inference of discrimination in the prime contract market.) The reasoning in the District Court decision is similar to the apparent direction from in the lower court ruling in Engineering Contractors of South Florida, Inc. v. Metropolitan Dade County. In this case, the Court appeared to have discounted the use of statistical evidence to show prima facie evidence of discrimination. For

Analysis of Essex County Procurement and Contracting: Final Report 61 example, in Engineering Contractors, the Court notes that...presence of disparities does not signify the presence of discrimination. The courts in other jurisdictions have been consistent in their support of a combination of statistical analysis of disparities and anecdotal evidence of discrimination in considering whether there is an adequate factual predicate for a local government MBE/WBE program. While some decisions point out that both types of evidence are not necessarily required, particularly if strong statistical evidence of discrimination is shown, the courts have generally held that a combination of statistical and anecdotal evidence would best meet the strict scrutiny test. For example, the Ninth Circuit in Coral Construction v. King County concluded that the combination of a proper statistical foundation and convincing anecdotal evidence was potent. The Court found that neither statistics, standing alone, nor anecdotal evidence, standing alone, is sufficient. Strict scrutiny demands a fuller story. Specifically, the Court noted that statistical evidence often does not fully account for the complex factors and motivations that can affect market outcomes, many of which may be entirely race neutral. Similarly, although anecdotal evidence of specific instances of discrimination can bring cold numbers convincingly to life, such evidence cannot on its own establish that there is systemic discrimination. Thus, a combination of evidence of statistical disparities in the utilization of MBE/WBEs and particularized anecdotal accounts of discrimination is required. In Associated General Contractors of California, Inc. v. Coalition for Economic Equity, et al., the Ninth Circuit commented favorably on the vast number of individual accounts of discrimination, which brings the cold numbers convincingly to life. The Court clearly approved of the combination of statistical and anecdotal evidence relied upon by the City of San Francisco in enacting its ordinance. The Court also rejected the plaintiff s argument that the City was required to identify specific bid practices that caused the statistical disparities. The Court stated that under Croson, statistical disparities alone would support a showing of discrimination sufficient to institute a race-conscious remedial plan. The Court further held that the plaintiff s argument would undercut Croson s recognition that a governmental actor may use its spending powers to remedy private discrimination. In O Donnell Construction Company v. District of Columbia, the Court of Appeals for the District of Columbia found the District of Columbia s quantitative and qualitative evidence to be lacking. Much of the factual predicate the District offered in support of the program was anecdotal. The Court held that Anecdotal evidence is most useful as a supplement to strong statistical evidence which the Council did not produce in this case. The Tenth Circuit in Concrete Works I specifically approved of Denver s use of anecdotal evidence of public and private race and gender discrimination in support of its program. The Court concluded that anecdotal evidence of discrimination alone would not meet the strict scrutiny standard; however, the evidence vividly complemented quantitative evidence of discrimination. In Concrete Works II, the Federal District Court elaborated further that discriminating behavior cannot be proved by objective evidence, and that in the absence of a direct admission by those accused, proof of race, ethnic, or gender discrimination depends upon inferences fairly drawn from circumstantial evidence. Statistical evidence bolstered by testimony of specific instances of discrimination has been successfully used to defend a Title VII challenge to an employment affirmative action provision. However, the court noted that there

Analysis of Essex County Procurement and Contracting: Final Report 62 are inherent limitations in attempting to collect and measure useful information about the construction industry because of the nearly infinite number of variables affecting the fate of firms operating within the special business environments of the many submarkets for products and services collectively called construction. The court further stated that the probative force of statistical disparity studies depends on whether the data used provide meaningful measurements of the number of minority firms qualified, willing, and able to perform a particular service as well as the number actually used in public contracting, directly or indirectly. As noted in Concrete Works II, this is a far more daunting task than comparing a single employer s history of employment of minorities and women with a relevant pool of workers available for the same types of work. b. narrow tailoring Courts are clear that race- and gender-based remedies must meet the strict scrutiny standard of review while race and gender-neutral measures need not. While some remedies such as MBE price preference or sheltered market programs clearly fall into the category of race and genderbased remedies, some other less direct methods to assist minority and women-owned firms may not. Several cases speak to this issue. The District Court s decision in Contractors Association of Eastern Pennsylvania provides additional examples of how courts might distinguish between neutral and race and genderbased remedies. The City of Philadelphia cited two programs as neutral initiatives that it had considered before enacting a race and gender-based remedy. One program provided financial and technical assistance to minority contractors who were unable to obtain credit because they had limited experience. The other program promoted the hiring of minorities on public construction sites. The Court commented that neither of these two programs was race neutral, as it understood the term to be applied in Croson. Moreover, based upon Adarand and prior U.S. Supreme Court cases, the operative determination as to whether a strict scrutiny standard of review should be applied rests not upon whether the government has established a preference based upon race, but merely whether the government has established a classification based upon race. See e.g., Adarand Constructors, Inc. v. Peña, 115 S.Ct. 2097 (1995); McLaughlin v. Florida, 379 U.S. 184 (1964); and Korematsu v. United States, 323 U.S. 214 (1944). A number of lower court decisions have favorably cited jurisdictions prior examination of neutral remedies in assessing whether programs were narrowly tailored. Examples are provided below. Examples of Narrow Tailoring Additional guidance is provided in legal precedents from other jurisdictions. In Concrete Works I, the District Court evaluated Denver s consideration of neutral remedies (even though it also spoke of Denver s good faith efforts program as a neutral remedy ). 22 The Court found that Denver enacted its ordinance after or in conjunction with race-neutral means of increasing MBE/WBE participation. The Court cited the following neutral measures: eliminating prequalification requirements. 22 The Tenth Circuit Court of Appeals did not address any of the narrow tailoring issues in its decision.

Analysis of Essex County Procurement and Contracting: Final Report 63 breaking down projects to facilitate small business participation. implementing bond guarantee programs. enacting a prompt payment ordinance. developing a contractor mentor program and a pre-apprenticeship program. improving instructional resources for inexperienced contractors. reaching out to contactors with information. The Court found that some race-neutral measures were not available to the City of Denver; for example, payment and performance bonds were required by state law, so the City was powerless to waive such bonding requirements. In sum, the Court held that the City had fulfilled this element of the narrow tailoring requirement by considering these neutral measures. [Concrete Works of Colorado, Inc. v. City and County of Denver, 823 F.Supp. 821 (D. Co., February 26, 1993)] In Coral Construction, the Ninth Circuit held that, while strict scrutiny requires serious consideration of race-neutral alternatives, strict scrutiny does not require exhaustion of every possible such alternative, however irrational, costly, unreasonable and unlikely to succeed such alternative might be. Particularly, an entity cannot be faulted for failing to exhaust race-neutral alternatives that are outside its authority. Also, the jurisdiction cannot be required to devote precious tax dollars to projects where potential for success is marginal at best. The Court referred favorably to King County s implementation of training sessions for small businesses and dissemination of information on accessing small business assistance programs in finding that the County had fulfilled its burden of considering race-neutral alternative programs. [Coral Construction Co. v. King County, 941 F.2d 910 (9 th Cir. 1991), cert. denied, 112 S. Ct. 875 (1992)] In another recent decision in Hershell Gill Consulting Engineers, Inc. v. Miami-Dade County, 333 F. Supp.2d 1305 (S.D. Fla., Aug. 24, 2004), the District Court held that Miami-Dade s MBE program was not narrowly tailored due to a failure to adequately consider a variety of race- and gender-neutral alternatives. Specifically, the Court held: Although narrow tailoring does not require exhaustion of every conceivable race-neutral alternative, it does require serious, good faith consideration of workable race-neutral alternatives, and the County failed to show the necessity of the relief chosen because the efficacy of alternative remedies had not been sufficiently explored. The County failed to show that its use of a small business program for construction had been ineffective, and/or that such a race-neutral approach would have been ineffective if applied to architectural and engineering contracts. However, in Adarand III, the Tenth Circuit Court of Appeals had found the record of Congress earlier use of race-neutral measures prior to incorporating race-based initiatives into the Small Business Administration programs to be sufficient to pass this aspect of narrow tailoring. [Adarand Constructors, Inc. v. Slater, 228 F.3d 1147, at 1176-1187 (10 th Cir. 2000)]

Analysis of Essex County Procurement and Contracting: Final Report 64 Demonstrated Consideration of Neutral Remedies The District Court in Contractors Association of Eastern Pennsylvania held that there was no evidence that the City of Philadelphia actually reviewed the effectiveness of the two programs the City cited as the neutral measures it considered. Further, the Court found that the evidence suggests that the previous programs cited as insufficient by the City had actually been successful. And, the Court further found that the City had not attempted to remedy barriers to all firms created by the City s procurement procedures. The Court urged the City to first consider relaxing its prequalification and bonding requirements for economically-disadvantaged contractors of all races. Training and financial assistance programs for all disadvantaged contractors were other neutral remedies suggested by the Court. In addition, the Court indicated that the City could vigorously enforce the anti-discrimination provisions of the City Charter and the Procurement Department s standard contracting requirements. In Contractors III, the Third Circuit held that the record supports the District Court s finding that alternatives to race-based preferences were available that would have been either race-neutral or, at least, less burdensome to non-minority contractors. Because the City failed to consider or adopt these alternatives, its race-based program was not narrowly tailored. In reaching this conclusion, the Court pointed specifically to the City s failure to consider a credit program for small or minority contractors. The City s apparent consideration of the alleged failure of the federal Small Business Administration to increase the number of minority and women-owned businesses is not constitutionally adequate consideration of the potential effectiveness of race-neutral measures for a particular industry in a particular locality. [Contractors Assoc. of Eastern Pennsylvania, Inc. v. City of Philadelphia, 739 F. Supp 227, 6 F.3d 990 (3 rd Cir. 1993)] Also, in Associated General Contractors of California, the Ninth Circuit held that an MBE program should be instituted either after, or in conjunction with, race-neutral measures. The Court held that the City of San Francisco considered race-neutral alternatives, but rejected them as not viable. The Court also found that the City had attempted to address discrimination in City contracting through a past race-neutral ordinance. [Associated General Contractors of California, Inc. v. Coalition for Economic Equity, et. al, 950 F.2d 1401 (9 th Cir. 1991), cert. denied 112 S. Ct. 1670 (1992)] In Engineering Contractors Association of South Florida, the Eleventh Circuit Court of Appeals agreed with the District Court that Dade County had not seriously considered most of the race and ethnicity-neutral alternatives available to it for remedying the effects of discrimination against MBEs. The Court criticized the County for failing to ferret out and respond to instances of discrimination potentially occurring in the County s own contracting process. The Court found that the County had taken no steps to inform, educate, discipline, or penalize its own officials and employees responsible for the misconduct. The first measure every government ought to undertake to eradicate discrimination is to clean its own house and to ensure that its own operations are run on a strictly race and ethnicity-neutral basis. The Court also criticized the County for failing to pass local ordinances to outlaw discrimination by local contractors, subcontractors, suppliers, bankers or insurers. It is important to note that the Eleventh Circuit did not hold the County to this level of narrow tailoring when considering its WBE program. [Engineering Contractors Ass n v. Metropolitan Dade County, 943 F. Supp. 1546 (S. D. Fla., 1996), aff d, 122 F.3d 895 (11 th Cir. 1997)]

Analysis of Essex County Procurement and Contracting: Final Report 65 Limited Scope of Remedies (Race, Gender, Geography, and Form) An additional element of narrow tailoring of remedies is the limitation of those remedies to identified discrimination and to firms affected by such discrimination. There are three parts to this element of narrow tailoring. First, courts have held that the remedies be limited to racial and ethnic groups for which evidence of discrimination exists. Second, Croson and post-croson decisions have indicated that the remedies should be limited to eradicating discrimination within the boundaries of the enacting jurisdiction. Finally, the remedies should focus upon the particular forms of identified discrimination. Subsequent decisions suggest a somewhat broader definition of the market area for which firms could be presumptively eligible for relief. For example, under controlling local precedent in Contractors II, the Third Circuit found that Philadelphia s ordinance was geographically targeted to Philadelphia businesses as waivers and exemptions are permitted where there exist an insufficient number of MBEs within the Philadelphia Standard Metropolitan Statistical Area. Upon remand, the District Court also approved of the limitation of the preference to the Philadelphia Metropolitan Area, but found that, as applied, the program was not geographically limited to this area. The Third Circuit s decision in Contractors III also held that a subcontracting-focused program was not narrowly tailored since there was no evidence of discrimination presented for subcontracting. We do not suggest that an appropriate remedial program for discrimination by a municipality in the award of primary contracts could never include a component that affects the subcontracting market in some way. We hold, however, that a program, like Philadelphia s current one, which focuses almost exclusively on the subcontracting market, is not narrowly tailored to address discrimination by the City in the market for prime contracts. The lack of fit between the alleged form of discrimination and the form of the remedy was also at issue in another controlling local precedent. In Association for Fairness in Business, Inc. v. New Jersey, in defending its MBE program, the State misplaced reliance on a 1993 Final Report that focused upon discrimination in State contract awards, and not the contracting practices of private casinos. Yet, it was the private casinos that were required to set aside contracts for M/WBEs under the State s program. Accordingly, the Court held that the program was not narrowly tailored. In Associated General Contractors of California, the Ninth Circuit commented favorably on the City of San Francisco s system for extending remedies only to those minority groups found to have previously received a lower percentage of specific types of contracts than their availability to perform such work would suggest. For example, remedies were not extended to Asian or Hispanic-owned architecture and engineering firms. The ordinance further limited the program to minority-owned firms that were economically disadvantaged. Denver s graduation provisions for MBEs and WBEs in its program also were cited favorably by the District Court in Concrete Works I. (Denver also required firms to have been in the construction business for at least three months to be eligible for the program.) The Court of Appeals for the District of Columbia found an absence of any findings with respect to discrimination against Hispanics, Asian Americans, Pacific Islanders, or Native Americans, all of whom were included in the District of Columbia Minority Contracting Act s definition of minority. This is one of the reasons the Court concluded that the District s program failed the narrow tailoring test (see O Donnell). The District Court in RGW also examined evidence of

Analysis of Essex County Procurement and Contracting: Final Report 66 discrimination against individual minority groups (African Americans, Hispanics and Asians) in ruling on BART s motion to dissolve or modify the Court s preliminary injunction. Moreover, the preliminary injunction was only dissolved in two of four counties comprising BART s service area due to completed disparity studies for those two counties. [O Donnell Construction Co. v. District of Columbia, 762 F. Supp. 354 (D.D.C. 1991), rev d, 963 F.2d 420 (D.C. App. 1992); RGW Construction, Inc. v. San Francisco BART, F. Supp. (Slip Op. N.D. Calif., Nov. 25, 1992)] The District Court s decision in Concrete Works I also considered whether Denver s goals program remedied only identified discrimination. Concrete Works argued that Denver s ordinance indiscriminately lumped ethnic groups together for the purpose of determining whether their members had suffered from discrimination. The Court held that it was not necessary that the City extend benefits only to MBEs that had individually demonstrated that they had been affected by discrimination. Citing Coral Construction, the Court reasoned that since Denver had shown evidence of systemic discrimination, it was fair to presume that an MBE was victimized by the discrimination. The Court also commented favorably on Denver s evidence of disparities for all four racial minority groups included in the remedies. [Concrete Works of Colorado, Inc. v. City and County of Denver, 823 F.Supp. 821 (D. Co., February 26, 1993)] Court decisions have differed somewhat on the issue of geographic limitation of remedies. In Coral Construction, the Ninth Circuit held that King County s program failed the narrow tailoring requirement because the program included many minority-owned firms that were outside the geographic boundaries of King County. While some of these firms had indicated that they had been discriminated against in the particular geographic areas in which they operated, there was no specific evidence that they had attempted to do business within King County. (In fact, the MBE that was awarded the contract over Coral Construction because of King County s MBE price preference was an Oregon-based firm.) The Court held that the proper question in choosing to extend a remedy was whether the business had been discriminated against in King County. The issue was not business location but business participation. Upon a finding of discrimination within the King County business community, an MBE could be presumptively eligible for relief if it had previously sought to do business within the County. [Coral Construction v. King County, 941 F.2d 910 (9 th Cir. 1991), cert. denied 112 S. Ct. 875 (1992)] Similarly, the District Court in RGW held that remedies could be extended only to firms located within the jurisdiction of the public entity or to those that had attempted to become or were active participants in the business communities lying within the boundaries of the jurisdiction. [RGW Construction, Inc. v. San Francisco BART, F. Supp. (Slip Op. N.D. Calif., Nov. 25, 1992)] Additionally, under the relevant local precedent in Association for Fairness in Business, Inc. v. New Jersey, the Federal District Court held that the Casino Control Act provision for a minority set-aside program was not narrowly tailored to the compelling interest that New Jersey asserted. Specifically, the definition of minorities included persons of Native American, Native Alaskan, Hawaiian, or Portuguese descent, without any evidence of discrimination against companies run by such individuals. Furthermore, there was no evidence in the record that New Jersey attempted race-neutral measures before adopting the minority set-aside program.

Analysis of Essex County Procurement and Contracting: Final Report 67 In Bilbo Freight Lines, the District Court struck down a State of Texas program to give preferential treatment to minorities and women in the issuance of certificates to conduct intrastate trucking operations. The District Court deemed the perceived barriers to entry and participation in the intrastate industry to be race and gender-neutral, affecting all small businesses. The Court found no evidence of gross statistical disparities between the number of minority and women-owned firms in the relevant market that were qualified to seek intrastate trucking authority and the number of minorities and women holding such authority. The statistical evidence presented by the State appeared to pertain to underutilization of existing MBE and WBE trucking firms, not disparities between the number of MBE and WBE trucking firms in existence and the number licensed by the State. The Court did not deem the former comparison to be relevant to the awarding of certificates. This underscores the importance of particularized findings of discrimination and remedies that are narrowly tailored to redress the identified form of discrimination. The decision suggests that the Court was interested in statistical evidence of disparities in the rates of business formation or ownership as a justification for a preference that eased market entry for MBEs; however, this evidence apparently was not produced. [Bilbo Freight Lines, Inc., et al. v. Dan Morales, et al, C. A. No. H-93-3808 (S.D. TX, Feb. 3, 1994)] Appropriate Goal Setting and Waivers Programs often contain two types of goals: annual goals that might be used as benchmarks for yearly evaluation of the operation of the program, and project-specific goals for MBE/WBE participation on a particular contract. Some jurisdictions subject to legal challenge have not distinguished these two types of goals; the annual goal for MBE or WBE participation was automatically applied as the project-specific goal. As for the controlling local precedent, the Third Circuit found the City of Philadelphia to be on weak ground in supporting its 15 percent goal for utilization of minority-owned construction firms. In practice, Philadelphia usually applied the 15 percent annual goal as the project-specific goal for each contract. The City s disparity study found availability of minority-owned construction firms to be 2.4 percent. In spite of the weakness in goal setting, the Third Circuit in Contractors II reversed the District Court s grant of summary judgment on this point as the Court believed the City had created a dispute of fact on whether the minority preference in the ordinance was narrowly tailored. The Court stated, We do not believe the goal must correspond precisely to the percentage of available contractors. Indeed, Croson does not impose this requirement. Furthermore, imposing a 15 percent goal for each contract may reflect the need to account for those contractors who receive a waiver because insufficient minority businesses are available, and the contracts exempted from the program. Upon remand, the District Court held that the 15 percent goal for minority participation was not selected for a remedial purpose. The Court found evidence that the minority composition of the local population, if anything, was the information used by the City to establish the goal. The Court criticized the City for setting this goal without collecting and considering information on availability of minority-owned firms in the Philadelphia area. In Contractors III, the Third Circuit agreed that the record indicated that the goals were established considering only the minority composition of the local population. Council made no effort... to determine how the Ordinance might be drafted to remedy particular discrimination to achieve, for example, the approximate market share for black contractors that would have existed, had the purported discrimination not occurred.

Analysis of Essex County Procurement and Contracting: Final Report 68 However, the Third Circuit also stated We do not suggest that the percentage of the preferred group in the universe of qualified contractors is necessarily the ceiling for all set-asides. It well may be that some premium could be justified under some circumstances. This issue has been addressed in a similar fashion by other circuits. Several courts have examined whether individual jurisdictions had incorporated flexibility and waivers into their programs, or whether the jurisdictions had applied rigid numerical quotas. For example, the Ninth Circuit approved of King County s allowances for waivers when neither an MBE nor a WBE were available to provide needed goods or services or when available MBEs and WBEs had given price quotes that were unreasonably high. The Ninth Circuit made similar findings related to the flexibility of San Francisco s bid preference program. In Concrete Works I, the District Court also examined Denver s ordinance for flexibility and opportunities for waivers and concluded that the City passed this test. Denver s ordinance provided an example of a system of annual goals (16 percent MBE and 12 percent WBE participation in the City s construction contracts measured by dollars), separate and apart from project by project goals (which could be set at zero), and was favorably reviewed by the court in Concrete Works I. There was no requirement that the City had to meet the 16 percent and 12 percent annual goals on individual contracts or on an annual basis. Likewise, Denver did not require that a contractor meet the project-specific goals; meeting the goals was but one avenue of complying with the good faith component of the program. Contractors had opportunity for waivers, goals were set on a project-by-project basis and goals were set only where there were qualified MBE/WBEs available. A bidder was not required to use an MBE or WBE as a subcontractor if the MBE or WBE failed to submit the lowest bid or was otherwise unqualified to perform the work. However, upon remand, Judge Matsch s decision in Concrete Works II criticized the City s failure to measure relative discrimination as experienced between different races and gender. The Court looked unfavorably upon the fact that both the MBE and WBE goals had been set at 10 percent with no apparent effort to use data from the Mayor s Office of Contract Compliance to periodically adjust the goals. Moreover, the Concrete Works II opinion cast doubt on the use of a contract goals program as an appropriate remedy for discrimination in access to credit, as there was no evidence that such unperformed contracts would be acceptable as collateral to banks. [The Court gave no indication that it had considered the value such contracts might have for demonstrating to banks that there was sufficient cash flow potential on the part of such discrimination victims for loan repayment.] Ultimately, however, in Concrete Works III, the Tenth Circuit Court of Appeals reversed Judge Matsch s decision and upheld the legality of the Denver MBE program on the issue of narrow tailoring of goals. Nevertheless, other jurisdictions have found that even flexible and aspirational annual goals may violate the law when they are applied improperly. In O Donnell, the Court of Appeals held that the District of Columbia s annual goal of 35 percent MBE participation was both inflexible as applied, and unsupported by the facts. As enacted, the percentage became far more than merely a hope, a wish or an aspiration. The Court specifically cited the goal setting process as another of the flaws in the District of Columbia s Minority Contracting Act which contributed to its finding in favor of the plaintiff. Although the Council had some statistics at its disposal when it originally enacted a goal of 25 percent in 1977, the Court held that the goal of 35 percent MBE utilization established in 1983 was not supported by any facts or findings. [O Donnell Construction Co. v. District of Columbia, 762 F. Supp. 354 (D.D.C. 1991), rev d, 963 F.2d 420 (D.C. App. 1992)]

Analysis of Essex County Procurement and Contracting: Final Report 69 Similarly, in Associated Utility Contractors, the Federal District Court for Maryland noted that the City of Baltimore enacted Ordinance 610 in 1990 to replace prior 1986 Ordinance 790 based upon the findings of a City Task Force. Prior Ordinance 790 had established set-aside goals of 20 percent of the value of subcontracts to be awarded to MBEs and 3 percent to WBEs. No disparity statistics had been offered to justify the prior Ordinance. It was supported only by a City Council finding that general societal discrimination had disadvantaged M/WBEs. The new Ordinance 610 did not establish any M/WBE goals, but mandated a procedure by which set-aside goals were to be established each year by the City for each category of contracting (e.g., public works, professional services, concession and purchasing contracts). However, as applied, the City used the program to simply reapply the old goals of 20 percent and 3 percent across the board for all contract categories. These same goals were adopted without variation and without support for each year from 1990 through 1999. No annual study was ever undertaken. Indeed, the City did not even collect the data that would have permitted such analysis. The City s disparity study was only commenced after the filing of this lawsuit. Periodic Review and Sunset Provisions The U.S. Supreme Court in Adarand reiterated that a program must be appropriately limited such that it will not last longer than the discriminatory effect it is designed to eliminate. Several lower courts have examined provisions for the periodic review and sunset provisions meant to comply with these requirements. In Concrete Works I, the limit of Denver s ordinance to five years duration and measures for periodic review were favorably reviewed by the District Court. The Court of Appeals in O Donnell criticized the District of Columbia s MBE program for its lack of sunset provisions. In Contractors Association of Eastern Pennsylvania, the District Court found Philadelphia s program not to be narrowly tailored in part because the City had approved an eleven and one-half year extension of the ordinance without a review of the appropriateness and efficacy of the program. c. forms of evidence required to satisfy strict scrutiny test The controlling local precedent from the Third Circuit Court of Appeals in Contractors II strongly endorses a combination of statistical and anecdotal evidence in establishing a prima facie case of discrimination, stating, Although anecdotal evidence alone may, in an exceptional case, be so dominant or pervasive that it passes muster under Croson, it is insufficient here. Upon remand of this case, the District Court held that the statistical disparity reported by Philadelphia s expert, standing alone, could not be conclusive without it being linked to additional evidence (even if the Court had accepted the City s statistical evidence, which it did not). (In Contractors III, however, the Third Circuit appears to have relied on the statistical evidence in concluding that it was a close call whether the City s evidence was adequate to provide a strong basis in evidence for an inference of discrimination in the prime contract market.) The reasoning in the District Court decision is similar to the apparent direction from the lower court ruling in Engineering Contractors of South Florida, Inc. v. Metropolitan Dade County. In that case, the Court appeared to have discounted the use of statistical evidence to show prima facie evidence of discrimination. In Engineering Contractors, the Court had noted that...presence of disparities does not signify the presence of discrimination.

Analysis of Essex County Procurement and Contracting: Final Report 70 The general legal trend in other jurisdictions has been similar. For example, the Ninth Circuit in Coral Construction v. King County concluded that the combination of a proper statistical foundation and convincing anecdotal evidence was potent. The Court found that neither statistics, standing alone, nor anecdotal evidence, standing alone, is sufficient. Strict scrutiny demands a fuller story. Specifically, the Court noted that statistical evidence often does not fully account for the complex factors and motivations that can affect market outcomes, many of which may be entirely race neutral. Similarly, although anecdotal evidence of specific instances of discrimination can bring cold numbers convincingly to life, such evidence cannot on its own establish that there is systemic discrimination. Thus, a combination of evidence of statistical disparities in the utilization of MBE/WBEs and particularized anecdotal accounts of discrimination is required. In Associated General Contractors of California, Inc. v. Coalition for Economic Equity, et al., the Ninth Circuit commented favorably on the vast number of individual accounts of discrimination, which brings the cold numbers convincingly to life. The Court clearly approved of the combination of statistical and anecdotal evidence relied upon by the City of San Francisco in enacting its ordinance. The Court also rejected the plaintiff s argument that the City was required to identify specific bid practices that caused the statistical disparities. The Court stated that under Croson, statistical disparities alone would support a showing of discrimination sufficient to institute a race-conscious remedial plan. The Court further held that the plaintiff s argument would undercut Croson s recognition that a governmental actor may use its spending powers to remedy private discrimination. d. strong basis in evidence test In Contractors III, the Third Circuit Court of Appeals upheld the district court opinion striking down Philadelphia s MBE program on the basis that the program was not narrowly tailored to serve a compelling interest. (91 F.3d 586) In doing so, the Court of Appeals held that there was an absence of a strong basis in evidence reflecting discrimination against black subcontractors by prime contractors or trade associations. The Court of Appeals also held that it was a close call whether the record in that case provided a strong basis in evidence for an inference of discrimination by the City against black construction firms in the prime contract market. The Court, however, declined to make that call and held that it was not necessary to do so. As Philadelphia s program focused almost exclusively on preferences to black subcontractors, the Court concluded it clearly was not narrowly tailored to address discrimination by the City in the market for prime contracts. Another decision that clearly delineated a jurisdiction s burden of production and the plaintiff s burden of proof in which a full disparity study had been performed for the jurisdiction was the Tenth Circuit decision in Concrete Works of Colorado, Inc. v. City and County of Denver I. The Tenth Circuit stated that although Croson places the burden of production on the jurisdiction to demonstrate a strong basis in evidence that its race and gender-based programs aim to remedy specifically identified past or present discrimination, the Fourteenth Amendment does not require a court to make an ultimate judicial finding of discrimination before a municipality may take affirmative steps to eradicate discrimination. The Court stated, We do not read Croson to require, nor do we embrace, an attempt to craft a precise mathematical formula to assess the quantum of evidence that rises to the Croson strong basis in evidence benchmark. That must

Analysis of Essex County Procurement and Contracting: Final Report 71 be evaluated on a case-by-case basis. Further, the adequacy of a municipality s showing of discrimination must be evaluated in the context of the breadth of the remedial program advanced by the municipality. Notwithstanding the burden of initial production that rests with the municipality, the Court was clear that the ultimate burden of proof remains with the challenging party to demonstrate the unconstitutionality of a race-based remedy. Thus, the Court stated, once Denver presented adequate statistical evidence of precisely defined discrimination in the Denver area construction market, it became incumbent upon Concrete Works either to establish that Denver s evidence did not constitute strong evidence of such discrimination or that the remedial statute was not narrowly drawn. In other words, once the municipality met its initial burden of coming forward with strong evidence to establish a prima facie case of marketplace discrimination, the burden then shifted to the plaintiff (Concrete Works) to prove by a preponderance of the evidence that the municipality s evidence did not show marketplace discrimination and that the municipality s conclusions were therefore not reasonable. Alternatively, the plaintiff could prevail also by proving by a preponderance of the evidence that the remedial statute was not narrowly tailored to remedy the identified discrimination. In the remand trial of Concrete Works II, Judge Matsch articulated a somewhat differing view of the standards of review and burdens of proof. He stated that based on Croson and Adarand, the challenged Denver ordinances must be analyzed skeptically with strict scrutiny to determine whether there is a strong showing that the purposes said to be served by those race and ethnic preferences are sufficiently compelling to be legitimate government interests, and whether the means adopted are narrowly tailored to meet those purposes. Stated differently, Judge Matsch held that the duty of the court is to look at disparity studies skeptically and under strict scrutiny to determine whether they provide strong evidence that non-minority contractors systematically exclude M/WBEs from business opportunities that would be available to them but for their race, ethnicity, and gender. However, Judge Matsch s view was specifically rejected by the 10 th Circuit Court of Appeals in Concrete Works III, which held that Judge Matsch failed to accord sufficient weight to the evidence presented by Denver due to his improper allocations of the burdens and standard of review. 3. Intermediate scrutiny standard: gender-based programs The affirmative action program at issue in Croson was a minority business participation program only, and did not include women-owned business enterprises. In the wake of the Croson decision, several federal appellate courts have evaluated gender-based affirmative action programs and have set forth the guidelines for evaluating such programs. Most of the appellate courts have adopted the less demanding intermediate scrutiny analysis, as opposed to the strict scrutiny analysis for race-conscious programs. For example, in Coral Construction, the Ninth Circuit applied an intermediate scrutiny standard in reviewing the WBE portion of King County s ordinance. Based upon anecdotal evidence of discrimination, the Ninth Circuit affirmed the grant of summary judgment in favor of the WBE portion of the County s program. In Concrete Works I, the Tenth Circuit also applied the intermediate level of scrutiny in evaluating the WBE portion of Denver s ordinance. Upon remand, the Federal District Court in Concrete Works II followed the direction of the Tenth Circuit and continued to apply the intermediate scrutiny standard to gender preferences. Under intermediate scrutiny, a gender classification is reviewed to ensure that it is substantially related to an important governmental objective, and there is a direct, substantial relationship

Analysis of Essex County Procurement and Contracting: Final Report 72 between the objective and the means chosen to accomplish the objective. Yet, courts are not clear as to how the necessary quantum of evidence differs between the strict scrutiny and intermediate scrutiny standards. The Third Circuit in Contractors II admitted, It is unclear whether statistical evidence as well as anecdotal evidence is required to establish the discrimination necessary to satisfy intermediate scrutiny, and if so, how much statistical evidence is necessary. The Third Circuit upheld the District Court s decision striking down the portion of Philadelphia s program pertaining to women-owned construction firms concluding that the City had no statistical evidence and very sketchy anecdotal evidence for WBE construction firms. The U.S. Supreme Court recently invalidated Virginia s maintenance of the single-sex Virginia Military Institution (VMI). United States v. Virginia, 116 S.Ct. 2264 (1996). Although the Court invalidated this gender-based classification under the Equal Protection Clause of the 14th Amendment, it apparently used a standard of review that is different from traditional intermediate scrutiny, yet apparently not identical to strict scrutiny. The Court held that [p]arties who seek to defend gender-based government action must demonstrate an exceedingly persuasive justification for that action. Accordingly, it remains unclear what precise standard of review is applicable to gender classifications and whether the Supreme Court is articulating a new level of heightened scrutiny for such cases. Id. at 2274. After the VMI decision, the Eleventh Circuit still applied intermediate scrutiny in examining the constitutionality of Dade County s WBE program. Unless and until the Supreme Court tells us otherwise, intermediate scrutiny remains the applicable constitutional standard in gender discrimination cases, and a gender preference may be upheld so long as it is substantially related to an important governmental objective. Further, the Court explains that the difference between the evidentiary foundation necessary to support a race-based program and the foundation necessary to support a gender-based program is one of degree, not of kind. Less evidence is needed. Also, there is no requirement that the government demonstrate discrimination by the government itself. There is also no requirement that gender-conscious programs be used only as a last resort. They only need to be based upon evidence of past discrimination in the economic sphere at which the program is directed. See Engineering Contractors Association of South Florida, Inc. et al. v. Metropolitan Dade County, et al. Similarly, in 2000, the Federal District Court of Maryland in Associated Utility Contractors invalidated a 3 percent set-aside goal for WBEs on the basis that the sheer absence of any preenactment evidence of discrimination precluded a showing that the gender-based preference was substantially related to achievement of the important objective of remedying gender discrimination. This is further indication that the intermediate scrutiny standard continues to be applied to gender-based preferences. Based upon this direction from the courts, we will apply the same approach for examining evidence of discrimination for women-owned firms as for minority-owned firms in this disparity study. In weighing whether such evidence meets the intermediate scrutiny test, we will take the courts direction that less evidence is required to support a gender-based program. The district court in Concrete Works II held that the Denver must show evidentiary support for the need for a remedial program to redress gender discrimination to satisfy the intermediate scrutiny standard for its gender-based WBE program. In its final analysis, the Concrete Works II court held that inconclusive evidence presented by the City was not sufficiently probative of discrimination that a gender-based goals program could be found to be substantially related

Analysis of Essex County Procurement and Contracting: Final Report 73 to the stated goal of remedying past discrimination or avoiding the City s participation in existing discrimination. B. Scope of Evidence 1. Use of post-enactment evidence A number of lower courts have held that a jurisdiction can utilize evidence of discrimination collected after enactment of an MBE program to support that program. (Use of post-enactment evidence is directly related to a state or local government s application of the findings of a disparity study, not necessarily the performance of the study itself.) For example, in Coral Construction, the only relevant statistical evidence that King County could proffer regarding underutilization of MBE/WBEs in its jurisdiction was post-enactment studies completed in 1990. While the Court ruled that a municipality must have some concrete evidence of discrimination in a particular industry before it may adopt a remedial program, it declined to require that all such evidence must be gathered prior to enactment of such a program. Instead, the Ninth Circuit held that the factual predicate for the program should be evaluated based upon all evidence presented to the court, whether such evidence was adduced before or after enactment of the program. This rule was designed to recognize the seemingly conflicting demands placed upon a state or municipality by the U.S. Constitution. On the one hand, the local government has the authority, and even the duty, to eradicate identified racial discrimination in both the public and private sectors. On the other hand, if a municipality fails to act immediately in remedying such identified discrimination while it waits the necessary months for further development of the record, it may risk constitutional culpability due to its inaction. This rule was intended to lessen the likelihood of such dilemmas. Apparently, at least in the Ninth Circuit, if a jurisdiction has some basis for believing that there may be a need for affirmative action to remedy marketplace discrimination, it will be permitted to enact an MBE/WBE program and subsequently enhance its factual predicate for that program and present the post-enactment evidence at trial. In Concrete Works I, the Tenth Circuit expressly approved of the District Court s consideration of post-enactment evidence. The Court reasoned that it would make little sense to strike down the ordinance solely because the evidence of discrimination before the City Council was insufficient without post-enactment evidence, only to watch the City Council reconvene immediately, incorporate the new evidence into a nearly identical ordinance, and arrive at a constitutionally adequate factual predicate. Upon remand, in Concrete Works II, the District Court commented that post-enactment evidence is admissible because it is useful in evaluating the remedial effects or shortcomings of the race- conscious program and whether deviation from the norm of equal treatment is necessary. However, in Associated Utility Contractors, the Federal District Court of Maryland relied upon the U.S. Supreme Court ruling in Shaw v. Hunt, 517 U.S. at 910 (1996) to hold that only evidence that was before the City of Baltimore at the time it adopted its 1999 set-aside goals can be considered to determine whether the challenged measure was justified by a strong basis in evidence. The Shaw case was decided in 1996 and clarified that the strong basis in evidence that satisfies the compelling interest prong of the strict scrutiny standard refers only to that evidence which is considered at the time of enactment of the challenged provision. Numerous

Analysis of Essex County Procurement and Contracting: Final Report 74 Circuit Court decisions that permitted such consideration of post-enactment evidence to establish a strong basis in evidence (e.g., Concrete Works, Coral Construction v. King County, and Harrison & Burrowes Bridge Constructors, Inc. v. Cuomo) were decided prior to the Supreme Court s ruling in Shaw and are therefore not controlling. See also West Tennessee Chapter of Assoc. Builders & Contractors, Inc. v. Board of Education of the Memphis City Schools, 64 F. Supp. 2d 714 (W.D. Tenn. 1999). In Associated Utility Contractors, it was undisputed that the City considered no evidence in 1999 before promulgating the construction subcontracting set-aside goals of 20 percent for MBEs and 3 percent for WBEs. Due to the sheer absence of any record of evidence, there was no dispute of material fact foreclosing Summary Judgment in favor of the Plaintiff. The City necessarily failed the strong basis in evidence test required of the 20 percent MBE goal. Nor was the 3 percent WBE preference shown to be substantially related to achievement of the important objective of remedying gender discrimination in 1999, in the construction industry. Nevertheless, Judge Davis commended the City for (finally) beginning to collect and analyze the data which the City Council directed it to begin collecting annually back in 1990 when the Ordinance was enacted. Judge Davis stated that presuming the data of the City s pending disparity study was reliable and complete, the city could soon have the statistical basis upon which to make the findings Ordinance 610 requires and which could justify the constitutionally required standards for the promulgation and implementation of narrowly tailored set-aside raceand gender-conscious goals. While the Third Circuit has not explicitly ruled on the appropriate use of post enactment evidence, due to the U.S. Supreme Court precedent in Shaw v. Hunt, the prudent course is to rely only upon evidence that has been gathered and analyzed prior to enactment of any race- or gender-conscious policies when determining if there is a strong basis in evidence that warrants adoption of such policies. Arguably, post enactment evidence might appropriately be used to defend such policies as being narrowly tailored, provided that such post enactment evidence establishes an ongoing need for a remedy as of the time of litigation. 2. Time frame for evidence The District Court in Contractors Association of Eastern Pennsylvania reviewed evidence of disparities in City contracting for a three-year period from 1979 through 1981. Although Philadelphia s program was struck down, the Court did not address the sufficiency of the length of the time period examined, except to note that anomalies in relatively short time frames are more likely to distort results. Instead the Court s decision rested largely upon criticism of the City of Philadelphia for not examining the extent of African American participation in federallyassisted public works contracts let by the City as a basis for rejecting the City s evidence that African American-owned firms had been underutilized in the local construction industry. Other lower court decisions after Croson have not specifically addressed the minimum number of years of data for required for statistical analysis of utilization. In Associated General Contractors of California, the Ninth Circuit denied plaintiff s motion for preliminary injunction based in part on City of San Francisco utilization data for a single fiscal year. The District Court in Phillips & Jordan v. Watts reviewed data for a two-year period of time. Its only criticism was that these two years were aggregated.

Analysis of Essex County Procurement and Contracting: Final Report 75 However, the Tenth Circuit opinion in Concrete Works I concludes that utilization data untainted by affirmative action programs are most relevant and persuasive in examining existence of disparities, even if the data represent a relatively small fraction of total contracts. Nevertheless, upon remand, the Federal District Court in Concrete Works II also criticized the City of Denver for largely focusing its analysis and evidence of utilization data on such contracts untainted by affirmative action programs. The Concrete Works II court reasoned that evidence of the effects of affirmative action programs on M/WBE utilization was relevant to the issue of whether the programs were successful in remedying effects of identified discrimination. Such effectiveness was deemed by the Federal District Court to be relevant to the issue of whether the programs are still needed and are therefore narrowly tailored. While Judge Matsch acknowledged that asking the question about what happens in the market without affirmative action programs may be consistent with scientific methodology, he held that such a methodological approach does not square with the applicable law. (Note, however, that Judge Matsch s decision was reversed by the Tenth Circuit Court of Appeals). The Ninth Circuit in RGW adopted a differing view from Judge Matsch as it examined information developed in a disparity study for Alameda County. The Court stated, [T]o the extent that the most recent data reflect the impact of operative DBE goals, then such data are not necessarily a reliable basis for concluding that remedial action is no longer warranted. Accordingly, we conclude that the 1985-87 period for non-federally funded contracts is the most relevant period along with the statistics for private sector contracting. The District Court in Engineering Contractors of South Florida also noted the value of preprogram statistical evidence, and recognizing that utilization data obtained after enactment of an MBE program can be skewed by the effects of the program stated,... the Court would prefer to rely on the data from 1982, the last year prior to the enactment of the ordinance, which seems to offer the best window on the County s treatment of black contractors apart from the set-aside program. 3. Geographic scope of evidence Under the controlling local precedent in Contractors II, the Third Circuit found that Philadelphia s ordinance was geographically targeted to Philadelphia businesses as waivers and exemptions are permitted where there exist an insufficient number of MBEs within the Philadelphia Standard Metropolitan Statistical Area. The District Court also cited three reasons for finding that the eight-county Philadelphia SMSA was the relevant geographic area. First, Philadelphia s ordinance was intended to remedy racial discrimination in the Philadelphia Metropolitan Area. Second, most of the City contracts and contract dollars were awarded to firms within this area. Finally, expanding the relevant geographic area beyond the Philadelphia Metropolitan Area would unnecessarily expand the number of potentially available contractors of all races without any corresponding evidence that those additional contractors were qualified, willing, and able to perform City contracts, or that African American contractors outside this area were discriminated against in the award of City contracts. Upon remand, while the District Court continued to approve of the limitation of the preference to the Philadelphia Metropolitan Area, it found that, as applied, the program was not geographically limited to this area. Accordingly, the general rule is that analysis of evidence gathered from the relevant metropolitan statistical area is appropriate, and jurisdictional political boundaries do not necessarily define the relevant geographic market for disparity study purposes.

Analysis of Essex County Procurement and Contracting: Final Report 76 A few other post-croson decisions have also considered the appropriate definition of the relevant geographic area for purposes of examining availability. (Such definitions of relevant geographic market also provide a framework for analyzing anecdotal evidence of discrimination, as well as quantitative evidence of marketplace discrimination). Several of these decisions have approved of defining the relevant geographic market area as the metropolitan area in which the jurisdiction is located if this represents the area from which the entity predominantly makes its purchases. However, the San Francisco city limits were the extent of the relevant geographic market area in the City s disparity study considered in Associated General Contractors of California (the City s program was limited to MBEs within the city limits). Similarly, in Cone Corporation, the Eleventh Circuit had before it availability statistics pertaining to the geographic boundaries of the county. Moreover, in Phillips & Jordan v. Watts, the Court criticized the disparity study for examining utilization and availability on a statewide basis, rather than district by district. The Court noted that contracts were being let on a district-by-district basis, and was concerned that geographic aggregation of the data might result in a finding of disparities where none exist at the district level. The only reasons apparently given by the study authors for the aggregated approach were administrative convenience and cost savings. Conversely, the Tenth Circuit rejected Concrete Works argument that Denver could not use empirical evidence of discrimination within the six-county Denver Metropolitan Statistical Area and was limited to data describing discrimination within its city limits. The Court held that the local construction market, not necessarily confined by jurisdictional boundaries, is the relevant area to consider evidence of discrimination. To confine the permissible data to a governmental body s strict geographic boundaries would ignore the economic reality that contracts are often awarded to firms situated in adjacent areas. This approach to defining the relevant geographic market area was subsequently adopted by the Federal District Court in Concrete Works II. 4. Public vs. private sector discrimination The first prong of the strict scrutiny analysis requires a firm or strong basis in evidence of either active participation by the government in prior discrimination, or government involvement as a passive participant in discrimination by the local industry. In Croson, the Supreme Court explained the passive participant concept only by stating that a governmental entity has a compelling interest in ensuring that its public dollars do not serve to finance the evils of private discrimination. A plurality of the Court stated that the Fourteenth Amendment permits raceconscious programs that seek both to eradicate discrimination by the governmental entity itself and to prevent the public entity from acting as a passive participant in a system of racial exclusion practiced by elements of the local construction industry by allowing tax dollars to finance the evil of private prejudice. Under the controlling local precedent of Contractors III, the Third Circuit outlined the potential for the City of Philadelphia to have been a passive participant in both prime contractors awards of subcontracts and contractor associations admission of members. The Court found that the City lacked evidence to support its claim of discrimination in either area. In regard to subcontracting, the Court identified deficiencies in both the statistical and anecdotal evidence

Analysis of Essex County Procurement and Contracting: Final Report 77 presented by the City. In examining the City s allegations of its passive participation in contractor associations discrimination, the Court concluded that racial discrimination can justify a race-based remedy only if the City has somehow participated in or supported that discrimination.... Contrary to the City s argument, nothing in Croson suggests that awarding contracts pursuant to a competitive bidding scheme and without reference to association membership could alone constitute passive participation by the City in membership discrimination by contractor associations. It did not require bidders to be association members, and nothing in the record suggests that it otherwise favored the associations or their members. While City dollars went to low bidding contractors who, in many instances, paid dues to the Associations, this would not appear to us to constitute support for the membership practices of associations any more than the payment of City dollars to low bidding contractors who do business with discriminatory labor unions constitutes support for those unions. Other recent lower court decisions have given more guidance on the passive participant concept, relying on evidence of local private industry discrimination, albeit in addition to findings that the governmental entity itself discriminated. For example, in Concrete Works I, the District Court held that it was enough for the City and County of Denver to show that it was a passive participant in the discrimination targeted by its programs, rather than having to show that it actively perpetrated such discrimination. The mere desire to prevent an infusion of tax dollars into a discriminatory industry may be sufficient to satisfy this requirement. In the appeal of Concrete Works I, the Tenth Circuit considered the evidence of disparities in the Denver Metropolitan Area construction industry in examining the factual predicate for Denver s MBE/WBE program. This evidence of disparities, the Court stated, gives rise to an inference that local prime contractors discriminated on the basis of race and gender. However, the Court appeared to search for more evidence of a linkage between Denver s award of public contracts and the evidence of industry-wide discrimination. The Court continued, Neither Croson nor its progeny clearly state whether private discrimination that is in no way funded with public tax dollars can, by itself, provide the requisite strong basis in evidence necessary to justify a municipality s affirmative action program. Although we do not read Croson as requiring the municipality to identify an exact linkage between its award of public contracts and private discrimination, such evidence would at least enhance the municipality s factual predicate for a race and gender-conscious program. Upon remand, the Federal District Court in Concrete Works II criticized the City of Denver for claiming to act so as to avoid being a passive participant in discrimination practiced by City contractors, while simultaneously taking no action to make such discriminating firms ineligible to do business with the City (e.g., debarment, suspension of contracts, termination of contracts). The court decisions that have explained and refined the passive participant concept have important implications for this disparity study. Current case law indicates that if evidence exists that a governmental entity is infusing public funds into a discriminatory industry, the governmental entity has a compelling interest in remedying the effects of such discrimination. To the extent possible, the methodology employed in this disparity study also will examine the linkage or nexus between local government procurement and any evidence of discrimination within the private marketplace.

Analysis of Essex County Procurement and Contracting: Final Report 78 C. Passive Participation in Private Sector Discrimination 1. Forms and uses of evidence of private sector discrimination In Concrete Works III, the 10 th Circuit Court of Appeals relied upon an extensive 10,000 page trial record that largely contained evidence of private sector discrimination. Based upon that record, the Court found that Denver had a sufficient strong basis in evidence to satisfy the compelling interest prong of the strict scrutiny standard. There were many different forms of evidence of private sector discrimination that were relied upon by that Court, including: Testimony from public reports, records, and hearings Disparity Study evidence from multiple studies examining disparities in M/WBE utilization within the broader marketplace Industry-based analysis of self-employment rates Regression analysis of relative earnings (controlling for firm characteristics) Regression analysis of survey results regarding private sector underutilization of M/WBE subcontractors Trial testimony from witnesses about such topics as double standards in performance, racial epithets on job sites, harassment and sabotage of jobsites, unequal access to relevant employment experience, price discrimination by suppliers, unequal access to capital, stereotypical attitudes by contractors, and unfair denials of contract awards. 2. Establishing requisite nexus with private sector discrimination The court decisions that have explained and refined the passive participant concept have further declared that if evidence exists that a governmental entity is infusing public funds into a discriminatory industry, the governmental entity has a compelling interest in remedying the effects of such discrimination. In the appeal of Concrete Works I, the Tenth Circuit considered the evidence of disparities in the Denver Metropolitan Area construction industry in examining the factual predicate for Denver s MBE/WBE program. This evidence of disparities, the Court stated, gives rise to an inference that local prime contractors discriminated on the basis of race and gender. However, the Court appeared to search for more evidence of a linkage between Denver s award of public contracts and the evidence of industry-wide discrimination. The Court continued, Neither Croson nor its progeny clearly state whether private discrimination that is in no way funded with public tax dollars can, by itself, provide the requisite strong basis in evidence necessary to justify a municipality s affirmative action program. Although we do not read Croson as requiring the municipality to identify an exact linkage between its award of public contracts and private discrimination, such evidence would at least enhance the municipality s factual predicate for a race and gender-conscious program. In Concrete Works III, the 10 th Circuit Court of Appeals commented favorably upon the City s trial evidence that linked specific contractors that had discriminated against M/WBE firms with City contract dollars, thus establishing an indirect contribution by the City to the private discrimination practiced by those contractors. Accordingly, wherever passive participation in private discrimination forms the basis for a government s compelling interest, its factual predicate for remedial relief should demonstrate the linkage between local government procurement and any evidence of discrimination within the private marketplace.

Analysis of Essex County Procurement and Contracting: Final Report 79 D. Analysis of Utilization Lower court decisions after Croson provide insights as to the specific statistical analyses appropriate for forming a factual predicate for minority business programs. These statistical analyses generally compare percentage utilization of minority and women-owned firms with percentage availability of MBEs and WBEs. We first examine issues related to analysis of MBE/WBE utilization. 1. Groups Most local government affirmative action programs pertain to groups previously determined to have been affected by discrimination and thus included in federal programs: African Americans, Hispanics, Asians, and Native Americans. Post-Croson decisions indicate that some analysis of utilization and availability by individual minority group is necessary. However, there is no indication from the courts that additional subdivisions beyond these four major minority groups are required. In O Donnell, the U.S. Court of Appeals for the District of Columbia held in favor of the plaintiff in part because the District of Columbia extended remedies to Hispanics, Asians, Pacific Islanders, and Native Americans without any findings with respect to discrimination in the construction industry for these groups. This raised questions concerning the remedial nature of the District s program and whether the remedy was narrowly tailored. Also, relevant court decisions indicate that, where availability is absent for a particular minority group, remedies cannot be extended to that group. For example, the Third Circuit struck down the City of Philadelphia s program with respect to Native American-owned construction firms because U.S. Census data showed no such firms within the Philadelphia Metropolitan Area. Varying definitions of racial, ethnic, and gender classifications by the U.S. Census and local governments can cause problems in statistical analysis. In Concrete Works II, the Federal District Court criticized a disparity study for failing to account for the difference between the Census definition of a minority- or woman-owned firm (i.e., as having at least 50 percent ownership by a minority person or woman) and the City of Denver definition (i.e., at least 51 percent ownership by such persons). The court reasoned that this might have resulted in an overstatement of WBE availability in Census data, which could have counted firms that were owned equally by husband and wife as WBEs. The court further held that it was a fundamental flaw that there was no objective criteria to define to which racial or ethnic group a person belonged. The court found it particularly problematic that various racial classifications were aggregated in different data sources without regard for the size of the businesses or the particular services or type of work in which they specialized under the faulty assumption that they were equally victimized by discrimination and equally entitled to preferential remedies. For example, the value of disparity ratio data was undermined by the combination of Asian and Native American firm data, particularly where 1997 data showed that Asian American firms outperformed all other groups. 2. Relevant time frame for statistical analysis As discussed above, lower court decisions after Croson have not specifically addressed the minimum number of years of utilization data for statistical analysis of utilization. Under the controlling local precedent, the District Court in Contractors Association of Eastern

Analysis of Essex County Procurement and Contracting: Final Report 80 Pennsylvania reviewed evidence of disparities in City contracting for a three-year period from 1979 through 1981. Although Philadelphia s program was struck down, the Court did not address the sufficiency of the length of the time period examined, except to note that anomalies in relatively short time frames are more likely to distort results. The Court did criticize the City of Philadelphia for not examining the extent of African American participation in federallyassisted public works contracts let by the City. Based in part on this criticism, the Court rejected the City s evidence that African American-owned firms had been underutilized in the local construction industry. Other lower court opinions have accepted and rejected varying time frames and periods for purposes of conducting statistical analyses. In Associated General Contractors of California, the Ninth Circuit denied plaintiff s motion for preliminary injunction based in part on City of San Francisco utilization data for a single fiscal year. Yet, the District Court in Phillips & Jordan v. Watts reviewed data for a two-year period of time, and its only criticism was that the data from those two years were aggregated. The Tenth Circuit in Concrete Works I gave somewhat different guidance in determining that utilization data untainted by affirmative action programs was most relevant and persuasive in examining the existence of disparities, even if the data represent a relatively small fraction of total contracts. Nevertheless, upon remand, the Federal District Court in Concrete Works II criticized the City of Denver for focusing its analysis and evidence of utilization data on such contracts untainted by affirmative action programs. The Concrete Works II court reasoned that evidence of the effects of affirmative action programs on M/WBE utilization was also relevant for purposes of determining whether the programs were successful in remedying effects of identified discrimination. Such effectiveness was deemed by the Federal District Court to be relevant to the issue of whether the programs are still needed and are therefore narrowly tailored. 3. Utilization as both prime contractors and subcontractors Croson and subsequent decisions emphasize the importance of attempting to examine, where relevant, both prime contract and subcontract utilization. In Croson, the U.S. Supreme Court found fault with statistics that only reported MBE utilization as prime contractors. Without any information on minority participation in subcontracting, it is quite simply impossible to evaluate overall minority representation in the city s construction expenditures. Some lower courts have followed this reasoning. The Federal District Court in Engineering Contractors Association of South Florida analyzed data separately for prime contractors and subcontractors, yet recognized that prime contractors sometimes perform as subcontractors, and that subcontractors sometimes perform as prime contractors. In that case, the court implicitly recognized the dilemma in attempting to draw conclusions from data that is ostensibly disaggregated between prime contractors and subcontractors when the pool of subcontractors is identified through the filings of release of lien forms. The court noted that such identified subcontractors may not in fact derive their primary source of income from subcontracting.

Analysis of Essex County Procurement and Contracting: Final Report 81 4. Measurement of utilization A number of cases in which utilization data were considered relied upon utilization as measured by the percentage of dollars going to minority and women-owned firms. Under controlling local precedent, the Third Circuit and the District Court in Contractors Association of Eastern Pennsylvania also considered utilization in terms of the percentage of contract dollars (for both City utilization of African American-owned firms and for utilization within the marketplace as a whole). Moreover, in Concrete Works I, the Tenth Circuit rejected plaintiff s argument that utilization should be measured by the number of MBE/WBEs receiving work. The Court stated that measurement of MBE/WBE utilization in terms of contract dollars provided a more accurate depiction of total utilization. The Ninth Circuit in Associated General Contractors of California examined MBE utilization based upon dollars, as did the District Court in Phillips & Jordan v. Watts. The District Court in RGW and the Eleventh Circuit in Cone Corporation v. Hillsborough County examined utilization in terms of both contract awards and contract dollars. At least one lower court decision appears to give greater weight to number of contract awards as a measure of utilization. The evidence submitted by Dade County in Engineering Contractors of South Florida included utilization in terms of MBEs proportion of numbers of prime contract awards and MBEs proportion of prime contract dollars. The Court focused on contract awards in determining that there was a lack of convincing evidence that there was any discernible discrimination occurring in the award of contracts to black-owned businesses. E. Analysis of Availability Again, lower court decisions have provided some additional guidance in how to consider qualifications, willingness, and ability in developing measures of MBE and WBE availability. Some of this guidance is conflicting. Courts have been clear that the utilization and availability statistics must pertain to the same industry (e.g., construction ). However, there is little indication from the balance of the cases that the analysis must be at the sub-industry level of detail. 1. Relevant geographic market area As discussed above, the first step in measuring availability is to define the relevant geographic market from which availability should be measured. A few post-croson decisions have provided guidance for defining the relevant geographic area for examining availability (as well as analyzing anecdotal evidence of discrimination and quantitative evidence of marketplace discrimination). Most of these decisions have approved of defining the relevant geographic market area as the metropolitan area in which the jurisdiction is located if this represents the area from which the entity predominantly makes its purchases. Under the controlling local precedent, both the Third Circuit and the District Court decisions in Contractors Association of Eastern Pennsylvania have applied the Philadelphia Standard Metropolitan Statistical Area as the relevant geographic market area for the analysis of statistical and anecdotal evidence of discrimination. The District Court cited three reasons for finding that the eight-county Philadelphia SMSA was the relevant geographic area. First, Philadelphia s ordinance was intended to remedy racial discrimination in the Philadelphia Metropolitan Area. Second, most of the City contracts and contract dollars were awarded to firms within this area.

Analysis of Essex County Procurement and Contracting: Final Report 82 Finally, expanding the relevant geographic area beyond the Philadelphia Metropolitan Area would unnecessarily expand the number of potentially available contractors of all races without any corresponding evidence that those additional contractors were qualified, willing and able to perform City contracts, or that African American contractors outside this area were discriminated against in the award of City contracts. Similarly, the Tenth Circuit rejected Concrete Works argument that Denver could not use empirical evidence of discrimination within the six county Denver Metropolitan Statistical Area and was limited to data describing discrimination within its city limits. The Court held that the local construction market, not necessarily confined by jurisdictional boundaries, is the relevant area to consider evidence of discrimination. To confine the permissible data to a governmental body s strict geographic boundaries would ignore the economic reality that contracts are often awarded to firms situated in adjacent areas. This approach to defining the relevant geographic market area was subsequently adopted by the Federal District Court in Concrete Works II. 2. Qualifications, willingness, ability, size, and capacity In the controlling local precedent of Contractors II, the Third Circuit criticized the City of Philadelphia for its comparison of the percentage of construction dollars going to African American and Hispanic firms (0.09 percent) with the percentage of all businesses owned by African Americans and Hispanics in Philadelphia (6.4 percent) because the latter figure did not indicate firms available or qualified to perform City construction contracts. This is another example of the importance of availability statistics pertaining to a specific industry (e.g., construction ). Upon remand to the District Court after Contractors II, the Court examined availability statistics for African American-owned construction firms. The District Court criticized availability statistics presented by the City for assuming that every African American contractor who was available was equally qualified, willing, and able to perform City public works contracts. The City s expert originally determined that there were 114 African American-owned construction companies in the Philadelphia Metropolitan Area in 1982. Correcting for firms located outside of the Metropolitan Area and for firms that were not construction contractors, availability was 57 firms. However, the City had a prequalifications process for its construction contracts. The plaintiff pointed out that only seven African American-owned firms had sought to prequalify to bid on City-financed prime construction contracts during the period for which the City examined utilization data. Also, the City s information on the total number of available firms in the marketplace did not assess the number of firms seeking prequalification. In sum, the Court found that the City s evidence failed to measure the relevant statistical pool necessary to perform an accurate disparity study in accordance with the standards set forth in Croson. However, under the controlling local precedent, the Third Circuit Court of Appeals found fault with the above analysis by the District Court. In Contractors III, the Third Circuit pointed out that the U.S. Supreme Court s admonitions concerning qualifications came in the context of using the percentage of a particular minority group in the general population as the measure of availability. The issue of qualifications can be approached at different levels of specificity, however, and some consideration of the practicality of various approaches is required. An

Analysis of Essex County Procurement and Contracting: Final Report 83 analysis is not devoid of probative value simply because it may theoretically be possible to adopt a more refined approach. In considering willingness, the Third Circuit states, In the absence of some reason to believe otherwise, one can normally assume that participants in a market with the ability to undertake gainful work will be willing to undertake it. Moreover, past discrimination in a marketplace may provide reason to believe the minorities who would otherwise be willing are discouraged from trying to secure the work. The Third Circuit also rejected the District Court s assertion that the City s availability statistics were flawed because they did not consider the City s prequalifications process. It would be highly impractical to review the hundreds of contracts awarded each year and compare them to each and every MBE. Dr. Brimmer chose instead to use as the relevant minority population the black firms listed in the 1982 OMO Directory (a local contractor directory). The Court pointed out that the small number of black firms seeking prequalifications might, in fact, corroborate the existence of discrimination. Other courts have provided conflicting guidance on the proper indicia of the relevant pool of qualified, willing, and able firms for purposes of measuring availability. For example, Defendants in Engineering Contractors Association of South Florida used bidders on prime contracts as a measure of availability with which to compare MBE utilization on these contracts. The Court appeared to adopt this analysis, however, it noted that it is not clear to the Court that the denominator used in the disparity analysis, that is the number of BBEs [black business enterprises] that bid on a Dade County contract within a certain SIC category, accurately reflects the relevant pool of willing, able and qualified minority firms. Dade County does not have a pre-qualification process through which a particular firm is qualified to do the work required under a specific contract. The only pre-bid qualification the County requires a contractor to meet is that the firm be a licensed contractor able to satisfy the bonding and insurance requirements for the contract being bid upon. This, of course, does not reflect a firm s ability to perform the work required under the contract. We are unable to interpret from this what the Court would see as a more accurate measure of availability. Courts also offer conflicting guidance regarding the issue of whether measures of availability must also take into consideration the size of a firm. The district court in Engineering Contractors Association of South Florida agreed with plaintiffs argument that disparities between the proportion of dollars going to MBEs and WBEs and availability of MBEs and WBEs could be explained by MBEs and WBEs being small. The defendants in Engineering Contractors Association of South Florida used a series of regression analyses to examine whether, controlling for different measures of firm size, disparities were still evident between the percentage of contract dollars going to MBEs and WBEs and availability based upon bidders. While disparities were still found in most cases, most did not reach the two standard deviation test for statistical significance and were not deemed probative by the Court. The Eleventh Circuit did not find fault with this analysis. Because they are bigger, bigger firms have a bigger chance to win bigger contracts. It follows that, all other factors being equal and in a perfectly nondiscriminatory market, one would expect the bigger (on average) non-mwbe firms to get a

Analysis of Essex County Procurement and Contracting: Final Report 84 disproportionately higher percentage of total construction dollars awarded than the smaller MWBE firms. Capacity is typically measured in one of three ways: (1) by the number of MBE/WBE firms relative to the total number of firms in the industry; (2) by MBE/WBE revenues as a percentage of industry revenue; or (3) by the number of employees of MBE/WBEs as a percentage of the industry employee total. The decisions in Concrete Works I and II provide considerable detailed guidance on this issue, some of which is inconsistent with the big vs. small argument described above. As initially addressed in the District Court and Tenth Circuit decisions in Concrete Works I, the primary comparison was between Denver s utilization of MBE/WBE firms on City construction contracts and the availability of MBE/WBEs to conduct that work. The availability analysis was based upon relative numbers of firms, not some weighting of firms by employee work force size or similar factors. The District Court dismissed the plaintiff s attempt to analyze disparities in MBE/WBE utilization on the basis of firm capacity instead of numerical availability. The District Court reasoned that capacity, or the ability of a firm to handle any given amount of business, is exceedingly difficult to define and even more difficult to quantify. Capacity is a function of many subjective, variable factors. Furthermore, the District Court concluded, while one might assume that current size reflects capacity, it does not follow that smaller firms have less capacity. Most firms have the ability and desire to expand to meet demand. Moreover, a firm s ability to break up a contract and subcontract its parts makes capacity virtually meaningless. The District Court in Concrete Works I also found that disparity analysis based upon measures of capacity also bears out what may be expected in a historically discriminatory industry, as discrimination continues to affect its victim s productivity beyond the time when the act of discrimination has ceased. Although it might appear that a minority contractor s size and productivity will eventually catch up to the non-minority s when the discriminatory acts cease, the District Court pointed out that the time value of money effectively prevents such catching-up. In other words, because an MBE/WBE s apparent capacity as measured by historical sales has been artificially constrained by marketplace discrimination, and because it has fewer employees as a result, the current relatively lower capacity of the MBE/WBE itself reflects the effects of discrimination. Nevertheless, if the effects of discrimination were removed, then the MBE/WBE s capacity could readily expand to meet increased demand, although not necessarily to the level of capacity it would have arrived at absent discrimination. In reviewing the District Court s grant of Denver s motion for summary judgment, the Tenth Circuit examined whether Concrete Works had raised any arguments that would represent a disputed fact issue. Concrete Works argued that because of the relatively small average size of local MBEs and WBEs, there might not have been MBEs available to perform work on City projects. While the Tenth Circuit added its weight to other appellate courts that have interpreted Croson to permit a local government to rely on general data reflecting the number of MBEs and WBEs in the marketplace, the Court concluded that the plaintiff had identified a legitimate factual dispute about whether reliance on the relative number of MBE/WBEs overstates the ability of MBE/WBEs to conduct business relative to the industry as a whole. This was one of the material issues of fact upon which the Tenth Circuit decided that summary judgment was not appropriate, and therefore reversed and remanded the case back to the District Court for resolution at trial. Even so, the Court stated that its finding of an issue of fact did not imply that

Analysis of Essex County Procurement and Contracting: Final Report 85 numerical availability is not an appropriate barometer against which to compare MBE/WBE utilization, nor that data simply based on raw numbers of MBEs and WBEs compared to the total number of firms in the market are not meaningful. Upon remand, the Federal District Court opinion by Judge Matsch in Concrete Works II rejected much of the reasoning that had been adopted by the same court under Judge Finesilver s opinion in Concrete Works I. Judge Matsch criticized a 1990 study for failing to include information about M/WBEs in the Denver MSA that were actually available and qualified to work on five specific housing projects. Judge Matsch specifically criticized a telephone survey in that study for failing to obtain information on the qualifications of respondent firms to perform City work, even though he later acknowledged that there was relative ease of entry for new firms in the construction industry and that there were minimum licensing requirements for most types of work. On the issue of capacity as a measure of availability, Judge Matsch referred to two City witnesses testimony that capacity of business firms cannot be measured objectively and that data on this subject cannot be obtained from contractors. The court further noted that one distinguishing characteristic of the construction industry is that most firms have few full-time employees and must grow or shrink their performance capacity to the volume of business they are doing. Their ability to expand to take on new contracts depends upon their access to increased resources, including workers with needed skills, equipment, materials, and operating capital. Judge Matsch further observed that large general contractors commonly use small firms specializing in particular trades or skills as subcontractors. The availability and capacity of large contractors to self-perform the work affects the degree of subcontracting. However, Judge Matsch held that it was an implausible assumption to accept, without qualification, that size elasticity means that all M/WBEs in the relevant market area would grow at will to develop capacity to meet the contract requirements of every project. [Judge Matsch failed to comment on the plausibility of an assumption that, absent discrimination and all other things being equal in an industry characterized by low barriers to entry and capacity expansion, M/WBEs would be able to grow and/or subcontract to the same degree as non-minority firms to meet contract requirements of every project.] Then, in Concrete Works III, the Tenth Circuit Court of Appeals relied, in part, upon a regression analysis of survey results that controlled for various firm characteristics, including indicia of firm size such as level of revenues and numbers of employees to conclude that M/WBE firms experienced disparate treatment on the basis of race and gender that was unrelated to their capacity. To place this issue of disparity measurements in proper context, these lower court decisions must be juxtaposed with the initial guidance on analysis of availability provided in Croson. The U.S. Supreme Court criticized a comparison of MBE utilization as prime contractors in city construction projects with the percentage of city residents that were minority. The Court contrasted this faulty analysis with the analysis in Ohio Contractors Association v. Keip in which the State of Ohio produced evidence of disparities between the MBE utilization and the percentage of minority businesses in the State. [Italics as written in Croson decision.]

Analysis of Essex County Procurement and Contracting: Final Report 86 F. Disparity: Methods for Comparing Utilization and Availability Several lower court decisions have relied upon disparity ratios or disparity indices to statistically compare utilization and availability. As an example, the Tenth Circuit in Concrete Works I relied upon disparity indices to examine differences between MBE utilization and availability. The ratios were determined by dividing utilization (the percentage of contract dollars going to MBEs) by the proportion of firms within the industry that were MBEs. Under that approach, a ratio of less than one, or unity, was determined to imply evidence of discrimination against minority-owned firms. Controlling local precedent from the Third Circuit in Contractors Association of Eastern Pennsylvania also relied upon disparity indices; however, these disparity indices were calculated and expressed by dividing the percentage of minority utilization by the percentage of minority availability, and then multiplying the resultant number by 100. This resulted in ratio ranges from 0 to 100, with 100 implying parity. The District Court in Engineering Contractors Association of South Florida v. Metropolitan Dade County used this approach as well. Several decisions have commented upon what absolute magnitude of disparity may constitute evidence of discrimination. In the controlling local precedent from Contractors III, the Third Circuit considered the disparity index of 22.5 shown by the City and concluded that There are circumstances in which a disparity index of 22.5 can constitute a strong basis in evidence for inferring the existence of discrimination. The Court compared this level of disparity with levels found in other cases to be sufficient to constitute a prima facie case (disparity indices of 22.4 and 50). The District Court in Engineering Contractors Association of South Florida used a threshold of 80 for considering whether the index was substantial (in addition to the statistical significance tests). In general, disparity indices of greater than 80 percent are not considered substantial. Even so, the Court did not find a disparity index of 65.4 percent to be relevant because the underlying data set was small and the disparity index was over 100 for the subsequent time period. (The Eleventh Circuit agreed with the 80 percent threshold: In general, and as the district court recognized, disparity indices of 80 percent or greater, which are close to full participation, are not considered indications of discrimination. ) While most courts have been clear on the appropriate statistical comparison utilization versus availability there is less direction on the level of statistical significance required to support a finding of disparity. The District Court in Engineering Contractors Association of South Florida applied a two standard deviation test to examine the evidence of disparities in Dade County contracting and within the marketplace. Upon appeal, the Eleventh Circuit agreed. However, several courts have recognized the difficulty in finding statistical significance when the number of available firms for a minority group is small. Some courts find that lack of statistical significance for these groups should not preclude an overall finding of evidence of discrimination, especially when availability may have been constrained by past marketplace discrimination. For example, the Ninth Circuit in RGW gives further direction on appropriate statistical analyses where sample sizes are small. In that case, the Court did not rule out findings of discrimination where sample sizes were too small to permit statistically significant results. As the Court indicated, This inability, however, does not preclude us from drawing inferences of discrimination from statistical disparities, where those inferences are supported by anecdotal or other evidence (emphasis added). Indeed, to rule otherwise would effectively preclude smaller jurisdictions from remedying discrimination within their borders.

Analysis of Essex County Procurement and Contracting: Final Report 87 Under our controlling local precedent in Contractors II, the Third Circuit found that the difference between 0 percent utilization and a combined 0.27 percent availability for Hispanicowned and Asian-owned construction firms did not rise to a significant statistical disparity. Also, there was no anecdotal evidence of discrimination against these groups. Based upon these two factors, the Court invalidated remedies in the City s ordinance related to Hispanic and Asian-owned construction firms. However, the Court did acknowledge that the small number of these firms does not eliminate the possibility of discrimination against these firms. Small numbers might reflect barriers to entry caused by discrimination. But, the Court deemed that the City had done nothing to investigate these hypotheses. The Court concluded, Plausible hypotheses do not make evidence. In Concrete Works II, the Federal District Court considered the results of a linear regression analysis to determine the effect of race and gender on the self-employment income of various ethnic groups. The 1997 regression analysis study used a PUMS data source from the 1990 Census of Population and controlled for numerous variables that researchers thought might affect rates of self-employment and utilization or income (e.g., race, gender, marital status, home ownership, the presence of an elderly person in the household, English fluency, mobility or personal care limitation, age, unearned income, household property value, monthly mortgage payment, residual household income, number of children in the household and education). The regression analysis study concluded that in both the Denver MSA and the State of Colorado white women, African American men, Hispanic men, and Native American men had lower selfemployment earnings than white men. Asian American men reported higher self-employment earnings than white men. No data were available for minority women. All of the disparities were considered statistically significant, with the exception of the reported higher earnings for Asian Americans (which was not commented upon by the researchers). Nevertheless, the District Court criticized the researchers failure to consider the effects of other variables such as prior business experience, religion, cultural history, and whether parents were self-employed. The District Court found that disparities in business formation rates by race and gender are not probative of discrimination in the marketplace, particularly where important factors are not controlled for (e.g., religion, cultural factors, and access to capital) and there are severe data problems due to the inconsistent Census data classifications of the race and gender of business owners. 1. Interpretation of results of disparity analysis There is a clear legal consensus that a significant statistical disparity between utilization and availability may constitute prima facie evidence of discrimination. Once such prima facie evidence is established, the burden of production then shifts to plaintiffs to demonstrate by a preponderance of the evidence that the statistical disparity is caused by factors that are unrelated to race or gender. As discussed above, under controlling Third Circuit precedent, When the municipality comes forward with evidence of facts alleged to justify its conclusions, the plaintiff has the burden of persuading the court that those facts are not accurate. (See Contractors III). Defendant jurisdictions then attempt to counteract the plaintiff s evidence by introducing evidence that tends to show that race and gender are factors that at least partially contribute to those statistical disparities. Regression analysis (as described above) and qualitative evidence (as described below) have been useful means for advancing a defendant jurisdiction s strong basis in evidence beyond that establishing a prima facie case.

Analysis of Essex County Procurement and Contracting: Final Report 88 G. Collection and Analysis of Qualitative Evidence of Discrimination Beyond the question as to the relevance of anecdotal evidence of discrimination in meeting the strict scrutiny standard, courts also have provided instruction on the types of qualitative information that might be used to meet this standard. Some decisions have also addressed methods of collecting this information. In Coral Construction, King County relied upon written affidavits from at least 57 minority or women contractors to provide anecdotal evidence of marketplace discrimination in King County. Many of those complaints of discrimination involved denial of access to private sector work. This evidence was viewed favorably by the Ninth Circuit Court of Appeals. In Concrete Works I, the Tenth Circuit specifically approved of Denver s use of anecdotal evidence of public and private race and gender discrimination in support of its program. Such evidence included testimony delivered in public hearings, interviews with MBE/WBEs, case studies conducted by the disparity study team, and reports generated during Denver compliance investigations. All of this evidence was taken at face value, with no further investigation or verification on the part of the City or its disparity study consultants. While the Court argued that anecdotal evidence about minority contractors experiences alone would not provide a strong basis in evidence sufficient to meet the strict scrutiny standard, personal accounts of actual discrimination or the effects of discriminatory practices may, however, vividly complement empirical evidence. The Court stated further, Moreover, anecdotal evidence of a municipality s institutional practices that exacerbate discriminatory market conditions are often particularly probative. Concrete Works cites no case, and we have found none, eschewing the consideration of anecdotal evidence. However, upon remand, the Federal District Court in Concrete Works II criticized several forms and aspects of anecdotal evidence presented at trial. A 1997 mail survey of M/WBE firms was criticized because it failed to ask respondents to limit their responses about perceptions of discrimination to experiences they may have had in the Denver MSA construction industry. Of 659 surveys mailed, 109 responses were received. (The Court did not comment on the adequacy of the 16.5 percent response rate). During trial, in order to save time, the Court limited the number of witnesses and the cross-examination of witnesses presenting anecdotal evidence of discrimination. Although some witnesses gave vivid descriptions of racial and gender hostility toward them while working on City projects, the reasons for such hostility were not revealed in the record. The Court held that it could not determine whether racial insults made by workers may fairly be imputed to the management of companies. The Court apparently discounted the relevance of anecdotal evidence of disparate treatment in pre-qualification requirements for M/WBE contractors on non-city projects. On the other hand, the Court held that anecdotes about racial and gender harassment on some private sector construction sites were relevant because they involved employees of contractors who did work for the City. Yet, Judge Matsch s decision also questioned whether anecdotal evidence of discrimination by City and prime contractor inspectors was probative of discrimination by prime contractors management. Furthermore, the Court questioned the relevance of anecdotal evidence of stereotypical attitudes on the part of government and private owners against using M/WBEs because it could not be determined whether such evidence reflected manifestations of societal prejudices or whether these phenomena could be fairly attributed to employers as business policy. The Court seemed to suggest that evidence of stereotypical attitudes on the part of government and private owners

Analysis of Essex County Procurement and Contracting: Final Report 89 is only relevant if it can be shown that such stereotypical attitudes are somehow different from societal views and constitute some form of official policy. In general, the Court discounted the value of anecdotal evidence as being more illustrative than probative. Apparently, numerous illustrative examples of specific instances of discrimination in the Denver marketplace were not considered to be probative by the Court. Then, in Concrete Works III, the Tenth Circuit Court of Appeals rejected much of the District Court s criticisms. There were several different types of anecdotal evidence that the City used to convince the Tenth Circuit that it had a compelling interest to remedy private sector discrimination. Among that voluminous and detailed anecdotal evidence cited and relied upon by the Tenth Circuit Court of the Appeals was the following: 1. Government Reports, Records, and Hearings Historical evidence of construction contracting practices prior to 1990: former City affirmative action officials testified minority contractors were available, but were effectively barred from City contracts due to rules, guidelines, and biases of the City. A voluntary program to promote MBE participation had little impact on how City projects were bid due to lack of enforcement. In 1977, the Minority Contractors Association filed a grievance with U.S. Department of Housing and Urban Development (HUD) that MBEs were not being used on federallyfunded projects in violation of federal statutes. The Department issued a report that stated the City failed to take reasonable actions to overcome the effects of conditions which resulted in limited participation of minority contractors on those federal contracts. The General Accounting Office (GAO) led an investigation into the Department of Public Works (DPW) compliance with federal affirmative action requirements. The 1978 GAO report concluded DPW contracting practices had a significant negative effect on MBE participation. These practices included requiring contractor prequalification, limited advertising on most bids, and inadequate time to submit a proposal or bid. From 1975 to 1977, only 4.96 percent of contract dollars were awarded to minority firms. In 1979, the U.S. DOT threatened to withdraw funds from Stapleton Airport unless measures were taken to increase minority participation. The U.S. DOT sent a letter to Denver s Mayor asserting that Denver s prequalification requirement, while neutral on its face, was unjustified and operated to bar minority contractors from obtaining DPW contracts. The U.S. DOT directed the City to eliminate or modify the prequalification and to adopt an affirmative action program. City Council public hearings in 1983 and 1988 produced testimony from MBE contractors and other individuals regarding utilization of MBEs on local construction projects and specific examples of discrimination encountered in the Denver construction industry, including:

Analysis of Essex County Procurement and Contracting: Final Report 90 MBE contractor testimony that they worked on projects that had federal requirements for MBE participation, but were almost completely excluded from City projects without affirmative action requirements. Stipulation from a representative of the Associated General Contractors of Colorado that there was discrimination in the construction industry against minorities and women. Testimony about the current MBE and WBE utilization on DPW projects, an assessment of MBE and WBE utilization on DPW projects, an assessment of MBE/WBE overall capabilities, the extent and impact of any past discriminatory practices or barriers to MBE/WBE participation, and the identification of special problems affecting MBEs and WBEs in specific areas of the construction industry. Responses from local contractors to questionnaires regarding these issues. 2. Disparity Study Evidence Disparity Study evidence included anecdotal evidence from interviews and public hearing testimony from MBEs, WBEs, majority-owned construction firms, and government officials that suggested that Denver employees and private contractors engaged in conduct designed to circumvent the goals program (e.g., use of change orders to avoid putting new work out for bid, characterizing major construction projects as remodeling because remodeling projects fell under the auspices of the Department of General Services which had no goals program for MBEs. Other identified evidence of resistance to use of MBEs and WBEs included: Prime contractors repeatedly calling WBEs for bids that they knew were out of business and counting those calls as good faith efforts to satisfy requirements of the goals program. Bid shopping to prevent MBEs and WBEs from submitting the lowest bid. 3. Trial Witness Testimony Trial testimony provided evidence of the following forms of marketplace discrimination: Double Standards. Testimony from a senior vice president of a large majority construction firm stated that when he worked in Denver he received credible complaints from minority and women construction firms that they were subject to different work rules than majority firms. Racial Epithets. The same large construction firm vice president testified that he frequently observed graffiti containing racial or gender epithets written on job sites in the Denver MSA. Refusals to Deal, Unfair Denial of Contract Awards, Stereotypical Attitudes. The same large construction firm vice president testified that, based on personal experiences, he believed that

Analysis of Essex County Procurement and Contracting: Final Report 91 many majority-owned firms refused to hire minority or women-owned subcontractors because they believed those firms were not competent. Unfair Denial of Contract Awards, Slow Payment, Price Discrimination by Suppliers, Double Standards in Performance, and Unequal Access to Relevant Employment Experience. M/WBEs testified that their bids were rejected even when they were the low bidder, and they were paid more slowly than majority subcontractors, charged higher prices by suppliers than white competitors, experienced difficulty joining trade unions, and experienced double standards in performance as compared to their majority counterparts. M/WBEs extensively testified detailing the difficulties of M/WBEs in obtaining credit; WBEs were required to have husbands or fathers as co-signers or in negotiations for loans. Stereotypical Attitudes, Jobsite Harassment. M/WBEs testified about racially and gendermotivated harassment experienced by M/WBEs at work sites. Women were called bitches and blacks were called nigger or dumb nigger. One 73-year-old truck driver was called a dumb f---ing Mexican. M/WBE employees were physically assaulted and fondled, spat upon with chewing tobacco, and pelted with two-inch bolts thrown by males from a height of 80 feet. Other courts have been critical of the nature and use of anecdotal evidence produced by the defendant jurisdictions. In Engineering Contractors Association of South Florida, the District Court cited three areas of concern with the anecdotal evidence presented by Dade County. First, the Court questioned whether those believing that they had been discriminated against had all the knowledge required of the perspectives of both parties involved in an incident as well as knowledge about how comparably placed persons of other races, ethnicities, and genders have been treated. The Court appeared to suggest that such knowledge was necessary for such anecdotal evidence to represent prima facie evidence of discrimination. The second area of concern cited by the District Court in Engineering Contractors Association of South Florida was interviewer bias or response bias in any interviewing or survey situation. This is closely related to the third area of concern cited by the Court: individuals having a vested interest in preserving a benefit motivated to view events in a manner that justifies the benefit. Without elaboration, the Court suggested, Attempts to investigate and verify the anecdotal evidence should be made. However, the Eleventh Circuit s opinion in Engineering Contractors Association of South Florida appeared to be more supportive of the value of anecdotal evidence in that case. Nevertheless, the Eleventh Circuit stated we believe that anecdotal evidence can play an important role in bolstering statistical evidence, but that only in the rare case will anecdotal evidence suffice standing alone. While such evidence can doubtless show the perception and, on occasion, the existence of discrimination, it needs statistical underpinnings or comparable proof to show that substantial amounts of business were actually lost to minority or female contractors as the result of the discrimination. In sum, while courts agree that anecdotal evidence, standing alone, would rarely if ever be sufficient to meet strict scrutiny, courts have taken different views of the probative value of the anecdotal evidence of discrimination collected through affidavits, interviews, public hearings and testimony in court

Analysis of Essex County Procurement and Contracting: Final Report 92 H. Narrow Tailoring of Remedies Post-Croson cases also have given considerable direction as to how remedies might be narrowly tailored. 1. Classification of neutral vs. race- and gender-based remedies Courts are clear that race and gender-based remedies must meet the strict scrutiny standard of review while race and gender-neutral measures need only meet an intermediate standard of scrutiny. While some remedies such as MBE price preference or sheltered market programs clearly fall into the category of race and gender-based remedies, some efforts to assist minority and women-owned firms may not. Several cases speak to this issue. Under relevant local precedent, the District Court s decision in Contractors Association of Eastern Pennsylvania provided some guidance on how courts might distinguish between neutral and race and gender-based remedies. The City of Philadelphia cited two programs as neutral initiatives that it had considered before enacting a race and gender-based remedy. One program provided financial and technical assistance to minority contractors who were unable to obtain credit because they had limited experience. The other program promoted the hiring of minorities on public construction sites. The Court commented that neither of these two programs was race neutral, as it understood the term to be applied in Croson. The City had erroneously considered that any policy that did not directly result in one contractor being selected over another contractor on the basis of race would not be viewed as race-conscious and would not be subject to strict scrutiny. Other lower court decisions have taken a different view. For example, the District Court in Concrete Works I came very close to holding in favor of the City based upon lack of any injury to the majority contractor bringing suit. The program challenged was a good faith efforts goals program that urged prime contractors to provide opportunities to MBE and WBE subcontractors to submit bids. In this program, the City would not deny any contract to a prime contractor based upon its race or gender. The Court held, We do not believe that Concrete Works had lost a contract due to a racial preference, but rather because Concrete Works failed to carry out the same race and gender-neutral requirements applicable to all contractors. From this language, it appears that the Court was not deeming Denver s good faith efforts goals program to meet the definition of a racial or gender preference. The Court also examined the assertion that the ability of MBEs and WBEs to use their own work in satisfaction of the project goals gave these firms an unfair competitive advantage over majority prime contractors. The Court recognized that Denver s ordinance contained no requirement that a contractor must meet the project goals in order to be deemed the lowest responsive bidder. MBE/WBEs and majority contractors alike had two routes of compliance open to them, each equally valid in establishing responsiveness: meet the goals or show good faith. Therefore, a non-minority contractor that established its good faith efforts would be on equal footing, if the bid amounts were equal, with a WBE or MBE that partially used its own work to meet project goals. In other words, non-minority contractors may be deemed responsive under race and gender-neutral means of establishing good faith. There is some force to this argument. However, the Court also stated that the ordinance offered fewer avenues by which to meet the good faith requirements to non-minority firms because of the ability of MBEs and WBEs to count their own dollars toward meeting the project goals. The Tenth Circuit also

Analysis of Essex County Procurement and Contracting: Final Report 93 examined the issue of whether Denver s ordinance placed a majority-owned contractor at any disadvantage in the bidding process. The Court held that the unequal nature of the bidding process lies in the ability of minority and women-owned prime contractors to use their own work to satisfy MBE and WBE participation goals. Without this aspect of the program, it is possible that the Court would have held that Denver s ordinance did not contain any elements which would be classified as race or gender-based remedies subject to a strict scrutiny analysis. With a similar set of facts, the Ninth Circuit in Monterey Mechanical Co. v. Wilson, et. al. reached the same conclusion as the Tenth Circuit. The Ninth Circuit opinion went further, however, by stating that a person suffers injury if the government encourages as a condition of granting him a benefit (e.g., a contract) or requires that he discriminate against others based on their race or sex. This suggests that a majority prime contractor bidding in a contracting process in which minority and majority prime contractors are treated equally may still be injured by an MBE/WBE program and have standing to sue. The Eleventh Circuit Court of Appeals in Cone Corporation followed logic very similar to the Concrete Works I decisions. Like Denver, Hillsborough County requires non-minority and minority prime contractors alike to comply with its subcontracting goals program. However, the Hillsborough County program reduced the MBE goal for minority general contractors who would do more than 50 percent of the work themselves. Consequently, the Eleventh Circuit remanded the case to the District Court for consideration of whether any injury to plaintiffs arose from this aspect of the program. Upon remand, the District Court held that the plaintiffs failed to identify any particularized injury related to the program, and Rule 11 sanctions were imposed against plaintiffs counsel for repeated failure to allege facts demonstrating injury. In New Concepts, Inc., et al. v. City of Albuquerque, the U.S. District Court for New Mexico struck down elements of the City of Albuquerque s ordinance giving preference to MBEs and WBEs. However, other portions of the ordinance were upheld because the Court deemed them to be race and gender-neutral. This case is also instructive in reviewing how courts define neutral versus race and gender-based remedies. The Court specifically approved the City s development of an MBE directory and continued tracking of MBE participation on City contracts. The Court also approved of training sessions and seminars for informing MBEs and other interested firms of business opportunities with the City. While these efforts specifically referenced MBEs, they also referenced other interested firms, and were thereby a permissible race and gender-neutral effort. In summary, upon Adarand and prior U.S. Supreme Court cases, the operative determination as to whether a strict scrutiny standard of review should be applied rests not upon whether the government has established a preference based upon race, but merely whether the government has established a classification based upon race. See e.g., Adarand Constructors, Inc. v. Peña, 115 S.Ct. 2097 (1995); McLaughlin v. Florida, 379 U.S. 184 (1964); and Korematsu v. United States, 323 U.S. 214 (1944). 2. Consideration of neutral remedies Recent court decisions have suggested that local governments consider a broad array of innovative race and gender-neutral programs and remedies as part of the narrow tailoring of remedies for marketplace discrimination. Among these neutral remedial approaches are the following:

Analysis of Essex County Procurement and Contracting: Final Report 94 1. Small Business Enterprise programs 2. Small Local Business Enterprise programs 3. Emerging Business programs 4. Private sector working capital funds 5. Linked Deposit Policies and Capital Access programs 6. E-commerce solutions (including automated centralized bidder registration process to facilitate industry-specific electronic outreach to prospective bidders, bid rotations, small contract award rotations) 7. Technical Assistance Referral Network 8. Debriefing of Losing Bidders 9. Bonding and Insurance Waivers or Requirement Reforms 10. Procurement Process Reforms (including re-packaging of smaller bid packages, multi-prime contracts, job order contracting, elimination of prequalification requirements, restrictive contract specification review, expedited payment of invoices, subcontract bid depositary) 11. Commercial Non-Discrimination Policies With Enforcement Mechanisms 12. Outreach As discussed above, the controlling local precedent in Contractors Association of Eastern Pennsylvania held that there was no evidence that the City of Philadelphia actually reviewed the effectiveness of the two programs the City cited as the neutral measures it considered. Further, the Court found that the evidence suggests that the previous programs cited as insufficient by the City had actually been successful. And, the Court found that the City had not attempted to remedy barriers to all firms created by the City s procurement procedures. The Court urged the City first to consider relaxing its prequalification and bonding requirements for economicallydisadvantaged contractors of all races. Training and financial assistance programs for all disadvantaged contractors were additional neutral remedies suggested by the Court. Further, the Court indicated that the City could vigorously enforce the anti-discrimination provisions of the City Charter and the Procurement Department s standard contracting requirements. In Contractors III, the Third Circuit held that the record supports the District Court s finding that alternatives to race-based preferences were available that would have been either race-neutral or, at least, less burdensome to non-minority contractors. Because the City failed to consider or adopt these alternatives, its race-based program was not narrowly tailored. In reaching this conclusion, the Court pointed specifically to the City s failure to consider a credit program for

Analysis of Essex County Procurement and Contracting: Final Report 95 small or minority contractors. The City s apparent consideration of the alleged failure of the federal Small Business Administration to increase the number of minority and women-owned businesses is not constitutionally adequate consideration of the potential effectiveness of raceneutral measures for a particular industry in a particular locality. 3. Flexibility and waivers In a number of cases, courts have examined whether individual jurisdictions had incorporated flexibility and waivers into their programs, or whether the jurisdictions had applied rigid numerical quotas. For example, the Ninth Circuit approved of King County s allowances for waivers when neither an MBE nor a WBE were available to provide needed goods or services or when available MBEs and WBEs had given price quotes that were unreasonably high. The Ninth Circuit made similar findings related to the flexibility of San Francisco s bid preference program. Based upon the Tenth Circuit Court of Appeal s decision in Adarand III, a new regulatory framework adopted by DOT in 2000 was determined to be narrowly tailored due to a requirement that DBE subcontracting goals be set on a contract-specific basis based upon availability. 4. Limitation of remedies to address identified discrimination and firms affected by discrimination There are three parts to this element of narrow tailoring. First, courts have held that the remedies be limited to racial and ethnic groups for which evidence of discrimination exists. Second, Croson and post-croson decisions have indicated that the remedies should be limited to eradicating discrimination within the boundaries of the enacting jurisdiction. Finally, the remedies should focus upon the particular forms of identified discrimination. A few courts have had difficulty with the concept of defining members of different racial groups and extending benefits solely based upon membership in those groups. For example, the Court in Houston Contractors Association expressed concern that there is no precise way to identify individuals by their race. It also found fault with Houston Metro s presumption that all bidders associated with disadvantaged groups are actually disadvantaged. It also criticized Metro for not accounting for the number of the disadvantaged employees working for a contractor. The Court ultimately concluded that by definition race has never been narrow or accurate. The form of the identified discrimination should also be reflected in the nature of the remedy. As discussed above, the Third Circuit s decision in Contractors III held that a subcontractingfocused program was not narrowly tailored since there was no evidence of discrimination presented for subcontracting. We do not suggest that an appropriate remedial program for discrimination by a municipality in the award of primary contracts could never include a component that affects the subcontracting market in some way. We hold, however, that a program, like Philadelphia s current one, which focuses almost exclusively on the subcontracting market, is not narrowly tailored to address discrimination by the City in the market for prime contracts.

Analysis of Essex County Procurement and Contracting: Final Report 96 5. Burden on the rights of third parties The District Court in Sherbrooke Sodding specifically addressed the issue of whether a white, male-owned firm shouldered an unconstitutional burden as a result of a race and gender-based program. Plaintiffs in this case argued that the DBE program s impact falls disproportionately hard on specialty subcontractors in less capital-intensive lines of business. Because of this, the burden of a DBE program falls heavily on the shoulders of plaintiffs and others similarly situated. The Court agreed. The Federal District Court in Houston Contractors Association was also very concerned about impacts on third parties. Citing prohibitions against collective and inter-generational guilt and against takings, the Court found that any racial preferences in government contracting are unconstitutional. In Concrete Works II, the Federal District Court criticized the City of Denver for failing to take regular action to discipline its own employees for discriminatory conduct, and for failure to adopt adequate means to receive and investigate claims of discriminatory conduct by its employees or to provide administrative remedies for resulting harms. The Court was especially critical of the City s efforts to pass off the burden of these remedial programs to third party contractors at the same time the City itself was unwilling to take effective measures regarding misconduct by its own employees. 6. Goal setting Programs often contain two types of goals: annual goals that might be used as benchmarks for yearly evaluation of the operation of the program, and project-specific goals for MBE/WBE participation on a particular contract. Some jurisdictions subject to legal challenge have not distinguished these two types of goals; the annual goal for MBE or WBE participation was automatically applied as the project-specific goal. 7. Periodic review, graduation, and sunset provisions The U.S. Supreme Court in Adarand reiterated that a program must be appropriately limited such that it will not last longer than the discriminatory effect it is designed to eliminate. Several lower courts have examined provisions for the periodic review and sunset provisions meant to comply with these requirements. In Concrete Works I, the limit of Denver s ordinance to five years duration and measures for periodic review were favorably reviewed by the District Court. The Court of Appeals in O Donnell criticized the District of Columbia s MBE program for its lack of sunset provisions. In Contractors Association of Eastern Pennsylvania, the District Court found Philadelphia s program not to be narrowly tailored in part because the City had approved an eleven and one-half year extension of the ordinance without a review of the appropriateness and efficacy of the program. In addition, Adarand I established the importance of graduation provisions (in addition to sunset provisions) to ensure that there is periodic individual inquiry to prevent the remedy from benefiting firms that have outgrown the need for the remedy.

APPENDIX C: ASSUMPTIONS Several assumptions were made over the course of this research project, which pose some limitations and constraints on how to interpret the research results. Some of the assumptions and resulting limitations and constraints are addressed below. The contract file we developed and used to conduct the analysis had several limitations. The file was created by merging an electronic file from the County, which contained contracts awarded between 2002 and 2004 for 19 of the 23 included agencies, with contract files received from the four additional agencies. The files were not comparable. None of the files provided industry classifications such as SIC or NAICS codes for the contracts. This type of classification is needed to perform the availability and utilization analysis. Since these industry classifications were not provided, we had to find a way to assign some type of industry classification to thousands of contracts. Fortunately, many of the contracts from the 19 agencies had some type of commodity code and/or a contract description. We used the contract description and commodity code to try to identify a three-digit NAICS code. This was done on a case-by-case basis. We made every effort to assign the most appropriate NAICS code. Then in an effort to tailor more narrowly the industry classification, from three-digits to sixdigits, the list of contracts and corresponding commodity codes was submitted to Periscope Holdings, Inc., a private company that provides a cross reference ( Crosswalk ) between the National Institute of Governmental Purchasing (NIGP) Commodity/Services Code with the NAICS codes. Unfortunately, since we were only able to provide the company with the threedigit NIGP codes, Periscope was not able to perfectly match the contracts to the corresponding six-digit NAICS code. Hence, in an effort to provide six-digit NAICS codes, some contracts were lumped into a common six-digit NAICS code rather than converted to a more specific sixdigit classification. The six-digit NAICS codes were used to calculate the utilization measures and were also used to calculate several of the weighted availability measures. Another problem with the contract file was that the names of the same firms were often spelled differently in different records. This created a problem when doing any type of firm-level analysis. Every effort was made to distinguish between similar but different companies and multiple listings of the same company with different spellings. A more serious problem is the fact that the contract file did not provide a way to distinguish between sole source contracts, EUS contracts, and bid contracts. Nor, did it provide a way to distinguish between contracts that were a part of the County bidding process and those that were a part of the State vending process. It would have been very helpful to be able to control for the type of contract in our analysis, particularly in our analysis of the probability of award. However, without any type of way to identify these contracts, we could not control for these factors. We asked the County Purchasing Office if the contract number could help identify the type of contract, but the office reported that it could not.

Analysis of Essex County Procurement and Contracting: Final Report 98 The bid analysis must be interpreted in light of various assumptions, similar to those we made for the contract file. First, there were a limited number of bids to analyze because not all contracts are put out for bid. In addition, we had to pull the bidder information by manually reviewing bid books. It is possible that there is some bidder information that was not available for our review and thus was not included in the bid database. In the availability analysis, we used different sources of information and each of these sources has their own limitations. The SMOBE/SWOBE reports are limited in that the latest available version was completed in 1997. In addition, we had to conduct this analysis using two-digit SIC codes. Ideally, one would prefer to use a more narrowly tailored classification such as a fourdigit SIC code or NAICS codes, which is the new industry standard. Another limitation in this dataset is that there is no classification for minority women; hence, there is the potential for double counting when adding women-owned firms and minority-owned firms. However, we attempted to account for this potential double counting by estimating the number of minority, women-owned firms and subtracting that number from the DBE count. In addition, for most of the analysis in this report, the primary geographic market was defined as PJM-2, which consists of 11 counties in New Jersey. The SMOBE/SWOBE, however, only reports statewide information and not county-level. Hence, we have to keep this distinction in mind when comparing the availability rate for this measure to the other three measures. A final limitation of the SMOBE/SWOBE reports is that it provides information on all women and minority-owned firms in the state and not just those who are ready, willing and able to work on County contracts. Hence, the availability rate might be slightly inflated since it captures firms that are interested in bidding on County contracts, as well as those who might not have an interest. Several assumptions were made about the DBE information used in the analysis. Unfortunately, there is no way to guarantee that the information is 100 percent reliable. The County of Essex does not have an official DBE list because it does not have a DBE program. As a result, it relies on other certification processes, as well as other objective and subjective sources of information. In our analysis, we uncovered firms who were listed as DBEs on one list but not on others, which caused us to question the reliability of some of the sources. The Dun & Bradstreet data have some limitations similar to those posed by the SMOBE/SWOBE reports. The Dun & Bradstreet search engine used to generate the total number of firms and the number of women and minority-owned firms allows searches to be defined by demographics, industry, and specialty data. Within the industry option, it only allows a search of the number of firms categorized by SIC code and not by NAICS code. In addition, the Dun & Bradstreet numbers could possibly over and underestimate the availability rate. It could be overestimated since the numbers represent all firms in the defined geographic market and not just firms who might actually be interested in bidding on County contracts nor do the firms on the D&B list have to go through a DBE certification process. The measure could be underestimated because it may not capture a large share of minority firms since the corporations registered with D&B tend to be larger firms as opposed to self-employed or sole proprietorships.

Analysis of Essex County Procurement and Contracting: Final Report 99 The qualitative analysis also has some limitations in that the respondents may not be a representative sample of the larger community of County contracts. Although about 9,000 mail surveys were distributed, only around 200 were received. In addition, even though nearly 1,000 web surveys were emailed, only around 200 were completed.

APPENDIX D: DESCRIPTION OF CONTRACTS AND DATA SOURCES To comprehensively understand bidding and contracting in Essex County, we collected data from 23 agencies, departments, and educational institutions. We collected both paper files and electronic data in order to create comprehensive data files that could be used in the analysis. This section outlines how these data were collected and recorded from the 23 agencies. I. Files from the Original 19 Agencies The original 19 agencies from which data were collected all share common recording and storage practices and most of the data were stored in the Hall of Records in Newark. Therefore, the same methods of collection were used to gather data from each of these agencies or departments. A. Bid files We reviewed the paper files for bids received in 2002, 2003, and 2004 that were stored in rooms 325 and 338 of the Hall of Records. These files contain information on the contracts that were put up for public bid to be awarded to the lowest responsive and responsible bid. We developed a final master bid file of these records. We had access to a set of bid books, small ledger-style notebooks in which the date of each bid opening, the names of the bidders, the bid amount, and a short phrase describing the service for which the bid would be performed was recorded. Some files listed in the bid books were not found in the Hall of Records. The location of any bid files not included in the excel data is unknown. Each of these paper files had bid applications for each of the bidders, including information such as contact/ownership information, insurance information, response to bid specifications (such as itemized bids or evidence that the company had suitable equipment to do the job), state business certification, number of employees, years in business, and various other pieces of firm background data. The files also included various correspondence surrounding the contract, such as letters between Essex County officials discussing the transfer of documents and copies of the newspaper notices that announced public bids. Copies of letters of complaint were also included, as were the County s responses to these letters, and when relevant, legal briefs and court rulings. Bids for construction projects included more detailed information, including a list of related business the firm had completed in the last five years, proof of surety bonding, and a list of possible subcontractors the firm would use if awarded the contract. The form to enter subcontractors names had an option to enter a dollar amount of the subcontract, but this amount was not always produced. In addition, because the bid file s scope ends at the final award of the

Analysis of Essex County Procurement and Contracting: Final Report 101 contract, it is not possible to determine the amount actually awarded to these subcontractors, or even if the subcontractor ended up being necessary to complete the project. These bid files were used to create an electronic master bid file used in the statistical bid analysis. In this database, we have 1,234 separate bidder information records for 515 bids, including 543 different bidder firms or organizations. B. Data Storage The bid master database includes three tabs: 1) Bids, 2) Requests for Bid, and 3) Subcontractors. Tab 1: Bids Column A Contract number. Unique number assigned to individual contracts by the study team. It is the primary link between the bid file and the contract file. Column B Bid number. Number assigned by the County when recording its bids. Column C DBE Status. Success of match between bidder name and the master DBE list assembled by the study team. Column D Column E Column F Column G Column H Column I Column J Column K Column L Bid name. Name of bid assigned by Essex County and found in the County s master bid books. Construction bid. Indicator of whether or not a bid falls under the rules for Construction/Public Works bids, which include mandatory disclosure of subcontractors, amount of related business done in last five years, financial statements, and surety bonding. Revenue bid. Indicator of whether or not a bid falls under the revenue bid category, meaning that the vendor pays the County for the privilege of selling its goods at a County event/facility. All of these contracts involve food sales/vending. Values: 1 (revenue) or 0 (non-revenue) Date. Month/year deadline for receiving bids. Number of bidders on each bid. Format of bid record. Bidder name. Vendor number. Unique number assigned to vendor by County; only available for vendors who appear in the County contract database. Bid amount.

Analysis of Essex County Procurement and Contracting: Final Report 102 Column M Contract amount. Amount attached to bid in the PO Contract Revised file. Column N Check amount. Amount attached to bid in the Check report 2002 2005 file. Bids were matched to these files by hand, using larger, bid amount, date, and bid name. Column O Bid result. The following describes the contract award process we used to analyze the master bid file. There are three ways a bid can be rejected: Rejection A Rejection B Rejection C All bids exceed County estimate for project County changes specifications for project, either due to a County error in the original specifications or a change in what the County wants done Bidder is determined to be unqualified to do the work, either as the result of the County's research or a complaint filed by another bidder The bidding process Step 1 Step 2 Step 3 Step 4 Step 5 Bids are received Bids are determined to be responsive Bid amounts are examined Bid amounts are compared against what the County has determined the maximum acceptable bid to be. If they are all above this amount, all bids are rejected and the contract may or may not be re-bid. (POSSIBLE REJECTION A) If the lowest responsive bidder is judged to be responsible, the contract is awarded. Otherwise, the second lowest responsive bidder is judged for responsibility, and so on. (POSSIBLE REJECTION C) At any point in this process up until the awarding of the contract, the County can decide to revise the specifications on the contract and request a new public bidding process. (POSSIBLE REJECTION B). Two additional points: 1. There a few (less than ten) 'Bid withdrawn' records, where a bidder dropped out of the formal bidding process (in an attempt to recoup their bond) because of an initial misjudgment of their ability to perform the work. This appears to be able to occur at any point between submission of bid and award of contract. 2. There are about 50 'No award' records, where there is no evidence either in the paper bid files or the electronic contract file that an award was made. There is also no evidence to suggest why no award was given, e.g., it could have been because all the bids were too high, because the County changed the project, because all the bidders were non-responsive, because all the bidders were non-responsible, or some combination of these.

Analysis of Essex County Procurement and Contracting: Final Report 103 Column P Column Q Column R Column S Column T Years in business. Vendor response to bid application question about how many years vendor has been in business. Employees. Vendor response to bid application question about how many employees vendor has. Amount of business. Vendor response to bid application question about how many dollars worth of related business vendor has done in the last five years. Assets. Total value of assets taken from financial statement submitted with bid. Alternate newspapers. County is bound by law to advertise all bids in the newspaper. All bids were announced in the Newark Star-Ledger, and this field indicates the selected bids announced in alternate newspapers. Column U Type of Operation. Column V Construction bond. Name of insurance company through which bidder for construction contract secured its surety bond. Column W Rejection reason. If bid rejected, the reason for rejection. Column X Notes. Extra notes on a specific bid. Tab 2: Requests for Bid This dataset includes all vendors who expressed formal interest in receiving a contract up for bid but did not actually submit a bid. The paper bid files include fax transmissions to various vendors indicating these requests, as well as sign-in sheets from public informational meetings prior to the bid submission deadline. Column A Column B Column C Column D Bid number Requester name DBE status Vendor number Tab 3: Subcontractors Bidders for construction contracts are required to submit a list of subcontractors. They do not always submit the amount of money each subcontractor will be paid, however. Neither the paper bid files nor the contract database indicates how much each subcontractor was eventually paid. Column A Column B Column C Column D Bid number Primary Bidder Subcontractor Subcontractor s DBE status

Analysis of Essex County Procurement and Contracting: Final Report 104 Column E Column F Subcontractor s vendor number Proposed amount of subcontract C. Contract files The electronic contract data were obtained from Essex County s Chief Financial Officer (CFO). The file we received contains all the records from the County contract database from 2002, 2003, and 2004. The fields from this file include department of contract, the purchase order number, the vendor name, vendor address, date of the contract, a short description of the contract, an account number, and a subcommodity code. The information provided by the description field was straight text, limiting its use in terms of classifying contracts by type of service except on a case-by-case basis. We were able to use the subcommodity code to map each contract into a type of service. In order to analyze the distribution of contracts in Essex County, we had to combine some of the records the CFO extracted from the database. That is, some individual contracts were represented by multiple records in the raw data file. The basic principle is that there are two ways a record should be considered part of the same contract. One, they share the same purchase order number. Two, they share the same vendor, year, and subcommodity code, but have different purchase orders because the purchases were made by different departments. One example of this would be a computer contract where the County agrees to buy some number of computers from a certain computer vendor, but allows each department to order their computers separately. This behavior would show up as different records in the database, although it would represent only one decision about to whom to award the contract. Based on a conversation with the CFO, we used the following process to roll records into contracts. First, we took all records from the County data system, the universe of contract data from 2002, 2003, and 2004. Then, we removed all records without purchase order numbers or vendor information. We combined records by purchase order. We assigned a single subcommodity code to each purchase order, based on the subcommodity code that represented the greatest number of dollars within a purchase order. We then identified purchase orders that matched one of the bid files we reviewed. These contracts were set aside. We combined the remaining records (with different purchase orders) by vendor, subcommodity code, and year. Finally, we appended the results to bid file records. The result of this process created the contract master file. This process left us with 11,260 contracts from 2002, 2003, and 2004. We also obtained a file from the CFO with information on how much money had been paid to date on specific contracts, and this amount was linked by purchase order number. D. Matching Process Following the contract and bid data collection, there were four types of vendors identified within the contract process: Vendors (firms/organizations with County contracts), Bidders (firms/organizations appearing in the bid files, both winners and losers), Requestors (firms/organizations appearing in the bid files as having requested a bid application but with no

Analysis of Essex County Procurement and Contracting: Final Report 105 evidence of ever having submitted a formal bid), and Subcontractors (firms/organizations listed in submitted bid applications as possible subcontractors to a lead bidder). All firms appearing in one of these four lists were checked manually against the master DBE list to determine which ones should be classified as DBEs. Whenever there was a questionable or ambiguous match, when available, the vendor address and type of service performed were used to confirm or refute the possible match. We input the list of all vendors, bidders, requestors, and possible subcontractors and the complete DBE list to receive an output of the matched DBE list. Eight DBE variable fields were added to contract and bid files. II. Files from Four Additional Agencies As discussed earlier, in addition to 19 agencies and departments who share common data collection and recording practices, four other County bodies were part of this study. The collection and recording of data from each of these bodies is discussed in below. A. Essex County College The electronic contract file for the County College was provided by Essex County College s Director of Purchasing and covered fiscal years 2002 through 2004. Contract records included purchase order number, vendor name and address, contract amount, date of contract, and a brief description of the contract. No subcommodity code system is maintained by the County College. We added the prefix CC to all County College purchase orders and added contract numbers for indexing as well as DBE classification variables. With distinct purchase order numbers and a lack of subcommodity codes, there was no method to combine at a base level the contract records. The option remains to combine contracts given to a single vendor by common year of contract. Without using this method, we have 3,283 Essex County College contracts. The database that includes data about the College contains the same information as the 19 agency contract file except for the following variations: Tab: ECC Contracts Column A Column B Column M Contract number. Contracts are numbered consecutively by vendor name (alphabetical), purchase order, and contract year. The CC prefix identifies these contracts as those of Essex County College. Bid number. Bid numbers were taken from Essex County College bid files. Vendor number. Essex County College does not attach ID numbers to vendors in the contract file.

Analysis of Essex County Procurement and Contracting: Final Report 106 Column N Column O Column P Column R Contract amount. Check amount. For the County College, these numbers were taken to be the same. The electronic file had only one contract amount. Subcommodity code. The College does not use a subcommodity code system. Source of contract. The County College bids were reviewed in the College s Office of Purchasing. The County College bid files contained somewhat less detailed information than the County files did, although this is probably because we established the template for data collection based on the contents of the main County files. For example, the County College files did not contain a sheet on which the vendor would record their tenure in business nor their number of employees, but it did request detailed information about the specifications for the contract that were difficult to measure in statistical outcomes. The County College bid files also covered contracts of all amounts, i.e., they were not concentrated among higher valued contracts and were also much more likely to cover broken up items, such as pieces of a science laboratory setup or general educational supplies. We collected information on 59 bids, for which there were 414 bidders, including 263 different firms. This file includes the same information as the 19 agency bid file except for the following variations: Tab: ECC Bids Column A Column B Column K Column T Column U Contract number. Bid number. Vendor number. Vendor numbers are not available for County College vendors. Contract amount. This is the amount attached to the bid in the College contract file. Check amount. For the County College, this is considered to be the same as the contract amount. B. Improvement Authority 1. Contracts The electronic contract files for the Essex County Improvement Authority came from two sources: the Improvement Authority s in-house system (2002 and first half of 2003) and the County system (second half of 2003 and 2004). The Improvement Authority switched over to the County contract system the same one that contained the contract data for the 19 agencies on July 1, 2003. As a result, we had to have the 2002 and first half of 2003 data pulled from the Authority s old system. The consulting firm that designed the Authority s system pulled this data for us, and we merged it with the second half of 2003 and 2004 data that we received from the CFO.

Analysis of Essex County Procurement and Contracting: Final Report 107 To merge the two data sources together, we began by pulling out the records from the 2003/2004 file with valid (non-empty) purchase order and vendor fields. We then matched the purchase order number to its associated check amount to eliminate repeats between the two systems and rolled up purchase orders into common records. This left us with a 2003/2004 contract file with purchase order numbers, vendor names, vendor numbers, contract dates, contract amounts, short contract descriptions, and vendor addresses. We then appended this to the contract file pulled from the old system for 2002 through June 30, 2003. This file included vendor name, contract date, purchase order number, contract amount, and a short contract description. Vendor address information was linked by vendor name from an additional file from the old system. This left us with a merged Improvement Authority contract file with 5,234 contracts. We input three raw input files: one file from the CFO and two files from Microshop Consulting. The output file includes the same information as the 19 agency contract file except for the following variations: Tab: ECIA Contracts Column A Column B Column M Column N Column O Column P Column R Contract number. Assigned by the study team to track contracts. Contracts are numbered consecutively by vendor name (alphabetical), contract year, and purchase order. The IA prefix identifies these contracts as those of the Improvement Authority. The gaps in sequence result from discovering that some contracts were part of the same bid after the contracts had already been numbered. These related contracts were combined at this later date and resulted in sequence gaps. Bid number. Taken from Improvement Authority contractor listing for Essex County Correctional Facility Project. Bid numbers link the bid file information to the contract file information. Vendor number. Number assigned by Improvement Authority. Contract amount. Check amount. For the Improvement Authority, these numbers were taken to be the same. The records from 2002 and the first half of 2003 did not have separate amounts attached to them. For the records from the County system, we used the check amount column to remove duplicates between the two sources (County system and old Improvement Authority system). Subcommodity code. Not applicable for ECIA contracts. Source of contract. Identifier indicating from which database the contract was pulled.

Analysis of Essex County Procurement and Contracting: Final Report 108 2. Bids Part of the study team reviewed paper bid files at the offices of the Improvement Authority. These files contained similar information to the construction files we reviewed for the primary 19 agencies bid amount, years in business, number of employees, amount of recent business done in the field, vendor assets, and correspondence surrounding the bid openings. Many of the bids reviewed covered projects that began prior to the 2002 start date, but had money paid on them during the 2002 to 2004 study period. The bid files provided to us by the Improvement Authority covered projects relating to the construction of the Essex County Correctional Facility. Following the completion of the bid file review, we identified records from the contract file covering work from the same bid and rolled these together. A similar process was carried out for the data covering the 19 agencies falling under the scope of the County data system. We collected information on 25 bids, for which there were 83 bidders, including 46 different firms. We input paper bids from the Authority to obtain an output file of Improvement Authority bids. This file includes the same information as the 19 agency bid file except for the following variations: Tab: ECIA Bids Column A Contract number. Column B Column K Column T Bid number. Vendor number. The vendor number was only available for vendors whose contracts appear in the County database, post July 1, 2003. Contract amount. This is the amount attached to the bid in the Improvement Authority contract file. Column U Check amount. For the Improvement Authority, this is considered to be the same as the contract amount. C. Vocational School The electronic contract file for Essex County Vocational School covered the school years 2002/2003, 2003/2004, and 2004/2005. Contract records included purchase order number, vendor name, contract amount, and date of contract. The Vocational School does not maintain a subcommodity code system or usable electronic contract descriptions. We added the prefix VS to all Vocational School purchase orders and added contract numbers for indexing as well as DBE classification variables. With distinct purchase order numbers but a lack of subcommodity codes, there was no method to combine at a base level the contract

Analysis of Essex County Procurement and Contracting: Final Report 109 records. The option remains to combine contracts given to a single vendor by common year of contract. No vendor address information was available in electronic form. Without using this method, we have 6,967 Essex County College contracts. This file includes the same information as the 19 agency contract file except for the following variations: Tab: Vocational Contracts Column A Column B Column M Column N Column O Column P Column R Contract number. Contracts are numbered consecutively by vendor name (alphabetical), year, and contract amount (descending). The VS prefix identifies these contracts as those from the Essex County Vocational School. Bid number. The single bid number was assigned by the study team. Vendor number. The vocational school does not attach ID numbers to vendors in the contract file. Contract amount. Check amount. For the vocational school, these numbers were taken to be the same. The electronic file had only one contract amount. Subcommodity code. The vocational school does not use a subcommodity code system. Source of contract. Values: Vocational School The study team was informed that the Vocational School only had one formal bid process during the period of the study. This bid was reviewed in the Vocational School s main administrative office in Verona. The bid file had information similar to a construction file from the primary County bid process, i.e., the files we examined at the Hall of Records. The single bid on which we collected information had 5 different bidders. This file includes the same information as the 19 agency bid file except for the following variations: Tab: Vocational Bids Column A Column B Column K Column T Contract number. Bid number. Vendor number. Vendor numbers are not available for vocational school vendors. Contract amount. This is the amount attached to the bid in the vocational school contract file.

Analysis of Essex County Procurement and Contracting: Final Report 110 Column U Check amount. For the vocational school, this is considered to be the same as the contract amount. D. Utilities Authority 1. Contracts The electronic contract file for the Utilities Authority included vendor name, contract amount, year of contract, and a product description of what the contract covered. The Utilities Authority does not maintain a subcommodity code system. We added contract numbers for indexing (with the prefix ECUA ) as well as DBE classification variables. Since the contract data was provided on an annual level, there was no need for purchase order numbers, which were not available anyway. Based on a discussion with the Utilities Authority s Finance Officer about the method of awarding contracts and the absence of an ability to look at any sub-year data, we considered the ECUA contracts to be given only at a yearly level. This method gives us 209 Essex County College contracts. This file includes the same information as the 19 agency bid file except for the following variations: Tab: ECUA Contracts Column A Column B Column M Column N Column O Column P Column R Contract number. Contracts are numbered consecutively by vendor name and year. The ECUA prefix identifies these contracts as those of the Essex County Utilities Authority. Bid number. The bid numbers were assigned by the study team to identify links between the contract and bid files. Vendor number. The Utilities Authority does not attach ID numbers to vendors in the contract file. Contract amount. Check amount. For the utilities authority, these numbers were taken to be the same. The electronic file had only one contract amount. Subcommodity code. The utilities authority does not use a subcommodity code system. Source of contract. 2. Bids

Analysis of Essex County Procurement and Contracting: Final Report 111 We reviewed two sets of bids for the Utilities Authority a five-year waste services contract and three yearly household hazardous waste collection contracts. The bid files were set up differently than the bid files we reviewed at the Hall of Records. There were no vendor information sheets indicating years in business, number of employees, etc. The files contained mostly information about the firm s ability to meet the bid specifications plans for completing the contracts and proof of equipment and facility access. We reviewed four bids with 11 bidders, 9 of whom were distinct. This file includes the same information as the 19 agency bid file except for the following variations: Tab: ECUA Bids Column A Column B Column K Column T Column U Column W Column X Column Y Contract number. Bid number. Vendor number. Vendor numbers are not available for the Authority s vendors. Contract amount. This is the amount attached to the bid in the Authority s contract file. Check amount. For the Utilities Authority, this is considered to be the same as the contract amount. Years in business. Employees. Amount of business. This information not included in the ECUA bid files. III. DBE List Creation We created a composite DBE list to match to the contract and bid files by combining DBE lists from different sources, including: Essex County Office of Cultural Diversity and Affirmative Action Port Authority of New York and New Jersey New Jersey Transit New Jersey Department of Commerce SAVI Database These lists were combined to create a single master list with fields including vendor name, contact person, vendor address, and contact information such as phone number, fax number, and email address. Some of the lists contained more detailed information, including NAICS codes and/or text descriptions of the type of work typically performed by the vendor.

Analysis of Essex County Procurement and Contracting: Final Report 112 The lists also included limited information as to which type of DBE a firm is, i.e., complete racial/gender identification of the firm s recorded owner. Only vendors appearing in the State SAVI database and registered as a DBE contained this information. We also had discussions with people familiar with New Jersey businesses to identify the race of minority vendors where possible. For vendors who were indicated to be DBEs but either did not appear on either the SAVI database nor were familiar to any of our experts, we classified them as DBE s only, with no further detail. There were seven business type variables included in the master file related to the DBE lists: female, African American, Hispanic, Asian, MBE, DBE, and SBE. Firms were classified according to the following steps: 1. If any specific racial ownership could be identified, the firm was coded both by that specific race (African American, Hispanic, or Asian) and as an MBE. 2. If female ownership could be identified, the firm was coded as a female-owned business. 3. If any of the race variables or the female variable was true, the firm was coded as a DBE. 4. If there was no specific information about racial ownership, but a list still recorded the firm as an MBE, the firm was coded as an MBE on our file. 5. If no information could be obtained about the nature of ownership but the firm still appeared on a list, the firm was coded as a DBE on our file with one exception. Some firms appearing on one older list are listed ONLY as SBE. This list includes some firms that are only SBEs (confirmed through SAVI), so we did not assume they were DBEs. 6. The only firms that have a positive SBE value are firms that otherwise appeared on one of the DBE lists. Because the one older list mentioned above has some firms that are only SBEs, merging the attached file with the vendor and bidder files will show some contracts going to firms that are SBEs but nothing else. Matching process Following the contract and bid data collection, there were four types of vendors identified within the contract process: Vendors (Firms/Organizations with County Contracts Bidders (Firms/Organizations appearing in the bid files, both winners and losers) Requestors (Firms/Organizations appearing in the bid files as having requested a bid application but with no evidence of ever having submitted a formal bid) Subcontractors (Firms/Organizations listed in submitted bid applications as possible subcontractors to a lead bidder)

Analysis of Essex County Procurement and Contracting: Final Report 113 All firms appearing on one of these four lists were checked manually against the master DBE list to determine which ones should be classified as DBEs. Whenever there was a questionable or ambiguous match, vendor addresses and type of service performed were used, when available, to confirm or refute the possible match.

APPENDIX E: SUMMARY OF ALL CONTRACTS ANALYZED After reviewing and collecting data from the original 19 County agencies, Essex County College, the Essex County Utilities Authority, Essex County Vocational School, and the Essex County Improvement Authority, we assembled data on over 26,000 contracts. The exact breakdown of the contracts (before the removal of intergovernmental transfers) as well as the number of vendors, bids, bidders, firms bidding, and winning bidders is outlined in Table E-1. Table E-1. Contracts, Vendors, and Bid Statistics 19 County agencies Essex County College Essex County Utilities Authority Essex County Vocational School Essex County Improvement Authority Contracts Vendors Bids Bidders Firms Winning bidding bidders 11,260 3,877 515 1,234 543 249 3,283 831 59 414 263 79 209 120 4 11 9 4 6,967 1,045 1 5 5 1 5,234 497 25 83 46 16 Note: Figures are not comparable across agencies due to different methods of defining and collecting data. The term contract in Table E-1 carries a slightly different meaning for each source of data. Due to differences in the respective data systems, it is not precisely correct to infer that a contract from any of the four additional agencies represents the same purchasing process as a contract originating from the 19 agencies under the County data system.

APPENDIX F: ANALYSIS OF MINORITY OR ETHNIC GROUPS For the purpose of this report, only three specific minority/ethnic classifications are used: 1) black, 2) Hispanic, and 3) Asian. Although the United States Census Bureau uses two additional minority/ethnic classifications American Indian/Alaska Native and Native Hawaiian/Other Pacific Islander, this report only focuses on the three major minority/ethnic classifications. These groups were selected because blacks, Hispanics, and Asians each account for 5 percent or more of the population of New Jersey and 3 percent or more of the population of Essex County. According to the 2000 U.S. Census, Asians accounted for 5.7 percent New Jersey s population and 3.7 percent of the Essex County population. Blacks accounted for 13.6 percent of the state population and 41.2 percent of the Essex County population. Hispanics accounted for 13.3 percent of the New Jersey population and 15.4 percent of the Essex County population. American Indians/Alaska Natives and Native Hawaiians/Other Pacific Islanders, however, accounted for less than 1 percent of the population of both New Jersey and Essex County. In addition to the three minority/ethnic classifications that are the focus of this report, various sections of the report will refer to other racial/minority related classifications such as Minority Business Enterprise (MBE), Minority/Women Business Enterprises (M/WBE) and Disadvantaged Business Enterprises (DBE). These terms are used to define the different types of minority and women-owned firms. These terms will be used interchangeably throughout the report. Whenever there is a reference to any of these three classifications, a footnote will be provided to describe how the term is being defined for purposes of that particular analysis. Typically, the term MBE refers to a business that is at least 51 percent owned by a minority person. In this instance, "minority" may not only refer to one of the three major minority/ethnic groups which are the focus of this report but may also encompass other minority classifications. For example, some of our data sources did not indicate a specific minority classification but merely designated the firm as a minority business. A M/WBE is a minority or woman-owned business, which means that the firm is at least 51 percent owned by a minority and/or female. This group is often counted as an MBE and a WBE, but whenever possible, measures were taken to avoid double counting this group. A DBE (disadvantaged business enterprise) is defined as a small firm that is owned and controlled by a socially and economically disadvantaged individual. Some minority groups and women are presumed to be disadvantaged. Typically, a firm has to apply to be certified as a DBE. The certification determination can be based upon "on-site visits, personal interviews, reviews of licenses, stock ownership, equipment, bonding capacity, work completed, resume of principal owners and financial capacity." Since the County of Essex does not have a formal certification process, DBE lists were obtained from other government agencies which have a certification process, including New Jersey

Analysis of Essex County Procurement and Contracting: Final Report 116 Transit and the Port Authority of New York and New Jersey. Firms on the lists of these agencies are considered certified DBEs. Nevertheless, in some sections of the report, the term DBE is used to refer to a firm that may or may not have gone through a formal DBE certification process but has been designated as an MBE, M/WBE, or WBE by a 1) a County of Essex source, such as the Essex County Office of Cultural Diversity and Affirmative Action or County of Essex Office of Purchasing ; 2) another government source such as the New Jersey Department of Commerce SAVI database, the City of East Orange, the City of Newark Vending and Construction List, or the Survey or Women and Minority-Owned Businesses; or 3) a private source such as Dun & Bradstreet.

APPENDIX G: GEOGRAPHIC MARKETPLACE To best understand a number of issues related to governmental purchasing, a unit of government must understand the geographic regions from which it attracts and hires vendors. This is called the geographic marketplace. To define Essex County s geographic marketplace, we collected data on the nature and location of contracts issued by Essex County between 2002 and 2004. First, we identified all the types of products and services that the County purchased during the relevant period. The list of these products and services are outlined in Table G-1. We then identified the size of all contracts and the states or country in which the appropriate vendors are located. Table G-2 outlines each state or country, the number of contracts in each state/country, the total amount of the contracts, and the percentage of the whole each state/country garnered. The first column includes all contracts; the second only those contracts for which a NAICS code, which identifies the product or service under contract, could be found. To use New Jersey as an example, 88.96 percent of all contracts, and 91.24 percent of NAICS identified contracts, were awarded to New Jersey-based vendors. This discrepancy is the result of varying record keeping in the County. Nineteen agencies kept records in a uniform manner, but four agencies: the County College, the Vocational School, the Improvement Authority, and the Utilities Authority kept records in a different manner and did not clearly indicate the type of each contract it awarded. Once we confirmed that New Jersey vendors had the lion s share of County contracts, we studied the distribution of these contacts among the state s 21 counties. Table G-3 shows that Essex County firms hold 51.46 percent of the County s contracts followed by Union and Morris County vendors. Given that only 11 of 21 counties have Essex County contracts, we began to look at those 11 counties as the geographic marketplace. These counties are the five counties including and contiguous to Essex and six counties around Trenton. To confirm the accuracy of this definition, we tested five other possible marketplace compositions by examining the number and amount of contracts across a number of population groups to determine which most accurately captured Essex County s market place. The results of the six models are outlined in Table G-4. The first three models are political jurisdiction models (PJM) and defined as: PJM-1, Essex County; PJM-2 Essex County, the four adjacent counties and six Trenton-area counties, and PJM-3 zip codes in New Jersey, New York, and Pennsylvania with contract awards that account for 1 percent or more of total dollars awarded in a contract year. The second three models are virtual marketplaces (VM), including: VM-1, the zip codes representing the intersection of contracts awarded, bid lists, and DBE lists; VM-2, the zip codes in Essex County and adjacent counties with at least 1 percent or more of total dollars awarded and at least one DBE or bidder from 2002 to 2004; and VM-3, the zip codes satisfying the following criteria: contracts awarded of $500,000 or more from 2002 to

Analysis of Essex County Procurement and Contracting: Final Report 118 2004 and at least one bidder or DBE from the zip codes for contracts awarded of $50,000 to $499,999 and representing at least 1 percent of all contracts awarded within the category, contracts $17,500 to $49,999, and NAICS categories accounting for 5 percent or more of total spending from 2002 to 2004. This analysis of different marketplace models showed that PJM-2 which included 11 New Jersey counties, captured the greatest number of all contracts, DBE contracts, MBE contracts, and black, Hispanic, and Asian contracts. This confirms that PJM-2 is the best-defined geographic marketplace for Essex County. NAICS 1997 Description Table G-1. Essex County s Relevant Industries 233210 Single Family Housing Construction 233320 Commercial and Institutional Building Construction 234110 Highway and Street Construction 234990 All Other Heavy Construction 235110 Plumbing, Heating, and Air-Conditioning Contractors 235310 Electrical Contractors 235710 Concrete Contractors 235920 Glass and Glazing Contractors 235990 All Other Special Trade Contractors 311119 Other Animal Food Manufacturing 311991 Perishable Prepared Food Manufacturing 313312 Textile and Fabric Finishing (except Broadwoven Fabric) Mills 313320 Fabric Coating Mills 314999 All Other Miscellaneous Textile Product Mills 323121 Tradebinding and Related Work 323122 Prepress Services 326199 All Other Plastics Product Manufacturing 332212 Hand and Edge Tool Manufacturing 332999 All Other Miscellaneous Fabricated Metal Product Manufacturing 333511 Industrial Mold Manufacturing 333912 Air and Gas Compressor Manufacturing 333922 Conveyor and Conveying Equipment Manufacturing 333997 Scale and Balance (except Laboratory) Manufacturing 334310 Audio and Video Equipment Manufacturing 336312 Gasoline Engine and Engine Parts Manufacturing 339111 Laboratory Apparatus and Furniture Manufacturing 339113 Surgical Appliance and Supplies Manufacturing 421120 Motor Vehicle Supplies and New Parts Wholesalers 421210 Furniture Wholesalers 421220 Home Furnishing Wholesalers 421310 Lumber, Plywood, Millwork, and Wood Panel Wholesalers 421320 Brick, Stone, and Related Construction Material Wholesalers 421330 Roofing, Siding, and Insulation Material Wholesalers 421410 Photographic Equipment and Supplies Wholesalers 421420 Office Equipment Wholesalers

Analysis of Essex County Procurement and Contracting: Final Report 119 Table G-1. Essex County s Relevant Industries 421430 Computer and Computer Peripheral Equipment and Software Wholesalers 421450 Medical, Dental, and Hospital Equipment and Supplies Wholesalers 421490 Other Professional Equipment and Supplies Wholesalers 421510 Metal Service Centers and Offices 421610 Electrical Apparatus and Equipment, Wiring Supplies, and Construction Material Wholesalers 421620 Electrical Appliance, Television, and Radio Set Wholesalers 421690 Other Electronic Parts and Equipment Wholesalers 421710 Hardware Wholesalers 421720 Plumbing and Heating Equipment and Supplies (Hydronics) Wholesalers 421730 Warm Air Heating and Air-Conditioning Equipment and Supplies Wholesalers 421740 Refrigeration Equipment and Supplies Wholesalers 421810 Construction and Mining (except Oil Well) Machinery and Equipment Wholesalers 421820 Farm and Garden Machinery and Equipment Wholesalers 421830 Industrial Machinery and Equipment Wholesalers 421840 Industrial Supplies Wholesalers 421850 Service Establishment Equipment and Supplies Wholesalers 421910 Sporting and Recreational Goods and Supplies Wholesalers 421930 Recyclable Material Wholesalers 421940 Jewelry, Watch, Precious Stone, and Precious Metal Wholesalers 421990 Other Miscellaneous Durable Goods Wholesalers 422110 Printing and Writing Paper Wholesalers 422120 Stationary and Office Supplies Wholesalers 422130 Industrial and Personal Service Paper Wholesalers 422210 Drugs and Druggists' Sundries Wholesalers 422310 Piece Goods, Notions, and Other Dry Goods Wholesalers 422420 Packaged Frozen Food Wholesalers 422490 Other Grocery and Related Products Wholesalers 422610 Plastics Materials and Basic Forms and Shapes Wholesalers 422690 Other Chemical and Allied Products Wholesalers 422710 Petroleum Bulk Stations and Terminals 422910 Farm Supplies Wholesalers 422930 Flower, Nursery Stock, and Florists' Supplies Wholesalers 422990 Other Miscellaneous Nondurable Goods Wholesalers 441310 Automotive Parts and Accessories Stores 441320 Tire Dealers 442210 Floor Covering Stores 442291 Window Treatment Stores 443111 Household Appliance Stores 443112 Radio, Television, and Other Electronics Stores 444120 Paint and Wallpaper Stores 444130 Hardware Stores 444190 Other Building Material Dealers 446130 Optical Goods Stores 448190 Other Clothing Stores 448320 Luggage and Leather Goods Stores 451110 Sporting Goods Stores 451140 Musical Instrument and Supplies Stores 453210 Office Supplies and Stationery Stores 453910 Pet and Pet Supplies Stores

Analysis of Essex County Procurement and Contracting: Final Report 120 Table G-1. Essex County s Relevant Industries 453920 Art Dealers 453998 All Other Miscellaneous Store Retailers (except Tobacco Stores) 454311 Heating Oil Dealers 487110 Scenic and Sightseeing Transportation, Land 488999 All Other Support Activities for Transportation 511130 Book Publishers 514120 Libraries and Archives 514210 Data Processing Services 522320 Financial Transactions Processing, Reserve, and Clearinghouse Activities 524210 Insurance Agencies and Brokerages 531210 Offices of Real Estate Agents and Brokers 532490 Other Commercial and Industrial Machinery and Equipment Rental and Leasing 541330 Engineering Services 561320 Temporary Help Services 561410 Document Preparation Services 561621 Security Systems Services (except Locksmiths) 561720 Janitorial Services 561740 Carpet and Upholstery Cleaning Services 561990 All Other Support Services 611710 Educational Support Services 621111 Offices of Physicians (except Mental Health Specialists) 621512 Diagnostic Imaging Centers 624190 Other Individual and Family Services 713990 All Other Amusement and Recreation Industries 722211 Limited-Service Restaurants 811111 General Automotive Repair 811121 Automotive Body, Paint, and Interior Repair and Maintenance 811213 Communication Equipment Repair and Maintenance 812320 Drycleaning and Laundry Services (except Coin-Operated) 813211 Grantmaking Foundations 813920 Professional Organizations Source: Essex County Contract Files 2002-2004 and NAICS Codes 1997

Analysis of Essex County Procurement and Contracting: Final Report 121 Table G-2. County Contracts by Vendors States/Countries All Contracts For contracts with NAICS codes state Number of Number of Contract Contract Amount Percent state Contracts Contracts Amount Percent AL 88 $30,616,821.01 3.00% AL 81 $30,608,711.67 3.20% AR 1 $466.00 0.00% AR 1 $466.00 0.00% AZ 89 $843,780.18 0.08% AZ 31 $825,435.49 0.09% CA 266 $4,312,765.82 0.42% CA 131 $3,886,288.12 0.41% Canada 21 $110,587.62 0.01% Canada 19 $109,987.72 0.01% CO 79 $422,162.94 0.04% CO 48 $285,841.02 0.03% CT 208 $2,394,805.13 0.23% CT 80 $2,074,351.71 0.22% DC 63 $292,323.96 0.03% DC 55 $260,609.11 0.03% DE 30 $1,406,083.55 0.14% DE 27 $1,323,781.55 0.14% FL 133 $2,580,767.63 0.25% FL 101 $2,505,473.68 0.26% Foreign 1 $1,197.00 0.00% Foreign 1 $1,197.00 0.00% GA 166 $6,665,510.75 0.65% GA 79 $5,163,212.80 0.54% IA 28 $3,421,044.19 0.34% IA 19 $3,396,663.08 0.35% ID 2 $6,640.00 0.00% ID 0.00% IL 481 $1,446,988.25 0.14% IL 117 $416,658.02 0.04% IN 45 $1,068,761.89 0.10% IN 27 $345,726.43 0.04% KS 23 $41,447.13 0.00% KS 14 $11,322.72 0.00% KY 144 $167,690.16 0.02% KY 58 $102,085.38 0.01% LA 12 $12,603.39 0.00% LA 6 $6,178.38 0.00% MA 116 $904,207.67 0.09% MA 74 $809,259.30 0.08% MD 86 $192,220.90 0.02% MD 74 $178,433.19 0.02% ME 4 $2,370.00 0.00% ME 0.00% MI 28 $124,286.96 0.01% MI 17 $76,150.39 0.01% MN 191 $1,230,783.97 0.12% MN 115 $921,041.70 0.10% MO 41 $193,577.70 0.02% MO 23 $35,337.85 0.00% MS 8 $12,784.39 0.00% MS 6 $12,384.39 0.00% MT 1 $253.62 0.00% MT 1 $253.62 0.00% NC 166 $920,957.01 0.09% NC 57 $604,599.42 0.06% ND 2 $855.65 0.00% ND 2 $855.65 0.00% NE 275 $173,410.54 0.02% NE 33 $102,991.26 0.01% NH 21 $485,424.17 0.05% NH 6 $51,820.00 0.01%

Analysis of Essex County Procurement and Contracting: Final Report 122 NJ 14,042 $907,393,915.20 88.96% NJ 8922 $872,999,943.43 91.24% NM 3 $1,874.66 0.00% NM 2 $1,685.66 0.00% NV 21 $3,780.63 0.00% NV 2 $1,504.96 0.00% NY 609 $15,528,701.29 1.52% NY 358 $13,634,600.54 1.42% OH 91 $273,224.95 0.03% OH 68 $107,273.12 0.01% OK 10 $54,791.41 0.01% OK 10 $54,791.41 0.01% OR 11 $14,130.98 0.00% OR 7 $10,781.65 0.00% PA 532 $8,066,392.13 0.79% PA 282 $6,148,632.19 0.64% RI 11 $6,811.77 0.00% RI 4 $1,843.64 0.00% SC 40 $3,213,668.29 0.32% SC 37 $3,212,173.44 0.34% SD 16 $417,900.62 0.04% SD 16 $417,900.62 0.04% TN 259 $129,725.47 0.01% TN 31 $112,988.51 0.01% TX 322 $4,037,123.38 0.40% TX 245 $3,524,881.60 0.37% UT 10 $23,379.71 0.00% UT 5 $2,717.74 0.00% VA 81 $611,606.58 0.06% VA 50 $521,446.85 0.05% VT 5 $3,899.60 0.00% VT 1 $85.90 0.00% WA 21 $58,421.47 0.01% WA 17 $55,437.72 0.01% WI 128 $167,794.02 0.02% WI 57 $75,933.67 0.01% WV 2 $18,276.64 0.00% WV 2 $18,276.64 0.00% WY 1 $654.04 0.00% WY 1 $654.04 0.00% Unknown 7,022 $19,870,666.51 1.95% Unknown 66 $1,815,686.01 0.19% Total 26,056 $1,019,950,318.53 100.00% Total 11,486 $956,836,355.99 100.00%

Analysis of Essex County Procurement and Contracting: Final Report 123 Table G-3. Distribution of Contracts in New Jersey FIPS County Number of Contracts Contract Amount Percent Counties in the Geographic Market Area 34001 Atlantic 98 $1,054,577.76 0.12% - 34003 Bergen 1,083 $38,966,600.12 4.32% 4.32% 34005 Burlington 91 $1,539,142.94 0.17% - 34007 Camden 151 $2,224,266.37 0.25% - 34009 Cape May 73 $970,770.17 0.11% - 34011 Cumberland 38 $986,470.33 0.11% - 34013 Essex 6,197 $464,345,009.23 51.46% 51.46% 34015 Gloucester 36 $470,841.10 0.05% - 34017 Hudson 312 $46,341,631.23 5.14% 5.14% 34019 Hunterdon 39 $2,968,272.38 0.33% 0.33% 34021 Mercer 400 $51,839,135.27 5.74% 5.74% 34023 Middlesex 1,199 $20,946,161.15 2.32% 2.32% 34025 Monmouth 396 $65,803,322.60 7.29% 7.29% 34027 Morris 1,225 $79,249,111.04 8.78% 8.78% 34029 Ocean 107 $2,510,299.46 0.28% - 34031 Passaic 818 $15,122,440.26 1.68% 1.68% 34033 Salem 7 $6,900.00 0.00% - 34035 Somerset 298 $2,172,030.54 0.24% 0.24% 34037 Sussex 63 $467,533.69 0.05% - 34039 Union 1,149 $104,138,952.32 11.54% 11.54% 34041 Warren 51 $257,815.47 0.03% - Total 13,831 $902,381,283.43 100.00% 98.84% Source: Essex County 23 Agency Contract Data 2002-2004

Analysis of Essex County Procurement and Contracting: Final Report 124 Table G-4. Distribution of Contracts by Geographic Market Area All DBEs MBEs WBEs Blacks Hispanics Asians Contracts Amounts Contracts Amounts Contracts Amounts Contracts Amounts Contracts Amounts Contracts Amounts Contracts Amounts PJM-1 PJM-2 PJM-3 VM-1 VM-2 VM-3 Total 6,197 13,116 3,418 1,666 3,405 8,772 26,056 23.8% 50.3% 13.1% 6.4% 13.1% 33.7% 100.0% $464,345,009 $891,892,666 $653,781,562 $154,261,656 $640,266,913 $861,857,092 $1,019,950,319 45.5% 87.4% 64.1% 15.1% 62.8% 84.5% 100.0% 613 943 282 165 282 505 1,215 50.5% 77.6% 23.2% 13.6% 23.2% 41.6% 100.0% $25,924,299 $78,416,874 $45,667,772 $68,257,824 $45,667,772 $79,569,212 $85,143,682 30.4% 92.1% 53.6% 80.2% 53.6% 93.5% 100.0% 457 571 177 72 177 240 723 63.2% 79.0% 24.5% 10.0% 24.5% 33.2% 100.0% $14,487,139 $30,477,080 $9,751,965 $25,856,188 $9,751,965 $27,763,351 $30,790,772 47.1% 99.0% 31.7% 84.0% 31.7% 90.2% 100.0% 57 128 59 49 59 92 170 33.5% 75.3% 34.7% 28.8% 34.7% 54.1% 100.0% $2,394,096 $16,530,014 $13,533,082 $15,828,722 $13,533,082 $16,252,912 $16,875,286 14.2% 98.0% 80.2% 93.8% 80.2% 96.3% 100.0% 427 479 153 20 153 181 620 68.9% 77.3% 24.7% 3.2% 24.7% 29.2% 100.0% $1,156,536 $2,046,852 $300,071 $667,429 $300,071 $1,068,878 $2,266,324 51.0% 90.3% 13.2% 29.4% 13.2% 47.2% 100.0% 72 117 30 27 30 94 144 50.0% 81.3% 20.8% 18.8% 20.8% 65.3% 100.0% $17,689,464 $24,247,956 $6,251,320 $20,072,237 $6,251,320 $27,807,506 $29,999,390 59.0% 80.8% 20.8% 66.9% 20.8% 92.7% 100.0% 18 59 8 29 8 28 64 28.1% 92.2% 12.5% 45.3% 12.5% 43.8% 100.0% $10,106,857 $13,656,348 $3,330,341 $3,512,567 $3,330,341 $13,099,775 $13,661,963 74.0% 100.0% 24.4% 25.7% 24.4% 95.9% 100.0%

APPENDIX H: AVAILABILITY ANALYSIS The detailed results of our comprehensive availability analysis are contained in this section. The first table, H-1 provides a summary of the four availability measures that were tested: 1) SWOBE/SMOBE, 2) Certified DBE, 3) Composite DBE, and 4) Dun & Bradstreet. In addition to providing an overall availability rate for each of these measures, the table also shows the availability rate for each of the three primary industries: 1) construction, 2) professional services and 3) supplies and equipment/other procurement services. The availability rates are also broken down by race and gender, and the availability rate for each group is compared to the utilization rate to determine if there are any disparities. The utilization of women and minorityowned firms is measured in three ways: 1) contract dollars, 2) number of contracts awarded, and 3) number of firms. The tables that follow H-1 are all of the background tables used to calculate the different availability rates. These tables show the individual SIC and NAICS codes that were used to calculate the availability rate, as well as the actual number of women and minority-owned firms that are available in each of the different industry classifications. The major findings stemming from these tables were discussed in the Findings Section of the report. 23 23 Throughout the tables in this Appendix, C stands for construction, P for professional services, and O other.

Analysis of Essex County Procurement and Contracting: Final Report 126 H-1. Summary of Availability and Utilization Analysis Availability Rate Utilization Rate Difference in Proportions Test Statistic and Significance Level SWOBE/ SMOBE Method Certified DBE List Method Composite DBE List Method Dun & Bradstreet Method D&B over $17,500 D&B under $17,500 Contract Dollars Contracts Firms Contract Dollars (4/5ths Rule) All Types of Contracting Non-Dbe 73.28% 96.09% 92.61% 91.76% 90.07% 88.67% 91.21% 92.81% 96.71% 1.00603 n= 364,652 14,977 14,860 97,052 12,173 12,173 5,666 >80% Contracts (z-test) DBE 26.72% 3.91% 7.39% 8.24% 9.93% 11.33% 8.79% 7.19% 3.29% 0.937429-12.454 n= 156,746 139 256 10,909 943 943 193 >80% MBE 10.91% 4.24% 4.51% 4.05% 3.42% 4.35% 1.76% 1.239766-4.069 n= 76,694 4,520 571 571 103 < 80% WBE 20.24% 6.61% 6.36% 8.63% 1.85% 0.98% 0.75% 3.572973-22.268-21.802 n= 103150 9210 128 128 44 < 80% Black 3.31% 0.46% 0.41% 0.40% 0.23% 3.65% 0.51% 2.00000 25.654 2.216 n= 12394 353 479 479 30 < 80% < 95% > 95% Hispanic 3.83% 1.28% 1.36% 1.29% 2.72% 0.89% 0.70% 0.470588 0.552-4.793 n= 14727 1434 117 117 41 >80% Asian 3.92% 0.86% 0.95% 1.02% 1.53% 0.45% 0.22% 0.562092-3.75-7.581 n= 16354 1338 59 59 13 >80% Construction Contracting Non-Dbe 88.54 % 94.02% 89.21% 91.95% 93.24% 93.00% 86.55% 92.46% 92.03% 1.062406 n= 55929 2035 2008 15386 731 731 439 >80% DBE 11.46 % 5.98% 10.79% 8.05% 8.11% 7.00% 13.45% 7.54% 7.97% 0.598461 2.351 1.476 n= 6684 71 98 1042 101 101 38 >80% MBE 6.31 % 3.87% 3.78% 3.59% 6.74% 3.44% 5.03% 0.574003 3.178 2.303 n= 5315 525 46 46 24 >80% WBE 9.58 % 4.82% 4.59% 4.10% 3.59% 1.94% 1.89% 1.34401-3.517-2.085 n= 4143 622 26 26 9 < 80% Firms (z-test) - 17.786-10.106

Analysis of Essex County Procurement and Contracting: Final Report 127 Black 1.34 % 0.41% 0.29% 0.41% 0.06% 0.22% 0.42% 7.066563-0.187-0.153 n= 1357 78 3 3 2 < 80% Hispanic 3.32 % 1.43% 1.41% 1.92% 7.08% 3.14% 3.14% 0.202001 4.597 2.778 n= 3041 260 42 42 15 >80% Asian 1.52 % 0.51% 0.51% 0.36% 3.20% 1.64% 0.63% 0.159162 2.835 1.057 n= 721 58 22 22 3 >80% Professional Services Non-Dbe 59.49 % 97.56% 95.04% 86.65% 88.40% 88.05% 97.60% 95.43% 96.82% 0.887809 n= 75365 4553 4499 53966 4819 4819 2377 >80% DBE 40.51 % 2.44% 4.96% 13.35% 11.60% 11.95% 2.40% 4.57% 3.18% 5.562204-13.464-7.884 n= 51319 53 107 4189 231 231 78 < 80% MBE 14.36 % 6.37% 5.09% 3.54% 0.74% 0.51% 1.83% 8.563348-4.946-2.528 n= 49135 1479 26 26 45 < 80% WBE 30.58-7.80% 8.18% 9.89% 0.36% 1.01% 0.41% 21.88569-25.214 % 11.218 n= 92506 3187 51 51 10 < 80% Black 5.34 % 0.74% 0.56% 0.36% 0.57% 0.48% 0.57% 1.291365 6.157 4.708 n= 14146 99 24 24 14 < 80% Hispanic 4.16 % 1.43% 1.29% 1.31% 0.47% 0.67% 0.73% 3.039039-0.016-0.014 n= 15934 410 34 34 18 < 80% Asian 5.29 % 1.66% 1.14% 0.85% 0.32% 0.50% 0.20% 5.187004-8.414-3.77 n= 20755 511 25 25 5 < 80% Supplies and Equipment Procurement & All Other Non-Dbe 72.36 % 99.40% 97.87% 90.58% 86.81% 87.09% 94.38% 97.14% 95.46% 0.959687 n= 28381 5 8389 8353 26448 4951 4951 1304 >80% DBE 27.64 0.60% 2.13% 9.42% 13.19% 12.91% 5.62% 2.86% 4.54% 1.677618-23.4 -

Analysis of Essex County Procurement and Contracting: Final Report 128 % 15.095 n= 48286 15 51 6930 146 146 62 < 80% 15.44 MBE 4.35% 6.29% 4.77% 0.30% 0.49% 1.76% 14.72026-9.375-9.113 % n= 22244 2516 25 25 24 < 80% 16.63 - WBE 8.03% 8.71% 9.69% 0.28% 0.33% 1.17% 28.5823-16.166 % 15.571 n= 31654 5401 17 17 16 < 80% 1.97 Black 0.47% 0.41% 0.28% 0.09% 0.41% 0.29% 5.484797-3.723-1.626 % n= 4735 176 21 21 4 < 80% 4.61 Hispanic 1.40% 1.38% 1.64% 0.14% 0.69% 0.81% 10.22995-7.918-4.435 % n= 7150 764 35 35 11 < 80% 8.88 Asian 1.08% 2.73% 1.38% 0.08% 0.22% 0.29% 13.46432-9.354-5.637 % n= 10954 769 11 11 4 < 80% Note: DBEs in SMOBE/SWOBE are estimated as the sum of WBEs and MBEs adjusted by the state average of overlap (4.43%)

Analysis of Essex County Procurement and Contracting: Final Report 129 Table H-2. Availability Measure: SMOBE/SWOBE Method SIC All Firms Woman Black Hispanic Asian Minority Utilization Weight Woman Black Hispanic Asian Minority Woman Black Hispanic Asian Minority (A) (B) (C) (D) (E) (F) (W) (B/A) (C/A) (D/A) (E/A) (F/A) (B/A)*W (C/A)*W (D/A)*W (E/A)*W (F/A)*W 15 13306 969 232 586 149 966 $51,550,563.89 5.99% 0.07 0.02 0.04 0.01 0.07 0.44% 0.10% 0.26% 0.07% 0.44% 16 1329 149 12 33 23 69 $241,480,357.01 28.08% 0.11 0.01 0.02 0.02 0.05 3.15% 0.25% 0.70% 0.49% 1.46% 17 47978 3025 1113 2422 549 4280 $84,605,147.73 9.84% 0.06 0.02 0.05 0.01 0.09 0.62% 0.23% 0.50% 0.11% 0.88% 20 942 148 18 68 96 177 $127,507.75 0.01% 0.16 0.02 0.07 0.10 0.19 0.00% 0.00% 0.00% 0.00% 0.00% 22 418 43 12 61 5 78 $1,475,290.33 0.17% 0.10 0.03 0.15 0.01 0.19 0.02% 0.00% 0.03% 0.00% 0.03% 23 1746 614 53 218 93 353 $99,715.17 0.01% 0.35 0.03 0.12 0.05 0.20 0.00% 0.00% 0.00% 0.00% 0.00% 27 4453 919 108 129 111 364 $7,784,652.34 0.91% 0.21 0.02 0.03 0.02 0.08 0.19% 0.02% 0.03% 0.02% 0.07% 30 700 52 0 0 33 33 $9,821.20 0.00% 0.07 0.00 0.00 0.05 0.05 0.00% 0.00% 0.00% 0.00% 0.00% 34 1656 173 6 34 41 80 $361,434.00 0.04% 0.10 0.00 0.02 0.02 0.05 0.00% 0.00% 0.00% 0.00% 0.00% 35 2367 185 27 84 45 148 $43,673.05 0.01% 0.08 0.01 0.04 0.02 0.06 0.00% 0.00% 0.00% 0.00% 0.00% 36 1054 121 7 11 65 83 $25,594.82 0.00% 0.11 0.01 0.01 0.06 0.08 0.00% 0.00% 0.00% 0.00% 0.00% 37 282 4 0 1 0 1 $520.12 0.00% 0.01 0.00 0.00 0.00 0.00 0.00% 0.00% 0.00% 0.00% 0.00% 38 563 17 0 6 31 37 $92,164.49 0.01% 0.03 0.00 0.01 0.06 0.07 0.00% 0.00% 0.00% 0.00% 0.00% 41 6230 529 1190 700 952 2670 $1,840,456.68 0.21% 0.08 0.19 0.11 0.15 0.43 0.02% 0.04% 0.02% 0.03% 0.09% 47 5516 1663 277 527 298 1045 $355,894.69 0.04% 0.30 0.05 0.10 0.05 0.19 0.01% 0.00% 0.00% 0.00% 0.01% 50 18348 2369 189 476 1406 2109 $29,014,888.55 3.37% 0.13 0.01 0.03 0.08 0.11 0.44% 0.03% 0.09% 0.26% 0.39% 51 13520 1952 198 439 1472 2118 $2,744,103.49 0.32% 0.14 0.01 0.03 0.11 0.16 0.05% 0.00% 0.01% 0.03% 0.05% 52 2162 300 32 60 7 105 $1,829,703.74 0.21% 0.14 0.01 0.03 0.00 0.05 0.03% 0.00% 0.01% 0.00% 0.01% 56 5052 2002 254 342 474 1052 $139,179.91 0.02% 0.40 0.05 0.07 0.09 0.21 0.01% 0.00% 0.00% 0.00% 0.00% 57 4345 857 104 196 193 496 $845,721.49 0.10% 0.20 0.02 0.05 0.04 0.11 0.02% 0.00% 0.00% 0.00% 0.01% 58 17533 3646 266 1213 2404 3837 $21,428,502.43 2.49% 0.21 0.02 0.07 0.14 0.22 0.52% 0.04% 0.17% 0.34% 0.55% 59 39797 16060 1994 2585 3228 7458 $617,886.70 0.07% 0.40 0.05 0.06 0.08 0.19 0.03% 0.00% 0.00% 0.01% 0.01% 60 652 30 18 15 2 35 $989,691.18 0.12% 0.05 0.03 0.02 0.00 0.05 0.01% 0.00% 0.00% 0.00% 0.01% 64 11818 1764 389 290 362 1064 $95,176,712.81 11.07% 0.15 0.03 0.02 0.03 0.09 1.65% 0.36% 0.27% 0.34% 1.00% 65 63614 16084 966 1341 2608 4771 $13,604,394.69 1.58% 0.25 0.02 0.02 0.04 0.07 0.40% 0.02% 0.03% 0.06% 0.12% 67 5152 942 48 15 125 188 $806,105.00 0.09% 0.18 0.01 0.00 0.02 0.04 0.02% 0.00% 0.00% 0.00% 0.00% 72 33014 11736 2292 2884 3464 8269 $25,639.96 0.00% 0.36 0.07 0.09 0.10 0.25 0.00% 0.00% 0.00% 0.00% 0.00% 73 72575 23594 3581 5171 5397 13413 $2,784,761.95 0.32% 0.33 0.05 0.07 0.07 0.18 0.11% 0.02% 0.02% 0.02% 0.06% 75 10519 471 465 1055 229 1713 $892,591.98 0.10% 0.04 0.04 0.10 0.02 0.16 0.00% 0.00% 0.01% 0.00% 0.02% 76 5516 514 189 298 123 585 $3,482,020.04 0.40% 0.09 0.03 0.05 0.02 0.11 0.04% 0.01% 0.02% 0.01% 0.04% 79 18222 4235 1083 571 289 1951 $1,044,462.22 0.12% 0.23 0.06 0.03 0.02 0.11 0.03% 0.01% 0.00% 0.00% 0.01% 80 38487 11414 1500 1522 4135 7015 $69,823,616.74 8.12% 0.30 0.04 0.04 0.11 0.18 2.41% 0.32% 0.32% 0.87% 1.48% 82 8395 3544 265 165 380 823 $42,872,098.82 4.99% 0.42 0.03 0.02 0.05 0.10 2.10% 0.16% 0.10% 0.23% 0.49% 83 11115 6665 1579 1092 270 2707 $75,234,343.64 8.75% 0.60 0.14 0.10 0.02 0.24 5.25% 1.24% 0.86% 0.21% 2.13% 87 53022 11513 1771 1515 3371 6601 $106,792,133.00 12.42% 0.22 0.03 0.03 0.06 0.12 2.70% 0.41% 0.35% 0.79% 1.55% 521398 103150 12394 14727 16354 76694 $860,001,351.61 100.0% 20.24% 3.31% 3.83% 3.92% 10.91%

Analysis of Essex County Procurement and Contracting: Final Report 130 Table H-3. Availability Measure: SMOBE/SWOBE Method for PJM-2 (By Industry) SIC All Firms Woman Black Hispanic Asian Minority Utilization Weight Woman Black Hispanic Asian Minority Woman Black Hispanic Asian Minority (A) (B) (C) (D) (E) (F) (W) (B/A) (C/A) (D/A) (E/A) (F/A) (B/A)*W (C/A)*W (D/A)*W (E/A)*W (F/A)*W 15 13306 969 232 586 149 966 $51,550,563.89 13.65% 0.07 0.02 0.04 0.01 0.07 0.99% 0.24% 0.60% 0.15% 0.99% 16 1329 149 12 33 23 69 $241,480,357.01 63.95% 0.11 0.01 0.02 0.02 0.05 7.17% 0.58% 1.59% 1.11% 3.32% 17 47978 3025 1113 2422 549 4280 $84,605,147.73 22.40% 0.06 0.02 0.05 0.01 0.09 1.41% 0.52% 1.13% 0.26% 2.00% C 62613 4143 1357 3041 721 5315 $377,636,068.63 9.58% 1.34% 3.32% 1.52% 6.31% 20 942 148 18 68 96 177 $127,507.75 0.19% 0.16 0.02 0.07 0.10 0.19 0.03% 0.00% 0.01% 0.02% 0.03% 22 418 43 12 61 5 78 $1,475,290.33 2.14% 0.10 0.03 0.15 0.01 0.19 0.22% 0.06% 0.31% 0.03% 0.40% 23 1746 614 53 218 93 353 $99,715.17 0.14% 0.35 0.03 0.12 0.05 0.20 0.05% 0.00% 0.02% 0.01% 0.03% 27 4453 919 108 129 111 364 $7,784,652.34 11.31% 0.21 0.02 0.03 0.02 0.08 2.33% 0.27% 0.33% 0.28% 0.92% 30 700 52 0 0 33 33 $9,821.20 0.01% 0.07 0.00 0.00 0.05 0.05 0.00% 0.00% 0.00% 0.00% 0.00% 34 1656 173 6 34 41 80 $361,434.00 0.53% 0.10 0.00 0.02 0.02 0.05 0.05% 0.00% 0.01% 0.01% 0.03% 35 2367 185 27 84 45 148 $43,673.05 0.06% 0.08 0.01 0.04 0.02 0.06 0.00% 0.00% 0.00% 0.00% 0.00% 36 1054 121 7 11 65 83 $25,594.82 0.04% 0.11 0.01 0.01 0.06 0.08 0.00% 0.00% 0.00% 0.00% 0.00% 37 282 4 0 1 0 1 $520.12 0.00% 0.01 0.00 0.00 0.00 0.00 0.00% 0.00% 0.00% 0.00% 0.00% 38 563 17 0 6 31 37 $92,164.49 0.13% 0.03 0.00 0.01 0.06 0.07 0.00% 0.00% 0.00% 0.01% 0.01% 41 6230 529 1190 700 952 2670 $1,840,456.68 2.67% 0.08 0.19 0.11 0.15 0.43 0.23% 0.51% 0.30% 0.41% 1.15% 47 5516 1663 277 527 298 1045 $355,894.69 0.52% 0.30 0.05 0.10 0.05 0.19 0.16% 0.03% 0.05% 0.03% 0.10% 50 18348 2369 189 476 1406 2109 $29,014,888.55 42.15% 0.13 0.01 0.03 0.08 0.11 5.44% 0.43% 1.09% 3.23% 4.84% 51 13520 1952 198 439 1472 2118 $2,744,103.49 3.99% 0.14 0.01 0.03 0.11 0.16 0.58% 0.06% 0.13% 0.43% 0.62% 52 2162 300 32 60 7 105 $1,829,703.74 2.66% 0.14 0.01 0.03 0.00 0.05 0.37% 0.04% 0.07% 0.01% 0.13% 56 5052 2002 254 342 474 1052 $139,179.91 0.20% 0.40 0.05 0.07 0.09 0.21 0.08% 0.01% 0.01% 0.02% 0.04% 57 4345 857 104 196 193 496 $845,721.49 1.23% 0.20 0.02 0.05 0.04 0.11 0.24% 0.03% 0.06% 0.05% 0.14% 58 17533 3646 266 1213 2404 3837 $21,428,502.43 31.13% 0.21 0.02 0.07 0.14 0.22 6.47% 0.47% 2.15% 4.27% 6.81% 59 39797 16060 1994 2585 3228 7458 $617,886.70 0.90% 0.40 0.05 0.06 0.08 0.19 0.36% 0.04% 0.06% 0.07% 0.17% O 126684 31654 4735 7150 10954 22244 $68,836,710.95 100.00% 16.63% 1.97% 4.61% 8.88% 15.44% 60 652 30 18 15 2 35 $989,691.18 0.24% 0.05 0.03 0.02 0.00 0.05 0.01% 0.01% 0.01% 0.00% 0.01% 64 11818 1764 389 290 362 1064 $95,176,712.81 23.02% 0.15 0.03 0.02 0.03 0.09 3.44% 0.76% 0.56% 0.71% 2.07% 65 63614 16084 966 1341 2608 4771 $13,604,394.69 3.29% 0.25 0.02 0.02 0.04 0.07 0.83% 0.05% 0.07% 0.13% 0.25% 67 5152 942 48 15 125 188 $806,105.00 0.19% 0.18 0.01 0.00 0.02 0.04 0.04% 0.00% 0.00% 0.00% 0.01% 72 33014 11736 2292 2884 3464 8269 $25,639.96 0.01% 0.36 0.07 0.09 0.10 0.25 0.00% 0.00% 0.00% 0.00% 0.00%

Analysis of Essex County Procurement and Contracting: Final Report 131 73 72575 23594 3581 5171 5397 13413 $2,784,761.95 0.67% 0.33 0.05 0.07 0.07 0.18 0.22% 0.03% 0.05% 0.05% 0.12% 75 10519 471 465 1055 229 1713 $892,591.98 0.22% 0.04 0.04 0.10 0.02 0.16 0.01% 0.01% 0.02% 0.00% 0.04% 76 5516 514 189 298 123 585 $3,482,020.04 0.84% 0.09 0.03 0.05 0.02 0.11 0.08% 0.03% 0.05% 0.02% 0.09% 79 18222 4235 1083 571 289 1951 $1,044,462.22 0.25% 0.23 0.06 0.03 0.02 0.11 0.06% 0.02% 0.01% 0.00% 0.03% 80 38487 11414 1500 1522 4135 7015 $69,823,616.74 16.88% 0.30 0.04 0.04 0.11 0.18 5.01% 0.66% 0.67% 1.81% 3.08% 82 8395 3544 265 165 380 823 $42,872,098.82 10.37% 0.42 0.03 0.02 0.05 0.10 4.38% 0.33% 0.20% 0.47% 1.02% 83 11115 6665 1579 1092 270 2707 $75,234,343.64 18.19% 0.60 0.14 0.10 0.02 0.24 10.91% 2.58% 1.79% 0.44% 4.43% 87 53022 11513 1771 1515 3371 6601 $106,792,133.00 25.82% 0.22 0.03 0.03 0.06 0.12 5.61% 0.86% 0.74% 1.64% 3.22% P 332101 92506 14146 15934 20755 49135 $413,528,572.03 100.0% 30.58% 5.34% 4.16% 5.29% 14.36%

Analysis of Essex County Procurement and Contracting: Final Report 132 Table H-4. Availability Measure: Certified DBE Method for PJM-2 NAICS Description Utilization Percent DBEs Non- DBE Total Firms DBE Share Non- DBE Share Weighted DBE Share Weighted Non-DBE Share 233210 Single Family Housing Construction $66,407.78 0.01% 3 350 353 0.01 0.99 0.00 0.00 233320 Commercial and Institutional Building Construction $51,484,156.11 5.99% 13 266 279 0.05 0.95 0.00 0.06 234110 Highway and Street Construction $22,271,098.62 2.59% 16 126 142 0.11 0.89 0.00 0.02 234990 All Other Heavy Construction $219,209,258.39 25.49% 9 118 127 0.07 0.93 0.02 0.24 235110 Plumbing, Heating, and Air-Conditioning Contractors $12,377,227.11 1.44% 1 338 339 0.00 1.00 0.00 0.01 235310 Electrical Contractors $49,991,576.26 5.81% 12 313 325 0.04 0.96 0.00 0.06 235710 Concrete Contractors $2,370,620.08 0.28% 15 193 208 0.07 0.93 0.00 0.00 235920 Glass and Glazing Contractors $9,686.67 0.00% 1 91 92 0.01 0.99 0.00 0.00 235990 All Other Special Trade Contractors $19,856,037.61 2.31% 1 240 241 0.00 1.00 0.00 0.02 311119 Other Animal Food Manufacturing $127,307.55 0.01% 0 3 3 0.00 1.00 0.00 0.00 311991 Perishable Prepared Food Manufacturing $200.20 0.00% 0 10 10 0.00 1.00 0.00 0.00 313312 Textile and Fabric Finishing (except Broadwoven Fabric) Mills $1,469,887.84 0.17% 0 25 25 0.00 1.00 0.00 0.00 313320 Fabric Coating Mills $5,402.49 0.00% 0 10 10 0.00 1.00 0.00 0.00 314999 All Other Miscellaneous Textile Product Mills $99,715.17 0.01% 0 59 59 0.00 1.00 0.00 0.00 323121 Tradebinding and Related Work $5,250.82 0.00% 0 38 38 0.00 1.00 0.00 0.00 323122 Prepress Services $1,545.77 0.00% 0 68 68 0.00 1.00 0.00 0.00 326199 All Other Plastics Product Manufacturing $9,821.20 0.00% 0 105 105 0.00 1.00 0.00 0.00 332212 Hand and Edge Tool Manufacturing $22,473.80 0.00% 0 28 28 0.00 1.00 0.00 0.00 332999 All Other Miscellaneous Fabricated Metal Product Manufacturing $361,434.00 0.04% 0 45 45 0.00 1.00 0.00 0.00 333511 Industrial Mold Manufacturing $12,164.90 0.00% 0 35 35 0.00 1.00 0.00 0.00 333912 Air and Gas Compressor Manufacturing $2,010.08 0.00% 0 9 9 0.00 1.00 0.00 0.00 333922 Conveyor and Conveying Equipment Manufacturing $7,024.27 0.00% 0 15 15 0.00 1.00 0.00 0.00 333997 Scale and Balance (except Laboratory) Manufacturing $0.00 0.00% 0 2 2 0.00 1.00 0.00 0.00 334310 Audio and Video Equipment Manufacturing $25,594.82 0.00% 0 11 11 0.00 1.00 0.00 0.00 336312 Gasoline Engine and Engine Parts Manufacturing $520.12 0.00% 0 8 8 0.00 1.00 0.00 0.00 339111 Laboratory Apparatus and Furniture Manufacturing $4,571.00 0.00% 0 16 16 0.00 1.00 0.00 0.00 339113 Surgical Appliance and Supplies Manufacturing $87,593.49 0.01% 1 30 31 0.03 0.97 0.00 0.00 421120 Motor Vehicle Supplies and New Parts Wholesalers $44,748.21 0.01% 1 168 169 0.01 0.99 0.00 0.00 421210 Furniture Wholesalers $91,759.70 0.01% 1 119 120 0.01 0.99 0.00 0.00

Analysis of Essex County Procurement and Contracting: Final Report 133 421220 Home Furnishing Wholesalers $779,493.72 0.09% 0 165 165 0.00 1.00 0.00 0.00 421310 Lumber, Plywood, Millwork, and Wood Panel Wholesalers $87,462.29 0.01% 0 106 106 0.00 1.00 0.00 0.00 421320 Brick, Stone, and Related Construction Material Wholesalers $21,453.39 0.00% 0 60 60 0.00 1.00 0.00 0.00 421330 Roofing, Siding, and Insulation Material Wholesalers $4,799.13 0.00% 0 30 30 0.00 1.00 0.00 0.00 421410 Photographic Equipment and Supplies Wholesalers $96,100.26 0.01% 0 46 46 0.00 1.00 0.00 0.00 421420 Office Equipment Wholesalers $1,524,600.00 0.18% 1 106 107 0.01 0.99 0.00 0.00 421430 Computer and Computer Peripheral Equipment and Software $4,986,717.78 0.58% Wholesalers 0 192 192 0.00 1.00 0.00 0.01 421450 Medical, Dental, and Hospital Equipment and Supplies $135,631.89 0.02% 0 Wholesalers 157 157 0.00 1.00 0.00 0.00 421490 Other Professional Equipment and Supplies Wholesalers $14,617,337.41 1.70% 1 75 76 0.01 0.99 0.00 0.02 421510 Metal Service Centers and Offices $58,510.46 0.01% 0 159 159 0.00 1.00 0.00 0.00 Electrical Apparatus and Equipment, Wiring Supplies, and 421610 $338,256.19 0.04% 0.01 0.99 0.00 0.00 Construction Material Wholesalers 2 172 174 421620 Electrical Appliance, Television, and Radio Set Wholesalers $25,918.67 0.00% 0 93 93 0.00 1.00 0.00 0.00 421690 Other Electronic Parts and Equipment Wholesalers $27,656.78 0.00% 0 218 218 0.00 1.00 0.00 0.00 421710 Hardware Wholesalers $22,540.11 0.00% 0 134 134 0.00 1.00 0.00 0.00 Plumbing and Heating Equipment and Supplies (Hydronics) 421720 $260,420.27 0.03% 0 0.00 1.00 0.00 0.00 Wholesalers 101 101 Warm Air Heating and Air-Conditioning Equipment and Supplies 421730 $380,575.03 0.04% 0 0.00 1.00 0.00 0.00 Wholesalers 78 78 421740 Refrigeration Equipment and Supplies Wholesalers $3,873.18 0.00% 0 25 25 0.00 1.00 0.00 0.00 Construction and Mining (except Oil Well) Machinery and 421810 $225,360.90 0.03% 0 0.00 1.00 0.00 0.00 Equipment Wholesalers 54 54 421820 Farm and Garden Machinery and Equipment Wholesalers $476,025.31 0.06% 0 46 46 0.00 1.00 0.00 0.00 421830 Industrial Machinery and Equipment Wholesalers $10,588.69 0.00% 0 254 254 0.00 1.00 0.00 0.00 421840 Industrial Supplies Wholesalers $10,810.54 0.00% 2 177 179 0.01 0.99 0.00 0.00 421850 Service Establishment Equipment and Supplies Wholesalers $299,985.40 0.03% 0 118 118 0.00 1.00 0.00 0.00 421910 Sporting and Recreational Goods and Supplies Wholesalers $117,236.61 0.01% 0 105 105 0.00 1.00 0.00 0.00 421930 Recyclable Material Wholesalers $522.69 0.00% 0 134 134 0.00 1.00 0.00 0.00 421940 Jewelry, Watch, Precious Stone, and Precious Metal Wholesalers $6,726.90 0.00% 0 139 139 0.00 1.00 0.00 0.00 421990 Other Miscellaneous Durable Goods Wholesalers $1,271,966.56 0.15% 2 214 216 0.01 0.99 0.00 0.00 422110 Printing and Writing Paper Wholesalers $13,579.05 0.00% 0 68 68 0.00 1.00 0.00 0.00 422120 Stationary and Office Supplies Wholesalers $14,403.00 0.00% 0 143 143 0.00 1.00 0.00 0.00 422130 Industrial and Personal Service Paper Wholesalers $77.80 0.00% 0 133 133 0.00 1.00 0.00 0.00 422210 Drugs and Druggists' Sundries Wholesalers $0.00 0.00% 0 184 184 0.00 1.00 0.00 0.00 422310 Piece Goods, Notions, and Other Dry Goods Wholesalers $0.00 0.00% 0 134 134 0.00 1.00 0.00 0.00

Analysis of Essex County Procurement and Contracting: Final Report 134 422420 Packaged Frozen Food Wholesalers $4,706.50 0.00% 0 70 70 0.00 1.00 0.00 0.00 422490 Other Grocery and Related Products Wholesalers $20,710.50 0.00% 0 206 206 0.00 1.00 0.00 0.00 422610 Plastics Materials and Basic Forms and Shapes Wholesalers $114.00 0.00% 0 107 107 0.00 1.00 0.00 0.00 422690 Other Chemical and Allied Products Wholesalers $452,334.70 0.05% 0 199 199 0.00 1.00 0.00 0.00 422710 Petroleum Bulk Stations and Terminals $287,522.66 0.03% 0 41 41 0.00 1.00 0.00 0.00 422910 Farm Supplies Wholesalers $611,099.24 0.07% 0 64 64 0.00 1.00 0.00 0.00 422930 Flower, Nursery Stock, and Florists' Supplies Wholesalers $106,102.23 0.01% 0 95 95 0.00 1.00 0.00 0.00 422990 Other Miscellaneous Nondurable Goods Wholesalers $0.00 0.00% 1 225 226 0.00 1.00 0.00 0.00 441310 Automotive Parts and Accessories Stores $384,072.86 0.04% 1 226 227 0.00 1.00 0.00 0.00 441320 Tire Dealers $69,722.14 0.01% 0 148 148 0.00 1.00 0.00 0.00 442210 Floor Covering Stores $136,883.08 0.02% 1 161 162 0.01 0.99 0.00 0.00 442291 Window Treatment Stores $847.51 0.00% 0 74 74 0.00 1.00 0.00 0.00 443111 Household Appliance Stores $24,983.12 0.00% 0 123 123 0.00 1.00 0.00 0.00 443112 Radio, Television, and Other Electronics Stores $815,496.41 0.09% 0 209 209 0.00 1.00 0.00 0.00 444120 Paint and Wallpaper Stores $61,855.87 0.01% 0 108 108 0.00 1.00 0.00 0.00 444130 Hardware Stores $1,767,847.87 0.21% 0 167 167 0.00 1.00 0.00 0.00 444190 Other Building Material Dealers $38,851.59 0.00% 0 270 270 0.00 1.00 0.00 0.00 446130 Optical Goods Stores $0.00 0.00% 0 155 155 0.00 1.00 0.00 0.00 448190 Other Clothing Stores $139,179.91 0.02% 0 133 133 0.00 1.00 0.00 0.00 448320 Luggage and Leather Goods Stores $600.00 0.00% 0 35 35 0.00 1.00 0.00 0.00 451110 Sporting Goods Stores $36,320.83 0.00% 0 213 213 0.00 1.00 0.00 0.00 451140 Musical Instrument and Supplies Stores $4,394.45 0.00% 0 58 58 0.00 1.00 0.00 0.00 453210 Office Supplies and Stationery Stores $2,458,280.81 0.29% 0 137 137 0.00 1.00 0.00 0.00 453910 Pet and Pet Supplies Stores $520.01 0.00% 0 130 130 0.00 1.00 0.00 0.00 453920 Art Dealers $2,278.20 0.00% 0 79 79 0.00 1.00 0.00 0.00 453998 All Other Miscellaneous Store Retailers (except Tobacco Stores) $578,167.66 0.07% 0 181 181 0.00 1.00 0.00 0.00 454311 Heating Oil Dealers $1,233,453.81 0.14% 1 126 127 0.01 0.99 0.00 0.00 487110 Scenic and Sightseeing Transportation, Land $1,840,456.68 0.21% 0 12 12 0.00 1.00 0.00 0.00 488999 All Other Support Activities for Transportation $355,894.69 0.04% 0 13 13 0.00 1.00 0.00 0.00 511130 Book Publishers $7,777,855.75 0.90% 1 56 57 0.02 0.98 0.00 0.01 514120 Libraries and Archives $38,281.26 0.00% 0 40 40 0.00 1.00 0.00 0.00 514210 Data Processing Services $507,766.34 0.06% 1 184 185 0.01 0.99 0.00 0.00 Financial Transactions Processing, Reserve, and Clearinghouse 522320 $989,691.18 0.12% 0 0.00 1.00 0.00 0.00 Activities 49 49 524210 Insurance Agencies and Brokerages $95,176,712.81 11.07% 1 309 310 0.00 1.00 0.00 0.11 531210 Offices of Real Estate Agents and Brokers $13,604,394.69 1.58% 3 275 278 0.01 0.99 0.00 0.02

Analysis of Essex County Procurement and Contracting: Final Report 135 Other Commercial and Industrial Machinery and Equipment Rental 532490 $966,355.08 0.11% 0 0.00 1.00 0.00 0.00 and Leasing 80 80 541330 Engineering Services $106,792,133.00 12.42% 28 279 307 0.09 0.91 0.01 0.11 561320 Temporary Help Services $0.00 0.00% 1 182 183 0.01 0.99 0.00 0.00 561410 Document Preparation Services $913,659.46 0.11% 1 88 89 0.01 0.99 0.00 0.00 561621 Security Systems Services (except Locksmiths) $193,009.07 0.02% 0 123 123 0.00 1.00 0.00 0.00 561720 Janitorial Services $102,545.52 0.01% 11 286 297 0.04 0.96 0.00 0.00 561740 Carpet and Upholstery Cleaning Services $8,862.32 0.00% 0 125 125 0.00 1.00 0.00 0.00 561990 All Other Support Services $101,426.48 0.01% 0 173 173 0.00 1.00 0.00 0.00 611710 Educational Support Services $42,833,817.56 4.98% 0 96 96 0.00 1.00 0.00 0.05 621111 Offices of Physicians (except Mental Health Specialists) $69,818,180.74 8.12% 1 335 336 0.00 1.00 0.00 0.08 621512 Diagnostic Imaging Centers $5,436.00 0.00% 0 95 95 0.00 1.00 0.00 0.00 624190 Other Individual and Family Services $3,485,607.34 0.41% 0 164 164 0.00 1.00 0.00 0.00 713990 All Other Amusement and Recreation Industries $1,044,462.22 0.12% 0 153 153 0.00 1.00 0.00 0.00 722211 Limited-Service Restaurants $21,428,502.43 2.49% 3 355 358 0.01 0.99 0.00 0.02 811111 General Automotive Repair $890,412.24 0.10% 0 315 315 0.00 1.00 0.00 0.00 811121 Automotive Body, Paint, and Interior Repair and Maintenance $2,179.74 0.00% 1 245 246 0.00 1.00 0.00 0.00 811213 Communication Equipment Repair and Maintenance $3,482,020.04 0.40% 1 40 41 0.02 0.98 0.00 0.00 812320 Dry-cleaning and Laundry Services (except Coin-Operated) $16,777.64 0.00% 0 277 277 0.00 1.00 0.00 0.00 813211 Grantmaking Foundations $806,105.00 0.09% 0 81 81 0.00 1.00 0.00 0.00 813920 Professional Organizations $71,748,736.30 8.34% 0 148 148 0.00 1.00 0.00 0.08 Grand Total $860,001,351.61 100.00% 139 14977 15116 0.04 0.96 Source: Essex County Contract Files 2002-2004

Analysis of Essex County Procurement and Contracting: Final Report 136 Table H-5. Availability Measure: Certified DBE Method for PJM-2 (By Industry) NAICS Description Utilization Percent DBEs Total Firms DBE Share Weighted Share 233210 Single Family Housing Construction $66,407.78 0.02% 3 353 0.0085 0.0000 233320 Commercial and Institutional Building Construction $51,484,156.11 13.63% 13 279 0.0466 0.0064 234110 Highway and Street Construction $22,271,098.62 5.90% 16 142 0.1127 0.0066 234990 All Other Heavy Construction $219,209,258.39 58.05% 9 127 0.0709 0.0411 235110 Plumbing, Heating, and Air-Conditioning Contractors $12,377,227.11 3.28% 1 339 0.0029 0.0001 235310 Electrical Contractors $49,991,576.26 13.24% 12 325 0.0369 0.0049 235710 Concrete Contractors $2,370,620.08 0.63% 15 208 0.0721 0.0005 235920 Glass and Glazing Contractors $9,686.67 0.00% 1 92 0.0109 0.0000 235990 All Other Special Trade Contractors $19,856,037.61 5.26% 1 241 0.0041 0.0002 C $377,636,068.63 100.00% 71 2106 5.98% 311119 Other Animal Food Manufacturing $127,307.55 0.32% 0 3 0.0000 0.0000 311991 Perishable Prepared Food Manufacturing $200.20 0.00% 0 10 0.0000 0.0000 313312 Textile and Fabric Finishing (except Broadwoven Fabric) Mills $1,469,887.84 3.71% 0 25 0.0000 0.0000 313320 Fabric Coating Mills $5,402.49 0.01% 0 10 0.0000 0.0000 314999 All Other Miscellaneous Textile Product Mills $99,715.17 0.25% 0 59 0.0000 0.0000 323121 Tradebinding and Related Work $5,250.82 0.01% 0 38 0.0000 0.0000 323122 Prepress Services $1,545.77 0.00% 0 68 0.0000 0.0000 326199 All Other Plastics Product Manufacturing $9,821.20 0.02% 0 105 0.0000 0.0000 332212 Hand and Edge Tool Manufacturing $22,473.80 0.06% 0 28 0.0000 0.0000 332999 All Other Miscellaneous Fabricated Metal Product Manufacturing $361,434.00 0.91% 0 45 0.0000 0.0000 333511 Industrial Mold Manufacturing $12,164.90 0.03% 0 35 0.0000 0.0000 333912 Air and Gas Compressor Manufacturing $2,010.08 0.01% 0 9 0.0000 0.0000 333922 Conveyor and Conveying Equipment Manufacturing $7,024.27 0.02% 0 15 0.0000 0.0000 333997 Scale and Balance (except Laboratory) Manufacturing $0.00 0.00% 0 2 0.0000 0.0000 334310 Audio and Video Equipment Manufacturing $25,594.82 0.06% 0 11 0.0000 0.0000 336312 Gasoline Engine and Engine Parts Manufacturing $520.12 0.00% 0 8 0.0000 0.0000 339111 Laboratory Apparatus and Furniture Manufacturing $4,571.00 0.01% 0 16 0.0000 0.0000 339113 Surgical Appliance and Supplies Manufacturing $87,593.49 0.22% 1 31 0.0323 0.0001 421120 Motor Vehicle Supplies and New Parts Wholesalers $44,748.21 0.11% 1 169 0.0059 0.0000 421210 Furniture Wholesalers $91,759.70 0.23% 1 120 0.0083 0.0000 421220 Home Furnishing Wholesalers $779,493.72 1.97% 0 165 0.0000 0.0000 421310 Lumber, Plywood, Millwork, and Wood Panel Wholesalers $87,462.29 0.22% 0 106 0.0000 0.0000 421320 Brick, Stone, and Related Construction Material Wholesalers $21,453.39 0.05% 0 60 0.0000 0.0000 421330 Roofing, Siding, and Insulation Material Wholesalers $4,799.13 0.01% 0 30 0.0000 0.0000 421410 Photographic Equipment and Supplies Wholesalers $96,100.26 0.24% 0 46 0.0000 0.0000

Analysis of Essex County Procurement and Contracting: Final Report 137 421420 Office Equipment Wholesalers $1,524,600.00 3.85% 1 107 0.0093 0.0004 421430 Computer and Computer Peripheral Equipment and Software Wholesalers $4,986,717.78 12.58% 0 192 0.0000 0.0000 421450 Medical, Dental, and Hospital Equipment and Supplies Wholesalers $135,631.89 0.34% 0 157 0.0000 0.0000 421490 Other Professional Equipment and Supplies Wholesalers $14,617,337.41 36.88% 1 76 0.0132 0.0049 421510 Metal Service Centers and Offices $58,510.46 0.15% 0 159 0.0000 0.0000 421610 Electrical Apparatus and Equipment, Wiring Supplies, and Construction $338,256.19 0.85% Material Wholesalers 2 174 0.0115 0.0001 421620 Electrical Appliance, Television, and Radio Set Wholesalers $25,918.67 0.07% 0 93 0.0000 0.0000 421690 Other Electronic Parts and Equipment Wholesalers $27,656.78 0.07% 0 218 0.0000 0.0000 421710 Hardware Wholesalers $22,540.11 0.06% 0 134 0.0000 0.0000 421720 Plumbing and Heating Equipment and Supplies (Hydronics) Wholesalers $260,420.27 0.66% 0 101 0.0000 0.0000 421730 Warm Air Heating and Air-Conditioning Equipment and Supplies $380,575.03 0.96% 0 Wholesalers 78 0.0000 0.0000 421740 Refrigeration Equipment and Supplies Wholesalers $3,873.18 0.01% 0 25 0.0000 0.0000 421810 Construction and Mining (except Oil Well) Machinery and Equipment $225,360.90 0.57% 0 Wholesalers 54 0.0000 0.0000 421820 Farm and Garden Machinery and Equipment Wholesalers $476,025.31 1.20% 0 46 0.0000 0.0000 421830 Industrial Machinery and Equipment Wholesalers $10,588.69 0.03% 0 254 0.0000 0.0000 421840 Industrial Supplies Wholesalers $10,810.54 0.03% 2 179 0.0112 0.0000 421850 Service Establishment Equipment and Supplies Wholesalers $299,985.40 0.76% 0 118 0.0000 0.0000 421910 Sporting and Recreational Goods and Supplies Wholesalers $117,236.61 0.30% 0 105 0.0000 0.0000 421930 Recyclable Material Wholesalers $522.69 0.00% 0 134 0.0000 0.0000 421940 Jewelry, Watch, Precious Stone, and Precious Metal Wholesalers $6,726.90 0.02% 0 139 0.0000 0.0000 421990 Other Miscellaneous Durable Goods Wholesalers $1,271,966.56 3.21% 2 216 0.0093 0.0003 422110 Printing and Writing Paper Wholesalers $13,579.05 0.03% 0 68 0.0000 0.0000 422120 Stationary and Office Supplies Wholesalers $14,403.00 0.04% 0 143 0.0000 0.0000 422130 Industrial and Personal Service Paper Wholesalers $77.80 0.00% 0 133 0.0000 0.0000 422210 Drugs and Druggists' Sundries Wholesalers $0.00 0.00% 0 184 0.0000 0.0000 422310 Piece Goods, Notions, and Other Dry Goods Wholesalers $0.00 0.00% 0 134 0.0000 0.0000 422420 Packaged Frozen Food Wholesalers $4,706.50 0.01% 0 70 0.0000 0.0000 422490 Other Grocery and Related Products Wholesalers $20,710.50 0.05% 0 206 0.0000 0.0000 422610 Plastics Materials and Basic Forms and Shapes Wholesalers $114.00 0.00% 0 107 0.0000 0.0000 422690 Other Chemical and Allied Products Wholesalers $452,334.70 1.14% 0 199 0.0000 0.0000 422710 Petroleum Bulk Stations and Terminals $287,522.66 0.73% 0 41 0.0000 0.0000 422910 Farm Supplies Wholesalers $611,099.24 1.54% 0 64 0.0000 0.0000 422930 Flower, Nursery Stock, and Florists' Supplies Wholesalers $106,102.23 0.27% 0 95 0.0000 0.0000 422990 Other Miscellaneous Nondurable Goods Wholesalers $0.00 0.00% 1 226 0.0044 0.0000 441310 Automotive Parts and Accessories Stores $384,072.86 0.97% 1 227 0.0044 0.0000 441320 Tire Dealers $69,722.14 0.18% 0 148 0.0000 0.0000

Analysis of Essex County Procurement and Contracting: Final Report 138 442210 Floor Covering Stores $136,883.08 0.35% 1 162 0.0062 0.0000 442291 Window Treatment Stores $847.51 0.00% 0 74 0.0000 0.0000 443111 Household Appliance Stores $24,983.12 0.06% 0 123 0.0000 0.0000 443112 Radio, Television, and Other Electronics Stores $815,496.41 2.06% 0 209 0.0000 0.0000 444120 Paint and Wallpaper Stores $61,855.87 0.16% 0 108 0.0000 0.0000 444130 Hardware Stores $1,767,847.87 4.46% 0 167 0.0000 0.0000 444190 Other Building Material Dealers $38,851.59 0.10% 0 270 0.0000 0.0000 446130 Optical Goods Stores $0.00 0.00% 0 155 0.0000 0.0000 448190 Other Clothing Stores $139,179.91 0.35% 0 133 0.0000 0.0000 448320 Luggage and Leather Goods Stores $600.00 0.00% 0 35 0.0000 0.0000 451110 Sporting Goods Stores $36,320.83 0.09% 0 213 0.0000 0.0000 451140 Musical Instrument and Supplies Stores $4,394.45 0.01% 0 58 0.0000 0.0000 453210 Office Supplies and Stationery Stores $2,458,280.81 6.20% 0 137 0.0000 0.0000 453910 Pet and Pet Supplies Stores $520.01 0.00% 0 130 0.0000 0.0000 453920 Art Dealers $2,278.20 0.01% 0 79 0.0000 0.0000 453998 All Other Miscellaneous Store Retailers (except Tobacco Stores) $578,167.66 1.46% 0 181 0.0000 0.0000 454311 Heating Oil Dealers $1,233,453.81 3.11% 1 127 0.0079 0.0002 487110 Scenic and Sightseeing Transportation, Land $1,840,456.68 4.64% 0 12 0.0000 0.0000 488999 All Other Support Activities for Transportation $355,894.69 0.90% 0 13 0.0000 0.0000 O $39,630,352.77 100.00% 15 8404 0.60% 511130 Book Publishers $7,777,855.75 1.76% 1 57 0.0175 0.0003 514120 Libraries and Archives $38,281.26 0.01% 0 40 0.0000 0.0000 514210 Data Processing Services $507,766.34 0.11% 1 185 0.0054 0.0000 522320 Financial Transactions Processing, Reserve, and Clearinghouse Activities $989,691.18 0.22% 0 49 0.0000 0.0000 524210 Insurance Agencies and Brokerages $95,176,712.81 21.50% 1 310 0.0032 0.0007 531210 Offices of Real Estate Agents and Brokers $13,604,394.69 3.07% 3 278 0.0108 0.0003 532490 Other Commercial and Industrial Machinery and Equipment Rental and $966,355.08 0.22% 0 Leasing 80 0.0000 0.0000 541330 Engineering Services $106,792,133.00 24.12% 28 307 0.0912 0.0220 561320 Temporary Help Services $0.00 0.00% 1 183 0.0055 0.0000 561410 Document Preparation Services $913,659.46 0.21% 1 89 0.0112 0.0000 561621 Security Systems Services (except Locksmiths) $193,009.07 0.04% 0 123 0.0000 0.0000 561720 Janitorial Services $102,545.52 0.02% 11 297 0.0370 0.0000 561740 Carpet and Upholstery Cleaning Services $8,862.32 0.00% 0 125 0.0000 0.0000 561990 All Other Support Services $101,426.48 0.02% 0 173 0.0000 0.0000 611710 Educational Support Services $42,833,817.56 9.67% 0 96 0.0000 0.0000 621111 Offices of Physicians (except Mental Health Specialists) $69,818,180.74 15.77% 1 336 0.0030 0.0005 621512 Diagnostic Imaging Centers $5,436.00 0.00% 0 95 0.0000 0.0000

Analysis of Essex County Procurement and Contracting: Final Report 139 624190 Other Individual and Family Services $3,485,607.34 0.79% 0 164 0.0000 0.0000 713990 All Other Amusement and Recreation Industries $1,044,462.22 0.24% 0 153 0.0000 0.0000 722211 Limited-Service Restaurants $21,428,502.43 4.84% 3 358 0.0084 0.0004 811111 General Automotive Repair $890,412.24 0.20% 0 315 0.0000 0.0000 811121 Automotive Body, Paint, and Interior Repair and Maintenance $2,179.74 0.00% 1 246 0.0041 0.0000 811213 Communication Equipment Repair and Maintenance $3,482,020.04 0.79% 1 41 0.0244 0.0002 812320 Dry-cleaning and Laundry Services (except Coin-Operated) $16,777.64 0.00% 0 277 0.0000 0.0000 813211 Grantmaking Foundations $806,105.00 0.18% 0 81 0.0000 0.0000 813920 Professional Organizations $71,748,736.30 16.21% 0 148 0.0000 0.0000 P $442,734,930.21 100.00% 53 4606 2.44%

Analysis of Essex County Procurement and Contracting: Final Report 140 Table H-6. Availability Measure: Consolidated DBE Method for PJM-2 NAICS Description Utilization Percent DBEs Total Firms DBE Share Weighted Share 233210 Single Family Housing Construction $66,407.78 0.01% 4 353 0.0113 0.0000 233320 Commercial and Institutional Building Construction $51,484,156.11 5.99% 25 279 0.0896 0.0054 234110 Highway and Street Construction $22,271,098.62 2.59% 16 142 0.1127 0.0029 234990 All Other Heavy Construction $219,209,258.39 25.49% 18 127 0.1417 0.0361 235110 Plumbing, Heating, and Air-Conditioning Contractors $12,377,227.11 1.44% 1 339 0.0029 0.0000 235310 Electrical Contractors $49,991,576.26 5.81% 13 325 0.0400 0.0023 235710 Concrete Contractors $2,370,620.08 0.28% 16 208 0.0769 0.0002 235920 Glass and Glazing Contractors $9,686.67 0.00% 1 92 0.0109 0.0000 235990 All Other Special Trade Contractors $19,856,037.61 2.31% 4 241 0.0166 0.0004 311119 Other Animal Food Manufacturing $127,307.55 0.01% 0 3 0.0000 0.0000 311991 Perishable Prepared Food Manufacturing $200.20 0.00% 0 10 0.0000 0.0000 313312 Textile and Fabric Finishing (except Broadwoven Fabric) Mills $1,469,887.84 0.17% 2 25 0.0800 0.0001 313320 Fabric Coating Mills $5,402.49 0.00% 0 10 0.0000 0.0000 314999 All Other Miscellaneous Textile Product Mills $99,715.17 0.01% 0 59 0.0000 0.0000 323121 Tradebinding and Related Work $5,250.82 0.00% 0 38 0.0000 0.0000 323122 Prepress Services $1,545.77 0.00% 0 68 0.0000 0.0000 326199 All Other Plastics Product Manufacturing $9,821.20 0.00% 1 105 0.0095 0.0000 332212 Hand and Edge Tool Manufacturing $22,473.80 0.00% 0 28 0.0000 0.0000 332999 All Other Miscellaneous Fabricated Metal Product Manufacturing $361,434.00 0.04% 0 45 0.0000 0.0000 333511 Industrial Mold Manufacturing $12,164.90 0.00% 0 35 0.0000 0.0000 333912 Air and Gas Compressor Manufacturing $2,010.08 0.00% 0 9 0.0000 0.0000 333922 Conveyor and Conveying Equipment Manufacturing $7,024.27 0.00% 0 15 0.0000 0.0000 333997 Scale and Balance (except Laboratory) Manufacturing $0.00 0.00% 0 2 0.0000 0.0000 334310 Audio and Video Equipment Manufacturing $25,594.82 0.00% 0 11 0.0000 0.0000 336312 Gasoline Engine and Engine Parts Manufacturing $520.12 0.00% 0 8 0.0000 0.0000 339111 Laboratory Apparatus and Furniture Manufacturing $4,571.00 0.00% 0 16 0.0000 0.0000 339113 Surgical Appliance and Supplies Manufacturing $87,593.49 0.01% 1 31 0.0323 0.0000 421120 Motor Vehicle Supplies and New Parts Wholesalers $44,748.21 0.01% 1 169 0.0059 0.0000 421210 Furniture Wholesalers $91,759.70 0.01% 2 120 0.0167 0.0000 421220 Home Furnishing Wholesalers $779,493.72 0.09% 0 165 0.0000 0.0000 421310 Lumber, Plywood, Millwork, and Wood Panel Wholesalers $87,462.29 0.01% 0 106 0.0000 0.0000 421320 Brick, Stone, and Related Construction Material Wholesalers $21,453.39 0.00% 1 60 0.0167 0.0000 421330 Roofing, Siding, and Insulation Material Wholesalers $4,799.13 0.00% 1 30 0.0333 0.0000 421410 Photographic Equipment and Supplies Wholesalers $96,100.26 0.01% 0 46 0.0000 0.0000 421420 Office Equipment Wholesalers $1,524,600.00 0.18% 1 107 0.0093 0.0000

Analysis of Essex County Procurement and Contracting: Final Report 141 421430 Computer and Computer Peripheral Equipment and Software Wholesalers $4,986,717.78 0.58% 4 192 0.0208 0.0001 421450 Medical, Dental, and Hospital Equipment and Supplies Wholesalers $135,631.89 0.02% 1 157 0.0064 0.0000 421490 Other Professional Equipment and Supplies Wholesalers $14,617,337.41 1.70% 1 76 0.0132 0.0002 421510 Metal Service Centers and Offices $58,510.46 0.01% 0 159 0.0000 0.0000 421610 Electrical Apparatus and Equipment, Wiring Supplies, and Construction $338,256.19 0.04% Material Wholesalers 6 174 0.0345 0.0000 421620 Electrical Appliance, Television, and Radio Set Wholesalers $25,918.67 0.00% 0 93 0.0000 0.0000 421690 Other Electronic Parts and Equipment Wholesalers $27,656.78 0.00% 1 218 0.0046 0.0000 421710 Hardware Wholesalers $22,540.11 0.00% 2 134 0.0149 0.0000 421720 Plumbing and Heating Equipment and Supplies (Hydronics) Wholesalers $260,420.27 0.03% 0 101 0.0000 0.0000 421730 Warm Air Heating and Air-Conditioning Equipment and Supplies $380,575.03 0.04% 0 Wholesalers 78 0.0000 0.0000 421740 Refrigeration Equipment and Supplies Wholesalers $3,873.18 0.00% 0 25 0.0000 0.0000 421810 Construction and Mining (except Oil Well) Machinery and Equipment $225,360.90 0.03% 0 Wholesalers 54 0.0000 0.0000 421820 Farm and Garden Machinery and Equipment Wholesalers $476,025.31 0.06% 0 46 0.0000 0.0000 421830 Industrial Machinery and Equipment Wholesalers $10,588.69 0.00% 0 254 0.0000 0.0000 421840 Industrial Supplies Wholesalers $10,810.54 0.00% 2 179 0.0112 0.0000 421850 Service Establishment Equipment and Supplies Wholesalers $299,985.40 0.03% 3 118 0.0254 0.0000 421910 Sporting and Recreational Goods and Supplies Wholesalers $117,236.61 0.01% 0 105 0.0000 0.0000 421930 Recyclable Material Wholesalers $522.69 0.00% 0 134 0.0000 0.0000 421940 Jewelry, Watch, Precious Stone, and Precious Metal Wholesalers $6,726.90 0.00% 0 139 0.0000 0.0000 421990 Other Miscellaneous Durable Goods Wholesalers $1,271,966.56 0.15% 3 216 0.0139 0.0000 422110 Printing and Writing Paper Wholesalers $13,579.05 0.00% 0 68 0.0000 0.0000 422120 Stationary and Office Supplies Wholesalers $14,403.00 0.00% 0 143 0.0000 0.0000 422130 Industrial and Personal Service Paper Wholesalers $77.80 0.00% 0 133 0.0000 0.0000 422210 Drugs and Druggists' Sundries Wholesalers $0.00 0.00% 0 184 0.0000 0.0000 422310 Piece Goods, Notions, and Other Dry Goods Wholesalers $0.00 0.00% 1 134 0.0075 0.0000 422420 Packaged Frozen Food Wholesalers $4,706.50 0.00% 0 70 0.0000 0.0000 422490 Other Grocery and Related Products Wholesalers $20,710.50 0.00% 0 206 0.0000 0.0000 422610 Plastics Materials and Basic Forms and Shapes Wholesalers $114.00 0.00% 0 107 0.0000 0.0000 422690 Other Chemical and Allied Products Wholesalers $452,334.70 0.05% 0 199 0.0000 0.0000 422710 Petroleum Bulk Stations and Terminals $287,522.66 0.03% 0 41 0.0000 0.0000 422910 Farm Supplies Wholesalers $611,099.24 0.07% 0 64 0.0000 0.0000 422930 Flower, Nursery Stock, and Florists' Supplies Wholesalers $106,102.23 0.01% 0 95 0.0000 0.0000 422990 Other Miscellaneous Nondurable Goods Wholesalers $0.00 0.00% 1 226 0.0044 0.0000 441310 Automotive Parts and Accessories Stores $384,072.86 0.04% 1 227 0.0044 0.0000 441320 Tire Dealers $69,722.14 0.01% 0 148 0.0000 0.0000 442210 Floor Covering Stores $136,883.08 0.02% 1 162 0.0062 0.0000 442291 Window Treatment Stores $847.51 0.00% 0 74 0.0000 0.0000

Analysis of Essex County Procurement and Contracting: Final Report 142 443111 Household Appliance Stores $24,983.12 0.00% 0 123 0.0000 0.0000 443112 Radio, Television, and Other Electronics Stores $815,496.41 0.09% 0 209 0.0000 0.0000 444120 Paint and Wallpaper Stores $61,855.87 0.01% 0 108 0.0000 0.0000 444130 Hardware Stores $1,767,847.87 0.21% 2 167 0.0120 0.0000 444190 Other Building Material Dealers $38,851.59 0.00% 0 270 0.0000 0.0000 446130 Optical Goods Stores $0.00 0.00% 0 155 0.0000 0.0000 448190 Other Clothing Stores $139,179.91 0.02% 2 133 0.0150 0.0000 448320 Luggage and Leather Goods Stores $600.00 0.00% 0 35 0.0000 0.0000 451110 Sporting Goods Stores $36,320.83 0.00% 0 213 0.0000 0.0000 451140 Musical Instrument and Supplies Stores $4,394.45 0.00% 0 58 0.0000 0.0000 453210 Office Supplies and Stationery Stores $2,458,280.81 0.29% 2 137 0.0146 0.0000 453910 Pet and Pet Supplies Stores $520.01 0.00% 0 130 0.0000 0.0000 453920 Art Dealers $2,278.20 0.00% 0 79 0.0000 0.0000 453998 All Other Miscellaneous Store Retailers (except Tobacco Stores) $578,167.66 0.07% 0 181 0.0000 0.0000 454311 Heating Oil Dealers $1,233,453.81 0.14% 2 127 0.0157 0.0000 487110 Scenic and Sightseeing Transportation, Land $1,840,456.68 0.21% 1 12 0.0833 0.0002 488999 All Other Support Activities for Transportation $355,894.69 0.04% 5 13 0.3846 0.0002 511130 Book Publishers $7,777,855.75 0.90% 10 57 0.1754 0.0016 514120 Libraries and Archives $38,281.26 0.00% 0 40 0.0000 0.0000 514210 Data Processing Services $507,766.34 0.06% 1 185 0.0054 0.0000 522320 Financial Transactions Processing, Reserve, and Clearinghouse Activities $989,691.18 0.12% 0 49 0.0000 0.0000 524210 Insurance Agencies and Brokerages $95,176,712.81 11.07% 2 310 0.0065 0.0007 531210 Offices of Real Estate Agents and Brokers $13,604,394.69 1.58% 3 278 0.0108 0.0002 532490 Other Commercial and Industrial Machinery and Equipment Rental and $966,355.08 0.11% 0 Leasing 80 0.0000 0.0000 541330 Engineering Services $106,792,133.00 12.42% 38 307 0.1238 0.0154 561320 Temporary Help Services $0.00 0.00% 1 183 0.0055 0.0000 561410 Document Preparation Services $913,659.46 0.11% 1 89 0.0112 0.0000 561621 Security Systems Services (except Locksmiths) $193,009.07 0.02% 2 123 0.0163 0.0000 561720 Janitorial Services $102,545.52 0.01% 11 297 0.0370 0.0000 561740 Carpet and Upholstery Cleaning Services $8,862.32 0.00% 0 125 0.0000 0.0000 561990 All Other Support Services $101,426.48 0.01% 2 173 0.0116 0.0000 611710 Educational Support Services $42,833,817.56 4.98% 4 96 0.0417 0.0021 621111 Offices of Physicians (except Mental Health Specialists) $69,818,180.74 8.12% 13 336 0.0387 0.0031 621512 Diagnostic Imaging Centers $5,436.00 0.00% 0 95 0.0000 0.0000 624190 Other Individual and Family Services $3,485,607.34 0.41% 1 164 0.0061 0.0000 713990 All Other Amusement and Recreation Industries $1,044,462.22 0.12% 0 153 0.0000 0.0000 722211 Limited-Service Restaurants $21,428,502.43 2.49% 4 358 0.0112 0.0003 811111 General Automotive Repair $890,412.24 0.10% 1 315 0.0032 0.0000 811121 Automotive Body, Paint, and Interior Repair and Maintenance $2,179.74 0.00% 1 246 0.0041 0.0000

Analysis of Essex County Procurement and Contracting: Final Report 143 811213 Communication Equipment Repair and Maintenance $3,482,020.04 0.40% 4 41 0.0976 0.0004 812320 Drycleaning and Laundry Services (except Coin-Operated) $16,777.64 0.00% 0 277 0.0000 0.0000 813211 Grantmaking Foundations $806,105.00 0.09% 5 81 0.0617 0.0001 813920 Professional Organizations $71,748,736.30 8.34% 3 148 0.0203 0.0017 Grand Total $860,001,351.61 100.00% 256 15116 7.39% Source: Essex County Contract Files 2002-2004

Analysis of Essex County Procurement and Contracting: Final Report 144 Table H-7. Availability Measure: Consolidated DBE Method for PJM-2 (By Industry) NAICS Description Utilization Percent DBEs Total DBE Weighted Firms Share Share 233210 Single Family Housing Construction $66,407.78 0.02% 4 353 0.0113 0.0000 233320 Commercial and Institutional Building Construction $51,484,156.11 13.63% 25 279 0.0896 0.0122 234110 Highway and Street Construction $22,271,098.62 5.90% 16 142 0.1127 0.0066 234990 All Other Heavy Construction $219,209,258.39 58.05% 18 127 0.1417 0.0823 235110 Plumbing, Heating, and Air-Conditioning Contractors $12,377,227.11 3.28% 1 339 0.0029 0.0001 235310 Electrical Contractors $49,991,576.26 13.24% 13 325 0.0400 0.0053 235710 Concrete Contractors $2,370,620.08 0.63% 16 208 0.0769 0.0005 235920 Glass and Glazing Contractors $9,686.67 0.00% 1 92 0.0109 0.0000 235990 All Other Special Trade Contractors $19,856,037.61 5.26% 4 241 0.0166 0.0009 C $377,636,068.63 100.00% 98 10.79% 311119 Other Animal Food Manufacturing $127,307.55 0.32% 0 3 0.0000 0.0000 311991 Perishable Prepared Food Manufacturing $200.20 0.00% 0 10 0.0000 0.0000 313312 Textile and Fabric Finishing (except Broadwoven Fabric) Mills $1,469,887.84 3.71% 2 25 0.0800 0.0030 313320 Fabric Coating Mills $5,402.49 0.01% 0 10 0.0000 0.0000 314999 All Other Miscellaneous Textile Product Mills $99,715.17 0.25% 0 59 0.0000 0.0000 323121 Tradebinding and Related Work $5,250.82 0.01% 0 38 0.0000 0.0000 323122 Prepress Services $1,545.77 0.00% 0 68 0.0000 0.0000 326199 All Other Plastics Product Manufacturing $9,821.20 0.02% 1 105 0.0095 0.0000 332212 Hand and Edge Tool Manufacturing $22,473.80 0.06% 0 28 0.0000 0.0000 332999 All Other Miscellaneous Fabricated Metal Product Manufacturing $361,434.00 0.91% 0 45 0.0000 0.0000 333511 Industrial Mold Manufacturing $12,164.90 0.03% 0 35 0.0000 0.0000 333912 Air and Gas Compressor Manufacturing $2,010.08 0.01% 0 9 0.0000 0.0000 333922 Conveyor and Conveying Equipment Manufacturing $7,024.27 0.02% 0 15 0.0000 0.0000 333997 Scale and Balance (except Laboratory) Manufacturing $0.00 0.00% 0 2 0.0000 0.0000 334310 Audio and Video Equipment Manufacturing $25,594.82 0.06% 0 11 0.0000 0.0000 336312 Gasoline Engine and Engine Parts Manufacturing $520.12 0.00% 0 8 0.0000 0.0000 339111 Laboratory Apparatus and Furniture Manufacturing $4,571.00 0.01% 0 16 0.0000 0.0000 339113 Surgical Appliance and Supplies Manufacturing $87,593.49 0.22% 1 31 0.0323 0.0001 421120 Motor Vehicle Supplies and New Parts Wholesalers $44,748.21 0.11% 1 169 0.0059 0.0000 421210 Furniture Wholesalers $91,759.70 0.23% 2 120 0.0167 0.0000 421220 Home Furnishing Wholesalers $779,493.72 1.97% 0 165 0.0000 0.0000 421310 Lumber, Plywood, Millwork, and Wood Panel Wholesalers $87,462.29 0.22% 0 106 0.0000 0.0000 421320 Brick, Stone, and Related Construction Material Wholesalers $21,453.39 0.05% 1 60 0.0167 0.0000 421330 Roofing, Siding, and Insulation Material Wholesalers $4,799.13 0.01% 1 30 0.0333 0.0000 421410 Photographic Equipment and Supplies Wholesalers $96,100.26 0.24% 0 46 0.0000 0.0000 421420 Office Equipment Wholesalers $1,524,600.00 3.85% 1 107 0.0093 0.0004

Analysis of Essex County Procurement and Contracting: Final Report 145 421430 Computer and Computer Peripheral Equipment and Software $4,986,717.78 12.58% Wholesalers 4 192 0.0208 0.0026 421450 Medical, Dental, and Hospital Equipment and Supplies Wholesalers $135,631.89 0.34% 1 157 0.0064 0.0000 421490 Other Professional Equipment and Supplies Wholesalers $14,617,337.41 36.88% 1 76 0.0132 0.0049 421510 Metal Service Centers and Offices $58,510.46 0.15% 0 159 0.0000 0.0000 421610 Electrical Apparatus and Equipment, Wiring Supplies, and Construction $338,256.19 0.85% Material Wholesalers 6 174 0.0345 0.0003 421620 Electrical Appliance, Television, and Radio Set Wholesalers $25,918.67 0.07% 0 93 0.0000 0.0000 421690 Other Electronic Parts and Equipment Wholesalers $27,656.78 0.07% 1 218 0.0046 0.0000 421710 Hardware Wholesalers $22,540.11 0.06% 2 134 0.0149 0.0000 421720 Plumbing and Heating Equipment and Supplies (Hydronics) Wholesalers $260,420.27 0.66% 0 101 0.0000 0.0000 421730 Warm Air Heating and Air-Conditioning Equipment and Supplies $380,575.03 0.96% 0 Wholesalers 78 0.0000 0.0000 421740 Refrigeration Equipment and Supplies Wholesalers $3,873.18 0.01% 0 25 0.0000 0.0000 421810 Construction and Mining (except Oil Well) Machinery and Equipment $225,360.90 0.57% 0 Wholesalers 54 0.0000 0.0000 421820 Farm and Garden Machinery and Equipment Wholesalers $476,025.31 1.20% 0 46 0.0000 0.0000 421830 Industrial Machinery and Equipment Wholesalers $10,588.69 0.03% 0 254 0.0000 0.0000 421840 Industrial Supplies Wholesalers $10,810.54 0.03% 2 179 0.0112 0.0000 421850 Service Establishment Equipment and Supplies Wholesalers $299,985.40 0.76% 3 118 0.0254 0.0002 421910 Sporting and Recreational Goods and Supplies Wholesalers $117,236.61 0.30% 0 105 0.0000 0.0000 421930 Recyclable Material Wholesalers $522.69 0.00% 0 134 0.0000 0.0000 421940 Jewelry, Watch, Precious Stone, and Precious Metal Wholesalers $6,726.90 0.02% 0 139 0.0000 0.0000 421990 Other Miscellaneous Durable Goods Wholesalers $1,271,966.56 3.21% 3 216 0.0139 0.0004 422110 Printing and Writing Paper Wholesalers $13,579.05 0.03% 0 68 0.0000 0.0000 422120 Stationary and Office Supplies Wholesalers $14,403.00 0.04% 0 143 0.0000 0.0000 422130 Industrial and Personal Service Paper Wholesalers $77.80 0.00% 0 133 0.0000 0.0000 422210 Drugs and Druggists' Sundries Wholesalers $0.00 0.00% 0 184 0.0000 0.0000 422310 Piece Goods, Notions, and Other Dry Goods Wholesalers $0.00 0.00% 1 134 0.0075 0.0000 422420 Packaged Frozen Food Wholesalers $4,706.50 0.01% 0 70 0.0000 0.0000 422490 Other Grocery and Related Products Wholesalers $20,710.50 0.05% 0 206 0.0000 0.0000 422610 Plastics Materials and Basic Forms and Shapes Wholesalers $114.00 0.00% 0 107 0.0000 0.0000 422690 Other Chemical and Allied Products Wholesalers $452,334.70 1.14% 0 199 0.0000 0.0000 422710 Petroleum Bulk Stations and Terminals $287,522.66 0.73% 0 41 0.0000 0.0000 422910 Farm Supplies Wholesalers $611,099.24 1.54% 0 64 0.0000 0.0000 422930 Flower, Nursery Stock, and Florists' Supplies Wholesalers $106,102.23 0.27% 0 95 0.0000 0.0000 422990 Other Miscellaneous Nondurable Goods Wholesalers $0.00 0.00% 1 226 0.0044 0.0000 441310 Automotive Parts and Accessories Stores $384,072.86 0.97% 1 227 0.0044 0.0000 441320 Tire Dealers $69,722.14 0.18% 0 148 0.0000 0.0000 442210 Floor Covering Stores $136,883.08 0.35% 1 162 0.0062 0.0000

Analysis of Essex County Procurement and Contracting: Final Report 146 442291 Window Treatment Stores $847.51 0.00% 0 74 0.0000 0.0000 443111 Household Appliance Stores $24,983.12 0.06% 0 123 0.0000 0.0000 443112 Radio, Television, and Other Electronics Stores $815,496.41 2.06% 0 209 0.0000 0.0000 444120 Paint and Wallpaper Stores $61,855.87 0.16% 0 108 0.0000 0.0000 444130 Hardware Stores $1,767,847.87 4.46% 2 167 0.0120 0.0005 444190 Other Building Material Dealers $38,851.59 0.10% 0 270 0.0000 0.0000 446130 Optical Goods Stores $0.00 0.00% 0 155 0.0000 0.0000 448190 Other Clothing Stores $139,179.91 0.35% 2 133 0.0150 0.0001 448320 Luggage and Leather Goods Stores $600.00 0.00% 0 35 0.0000 0.0000 451110 Sporting Goods Stores $36,320.83 0.09% 0 213 0.0000 0.0000 451140 Musical Instrument and Supplies Stores $4,394.45 0.01% 0 58 0.0000 0.0000 453210 Office Supplies and Stationery Stores $2,458,280.81 6.20% 2 137 0.0146 0.0009 453910 Pet and Pet Supplies Stores $520.01 0.00% 0 130 0.0000 0.0000 453920 Art Dealers $2,278.20 0.01% 0 79 0.0000 0.0000 453998 All Other Miscellaneous Store Retailers (except Tobacco Stores) $578,167.66 1.46% 0 181 0.0000 0.0000 454311 Heating Oil Dealers $1,233,453.81 3.11% 2 127 0.0157 0.0005 487110 Scenic and Sightseeing Transportation, Land $1,840,456.68 4.64% 1 12 0.0833 0.0039 488999 All Other Support Activities for Transportation $355,894.69 0.90% 5 13 0.3846 0.0035 O $39,630,352.77 100.00% 51 2.13% 511130 Book Publishers $7,777,855.75 1.76% 10 57 0.1754 0.0031 514120 Libraries and Archives $38,281.26 0.01% 0 40 0.0000 0.0000 514210 Data Processing Services $507,766.34 0.11% 1 185 0.0054 0.0000 522320 Financial Transactions Processing, Reserve, and Clearinghouse $989,691.18 0.22% 0 Activities 49 0.0000 0.0000 524210 Insurance Agencies and Brokerages $95,176,712.81 21.50% 2 310 0.0065 0.0014 531210 Offices of Real Estate Agents and Brokers $13,604,394.69 3.07% 3 278 0.0108 0.0003 532490 Other Commercial and Industrial Machinery and Equipment Rental and $966,355.08 0.22% 0 Leasing 80 0.0000 0.0000 541330 Engineering Services $106,792,133.00 24.12% 38 307 0.1238 0.0299 561320 Temporary Help Services $0.00 0.00% 1 183 0.0055 0.0000 561410 Document Preparation Services $913,659.46 0.21% 1 89 0.0112 0.0000 561621 Security Systems Services (except Locksmiths) $193,009.07 0.04% 2 123 0.0163 0.0000 561720 Janitorial Services $102,545.52 0.02% 11 297 0.0370 0.0000 561740 Carpet and Upholstery Cleaning Services $8,862.32 0.00% 0 125 0.0000 0.0000 561990 All Other Support Services $101,426.48 0.02% 2 173 0.0116 0.0000 611710 Educational Support Services $42,833,817.56 9.67% 4 96 0.0417 0.0040 621111 Offices of Physicians (except Mental Health Specialists) $69,818,180.74 15.77% 13 336 0.0387 0.0061 621512 Diagnostic Imaging Centers $5,436.00 0.00% 0 95 0.0000 0.0000 624190 Other Individual and Family Services $3,485,607.34 0.79% 1 164 0.0061 0.0000

Analysis of Essex County Procurement and Contracting: Final Report 147 713990 All Other Amusement and Recreation Industries $1,044,462.22 0.24% 0 153 0.0000 0.0000 722211 Limited-Service Restaurants $21,428,502.43 4.84% 4 358 0.0112 0.0005 811111 General Automotive Repair $890,412.24 0.20% 1 315 0.0032 0.0000 811121 Automotive Body, Paint, and Interior Repair and Maintenance $2,179.74 0.00% 1 246 0.0041 0.0000 811213 Communication Equipment Repair and Maintenance $3,482,020.04 0.79% 4 41 0.0976 0.0008 812320 Drycleaning and Laundry Services (except Coin-Operated) $16,777.64 0.00% 0 277 0.0000 0.0000 813211 Grantmaking Foundations $806,105.00 0.18% 5 81 0.0617 0.0001 813920 Professional Organizations $71,748,736.30 16.21% 3 148 0.0203 0.0033 P $442,734,930.21 100.00% 107 4.96% Source: Essex County Contract Files 2002-2004

Analysis of Essex County Procurement and Contracting: Final Report 148 NAICS Utilization Percent Total Firms Minority Firms Women Firms Table H-8. Availability Measure: D&B Method for PJM-2 Black Firms Hispanic Firms Asian Firms Minority Share 233210 $66,407.78 0.01% 6118 152 155 17 82 20 0.0248 0.0253 0.0028 0.0134 0.0033 0.0000 0.0000 0.0000 0.0000 0.0000 233320 $51,484,156.11 5.99% 958 71 77 17 21 8 0.0741 0.0804 0.0177 0.0219 0.0084 0.0044 0.0048 0.0011 0.0013 0.0005 234110 $22,271,098.62 2.59% 472 22 30 3 11 1 0.0466 0.0636 0.0064 0.0233 0.0021 0.0012 0.0016 0.0002 0.0006 0.0001 234990 $219,209,258.39 25.49% 189 6 8 0 2 1 0.0317 0.0423 0.0000 0.0106 0.0053 0.0081 0.0108 0.0000 0.0027 0.0013 235110 $12,377,227.11 1.44% 3377 81 117 11 38 10 0.0240 0.0346 0.0033 0.0113 0.0030 0.0003 0.0005 0.0000 0.0002 0.0000 235310 $49,991,576.26 5.81% 2680 92 93 19 44 10 0.0343 0.0347 0.0071 0.0164 0.0037 0.0020 0.0020 0.0004 0.0010 0.0002 235710 $2,370,620.08 0.28% 395 30 25 2 20 1 0.0759 0.0633 0.0051 0.0506 0.0025 0.0002 0.0002 0.0000 0.0001 0.0000 235920 $9,686.67 0.00% 129 5 6 0 4 1 0.0388 0.0465 0.0000 0.0310 0.0078 0.0000 0.0000 0.0000 0.0000 0.0000 235990 $19,856,037.61 2.31% 2110 66 111 9 38 6 0.0313 0.0526 0.0043 0.0180 0.0028 0.0007 0.0012 0.0001 0.0004 0.0001 311119 $127,307.55 0.01% 7 0 1 0 0 0 0.0000 0.1429 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 311991 $200.20 0.00% 74 2 12 0 1 0 0.0270 0.1622 0.0000 0.0135 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 313312 $1,469,887.84 0.17% 14 2 2 0 1 0 0.1429 0.1429 0.0000 0.0714 0.0000 0.0002 0.0002 0.0000 0.0001 0.0000 313320 $5,402.49 0.00% 14 2 4 0 1 0 0.1429 0.2857 0.0000 0.0714 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 314999 $99,715.17 0.01% 39 0 11 0 0 0 0.0000 0.2821 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 323121 $5,250.82 0.00% 49 2 6 0 2 0 0.0408 0.1224 0.0000 0.0408 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 323122 $1,545.77 0.00% 73 2 17 0 1 1 0.0274 0.2329 0.0000 0.0137 0.0137 0.0000 0.0000 0.0000 0.0000 0.0000 326199 $9,821.20 0.00% 289 13 13 0 7 3 0.0450 0.0450 0.0000 0.0242 0.0104 0.0000 0.0000 0.0000 0.0000 0.0000 332212 $22,473.80 0.00% 31 0 2 0 0 0 0.0000 0.0645 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 332999 $361,434.00 0.04% 43 2 6 0 0 1 0.0465 0.1395 0.0000 0.0000 0.0233 0.0000 0.0001 0.0000 0.0000 0.0000 333511 $12,164.90 0.00% 143 4 7 1 2 0 0.0280 0.0490 0.0070 0.0140 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 333912 $2,010.08 0.00% 10 0 1 0 0 0 0.0000 0.1000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 333922 $7,024.27 0.00% 24 0 1 0 0 0 0.0000 0.0417 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 333997 $0.00 0.00% 3 1 1 0 0 0 0.3333 0.3333 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 334310 $25,594.82 0.00% 64 2 2 0 1 0 0.0313 0.0313 0.0000 0.0156 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 336312 $520.12 0.00% 41 3 1 1 0 1 0.0732 0.0244 0.0244 0.0000 0.0244 0.0000 0.0000 0.0000 0.0000 0.0000 339111 $4,571.00 0.00% 32 1 2 0 0 1 0.0313 0.0625 0.0000 0.0000 0.0313 0.0000 0.0000 0.0000 0.0000 0.0000 339113 $87,593.49 0.01% 97 4 4 0 1 1 0.0412 0.0412 0.0000 0.0103 0.0103 0.0000 0.0000 0.0000 0.0000 0.0000 421120 $44,748.21 0.01% 574 15 30 0 7 5 0.0261 0.0523 0.0000 0.0122 0.0087 0.0000 0.0000 0.0000 0.0000 0.0000 421210 $91,759.70 0.01% 289 21 26 3 5 9 0.0727 0.0900 0.0104 0.0173 0.0311 0.0000 0.0000 0.0000 0.0000 0.0000 421220 $779,493.72 0.09% 455 32 41 1 4 13 0.0703 0.0901 0.0022 0.0088 0.0286 0.0001 0.0001 0.0000 0.0000 0.0000 421310 $87,462.29 0.01% 275 7 23 3 0 2 0.0255 0.0836 0.0109 0.0000 0.0073 0.0000 0.0000 0.0000 0.0000 0.0000 421320 $21,453.39 0.00% 249 13 20 0 4 4 0.0522 0.0803 0.0000 0.0161 0.0161 0.0000 0.0000 0.0000 0.0000 0.0000 Women Share Black Share Hispanic Share Asian Share Weighted Minority Share Weighted Women Share Weighted Black Share Weighted Hispanic Share Weighted Asian Share

Analysis of Essex County Procurement and Contracting: Final Report 149 421330 $4,799.13 0.00% 71 0 3 0 0 0 0.0000 0.0423 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 421410 $96,100.26 0.01% 49 1 2 0 0 1 0.0204 0.0408 0.0000 0.0000 0.0204 0.0000 0.0000 0.0000 0.0000 0.0000 421420 $1,524,600.00 0.18% 188 10 19 0 2 3 0.0532 0.1011 0.0000 0.0106 0.0160 0.0001 0.0002 0.0000 0.0000 0.0000 421430 $4,986,717.78 0.58% 573 67 52 8 8 32 0.1169 0.0908 0.0140 0.0140 0.0558 0.0007 0.0005 0.0001 0.0001 0.0003 421450 $135,631.89 0.02% 492 26 49 1 4 10 0.0528 0.0996 0.0020 0.0081 0.0203 0.0000 0.0000 0.0000 0.0000 0.0000 421490 $14,617,337.41 1.70% 183 12 11 2 2 2 0.0656 0.0601 0.0109 0.0109 0.0109 0.0011 0.0010 0.0002 0.0002 0.0002 421510 $58,510.46 0.01% 290 16 22 2 2 6 0.0552 0.0759 0.0069 0.0069 0.0207 0.0000 0.0000 0.0000 0.0000 0.0000 421610 $338,256.19 0.04% 560 24 51 2 10 9 0.0429 0.0911 0.0036 0.0179 0.0161 0.0000 0.0000 0.0000 0.0000 0.0000 421620 $25,918.67 0.00% 138 10 8 1 1 6 0.0725 0.0580 0.0072 0.0072 0.0435 0.0000 0.0000 0.0000 0.0000 0.0000 421690 $27,656.78 0.00% 667 45 58 2 8 19 0.0675 0.0870 0.0030 0.0120 0.0285 0.0000 0.0000 0.0000 0.0000 0.0000 421710 $22,540.11 0.00% 190 7 12 0 2 4 0.0368 0.0632 0.0000 0.0105 0.0211 0.0000 0.0000 0.0000 0.0000 0.0000 421720 $260,420.27 0.03% 262 9 14 0 5 3 0.0344 0.0534 0.0000 0.0191 0.0115 0.0000 0.0000 0.0000 0.0000 0.0000 421730 $380,575.03 0.04% 126 4 9 0 3 0 0.0317 0.0714 0.0000 0.0238 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 421740 $3,873.18 0.00% 58 2 3 0 0 1 0.0345 0.0517 0.0000 0.0000 0.0172 0.0000 0.0000 0.0000 0.0000 0.0000 421810 $225,360.90 0.03% 172 8 9 0 3 4 0.0465 0.0523 0.0000 0.0174 0.0233 0.0000 0.0000 0.0000 0.0000 0.0000 421820 $476,025.31 0.06% 87 1 3 0 0 0 0.0115 0.0345 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 421830 $10,588.69 0.00% 1015 38 64 5 6 16 0.0374 0.0631 0.0049 0.0059 0.0158 0.0000 0.0000 0.0000 0.0000 0.0000 421840 $10,810.54 0.00% 500 14 54 1 4 1 0.0280 0.1080 0.0020 0.0080 0.0020 0.0000 0.0000 0.0000 0.0000 0.0000 421850 $299,985.40 0.03% 533 33 67 5 8 11 0.0619 0.1257 0.0094 0.0150 0.0206 0.0000 0.0000 0.0000 0.0000 0.0000 421910 $117,236.61 0.01% 180 6 9 1 0 2 0.0333 0.0500 0.0056 0.0000 0.0111 0.0000 0.0000 0.0000 0.0000 0.0000 421930 $522.69 0.00% 209 5 8 0 2 2 0.0239 0.0383 0.0000 0.0096 0.0096 0.0000 0.0000 0.0000 0.0000 0.0000 421940 $6,726.90 0.00% 303 18 41 0 5 4 0.0594 0.1353 0.0000 0.0165 0.0132 0.0000 0.0000 0.0000 0.0000 0.0000 421990 $1,271,966.56 0.15% 545 18 50 3 4 7 0.0330 0.0917 0.0055 0.0073 0.0128 0.0000 0.0001 0.0000 0.0000 0.0000 422110 $13,579.05 0.00% 62 4 6 0 1 2 0.0645 0.0968 0.0000 0.0161 0.0323 0.0000 0.0000 0.0000 0.0000 0.0000 422120 $14,403.00 0.00% 329 17 45 2 5 5 0.0517 0.1368 0.0061 0.0152 0.0152 0.0000 0.0000 0.0000 0.0000 0.0000 422130 $77.80 0.00% 218 6 24 1 2 0 0.0275 0.1101 0.0046 0.0092 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 422210 $0.00 0.00% 530 32 45 3 10 9 0.0604 0.0849 0.0057 0.0189 0.0170 0.0000 0.0000 0.0000 0.0000 0.0000 422310 $0.00 0.00% 297 22 33 0 3 10 0.0741 0.1111 0.0000 0.0101 0.0337 0.0000 0.0000 0.0000 0.0000 0.0000 422420 $4,706.50 0.00% 54 0 7 0 0 0 0.0000 0.1296 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 422490 $20,710.50 0.00% 618 31 54 1 15 10 0.0502 0.0874 0.0016 0.0243 0.0162 0.0000 0.0000 0.0000 0.0000 0.0000 422610 $114.00 0.00% 142 10 13 1 1 5 0.0704 0.0915 0.0070 0.0070 0.0352 0.0000 0.0000 0.0000 0.0000 0.0000 422690 $452,334.70 0.05% 465 20 31 3 4 6 0.0430 0.0667 0.0065 0.0086 0.0129 0.0000 0.0000 0.0000 0.0000 0.0000 422710 $287,522.66 0.03% 10 0 1 0 0 0 0.0000 0.1000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 422910 $611,099.24 0.07% 76 2 5 0 0 1 0.0263 0.0658 0.0000 0.0000 0.0132 0.0000 0.0000 0.0000 0.0000 0.0000 422930 $106,102.23 0.01% 151 8 12 0 4 1 0.0530 0.0795 0.0000 0.0265 0.0066 0.0000 0.0000 0.0000 0.0000 0.0000 422990 $0.00 0.00% 1525 80 156 4 15 39 0.0525 0.1023 0.0026 0.0098 0.0256 0.0000 0.0000 0.0000 0.0000 0.0000

Analysis of Essex County Procurement and Contracting: Final Report 150 441310 $384,072.86 0.04% 1076 44 41 4 19 7 0.0409 0.0381 0.0037 0.0177 0.0065 0.0000 0.0000 0.0000 0.0000 0.0000 441320 $69,722.14 0.01% 801 37 18 1 19 5 0.0462 0.0225 0.0012 0.0237 0.0062 0.0000 0.0000 0.0000 0.0000 0.0000 442210 $136,883.08 0.02% 652 21 51 1 14 2 0.0322 0.0782 0.0015 0.0215 0.0031 0.0000 0.0000 0.0000 0.0000 0.0000 442291 $847.51 0.00% 550 18 80 4 4 6 0.0327 0.1455 0.0073 0.0073 0.0109 0.0000 0.0000 0.0000 0.0000 0.0000 443111 $24,983.12 0.00% 300 11 20 0 8 1 0.0367 0.0667 0.0000 0.0267 0.0033 0.0000 0.0000 0.0000 0.0000 0.0000 443112 $815,496.41 0.09% 511 28 24 1 13 5 0.0548 0.0470 0.0020 0.0254 0.0098 0.0001 0.0000 0.0000 0.0000 0.0000 444120 $61,855.87 0.01% 336 8 24 1 4 1 0.0238 0.0714 0.0030 0.0119 0.0030 0.0000 0.0000 0.0000 0.0000 0.0000 444130 $1,767,847.87 0.21% 390 17 17 1 9 3 0.0436 0.0436 0.0026 0.0231 0.0077 0.0001 0.0001 0.0000 0.0000 0.0000 444190 $38,851.59 0.00% 275 7 23 3 0 2 0.0255 0.0836 0.0109 0.0000 0.0073 0.0000 0.0000 0.0000 0.0000 0.0000 446130 $0.00 0.00% 390 17 17 1 9 3 0.0436 0.0436 0.0026 0.0231 0.0077 0.0000 0.0000 0.0000 0.0000 0.0000 448190 $139,179.91 0.02% 919 73 173 0 21 28 0.0794 0.1882 0.0000 0.0229 0.0305 0.0000 0.0000 0.0000 0.0000 0.0000 448320 $600.00 0.00% 61 6 7 0 1 2 0.0984 0.1148 0.0000 0.0164 0.0328 0.0000 0.0000 0.0000 0.0000 0.0000 451110 $36,320.83 0.00% 764 24 53 1 7 8 0.0314 0.0694 0.0013 0.0092 0.0105 0.0000 0.0000 0.0000 0.0000 0.0000 451140 $4,394.45 0.00% 173 6 13 0 2 3 0.0347 0.0751 0.0000 0.0116 0.0173 0.0000 0.0000 0.0000 0.0000 0.0000 453210 $2,458,280.81 0.29% 356 19 49 2 5 5 0.0534 0.1376 0.0056 0.0140 0.0140 0.0002 0.0004 0.0000 0.0000 0.0000 453910 $520.01 0.00% 3024 115 398 4 25 48 0.0380 0.1316 0.0013 0.0083 0.0159 0.0000 0.0000 0.0000 0.0000 0.0000 453920 $2,278.20 0.00% 3024 115 398 4 25 48 0.0380 0.1316 0.0013 0.0083 0.0159 0.0000 0.0000 0.0000 0.0000 0.0000 453998 $578,167.66 0.07% 3024 115 398 4 25 48 0.0380 0.1316 0.0013 0.0083 0.0159 0.0000 0.0001 0.0000 0.0000 0.0000 454311 $1,233,453.81 0.14% 223 6 14 2 2 1 0.0269 0.0628 0.0090 0.0090 0.0045 0.0000 0.0001 0.0000 0.0000 0.0000 487110 $1,840,456.68 0.21% 1206 46 74 8 15 12 0.0381 0.0614 0.0066 0.0124 0.0100 0.0001 0.0001 0.0000 0.0000 0.0000 488999 $355,894.69 0.04% 566 12 12 0 7 1 0.0212 0.0212 0.0000 0.0124 0.0018 0.0000 0.0000 0.0000 0.0000 0.0000 511130 $7,777,855.75 0.90% 248 5 30 1 1 0 0.0202 0.1210 0.0040 0.0040 0.0000 0.0002 0.0011 0.0000 0.0000 0.0000 514120 $38,281.26 0.00% 411 1 4 1 0 0 0.0024 0.0097 0.0024 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 514210 $507,766.34 0.06% 765 38 99 4 13 8 0.0497 0.1294 0.0052 0.0170 0.0105 0.0000 0.0001 0.0000 0.0000 0.0000 522320 $989,691.18 0.12% 339 18 24 0 8 1 0.0531 0.0708 0.0000 0.0236 0.0029 0.0001 0.0001 0.0000 0.0000 0.0000 524210 $95,176,712.81 11.07% 3013 81 247 8 38 6 0.0269 0.0820 0.0027 0.0126 0.0020 0.0030 0.0091 0.0003 0.0014 0.0002 531210 $13,604,394.69 1.58% 5964 133 537 17 49 24 0.0223 0.0900 0.0029 0.0082 0.0040 0.0004 0.0014 0.0000 0.0001 0.0001 532490 $966,355.08 0.11% 582 11 31 2 4 2 0.0189 0.0533 0.0034 0.0069 0.0034 0.0000 0.0001 0.0000 0.0000 0.0000 541330 $106,792,133.00 12.42% 1704 133 112 21 22 38 0.0781 0.0657 0.0123 0.0129 0.0223 0.0097 0.0082 0.0015 0.0016 0.0028 561320 $0.00 0.00% 696 25 98 9 8 1 0.0359 0.1408 0.0129 0.0115 0.0014 0.0000 0.0000 0.0000 0.0000 0.0000 561410 $913,659.46 0.11% 389 14 169 3 2 3 0.0360 0.4344 0.0077 0.0051 0.0077 0.0000 0.0005 0.0000 0.0000 0.0000 561621 $193,009.07 0.02% 234 8 12 0 3 0 0.0342 0.0513 0.0000 0.0128 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 561720 $102,545.52 0.01% 1701 125 218 32 65 6 0.0735 0.1282 0.0188 0.0382 0.0035 0.0000 0.0000 0.0000 0.0000 0.0000 561740 $8,862.32 0.00% 296 10 12 2 4 3 0.0338 0.0405 0.0068 0.0135 0.0101 0.0000 0.0000 0.0000 0.0000 0.0000 561990 $101,426.48 0.01% 11227 321 1372 41 82 72 0.0286 0.1222 0.0037 0.0073 0.0064 0.0000 0.0000 0.0000 0.0000 0.0000 611710 $42,833,817.56 4.98% 1112 72 202 9 20 21 0.0647 0.1817 0.0081 0.0180 0.0189 0.0032 0.0090 0.0004 0.0009 0.0009

Analysis of Essex County Procurement and Contracting: Final Report 151 621111 $69,818,180.74 8.12% 8488 400 883 4 111 74 0.0471 0.1040 0.0005 0.0131 0.0087 0.0038 0.0084 0.0000 0.0011 0.0007 621512 $5,436.00 0.00% 392 9 21 0 1 5 0.0230 0.0536 0.0000 0.0026 0.0128 0.0000 0.0000 0.0000 0.0000 0.0000 624190 $3,485,607.34 0.41% 2800 0 1 0 0 0 0.0000 0.0004 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 713990 $1,044,462.22 0.12% 1436 52 186 0 9 26 0.0362 0.1295 0.0000 0.0063 0.0181 0.0000 0.0002 0.0000 0.0000 0.0000 722211 $21,428,502.43 2.49% 10968 667 862 18 204 302 0.0608 0.0786 0.0016 0.0186 0.0275 0.0015 0.0020 0.0000 0.0005 0.0007 811111 $890,412.24 0.10% 2391 110 73 2 64 16 0.0460 0.0305 0.0008 0.0268 0.0067 0.0000 0.0000 0.0000 0.0000 0.0000 811121 $2,179.74 0.00% 1108 54 36 1 28 10 0.0487 0.0325 0.0009 0.0253 0.0090 0.0000 0.0000 0.0000 0.0000 0.0000 811213 $3,482,020.04 0.40% 230 20 14 0 10 6 0.0870 0.0609 0.0000 0.0435 0.0261 0.0004 0.0002 0.0000 0.0002 0.0001 812320 $16,777.64 0.00% 1290 207 150 0 18 144 0.1605 0.1163 0.0000 0.0140 0.1116 0.0000 0.0000 0.0000 0.0000 0.0000 813211 $806,105.00 0.09% 99 2 5 1 0 1 0.0202 0.0505 0.0101 0.0000 0.0101 0.0000 0.0000 0.0000 0.0000 0.0000 813920 $71,748,736.30 8.34% 272 0 3 0 0 0 0.0000 0.0110 0.0000 0.0000 0.0000 0.0000 0.0009 0.0000 0.0000 0.0000 $860,001,351.61 100.00% 107961 4520 9210 353 1434 1338 4.24% 6.61% 0.46% 1.28% 0.86% Source: Essex County Contract Files 2002-2004

Analysis of Essex County Procurement and Contracting: Final Report 152 Table H-9. Availability Measure: D&B Method for PJM-2 (By Industry) NAICS Utilization Percent Total Firms Minority Firms Women Firms Black Firms Hispanic Firms Asian Firms Minority Share Women Share Black Share Hispanic Share Asian Share Weighted Minority Share Weighted Women Share Weighted Black Share Weighted Hispanic Share Weighted Asian Share 233210 $66,407.78 0.02% 6118 152 155 17 82 20 0.025 0.025 0.003 0.013 0.003 0.0000 0.0000 0.0000 0.0000 0.0000 233320 $51,484,156.11 13.63% 958 71 77 17 21 8 0.074 0.080 0.018 0.022 0.008 0.0101 0.0110 0.0024 0.0030 0.0011 234110 $22,271,098.62 5.90% 472 22 30 3 11 1 0.047 0.064 0.006 0.023 0.002 0.0027 0.0037 0.0004 0.0014 0.0001 234990 $219,209,258.39 58.05% 189 6 8 0 2 1 0.032 0.042 0.000 0.011 0.005 0.0184 0.0246 0.0000 0.0061 0.0031 235110 $12,377,227.11 3.28% 3377 81 117 11 38 10 0.024 0.035 0.003 0.011 0.003 0.0008 0.0011 0.0001 0.0004 0.0001 235310 $49,991,576.26 13.24% 2680 92 93 19 44 10 0.034 0.035 0.007 0.016 0.004 0.0045 0.0046 0.0009 0.0022 0.0005 235710 $2,370,620.08 0.63% 395 30 25 2 20 1 0.076 0.063 0.005 0.051 0.003 0.0005 0.0004 0.0000 0.0003 0.0000 235920 $9,686.67 0.00% 129 5 6 0 4 1 0.039 0.047 0.000 0.031 0.008 0.0000 0.0000 0.0000 0.0000 0.0000 235990 $19,856,037.61 5.26% 2110 66 111 9 38 6 0.031 0.053 0.004 0.018 0.003 0.0016 0.0028 0.0002 0.0009 0.0001 $377,636,068.63 100.00% 16428 525 622 78 260 58 3.87% 4.82% 0.41% 1.43% 0.51% 311119 $127,307.55 0.32% 7 0 1 0 0 0 0.000 0.143 0.000 0.000 0.000 0.0000 0.0005 0.0000 0.0000 0.0000 311991 $200.20 0.00% 74 2 12 0 1 0 0.027 0.162 0.000 0.014 0.000 0.0000 0.0000 0.0000 0.0000 0.0000 313312 $1,469,887.84 3.71% 14 2 2 0 1 0 0.143 0.143 0.000 0.071 0.000 0.0053 0.0053 0.0000 0.0026 0.0000 313320 $5,402.49 0.01% 14 2 4 0 1 0 0.143 0.286 0.000 0.071 0.000 0.0000 0.0000 0.0000 0.0000 0.0000 314999 $99,715.17 0.25% 39 0 11 0 0 0 0.000 0.282 0.000 0.000 0.000 0.0000 0.0007 0.0000 0.0000 0.0000 323121 $5,250.82 0.01% 49 2 6 0 2 0 0.041 0.122 0.000 0.041 0.000 0.0000 0.0000 0.0000 0.0000 0.0000 323122 $1,545.77 0.00% 73 2 17 0 1 1 0.027 0.233 0.000 0.014 0.014 0.0000 0.0000 0.0000 0.0000 0.0000 326199 $9,821.20 0.02% 289 13 13 0 7 3 0.045 0.045 0.000 0.024 0.010 0.0000 0.0000 0.0000 0.0000 0.0000 332212 $22,473.80 0.06% 31 0 2 0 0 0 0.000 0.065 0.000 0.000 0.000 0.0000 0.0000 0.0000 0.0000 0.0000 332999 $361,434.00 0.91% 43 2 6 0 0 1 0.047 0.140 0.000 0.000 0.023 0.0004 0.0013 0.0000 0.0000 0.0002 333511 $12,164.90 0.03% 143 4 7 1 2 0 0.028 0.049 0.007 0.014 0.000 0.0000 0.0000 0.0000 0.0000 0.0000 333912 $2,010.08 0.01% 10 0 1 0 0 0 0.000 0.100 0.000 0.000 0.000 0.0000 0.0000 0.0000 0.0000 0.0000 333922 $7,024.27 0.02% 24 0 1 0 0 0 0.000 0.042 0.000 0.000 0.000 0.0000 0.0000 0.0000 0.0000 0.0000 333997 $0.00 0.00% 3 1 1 0 0 0 0.333 0.333 0.000 0.000 0.000 0.0000 0.0000 0.0000 0.0000 0.0000 334310 $25,594.82 0.06% 64 2 2 0 1 0 0.031 0.031 0.000 0.016 0.000 0.0000 0.0000 0.0000 0.0000 0.0000 336312 $520.12 0.00% 41 3 1 1 0 1 0.073 0.024 0.024 0.000 0.024 0.0000 0.0000 0.0000 0.0000 0.0000 339111 $4,571.00 0.01% 32 1 2 0 0 1 0.031 0.063 0.000 0.000 0.031 0.0000 0.0000 0.0000 0.0000 0.0000 339113 $87,593.49 0.22% 97 4 4 0 1 1 0.041 0.041 0.000 0.010 0.010 0.0001 0.0001 0.0000 0.0000 0.0000 421120 $44,748.21 0.11% 574 15 30 0 7 5 0.026 0.052 0.000 0.012 0.009 0.0000 0.0001 0.0000 0.0000 0.0000 421210 $91,759.70 0.23% 289 21 26 3 5 9 0.073 0.090 0.010 0.017 0.031 0.0002 0.0002 0.0000 0.0000 0.0001 421220 $779,493.72 1.97% 455 32 41 1 4 13 0.070 0.090 0.002 0.009 0.029 0.0014 0.0018 0.0000 0.0002 0.0006

Analysis of Essex County Procurement and Contracting: Final Report 153 421310 $87,462.29 0.22% 275 7 23 3 0 2 0.025 0.084 0.011 0.000 0.007 0.0001 0.0002 0.0000 0.0000 0.0000 421320 $21,453.39 0.05% 249 13 20 0 4 4 0.052 0.080 0.000 0.016 0.016 0.0000 0.0000 0.0000 0.0000 0.0000 421330 $4,799.13 0.01% 71 0 3 0 0 0 0.000 0.042 0.000 0.000 0.000 0.0000 0.0000 0.0000 0.0000 0.0000 421410 $96,100.26 0.24% 49 1 2 0 0 1 0.020 0.041 0.000 0.000 0.020 0.0000 0.0001 0.0000 0.0000 0.0000 421420 $1,524,600.00 3.85% 188 10 19 0 2 3 0.053 0.101 0.000 0.011 0.016 0.0020 0.0039 0.0000 0.0004 0.0006 421430 $4,986,717.78 12.58% 573 67 52 8 8 32 0.117 0.091 0.014 0.014 0.056 0.0147 0.0114 0.0018 0.0018 0.0070 421450 $135,631.89 0.34% 492 26 49 1 4 10 0.053 0.100 0.002 0.008 0.020 0.0002 0.0003 0.0000 0.0000 0.0001 421490 $14,617,337.41 36.88% 183 12 11 2 2 2 0.066 0.060 0.011 0.011 0.011 0.0242 0.0222 0.0040 0.0040 0.0040 421510 $58,510.46 0.15% 290 16 22 2 2 6 0.055 0.076 0.007 0.007 0.021 0.0001 0.0001 0.0000 0.0000 0.0000 421610 $338,256.19 0.85% 560 24 51 2 10 9 0.043 0.091 0.004 0.018 0.016 0.0004 0.0008 0.0000 0.0002 0.0001 421620 $25,918.67 0.07% 138 10 8 1 1 6 0.072 0.058 0.007 0.007 0.043 0.0000 0.0000 0.0000 0.0000 0.0000 421690 $27,656.78 0.07% 667 45 58 2 8 19 0.067 0.087 0.003 0.012 0.028 0.0000 0.0001 0.0000 0.0000 0.0000 421710 $22,540.11 0.06% 190 7 12 0 2 4 0.037 0.063 0.000 0.011 0.021 0.0000 0.0000 0.0000 0.0000 0.0000 421720 $260,420.27 0.66% 262 9 14 0 5 3 0.034 0.053 0.000 0.019 0.011 0.0002 0.0004 0.0000 0.0001 0.0001 421730 $380,575.03 0.96% 126 4 9 0 3 0 0.032 0.071 0.000 0.024 0.000 0.0003 0.0007 0.0000 0.0002 0.0000 421740 $3,873.18 0.01% 58 2 3 0 0 1 0.034 0.052 0.000 0.000 0.017 0.0000 0.0000 0.0000 0.0000 0.0000 421810 $225,360.90 0.57% 172 8 9 0 3 4 0.047 0.052 0.000 0.017 0.023 0.0003 0.0003 0.0000 0.0001 0.0001 421820 $476,025.31 1.20% 87 1 3 0 0 0 0.011 0.034 0.000 0.000 0.000 0.0001 0.0004 0.0000 0.0000 0.0000 421830 $10,588.69 0.03% 1015 38 64 5 6 16 0.037 0.063 0.005 0.006 0.016 0.0000 0.0000 0.0000 0.0000 0.0000 421840 $10,810.54 0.03% 500 14 54 1 4 1 0.028 0.108 0.002 0.008 0.002 0.0000 0.0000 0.0000 0.0000 0.0000 421850 $299,985.40 0.76% 533 33 67 5 8 11 0.062 0.126 0.009 0.015 0.021 0.0005 0.0010 0.0001 0.0001 0.0002 421910 $117,236.61 0.30% 180 6 9 1 0 2 0.033 0.050 0.006 0.000 0.011 0.0001 0.0001 0.0000 0.0000 0.0000 421930 $522.69 0.00% 209 5 8 0 2 2 0.024 0.038 0.000 0.010 0.010 0.0000 0.0000 0.0000 0.0000 0.0000 421940 $6,726.90 0.02% 303 18 41 0 5 4 0.059 0.135 0.000 0.017 0.013 0.0000 0.0000 0.0000 0.0000 0.0000 421990 $1,271,966.56 3.21% 545 18 50 3 4 7 0.033 0.092 0.006 0.007 0.013 0.0011 0.0029 0.0002 0.0002 0.0004 422110 $13,579.05 0.03% 62 4 6 0 1 2 0.065 0.097 0.000 0.016 0.032 0.0000 0.0000 0.0000 0.0000 0.0000 422120 $14,403.00 0.04% 329 17 45 2 5 5 0.052 0.137 0.006 0.015 0.015 0.0000 0.0000 0.0000 0.0000 0.0000 422130 $77.80 0.00% 218 6 24 1 2 0 0.028 0.110 0.005 0.009 0.000 0.0000 0.0000 0.0000 0.0000 0.0000 422210 $0.00 0.00% 530 32 45 3 10 9 0.060 0.085 0.006 0.019 0.017 0.0000 0.0000 0.0000 0.0000 0.0000 422310 $0.00 0.00% 297 22 33 0 3 10 0.074 0.111 0.000 0.010 0.034 0.0000 0.0000 0.0000 0.0000 0.0000 422420 $4,706.50 0.01% 54 0 7 0 0 0 0.000 0.130 0.000 0.000 0.000 0.0000 0.0000 0.0000 0.0000 0.0000 422490 $20,710.50 0.05% 618 31 54 1 15 10 0.050 0.087 0.002 0.024 0.016 0.0000 0.0000 0.0000 0.0000 0.0000 422610 $114.00 0.00% 142 10 13 1 1 5 0.070 0.092 0.007 0.007 0.035 0.0000 0.0000 0.0000 0.0000 0.0000 422690 $452,334.70 1.14% 465 20 31 3 4 6 0.043 0.067 0.006 0.009 0.013 0.0005 0.0008 0.0001 0.0001 0.0001 422710 $287,522.66 0.73% 10 0 1 0 0 0 0.000 0.100 0.000 0.000 0.000 0.0000 0.0007 0.0000 0.0000 0.0000 422910 $611,099.24 1.54% 76 2 5 0 0 1 0.026 0.066 0.000 0.000 0.013 0.0004 0.0010 0.0000 0.0000 0.0002 422930 $106,102.23 0.27% 151 8 12 0 4 1 0.053 0.079 0.000 0.026 0.007 0.0001 0.0002 0.0000 0.0001 0.0000

Analysis of Essex County Procurement and Contracting: Final Report 154 422990 $0.00 0.00% 1525 80 156 4 15 39 0.052 0.102 0.003 0.010 0.026 0.0000 0.0000 0.0000 0.0000 0.0000 441310 $384,072.86 0.97% 1076 44 41 4 19 7 0.041 0.038 0.004 0.018 0.007 0.0004 0.0004 0.0000 0.0002 0.0001 441320 $69,722.14 0.18% 801 37 18 1 19 5 0.046 0.022 0.001 0.024 0.006 0.0001 0.0000 0.0000 0.0000 0.0000 442210 $136,883.08 0.35% 652 21 51 1 14 2 0.032 0.078 0.002 0.021 0.003 0.0001 0.0003 0.0000 0.0001 0.0000 442291 $847.51 0.00% 550 18 80 4 4 6 0.033 0.145 0.007 0.007 0.011 0.0000 0.0000 0.0000 0.0000 0.0000 443111 $24,983.12 0.06% 300 11 20 0 8 1 0.037 0.067 0.000 0.027 0.003 0.0000 0.0000 0.0000 0.0000 0.0000 443112 $815,496.41 2.06% 511 28 24 1 13 5 0.055 0.047 0.002 0.025 0.010 0.0011 0.0010 0.0000 0.0005 0.0002 444120 $61,855.87 0.16% 336 8 24 1 4 1 0.024 0.071 0.003 0.012 0.003 0.0000 0.0001 0.0000 0.0000 0.0000 444130 $1,767,847.87 4.46% 390 17 17 1 9 3 0.044 0.044 0.003 0.023 0.008 0.0019 0.0019 0.0001 0.0010 0.0003 444190 $38,851.59 0.10% 275 7 23 3 0 2 0.025 0.084 0.011 0.000 0.007 0.0000 0.0001 0.0000 0.0000 0.0000 446130 $0.00 0.00% 390 17 17 1 9 3 0.044 0.044 0.003 0.023 0.008 0.0000 0.0000 0.0000 0.0000 0.0000 448190 $139,179.91 0.35% 919 73 173 0 21 28 0.079 0.188 0.000 0.023 0.030 0.0003 0.0007 0.0000 0.0001 0.0001 448320 $600.00 0.00% 61 6 7 0 1 2 0.098 0.115 0.000 0.016 0.033 0.0000 0.0000 0.0000 0.0000 0.0000 451110 $36,320.83 0.09% 764 24 53 1 7 8 0.031 0.069 0.001 0.009 0.010 0.0000 0.0001 0.0000 0.0000 0.0000 451140 $4,394.45 0.01% 173 6 13 0 2 3 0.035 0.075 0.000 0.012 0.017 0.0000 0.0000 0.0000 0.0000 0.0000 453210 $2,458,280.81 6.20% 356 19 49 2 5 5 0.053 0.138 0.006 0.014 0.014 0.0033 0.0085 0.0003 0.0009 0.0009 453910 $520.01 0.00% 3024 115 398 4 25 48 0.038 0.132 0.001 0.008 0.016 0.0000 0.0000 0.0000 0.0000 0.0000 453920 $2,278.20 0.01% 3024 115 398 4 25 48 0.038 0.132 0.001 0.008 0.016 0.0000 0.0000 0.0000 0.0000 0.0000 453998 $578,167.66 1.46% 3024 115 398 4 25 48 0.038 0.132 0.001 0.008 0.016 0.0006 0.0019 0.0000 0.0001 0.0002 454311 $1,233,453.81 3.11% 223 6 14 2 2 1 0.027 0.063 0.009 0.009 0.004 0.0008 0.0020 0.0003 0.0003 0.0001 487110 $1,840,456.68 4.64% 1206 46 74 8 15 12 0.038 0.061 0.007 0.012 0.010 0.0018 0.0028 0.0003 0.0006 0.0005 488999 $355,894.69 0.90% 566 12 12 0 7 1 0.021 0.021 0.000 0.012 0.002 0.0002 0.0002 0.0000 0.0001 0.0000 P $39,630,352.77 100.00% 33378 1479 3187 99 410 511 6.37% 7.80% 0.74% 1.43% 1.66% 511130 $7,777,855.75 1.76% 248 5 30 1 1 0 0.020 0.121 0.004 0.004 0.000 0.0004 0.0021 0.0001 0.0001 0.0000 514120 $38,281.26 0.01% 411 1 4 1 0 0 0.002 0.010 0.002 0.000 0.000 0.0000 0.0000 0.0000 0.0000 0.0000 514210 $507,766.34 0.11% 765 38 99 4 13 8 0.050 0.129 0.005 0.017 0.010 0.0001 0.0001 0.0000 0.0000 0.0000 522320 $989,691.18 0.22% 339 18 24 0 8 1 0.053 0.071 0.000 0.024 0.003 0.0001 0.0002 0.0000 0.0001 0.0000 524210 $95,176,712.81 21.50% 3013 81 247 8 38 6 0.027 0.082 0.003 0.013 0.002 0.0058 0.0176 0.0006 0.0027 0.0004 531210 $13,604,394.69 3.07% 5964 133 537 17 49 24 0.022 0.090 0.003 0.008 0.004 0.0007 0.0028 0.0001 0.0003 0.0001 532490 $966,355.08 0.22% 582 11 31 2 4 2 0.019 0.053 0.003 0.007 0.003 0.0000 0.0001 0.0000 0.0000 0.0000 541330 $106,792,133.00 24.12% 1704 133 112 21 22 38 0.078 0.066 0.012 0.013 0.022 0.0188 0.0159 0.0030 0.0031 0.0054 561320 $0.00 0.00% 696 25 98 9 8 1 0.036 0.141 0.013 0.011 0.001 0.0000 0.0000 0.0000 0.0000 0.0000 561410 $913,659.46 0.21% 389 14 169 3 2 3 0.036 0.434 0.008 0.005 0.008 0.0001 0.0009 0.0000 0.0000 0.0000 561621 $193,009.07 0.04% 234 8 12 0 3 0 0.034 0.051 0.000 0.013 0.000 0.0000 0.0000 0.0000 0.0000 0.0000 561720 $102,545.52 0.02% 1701 125 218 32 65 6 0.073 0.128 0.019 0.038 0.004 0.0000 0.0000 0.0000 0.0000 0.0000 561740 $8,862.32 0.00% 296 10 12 2 4 3 0.034 0.041 0.007 0.014 0.010 0.0000 0.0000 0.0000 0.0000 0.0000

Analysis of Essex County Procurement and Contracting: Final Report 155 561990 $101,426.48 0.02% 11227 321 1372 41 82 72 0.029 0.122 0.004 0.007 0.006 0.0000 0.0000 0.0000 0.0000 0.0000 611710 $42,833,817.56 9.67% 1112 72 202 9 20 21 0.065 0.182 0.008 0.018 0.019 0.0063 0.0176 0.0008 0.0017 0.0018 621111 $69,818,180.74 15.77% 8488 400 883 4 111 74 0.047 0.104 0.000 0.013 0.009 0.0074 0.0164 0.0001 0.0021 0.0014 621512 $5,436.00 0.00% 392 9 21 0 1 5 0.023 0.054 0.000 0.003 0.013 0.0000 0.0000 0.0000 0.0000 0.0000 624190 $3,485,607.34 0.79% 2800 0 1 0 0 0 0.000 0.000 0.000 0.000 0.000 0.0000 0.0000 0.0000 0.0000 0.0000 713990 $1,044,462.22 0.24% 1436 52 186 0 9 26 0.036 0.130 0.000 0.006 0.018 0.0001 0.0003 0.0000 0.0000 0.0000 722211 $21,428,502.43 4.84% 10968 667 862 18 204 302 0.061 0.079 0.002 0.019 0.028 0.0029 0.0038 0.0001 0.0009 0.0013 811111 $890,412.24 0.20% 2391 110 73 2 64 16 0.046 0.031 0.001 0.027 0.007 0.0001 0.0001 0.0000 0.0001 0.0000 811121 $2,179.74 0.00% 1108 54 36 1 28 10 0.049 0.032 0.001 0.025 0.009 0.0000 0.0000 0.0000 0.0000 0.0000 811213 $3,482,020.04 0.79% 230 20 14 0 10 6 0.087 0.061 0.000 0.043 0.026 0.0007 0.0005 0.0000 0.0003 0.0002 812320 $16,777.64 0.00% 1290 207 150 0 18 144 0.160 0.116 0.000 0.014 0.112 0.0000 0.0000 0.0000 0.0000 0.0000 813211 $806,105.00 0.18% 99 2 5 1 0 1 0.020 0.051 0.010 0.000 0.010 0.0000 0.0001 0.0000 0.0000 0.0000 813920 $71,748,736.30 16.21% 272 0 3 0 0 0 0.000 0.011 0.000 0.000 0.000 0.0000 0.0018 0.0000 0.0000 0.0000 O $442,734,930.21 100.00% 58155 2516 5401 176 764 769 4.35% 8.03% 0.47% 1.14% 1.08% Source: Essex County Contract Files 2002-2004

Analysis of Essex County Procurement and Contracting: Final Report 156 SIC Code Utilization Weight 15 Building Cnstrctn - General Contractors & Operative Builders 16 Heavy Cnstrctn, Except Building Construction - Contractors 17 Construction - Special Trade Contractors 01 Agricultural Production - Crops 07 Agricultural Services 23 Apparel, Finished Prdcts from Fabrics & Similar Materials 24 Lumber and Wood Products, Except Furniture 26 Paper and Allied Products 27 Printing, Publishing and Allied Industries 28 Chemicals and Allied Products 32 Stone, Clay, Glass, and Concrete Products 33 Primary Metal Industries 34 Fabricated Metal Prdcts, Except Machinery & Transport Eqpmnt 35 Industrial and Commercial Machinery and Computer Equipment H-10: Dun & Bradstreet Weighted Availability Measure for PJM2 (Two-Digit SIC Codes for Under 17,500) Total Firms DBE* Firms MBE Firms WBE Firms MWBE Firms Black Firms Hispanic Firms $1,410,233.13 6.46% 8238 497 282 279 64 41 129 34 0.390% 0.221% 0.219% 0.050% 0.032% 0.101% 0.027% $524,112.39 2.40% 845 83 40 49 6 3 21 3 0.236% 0.114% 0.139% 0.017% 0.009% 0.060% 0.009% $1,551,834.02 7.11% 15028 1041 503 626 88 53 309 46 0.493% 0.238% 0.296% 0.042% 0.025% 0.146% 0.022% Asian Firms DBE* Share MBE Share WBE Share MWBE Share Black Share Hispanic Share $0.00 0 509 51 8 46 3 2 1 3 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% $0.00 0 4652 442 105 370 33 6 55 14 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% $36,588.17 0.17% 766 161 41 141 21 3 18 12 0.035% 0.009% 0.031% 0.005% 0.001% 0.004% 0.003% $0.00 0.00% 468 39 11 33 5 1 2 3 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% $0.00 0.00% 389 44 11 37 4 3 1 2 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% $1,247,738.81 5.72% 2889 376 85 322 31 14 30 15 0.744% 0.168% 0.637% 0.061% 0.028% 0.059% 0.030% $0.00 0.00% 1209 91 34 65 8 3 5 12 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% $0.00 0.00% 381 48 14 37 3 8 3 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% $0.00 0.00% 211 15 7 11 3 1 3 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% $4,000.00 0.02% 1074 105 38 77 10 4 18 7 0.002% 0.001% 0.001% 0.000% 0.000% 0.000% 0.000% $46,539.09 0.21% 1637 155 73 101 19 5 27 18 0.020% 0.010% 0.013% 0.002% 0.001% 0.004% 0.002% Asian Share

Analysis of Essex County Procurement and Contracting: Final Report 157 36 Electronic, Elctrcl Eqpmnt & Cmpnts, Excpt $33,457.72 0.15% 1032 97 51 60 14 3 8 20 0.014% 0.008% 0.009% 0.002% 0.000% 0.001% 0.003% Computer Eqpmnt 37 Transportation Equipment $601.68 0.00% 195 12 5 7 2 1 1 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 38 Mesr/Anlyz/Cntrl Instrmnts; Photo/Med/Opt $49,119.48 0.23% 668 62 27 41 6 3 5 8 0.021% 0.009% 0.014% 0.002% 0.001% 0.002% 0.003% Gds; Watchs/Clocks 39 Miscellaneous Manufacturing $0.00 0.00% 1279 198 42 169 13 2 17 9 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% Industries 41 Local, Suburban Transit & Interurbn Hgwy $100,786.00 0.46% 1797 177 78 123 24 14 31 12 0.045% 0.020% 0.032% 0.006% 0.004% 0.008% 0.003% Passenger Transport 42 Motor Freight Transportation $0.00 0.00% 3948 334 160 207 33 19 89 24 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 47 Transportation Services $733,340.69 3.36% 2892 522 192 414 84 12 90 30 0.606% 0.223% 0.481% 0.098% 0.014% 0.105% 0.035% 48 Communications $0.00 0.00% 1991 156 63 111 18 13 16 15 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 49 Electric, Gas and Sanitary $0.00 0.00% 802 34 16 24 6 2 9 1 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% Services 50 Wholesale Trade - Durable $4,790,354.61 21.95% 9924 1177 507 821 151 43 112 200 2.603% 1.121% 1.816% 0.334% 0.095% 0.248% 0.442% Goods 51 Wholesale Trade - $656,223.46 3.01% 6515 895 383 622 110 19 112 138 0.413% 0.177% 0.287% 0.051% 0.009% 0.052% 0.064% Nondurable Goods 52 Building Matrials, Hrdwr, Garden Supply & $625,772.34 2.87% 1702 145 51 107 13 3 23 9 0.244% 0.086% 0.180% 0.022% 0.005% 0.039% 0.015% Mobile Home Dealrs 54 Food Stores $0.00 0.00% 6547 1000 501 658 159 8 210 105 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 55 Automotive Dealers and Gasoline Service $0.00 0.00% 4041 246 138 130 22 4 52 30 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% Stations 56 Apparel and Accessory Stores $237,158.00 1.09% 4634 886 217 787 118 3 75 68 0.208% 0.051% 0.185% 0.028% 0.001% 0.018% 0.016%

Analysis of Essex County Procurement and Contracting: Final Report 158 57 Home Furniture, Furnishings and $269,620.59 1.24% 4481 523 198 386 61 21 78 48 0.144% 0.055% 0.106% 0.017% 0.006% 0.022% 0.013% Equipment Stores 58 Eating and Drinking Places $143,009.91 0.66% 11891 1449 694 935 180 21 225 305 0.080% 0.038% 0.052% 0.010% 0.001% 0.012% 0.017% 59 Misc Retail $446,507.35 2.05% 13926 2496 659 2117 280 26 201 160 0.367% 0.097% 0.311% 0.041% 0.004% 0.030% 0.024% 60 Depository Institutions $224,983.96 1.03% 2591 50 25 35 10 8 4 0.020% 0.010% 0.014% 0.004% 0.000% 0.003% 0.002% 63 Insurance Carriers $0.00 0.00% 561 26 8 20 2 1 3 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 64 Insurance Agents, Brokers $77,074.15 0.35% 2964 289 81 246 38 8 38 6 0.034% 0.010% 0.029% 0.005% 0.001% 0.005% 0.001% and Service 65 Real Estate $289,207.83 1.33% 9878 841 190 729 78 24 74 28 0.113% 0.025% 0.098% 0.010% 0.003% 0.010% 0.004% 70 Hotels, Rooming Houses, Camps, and Other $0.00 0.00% 833 92 36 69 13 1 4 11 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% Lodging Places 72 Personal Serv $26,619.06 0.12% 12749 3308 998 2850 540 25 287 358 0.032% 0.010% 0.027% 0.005% 0.000% 0.003% 0.003% 73 Bus Services $974,030.69 4.46% 27336 4370 1418 3510 558 203 308 330 0.713% 0.231% 0.573% 0.091% 0.033% 0.050% 0.054% 75 Automotive Repair, Services $310,815.23 1.42% 6378 386 235 201 50 7 133 33 0.086% 0.052% 0.045% 0.011% 0.002% 0.030% 0.007% and Parking 76 Miscellaneous Repair Services $640,154.62 2.93% 4101 307 137 207 37 13 72 20 0.220% 0.098% 0.148% 0.026% 0.009% 0.051% 0.014% 78 Motion Pictures $0.00 0.00% 1630 113 31 91 9 3 11 6 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 79 Amusement & Recreation Serv $13,333.32 0.06% 4841 696 110 626 40 9 27 29 0.009% 0.001% 0.008% 0.001% 0.000% 0.000% 0.000% 80 Health Services $2,473,440.43 11.33% 18800 2320 655 1974 309 11 172 139 1.399% 0.395% 1.190% 0.186% 0.007% 0.104% 0.084% 81 Legal Services $0.00 0.00% 6593 650 121 578 49 1 51 24 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 82 Educational Services $436,160.95 2.00% 4733 308 101 253 46 18 28 26 0.130% 0.043% 0.107% 0.019% 0.008% 0.012% 0.011% 83 Social Services $1,134,263.50 5.20% 6146 508 69 501 62 7 2 7 0.430% 0.058% 0.424% 0.052% 0.006% 0.002% 0.006% 87 Engineering, Accounting, Research, Management & Related Svcs $2,318,410.01 10.62% 19538 2718 863 2137 282 174 210 193 1.478% 0.469% 1.162% 0.153% 0.095% 0.114% 0.105% 89 Services, NEC $0.00 0.00% 2040 315 51 281 17 10 12 16 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 99 Nonclassifiable Establishments $0.00 0.00% 14196 146 37 119 10 8 11 11 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% Total $21,825,491.19 100.00% 145908 17443 5166 14427 2150 522 1449 1244 11.33% 4.05% 8.63% 1.35% 0.40% 1.29% 1.02%

Analysis of Essex County Procurement and Contracting: Final Report 159 SIC Code Utilization Weight H-11: Dun & Bradstreet Weighted Availability Measure for PJM2 (Four-Digit SIC Codes for Over 17,500) Total Firms DBE* Firms MBE Firm WBE Firms MWBE Firms Black Firms Hispanic Firms 0119 15 2 0 2 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 0161 40 2 0 2 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 0219 3 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 0742 375 44 5 40 1 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 0781 1308 70 24 54 8 2 12 4 0.000 0.000 0.000 0.000 0.000 0.000 0782 2040 123 58 76 11 2 38 8 0.000 0.000 0.000 0.000 0.000 0.000 0783 337 11 1 11 1 0 0 1 0.000 0.000 0.000 0.000 0.000 0.000 0971 9 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 1423 3 2 1 1 0 0 1 0.000 0.000 0.000 0.000 0.000 0.000 1429 6 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 1521 $ 27,426.33 0.00004 6049 277 152 154 29 17 82 20 0.000 0.000 0.000 0.000 0.000 0.000 1522 789 65 39 34 8 4 20 3 0.000 0.000 0.000 0.000 0.000 0.000 1541 $ 54,697,011.01 0.07065 281 25 16 11 2 3 4 2 0.006 0.004 0.003 0.001 0.001 0.001 1542 916 121 70 75 24 16 21 8 0.000 0.000 0.000 0.000 0.000 0.000 1611 $ 26,958,273.77 0.03482 457 47 22 30 5 3 11 1 0.004 0.002 0.002 0.000 0.001 0.000 1622 27 6 2 4 0 1 1 0.000 0.000 0.000 0.000 0.000 0.000 1623 187 16 10 7 1 0 7 0 0.000 0.000 0.000 0.000 0.000 0.000 1629 $ 231,756,962.76 0.29934 173 14 6 8 0 2 1 0.024 0.010 0.014 0.000 0.003 0.002 1711 $ 12,258,444.07 0.01583 3359 183 81 117 15 11 38 10 0.001 0.000 0.001 0.000 0.000 0.000 1721 1668 137 72 70 5 4 59 5 0.000 0.000 0.000 0.000 0.000 0.000 1731 $ 53,552,933.85 0.06917 2662 171 91 93 13 18 44 10 0.004 0.002 0.002 0.000 0.001 0.000 1741 742 66 43 32 9 3 28 1 0.000 0.000 0.000 0.000 0.000 0.000 1742 317 29 15 17 3 3 8 1 0.000 0.000 0.000 0.000 0.000 0.000 1761 978 60 27 36 3 1 17 3 0.000 0.000 0.000 0.000 0.000 0.000 1771 $ 3,026,672.26 0.00391 390 50 30 25 5 2 20 1 0.001 0.000 0.000 0.000 0.000 0.000 1794 553 37 8 31 2 2 4 1 0.000 0.000 0.000 0.000 0.000 0.000 1795 $ 19,797,752.36 0.02557 104 9 2 7 0 2 0 0.002 0.000 0.002 0.000 0.000 0.000 1799 2087 156 66 111 21 9 38 6 0.000 0.000 0.000 0.000 0.000 0.000 2047 7 1 0 1 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 2399 34 8 0 8 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 2711 $ 7,884,892.62 0.01018 240 28 8 21 1 1 4 2 0.001 0.000 0.001 0.000 0.000 0.000 2731 244 35 5 30 1 1 0 0.000 0.000 0.000 0.000 0.000 0.000 2741 422 41 2 40 1 0 0 1 0.000 0.000 0.000 0.000 0.000 0.000 Asian Firms DBE Share Minority Share Women Share Black Share Hispanic Share Asian Share

Analysis of Essex County Procurement and Contracting: Final Report 160 2752 859 118 34 97 13 8 11 3 0.000 0.000 0.000 0.000 0.000 0.000 3269 18 8 1 8 1 0 1 0 0.000 0.000 0.000 0.000 0.000 0.000 3363 5 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 3446 92 9 5 4 0 3 0 0.000 0.000 0.000 0.000 0.000 0.000 3563 9 1 0 1 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 3579 17 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 3589 69 7 4 5 2 1 2 1 0.000 0.000 0.000 0.000 0.000 0.000 3599 480 44 21 26 3 2 12 4 0.000 0.000 0.000 0.000 0.000 0.000 3663 89 6 3 3 0 0 3 0.000 0.000 0.000 0.000 0.000 0.000 3669 37 3 1 2 0 1 0 0.000 0.000 0.000 0.000 0.000 0.000 3823 51 4 2 3 1 2 0 0 0.000 0.000 0.000 0.000 0.000 0.000 3842 95 7 4 4 1 0 1 1 0.000 0.000 0.000 0.000 0.000 0.000 3844 7 1 0 1 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 3861 50 4 2 2 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 3993 404 66 14 56 4 1 7 2 0.000 0.000 0.000 0.000 0.000 0.000 4131 33 3 2 2 1 0 1 0 0.000 0.000 0.000 0.000 0.000 0.000 4212 1907 178 98 95 15 11 64 10 0.000 0.000 0.000 0.000 0.000 0.000 4213 776 67 26 50 9 4 13 2 0.000 0.000 0.000 0.000 0.000 0.000 4214 222 24 7 18 1 2 3 1 0.000 0.000 0.000 0.000 0.000 0.000 4311 391 1 0 1 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 4724 1317 388 129 328 69 7 67 10 0.000 0.000 0.000 0.000 0.000 0.000 4812 392 20 10 13 3 1 2 4 0.000 0.000 0.000 0.000 0.000 0.000 4813 894 81 35 57 11 7 8 8 0.000 0.000 0.000 0.000 0.000 0.000 4931 17 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 4953 392 26 11 19 4 1 7 0 0.000 0.000 0.000 0.000 0.000 0.000 4959 108 7 4 4 1 1 2 0 0.000 0.000 0.000 0.000 0.000 0.000 5012 176 11 5 9 3 0 2 1 0.000 0.000 0.000 0.000 0.000 0.000 5013 $ 146,371.21 0.00019 568 42 15 30 3 0 7 5 0.000 0.000 0.000 0.000 0.000 0.000 5021 284 37 21 26 10 3 5 9 0.000 0.000 0.000 0.000 0.000 0.000 5023 $ 100,946.74 0.00013 445 66 31 41 6 1 4 13 0.000 0.000 0.000 0.000 0.000 0.000 5031 266 27 7 22 2 3 0 2 0.000 0.000 0.000 0.000 0.000 0.000 5032 242 29 13 20 4 0 4 4 0.000 0.000 0.000 0.000 0.000 0.000 5039 $ 1,557,178.87 0.00201 64 2 1 1 0 1 0 0.000 0.000 0.000 0.000 0.000 0.000 5043 $ 1,741,437.55 0.00225 46 2 1 2 1 0 0 1 0.000 0.000 0.000 0.000 0.000 0.000 5044 $ 3,266,956.46 0.00422 184 27 10 19 2 0 2 3 0.001 0.000 0.000 0.000 0.000 0.000 5045 $ 10,123,797.39 0.01308 558 97 67 52 22 8 8 32 0.002 0.002 0.001 0.000 0.000 0.001

Analysis of Essex County Procurement and Contracting: Final Report 161 5047 $ 14,974,077.47 0.01934 481 67 26 49 8 1 4 10 0.003 0.001 0.002 0.000 0.000 0.000 5051 $ 30,569.20 0.00004 283 33 16 22 5 2 2 6 0.000 0.000 0.000 0.000 0.000 0.000 5063 553 70 24 51 5 2 10 9 0.000 0.000 0.000 0.000 0.000 0.000 5064 $ 152,482.92 0.00020 136 15 10 8 3 1 1 6 0.000 0.000 0.000 0.000 0.000 0.000 5074 260 22 9 14 1 0 5 3 0.000 0.000 0.000 0.000 0.000 0.000 5082 $ 302,188.00 0.00039 170 13 8 9 4 0 3 4 0.000 0.000 0.000 0.000 0.000 0.000 5083 $ 426,538.28 0.00055 85 3 1 2 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 5084 988 94 38 64 8 5 6 16 0.000 0.000 0.000 0.000 0.000 0.000 5087 $ 622,705.26 0.00080 519 88 33 67 12 5 8 11 0.000 0.000 0.000 0.000 0.000 0.000 5088 102 17 5 12 1 2 2 0.000 0.000 0.000 0.000 0.000 0.000 5091 $ 436,204.32 0.00056 180 12 6 9 3 1 0 2 0.000 0.000 0.000 0.000 0.000 0.000 5092 $ 1,452,999.63 0.00188 140 35 16 24 5 1 0 11 0.000 0.000 0.000 0.000 0.000 0.000 5111 62 8 4 6 2 0 1 2 0.000 0.000 0.000 0.000 0.000 0.000 5112 322 53 17 45 9 2 5 5 0.000 0.000 0.000 0.000 0.000 0.000 5113 212 29 6 24 1 1 2 0 0.000 0.000 0.000 0.000 0.000 0.000 5122 512 65 32 45 12 3 10 9 0.000 0.000 0.000 0.000 0.000 0.000 5136 $ 291,494.84 0.00038 255 42 23 24 5 0 3 12 0.000 0.000 0.000 0.000 0.000 0.000 5137 366 85 35 67 17 0 7 13 0.000 0.000 0.000 0.000 0.000 0.000 5141 $ 160,211.05 0.00021 346 59 33 36 10 1 15 7 0.000 0.000 0.000 0.000 0.000 0.000 5162 138 19 10 13 4 1 1 5 0.000 0.000 0.000 0.000 0.000 0.000 5169 $ 469,788.38 0.00061 443 47 20 30 3 3 4 6 0.000 0.000 0.000 0.000 0.000 0.000 5191 72 6 2 4 0 0 1 0.000 0.000 0.000 0.000 0.000 0.000 5193 147 20 8 12 0 4 1 0.000 0.000 0.000 0.000 0.000 0.000 5194 $ 568,670.72 0.00073 51 3 3 0 0 1 2 0.000 0.000 0.000 0.000 0.000 0.000 5199 1489 215 80 155 20 4 15 39 0.000 0.000 0.000 0.000 0.000 0.000 5261 310 37 9 30 2 0 4 2 0.000 0.000 0.000 0.000 0.000 0.000 5411 3894 611 349 371 109 2 151 68 0.000 0.000 0.000 0.000 0.000 0.000 5499 641 131 39 112 20 2 12 7 0.000 0.000 0.000 0.000 0.000 0.000 5511 669 33 17 19 3 2 5 5 0.000 0.000 0.000 0.000 0.000 0.000 5699 $ 480,510.81 0.00062 890 202 71 169 38 0 20 27 0.000 0.000 0.000 0.000 0.000 0.000 5712 1142 134 47 100 13 4 21 11 0.000 0.000 0.000 0.000 0.000 0.000 5713 646 63 21 51 9 1 14 2 0.000 0.000 0.000 0.000 0.000 0.000 5722 296 27 11 20 4 0 8 1 0.000 0.000 0.000 0.000 0.000 0.000 5731 $ 819,103.35 0.00106 507 41 28 24 11 1 13 5 0.000 0.000 0.000 0.000 0.000 0.000 5734 733 97 54 57 14 11 7 18 0.000 0.000 0.000 0.000 0.000 0.000 5812 $ 21,498,513.57 0.02777 10861 1351 666 858 173 18 203 302 0.003 0.002 0.002 0.000 0.001 0.001

Analysis of Essex County Procurement and Contracting: Final Report 162 5912 1182 71 41 43 13 1 13 11 0.000 0.000 0.000 0.000 0.000 0.000 5941 $ 29,823.72 0.00004 754 68 24 52 8 1 7 8 0.000 0.000 0.000 0.000 0.000 0.000 5943 212 53 14 46 7 0 2 4 0.000 0.000 0.000 0.000 0.000 0.000 5961 492 109 23 98 12 1 6 8 0.000 0.000 0.000 0.000 0.000 0.000 5963 230 50 7 46 3 0 5 0 0.000 0.000 0.000 0.000 0.000 0.000 5983 $ 1,110,894.84 0.00143 218 18 6 14 2 2 2 1 0.000 0.000 0.000 0.000 0.000 0.000 5994 83 17 8 13 4 0 0 2 0.000 0.000 0.000 0.000 0.000 0.000 5995 456 39 15 31 7 1 9 1 0.000 0.000 0.000 0.000 0.000 0.000 5999 2989 467 115 397 45 4 25 48 0.000 0.000 0.000 0.000 0.000 0.000 6021 715 3 1 2 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 6022 665 6 4 4 2 0 0 2 0.000 0.000 0.000 0.000 0.000 0.000 6029 117 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 6036 170 1 1 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 6111 66 12 5 8 1 1 0 2 0.000 0.000 0.000 0.000 0.000 0.000 6159 101 3 2 2 1 0 0 1 0.000 0.000 0.000 0.000 0.000 0.000 6321 46 2 0 2 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 6324 27 2 2 1 1 0 1 0 0.000 0.000 0.000 0.000 0.000 0.000 6331 102 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 6351 61 2 1 1 0 0 1 0.000 0.000 0.000 0.000 0.000 0.000 6411 $ 98,717,226.34 0.12750 2951 290 81 247 38 8 38 6 0.013 0.003 0.011 0.000 0.002 0.000 6512 1539 72 17 60 5 1 10 1 0.000 0.000 0.000 0.000 0.000 0.000 6513 1262 59 15 52 8 1 6 0 0.000 0.000 0.000 0.000 0.000 0.000 6531 $ 14,628,397.00 0.01889 5727 615 133 535 53 17 49 24 0.002 0.000 0.002 0.000 0.000 0.000 6552 490 24 9 19 4 2 4 0 0.000 0.000 0.000 0.000 0.000 0.000 6799 553 12 2 11 1 2 0 0 0.000 0.000 0.000 0.000 0.000 0.000 7011 638 66 32 44 10 0 3 12 0.000 0.000 0.000 0.000 0.000 0.000 7032 101 9 0 9 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 7213 38 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 7231 5726 2068 491 1948 371 7 158 136 0.000 0.000 0.000 0.000 0.000 0.000 7261 482 48 15 43 10 0 6 0 0.000 0.000 0.000 0.000 0.000 0.000 7299 1569 361 41 344 24 6 13 6 0.000 0.000 0.000 0.000 0.000 0.000 7311 857 170 22 155 7 0 11 4 0.000 0.000 0.000 0.000 0.000 0.000 7338 $ 966,520.89 0.00125 385 172 14 168 10 3 2 3 0.001 0.000 0.001 0.000 0.000 0.000 7342 $ 76,506.12 0.00010 466 38 26 20 8 8 10 2 0.000 0.000 0.000 0.000 0.000 0.000 7349 1686 291 125 218 52 32 65 6 0.000 0.000 0.000 0.000 0.000 0.000 7353 61 3 2 1 0 1 0 0.000 0.000 0.000 0.000 0.000 0.000

Analysis of Essex County Procurement and Contracting: Final Report 163 7359 560 38 11 30 3 2 4 2 0.000 0.000 0.000 0.000 0.000 0.000 7361 1061 224 48 209 33 9 12 5 0.000 0.000 0.000 0.000 0.000 0.000 7363 688 113 25 98 10 9 8 1 0.000 0.000 0.000 0.000 0.000 0.000 7371 1815 303 189 183 69 7 13 55 0.000 0.000 0.000 0.000 0.000 0.000 7373 666 105 69 55 19 12 13 22 0.000 0.000 0.000 0.000 0.000 0.000 7378 314 41 19 27 5 3 3 9 0.000 0.000 0.000 0.000 0.000 0.000 7379 2888 572 339 352 119 44 36 95 0.000 0.000 0.000 0.000 0.000 0.000 7381 423 27 10 19 2 5 3 1 0.000 0.000 0.000 0.000 0.000 0.000 7382 $ 128,660.00 0.00017 232 19 8 12 1 2 3 0 0.000 0.000 0.000 0.000 0.000 0.000 7389 11015 1531 319 1368 156 41 82 72 0.000 0.000 0.000 0.000 0.000 0.000 7515 48 2 0 2 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 7519 33 2 0 2 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 7521 116 5 2 3 0 2 0 0.000 0.000 0.000 0.000 0.000 0.000 7532 $ 4,497,693.82 0.00581 1105 79 54 36 11 1 28 10 0.000 0.000 0.000 0.000 0.000 0.000 7534 48 4 2 2 0 2 0 0.000 0.000 0.000 0.000 0.000 0.000 7538 $ 593,600.06 0.00077 2357 167 110 72 15 2 64 16 0.000 0.000 0.000 0.000 0.000 0.000 7623 179 13 8 5 1 6 1 0.000 0.000 0.000 0.000 0.000 0.000 7629 331 18 14 7 3 0 6 4 0.000 0.000 0.000 0.000 0.000 0.000 7694 31 4 2 2 0 2 0 0.000 0.000 0.000 0.000 0.000 0.000 7699 2777 178 63 138 23 11 30 6 0.000 0.000 0.000 0.000 0.000 0.000 7812 849 51 14 42 5 3 6 2 0.000 0.000 0.000 0.000 0.000 0.000 7819 192 15 1 14 0 1 0 0.000 0.000 0.000 0.000 0.000 0.000 7922 300 33 2 31 1 0 0 0.000 0.000 0.000 0.000 0.000 0.000 7991 727 130 16 123 9 1 8 1 0.000 0.000 0.000 0.000 0.000 0.000 7999 1404 226 52 186 12 0 9 26 0.000 0.000 0.000 0.000 0.000 0.000 8011 $ 68,367,399.31 0.08830 8401 1105 400 882 177 4 111 74 0.012 0.004 0.009 0.000 0.001 0.001 8021 3480 357 120 291 54 2 27 38 0.000 0.000 0.000 0.000 0.000 0.000 8031 78 10 2 8 0 1 1 0.000 0.000 0.000 0.000 0.000 0.000 8043 386 34 10 30 6 0 2 0 0.000 0.000 0.000 0.000 0.000 0.000 8049 1740 394 33 385 24 0 6 5 0.000 0.000 0.000 0.000 0.000 0.000 8051 246 11 2 10 1 0 2 0 0.000 0.000 0.000 0.000 0.000 0.000 8059 108 14 2 13 1 0 1 0 0.000 0.000 0.000 0.000 0.000 0.000 8062 185 2 0 2 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 8069 128 14 0 14 0 0 0.000 0.000 0.000 0.000 0.000 0.000 8071 385 25 9 21 5 0 1 5 0.000 0.000 0.000 0.000 0.000 0.000 8072 $ 72,815.60 0.00009 357 52 26 33 7 0 12 10 0.000 0.000 0.000 0.000 0.000 0.000

Analysis of Essex County Procurement and Contracting: Final Report 164 8082 284 48 13 46 11 4 0 2 0.000 0.000 0.000 0.000 0.000 0.000 8093 499 55 7 54 6 0 0 2 0.000 0.000 0.000 0.000 0.000 0.000 8099 703 49 7 45 3 1 0 1 0.000 0.000 0.000 0.000 0.000 0.000 8111 6592 652 123 578 49 1 51 25 0.000 0.000 0.000 0.000 0.000 0.000 8211 2585 1 0 1 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 8221 288 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 8222 51 3 3 0 0 2 1 0.000 0.000 0.000 0.000 0.000 0.000 8231 $ 38,194.64 0.00005 406 5 1 4 1 0 0 0.000 0.000 0.000 0.000 0.000 0.000 8243 129 45 18 31 4 7 3 3 0.000 0.000 0.000 0.000 0.000 0.000 8244 34 3 2 2 1 0 2 0 0.000 0.000 0.000 0.000 0.000 0.000 8249 131 21 6 16 1 1 1 2 0.000 0.000 0.000 0.000 0.000 0.000 8299 1096 232 71 201 40 9 20 20 0.000 0.000 0.000 0.000 0.000 0.000 8322 $ 3,713,653.20 0.00480 2742 1 0 1 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 8331 190 1 0 1 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 8351 2425 505 69 498 62 7 2 7 0.000 0.000 0.000 0.000 0.000 0.000 8361 272 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 8399 427 2 0 2 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 8611 412 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 8621 259 3 0 3 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 8631 496 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 8641 1533 1 0 1 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 8661 5518 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 8699 861 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 8711 $ 111,707,799.92 0.14428 1669 211 132 111 32 20 22 38 0.018 0.011 0.010 0.002 0.002 0.003 8712 866 107 55 62 10 6 27 9 0.000 0.000 0.000 0.000 0.000 0.000 8713 186 10 6 7 3 1 1 1 0.000 0.000 0.000 0.000 0.000 0.000 8721 3395 395 111 318 34 9 43 27 0.000 0.000 0.000 0.000 0.000 0.000 8733 243 33 12 23 2 0 1 5 0.000 0.000 0.000 0.000 0.000 0.000 8741 1266 106 29 85 8 7 7 5 0.000 0.000 0.000 0.000 0.000 0.000 8742 5454 905 252 754 101 65 56 53 0.000 0.000 0.000 0.000 0.000 0.000 8743 535 126 13 120 7 5 4 1 0.000 0.000 0.000 0.000 0.000 0.000 8744 47 14 5 11 2 1 1 0 0.000 0.000 0.000 0.000 0.000 0.000 8748 4666 655 199 521 65 51 45 40 0.000 0.000 0.000 0.000 0.000 0.000 9111 487 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 9121 200 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 9131 27 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000

Analysis of Essex County Procurement and Contracting: Final Report 165 9199 139 1 1 1 1 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 9211 155 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 9222 75 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 9223 50 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 9224 377 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 9411 89 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 9441 147 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 9511 129 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 9531 141 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 9532 58 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 9611 49 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 9641 53 0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 9999 13446 138 38 110 10 8 12 11 0.000 0.000 0.000 0.000 0.000 0.000 Total $ 774,232,300.51 1.00000 208039 22563 7664 17721 2822 702 2516 1843 9.93% 4.51% 6.36% 0.41% 1.36% 0.95%

Analysis of Essex County Procurement and Contracting: Final Report 166 Table H-12 Dun & Bradstreet Weighted Availability Measure for PJM2 (Two Digit SIC Codes for Under 17,500) By Industry Type SIC Code Utilization Weight Total DBE MBE WBE MWBE Black Hispanic Asian C C C 15 Building Cnstrctn - General Contractors & Operative Builders 16 Heavy Cnstrctn, Except Building Constr- Contractors 17 Constr Special Trade Contractors $1,410,233.13 40.45% 8238 497 282 279 64 41 129 34 2.440% 1.385% 1.370% 0.314% 0.201% 0.633% 0.167% $524,112.39 15.03% 845 83 40 49 6 3 21 3 1.477% 0.712% 0.872% 0.107% 0.053% 0.374% 0.053% $1,551,834.02 44.51% 15028 1041 503 626 88 53 309 46 3.084% 1.490% 1.854% 0.261% 0.157% 0.915% 0.136% Subtotal $3,486,179.54 100% 24111 1621 825 954 158 97 459 83 7.00% 3.59% 4.10% 0.68% 0.41% 1.92% 0.36% O 01 Agr Prod Crops $0.00 0 509 51 8 46 3 2 1 3 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% O 07 Agr Services $0.00 0 4652 442 105 370 33 6 55 14 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% O 23 Apparel, Finished Prdcts from Fabrics & $36,588.17 0.39% 766 161 41 141 21 3 18 12 0.082% 0.021% 0.071% 0.011% 0.002% 0.009% 0.006% Similar Materials O 24 Lumber and Wood Products, $0.00 0.00% 468 39 11 33 5 1 2 3 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% Except Furniture O 26 Paper and Allied Products $0.00 0 389 44 11 37 4 3 1 2 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% O 27 Printing, Publishing and $1,247,738.81 13.24% 2889 376 85 322 31 14 30 15 1.724% 0.390% 1.476% 0.142% 0.064% 0.138% 0.069% Allied Industries O 28 Chemicals and Allied Products $0.00 0 1209 91 34 65 8 3 5 12 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 32 Stone, Clay, O Glass, and $0.00 0 381 48 14 37 3 8 3 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% Concrete Products O 33 Primary Metal Industries $0.00 0 211 15 7 11 3 1 3 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% O 34 Fabricated Metal Prdcts, $4,000.00 0.04% 1074 105 38 77 10 4 18 7 0.004% 0.002% 0.003% 0.000% 0.000% 0.001% 0.000% O O 35 Industrial and Commercial Machinery and Comp Equip 36 Electronic, Elctrcl Eqpmnt & Cmpnts, Excpt Comp Eqpmnt $46,539.09 0.49% 1637 155 73 101 19 5 27 18 0.047% 0.022% 0.030% 0.006% 0.002% 0.008% 0.005% $33,457.72 0.36% 1032 97 51 60 14 3 8 20 0.033% 0.018% 0.021% 0.005% 0.001% 0.003% 0.007% DBE* Share MBE Share WBE Share MWBE Share Black Share Hispanic Share Asian Share

Analysis of Essex County Procurement and Contracting: Final Report 167 O O O 37 Transp Equipment 38 Instrum Photo/Med/Opt Watches/Clocks 39 Misc Manufact Industries $601.68 0.01% 195 12 5 7 2 1 1 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% $49,119.48 0.52% 668 62 27 41 6 3 5 8 0.048% 0.021% 0.032% 0.005% 0.002% 0.004% 0.006% $0.00 0 1279 198 42 169 13 2 17 9 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% O 41 Local, Suburban Transit & Interurbn Hgwy $100,786.00 1.07% 1797 177 78 123 24 14 31 12 0.105% 0.046% 0.073% 0.014% 0.008% 0.018% 0.007% Passenger Transport O 42 Motor Freight Transp $0.00 0 3948 334 160 207 33 19 89 24 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% O 47 Transp Services $733,340.69 7.78% 2892 522 192 414 84 12 90 30 1.405% 0.517% 1.114% 0.226% 0.032% 0.242% 0.081% O 48 Communi-cations $0.00 0 1991 156 63 111 18 13 16 15 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% O 49 Electric, Gas and Sanitary $0.00 0 802 34 16 24 6 2 9 1 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% Services O 50 Wholesale Trade - Durable $4,790,354.61 50.85% 9924 1177 507 821 151 43 112 200 6.031% 2.598% 4.207% 0.774% 0.220% 0.574% 1.025% Goods O 51 Wholesale Trade - $656,223.46 6.97% 6515 895 383 622 110 19 112 138 0.957% 0.409% 0.665% 0.118% 0.020% 0.120% 0.148% Nondurable Goods O 52 Bldg Mat, Hrdwr, Garden Supply & Mobile $625,772.34 6.64% 1702 145 51 107 13 3 23 9 0.566% 0.199% 0.418% 0.051% 0.012% 0.090% 0.035% Home Dealrs O 54 Food Stores $0.00 0 6547 1000 501 658 159 8 210 105 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% O 55 Automotive Dealers and Gasoline Service $0.00 0 4041 246 138 130 22 4 52 30 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% Stations O 56 Apparel Accessory Stores $237,158.00 2.52% 4634 886 217 787 118 3 75 68 0.481% 0.118% 0.428% 0.064% 0.002% 0.041% 0.037% O 57 Home Furniture, Furnishings and $269,620.59 2.86% 4481 523 198 386 61 21 78 48 0.334% 0.126% 0.247% 0.039% 0.013% 0.050% 0.031% Equipment Stores O 58 Eating and Drinking Places $143,009.91 1.52% 11891 1449 694 935 180 21 225 305 0.185% 0.089% 0.119% 0.023% 0.003% 0.029% 0.039% O 59 Miscellaneous Retail $446,507.35 4.74% 13926 2496 659 2117 280 26 201 160 0.849% 0.224% 0.721% 0.095% 0.009% 0.068% 0.054% Subtotal $9,420,817.90 100% 92450 1193 4409 8959 1432 259 1520 1275 12.85% 4.80% 9.62% 1.57% 0.39% 1.39% 1.55%

Analysis of Essex County Procurement and Contracting: Final Report 168 6 P 60 Depository Institutions $224,983.96 2.52% 2591 50 25 35 10 8 4 0.049% 0.024% 0.034% 0.010% 0.000% 0.008% 0.004% P 63 Insurance Carriers $0.00 0 561 26 8 20 2 1 3 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 64 Insurance P Agents, Brokers $77,074.15 0.86% 2964 289 81 246 38 8 38 6 0.084% 0.024% 0.072% 0.011% 0.002% 0.011% 0.002% and Service P 65 Real Estate $289,207.83 3.24% 9878 841 190 729 78 24 74 28 0.276% 0.062% 0.239% 0.026% 0.008% 0.024% 0.009% P 70 Hotels, Rooming Houses, Camps, and Other $0.00 0 833 92 36 69 13 1 4 11 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% Lodging P 72 Personal Services $26,619.06 0.30% 12749 3308 998 2850 540 25 287 358 0.077% 0.023% 0.067% 0.013% 0.001% 0.007% 0.008% P 73 Business Services $974,030.69 10.92% 27336 4370 1418 3510 558 203 308 330 1.746% 0.567% 1.402% 0.223% 0.081% 0.123% 0.132% P 75 Automotive Repair, Srv and $310,815.23 3.49% 6378 386 235 201 50 7 133 33 0.211% 0.128% 0.110% 0.027% 0.004% 0.073% 0.018% Parking P 76 Misc Repair Services $640,154.62 7.18% 4101 307 137 207 37 13 72 20 0.537% 0.240% 0.362% 0.065% 0.023% 0.126% 0.035% P 78 Motion Pictures $0.00 0 1630 113 31 91 9 3 11 6 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% P 79 Amusement and Recr Services $13,333.32 0.15% 4841 696 110 626 40 9 27 29 0.021% 0.003% 0.019% 0.001% 0.000% 0.001% 0.001% P 80 Health Services $2,473,440.43 27.73% 18800 2320 655 1974 309 11 172 139 3.422% 0.966% 2.912% 0.456% 0.016% 0.254% 0.205% P 81 Legal Services $0.00 0 6593 650 121 578 49 1 51 24 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% P 82 Educational Services $436,160.95 4.89% 4733 308 101 253 46 18 28 26 0.318% 0.104% 0.261% 0.048% 0.019% 0.029% 0.027% P 83 Social Services $1,134,263.50 12.72% 6146 508 69 501 62 7 2 7 1.051% 0.143% 1.037% 0.128% 0.014% 0.004% 0.014% P 87 Eng, Accounting, Research, Mngmt & Related Svcs $2,318,410.01 26.00% 19538 2718 863 2137 282 174 210 193 3.616% 1.148% 2.843% 0.375% 0.232% 0.279% 0.257% P 89 Services, NEC $0.00 0 2040 315 51 281 17 10 12 16 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% P 99 Nonclassifiable Establishments Subtotal $8,918,493.75 100% 14590 8 $0.00 0 14196 146 37 119 10 8 11 11 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 0.000% 1744 3 5166 14427 2150 522 1449 1244 11.41% 3.43% 9.36% 1.38% 0.40% 0.94% 0.71%

Analysis of Essex County Procurement and Contracting: Final Report 169 TYPE SIC Code Utilization Weight Table H-13 Dun & Bradstreet Weighted Availability Measure for PJM2 (Four Digit SIC Codes for Over 17,500) By Industry Total Firms DBE* Firms MBE Firms WBE Firms MWBE Firms C 1423 3 2 1 1 0 0 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% C 1429 6 0 0 0 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% C 1521 $27,426.33 0.00007 6049 277 152 154 29 17 82 20 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% C 1522 789 65 39 34 8 4 20 3 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% C 1541 $54,697,011.01 0.13604 281 25 16 11 2 3 4 2 1.21% 0.77% 0.53% 0.10% 0.15% 0.19% 0.10% C 1542 916 121 70 75 24 16 21 8 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% C 1611 $26,958,273.77 0.06705 457 47 22 30 5 3 11 1 0.69% 0.32% 0.44% 0.07% 0.04% 0.16% 0.01% C 1622 27 6 2 4 0 1 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% C 1623 187 16 10 7 1 0 7 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% C 1629 $231,756,962.76 0.57640 173 14 6 8 0 2 1 4.66% 2.00% 2.67% 0.00% 0.00% 0.67% 0.33% C 1711 $12,258,444.07 0.03049 3359 183 81 117 15 11 38 10 0.17% 0.07% 0.11% 0.01% 0.01% 0.03% 0.01% C 1721 1668 137 72 70 5 4 59 5 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% C 1731 $53,552,933.85 0.13319 2662 171 91 93 13 18 44 10 0.86% 0.46% 0.47% 0.07% 0.09% 0.22% 0.05% C 1741 742 66 43 32 9 3 28 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% C 1742 317 29 15 17 3 3 8 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% C 1761 978 60 27 36 3 1 17 3 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% C 1771 $3,026,672.26 0.00753 390 50 30 25 5 2 20 1 0.10% 0.06% 0.05% 0.01% 0.00% 0.04% 0.00% C 1794 553 37 8 31 2 2 4 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% C 1795 $19,797,752.36 0.04924 104 9 2 7 0 2 0 0.43% 0.09% 0.33% 0.00% 0.00% 0.09% 0.00% C 1799 2087 156 66 111 21 9 38 6 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Subtotal $402,075,476.41 1 21748 1471 753 863 145 96 406 75 8.11% 3.78% 4.59% 0.26% 0.29% 1.41% 0.51% Black Firms Hispanic Firms Asian Firms DBE* Share MBE Share WBE Share MWBE Share Black Share Hispanic Share Asian Share O 0119 15 2 0 2 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 0161 40 2 0 2 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 0219 3 0 0 0 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 0742 375 44 5 40 1 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 0781 1308 70 24 54 8 2 12 4 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 0782 2040 123 58 76 11 2 38 8 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 0783 337 11 1 11 1 0 0 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 0971 9 0 0 0 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 2047 7 1 0 1 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

Analysis of Essex County Procurement and Contracting: Final Report 170 O 2399 34 8 0 8 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 2711 $7,884,892.62 0.11486 240 28 8 21 1 1 4 2 1.34% 0.38% 1.01% 0.05% 0.05% 0.19% 0.10% O 2731 244 35 5 30 1 1 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 2741 422 41 2 40 1 0 0 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 2752 859 118 34 97 13 8 11 3 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 3269 18 8 1 8 1 0 1 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 3363 5 0 0 0 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 3446 92 9 5 4 0 3 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 3563 9 1 0 1 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 3579 17 0 0 0 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 3589 69 7 4 5 2 1 2 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 3599 480 44 21 26 3 2 12 4 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 3663 89 6 3 3 0 0 3 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 3669 37 3 1 2 0 1 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 3823 51 4 2 3 1 2 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 3842 95 7 4 4 1 0 1 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 3844 7 1 0 1 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 3861 50 4 2 2 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 3993 404 66 14 56 4 1 7 2 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 4131 33 3 2 2 1 0 1 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 4212 1907 178 98 95 15 11 64 10 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 4213 776 67 26 50 9 4 13 2 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 4214 222 24 7 18 1 2 3 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 4724 1317 388 129 328 69 7 67 10 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 4812 392 20 10 13 3 1 2 4 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 4813 894 81 35 57 11 7 8 8 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 4931 17 0 0 0 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 4953 392 26 11 19 4 1 7 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 4959 108 7 4 4 1 1 2 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5012 176 11 5 9 3 0 2 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5013 $146,371.21 0.00213 568 42 15 30 3 0 7 5 0.02% 0.01% 0.01% 0.00% 0.00% 0.00% 0.00% O 5021 284 37 21 26 10 3 5 9 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5023 $100,946.74 0.00147 445 66 31 41 6 1 4 13 0.02% 0.01% 0.01% 0.00% 0.00% 0.00% 0.00% O 5031 266 27 7 22 2 3 0 2 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5032 242 29 13 20 4 0 4 4 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5039 $1,557,178.87 0.02268 64 2 1 1 0 1 0 0.07% 0.04% 0.04% 0.00% 0.00% 0.04% 0.00%

Analysis of Essex County Procurement and Contracting: Final Report 171 O 5043 $1,741,437.55 0.02537 46 2 1 2 1 0 0 1 0.11% 0.06% 0.11% 0.06% 0.00% 0.00% 0.06% O 5044 $3,266,956.46 0.04759 184 27 10 19 2 0 2 3 0.70% 0.26% 0.49% 0.05% 0.00% 0.05% 0.08% O 5045 $10,123,797.39 0.14747 558 97 67 52 22 8 8 32 2.56% 1.77% 1.37% 0.58% 0.21% 0.21% 0.85% O 5047 $14,974,077.47 0.21813 481 67 26 49 8 1 4 10 3.04% 1.18% 2.22% 0.36% 0.05% 0.18% 0.45% O 5051 $30,569.20 0.00045 283 33 16 22 5 2 2 6 0.01% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5063 553 70 24 51 5 2 10 9 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5064 $152,482.92 0.00222 136 15 10 8 3 1 1 6 0.02% 0.02% 0.01% 0.00% 0.00% 0.00% 0.01% O 5074 260 22 9 14 1 0 5 3 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5082 $302,188.00 0.00440 170 13 8 9 4 0 3 4 0.03% 0.02% 0.02% 0.01% 0.00% 0.01% 0.01% O 5083 $426,538.28 0.00621 85 3 1 2 0 0 0 0.02% 0.01% 0.01% 0.00% 0.00% 0.00% 0.00% O 5084 988 94 38 64 8 5 6 16 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5087 $622,705.26 0.00907 519 88 33 67 12 5 8 11 0.15% 0.06% 0.12% 0.02% 0.01% 0.01% 0.02% O 5088 102 17 5 12 1 2 2 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5091 $436,204.32 0.00635 180 12 6 9 3 1 0 2 0.04% 0.02% 0.03% 0.01% 0.00% 0.00% 0.01% O 5092 $1,452,999.63 0.02117 140 35 16 24 5 1 0 11 0.53% 0.24% 0.36% 0.08% 0.02% 0.00% 0.17% O 5111 62 8 4 6 2 0 1 2 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5112 322 53 17 45 9 2 5 5 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5113 212 29 6 24 1 1 2 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5122 512 65 32 45 12 3 10 9 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5136 $291,494.84 0.00425 255 42 23 24 5 0 3 12 0.07% 0.04% 0.04% 0.01% 0.00% 0.00% 0.02% O 5137 366 85 35 67 17 0 7 13 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5141 $160,211.05 0.00233 346 59 33 36 10 1 15 7 0.04% 0.02% 0.02% 0.01% 0.00% 0.01% 0.00% O 5162 138 19 10 13 4 1 1 5 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5169 $469,788.38 0.00684 443 47 20 30 3 3 4 6 0.07% 0.03% 0.05% 0.00% 0.00% 0.01% 0.01% O 5191 72 6 2 4 0 0 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5193 147 20 8 12 0 4 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5194 $568,670.72 0.00828 51 3 3 0 0 1 2 0.05% 0.05% 0.00% 0.00% 0.00% 0.02% 0.03% O 5199 1489 215 80 155 20 4 15 39 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5261 310 37 9 30 2 0 4 2 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5411 3894 611 349 371 109 2 151 68 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5499 641 131 39 112 20 2 12 7 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5511 669 33 17 19 3 2 5 5 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5699 $480,510.81 0.00700 890 202 71 169 38 0 20 27 0.16% 0.06% 0.13% 0.03% 0.00% 0.02% 0.02% O 5712 1142 134 47 100 13 4 21 11 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5713 646 63 21 51 9 1 14 2 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5722 296 27 11 20 4 0 8 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

Analysis of Essex County Procurement and Contracting: Final Report 172 O 5731 $819,103.35 0.01193 507 41 28 24 11 1 13 5 0.10% 0.07% 0.06% 0.03% 0.00% 0.03% 0.01% O 5734 733 97 54 57 14 11 7 18 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5812 $21,498,513.57 0.31317 10861 1351 666 858 173 18 203 302 3.90% 1.92% 2.47% 0.50% 0.05% 0.59% 0.87% O 5912 1182 71 41 43 13 1 13 11 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5941 $29,823.72 0.00043 754 68 24 52 8 1 7 8 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5943 212 53 14 46 7 0 2 4 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5961 492 109 23 98 12 1 6 8 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5963 230 50 7 46 3 0 5 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5983 $1,110,894.84 0.01618 218 18 6 14 2 2 2 1 0.13% 0.04% 0.10% 0.01% 0.01% 0.01% 0.01% O 5994 83 17 8 13 4 0 0 2 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5995 456 39 15 31 7 1 9 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% O 5999 2989 467 115 397 45 4 25 48 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Subtotal $68,648,357.20 1 51764 6516 2717 4648 849 154 929 848 13.19% 6.29% 8.71% 1.81% 0.41% 1.38% 2.73% 0 P 6021 715 3 1 2 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 6022 665 6 4 4 2 0 0 2 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 6029 117 0 0 0 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 6036 170 1 1 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 6111 66 12 5 8 1 1 0 2 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 6159 101 3 2 2 1 0 0 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 6321 46 2 0 2 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 6324 27 2 2 1 1 0 1 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 6331 102 0 0 0 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 6351 61 2 1 1 0 0 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 6411 $98,717,226.34 0.32525 2951 290 81 247 38 8 38 6 3.20% 0.89% 2.72% 0.42% 0.09% 0.42% 0.07% P 6512 1539 72 17 60 5 1 10 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 6513 1262 59 15 52 8 1 6 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 6531 $14,628,397.00 0.04820 5727 615 133 535 53 17 49 24 0.52% 0.11% 0.45% 0.04% 0.01% 0.04% 0.02% P 6552 490 24 9 19 4 2 4 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 6799 553 12 2 11 1 2 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7011 638 66 32 44 10 0 3 12 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7032 101 9 0 9 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7213 38 0 0 0 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7231 5726 2068 491 1948 371 7 158 136 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7261 482 48 15 43 10 0 6 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7299 1569 361 41 344 24 6 13 6 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

Analysis of Essex County Procurement and Contracting: Final Report 173 P 7311 857 170 22 155 7 0 11 4 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7338 $966,520.89 0.00318 385 172 14 168 10 3 2 3 0.14% 0.01% 0.14% 0.01% 0.00% 0.00% 0.00% P 7342 $76,506.12 0.00025 466 38 26 20 8 8 10 2 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7349 1686 291 125 218 52 32 65 6 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7353 61 3 2 1 0 1 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7359 560 38 11 30 3 2 4 2 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7361 1061 224 48 209 33 9 12 5 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7363 688 113 25 98 10 9 8 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7371 1815 303 189 183 69 7 13 55 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7373 666 105 69 55 19 12 13 22 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7378 314 41 19 27 5 3 3 9 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7379 2888 572 339 352 119 44 36 95 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7381 423 27 10 19 2 5 3 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7382 $128,660.00 0.00042 232 19 8 12 1 2 3 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7389 11015 1531 319 1368 156 41 82 72 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7515 48 2 0 2 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7519 33 2 0 2 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7521 116 5 2 3 0 2 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7532 $4,497,693.82 0.01482 1105 79 54 36 11 1 28 10 0.11% 0.07% 0.05% 0.01% 0.00% 0.04% 0.01% P 7534 48 4 2 2 0 2 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7538 $593,600.06 0.00196 2357 167 110 72 15 2 64 16 0.01% 0.01% 0.01% 0.00% 0.00% 0.01% 0.00% P 7623 179 13 8 5 1 6 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7629 331 18 14 7 3 0 6 4 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7694 31 4 2 2 0 2 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7699 2777 178 63 138 23 11 30 6 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7812 849 51 14 42 5 3 6 2 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7819 192 15 1 14 0 1 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7922 300 33 2 31 1 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7991 727 130 16 123 9 1 8 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 7999 1404 226 52 186 12 0 9 26 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8011 $68,367,399.31 0.22526 8401 1105 400 882 177 4 111 74 2.96% 1.07% 2.36% 0.47% 0.01% 0.30% 0.20% P 8021 3480 357 120 291 54 2 27 38 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8031 78 10 2 8 0 1 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8043 386 34 10 30 6 0 2 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8049 1740 394 33 385 24 0 6 5 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8051 246 11 2 10 1 0 2 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%

Analysis of Essex County Procurement and Contracting: Final Report 174 P 8059 108 14 2 13 1 0 1 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8062 185 2 0 2 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8069 128 14 0 14 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8071 385 25 9 21 5 0 1 5 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8072 $72,815.60 0.00024 357 52 26 33 7 0 12 10 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8082 284 48 13 46 11 4 0 2 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8093 499 55 7 54 6 0 0 2 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8099 703 49 7 45 3 1 0 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8111 6592 652 123 578 49 1 51 25 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8211 2585 1 0 1 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8221 288 0 0 0 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8222 51 3 3 0 0 2 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8231 $38,194.64 0.00013 406 5 1 4 1 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8243 129 45 18 31 4 7 3 3 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8244 34 3 2 2 1 0 2 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8249 131 21 6 16 1 1 1 2 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8299 1096 232 71 201 40 9 20 20 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8322 $3,713,653.20 0.01224 2742 1 0 1 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8331 190 1 0 1 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8351 2425 505 69 498 62 7 2 7 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8361 272 0 0 0 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8399 427 2 0 2 0 0 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8711 $111,707,799.92 0.36805 1669 211 132 111 32 20 22 38 4.65% 2.91% 2.45% 0.71% 0.44% 0.49% 0.84% P 8712 866 107 55 62 10 6 27 9 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8713 186 10 6 7 3 1 1 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8721 3395 395 111 318 34 9 43 27 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8733 243 33 12 23 2 0 1 5 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8741 1266 106 29 85 8 7 7 5 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8742 5454 905 252 754 101 65 56 53 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8743 535 126 13 120 7 5 4 1 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8744 47 14 5 11 2 1 1 0 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 8748 4666 655 199 521 65 51 45 40 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% P 9999 13446 138 38 110 10 8 12 11 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% Subtotal $303,508,466.90 1 122881 14570 4193 12204 1827 452 1181 920 11.60% 5.09% 8.18% 1.67% 0.56% 1.29% 1.14%

APPENDIX I: UTILIZATION ANALYSIS To best comprehend how many businesses ready, willing, and able to do business with Essex County are actually winning work from the County, we undertook a utilization analysis, which calculated how many firms of different types are working with Essex County. The results of this analysis are outlined in Tables I-1 (I-1 through I-1g) through I-2. The first step in this analysis (Table I-1) was understanding how many contracts were awarded to the private sector and how many were intergovernmental transfers between different levels of city, county, state, and federal government. In Essex County, almost 92 percent of the contract dollars awarded by the 23 agencies and departments studied were awarded to the private sector. This amounted to $1.125 billion. Of the contracts awarded to the private sector, almost 92 percent were awarded to for-profit firms. Of the dollars awarded, 17.52 percent were awarded to firms that qualified as DBEs, WBEs, MBEs, black, Hispanic, or Asians. Over 8 percent of the contracts went to DBEs, while barely one-fifth of 1 percent were awarded to black-owned firms. Tables I-1a through I-1g break down the utilization discussed in Table I-1 by different population groups. For example, among contracts won by DBEs (I-1a), 87.2 percent contracts won were valued at less than $17,500, but 69.3 percent of the dollars awarded to DBEs were in the form of contracts valued at over $1 million. Thus, a large percentage of the dollars awarded to DBEs were concentrated in a few contracts. The last column in I-1a shows that DBEs win less than 5 percent of all contracts awarded to the private sector and less than less than 9 percent of contract dollars awarded. Interestingly, similar percentages emerge in terms of the number of contracts and value of contracts won by women. But, women win an even smaller share of the general contract pool. They win less than 1 percent of all contracts and less than 2 percent of the contract dollars. If one skips over Table I-1c for just a moment and reviews the tables for Hispanics (I-1d), Asians (I-1e), MBEs (I-1f), and SBEs (I-1g), the trends are similar to those seen with DBEs and women. The majority of contracts awarded to each population group are among contracts valued at $17,500 or less, but the large majority of dollars earned are from contracts valued over $1 million. For example, 73.6 percent of contracts awarded to Hispanics and 57.8 percent awarded to Asians are worth less than $17,500, but 69.1 percent of the dollars awarded to Hispanics and 52.4 percent of the dollars awarded to Asians are from contracts worth over $1 million. The numbers for MBEs and SBEs are not very different. However, all these groups win a very small percentage of the total number of contracts and dollars. Hispanics win 0.55 percent of all contracts and 2.95 percent of contract dollars while Asians win 0.25 percent of contracts and 1.34 percent of the dollars. The numbers are slightly higher among MBEs and SBEs. In terms of the number of contracts awarded, MBEs win 2.77 percent of the contracts while SBEs win 3.14 percent. Of the dollars awarded, MBEs win 3.02 percent and SBEs 6.03 percent.

Analysis of Essex County Procurement and Contracting: Final Report 176 To return to blacks: of all the groups outlined in Table I-1, some of the most interesting results emerge in Table I-1c, which profiles utilization in County contracting of black-owned firms. Black firms were not awarded any contracts valued between $250,000 and over $1 million. Thus, blacks will not have the benefit of other groups in which they can earn a lot of money from large contracts. Of all the other groups analyzed, none faced this vacuum. All groups had at least a couple of contracts in all value segments. However, of the three value segments in which blacks did win contracts, they did follow the same patterns as the other groups the majority of the contracts won were for less than $17,500 but the majority of dollars earned were for contracts with the top value. In the case of blacks, though, this top value was between $50,000 and $249,999 not greater than $1 million. The other interesting observation among blacks was, that unlike the other groups in which the percentage of contracts won was smaller than the percentage of dollars won, blacks won a larger share of contracts but the percentage of dollars garnered was lower. To clarify, compare Asians, women, and blacks. Asians won 0.25 percent of all contracts but 1.34 percent of all dollars awarded. Or, look at women, women won 0.65 percent of all contracts but 1.65 percent of the dollars. In contrast with these two groups and all others studied, blacks won 2.38 percent of all contracts but only.22 percent of all dollars. Thus, all groups other than blacks are able to make up for their small share of contracts, in part, by winning a few large contracts. In contrast, blacks have to win a lot more contracts for a smaller number of dollars. Finally, we wanted to know if any of these numbers changed if they looked only at contracts awarded within the defined geographic marketplace instead of all contracts bid out (U-2). The answer is yes and no. In terms of number of contracts awarded, all groups won a larger percentage of contracts within the marketplace (PJM-2). For example, DBEs increased by 54 percent and Hispanics 61 percent. The smallest increase (50 percent) was among WBEs and the largest among Asians (80 percent). In terms of dollars won, the results were not as dramatic, but all groups but one increased their share. DBEs went from 8.35 percent to 8.79 percent of contract dollars while blacks went from 0.22 to 0.23 percent of dollars. Hispanics, though, saw a decrease from 2.94 percent to 2.72 percent. Thus, a range of business enterprises, in general, has a better business experience within the County s geographic marketplace than in the overall marketplace

Analysis of Essex County Procurement and Contracting: Final Report 177 Table I-1. Number of Contracts and Contract Amounts N Contracts Percent for-profit 25,053 $937,821,199.05 91.95% Private Sector not-for-profit 1,003 $82,129,119.48 8.05% Total 26,056 $1,019,950,318.53 for-profit 794 $63,556,352.76 60.49% Intergovernmental Transfers not-for-profit 103 $41,513,123.95 39.51% Total 897 $105,069,476.71 Grand Total 26,953 $1,125,019,795.24 23 Essex County Contracts 2002-04 Non-DBE 24,841 $934,806,636.20 91.65% DBE 1215 $85,143,682.33 8.35% WBE 170 $16,875,286.26 1.65% MBE 723 $30,790,771.79 3.02% Black 620 $2,266,323.72 0.22% Hispanic 144 $29,999,389.58 2.94% Asian 64 $13,661,962.84 1.34% Total 26,056 $1,019,950,318.53

Analysis of Essex County Procurement and Contracting: Final Report 178 Number (A) Table I-1a. Utilization Analysis by Contract Size: DBE All Contracts DBE Prime Contracts DBE Share Dist. Amount (B) Dist. Number (C) Dist. Amount (D) Dist. Number (C/A) Amount (D/B) Over $1 million 155 0.6% $678,550,765.00 66.5% 19 1.6% $59,026,710.31 69.3% 12.26% 8.70% Between $500,000 and $999,999 Between $250,000 and $499,999 Between $50,000 and $249,999 116 0.4% $79,457,456.47 7.8% 13 1.1% $9,483,177.44 11.1% 11.21% 11.93% 223 0.9% $78,494,079.56 7.7% 21 1.7% $7,059,206.32 8.3% 9.42% 8.99% 993 3.8% $109,104,578.00 10.7% 61 5.0% $6,905,551.73 8.1% 6.14% 6.33% Between $17,500 and $49,999 1,143 4.4% $33,350,335.24 3.3% 41 3.4% $1,101,964.86 1.3% 3.59% 3.30% Under $17,500 23,426 89.9% $40,993,104.77 4.0% 1,060 87.2% $1,567,071.67 1.8% 4.52% 3.82% Total 26,056 100.0% $1,019,950,319.04 100.0% 1,215 100.0% $85,143,682.33 100.0% 4.66% 8.35% Source: Essex County Contract Files 2002-2004 Note: All 23 Essex County Agency Contracts

Analysis of Essex County Procurement and Contracting: Final Report 179 Number (A) Table I-1b. Utilization Analysis by Contract Size: Female All Contracts Female Prime Contracts Female Share Dist. Amount (B) Dist. Number (C) Dist. Amount (D) Dist. Number (C/A) Over $1 million 155 0.6% $678,550,765.00 66.5% 4 2.4% $10,261,273.45 60.8% 2.58% 1.51% Between $500,000 and $999,999 116 0.4% $79,457,456.47 7.8% 2 1.2% $1,626,868.89 9.6% 1.72% 2.05% Between $250,000 and $499,999 223 0.9% $78,494,079.56 7.7% 10 5.9% $3,162,580.30 18.7% 4.48% 4.03% Between $50,000 and $249,999 993 3.8% $109,104,578.00 10.7% 12 7.1% $1,309,655.92 7.8% 1.21% 1.20% Between $17,500 and $49,999 1,143 4.4% $33,350,335.24 3.3% 8 4.7% $220,521.50 1.3% 0.70% 0.66% Under $17,500 23,426 89.9% $40,993,104.77 4.0% 134 78.8% $294,386.20 1.7% 0.57% 0.72% Total 26,056 100.0% $1,019,950,319.04 100.0% 170 100.0% $16,875,286.26 100.0% 0.65% 1.65% Source: Essex County Contract Files 2002-2004 Note: All 23 Essex County Agency Contracts Amount (D/B)

Analysis of Essex County Procurement and Contracting: Final Report 180 Number (A) Table I-1c. Utilization Analysis by Contract Size: Black All Contracts Black Prime Contracts Black Share Dist. Amount (B) Dist. Number (C) Dist. Amount (D) Dist. Number (C/A) Amount (D/B) Over $1 million 155 0.6% $678,550,765.00 66.5% - - - - - - Between $500,000 and $999,999 Between $250,000 and $499,999 Between $50,000 and $249,999 116 0.4% $79,457,456.47 7.8% - - - - - - 223 0.9% $78,494,079.56 7.7% - - - - - - 993 3.8% $109,104,578.00 10.7% 10 1.6% $1,237,049.34 54.6% 1.01% 1.13% Between $17,500 and $49,999 1,143 4.4% $33,350,335.24 3.3% 18 2.9% $454,963.47 20.1% 1.57% 1.36% Under $17,500 23,426 89.9% $40,993,104.77 4.0% 592 95.5% $574,310.91 25.3% 2.53% 1.40% Total 26,056 100.0% $1,019,950,319.04 100.0% 620 100.0% $2,266,323.72 100.0% 2.38% 0.22% Source: Essex County Contract Files 2002-2004 Note: All 23 Essex County Agency Contracts

Analysis of Essex County Procurement and Contracting: Final Report 181 Number (A) Table I-1d. Utilization Analysis by Contract Size: Hispanic All Contracts Hispanic Prime Contracts Hispanic Share Dist. Amount (B) Dist. Number (C) Dist. Amount (D) Dist. Number (C/A) Amount (D/B) Over $1 million 155 0.6% $678,550,765.00 66.5% 9 6.3% $20,719,468.71 69.1% 5.81% 3.05% Between $500,000 and $999,999 Between $250,000 and $499,999 Between $50,000 and $249,999 116 0.4% $79,457,456.47 7.8% 7 4.9% $5,251,816.55 17.5% 6.03% 6.61% 223 0.9% $78,494,079.56 7.7% 7 4.9% $2,471,881.63 8.2% 3.14% 3.15% 993 3.8% $109,104,578.00 10.7% 8 5.6% $1,124,824.93 3.7% 0.81% 1.03% Between $17,500 and $49,999 1,143 4.4% $33,350,335.24 3.3% 7 4.9% $181,029.70 0.6% 0.61% 0.54% Under $17,500 23,426 89.9% $40,993,104.77 4.0% 106 73.6% $250,368.06 0.8% 0.45% 0.61% Total 26,056 100.0% $1,019,950,319.04 100.0% 144 100.0% $29,999,389.58 100.0% 0.55% 2.94% Source: Essex County Contract Files 2002-2004 Note: All 23 Essex County Agency Contracts

Analysis of Essex County Procurement and Contracting: Final Report 182 Number (A) Table I-1e. Utilization Analysis by Contract Size: Asian All Contracts Asian Prime Contracts Asian Share Dist. Amount (B) Dist. Number (C) Dist. Amount (D) Dist. Number (C/A) Amount (D/B) Over $1 million 155 0.6% $678,550,765.00 66.5% 4 6.3% $7,157,487.90 52.4% 2.58% 1.05% Between $500,000 and $999,999 Between $250,000 and $499,999 Between $50,000 and $249,999 116 0.4% $79,457,456.47 7.8% 5 7.8% $3,140,370.25 23.0% 4.31% 3.95% 223 0.9% $78,494,079.56 7.7% 5 7.8% $1,904,222.68 13.9% 2.24% 2.43% 993 3.8% $109,104,578.00 10.7% 11 17.2% $1,293,538.36 9.5% 1.11% 1.19% Between $17,500 and $49,999 1,143 4.4% $33,350,335.24 3.3% 2 3.1% $58,687.39 0.4% 0.17% 0.18% Under $17,500 23,426 89.9% $40,993,104.77 4.0% 37 57.8% $107,656.26 0.8% 0.16% 0.26% Total 26,056 100.0% $1,019,950,319.04 100.0% 64 100.0% $13,661,962.84 100.0% 0.25% 1.34% Source: Essex County Contract Files 2002-2004 Note: All 23 Essex County Agency Contracts

Analysis of Essex County Procurement and Contracting: Final Report 183 Number (A) Table I-1f. Utilization Analysis by Contract Size: MBE All Contracts MBE Prime Contracts MBE Share Dist. Amount (B) Dist. Number (C) Dist. Amount (D) Dist. Number (C/A) Amount (D/B) Over $1 million 155 0.6% $678,550,765.00 66.5% 8 1.1% $18,023,799.81 58.5% 5.16% 2.66% Between $500,000 and $999,999 Between $250,000 and $499,999 Between $50,000 and $249,999 116 0.4% $79,457,456.47 7.8% 6 0.8% $4,361,168.26 14.2% 5.17% 5.49% 223 0.9% $78,494,079.56 7.7% 11 1.5% $4,075,092.21 13.2% 4.93% 5.19% 993 3.8% $109,104,578.00 10.7% 24 3.3% $3,040,593.23 9.9% 2.42% 2.79% Between $17,500 and $49,999 1,143 4.4% $33,350,335.24 3.3% 20 2.8% $548,167.89 1.8% 1.75% 1.64% Under $17,500 23,426 89.9% $40,993,104.77 4.0% 654 90.5% $741,950.39 2.4% 2.79% 1.81% Total 26,056 100.0% $1,019,950,319.04 100.0% 723 100.0% $30,790,771.79 100.0% 2.77% 3.02% Source: Essex County Contract Files 2002-2004 Note: All 23 Essex County Agency Contracts

Analysis of Essex County Procurement and Contracting: Final Report 184 Number (A) Table I-1g. Utilization Analysis by Contract Size: SBE All Contracts SBE Prime Contracts SBE Share Dist. Amount (B) Dist. Number (C) Dist. Amount (D) Dist. Number (C/A) Amount (D/B) Over $1 million 155 0.6% $678,550,765.00 66.5% 17 2.1% $44,979,810.15 73.1% 10.97% 6.63% Between $500,000 and $999,999 Between $250,000 and $499,999 Between $50,000 and $249,999 116 0.4% $79,457,456.47 7.8% 9 1.1% $6,913,421.69 11.2% 7.76% 8.70% 223 0.9% $78,494,079.56 7.7% 16 2.0% $5,505,769.29 9.0% 7.17% 7.01% 993 3.8% $109,104,578.00 10.7% 24 2.9% $2,409,743.93 3.9% 2.42% 2.21% Between $17,500 and $49,999 1,143 4.4% $33,350,335.24 3.3% 20 2.4% $589,196.93 1.0% 1.75% 1.77% Under $17,500 23,426 89.9% $40,993,104.77 4.0% 731 89.5% $1,100,942.79 1.8% 3.12% 2.69% Total 26,056 100.0% $1,019,950,319.04 100.0% 817 100.0% $61,498,884.78 100.0% 3.14% 6.03% Source: Essex County Contract Files 2002-2004 Note: All 23 Essex County Agency Contracts

Analysis of Essex County Procurement and Contracting: Final Report 185 DBE MBE WBE Black Hispanic Asian Total Contra cts 1,215 723 170 620 144 64 26,056 For-profits vs. Not-for-profits Contra cts for-profits 25,053 not-forprofits 1,003 Total 26,056 Essex County 23 Agency Contract Files 2002-04 Table I-2. Utilization Analysis All Contracts PJM-2 Percent Amount Percent Contracts Percent Amount Percent 4.66% $85,143,682.33 8.35% DBE 943 7.19% $78,416,873.94 8.79% 2.77% $30,790,771.79 3.02% MBE 571 4.35% $30,477,079.51 3.42% 0.65% $16,875,286.26 1.65% WBE 128 0.98% $16,530,014.28 1.85% 2.38% $2,266,323.72 0.22% Black 479 3.65% $2,046,851.94 0.23% 0.55% $29,999,389.58 2.94% Hispanic 117 0.89% $24,247,956.40 2.72% 0.25% $13,661,962.84 1.34% Asian 59 0.45% $13,656,347.84 1.53% $1,019,950,318.53 Total 13,116 $891,892,666.13 Percent Amount Percent Contracts Percent Amount Percent 96.15% $937,821,199.05 91.95% for-profits 12,416 94.66% $811,538,608.60 90.99% 3.85% $82,129,119.48 8.05% not-forprofits 700 5.34% $80,354,057.53 9.01% $1,019,950,318.53 Total 13,116 $891,892,666.13

APPENDIX J: PASSIVE DISCRIMINATION I. Employment A. Introduction In this section, we estimate Essex County s racial, ethnic, and gender differences in employmentpopulation ratios, unemployment, wages, and self-employment. We believe identifying these differences is important because significant barriers in employment and earnings make it more difficult to save and secure the necessary capital to become self-employed, which is one direct avenue to DBE status. We use micro data from the 1 percent sample from the 2000 U.S. Census to demonstrate that Essex County women and minorities have higher unemployment rates, lower employmentpopulation ratios, and lower wages than comparable white men in Essex County. We also show that Essex County women and minorities have lower self-employment rates. These labor market disadvantages are worse for women and minorities in Essex County than for women and minorities in the rest of New Jersey. Most of the lower employment, higher unemployment, lower wages, and lower self-employment among the County s minorities and women cannot be explained by racial and gender differences in characteristics such as educational attainment and labor market experience. Further, among employed Essex County residents, women and minorities are less likely to be employed in construction and professional services sectors than are white males. These sectors are of particular importance because, from 2002 to 2004, they constituted over $800 million in contracts awarded by Essex County. Employment in these sectors also serves as a pipeline to becoming self-employed. B. Basic Results We use micro data from the New Jersey portion of 2000 U.S. Census. Our Essex County sample consists of 18,401 men and women, who are between 25 and 65 years of age and who are not enrolled in school. African Americans, Hispanics, and Asians comprise 35.5, 15.2, and 4.4 percent of the sample respectively. Our state level sample is limited to 215,656 men and women. African Americans, Hispanics, and Asians comprise 11.1, 12.1, and 5.9 percent of the state s population. We compare the employment-population ratio, unemployment rate, and hourly wages of Essex County s minorities and women to Essex County s white men. 24 We also compare these labor 24 We construct dummy variables for self-employment, employment, unemployment, and the logarithm of hourly wages. Self-employment equals 1 if the individual identified themselves as self employed in either a unincorporated or incorporated business, professional practice, or farm, and 0 if they are otherwise employed. The employment-population ratio is defined as the ratio of the number of employed to the sum of the number looking for work, the number working, the number with a job but not working, and all those who are out of the labor

Analysis of Essex County Procurement and Contracting: Final Report 187 market outcomes of Essex County s minorities and women to the outcomes of minorities and women in the rest of New Jersey. To identify the portion of the observed gaps in employment, unemployment, wages, and self-employment that can be explained by differences in educational attainment and other factors, we estimate regression models that control for racial and gender differences in educational attainment, potential experience, marital status, immigration status, English-language ability, veteran status, disability status, and industry characteristics of the groups. Table J-1 presents summary statistics for the Essex County and New Jersey samples. In Essex County, white men typically have higher employment population ratios, lower unemployment rates, higher wages, and higher self-employment rates than do most other Essex County demographic groups. In many cases, the Essex County gaps exceed the statewide employment, unemployment, wage gaps, and self-employment rates. More specifically, in Essex County, white and Asian males have the highest employmentpopulation ratios, exceeding 80 percent. Black and Hispanic males have employment-population ratios that are 23.6 to 20.4 points lower than white males. Women s employment-population ratios are lower than white men s employment-population ratios in Essex County. Just over twothirds of the county s white and Asian female civilian population is employed. Hispanic and black women have employment-population ratios that are 21.6 and 34.2 points below that of white men. The black and Hispanic gaps in Essex County exceed their comparable state-level gaps. The racial and ethnic gaps in unemployment are a major contributor to the lower employmentpopulation ratios of minorities. 25 Table J-1 demonstrates that Essex County whites and Asians have unemployment rates that range from 2.1 to 3.6 percent. Those groups with notably higher unemployment rates than whites and Asians are blacks and Hispanics. Both have double the white unemployment rates. Further, the black and Hispanic disadvantages in Essex County are larger than the state-level disadvantages. For example, Essex County black males have an unemployment rate of 12.3 percent as compared to 9.3 percent for blacks at the state-level. The average log hourly wages reported in Table J-1 indicate that Essex County white males have higher wages than all other demographic groups in Essex County. The sizes of the Essex County wage gaps are similar to the state level gaps, with two key exceptions. The earnings of Essex County Asian and white females relative to white males are greater than at the state level. For instance, Asian females in Essex County earn nearly 80 percent of what white males earn in Essex County, whereas at the state level Asian females earn only 66 percent of white males. force. The unemployment rate is the ratio of the number of unemployed to the sum of the number looking for work and the number working. The logarithm of real hourly earnings is constructed from the respondent s pay status. If the respondent reported that they are paid on an hourly basis, we took the logarithm of their hourly wage. If the respondent reported that they are paid on a weekly basis, we took the logarithm of the ratio of their usual weekly earnings and usual hours worked per week. 25 The employment-population ratio equals the product of one minus the unemployment rate and the labor force participation ratio.

Analysis of Essex County Procurement and Contracting: Final Report 188 Additionally, in Essex County, the black-white male gap is 59 percent, compared to a 62 percent gap at the state-level. The Essex County self-employment rate of white males is 7.7 points higher than the selfemployment rate of white females. The self-employment rate of white males is 11.3 points higher than black males and 12.9 points higher than black females. Hispanics also have selfemployment rates that are considerably lower than the self-employment rates of whites. Although not as large as blacks and Hispanics, the standard errors of Asian men and women are 4.4 points and 9.0 points less than white men s self-employment rates. All of the Essex County self-employment gaps exceed their comparable state-level gaps, particularly for black men, black women, and Hispanic women. These outcomes demonstrate that racial, ethnic, and gender inequality are key features of the Essex County labor market. What role do racial and gender differences in educational attainment, potential experience, marital status, language skills, veteran status, immigrant and disability status, and industry of employment play in generating the gaps shown in Table J-1? To help answer this question, Table J-1 reports summary statistics by race and gender for these variables. Differences in educational attainment are a very important contributor to the observed gaps. The percentage of black men with at least a B.A. degree is 13.6 percent. Only 10 to 13 percent of Hispanics have at least a B.A. degree. The percentage of white men in Essex County with at least a B.A. degree is 49 percent. For Asians, over 60 percent have completed at least a B.A. degree. This higher educational achievement among Asians relative to whites offsets any discrimination that these groups may face. Racial and ethnic differences in industry of employment, marital status, and immigrant status will explain some of the observed differences in employment, unemployment, wages, and self-employment. C. Results To measure the contributions of racial, ethnic, and gender differences in educational attainment, language skills, immigrant status, and industry of employment to the observed differences in employment, unemployment, hourly wages, and self-employment, we first regress each outcome only on race and gender differences to obtain the raw or unadjusted gaps. We then add to this regression variables that capture the following factors: education, English-language ability, immigration status, industry, age, marital status, veteran status, and disability status. The coefficients on the racial and gender dummy variables measure the unexplained gaps. They are interpreted as measuring discrimination and/or omitted variables that are correlated both with race/gender and the labor market outcomes. Panel A of Table J-2 reports the regression-adjusted gaps in employment-population ratios between white males and other groups. Even after adding our observable characteristics to the regressions, blacks and Hispanics still have lower employment-population ratios than whites. Large portions of the racial and ethnic differences remain unexplained. For example, in Essex County, more than half of the gap between white and black males cannot be explained by differences in observable characteristics. Discrimination in the labor market may be contributing to the remaining gap. Similarly, about 48 percent of the gap between white and Hispanic males cannot be explained.

Analysis of Essex County Procurement and Contracting: Final Report 189 Controlling for differences in personal characteristics actually increases the gap in employmentpopulation ratios between white and Asian males. As noted in Table J-1, Asian males have higher education levels than white males in Essex County. Thus, virtually all of the 4.7 point adjusted gap may be attributed to some form of discrimination. For white women, even after adding controls for personal characteristics, their employment-population ratios remain 9.7 points lower than white men. For black and Hispanic women, the gaps are 11.4 and 13.4 points. For Asian females, the unexplained gap falls between black and Hispanic women. The lower employment-population ratios of minorities and women are partially due to their higher unemployment rates than white men. Most of the higher relative gaps cannot be explained by racial, ethnic, and gender differences in personal characteristics. The gap in unemployment rates between the Hispanic and white men are the exception. The gap can be fully explained by personal characteristics. More specifically, Panel B of Table J-2 reports that the Essex County black-white gap in unemployment rates narrows slightly from 8.7 to 5.7 percent after controlling for differences in human capital and other characteristics. This narrowing means that nearly two-thirds of the gap is not explained by differences in education and experience. Similarly, 70 percent of the black female-white male gap remains. The state results are similar to those for Essex County. Additionally, the unemployment rate gap between Hispanic females and white males shrinks after adding controls, but remains 5 percentage points. Interestingly, the gap between Hispanic and white males can be fully explained. The coefficients displayed in Table J-2C report the unexplained portion of the wage gap with white males. Moreover, for blacks and Hispanics of both genders the unexplained portion of the wage gap relative to white males is consistently the largest in Essex County. For instance, 31 percent of the black male vs. white male wage gap in Essex (59 percent differential) remains unexplained after controlling for human capital and other factors. In contrast, at the state level, only 19 percent of the black-white male gap remains unexplained. Similarly, Essex County Hispanic females earn just 41 percent of the average white male, and 53 percent of this gap remains unexplained after accounting for human capital and other differences. Meanwhile, at the state level just 42 percent of the gap remains unexplained. The greater unexplained gaps in Essex County suggest that factors contributing to the unexplained gap, such as discrimination, are larger in Essex County than the rest of New Jersey. As with the employment-population ratio and unemployment rate, three general trends appear in the wage gaps. First, all race and gender groups earn wages lower than white males across all counties in New Jersey. Second, much of the wage gap relative to white males cannot be explained by human capital and other factors. Third, the unexplained portion of the wage gap is largest in Essex County, suggesting that structural factors that contribute to the gap are greater in Essex County than elsewhere in the state.

Analysis of Essex County Procurement and Contracting: Final Report 190 A large number of Essex County contracts are awarded to construction and professional services firms. Employment in these sectors is also a direct pipeline to self-employment. As such, we examine how the probability of employment in these industries varies by race and gender. We estimate a probit model that predicts the probability that the i th individual works in the construction industry. As in earlier models, we initially included only race and gender dummy variables, but then add our measures of personal characteristics: educational attainment, potential experience, marital status, immigration status, English-language ability, veteran status, and disability status. We estimate a similar model but predict whether an individual is employed in professional services. Table J-3 reports the results. Three general trends emerge from the construction model. First, in Essex County, all racialgender groups, with the exception of black females, have a lower likelihood of being employed in construction than do white males. Specifically, white females, black males, and Asian males have a 2 to 8 point lower probability of employment in the construction sector. Second, very little of the disadvantage can be explained by characteristics. Third, the disadvantage experienced by these groups in Essex County is slightly smaller than the disadvantage estimated at the state level. Similar patterns exist in the professional services probit models. All racial-gender groups, with the exception of Asian males and black females, have a lower likelihood of being employed in professional services than do white males. Black males and Hispanic males have the largest disadvantage, with both groups having a 4 point lower probability even after controlling for differences in education, potential experience, etc. The lower probabilities are larger in Essex County than that at the state level. Table J-4 reports estimated racial and gender self-employment gaps. The benchmark group in all cases is white males. The first row provides estimates of the gaps for Essex County. The second row presents estimates of the gaps in New Jersey and the remaining rows provide estimates of the gap in New Jersey s other counties. Three main conclusions flow from the self-employment analysis. First, white males are more likely to be self-employed than are other demographic groups. Second, the gaps in self-employment rates between white males and other groups cannot be explained by observable differences in education and potential experience. Third, the Essex County gender and racial gaps exceed the self-employment gaps in many other New Jersey counties. The estimates suggest that discrimination may play a role in self-employment outcomes. For example, the gap between white and black males in Essex County starts at 11.3 percent and falls to 8.5 percent when our controls are added. Therefore, 75 percent of the difference cannot be explained by our observable measures. The unexplained gap between white males and other groups is greater in Essex County than in the rest of New Jersey for the following groups: white females, black males, black females, Asian females, and Hispanic females. The self-employment gap between white males and Asians increased after controlling for our measures of observable characteristics. For Asian males, the unadjusted gap with white males is 4.4 percent. After controlling for individual characteristics, the difference between white males and Asian males increased to 5.2 percent. The result is apparently caused by the fact that Essex

Analysis of Essex County Procurement and Contracting: Final Report 191 County Asians have higher educational attainments than whites. The higher levels offset the disadvantage that discrimination and other barriers to self-employment that Asians may face. D. Summary This section documents Essex County s racial and gender differences in employment-population ratios, unemployment rates, wages, and self-employment. Comparisons are also made to the rest of the state and New Jersey s 20 counties. Using data from the 2000 U.S. Census, we find that generally blacks, Hispanics, and white women tend to have worse labor market outcomes than white males. Further, a significant portion of the differences between minority group outcomes and white-male outcomes cannot be explained by age, different levels of human capital, marital status, language spoken, veteran status, disability, immigrant status, and industry of employment. Labor market discrimination in the labor market probably contributes to the unexplained portion of self-employment, employment, unemployment, and wage gaps. Regardless of discrimination s existence and persistence, women and minorities lower employment-population ratios, higher unemployment rates, and lower wages serve as key barriers to self-employment and the ability to compete for Essex County contracts. II. Size and Capacity of Business Before conducting the availability/utilization analysis, we looked at the minority and female populations in Essex County as compared to New Jersey and the United States as a whole. We sought to understand how these populations were reflected in business ownership and capacity. Tables J-6 through J-13 illustrate our findings. The percentage of the Essex County population identified as minority is significantly higher in Essex County than in New Jersey or the U.S. Essex County has a minority population of 60.5 percent as compared to 32.8 percent in New Jersey and 29.3 percent in the U.S. Of the four main minority groups, blacks in Essex County constitute the largest segment of this group with 41.2 percent of the county s population or three times the percentage of blacks in New Jersey or the U.S. In addition, Essex County has a slightly higher percentage female population than New Jersey or the U.S. Tables J-7 through J-9 illustrate that minority-owned firms in Essex County constitute a greater percentage of businesses in the county than do minority firms in New Jersey or the U.S. Minority firms comprise 22.52 percent of all firms in Essex County as compared to 15.64 percent and 14.6 percent of firms in New Jersey and the U.S., respectively. However, despite the larger percentage of minority-owned firms in Essex County, these firms comprise a lower percentage of sales than do their counterparts at the state and national levels. In Essex County, minority firms constitute 2.93 percent of total sales. In contrast, minority firms generate 3.5 percent of sales in New Jersey and 3.19 percent of firms in the U.S.. For women-owned firms, the numbers are slightly different. Women constitute a smaller presence in Essex County than in New Jersey or the U.S., but they also generate a smaller percentage of sales. In Essex County, women-owned firms constitute 21.53 percent of firms and

Analysis of Essex County Procurement and Contracting: Final Report 192 3.19 percent of sales as compared to 23.74 percent of firms and 4.35 percent of sales in New Jersey and 26.02 percent of firms and 4.41 percent of sales in the U.S. To better understand the gaps between the percentage of firms and percentage of sales they generate, we developed disproportionality ratios to compare percent sales to percent firms (sales/firms). If the percent of sales generated were equal to the percent of firms owned by a given group in the marketplace the ratio would be 1. If the percentage of sales is lower than the percentage of firms the ratio is less than one and if the sales are higher than the percentage of firms it is greater than 1. Table J-10 shows that the ratio for all minority-owned firms in Essex County is.1301 as compared to.2237 in New Jersey and.2183 in the U.S. Among individual minority groups, blacks have the lowest ratio in all geographic regions, but it is lowest in Essex County. In Essex, the ratio is.0696, in New Jersey.0773, and in the U.S..0971. In Essex County, this means that although black-owned firms constitute 12.29 percent of all firms they only generate 0.86 percent of sales. Although the percent of sales generated is higher in Essex County than New Jersey or the U.S., the larger percentage of firms in Essex County means that the disproportionality ratio is lower than at the state and national level. For women, similar ratios emerge as compared to the combined percentage of minority firms. Interestingly, when only firms with paid employees are considered (Table J-11), the ratio improves considerably for black-owned firms. To better understand how minority business in Essex County compared to other communities nationwide, we collected data on all U.S. counties that were identified as having at least one minority and/or woman-owned business. The results of our survey are found in Table J-12. We looked at business capacity among minority and woman-owned firms in these counties nationwide to compare them to business behavior in Essex County. We took the disproportionality ratios from the identified counties and applied Essex County characteristics such as population, unemployment, education, etc. to determine how Essex County M/WBEs should be performing if they matched national patterns. The results of this analysis can be found in table J-13. In that table, one can see that the ratio would 35 percent higher for all minority businesses and close to 20 percent higher for black-owned businesses. The ratio was basically unchanged among women. Therefore, these tables illustrate that minority and woman-owned businesses in Essex County are under performing their presence in the marketplace. III. Credit Markets Access to credit markets is essential to founding and maintaining businesses. In particular, refinancing loans are an important source of capital. Therefore, we conducted a number of analyses to understand how loan denials compare across population groups and in turn affect the

Analysis of Essex County Procurement and Contracting: Final Report 193 ability of different groups to open and grow their own businesses. The results of these analyses are demonstrated in tables J-14 through J-19. Table J-14 shows that denial rates are significantly higher among American Indians, blacks, and Hispanics than whites. Asians had only a slightly higher denial rate than whites. These rates were true among all loans and when only refinancing loans were considered. Once we established loan denial rates, we wanted to understand the reasons given for loan denials (Table J-15). Credit history emerged as the main reason for denial among all groups, but particularly among blacks. More than one third of all black loans are denied due to credit history. The next step in understanding loan denials is considering how much of the difference in denial rates across racial groups can be explained by factors such as credit history and how much, if any, is unexplained by identifiable factors. First, looking at refinancing loans only, we ran a regression to identify loans in which bad credit was given as the first, second, or third reason a loan was denied (Table J-16). Of 26,822 loans evaluated, 8727 were identified as having bad credit denials. We, then, took those loans and determined how much of an impact bad credit had on the denials. In these regressions we controlled for amount of loan, type of borrower, type of lender, and the ratio between loan amount and applicant income. We determined that although bad credit is a significant factor, particularly among blacks, there is a gap between denial rates for whites and other groups that cannot be explained. In other words, blacks and other minority groups are more likely to be denied for loans than whites and the evidence does not explain why. We also examined the behavior of financial institutions that accept loan applications from Essex County businesses to determine if differences in denial emerged among institutions. Table J-17 illustrates the overall loan acceptances and denials by race and gender. Some interesting numbers emerge, such as Ameriquest denies 71 percent of all applications, but their denial rate is higher for whites than for blacks or Hispanics. Or, PNC denies more than a third of all applications but their denial rates of American Indians, blacks, and Hispanics are more than twice that of white denials. However, this raw data does not confirm any conclusions. Therefore, we ran disproportionality ratios in denial rates between whites and blacks (blacks/whites) and whites and Hispanics (Hispanic/white) as well as between women and men (women/men). A ratio of 1 would mean loan applications by the two groups being compared are equal. A ratio of less than 1 means whites or men are denied at higher rates than the group to which they are being compared and if the ratio is greater than 1, whites or men are denied at lower rates. Table J-18 outlines these ratios for all institutions including the aforementioned Ameriquest and PNC. Ameriquest has a 0.92 black/white ratio meaning the black denial rate is 92 percent that of the white denial rate at this institution. Meanwhile, PNC has a black/white rate of 2.76 meaning that blacks are denied at almost 2.8 times a greater rate than whites.

Analysis of Essex County Procurement and Contracting: Final Report 194 To understand what portion of the unexplained gap in loan denial could be attributed to discrimination, we took the regressions outlined in Table J-16 and included them in residual difference analysis we conducted on all financial institutions. A negative value derived from these regressions indicates the possibility of discrimination and a positive value indicates an absence of discrimination. However, before concluding that discrimination has had a measurable effect on denial rates, we had to consider if the results are statistically significant and this varies from institution to institution. A result is statistically significant if the mean is less than 2 times the standard deviation. So, what do these findings tell us about access to credit markets and by extension the presence of passive discrimination in the marketplace? Minority populations, and particularly blacks, do find less access to credit markets as illustrated by their overall denial rates and some of this access is the result of discrimination in the market.

Analysis of Essex County Procurement and Contracting: Final Report 195 Table J-1. Summary Statistics for New Jersey and Essex County Panel A: Essex County Male Female Outcome Variables White Black Asian Hispanic White Black Asian Hispanic Self-Employed 16.2% 4.9% 11.8% 8.8% 8.5% 3.3% 7.2% 4.5% Employment-Population Ratio 83.8% 60.2% 82.5% 63.4% 67.2% 61.2% 66.7% 49.6% Unemployment Rate 3.6% 12.3% 2.9% 8.1% 3.5% 9.6% 2.1% 12.1% Logarithm of Hourly Wages 3.28 2.76 3.09 2.57 2.94 2.69 3.07 2.41 Predictor Variables Potential Experience 30.2 30.2 27.5 29.4 30.7 30.6 27.2 30.5 High School Dropout 12% 26% 7% 46% 11% 23% 9% 42% High School Graduate 20.3% 35.0% 11.5% 29.5% 24.1% 32.6% 12.0% 28.4% Some College 18.2% 25.1% 13.8% 14.7% 18.9% 28.7% 14.8% 18.2% College Graduate 25.8% 9.4% 33.6% 6.0% 28.2% 10.7% 42.3% 7.2% Advanced Degree 23.9% 4.2% 34.1% 3.7% 18.1% 4.8% 21.8% 3.8% Never Married 20.4% 35.7% 13.0% 28.2% 15.1% 37.3% 12.4% 20.4% Married 70.4 45.6 84.0 58.4 69.2 33.6 79.9 53.8 Separated/Divorced 8.3 16.4 1.6 12.2 12.3 21.4 5.0 20.5 Speak a Second Language 5.1% 1.6% 9.3% 30.1% 5.2% 2.1% 13.2% 32.7% Veteran 15.8% 16.7% 2.0% 5.9% 0.5% 1.7% 0.5% 0.7% Disability 16.3% 32.5% 17.8% 34.8% 13.0% 30.8% 18.2% 31.9% Immigrant 17.0% 20.8% 91.7% 53.7% 16.3% 18.3% 93.5% 46.5% Notes: The data come from the 2000 U.S. Census for New Jersey. The samples are comprised of men and women who are between 25 and 65 years of age and who are not currently enrolled in school. There are 18,401 Essex County residents of whom 35.5 percent are African American, 15.2 percent Hispanic, and 4.4 percent Asian. Self-employed is defined as 1 if an individual identified him/herself as selfemployed in either an unincorporated or incorporated business, professional practice, or farm, 0 for those who did not identify as selfemployed. Employment-Population Ratio is defined as 1 if the individual is employed and 0 if the individual is unemployed or out of the labor force. The unemployment rate is defined as 1 if the individual is unemployed and 0 if the individual is employed. An individual s log hourly wage is defined as follows: if the respondent reported that they are paid on an hourly basis, we took the logarithm of their hourly wage. If the respondent reported that they are paid on a weekly basis, we took the logarithm of the ratio of their usual weekly earnings and usual hours worked per week.

Analysis of Essex County Procurement and Contracting: Final Report 196 Table J-1 cont. Summary Statistics for New Jersey and Essex County Panel B: New Jersey Men Women Outcome Variables White Black Asian Hispanic White Black Asian Hispanic Self-Employed 14.4% 5.3% 11.9% 7.3% 6.8% 2.6% 7.5% 4.7% Employment-Population Ratio 84.9% 63.8% 84.3% 68.3% 68.8% 65.3% 62.0% 55.0% Unemployment Rate 3.2% 9.3% 3.4% 6.5% 3.2% 7.9% 4.0% 10.1% Log of Hourly Wage 3.14 2.81 3.10 2.61 2.81 2.70 2.87 2.44 Predictor Variables: Potential Experience 30.3 29.8 26.5 28.9 30.5 30.2 26.8 29.5 High School Dropout 9% 24% 8% 42% 8% 20% 11% 38% High School Graduate 28.7% 35.0% 9.7% 28.3% 31.5% 33.0% 13.7% 28.5% Some College 24.1% 25.4% 13.7% 18.3% 26.4% 29.4% 14.9% 20.9% College Graduate 23.2% 10.6% 34.4% 6.8% 23.0% 12.1% 40.0% 8.6% Advanced Degree 14.8% 5.1% 34.0% 4.1% 11.4% 5.3% 20.0% 4.3% Never Married 18.1% 31.6% 13.8% 24.7% 13.2% 33.2% 9.6% 18.1% Married 71.2 51.2 83.1 61.7 69.4 38.8 82.1 58.2 Separated/Divorced 9.8 15.3 2.6 12.8 13.6 21.5 5.3 20.2 Speak a Second Language 1.4% 1.0% 10.5% 31.0% 1.5% 1.3% 17.8% 34.1% Veteran 19.9% 19.6% 2.5% 7.0% 0.8% 1.9% 0.2% 0.5% Disability 15.2% 28.0% 16.9% 28.8% 13.7% 27.6% 14.4% 26.7% Immigrant 9.2% 15.2% 94.3% 60.9% 8.8% 14.4% 94.4% 57.2% Notes: The data come from the 2000 U.S. Census for New Jersey. The samples are comprised of men and women who are between 25 and 65 years of age and who are not currently enrolled in school. There are 18,401 Essex County residents of whom 35.5 percent are African American, 15.2 percent Hispanic, and 4.4 percent Asian. Self-employed is defined as 1 if an individual identified him/herself as self-employed in either an unincorporated or incorporated business, professional practice, or farm, 0 for those who did not identify as self-employed. Employment-Population Ratio is defined as 1 if the individual is employed and 0 if the individual is unemployed or out of the labor force. The unemployment rate is defined as 1 if the individual is unemployed and 0 if the individual is employed. An individual s log hourly wage is defined as follows. If the respondent reported that they are paid on an hourly basis, we took the logarithm of their hourly wage. If the respondent reported that they are paid on a weekly basis, we took the logarithm of the ratio of their usual weekly earnings and usual hours worked per week.

Analysis of Essex County Procurement and Contracting: Final Report 197 Table J-2. Differences in Economic Outcomes Relative to White Males by Geographic Area Panel A: Employment-Population Ratio Males Females Geographic Area Black Asian Hispanic White Black Asian Hispanic Essex -0.125 a -0.044 b -0.097 a -0.097 a -0.114 a -0.113 a -0.134 a New Jersey -0.122 a -0.028 a -0.092 a -0.085 a -0.105 a -0.107 a -0.14 a Atlantic -0.086 a -0.015-0.172 a -0.072 a -0.093 a -0.072 b -0.113 a Bergen -0.055 a -0.024 b -0.048 a -0.089 a -0.075 a -0.094 a -0.108 a Burlington -0.127 a -0.031-0.108 a -0.08 a -0.094 a -0.072 a -0.152 a Camden -0.128 a -0.044 c -0.101 a -0.075 a -0.141 a -0.091 a -0.187 a Cape May -0.073-0.409 b -0.083-0.059 a -0.052-0.21-0.173 a Cumberland -0.274 a -0.013-0.123 a -0.022-0.044 c -0.031-0.073 b Hudson -0.139 a -0.036 b -0.065 a -0.042 a -0.109 a -0.083 a -0.13 a Hunterdon -0.51 a -0.006-0.115 b -0.101 a -0.594 a -0.151 a -0.275 a Mercer -0.147 a -0.043-0.107 a -0.064 a -0.088 a -0.135 a -0.128 a Middlesex -0.076 a -0.002-0.072 a -0.072 a -0.05 a -0.101 a -0.129 a Monmouth -0.109 a 0.055 a -0.066 a -0.091 a -0.108 a -0.072 a -0.176 a Morris -0.05 c -0.049 a -0.091 a -0.102 a -0.126 a -0.094 a -0.138 a Ocean -0.073 a 0.046-0.049 b -0.089 a -0.091 a -0.075 c -0.152 a Passaic -0.168 a -0.043-0.109 a -0.101 a -0.145 a -0.12 a -0.168 a Somerset -0.056 a -0.011-0.049 b -0.089 a -0.076 a -0.11 a -0.103 a Sussex 0.03 0.001-0.052-0.1 a -0.109-0.035-0.203 a Union -0.077 a -0.069 a -0.097 a -0.096 a -0.109 a -0.119 a -0.135 a Warren -0.031 0.044-0.014-0.076 a -0.188 a -0.233 a -0.01 Gloucester & Salem -0.062 a 0.013-0.129 a -0.061 a -0.072 a -0.157 a -0.071 b Notes: The data come from the 2000 U.S. Census for New Jersey. The samples are comprised of men and women who are between 25 and 65 years of age and who are not currently enrolled in school. There are 18,401 Essex County residents of whom 35.5 percent are African American, 15.2 percent Hispanic, and 4.4 percent Asian. Employment-Population Ratio is defined as 1 if the individual is employed and 0 if the individual is unemployed or out of the labor force. The entries in the table are the coefficients of a regression of the self-employment dummy variable on dummy variables for each gender and race group, where white males are the reference group. Controls for education, English-language ability, immigration status, industry, age, marital status, veteran status, and disability status are also included. A c denotes statistical significance at the 10 percent level. A b denotes statistical significance at the 5 percent level of significance. An a denotes statistical significance at the 1 percent level of significance. The standard errors are located in Table J-5. Table J-2 cont. Differences in Economic Outcomes Relative to White Males by Geographic Area

Analysis of Essex County Procurement and Contracting: Final Report 198 Panel B: Unemployment Rate Males Females Geographic Area Black Asian Hispanic White Black Asian Hispanic Essex 0.057 a 0.008 0.009 0.007 0.042 a 0.006 0.05 a New Jersey 0.041 a 0.014 a 0.007 a 0.004 a 0.031 a 0.015 a 0.042 a Atlantic 0.041 a 0.000001 0.059 a 0.001 0.026 b 0.011 0.03 c Bergen 0.027 a -0.003 0.004 0.004 0.003 0.0002 0.006 Burlington 0.024 a 0.001 0.01 0.005 0.029 a -0.016 0.043 a Camden 0.047 a 0.009 0.022 c 0.002 0.047 a -0.001 0.073 a Cape May 0.058 0.028-0.049 0.029 b 0.05 0.022 0.055 Cumberland 0.062 a -0.018 0.021 0.021 0.048 b -0.031 0.069 a Hudson 0.068 a 0.019 0.01-0.004 0.057 a 0.011 0.055 a Hunterdon 0.006-0.002 0.014 0.009-0.001 0.001 0.12 a Mercer 0.045 a 0.056 a -0.009-0.011 c 0.025 b 0.065 a 0.014 Middlesex 0.013 0.012 c -0.005-0.001 0.013 0.015 c 0.047 a Monmouth 0.033 a -0.001 0.012 0.001 0.022 b 0.021 c 0.042 a Morris -0.017 0.027 a 0.009 0.005 0.011 0.023 b 0.031 a Ocean 0.019 0.02 0.02 0.004-0.01 0.015 0.033 b Passaic 0.072 a 0.022-0.002 0.016 b 0.036 a 0.045 b 0.053 a Somerset 0.017 0.005-0.003 0.001 0.037 a 0.009 0.022 c Sussex -0.014 0.036-0.031 0.01 0.083-0.003 0.041 c Union 0.024 a 0.03 b -0.004-0.002 0.034 a 0.025 c 0.035 a Warren 0.002-0.03 0.021-0.002 0.022-0.03-0.049 Gloucester & Salem 0.019 0.022 0.079 a 0.002 0.008 0.029 0.0001 Notes: The data come from the 2000 U.S. Census for New Jersey. The samples are comprised of men and women who are between 25 and 65 years of age and who are not currently enrolled in school. There are 18,401 Essex County residents of whom 35.5 percent are African American, 15.2 percent Hispanic, and 4.4 percent Asian. Employment-Population Ratio is defined as 1 if the individual is employed and 0 if the individual is unemployed or out of the labor force. The entries in the table are the coefficients of a regression of the self-employment dummy variable on dummy variables for each gender and race group, where white males are the reference group. Controls for education, English-language ability, immigration status, industry, age, marital status, veteran status, and disability status are also included. A c denotes statistical significance at the 10 percent level. A b denotes statistical significance at the 5 percent level of significance. An a denotes statistical significance at the 1 percent level of significance. The standard errors are located in Table J-5.

Analysis of Essex County Procurement and Contracting: Final Report 199 Table J-2 cont. Differences in Economic Outcomes Relative to White Males by Geographic Area Panel C: Log Hourly Wage Males Females Geographic Area Black Asian Hispanic White Black Asian Hispanic Essex -0.305 a -0.172 a -0.305 a -0.318 a -0.403 a -0.184 a -0.533 a New Jersey -0.19 a -0.1 a -0.176 a -0.311 a -0.323 a -0.273 a -0.417 a Atlantic -0.175 a -0.25 a -0.211 a -0.295 a -0.319 a -0.15 b -0.352 a Bergen -0.171 a -0.099 a -0.178 a -0.32 a -0.288 a -0.335 a -0.413 a Burlington -0.129 a -0.076-0.027-0.314 a -0.24 a -0.268 a -0.348 a Camden -0.134 a -0.198 a -0.143 a -0.279 a -0.262 a -0.363 a -0.38 a Cape May -0.211 c -0.01-0.084-0.304 a -0.197 b -0.34-0.099 Cumberland -0.114 a 0.129-0.226 a -0.332 a -0.289 a -0.143-0.424 a Hudson -0.166 a -0.133 a -0.166 a -0.159 a -0.292 a -0.154 a -0.367 a Hunterdon -0.313-0.277 b -0.278 b -0.276 a 0.108-0.524 a -0.294 b Mercer -0.204 a 0.017-0.148 a -0.282 a -0.383 a -0.203 a -0.378 a Middlesex -0.096 a -0.032-0.113 a -0.278 a -0.235 a -0.273 a -0.357 a Monmouth -0.176 a -0.096 b -0.03-0.352 a -0.384 a -0.311 a -0.387 a Morris -0.238 a -0.142 a -0.164 a -0.323 a -0.327 a -0.321 a -0.454 a Ocean -0.183 a -0.087-0.129 a -0.335 a -0.414 a -0.318 a -0.468 a Passaic -0.206 a -0.122 b -0.201 a -0.262 a -0.321 a -0.183 a -0.449 a Somerset -0.144 a -0.103 b -0.192 a -0.288 a -0.249 a -0.332 a -0.459 a Sussex -0.026-0.336 b -0.09-0.354 a -0.362 c -0.323 b -0.335 a Union -0.253 a -0.088 c -0.234 a -0.328 a -0.383 a -0.211 a -0.488 a Warren -0.006-0.223-0.224 b -0.307 a -0.08-0.294-0.51 a Gloucester & Salem -0.128 a -0.081-0.167 b -0.328 a -0.351 a -0.234 b -0.427 a Notes: The data come from the 2000 U.S. Census for New Jersey. The samples are comprised of men and women who are between 25 and 65 years of age and who are not currently enrolled in school. There are 18,401 Essex County residents of whom 35.5 percent are African American, 15.2 percent Hispanic, and 4.4 percent Asian. Employment-Population Ratio is defined as 1 if the individual is employed and 0 if the individual is unemployed or out of the labor force. The entries in the table are the coefficients of a regression of the self-employment dummy variable on dummy variables for each gender and race group, where white males are the reference group. Controls for education, English-language ability, immigration status, industry, age, marital status, veteran status, and disability status are also included. A c denotes statistical significance at the 10 percent level. A b denotes statistical significance at the 5 percent level of significance. An a denotes statistical significance at the 1 percent level of significance. The standard errors are located in Table J-5.

Analysis of Essex County Procurement and Contracting: Final Report 200 Table J-3. Employment in Construction and Professional Services Panel A: Construction Essex New Jersey Unadjusted Adjusted Unadjusted Adjusted Demographic Group DP/Dx Std. Error DP/Dx Std. Error DP/Dx Std. Error DP/Dx Std. Error MALES Black -0.022 a 0.003-0.023 a 0.003-0.021 a 0.001-0.023 a 0.001 Asian -0.025 a 0.003-0.021 a 0.002-0.039 a 0.001-0.033 a 0.001 Other 0.021 b 0.01-0.002 0.006-0.004 0.003-0.01 a 0.002 Hispanic 0.007 c 0.004-0.016 a 0.002-0.007 a 0.001-0.023 a 0.001 FEMALES White -0.082 a 0.006-0.07 a 0.006-0.09 a 0.001-0.083 a 0.001 Black 0.00041 0.008 0.003 0.007-0.013 a 0.004-0.01 b 0.003 Asian 0.043 c 0.035 0.036 c 0.03 0.05 a 0.012 0.034 a 0.01 Other -0.019 0.009-0.019 0.006-0.01 0.007-0.013 c 0.006 Hispanic -0.019 b 0.006-0.015 b 0.005-0.013 a 0.003-0.01 a 0.003 Panel B: Professional Services Essex New Jersey Unadjusted Adjusted Unadjusted Adjusted Demographic Group Df/Dx std. Error Df/Dx std. Error Df/Dx std. Error Df/Dx Std. Error MALES Black -0.071 a 0.005-0.043 a 0.006-0.05 a 0.002-0.032 a 0.002 Asian -0.005 0.011-0.007 0.01 0.064 a 0.004 0.031 a 0.004 Other -0.039 a 0.009-0.018 0.011-0.028 a 0.005-0.016 a 0.005 Hispanic -0.066 a 0.004-0.039 a 0.006-0.058 a 0.002-0.028 a 0.002 FEMALES White -0.002 0.005 0.002 0.005-0.004 a 0.001-0.001 0.001 Black 0.00269 0.01-0.003 0.008 0.01 c 0.005 0.003 0.004 Asian -0.028 b 0.011-0.025 b 0.01-0.035 a 0.003-0.028 a 0.003 Other -0.012 0.021-0.017 0.017 0.013 0.011 0.011 0.01 Hispanic 0.033 b 0.018 0.021 0.016 0.031 a 0.006 0.018 a 0.005 Notes: The data come from the 2000 U.S. Census for New Jersey. The samples are comprised of men and women who are between 25 and 65 years of age and who are not currently enrolled in school. There are 15,835 residents employed in Essex County and 191,685 employed in New Jersey. The employment in construction is defined as 1 if the individual is employed in the construction industry and 0 if the individual is employed in a different industry. Employment in professional services is defined analogously. The entries in the columns labeled unadjusted are the coefficients for each gender and race group dummy variable, where white males are the reference group. No other variables are in the unadjusted model. The columns labeled adjusted include controls for education, English-language ability, immigration status, industry, age, marital status, veteran status, and disability status. A c denotes statistical significance at the 10 percent level. A b denotes statistical significance at the 5 percent level. An a denotes statistical significance at the 1 percent level of significance.

Analysis of Essex County Procurement and Contracting: Final Report 201 Table J-4. Differences in Self-Employment Relative to White Males by Geographic Area Males Females Geographic Area Black Asian Hispanic White Black Asian Hispanic Essex -0.085 a -0.052 a -0.05 a -0.067 a -0.093 a -0.093 a -0.079 a New Jersey -0.064 a -0.04 a -0.056 a -0.062 a -0.078 a -0.075 a -0.066 a Atlantic -0.09 a -0.037-0.051 a -0.072 a -0.098 a -0.048 c -0.088 a Bergen -0.071 a -0.029 b -0.044 a -0.091 a -0.094 a -0.087 a -0.077 a Burlington -0.048 a -0.01-0.077 a -0.052 a -0.063 a -0.056 b -0.025 Camden -0.05 a 0.004-0.053 a -0.052 a -0.067 a -0.053 b -0.063 a Cape May -0.041-0.129-0.078-0.092 a -0.112 b -0.058-0.113 c Cumberland -0.042 a 0.146 b -0.047 b -0.046 a -0.041 b 0.065-0.048 b Hudson -0.032 a -0.035 a -0.037 a -0.023 a -0.051 a -0.043 a -0.046 a Hunterdon 0.029-0.08-0.032-0.055 a -0.095 c -0.081-0.1 c Mercer -0.039 a -0.05 b -0.046 a -0.029 a -0.032 b -0.031-0.007 Middlesex -0.038 a -0.042 a -0.052 a -0.055 a -0.065 a -0.062 a -0.057 a Monmouth -0.093 a -0.055 a -0.08 a -0.068 a -0.091 a -0.103 a -0.065 a Morris -0.068 a -0.042 b -0.089 a -0.061 a -0.084 a -0.105 a -0.065 a Ocean -0.044 c 0.013-0.041 b -0.059 a -0.062 b -0.1 b -0.07 a Passaic -0.091 a -0.039 b -0.077 a -0.074 a -0.09 a -0.101 a -0.084 a Somerset -0.052 a -0.05 b -0.077 a -0.073 a -0.105 a -0.051 b -0.072 a Sussex -0.082-0.005-0.045-0.059 a -0.12-0.116 c -0.055 Union -0.07 a -0.038 b -0.062 a -0.065 a -0.099 a -0.077 a -0.078 a Warren -0.122 a 0.023-0.069-0.055 a -0.048-0.107-0.117 b Gloucester & Salem -0.028 c 0.047-0.094 a -0.052 a -0.068 a -0.071 c -0.081 a Notes: The data come from the 2000 U.S. Census for New Jersey. The samples are comprised of men and women who are between 25 and 65 years of age who are not currently enrolled in school. There are 18,401 Essex County residents of whom 35.5 percent are African American, 15.2 percent Hispanic, and 4.4 percent Asian. Self-employed is defined as 1 if an individual identified themselves as self employed in either an unincorporated or incorporated business, professional practice, or farm. It is 0 if the response was anything else. The entries in the table are the coefficients of a regression of the self-employment dummy variable on dummy variables for each gender and race group, where white males are the reference group. Controls for education, English-language ability, immigration status, industry, age, marital status, veteran status, and disability status are also included. A c denotes statistical significance at the 10 percent level. A b denotes statistical significance at the 5 percent level. An a denotes statistical significance at the 1 percent level of significance. The standard errors are located in Table J-5. Table J-5. Differences in Economic Outcomes between White Males and Other Groups

Analysis of Essex County Procurement and Contracting: Final Report 202 Panel A: Self-Employment Black Males Asian Males Hispanic Males White Females Black Females Asian Females Hispanic Females Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Atlantic -0.09 a 0.016-0.037 0.026-0.051 a 0.018-0.072 a 0.01-0.098 a 0.015-0.048 c 0.026-0.088 a 0.019 Bergen -0.071 a 0.016-0.029 b 0.011-0.044 a 0.011-0.091 a 0.006-0.094 a 0.015-0.087 a 0.012-0.077 a 0.012 Burlington -0.048 a 0.011-0.01 0.025-0.077 a 0.02-0.052 a 0.007-0.063 a 0.011-0.056 b 0.024-0.025 0.021 Camden -0.05 a 0.011 0.004 0.021-0.053 a 0.015-0.052 a 0.007-0.067 a 0.011-0.053 b 0.022-0.063 a 0.015 Cape May -0.041 0.048-0.129 0.152-0.078 0.055-0.092 a 0.016-0.112 b 0.044-0.058 0.152-0.113 c 0.058 Cumberland -0.042 ab 0.017 0.146 b 0.069-0.047 b 0.02-0.046 a 0.012-0.041 b 0.02 0.065 0.081-0.048 b 0.023 Essex -0.085 a 0.007-0.052 a 0.015-0.05 a 0.009-0.067 a 0.007-0.093 a 0.007-0.093 a 0.016-0.079 a 0.01 Hudson -0.032 a 0.011-0.035 a 0.011-0.037 a 0.008-0.023 a 0.007-0.051 a 0.011-0.043 a 0.012-0.046 a 0.008 Hunterdon 0.029 0.098-0.08 0.058-0.032 0.055-0.055 a 0.012-0.095 c 0.056-0.081 0.059-0.1 c 0.052 Mercer -0.039 a 0.013-0.05 b 0.02-0.046 a 0.017-0.029 a 0.008-0.032 b 0.013-0.031 0.022-0.007 0.018 Middlesex -0.038 a 0.01-0.042 a 0.009-0.052 a 0.009-0.055 a 0.005-0.065 a 0.01-0.062 a 0.01-0.057 a 0.009 Monmouth -0.093 a 0.015-0.055 a 0.019-0.08 a 0.015-0.068 a 0.006-0.091 a 0.014-0.103 a 0.02-0.065 a 0.016 Morris -0.068 a 0.026-0.042 b 0.018-0.089 a 0.017-0.061 a 0.007-0.084 a 0.024-0.105 a 0.019-0.065 a 0.018 Ocean -0.044 c 0.025 0.013 0.038-0.041 b 0.021-0.059 a 0.007-0.062 b 0.027-0.1 b 0.04-0.07 a 0.022 Passaic -0.091 a 0.012-0.039 b 0.019-0.077 a 0.009-0.074 a 0.007-0.09 a 0.012-0.101 a 0.02-0.084 a 0.01 Somerset -0.052 ab 0.021-0.05 b 0.02-0.077 a 0.019-0.073 a 0.008-0.105 a 0.019-0.051 b 0.021-0.072 a 0.02 Sussex -0.082 0.072-0.005 0.06-0.045 0.038-0.059 a 0.01-0.12 0.087-0.116 c 0.059-0.055 0.035 Union -0.07 a 0.01-0.038 b 0.018-0.062 a 0.01-0.065 a 0.007-0.099 a 0.01-0.077 a 0.019-0.078 a 0.011 Warren -0.122 ab 0.06 0.023 0.119-0.069 0.048-0.055 a 0.013-0.048 0.067-0.107 0.092-0.117 b 0.054 Gloucester & Salem -0.028 c 0.016 0.047 0.039-0.094 a 0.03-0.052 a 0.007-0.068 a 0.016-0.071 c 0.04-0.081 a 0.031 New Jersey -0.064 a 0.003-0.04 a 0.004-0.056 a 0.003-0.062 a 0.002-0.078 a 0.003-0.075 a 0.004-0.066 a 0.003 Notes: See text for detailed description. Table J-5 cont. Differences in Economic Outcomes between White Males and Other Groups

Analysis of Essex County Procurement and Contracting: Final Report 203 Panel B: Employment to Population Ratio Black Males Asian Males Hispanic Males White Females Black Females Asian Females Hispanic Females Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Atlantic -0.086 a 0.019-0.015 0.033-0.172 a 0.023-0.072 a 0.012-0.093 a 0.018-0.072 b 0.031-0.113 a 0.023 Bergen -0.055 a 0.016-0.024 b 0.012-0.048 a 0.011-0.089 a 0.006-0.075 a 0.015-0.094 a 0.011-0.108 a 0.011 Burlington -0.127 a 0.013-0.031 0.031-0.108 a 0.025-0.08 a 0.008-0.094 a 0.014-0.072 a 0.028-0.152 a 0.025 Camden -0.128 a 0.013-0.044 c 0.026-0.101 a 0.018-0.075 a 0.008-0.141 a 0.013-0.091 a 0.026-0.187 a 0.017 Cape May -0.073 0.047-0.409 b 0.168-0.083 0.057-0.059 a 0.017-0.052 0.045-0.21 0.168-0.173 a 0.059 Cumberland -0.274 a 0.023-0.013 0.096-0.123 a 0.027-0.022 0.017-0.044 c 0.027-0.031 0.103-0.073 b 0.03 Essex -0.125 a 0.01-0.044 b 0.02-0.097 a 0.012-0.097 a 0.009-0.114 a 0.01-0.113 a 0.02-0.134 a 0.013 Hudson -0.139 a 0.016-0.036 b 0.017-0.065 a 0.011-0.042 a 0.011-0.109 a 0.015-0.083 a 0.017-0.13 a 0.011 Hunterdon -0.51 a 0.093-0.006 0.056-0.115 b 0.051-0.101 a 0.012-0.594 a 0.05-0.151 a 0.051-0.275 a 0.048 Mercer -0.147 a 0.016-0.043 0.027-0.107 a 0.021-0.064 a 0.01-0.088 a 0.016-0.135 a 0.028-0.128 a 0.022 Middlesex -0.076 a 0.014-0.002 0.012-0.072 a 0.012-0.072 a 0.007-0.05 a 0.013-0.101 a 0.012-0.129 a 0.012 Monmouth -0.109 a 0.016 0.055 a 0.02-0.066 a 0.016-0.091 a 0.006-0.108 a 0.015-0.072 a 0.02-0.176 a 0.016 Morris -0.05 c 0.027-0.049 a 0.019-0.091 a 0.018-0.102 a 0.007-0.126 a 0.025-0.094 a 0.019-0.138 a 0.018 Ocean -0.073 ab 0.029 0.046 0.044-0.049 b 0.024-0.089 a 0.008-0.091 a 0.03-0.075 c 0.042-0.152 a 0.024 Passaic -0.168 a 0.017-0.043 0.027-0.109 a 0.013-0.101 a 0.01-0.145 a 0.016-0.12 a 0.026-0.168 a 0.013 Somerset -0.056 ab 0.023-0.011 0.022-0.049 b 0.02-0.089 a 0.009-0.076 a 0.021-0.11 a 0.022-0.103 a 0.021 Sussex 0.03 0.078 0.001 0.066-0.052 0.042-0.1 a 0.011-0.109 0.093-0.035 0.062-0.203 a 0.036 Union -0.077 a 0.013-0.069 a 0.024-0.097 a 0.013-0.096 a 0.009-0.109 a 0.012-0.119 a 0.023-0.135 a 0.014 Warren -0.031 0.061 0.044 0.124-0.014 0.048-0.076 a 0.013-0.188 a 0.061-0.233 a 0.086-0.01 0.052 Gloucester & Salem -0.062 a 0.019 0.013 0.049-0.129 a 0.036-0.061 a 0.009-0.072 a 0.018-0.157 a 0.046-0.071 b 0.036 New Jersey -0.122 a 0.004-0.028 a 0.005-0.092 a 0.004-0.085 a 0.002-0.105 a 0.004-0.107 a 0.005-0.14 a 0.004 Notes: See text for detailed description.

Analysis of Essex County Procurement and Contracting: Final Report 204 Table J-5 cont. Differences in Economic Outcomes between White Males and Other Groups Panel C: Unemployment Rates Black Males Asian Males Hispanic Males White Females Black Females Asian Females Hispanic Females Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Atlantic 0.041 a 0.013 0.000001 0.021 0.059 a 0.016 0.001 0.008 0.026 b 0.012 0.011 0.022 0.03 c 0.016 Bergen 0.027 a 0.009-0.003 0.006 0.004 0.006 0.004 0.003 0.003 0.009 0.0002 0.007 0.006 0.007 Burlington 0.024 a 0.007 0.001 0.016 0.01 0.014 0.005 0.004 0.029 a 0.007-0.016 0.016 0.043 a 0.014 Camden 0.047 a 0.009 0.009 0.017 0.022 c 0.012 0.002 0.005 0.047 a 0.009-0.001 0.018 0.073 a 0.013 Cape May 0.058 0.041 0.028 0.18-0.049 0.05 0.029 b 0.013 0.05 0.036 0.022 0.148 0.055 0.051 Cumberland 0.062 a 0.022-0.018 0.072 0.021 0.023 0.021 0.013 0.048 b 0.02-0.031 0.086 0.069 a 0.024 Essex 0.057 a 0.007 0.008 0.014 0.009 0.009 0.007 0.006 0.042 a 0.007 0.006 0.015 0.05 a 0.01 Hudson 0.068 a 0.012 0.019 0.012 0.01 0.008-0.004 0.008 0.057 a 0.012 0.011 0.013 0.055 a 0.009 Hunterdon 0.006 0.072-0.002 0.026 0.014 0.027 0.009 0.006-0.001 0.059 0.001 0.029 0.12 a 0.027 Mercer 0.045 a 0.01 0.056 a 0.015-0.009 0.014-0.011 c 0.006 0.025 b 0.01 0.065 a 0.018 0.014 0.015 Middlesex 0.013 0.008 0.012 c 0.007-0.005 0.007-0.001 0.004 0.013 0.008 0.015 c 0.008 0.047 a 0.008 Monmouth 0.033 a 0.01-0.001 0.011 0.012 0.01 0.001 0.004 0.022 b 0.009 0.021 c 0.012 0.042 a 0.011 Morris -0.017 0.014 0.027 a 0.009 0.009 0.009 0.005 0.004 0.011 0.013 0.023 b 0.01 0.031 a 0.01 Ocean 0.019 0.017 0.02 0.024 0.02 0.014 0.004 0.005-0.01 0.018 0.015 0.027 0.033 b 0.015 Passaic 0.072 a 0.012 0.022 0.017-0.002 0.009 0.016 b 0.007 0.036 a 0.011 0.045 b 0.019 0.053 a 0.01 Somerset 0.017 0.012 0.005 0.012-0.003 0.011 0.001 0.005 0.037 a 0.011 0.009 0.013 0.022 c 0.012 Sussex -0.014 0.042 0.036 0.035-0.031 0.023 0.01 0.006 0.083 0.052-0.003 0.036 0.041 c 0.023 Union 0.024 a 0.008 0.03 b 0.014-0.004 0.008 0.0003 0.006 0.034 a 0.008 0.025 c 0.015 0.035 a 0.009 Warren 0.002 0.035-0.03 0.067 0.021 0.027-0.002 0.008 0.022 0.044-0.03 0.063-0.049 0.032 Gloucester & Salem 0.019 0.012 0.022 0.028 0.079 a 0.022 0.002 0.005 0.008 0.011 0.029 0.031 0.0001 0.023 New Jersey 0.041 a 0.002 0.014 a 0.003 0.007 a 0.002 0.004 a 0.001 0.031 a 0.002 0.015 a 0.003 0.042 a 0.003 Notes: See text for detailed description.

Analysis of Essex County Procurement and Contracting: Final Report 205 Table J-5 cont. Differences in Economic Outcomes between White Males and Other Groups Panel D: Log Hourly Wage Black Males Asian Males Hispanic Males White Females Black Females Asian Females Hispanic Females Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Atlantic -0.175 a 0.04-0.25 a 0.063-0.211 a 0.049-0.295 a 0.023-0.319 a 0.037-0.15 b 0.066-0.352 a 0.048 Bergen -0.171 a 0.038-0.099 a 0.027-0.178 a 0.027-0.32 a 0.013-0.288 a 0.035-0.335 a 0.03-0.413 a 0.028 Burlington -0.129 a 0.028-0.076 0.062-0.027 0.053-0.314 a 0.016-0.24 a 0.028-0.268 a 0.06-0.348 a 0.054 Camden -0.134 a 0.028-0.198 a 0.052-0.143 a 0.038-0.279 a 0.016-0.262 a 0.028-0.363 a 0.055-0.38 a 0.042 Cape May -0.211 c 0.115-0.01 0.455-0.084 0.141-0.304 a 0.036-0.197 b 0.099-0.34 0.456-0.099 0.143 Cumberland -0.114 ab 0.054 0.129 0.18-0.226 a 0.055-0.332 a 0.03-0.289 a 0.05-0.143 0.253-0.424 a 0.059 Essex -0.305 a 0.022-0.172 a 0.042-0.305 a 0.029-0.318 a 0.019-0.403 a 0.021-0.184 a 0.046-0.533 a 0.031 Hudson -0.166 a 0.034-0.133 a 0.033-0.166 a 0.023-0.159 a 0.021-0.292 a 0.033-0.154 a 0.036-0.367 a 0.024 Hunterdon -0.313 0.369-0.277 b 0.122-0.278 b 0.132-0.276 a 0.027 0.108 0.263-0.524 a 0.134-0.294 b 0.13 Mercer -0.204 a 0.037 0.017 0.053-0.148 a 0.047-0.282 a 0.021-0.383 a 0.035-0.203 a 0.062-0.378 a 0.052 Middlesex -0.096 a 0.028-0.032 0.024-0.113 a 0.024-0.278 a 0.013-0.235 a 0.026-0.273 a 0.026-0.357 a 0.026 Monmouth -0.176 a 0.038-0.096 b 0.044-0.03 0.038-0.352 a 0.015-0.384 a 0.036-0.311 a 0.048-0.387 a 0.043 Morris -0.238 a 0.061-0.142 a 0.042-0.164 a 0.042-0.323 a 0.016-0.327 a 0.057-0.321 a 0.046-0.454 a 0.045 Ocean -0.183 a 0.06-0.087 0.088-0.129 a 0.049-0.335 a 0.017-0.414 a 0.063-0.318 a 0.097-0.468 a 0.055 Passaic -0.206 a 0.036-0.122 b 0.05-0.201 a 0.026-0.262 a 0.02-0.321 a 0.034-0.183 a 0.055-0.449 a 0.029 Somerset -0.144 a 0.05-0.103 b 0.047-0.192 a 0.046-0.288 a 0.02-0.249 a 0.046-0.332 a 0.052-0.459 a 0.051 Sussex -0.026 0.162-0.336 b 0.131-0.09 0.084-0.354 a 0.023-0.362 c 0.195-0.323 b 0.128-0.335 a 0.088 Union -0.253 a 0.027-0.088 c 0.048-0.234 a 0.028-0.328 a 0.019-0.383 a 0.026-0.211 a 0.052-0.488 a 0.03 Warren -0.006 0.121-0.223 0.223-0.224 b 0.096-0.307 a 0.027-0.08 0.157-0.294 0.21-0.51 a 0.11 Gloucester & Salem -0.128 a 0.039-0.081 0.093-0.167 b 0.077-0.328 a 0.017-0.351 a 0.038-0.234 b 0.106-0.427 a 0.074 New Jersey -0.19 a 0.008-0.1 a 0.01-0.176 a 0.008-0.311 a 0.004-0.323 a 0.008-0.273 a 0.011-0.417 a 0.009 Notes: See text for detailed description.

Analysis of Essex County Procurement and Contracting: Final Report 206 Table J-6. Analysis of Business Size and Capacity Percentage of Minority and Female Population Geography: Essex County, State of NJ, and US Essex County State of New Jersey U.S. Total Minority 26 60.5% 32.8% 29.3% Asian and Pacific Islander 3.7% 5.7% 3.6% Black 41.2% 13.6% 12.3% Hispanic 15.4% 13.3% 12.5% American Indian and Alaska Natives 0.2% 0.2% 0.9% Female 52.4% 51.5% 50.9% Source: U.S. Census 2000 26 This figure may be slightly larger than the actual total number of minorities because this figure is a sum of all minority groups and may double count black Hispanics.

Analysis of Essex County Procurement and Contracting: Final Report 207 Table J-7. Analysis of Business Size and Capacity Geography: Essex County, NJ SIC code Total Total Total Total Total Total Total SIC Industry Description All All All All All All All industries industries industries industries industries industries industries Group codes All Firms American Asian and Indian Total Pacific Black Hispanic and Minority Islander Alaska Women Natives Total number of firms 56,174 2,731 6,905 3,305 180 12,652 12,097 Sales of all firms ($1,000) 71,225,97 5 718,257 609,422 760,809 20,906 2,087,242 2,783,404 Total number of emp firms 1 17,448 975 777 921 35 2,635 2,279 Sales of emp firms ($1,000) 69,055,97 5 636,186 443,701 692,572 17,837 1,771,827 2,370,306 Number of employees 307,991 3,174 5,447 3,676 197 12,403 17,413 Annual payroll ($1,000) 10,812,08 1 78,685 101,606 77,216 4,710 260,966 466,325 Total number of non-emp firms 2 38,726 1,756 6,128 2,384 145 10,017 9,818 Non-emp firms' sales ($1,000) 2,170,000 82,071 165,721 68,237 3,069 315,416 413,098 Percent Total number of firms 100.00% 4.86% 12.29% 5.88% 0.32% 22.52% 21.53% Sales of all firms ($1,000) 100.00% 1.01% 0.86% 1.07% 0.03% 2.93% 3.91% Total number of emp firms 100.00% 5.59% 4.45% 5.28% 0.20% 15.10% 13.06% Sales of emp firms ($1,000) 100.00% 0.92% 0.64% 1.00% 0.03% 2.57% 3.43% Number of employees 100.00% 1.03% 1.77% 1.19% 0.06% 4.03% 5.65% Annual payroll ($1,000) 100.00% 0.73% 0.94% 0.71% 0.04% 2.41% 4.31% Total number of non-emp firms 100.00% 4.53% 15.82% 6.16% 0.37% 25.87% 25.35% Non-emp firms' sales ($1,000) 100.00% 3.78% 7.64% 3.14% 0.14% 14.54% 19.04% Source: Survey of Minority and Women-Owned Business Enterprises 1997 1 Number of firms with paid employees 2 Number of firms without paid employees

Analysis of Essex County Procurement and Contracting: Final Report 208 Table J-8. Analysis of Business Size and Capacity Geography: State of New Jersey SIC code Total Total Total Total Total Total Total All All All All All All All SIC Industry Description industries industries industries industries industries industries industries American Asian and Indian and Total Group codes All Firms Pacific Black Hispanic Women Alaska Minority Islander Natives Total number of firms 654,227 41,432 26,500 36,116 2,375 102,295 155,345 Sales of all firms ($1,000) 690,007,714 16,734,070 2,160,441 5,107,287 364,056 24,138,905 30,000,725 Total number of emp firms 1 194,118 15,154 3,236 7,355 461 25,625 29,047 Sales of emp firms ($1,000) 667,137,703 15,436,316 1,580,808 4,276,407 300,492 21,458,192 26,434,850 Number of employees 3,298,375 79,640 16,862 28,134 2,347 125,819 247,040 Annual payroll ($1,000) 113,091,959 2,314,294 375,877 665,530 57,733 3,398,780 5,398,593 Total number of non-emp firms 2 460,109 26,278 23,264 28,761 1,913 76,669 126,298 Non-emp firms' sales ($1,000) 22,870,011 1,297,754 579,634 830,879 63,564 2,680,713 3,565,875 Percent Total number of firms 100.00% 6.33% 4.05% 5.52% 0.36% 15.64% 23.74% Sales of all firms ($1,000) 100.00% 2.43% 0.31% 0.74% 0.05% 3.50% 4.35% Total number of emp firms 100.00% 7.81% 1.67% 3.79% 0.24% 13.20% 14.96% Sales of emp firms ($1,000) 100.00% 2.31% 0.24% 0.64% 0.05% 3.22% 3.96% Number of employees 100.00% 2.41% 0.51% 0.85% 0.07% 3.81% 7.49% Annual payroll ($1,000) 100.00% 2.05% 0.33% 0.59% 0.05% 3.01% 4.77% Total number of non-emp firms 100.00% 5.71% 5.06% 6.25% 0.42% 16.66% 27.45% Non-emp firms' sales ($1,000) 100.00% 5.67% 2.53% 3.63% 0.28% 11.72% 15.59% Source: Survey of Minority and Women-Owned Business Enterprises 1997 1 Number of firms with paid employees 2 Number of firms without paid employees

Analysis of Essex County Procurement and Contracting: Final Report 209 Table J-9. Analysis of Business Size and Capacity Geography: United States SIC code Total Total Total Total Total Total Total All All All All All All SIC Industry Description All industries industries industries industries industries industries industries American Asian and Indian and Total Group codes All Firms Pacific Black Hispanic Women Alaska Minority Islander Natives Total number of firms 20,821,934 912,960 823,499 1,199,896 197,300 3,039,033 5,417,034 Sales of all firms ($1,000) 18,553,243,047 306,932,982 71,214,662 186,274,582 34,343,907 591,259,123 818,669,084 Total number of emp firms 1 5,295,151 289,999 93,235 211,884 33,277 615,222 846,780 Sales of emp firms ($1,000) 17,907,940,321 278,294,345 56,377,860 158,674,537 29,226,260 516,979,920 717,763,965 Number of employees 103,359,815 2,203,079 718,341 1,388,746 298,661 4,514,699 7,076,081 Annual payroll ($1,000) 2,936,492,940 46,179,519 14,322,312 29,830,028 6,624,235 95,528,782 149,115,699 Total number of non-emp firms 2 15,526,783 622,961 730,264 988,012 164,023 2,423,811 4,570,254 Non-emp firms' sales ($1,000) 645,302,726 28,638,638 14,836,803 27,600,045 5,117,647 74,279,203 100,905,119 Percent Total number of firms 100.00% 4.38% 3.95% 5.76% 0.95% 14.60% 26.02% Sales of all firms ($1,000) 100.00% 1.65% 0.38% 1.00% 0.19% 3.19% 4.41% Total number of emp firms 100.00% 5.48% 1.76% 4.00% 0.63% 11.62% 15.99% Sales of emp firms ($1,000) 100.00% 1.55% 0.31% 0.89% 0.16% 2.89% 4.01% Number of employees 100.00% 2.13% 0.69% 1.34% 0.29% 4.37% 6.85% Annual payroll ($1,000) 100.00% 1.57% 0.49% 1.02% 0.23% 3.25% 5.08% Total number of non-emp firms 100.00% 4.01% 4.70% 6.36% 1.06% 15.61% 29.43% Non-emp firms' sales ($1,000) 100.00% 4.44% 2.30% 4.28% 0.79% 11.51% 15.64% Source: Survey of Minority and Women-Owned Business Enterprises 1997 1 Number of firms with paid employees 2 Number of firms without paid employees

Analysis of Essex County Procurement and Contracting: Final Report 210 Table J-10. Analysis of Business Size and Capacity Disproportionality of Minority and Women-Owned Businesses: All Firms Geography: Essex County, State of NJ, and US Essex County State of New Jersey U.S. Group Total number of firms Sales of All Firms Ratio (Number of Firms to Sales) Total number of firms Sales of All Firms Ratio (Number of Firms to Sales) Total number of firms Sales of All Firms Ratio (Number of Firms to Sales) (A) (B) (B/A) (A) (B) (B/A) (A) (B) (B/A) Total Minority 22.52% 2.93% 0.1301 15.64% 3.50% 0.2237 14.60% 3.19% 0.2183 Asian and Pacific Islander 4.86% 1.01% 0.2074 6.33% 2.43% 0.3829 4.38% 1.65% 0.3773 Black 12.29% 0.86% 0.0696 4.05% 0.31% 0.0773 3.95% 0.38% 0.0971 Hispanic 5.88% 1.07% 0.1816 5.52% 0.74% 0.1341 5.76% 1.00% 0.1742 American Indian and Alaska Natives 0.32% 0.03% 0.0916 0.36% 0.05% 0.1453 0.95% 0.19% 0.1954 Women 21.53% 3.91% 0.1815 23.74% 4.35% 0.1831 26.02% 4.41% 0.1696 Source: Survey of Minority and Women-Owned Business Enterprises 1997

Analysis of Essex County Procurement and Contracting: Final Report 211 Table J-11. Analysis of Business Size and Capacity Disproportionality of Minority- and Women-Owned Businesses: Firms with Paid Employees Geography: Essex County, State of NJ, and US Essex County State of New Jersey U.S. Group Total number of firms Sales of All Firms Ratio (Number of Firms to Sales) Total number of firms Sales of All Firms Ratio (Number of Firms to Sales) Total number of firms Sales of All Firms Ratio (Number of Firms to Sales) (A) (B) (B/A) (A) (B) (B/A) (A) (B) (B/A) Total Minority 15.10% 2.57% 0.170 13.20% 3.22% 0.244 11.62% 2.89% 0.248 Asian and Pacific Islander 5.59% 0.92% 0.165 7.81% 2.31% 0.296 5.48% 1.55% 0.284 Black 4.45% 0.64% 0.144 1.67% 0.24% 0.142 1.76% 0.31% 0.179 Hispanic 5.28% 1.00% 0.190 3.79% 0.64% 0.169 4.00% 0.89% 0.221 American Indian and Alaska Natives 0.20% 0.03% 0.129 0.24% 0.05% 0.190 0.63% 0.16% 0.260 Women 13.06% 3.43% 0.263 14.96% 3.96% 0.265 15.99% 4.01% 0.251 Source: Survey of Minority and Women-Owned Business Enterprises 1997

Analysis of Essex County Procurement and Contracting: Final Report 212 Variable Table J-12. Analysis of Business Size and Capacity: Regression Analysis of Disproportionality by Race All Minority Women Asian Black Hispanic Native American Coeff. Est. Constant -0.3149 Percent Change in Population 1990-97 High School Graduate or higher(%) 1990 Percent of White Population 1996 Payroll(in million dollars) 1997 Unemployment Rate(%) 1996 t- stat - 4.6 3 0.1661 2.6 1 0.3704 4.2 7 0.2360 5.1 6 0.0173 0.8 8 1.3903 5.6 5 ** Coeff. Est. t- stat * 0.4178 9.1 4 ** * 0.2574 6.0 2 ** * -0.4007 ** - 7.6 7 * 0.0898 2.7 9 ** -0.0003-0.0 1 * 0.1113 0.6 5 ** Coeff. Est. * -0.1360 ** t- stat - 0.6 9 * 0.1432 1.0 9 ** * 0.1628 0.7 3 ** * 0.3037 2.6 8 0.0661 1.6 5 1.4519 2.4 6 ** Coeff. Est. -0.0592 t- stat - 1.2 3 0.1306 2.9 3 0.1249 1.9 9 * 0.0512 1.4 2 ** 0.1051 1.3 2 0.2704 1.2 2 ** Coeff. Est. -0.2198 t- stat - 1.9 6 * 0.1731 2.4 7 ** 0.2940 2.5 1 0.0796 1.2 1 0.0610 1.6 3 1.5184 5.5 8 ** ** ** ** Coeff. Est. -0.2243-0.0194 t- stat - 1.4 3-0.1 7 0.5359 2.9 6-0.0807-0.9 3 0.4361 2.0 8 * 1.6511 3.2 8 Number of Observations 1340 2591 446 653 521 368 Adjusted R-Square 0.064 0.0374 0.0301 0.0409 0.0807 0.0402 Mean of Dependent Variable 0.2472 0.2433 0.3409 0.0963 0.1884 0.2169 F Value 19.33 21.12 3.77 6.57 10.14 4.08 p-value < 0.0001 < 0.0001 0.0024 < 0.0001 < 0.0001 0.0013 *** significant at 99%, ** at 95%, and * at 90% Dependent Variable = Percent Sales of Minority Firms/Percent Minority Firms Source: Survey of Minority- and Women-Owned Business Enterprises 1997, County and City Databook 2000, and US Counties 1998 ** * ** ** *

Analysis of Essex County Procurement and Contracting: Final Report 213 Table J-13. Analysis of Size and Capacity: Regression of Disproportionality Observed Essex County Estimated from National Coefficient New Jersey Minority Firms 13.01% 17.58% 22.37% 21.83% Asian Firms 20.74% 25.32% 38.29% 37.73% Black Firms 6.96% 8.25% 7.73% 9.71% Hispanic Firms 18.16% 14.41% 13.41% 17.42% Native American Firms 9.16% 23.84% 14.53% 19.54% Women Firms 18.15% 18.20% 18.31% 16.96% Disproportionality = Percent Sales of Minority Firms/Percent Minority Firms Source: SMWOBE 1997, County and City Databook 2000, and US Counties 1998 US

Analysis of Essex County Procurement and Contracting: Final Report 214 Table J-14. Loan Denial Rates by Race in 2003: Essex County, NJ All Loans Refinancing Only N Denial Rate N Denial Rate American Indian 298 Asian 2,692 Black 13,620 Hispanic 6,966 White 28,635 Overall * 74,623 25.2% 207 25.6% 11.1% 1,888 10.2% 28.2% 8,993 29.6% 22.6% 4,119 23.4% 9.8% 21,931 9.5% 17.5% 53,130 17.4% Source: HMDA 2003 * Unidentified races are included.

Analysis of Essex County Procurement and Contracting: Final Report 215 Table J-15. Reasons for Loan Denial by Race in 2003: Essex County, NJ (Refinancing Loans only) All American Indian Asian Black Hispanic White N % N % N % N % N % N % Debt-to-Income Ratio 1490 16.7% 11 20.4% 37 17.2% 444 16.0% 205 20.3% 361 17.2% Employment History 116 1.3% 0 0.0% 3 1.4% 21 0.8% 17 1.7% 44 2.1% Credit History 2779 31.2% 12 22.2% 52 24.2% 1038 37.5% 322 31.8% 556 26.6% Collateral 1105 12.4% 10 18.5% 24 11.2% 376 13.6% 130 12.8% 196 9.4% Insufficient Cash 105 1.2% 0 0.0% 3 1.4% 33 1.2% 16 1.6% 18 0.9% Unverifiable Information 292 3.3% 0 0.0% 16 7.4% 50 1.8% 29 2.9% 118 5.6% Credit Application Incomplete 1489 16.7% 7 13.0% 46 21.4% 322 11.6% 149 14.7% 451 21.5% Mortgage Insurance Denied 2 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 0.0% Other 1518 17.1% 14 25.9% 34 15.8% 487 17.6% 144 14.2% 348 16.6% Total 8896 100.0% 54 100.0% 215 100.0% 2771 100.0% 1012 100.0% 2093 100.0% Source: HMDA 2003 Note: Multiple reasons are counted individually if there are more than one reason for denial.

Analysis of Essex County Procurement and Contracting: Final Report 216 Table J-16. Logistic Regressions for Bad Credit and Loan Denial Rates Essex County (Refinancing Loans Only) Bad Credit Regression Loan Denial Regression Variable Coeff. Est. Std. Err. p-value Coeff. Est. St d. Err p-value. Constant -1.3113 0.166 <.0001 *** -10.9248 1.1 76 <.0001 *** Race: American Indian - - - - - - Race: Asian - - - - - - Race: Black - - - 0.8087 0.1 <.0001 *** 18 Race: Hispanic - - - - - - Gender: Female - - - 0.2848 0.1 0.0070 ** 06 Loan Type: Conventional - - - 0.8437 1.1 19 Loan Type: FHA - - - -0.7537 1.1 43 Purchaser: OCC - - - 4.1402 0.3 21 Purchaser: FRB - - - 3.0893 0.3 36 Purchaser: FDIC - - - 1.9342 0.3 24 Purchaser: OTS - - - 2.5194 0.3 13 Purchaser: HUD - - - 3.0860 0.3 Percent of Minority Population in Census Tract Ratio of Applicant Income to Median Income Ratio of Owner Occupied Units to All Dwellings 08 0.4508 0.5097 <.0001 *** <.0001 *** <.0001 *** <.0001 *** <.0001 *** 0.0049 0.001 <.0001 *** - - - -0.0074 0.010 0.4477 - - - 0.2446 0.160 0.1254 - - - Predicted Probability of Bad Credit - - - 30.2091 0.5 89 Loan-to-Income Ratio - - - 0.0371 0.0 09 Number of Observations 8727 26882 Max-Rescaled R-Square 0.0046 0.9095-2 Log L 10626.9 3331.8 Likelihood Ratio 28.20 20842.7 p-value < 0.0001 < 0.0001 Source: HMDA 2003 *** significant at 99% <.0001 *** <.0001 ***

Analysis of Essex County Procurement and Contracting: Final Report 217 Table J-17. Unadjusted Loan Denial Rates by Lender Essex County (Refinancing Loans Only) Loan Application Denial Rates by Race by Gender Bank Name Denial Am Accepted Denied Total Rate Indian Asian Black Hispanic White Female Male 1 WELLS FARGO HOME MORTGAGE 3629 252 3881 6.5% 0.0% 3.7% 13.4% 13.3% 3.2% 8.0% 4.7% 2 WASHINGTON MUTUAL BANK, FA 2894 421 3315 12.7% 40.0% 19.8% 22.9% 25.4% 13.6% 18.3% 16.8% 3 FLEET NATIONAL BANK 2066 627 2693 23.3% 44.4% 24.3% 40.9% 42.5% 14.6% 29.1% 22.1% 4 COUNTRYWIDE HOME LOANS 2560 101 2661 3.8% 6.1% 1.1% 7.5% 4.3% 2.9% 4.6% 3.9% 5 WACHOVIA BANK 1645 674 2319 29.1% 14.0% 23.6% 39.9% 46.3% 14.8% 32.8% 28.6% 6 CHASE MANHATTAN MORTGAGE CORP 1970 224 2194 10.2% 25.0% 6.2% 16.4% 11.3% 7.3% 11.6% 9.4% 7 AMERIQUEST MORTGAGE COMPANY 479 1175 1654 71.0% 77.8% 75.0% 68.4% 67.5% 74.4% 66.9% 72.3% 8 GMAC MORTGAGE CORPORATION 1340 144 1484 9.7% 0.0% 1.3% 17.3% 15.3% 3.5% 3.7% 3.9% 9 CENDANT MORTGAGE 1250 3 1253 0.2% 0.0% 0.0% 0.0% 4.0% 0.0% 1.0% 0.0% 10 WELLS FARGO FUNDING 1109 2 1111 0.2% 0.0% 0.0% 0.0% 0.0% 0.2% 0.0% 0.2% 11 PNC BANK NA 674 390 1064 36.7% 100.0% 28.6% 55.5% 44.4% 20.1% 37.9% 31.3% 12 CITIMORTGAGE, INC 1039 24 1063 2.3% 0.0% 0.0% 4.6% 1.5% 0.7% 1.8% 1.2% 13 NATIONAL CITY MORTGAGE CORPORA 840 36 876 4.1% 0.0% 0.0% 6.4% 4.6% 2.5% 4.3% 3.8% 14 HUDSON CITY SAVINGS BANK 668 36 704 5.1% 0.0% 6.5% 13.8% 12.5% 3.8% 6.3% 5.0% 15 VALLEY NATIONAL BANK 676 24 700 3.4% - 6.7% 3.9% 18.6% 2.1% 4.9% 3.4% 16 KEYBANK USA, N.A. 391 300 691 43.4% 0.0% 30.8% 36.2% 29.7% 27.8% 36.7% 27.4% 17 PRINCIPAL RESIDENTIAL MTG, INC 659 14 673 2.1% - 0.0% 10.3% 4.2% 0.9% 8.5% 0.8% 18 BANK OF AMERICA, N.A. 658 12 670 1.8% 0.0% 0.0% 21.4% 27.3% 6.4% 8.8% 10.1% 19 ABN AMRO MORTGAGE GROUP, INC. 640 25 665 3.8% - 0.0% 13.9% 3.1% 3.6% 2.8% 4.6% 20 OPTION ONE MORTGAGE CORP. 418 241 659 36.6% 33.3% 28.6% 39.9% 39.8% 32.8% 32.9% 40.6% 21 NEW CENTURY MORTGAGE CORP. 373 150 523 28.7% 100.0% 0.0% 18.2% 10.7% 17.7% 20.1% 14.8% 22 ARGENT MORTGAGE COMPANY 448 47 495 9.5% 50.0% 14.3% 9.8% 8.3% 11.1% 10.1% 10.8% 23 GMAC BANK 485 4 489 0.8% 0.0% 0.0% 0.0% 0.0% 0.8% 0.0% 0.7% 24 FLAGSTAR BANK 458 22 480 4.6% 0.0% 0.0% 6.4% 10.5% 3.2% 1.8% 5.0% 25 HSBC MORTGAGE CORPORATION 434 8 442 1.8% - 2.9% 3.3% 0.0% 1.8% 2.8% 1.7% 26 LEHMAN BROTHERS BANK 419 8 427 1.9% 0.0% 9.1% 0.0% 2.7% 0.7% 1.1% 1.0% 27 FREMONT INVESTMENT & LOAN 290 131 421 31.1% 0.0% 20.0% 30.2% 34.6% 37.1% 32.3% 34.2% 28 FIRST HORIZON HOME LOAN CORP 324 93 417 22.3% 50.0% 60.0% 29.8% 44.1% 21.2% 36.8% 24.4% 29 GREENWICH HOME MORTGAGE 409 3 412 0.7% - 0.0% 0.0% 2.9% 0.7% 1.0% 0.8% 30 BANK ONE, NA 197 147 344 42.7% 0.0% 14.3% 67.3% 40.0% 38.8% 56.5% 37.2% 31 SIB MORTGAGE CORP. 310 22 332 6.6% 0.0% 12.5% 22.2% 0.0% 6.0% 7.5% 8.4% 32 INDYMAC BANK, F.S.B. 242 89 331 26.9% 100.0% 16.7% 21.9% 29.4% 26.3% 28.8% 23.7% 33 RESIDENTIAL FUNDING CORPORATIO 331 0 331 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

Analysis of Essex County Procurement and Contracting: Final Report 218 34 FIRST INTERSTATE FINANCIAL COR 314 14 328 4.3% 0.0% 0.0% 0.0% 5.6% 5.1% 1.3% 5.9% 35 CHASE MANHATTAN BANK USA, NA 83 242 325 74.5% 75.0% 50.0% 72.3% 78.6% 64.1% 68.7% 70.4% 36 SOVEREIGN BANK 294 23 317 7.3% - 0.0% 16.7% 0.0% 7.8% 15.8% 5.3% 37 PENN FEDERAL SAVINGS BANK 297 18 315 5.7% 0.0% 0.0% 0.0% 30.0% 4.8% 6.3% 5.9% 38 TRUST COMPANY OF NEW JERSEY 280 11 291 3.8% 0.0% 12.5% 7.7% 12.5% 2.5% 3.6% 3.6% 39 THE PROVIDENT BANK 269 22 291 7.6% 0.0% 0.0% 26.9% 17.7% 3.0% 9.2% 5.7% 40 HOUSEHOLD FINANCE CORPORATION 58 230 288 79.9% - 100.0% 81.7% 87.5% 81.8% 77.1% 90.0% 41 E-LOAN, INC. 111 175 286 61.2% 100.0% 37.5% 87.9% 63.2% 50.5% 58.7% 60.9% 42 HOMECOMINGS FINANCIAL NETWORK 266 13 279 4.7% - 0.0% 16.7% 10.0% 1.3% 1.8% 4.4% 43 AMERICAN BUSINESS FINANCIAL 83 180 263 68.4% 100.0% 100.0% 70.0% 82.8% 56.4% 70.0% 69.5% 44 BENEFICIAL 42 217 259 83.8% - - 74.4% 100.0% 100.0% 70.8% 86.2% 45 JPMORGAN CHASE BANK 222 35 257 13.6% 0.0% 0.0% 18.8% 22.2% 15.7% 18.2% 15.0% 46 INDEPENDENCE COMMUNITY BANK 174 79 253 31.2% 0.0% 25.0% 46.2% 50.0% 19.8% 34.4% 29.8% 47 DELTA FUNDING CORPORATION 183 68 251 27.1% 0.0% - 28.6% 18.2% 30.4% 28.0% 26.7% 48 WORLD SAVINGS BANK 205 40 245 16.3% - 37.5% 25.0% 21.4% 11.5% 21.7% 14.1% 49 MERRILL LYNCH CREDIT CORP. 234 9 243 3.7% 0.0% 0.0% 5.3% 0.0% 3.0% 0.0% 4.2% 50 CITICORP TRUST BANK, FSB 195 44 239 18.4% - 0.0% 14.9% 5.8% 42.4% 23.9% 11.7% 51 HOMEOWNERS LOAN CORPORATION 193 43 236 18.2% 0.0% 0.0% 16.1% 33.3% 3.7% 20.0% 14.7% 52 EMIGRANT SAVINGS BANK 126 99 225 44.0% - 37.5% 59.2% 19.2% 31.0% 49.2% 43.8% 53 GREENPOINT MORTGAGE FUNDING, I 168 50 218 22.9% - 11.1% 27.7% 30.0% 15.7% 24.2% 18.6% 54 CITIBANK, FSB 201 11 212 5.2% 0.0% 11.1% 13.3% 0.0% 1.7% 3.2% 3.8% 55 GATEWAY FUNDING DIV MTG SVCS 201 9 210 4.3% - 16.7% 4.2% 14.3% 1.8% 7.9% 3.0% 56 BNC MORTGAGE 180 26 206 12.6% 0.0% 0.0% 10.0% 4.6% 13.7% 8.8% 11.5% 57 CITIFINANCIAL SERVICES, INC. 96 98 194 50.5% - - 45.6% 43.5% 20.0% 36.6% 44.4% 58 INVESTORS SAVINGS BANK 185 7 192 3.6% - 16.7% 25.0% 0.0% 6.4% 18.8% 5.5% 59 HOMESTAR MORTGAGE SVCS, LLC 184 8 192 4.2% 0.0% 0.0% 11.1% 0.0% 0.0% 2.9% 4.1% 60 WALL STREET FINANCIAL CORPORAT 188 3 191 1.6% 0.0% 0.0% 2.7% 0.0% 2.5% 2.0% 1.6% 61 CITIFINANCIAL MORTGAGE COMPANY 79 109 188 58.0% - 50.0% 78.6% 86.7% 75.0% 71.0% 82.1% 62 SECURITY ATLANTIC WHOLSALE LEN 135 53 188 28.2% 0.0% 0.0% 26.9% 25.0% 30.8% 34.9% 22.4% 63 FULL SPECTRUM LENDING 133 53 186 28.5% 100.0% 66.7% 34.5% 24.1% 13.3% 34.5% 22.0% 64 AMERICAN SAVINGS BANK OF NJ 182 1 183 0.5% - 0.0% - 0.0% 0.7% 0.0% 0.7% 65 CENTEX HOME EQUITY COMPANY, LL 31 151 182 83.0% - 0.0% 20.0% 0.0% 0.0% 20.0% 0.0% 66 CITIZENS BANK OF PENNSYLVANIA 126 52 178 29.2% 0.0% 20.0% 83.3% 60.0% 22.8% 40.0% 26.7% 67 PROVIDENT FUNDING ASSOCIATES, 172 1 173 0.6% - 0.0% 0.0% 0.0% 0.8% 0.0% 0.7% 68 COLUMBIA BANK 166 2 168 1.2% - 0.0% 0.0% 0.0% 2.0% 0.0% 1.9% 69 FINANCE AMERICA, LLC 23 137 160 85.6% - - 90.9% 100.0% 63.6% 84.6% 81.8% 70 BNY MORTGAGE COMPANY LLC 139 19 158 12.0% - 12.5% 29.4% 40.0% 8.6% 17.9% 13.0% 71 ALLIANCE MORTGAGE COMPANY 157 0 157 0.0% - 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 72 US BANK, N.A. 146 7 153 4.6% - 25.0% 25.0% 0.0% 5.9% 14.3% 12.5%

Analysis of Essex County Procurement and Contracting: Final Report 219 73 AMERICAN HOME MORTGAGE 131 15 146 10.3% - 0.0% 50.0% 14.3% 10.1% 16.0% 9.3% 74 OHIO SAVINGS BANK 132 4 136 2.9% - 0.0% 25.0% 0.0% 1.0% 0.0% 2.2% 75 COMMERCE BANK, NA 134 2 136 1.5% - 0.0% 18.2% 0.0% 0.0% 6.9% 0.0% 76 MBNA AMERICA (DELAWARE), N.A. 24 107 131 81.7% 100.0% 100.0% 75.0% 70.0% 85.4% 77.3% 81.0% 77 EQUITY ONE, INC. 83 44 127 34.6% - 0.0% 31.3% 26.3% 31.6% 21.2% 36.8% 78 FIRST MAGNUS FINANCIAL CORP 110 17 127 13.4% 100.0% 100.0% 30.0% 10.0% 3.9% 21.9% 11.3% 79 AMERICAN MORTGAGE NETWORK, INC 120 1 121 0.8% - 0.0% 0.0% 0.0% 1.3% 0.0% 1.2% 80 ING BANK, FSB 92 26 118 22.0% - 0.0% 50.0% 33.3% 28.2% 23.5% 33.3% 81 FREEDOM MORTGAGE CORPORATION 113 3 116 2.6% - 0.0% 0.0% 0.0% 4.0% 0.0% 2.9% 82 U.S. MORTGAGE CORP. 111 5 116 4.3% - 66.7% 10.5% 0.0% 2.7% 0.0% 8.5% 83 ADVANCED FINANCIAL SERVICES, I 36 76 112 67.9% - 100.0% 47.1% 40.0% 46.7% 66.7% 34.8% 84 QUICKEN LOANS, INC. 96 15 111 13.5% 0.0% 33.3% 25.0% 0.0% 5.3% 10.0% 9.5% 85 PAN AMERICAN BANK 105 3 108 2.8% - 0.0% 0.0% 0.0% 3.5% 0.0% 5.1% 86 H&R BLOCK,ORTGAGE CORPORATION 69 39 108 36.1% - 0.0% 34.6% 20.0% 15.8% 25.0% 24.1% 87 MIDFIRST BANK 51 55 106 51.9% - 0.0% 54.1% 75.0% 37.5% 52.5% 53.2% 88 IRWIN MORTGAGE CORP 96 10 106 9.4% - 0.0% 5.3% 5.6% 19.2% 4.4% 11.3% 89 E*TRADE BANK 65 38 103 36.9% - 50.0% 100.0% 0.0% 19.1% 50.0% 25.0% 90 NOVASTAR MORTGAGE INC 79 23 102 22.5% - 0.0% 25.0% 0.0% 10.0% 11.1% 12.5% 91 MORTGAGEIT INC 97 3 100 3.0% - 0.0% 2.4% 0.0% 6.9% 3.7% 3.2% Overall 38590 8294 46884 17.7% 23.1% 10.7% 30.0% 23.7% 9.6% 20.5% 14.4% Source: Home Mortgage Disclosure Act (HMDA) Data 2003 Note: Banks with at least 100 loan applications are listed. Banks are sorted by the size of loan applications.

Analysis of Essex County Procurement and Contracting: Final Report 220 Table J-18. Disproportionality in Unadjusted Loan Denial Rates Essex County (Refinancing Loans Only) Loan Application Disproportionality Bank Name Denial Black-to- Hispanic-to- Female-to- Total Rate White White Male 1 WELLS FARGO HOME MORTGAGE 3881 6.5% 4.15 4.12 1.68 2 WASHINGTON MUTUAL BANK, FA 3315 12.7% 1.68 1.87 1.09 3 FLEET NATIONAL BANK 2693 23.3% 2.81 2.92 1.31 4 COUNTRYWIDE HOME LOANS 2661 3.8% 2.61 1.47 1.18 5 WACHOVIA BANK 2319 29.1% 2.71 3.14 1.15 6 CHASE MANHATTAN MORTGAGE CORP 2194 10.2% 2.26 1.56 1.23 7 AMERIQUEST MORTGAGE COMPANY 1654 71.0% 0.92 0.91 0.92 8 GMAC MORTGAGE CORPORATION 1484 9.7% 4.93 4.36 0.96 9 CENDANT MORTGAGE 1253 0.2% - - - 10 WELLS FARGO FUNDING 1111 0.2% 0.00 0.00 0.00 11 PNC BANK NA 1064 36.7% 2.76 2.21 1.21 12 CITIMORTGAGE, INC 1063 2.3% 6.89 2.23 1.47 13 NATIONAL CITY MORTGAGE CORPORA 876 4.1% 2.60 1.83 1.13 14 HUDSON CITY SAVINGS BANK 704 5.1% 3.68 3.33 1.26 15 VALLEY NATIONAL BANK 700 3.4% 1.88 9.07 1.46 16 KEYBANK USA, N.A. 691 43.4% 1.30 1.07 1.34 17 PRINCIPAL RESIDENTIAL MTG, INC 673 2.1% 11.12 4.48 10.38 18 BANK OF AMERICA, N.A. 670 1.8% 3.34 4.25 0.87 19 ABN AMRO MORTGAGE GROUP, INC. 665 3.8% 3.84 0.86 0.60 20 OPTION ONE MORTGAGE CORP. 659 36.6% 1.21 1.21 0.81 21 NEW CENTURY MORTGAGE CORP. 523 28.7% 1.03 0.61 1.36 22 ARGENT MORTGAGE COMPANY 495 9.5% 0.88 0.75 0.93 23 GMAC BANK 489 0.8% 0.00 0.00 0.00 24 FLAGSTAR BANK 480 4.6% 1.98 3.26 0.36 25 HSBC MORTGAGE CORPORATION 442 1.8% 1.86 0.00 1.65 26 LEHMAN BROTHERS BANK 427 1.9% 0.00 4.09 1.06 27 FREMONT INVESTMENT & LOAN 421 31.1% 0.81 0.93 0.94 28 FIRST HORIZON HOME LOAN CORP 417 22.3% 1.41 2.08 1.51 29 GREENWICH HOME MORTGAGE 412 0.7% 0.00 4.21 1.37 30 BANK ONE, NA 344 42.7% 1.73 1.03 1.52 31 SIB MORTGAGE CORP. 332 6.6% 3.71 0.00 0.89 32 INDYMAC BANK, F.S.B. 331 26.9% 0.83 1.12 1.22 33 RESIDENTIAL FUNDING CORPORATIO 331 0.0% - - - 34 FIRST INTERSTATE FINANCIAL COR 328 4.3% 0.00 1.09 0.22 35 CHASE MANHATTAN BANK USA, NA 325 74.5% 1.13 1.23 0.98 36 SOVEREIGN BANK 317 7.3% 2.13 0.00 3.00 37 PENN FEDERAL SAVINGS BANK 315 5.7% 0.00 6.25 1.05 38 TRUST COMPANY OF NEW JERSEY 291 3.8% 3.04 4.94 1.01 39 THE PROVIDENT BANK 291 7.6% 8.94 5.86 1.63 40 HOUSEHOLD FINANCE CORPORATION 288 79.9% 1.00 1.07 0.86 41 E-LOAN, INC. 286 61.2% 1.74 1.25 0.96 42 HOMECOMINGS FINANCIAL NETWORK 279 4.7% 13.34 8.00 0.41 43 AMERICAN BUSINESS FINANCIAL 263 68.4% 1.24 1.47 1.01 44 BENEFICIAL 259 83.8% 0.74 1.00 0.82 45 JPMORGAN CHASE BANK 257 13.6% 1.20 1.42 1.21 46 INDEPENDENCE COMMUNITY BANK 253 31.2% 2.34 2.53 1.15 47 DELTA FUNDING CORPORATION 251 27.1% 0.94 0.60 1.05 48 WORLD SAVINGS BANK 245 16.3% 2.17 1.86 1.55

Analysis of Essex County Procurement and Contracting: Final Report 221 49 MERRILL LYNCH CREDIT CORP. 243 3.7% 1.76 0.00 0.00 50 CITICORP TRUST BANK, FSB 239 18.4% 0.35 0.14 2.04 51 HOMEOWNERS LOAN CORPORATION 236 18.2% 4.34 9.01 1.36 52 EMIGRANT SAVINGS BANK 225 44.0% 1.91 0.62 1.12 53 GREENPOINT MORTGAGE FUNDING, I 218 22.9% 1.77 1.92 1.30 54 CITIBANK, FSB 212 5.2% 7.66 0.00 0.86 55 GATEWAY FUNDING DIV MTG SVCS 210 4.3% 2.38 8.17 2.64 56 BNC MORTGAGE 206 12.6% 0.73 0.33 0.77 57 CITIFINANCIAL SERVICES, INC. 194 50.5% 2.28 2.17 0.82 58 INVESTORS SAVINGS BANK 192 3.6% 3.90 0.00 3.42 59 HOMESTAR MORTGAGE SVCS, LLC 192 4.2% - - 0.70 60 WALL STREET FINANCIAL CORPORAT 191 1.6% 1.07 0.00 1.25 61 CITIFINANCIAL MORTGAGE COMPANY 188 58.0% 1.05 1.16 0.86 62 SECURITY ATLANTIC WHOLSALE LEN 188 28.2% 0.87 0.81 1.56 63 FULL SPECTRUM LENDING 186 28.5% 2.59 1.81 1.57 64 AMERICAN SAVINGS BANK OF NJ 183 0.5% - 0.00 0.00 65 CENTEX HOME EQUITY COMPANY, LL 182 83.0% - - - 66 CITIZENS BANK OF PENNSYLVANIA 178 29.2% 3.65 2.63 1.50 67 PROVIDENT FUNDING ASSOCIATES, 173 0.6% 0.00 0.00 0.00 68 COLUMBIA BANK 168 1.2% 0.00 0.00 0.00 69 FINANCE AMERICA, LLC 160 85.6% 1.43 1.57 1.03 70 BNY MORTGAGE COMPANY LLC 158 12.0% 3.43 4.67 1.37 71 ALLIANCE MORTGAGE COMPANY 157 0.0% - - - 72 US BANK, N.A. 153 4.6% 4.25 0.00 1.14 73 AMERICAN HOME MORTGAGE 146 10.3% 4.95 1.41 1.72 74 OHIO SAVINGS BANK 136 2.9% 25.00 0.00 0.00 75 COMMERCE BANK, NA 136 1.5% - - - 76 MBNA AMERICA (DELAWARE), N.A. 131 81.7% 0.88 0.82 0.95 77 EQUITY ONE, INC. 127 34.6% 0.99 0.83 0.58 78 FIRST MAGNUS FINANCIAL CORP 127 13.4% 7.65 2.55 1.94 79 AMERICAN MORTGAGE NETWORK, INC 121 0.8% 0.00 0.00 0.00 80 ING BANK, FSB 118 22.0% 1.77 1.18 0.71 81 FREEDOM MORTGAGE CORPORATION 116 2.6% 0.00 0.00 0.00 82 U.S. MORTGAGE CORP. 116 4.3% 3.90 0.00 0.00 83 ADVANCED FINANCIAL SERVICES, I 112 67.9% 1.01 0.86 1.92 84 QUICKEN LOANS, INC. 111 13.5% 4.75 0.00 1.05 85 PAN AMERICAN BANK 108 2.8% 0.00 0.00 0.00 86 H&R BLOCK,ORTGAGE CORPORATION 108 36.1% 2.19 1.27 1.04 87 MIDFIRST BANK 106 51.9% 1.44 2.00 0.99 88 IRWIN MORTGAGE CORP 106 9.4% 0.27 0.29 0.39 89 E*TRADE BANK 103 36.9% 5.25 0.00 2.00 90 NOVASTAR MORTGAGE INC 102 22.5% 2.50 0.00 0.89 91 MORTGAGEIT INC 100 3.0% 0.35 0.00 1.15 Overall 46884 17.7% 3.13 2.47 1.43 Source: Home Mortgage Disclosure Act (HMDA) Data 2003 Note: Banks with at least 100 loan applications are listed. Banks are sorted by the size of loan applications. Disparity ratios are not reported when none of white/male applications were denied.

Analysis of Essex County Procurement and Contracting: Final Report 222 Table J-19. Residual Difference Analysis: Black vs. White Essex County (Refinancing Loans Only) Bank Name N Mean Std Dev Minimum Maximum E*TRADE BANK 3-0.2022 0.1245-0.3451-0.1173 INVESTORS SAVINGS BANK 3-0.1818 0.1578-0.2816 0.0001 CITIZENS BANK OF PENNSYLVANIA 12-0.1120 0.0736-0.2318 0.0001 INDEPENDENCE COMMUNITY BANK 26-0.0632 0.0745-0.2056 0.0003 PENN FEDERAL SAVINGS BANK 8-0.0629 0.1278-0.3517 0.0001 E-LOAN, INC. 33-0.0610 0.0414-0.1531 0.0016 EMIGRANT SAVINGS BANK 98-0.0557 0.0580-0.3000-0.0002 OHIO SAVINGS BANK 3-0.0511 0.0879-0.1526-0.0003 ING BANK, FSB 8-0.0507 0.0643-0.1656-0.0002 CITIFINANCIAL MORTGAGE COMPANY 42-0.0484 0.0406-0.1637 0.0010 INDYMAC BANK, F.S.B. 22-0.0434 0.0951-0.3558-0.0001 FREMONT INVESTMENT & LOAN 115-0.0421 0.0703-0.2498 0.0004 MIDFIRST BANK 68-0.0382 0.0804-0.5365 0.0269 SIB MORTGAGE CORP. 36-0.0377 0.0793-0.2993 0.0006 TRUST COMPANY OF NEW JERSEY 13-0.0364 0.0759-0.2444 0.0003 AMERICAN HOME MORTGAGE 2-0.0359 0.0520-0.0726 0.0009 WORLD SAVINGS BANK 40-0.0354 0.0705-0.2700-0.0003 FINANCE AMERICA, LLC 22-0.0341 0.0242-0.1003 0.0014 THE PROVIDENT BANK 26-0.0335 0.0666-0.1787 0.0048 GREENPOINT MORTGAGE FUNDING, I 39-0.0332 0.0611-0.1960 0.0004 CITIBANK, FSB 28-0.0328 0.0766-0.3021-0.0003 WASHINGTON MUTUAL BANK, FA 275-0.0324 0.0643-0.3010 0.0091 HOUSEHOLD FINANCE CORPORATION 70-0.0296 0.0195-0.0980 0.0016 HUDSON CITY SAVINGS BANK 46-0.0275 0.0689-0.2590 0.0029 AMERIQUEST MORTGAGE COMPANY 665-0.0263 0.0248-0.1445 0.0029 BENEFICIAL 43-0.0250 0.0168-0.0460 0.0016 CITIFINANCIAL SERVICES, INC. 57-0.0240 0.0357-0.1570 0.0012 AMERICAN BUSINESS FINANCIAL 74-0.0233 0.0205-0.0873 0.0016 SOVEREIGN BANK 23-0.0213 0.0528-0.2167-0.0001 FULL SPECTRUM LENDING 87-0.0184 0.0294-0.1266 0.0014 OPTION ONE MORTGAGE CORP. 193-0.0149 0.0224-0.0923 0.0019 CITICORP TRUST BANK, FSB 87-0.0141 0.0356-0.1880-0.0002 H&R BLOCK,ORTGAGE CORPORATION 26-0.0140 0.0188-0.0532 0.0018 ADVANCED FINANCIAL SERVICES, I 17-0.0134 0.0184-0.0560 0.0018 EQUITY ONE, INC. 31-0.0127 0.0224-0.0744 0.0015 BANK ONE, NA 53-0.0126 0.0149-0.0604 0.0048 JPMORGAN CHASE BANK 25-0.0121 0.0289-0.1137 0.0013 BNY MORTGAGE COMPANY LLC 12-0.0116 0.0278-0.0932 0.0011 CHASE MANHATTAN BANK USA, NA 100-0.0110 0.0113-0.0453 0.0050 GMAC MORTGAGE CORPORATION 88-0.0105 0.0262-0.1220 0.0016 DELTA FUNDING CORPORATION 35-0.0087 0.0171-0.0556 0.0021 CHASE MANHATTAN MORTGAGE CORP 272-0.0087 0.0300-0.1336 0.2457 PNC BANK NA 254-0.0086 0.0137-0.1070 0.0044 PROVIDENT FUNDING ASSOCIATES, 11-0.0081 0.0231-0.0735 0.0015 NOVASTAR MORTGAGE INC 10-0.0071 0.0170-0.0396 0.0014

Analysis of Essex County Procurement and Contracting: Final Report 223 NEW CENTURY MORTGAGE CORP. 159-0.0070 0.0190-0.0904 0.0016 IRWIN MORTGAGE CORP 9-0.0068 0.0149-0.0336 0.0009 QUICKEN LOANS, INC. 4-0.0066 0.0155-0.0298 0.0015 MBNA AMERICA (DELAWARE), N.A. 23-0.0065 0.0109-0.0303 0.0044 FLAGSTAR BANK 47-0.0064 0.0237-0.1133 0.0000 CENTEX HOME EQUITY COMPANY, LL 5-0.0061 0.0164-0.0355 0.0013 FLEET NATIONAL BANK 421-0.0058 0.0139-0.0783 0.0536 WACHOVIA BANK 798-0.0041 0.0102-0.0580 0.0056 HOMECOMINGS FINANCIAL NETWORK 24-0.0040 0.0123-0.0364 0.0026 GREENWICH HOME MORTGAGE 28-0.0038 0.0183-0.0774 0.0017 HOMESTAR MORTGAGE SVCS, LLC 47-0.0032 0.0129-0.0479 0.0018 COMMERCE BANK, NA 11-0.0029 0.0142-0.0411 0.0041 US BANK, N.A. 5-0.0026 0.0088-0.0158 0.0036 KEYBANK USA, N.A. 183-0.0024 0.0093-0.0478 0.0049 BNC MORTGAGE 90-0.0020 0.0100-0.0439 0.0017 BANK OF AMERICA, N.A. 12-0.0018 0.0082-0.0177 0.0034 ARGENT MORTGAGE COMPANY 112-0.0018 0.0098-0.0411 0.0064 MERRILL LYNCH CREDIT CORP. 16-0.0010 0.0084-0.0324 0.0016 COUNTRYWIDE HOME LOANS 412-0.0008 0.0235-0.1113 0.2757 WELLS FARGO HOME MORTGAGE 302-0.0007 0.0122-0.0671 0.0711 HSBC MORTGAGE CORPORATION 29-0.0004 0.0060-0.0315 0.0010 COLUMBIA BANK 2-0.0004 0.0001-0.0005-0.0004 GMAC BANK 24-0.0004 0.0001-0.0006-0.0002 LEHMAN BROTHERS BANK 42-0.0004 0.0003-0.0016 0.0005 PAN AMERICAN BANK 2 0.0000 0.0000 0.0000 0.0000 ABN AMRO MORTGAGE GROUP, INC. 36 0.0004 0.0067-0.0274 0.0041 WALL STREET FINANCIAL CORPORAT 51 0.0004 0.0300-0.0965 0.1706 CENDANT MORTGAGE 130 0.0011 0.0002 0.0007 0.0020 ALLIANCE MORTGAGE COMPANY 11 0.0011 0.0002 0.0008 0.0015 FREEDOM MORTGAGE CORPORATION 12 0.0011 0.0002 0.0008 0.0014 AMERICAN MORTGAGE NETWORK, INC 10 0.0013 0.0003 0.0009 0.0019 VALLEY NATIONAL BANK 25 0.0014 0.0049-0.0195 0.0038 MORTGAGEIT INC 1 0.0014-0.0014 0.0014 FIRST INTERSTATE FINANCIAL COR 2 0.0015 0.0000 0.0015 0.0015 HOMEOWNERS LOAN CORPORATION 112 0.0017 0.0057-0.0178 0.0106 FIRST HORIZON HOME LOAN CORP 44 0.0018 0.0189-0.0444 0.0613 GATEWAY FUNDING DIV MTG SVCS 33 0.0027 0.0151-0.0294 0.0812 CITIMORTGAGE, INC 117 0.0040 0.0127-0.0153 0.1259 NATIONAL CITY MORTGAGE CORPORA 114 0.0048 0.0139-0.0392 0.0777 U.S. MORTGAGE CORP. 15 0.0118 0.0411 0.0008 0.1602 PRINCIPAL RESIDENTIAL MTG, INC 16 0.0171 0.0779-0.0267 0.3072 FIRST MAGNUS FINANCIAL CORP 23 0.0377 0.0951-0.0419 0.3796 WELLS FARGO FUNDING 49 0.0432 0.1960 0.0021 0.9963 SECURITY ATLANTIC WHOLSALE LEN 55 0.0795 0.1022 0.0010 0.4012 Source: HMDA 2003 Note: The residual difference value was estimated for the individual black applicant using the underlying regression shown in Table J-16, which is the difference between the estimated loan rejection rate of a black applicant if he/she were treated like a white applicant minus the estimated loan rejection rate of the black applicant.

APPENDIX K: STATISTICAL DISPARITY ANALYSIS I. Bids A. Summary The analysis finds that Essex County DBEs have lower probabilities of winning a bid than do non-dbe firms. They also have higher probabilities of being part of a bidding process where all participant bids are rejected. However, during the initial submission portion of the procurement process, they are just as responsive to the County s specifications as non-dbe firms. Essex County DBEs appear to submit higher bid amounts, which explains some of their higher loss and rejection rates. Another important source of the differences between DBEs and non-dbes is that DBE status is positively correlated with bidding on construction bids and construction bids have a lower likelihood of being awarded a contract than do other types of bids. Utilizing DBE information on gender, race, and ethnicity, we find that the general results are solely driven by the experiences of female DBEs, followed by Hispanic and Asian DBEs. Blacks comprise too small a part of the DBE population to make any general conclusions. B. Data The data analyzed come from Essex County s 2002, 2003, and 2004 contract and bid information for seven outcomes. The bid outcomes are: Win: won bid or piece of bid; Loser: lost bid; No Award: bid on which no contract was granted; Non-responsive: bid judged non-responsive by County officials, i.e., County determined that part of the requested bid response was missing; Rejected: bid rejected by the County, usually because either the price was too high, the original contract specifications were changed, or another bidder lodged a successful protest of a competitor s bid response (includes non-responsible); Bid Withdrawn: bidder formally withdrew bid, usually due to inability to meet specifications; and Winner-Contract Withdrawn: bidder was awarded contract, but County later withdrew offer due to inability to meet specifications. The outcomes are consolidated into three bid outcomes: 1) winning versus losing bid, 2) rejected versus winning bid, and 3) responsive versus non-responsive bid (rejected or winning). The statistics for winning bids come from 1,377 firms. Statistics for the rejected bids are based on 692. There are two types of responsive bids. The first is based on 751 bids and the second is based on 628 bids. To be included in each outcome s sample, the firm must have complete information on the following characteristics: month of bid, year of bid, newspaper advertisement, age of firm, number of employees, bid amount, type of bid, and whether the bid is for a revenue contract. Construction is defined as an indicator of whether or not a bid falls under the rules for construction/public works bids. Revenue is defined as an indicator of whether or not a bid falls under the revenue bid category, meaning that the vendor pays the County for the privilege of selling its goods at a County event/facility.

Analysis of Essex County Procurement and Contracting: Final Report 225 A series of dummy variables to capture the bid record s format are as follows: Bid Amount; Bid in Pieces (bid amount recorded in pieces, used for contracts where vendors were bidding to deliver multiple types of goods/services, and where the County was able to split the award and distribute it among different vendors); Unit Amount (bid amount recorded in unit amount, for contracts where the County buys multiple units of the same item, but the bids are delivered at the item cost level); Missing Bid (bid amount was recorded from the County master bid book, but the physical file was not found); and No Bids (contracts for which no bids were received.) Years in Business: vendor response to bid application question about how many years vendor has been in business. Employees: vendor response to bid application question about how many employees vendor has. Alternate newspapers: County is bound by law to advertise all bids in the newspaper. All bids were announced in the Newark Star-Ledger, and this field indicates the selected bids announced in alternate newspapers: El Nuevo Coqui, Imperio, and Trenton Times. Table K-1 indicates that non-construction DBE firms are 5 percentage points less likely to win a bid. They have a 26 percent likelihood of rejection, compared to a 10 percent probability of rejection. Non-construction DBE firms are just as responsive as non-construction and non-dbe firms. Over 90 percent of each group s bids are viewed as responsive. The average bid amount of DBE firms exceeds the average bid of non-dbe firms. They also have fewer employees and are typically younger than non-dbe firms. Female and Asian DBEs comprise most of the nonconstruction DBEs. The table s entries for construction bids are similar. DBEs have a slightly lower likelihood of winning the bid, a higher probability of rejection, and are more responsive than non-dbes. Onequarter of each group s bids are winning bids. Almost one-half of DBE bids are rejected, compared to a rejection rate of 38 percent for non-dbe bids. Non-DBE construction firms submit higher bids, have more employees, and are older than DBE construction firms. The detailed information on type of DBE reveals that most construction DBEs are female, Hispanic, and Asian. C. Results To identify how much of the gap in win probabilities can be explained by differences in characteristics of the contract and firms, we estimate probit models. The dependent variable is whether the firm won or lost the bid. The predictor variables are the firm s DBE status, the bid was or was not a revenue bid, the format of the bid, the firm s bid amount, the firm s number of employees, its age, the bid was or was not advertised in alternative newspapers, the bid was on a construction contract, and month and year dummy variables. To isolate the impact of each variable, we start with a probit of the win dummy variable and DBE status and then incrementally add the characteristics of the contract and firm. Table K-2 reports the changes in the probability of winning with respect to changes in the predictor variables. The partial derivatives are evaluated at the sample means of the predictor variables. DBE firms have a 12 point lower likelihood of winning a bid; however, the gap falls to an imprecisely estimated gap of 4 points when we control for the characteristics of the firms. Most of the 12 point disparity can be explained by DBE and non-dbe differences in bid amount

Analysis of Essex County Procurement and Contracting: Final Report 226 and whether the bid was for a construction contract. As shown in the summary statistics DBEs submit higher construction bids than non-dbe firms. Panel B of Table K-2 shows that DBE bids have a 25 point higher likelihood of being rejected than non-dbe firms. The gap falls to 11 points after controlling for differences in bid amounts. Controlling for whether the contract is a construction contract further reduces the gap to a statistically significant 8 points. Panels C and D indicate that DBE firms are no less responsive to the County s criteria for bidding on contracts than non-dbe firms. After controlling for the characteristics of the firms and the contracts, the 5 point higher likelihood of DBEs falls to between 2.4 to 3.0 points. Table K-3 presents probit estimates for the non-construction bids. They comprise the majority of bids in our data. They generate similar results. The DBE disadvantage associated with submitting a winning bid is fully explained by DBE and non-dbe differences. A DBE s higher likelihood of having its bid rejected remains and its bids are just as responsive as non-dbe bids. We now disaggregate the DBE status variable into four categories: female, African American, Asian, and Hispanic. We re-estimate our probit models with this more detailed information. Tables K-4 and K-5 report differences in DBE and non-dbe status for the pooled and nonconstruction samples, respectively. Model 1 only includes dummy variables for DBE status. Model 2 adds information on revenue bid, format of bid, and bid amount. Model 3 adds the number of employees and age of the firm. Model 4 adds information on where the bid was advertised. Model 5 adds month and year dummy variables. Table K-4 indicates that female, Hispanic, and Asian DBEs have probabilities of submitting winning bids that are 18, 11, and 13 points less likely than non-dbe firms. The female estimate is statistically significant, while the Hispanic and Asian coefficients are marginally significant. African American DBEs have higher probabilities of winning, but the estimate is quite imprecise. Controlling for characteristics of the firms and contracts fails to explain why female DBEs have lower probabilities of winning, but explains all of the Asian and Hispanic disadvantages. The black DBE dummy variable increases as controls are included, but the standard errors are quite large, indicating no reliability in the estimated gap. Panel B indicates that the Female and Hispanic DBEs experience higher probabilities of rejection in the range of 28 to 30 points. The Asian disadvantage is 12 points higher than non-dbe firms. When the characteristics of the firms and contracts are added, the female disadvantage falls to 15 points, but remains precisely estimated. The Hispanic and Asian disadvantages are fully explained. Panel C indicates that female DBEs were driving the responsiveness results in Table K-3. Women DBEs are just as responsive to the County s specifications and criteria for bidding on its contracts. Table K-5 reports the estimated disaggregated differences between DBEs and non-dbes for the non-construction bids. Female s lower likelihood of submitting a winning dissipates. The higher

Analysis of Essex County Procurement and Contracting: Final Report 227 rejection rates for female DBEs remain and they remain just as responsive to the County specifications. II. Contracts Table K-6a examines the average contract amounts for DBEs vs. non-dbes. The average contract size for DBEs between 2002 and 2004 was $70,077. The average non-dbe contract was $37,623. This difference is statistically significant and yields the unexpected and unusual result that the average size of awards going to disadvantaged business enterprises exceeds that going to other firms. Part of the anomaly arises from the skewed distribution of contracts received by DBEs. Table I-1a shows that although 0.6 percent of all contracts awarded were for amounts over $1 million, 1.6 percent of DBE contracts were for amounts over $1M. Further, although DBEs account for only 4.66 percent of all contracts awarded, they account for 12.66 percent of all contracts awarded for amounts over $1million. Thus, the finding of higher average contract amounts for DBEs than for non-dbes is essentially an artifact of the different distribution of contracts awarded to DBEs vs. non-dbes. To see more clearly how the average size of contracts might obscure underlying disparities, we computed the mean contract amounts for DBEs and non-dbes for different dollar ranges. Table K-6a shows that the average contract amount for DBEs with contracts over $1 million was $3.1 million while for non-dbes it was $4.6 million, a disparity that is statistically significant at the 95 percent level of confidence. At the lower end of the distribution, the average contract size awarded to DBEs for contracts under $17,500 was $1,478, while for non-dbes it was $1,762 -- again a statistically significant adverse disparity. Table K-7a further breaks down this disparity in contract amounts by industry. Within the construction industry the average contract amount for DBEs was $540,127 while for non-dbes, it was $283,529, a statistically significant advantage to DBEs. When this comparison is made separately for contracts above and below $17,500, the differences are no longer statistically significant. No statistically significant differences in contract amounts are found for supplies and equipment contracts. The difference between DBE and non-dbe contract amounts for professional services is barely significant at the 5 percent level overall, although the differences are significant for professional service contracts over $17,500. 27 The conclusion then from examining disparities in contract amounts between DBEs and non- DBEs is that on the surface there appears to be an advantage enjoyed by DBEs over non-dbes and that advantage seems to come by way of higher average contract awards. When one looks below the surface at smaller contracts and at professional services, the advantage is no longer as apparent. 27 The remaining contracts include those that could not be classified because they did not have product codes or NAICs codes.

Analysis of Essex County Procurement and Contracting: Final Report 228 Of course, another way of looking below the surface is to disaggregate the DBE population into portions that reflect specific racial, ethnic, and gender groups. Recall, however, how we defined DBE. In this report, DBEs are understood to be firms that when matched with the various vendor lists, certification lists, and Dun & Bradstreet lists were found to be identified as one or more of the following: DBE, MBE, WBE, African American, Asian American, American Indian, Hispanic, or other. Because of the nature of this type of matching, it is entirely possible for there to be an overall advantage registered for DBEs when no such advantage exists for all specific subgroups. Thus, as an illustration, we look at the average contract amounts for females, African Americans, Hispanics, and Asians. The average contract amount for females was $99,266, far above the average for non-dbes, although not a statistically significant advantage. However, within professional services, the average contract amount for females was only a third of that of non-dbes. This result is documented in Table K-7b. The average contract amount for Hispanics and Asian Americans was $208,329 and $213,428, both far above the average non-dbe contract award of $37,632. By way of contrast, the mean African American contract size was only $3,655. This means that the average non-dbe contract was more than 10 times larger than the average African American contract. These results are documented in Tables K-7c through K-7e. So, the main interpretation of the results shown in these tables that report on disparities in contract amounts is that there appear to be a few Hispanic and Asian firms many in the construction industry that have much larger than average contract sizes, creating the illusion that there are advantages that DBEs experience via contracting with Essex County. This illusion is shattered when one looks more closely at race and gender, wherein African Americans have lower than average contract amounts and within professional services where females have lower contract amounts. III. Measures of Discrimination A more narrow definition of discrimination is used in regression analysis. Herein, the test is whether controlling for other relevant factors there is an adverse impact on specific groups in the amount of contracts awarded. The reason this is considered a more narrow measure of discrimination is that it takes account of certain variables such as length of time in business and size of business firm which themselves may be the result of discriminatory processes. The method, nonetheless, answers the question of whether there is differential treatment of identically situated firms. That is, it addresses the question of whether firms of the same size, same tenure, same industry, same year, and same location experience differ in the amounts of contracts awarded solely as a result of their race, ethnicity, or gender. The dependent variable in the regression results displayed in Tables K-11, K-11a, K-12 and K- 12a is the log of the amount of contract awarded. Tables K-11a and K-12a display the results separately by industry. Tables K-11 and K-12 display the results separately by contracts under $17,500 and those over $17,500.

Analysis of Essex County Procurement and Contracting: Final Report 229 The results show the following patterns of adverse discrimination: a. For all contracts under $17,500, DBEs experience average awards that are 11 percent lower than non-dbes (Table K-11) b. African American firms receive average contract awards that are 64 percent lower than non-dbes (Table K-12) The results also show that: a. For all contracts over $17,500, DBEs as a group have statistically significant higher average contract awards relative to non-dbes. However, when combining contracts over $17,500 and under $17,500 there is no statistically significant difference between DBE and non-dbe contract awards, controlling for location, size, tenure, year of award and industry. (Table K-11) b. When looking separately at construction, professional services, and supplies and equipment, DBEs do not experience adverse discrimination (Table K-11a) c. When looking separately at construction and supplies and equipment, the difference between contract amounts awarded to African American-owned firms and non-dbes is not statistically significant. (Table K-12a) d. Female-owned firms do not face a statistically significant lower contract amount, once one controls for relevant factors such as size of firm, industry, and tenure. (Tables K-12 and K-12a) e. Hispanic-owned firms have lower average contract amounts within professional services and supplies and equipment purchases, but these differences are not statistically significant once one controls for relevant factors. They do not experience a disadvantage in construction or overall. (Tables K-12 and K-12a) f. There is no statistically significant difference between the contract awards to Asianowned firms and non-dbes overall, small purchases, or for supplies and equipment purchases. In other areas there is no adverse discriminatory impact on Asian-owned businesses.(tables K-12 and K-12a) In summary, the regression analysis provides us with compelling evidence of adverse discrimination against African American firms as a group across all contracts and DBEs as a group among small contracts. The fact that the differences in contract amount between non- DBEs and females and between non-dbes and African Americans are no longer statistically significant once one looks at narrower industry groupings may be a reflection of the smaller number of observations required to perform this regression analysis.

Analysis of Essex County Procurement and Contracting: Final Report 230 Table K-1: Summary Statistics by DBE Status, Type of Bid, and Bid Outcome DBE Non-construction Non-DBE Non-construction Responsive Responsive Variables Win = 1 Reject = 1 Def 1 = 1 Def 2 = 1 Win = 1 Reject = 1 Def 1 = 1 Def 2 = 1 Bid Outcome = 1 0.42 0.26 0.93 0.91 0.47 0.10 0.92 0.91 Revenue Contract =1 0.01 0.00 0.00 0.00 0.05 0.06 0.05 0.06 Bid in Pieces = 1 0.30 0.31 0.31 0.38 0.41 0.42 0.38 0.41 Bid Withdrawn = 1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Missing Bid = 1 0.14 0.07 0.07 0.09 0.11 0.06 0.05 0.05 No Bid = 1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Non-responsive Bid = 1 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 Specifications of Bid Revised = 1 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.00 Discount Bid = 1 0.07 0.02 0.02 0.03 0.00 0.00 0.00 0.00 Missing Value = 1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Bid Amount 341517 379928 356425 341564 65364 59647 57099 43122 Bid Amount Missing = 1 0.50 0.40 0.40 0.50 0.52 0.48 0.46 0.48 Number of Employees 9.04 53.05 49.51 13.59 26.91 49.04 45.37 29.37 Number of Employees Missing =1 0.82 0.76 0.78 0.85 0.87 0.79 0.80 0.82 Age of Firm (in Years) 5.99 6.52 6.78 5.76 8.66 11.44 11.75 10.93 Age of Firm (in Years) Missing = 1 0.57 0.50 0.49 0.50 0.65 0.58 0.57 0.58 Bid not advertised = 1 0.96 0.88 0.87 0.88 0.92 0.91 0.91 0.93 El Nuevo Coqui = 1 0.03 0.10 0.11 0.09 0.08 0.09 0.08 0.07 Imperio = 1 0.01 0.02 0.02 0.03 0.00 0.01 0.01 0.01 Trenton Times = 1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2003 = 1 0.31 0.31 0.29 0.24 0.30 0.35 0.35 0.34 2004 = 1 0.30 0.43 0.44 0.41 0.33 0.32 0.32 0.31 February = 1 0.11 0.10 0.09 0.12 0.11 0.11 0.11 0.11 March = 1 0.09 0.12 0.13 0.09 0.10 0.11 0.11 0.11 April = 1 0.12 0.07 0.07 0.09 0.12 0.07 0.07 0.07 May = 1 0.07 0.10 0.09 0.09 0.10 0.09 0.09 0.09 June = 1 0.11 0.10 0.11 0.15 0.05 0.06 0.06 0.06 July = 1 0.09 0.05 0.04 0.06 0.08 0.05 0.06 0.06 August = 1 0.08 0.12 0.11 0.12 0.10 0.10 0.11 0.11 September = 1 0.03 0.00 0.02 0.03 0.05 0.06 0.06 0.07 October = 1 0.07 0.05 0.04 0.06 0.07 0.08 0.08 0.08 November = 1 0.03 0.17 0.16 0.03 0.09 0.12 0.12 0.09 December = 1 0.09 0.07 0.07 0.09 0.08 0.10 0.09 0.10 Female DBE =1 0.50 0.45 0.44 0.38 African American DBE =1 0.03 0.00 0.00 0.00 Hispanic DBE = 1 0.09 0.10 0.09 0.09 Asian DBE = 1 0.14 0.12 0.11 0.15 Notes: See end of table.

Analysis of Essex County Procurement and Contracting: Final Report 231 Table K-1 cont. Summary Statistics by DBE Status, Type of Bid, and Bid Outcome DBE Construction Non-DBE Construction Responsive Responsive Variables Win = 1 Reject = 1 Def 1 = 1 Def 2 = 1 Win = 1 Reject = 1 Def 1 = 1 Def 2 = 1 Bid Outcome = 1 0.24 0.48 1.00 1.00 0.25 0.39 0.89 0.83 Revenue Contract =1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Bid in Pieces = 1 0.00 0.00 0.00 0.00 0.03 0.06 0.05 0.08 Bid Withdrawn = 1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Missing Bid = 1 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 No Bid = 1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Non-responsive Bid = 1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Specifications of Bid Revised = 1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Discount Bid = 1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Missing Value = 1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Bid Amount 1171374 1627125 1627125 781854 2131109 2277893 2228369 1761582 Bid Amount Missing = 1 0.01 0.00 0.00 0.00 0.03 0.06 0.05 0.08 Number of Employees 18.41 21.64 21.64 23.34 58.97 48.27 44.97 56.55 Number of Employees Missing =1 0.10 0.11 0.11 0.14 0.22 0.19 0.18 0.18 Age of Firm (in Years) 15.50 14.38 14.38 12.97 17.13 18.48 18.88 19.89 Age of Firm (in Years) Missing = 1 0.10 0.09 0.09 0.14 0.19 0.10 0.10 0.09 Bid not advertised = 1 0.93 0.91 0.91 0.90 0.90 0.93 0.93 0.91 El Nuevo Coqui = 1 0.04 0.07 0.07 0.07 0.06 0.06 0.05 0.06 Imperio = 1 0.02 0.02 0.02 0.03 0.02 0.00 0.00 0.00 Trenton Times 0.01 0.00 0.00 0.00 0.02 0.01 0.02 0.03 2003 = 1 0.40 0.38 0.38 0.34 0.38 0.37 0.38 0.38 2004 = 1 0.48 0.55 0.55 0.59 0.36 0.42 0.44 0.44 February = 1 0.01 0.02 0.02 0.03 0.04 0.03 0.03 0.05 March = 1 0.12 0.13 0.13 0.10 0.07 0.18 0.18 0.11 April = 1 0.12 0.11 0.11 0.14 0.08 0.06 0.05 0.06 May = 1 0.18 0.09 0.09 0.17 0.24 0.14 0.13 0.18 June = 1 0.07 0.13 0.13 0.07 0.04 0.08 0.08 0.08 July = 1 0.02 0.04 0.04 0.03 0.03 0.03 0.03 0.03 August = 1 0.02 0.00 0.00 0.00 0.08 0.08 0.09 0.14 September = 1 0.05 0.16 0.16 0.10 0.07 0.16 0.18 0.11 October = 1 0.11 0.07 0.07 0.10 0.07 0.06 0.07 0.11 November = 1 0.12 0.16 0.16 0.07 0.09 0.10 0.09 0.08 December = 1 0.17 0.09 0.09 0.14 0.12 0.04 0.04 0.05 Female DBE =1 0.36 0.32 0.32 0.28 African American DBE =1 0.02 0.04 0.04 0.07 Hispanic DBE = 1 0.32 0.41 0.41 0.41 Asian DBE = 1 0.20 0.16 0.16 0.17

Analysis of Essex County Procurement and Contracting: Final Report 232 Notes: The data come from Essex County, NJ s 2002, 2003, and 2004 contract and bid information for seven outcomes. The following describe the bid outcomes: Win: won bid, or piece of bid; Loser: lost bid; No Award: bid on which no contract was granted; Nonresponsive: Bid judged non-responsive by County officials, i.e., County determined that part of the requested bid response was missing; Rejected: bid rejected by the County, usually because either the price was too high, the original contract specifications were changed, or another bidder lodged a successful protest of a competitor s bid response. (Includes non-responsible); Bid Withdrawn: bidder formally withdrew bid, usually due to inability to meet specifications; and Winner-Contract Withdrawn: bidder was awarded contract, but County later withdrew offer due to inability to meet specifications. The outcomes are consolidated into three bid outcomes: 1) winning versus losing bid, 2) rejected versus winning bid, and 3) responsive versus non-responsive bid (rejected or winning). The statistics for winning bids come from 1,377 firms. Statistics for the rejected bids are based on 692. There are two types of responsive bids. The first is based on 751 bids and the second is based on 628 bids. To be included in each outcome s sample, the firm must have complete information on the following characteristics: month of bid, year of bid, newspaper advertisement, age of firm, number of employees, bid amount, type of bid, and whether the bid is for a revenue contract. Construction is defined as an indicator of whether or not a bid falls under the rules for construction/public works bids. Revenue is defined as an indicator of whether or not a bid falls under the revenue bid category, meaning that the vendor pays the County for the privilege of selling its goods at a County event/facility. A series of dummy variables to capture the bid record s format are as follows: Bid Amount; Bid in Pieces (bid amount recorded in pieces, used for contracts where vendors were bidding to deliver multiple types of goods/services, and where the County was able to split the award and distribute among different vendors); Unit Amount (bid amount recorded in unit amount, for contracts where the County buys multiple units of the same item, but the bids are delivered at the item cost level); Missing Bid (Bid amount was recorded from the County master bid book, but physical file was not found); and No Bids (Contracts for which no bids were received.) Years in business: Vendor response to bid application question about how many years vendor has been in business. Employees: vendor response to bid application question about how many employees vendor has. Alternate newspapers: County is bound by law to advertise all bids in the newspaper. All bids were announced in the Newark Star-Ledger, and this field indicates the selected bids announced in alternate newspapers: El Nuevo Coqui, Imperio, and Trenton Times.

Analysis of Essex County Procurement and Contracting: Final Report 233 Panel A: Determinants of Winning Bid Table K-2. Analysis of Discrimination in Bidding Model 1 Model 2 Model 3 Model 4 Model 5 Variable df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err DBE = 1-0.123 0.036-0.069 0.039-0.082 0.042-0.036 0.044-0.039 0.045 Revenue =1 0.113 0.072 0.173 0.073 0.128 0.074 0.168 0.077 Bid in Pieces = 1 0.242 0.187 0.247 0.197 0.279 0.198 0.365 0.200 Missing Bid = 1-0.023 0.192-0.018 0.202 0.014 0.208 0.094 0.225 Bid Amount*1000000-0.093 0.022-0.110 0.026-0.067 0.026-0.058 0.025 Missing Bid Amount -0.154 0.181-0.086 0.198-0.143 0.197-0.214 0.202 Number of Employees*1000 0.276 0.161 0.208 0.158 0.249 0.161 Missing Number of Employees -0.045 0.040-0.163 0.047-0.172 0.047 Age of Firm 0.001 0.001 0.001 0.001 0.001 0.001 Missing Age of Firm -0.072 0.049-0.101 0.050-0.093 0.051 El Nuevo Coqui = 1-0.079 0.053-0.080 0.059 Imperio = 1 0.019 0.137-0.070 0.135 Trenton Times = 1-0.046 0.256-0.190 0.208 Construction = 1-0.270 0.044-0.283 0.043 Probability of Winning Bid 0.413 0.413 0.413 0.413 0.413 Notes: Entries are partial derivatives from probit models of bid outcome on a variety of characteristics: DBE status, revenue bid, format of bid, bid amount, number of employees, age of the firm, alternative advertising, and construction bid. Model 5 contains month and year dummy variables.

Analysis of Essex County Procurement and Contracting: Final Report 234 Table K-2 cont. Analysis of Discrimination in Bidding Panel B: Determinants of Rejected Bid Model 1 Model 2 Model 3 Model 4 Model 5 Variable df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err DBE = 1 0.245 0.051 0.108 0.046 0.113 0.048 0.110 0.051 0.081 0.041 Bid in Pieces = 1-0.314 0.126-0.299 0.132-0.267 0.115-0.212 0.089 Missing Bid = 1-0.149 0.022-0.144 0.023-0.141 0.024-0.094 0.018 Bid Amount*1000000 0.092 0.019 0.086 0.019 0.085 0.021 0.054 0.018 Missing Bid Amount = 1 0.229 0.231 0.211 0.237 0.173 0.202 0.100 0.141 Number of Employees*1000 0.001 0.132-0.002 0.136 0.009 0.080 Missing Number of Employees = 1-0.056 0.041-0.046 0.043-0.043 0.034 Age of Firm 0.001 0.001 0.002 0.001 0.001 0.001 Missing Age of Firm = 1 0.060 0.045 0.083 0.049 0.088 0.040 El Nuevo Coqui = 1 0.132 0.069 0.204 0.082 Construction = 1 0.043 0.049 0.031 0.039 Probability of Rejected Bid 0.178 0.182 0.182 0.184 0.184 Notes: Entries are partial derivatives from probit models of bid outcome on a variety of characteristics: DBE status, revenue bid, format of bid, bid amount, number of employees, age of the firm, alternative advertising, and construction bid. Model 5 contains month and year dummy variables.

Analysis of Essex County Procurement and Contracting: Final Report 235 Table K-2 cont. Analysis of Discrimination in Bidding Panel C: Determinants of Responsive Bid Type #1 Model 1 Model 2 Model 3 Model 4 Variable df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err DBE = 1 0.056 0.020 0.042 0.014 0.037 0.016 0.030 0.015 Revenue = 1 0.045 0.015 0.043 0.015 0.029 0.020 Bid in Pieces = 1 0.103 0.015 0.105 0.015 0.094 0.016 Bid Amount*1000000 0.0139 0.0124 0.0135 0.0153 0.0146 0.0132 El Nuevo Coqui = 1 0.0292 0.0176 0.0358 0.0115 Trenton Times = 1-0.4057 0.3718-0.5082 0.3956 Construction = 1 0.0172 0.0213 0.0175 0.0177 Probability of Responsive Bid Type #1 0.921 0.924 0.923 0.918 Panel D: Determinants of Responsive Bid Type #2 Variable df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err DBE = 1 0.051 0.030 0.039 0.018 0.034 0.022 0.024 0.021 Revenue = 1 0.054 0.016 0.053 0.016 0.040 0.017 Bid in Pieces = 1 0.135 0.019 0.138 0.020 0.126 0.021 Bid Amount*1000000 0.0014 0.0141 0.0025 0.0168 0.0043 0.0131 El Nuevo Coqui = 1 0.0274 0.0230 0.0341 0.0141 Trenton Times = 1-0.2707 0.3432-0.3521 0.3874 Construction = 1 0.0144 0.0242 0.0142 0.0191 Probability of Responsive Bid Type #2 0.906 0.908 0.908 0.901 Notes: Entries are partial derivatives from probit models of bid outcome on a variety of characteristics: DBE status, revenue bid, format of bid, bid amount, alternative advertising, and construction bid. Model 4 contains month and year dummy variables.

Analysis of Essex County Procurement and Contracting: Final Report 236 Table K-3. Non-Construction Regressions Panel A: Determinants of Winning Bid Model 1 Model 2 Model 3 Model 4 Model 5 Variable df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err DBE = 1-0.051 0.060 0.000 0.064 0.007 0.069-0.004 0.070 0.006 0.073 Revenue = 1 0.041 0.074 0.053 0.076 0.069 0.078 0.116 0.081 Bid in Pieces = 1 0.277 0.186 0.335 0.214 0.325 0.215 0.453 0.208 Missing Bid = 1 0.016 0.202 0.084 0.237 0.076 0.238 0.202 0.237 Bid Amount*1000000-0.370 0.156-0.671 0.171-0.665 0.172-0.643 0.178 Missing Bid Amount = 1-0.272 0.186-0.231 0.227-0.238 0.226-0.358 0.226 Number of Employees*1000 0.4441 0.3277 0.4462 0.3160 0.5005 0.3194 Missing Number of Employees = 1-0.315 0.049-0.310 0.049-0.311 0.050 Age of Firm 0.001 0.002 0.001 0.002 0.001 0.002 Missing Age of Firm = 1-0.126 0.057-0.136 0.057-0.126 0.060 El Nuevo Coqui = 1-0.125 0.061-0.110 0.069 Probability of Winning Bid 0.466 0.466 0.466 0.464 0.464 Panel B: Determinants of Rejected Bid Variable df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err DBE = 1 0.163 0.069 0.128 0.065 0.147 0.069 0.150 0.071 0.108 0.054 Bid in Pieces = 1-0.286 0.145-0.013 0.070 0.007 0.064-0.150 0.082 Missing Bid = 1-0.103 0.020 0.526 0.415 0.461 0.362-0.048 0.014 Bid Amount*1000000 0.062 0.044 0.050 0.044 0.056 0.044 0.008 0.022 Missing Bid Amount = 1 0.159 0.152-0.125 0.073-0.132 0.067 0.029 0.061 Number of Employees*1000 0.00009 0.00011 0.00007 0.00012 0.00004 0.00006 Missing Number of Employees = 1-0.0551 0.0418-0.0472 0.0398-0.0386 0.0295 Age of Firm 0.001 0.001 0.002 0.001 0.001 0.001 Missing Age of Firm = 1 0.052 0.034 0.073 0.036 0.058 0.025 El Nuevo Coqui = 1 0.000 0.000 0.153 0.072 0.259 0.104 Probability of Rejected Bid 0.112 0.113 0.113 0.114 0.000 0.122 0.000 Notes: Entries are partial derivatives from probit models of bid outcome on a variety of characteristics: DBE status, format of bid, bid amount, number of employees, age of the firm, alternative advertising, and construction bid. Model 5 contains month and year dummy variables.

Analysis of Essex County Procurement and Contracting: Final Report 237 Table K-3. Non-Construction Regressions Panel C: Determinants of Responsive Bid Type #1 Model 1 Model 2 Model 3 Model 4 Model 5 Variable df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err DBE = 1 0.0153 0.0390-0.0052 0.0346-0.0056 0.0357-0.0092 0.0379-0.0046 0.0333 Revenue = 1 0.0401 0.0127 0.0402 0.0128 0.0386 0.0130 0.0281 0.0161 Bid in Pieces = 1 0.1185 0.0196 0.1191 0.0204 0.1198 0.0206 0.0000 0.0000 Bid Amount*1000000 0.2270 0.1280 0.2260 0.1260 0.2200 0.1250 0.0000 0.0000 Age of Firm 0.0000 0.0005 0.0000 0.0005 0.0001 0.0005 Missing Age of Firm = 1-0.0022 0.0204 0.0007 0.0211 0.0098 0.0195 El Nuevo Coqui = 1 0.0229 0.0175 0.0268 0.0134 Probability of Responsive Bid Type #1 0.919 0.922 0.922 0.921 0.916 Panel D: Determinants of Responsive Bid Type #2 Variable df/dx Std. Err df/dx df/dx df/dx Std. Err df/dx Std. Err DBE = 1 0.0019 0.0504-0.0374 0.0585-0.0408 0.0613-0.0467 0.0646-0.0390 0.0599 Revenue = 1 0.0453 0.0134 0.0452 0.0136 0.0442 0.0134 0.0337 0.0166 Bid in Pieces = 1 0.1469 0.0228 0.1480 0.0240 0.1486 0.0241 0.1337 0.0418 Bid Amount*1000000 0.2360 0.1440 0.2290 0.1410 0.2320 0.1420 0.1340 0.1650 Age of Firm -0.0002 0.0006-0.0003 0.0006 0.0000 0.0005 Missing Age of Firm = 1-0.0087 0.0236-0.0074 0.0240-0.0014 0.0204 El Nuevo Coqui = 1 0.0196 0.0211 0.0204 0.0159 Probability of Responsive Bid Type #2 0.910 0.913 0.913 0.912 0.906 Notes: Entries are partial derivatives from probit models of bid outcome on a variety of characteristics: DBE status, revenue bid, format of bid, bid amount, number of employees, age of the firm, alternative advertising, and construction bid. Model 5 contains month and year dummy variables.

Analysis of Essex County Procurement and Contracting: Final Report 238 Table K-4. Pooled Disaggregated Probit Results Panel A: Probability of Winning Model 1 Model 2 Model 3 Model 4 Model 5 DBE Variable df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err Female =1-0.1849 0.0497-0.1567 0.0523-0.1713 0.0524-0.1417 0.0567-0.1489 0.0568 Black = 1 0.1714 0.2192 0.1723 0.2278 0.2104 0.2439 0.3183 0.2549 0.2766 0.2688 Hispanic = 1-0.1072 0.0684-0.0574 0.0740-0.0820 0.0762 0.0044 0.0842 0.0241 0.0872 Asian = 1-0.1347 0.0777-0.0957 0.0801-0.1055 0.0806-0.0474 0.0879-0.0434 0.0894 Panel B: Probability of Rejection DBE Variable df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err Female =1 0.2956 0.0824 0.1695 0.0777 0.1801 0.0800 0.1748 0.0810 0.1460 0.0736 Hispanic = 1 0.2795 0.0960 0.0425 0.0654 0.0298 0.0619 0.0099 0.0596 0.0230 0.0547 Asian = 1 0.1178 0.1200 0.0013 0.0678 0.0070 0.0716 0.0052 0.0731-0.0138 0.0639 Panel C: Probability of Responsiveness Type #1 DBE Variable df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err Female =1 0.0576 0.0302 0.0390 0.0209 0.0372 0.0224 0.0359 0.0237 0.0319 0.0198 Panel D: Probability of Responsiveness Type #2 DBE Variable df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err Female =1 0.0524 0.0482 0.0295 0.0379 0.0251 0.0425 0.0245 0.0443 0.0172 0.0417 Notes: Entries are differences in DBE and non-dbe status from probit models of bid outcomes on a variety of characteristics. Model 1 only includes dummy variables for DBE status. Model 2 adds information on revenue bid, format of bid, and bid amount. Model 3 adds the number of employees and age of the firm. Model 4 adds information on where the bid was advertised and whether it is a construction contract. Model 5 adds month and year dummy variables.

Analysis of Essex County Procurement and Contracting: Final Report 239 Table K-5. Non-construction Disaggregated Probit Results Panel A: Probability of Winning Model 1 Model 2 Model 3 Model 4 Model 5 Variable df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err Female = 1-0.1280 0.0820-0.0946 0.0843-0.0827 0.0886-0.0826 0.0898-0.0715 0.0928 Hispanic = 1-0.0261 0.1868 0.0202 0.1965 0.1066 0.2264 0.0862 0.2294 0.1436 0.2260 Asian = 1 0.0285 0.1590 0.0728 0.1476 0.0920 0.1520 0.0524 0.1658 0.0656 0.1671 Panel B: Probability of Rejection Variable df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err Female = 1 0.2670 0.1118 0.1814 0.0965 0.2067 0.1011 0.1970 0.1039 0.1233 0.0912 Hispanic = 1 0.1522 0.2219 0.0372 0.1407 0.0453 0.1412 0.0129 0.1012 0.2293 0.1934 Panel C: Probability of Responsiveness Type #1 Variable df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err Female = 1 0.0332 0.0501 0.0096 0.0429 0.0096 0.0435 0.0075 0.0462 0.0150 0.0329 Panel D: Probability of Responsiveness Type #2 Variable df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err df/dx Std. Err Female = 1 0.0149 0.0751-0.0281 0.0920-0.0312 0.0958-0.0361 0.1004-0.0218 0.0833 Notes: Entries are differences in DBE and non-dbe status from probit models of bid outcomes on a variety of characteristics. Model 1 only includes dummy variables for DBE status. Model 2 adds information on revenue bid, format of bid, and bid amount. Model 3 adds the number of employees and age of the firm. Model 4 adds information on where the bid was advertised and whether the bid is a construction contract. Model 5 adds month and year dummy variables.

Analysis of Essex County Procurement and Contracting: Final Report 240 Table K-6a. Mean Difference Tests: DBEs vs. Non-DBEs DBEs Non-DBEs N Amount N Amount t-stat p-value All Contracts 1,215 $70,077.00 24,841 $37,632.00-2.34 0.0196 by Contract Size Over $1 Million 19 $3,110,000.00 136 $4,560,000.00 2.01 0.0477 Between $500,000 and $999,999 13 $729,475.00 103 $679,362.00-1.16 0.2475 Between $250,000 and $499,999 21 $336,153.00 202 $353,638.00 1.07 0.2850 Between $50,000 and $249,999 61 $113,206.00 932 $109,656.00-0.50 0.6182 Between $17,500 and $49,999 41 $26,877.00 1,102 $29,263.00 1.64 0.1017 Under $17,500 1,060 $1,478.40 22,366 $1,762.80 3.46 0.0006 Source: 23 Agency Essex County Contract Files 2002-2004 Table K-6b. Mean Difference Tests: Females vs. Non-DBEs Female Non-DBEs N Amount N Amount t-stat p-value All Contracts 170 $99,266.00 24,841 $37,632.00-1.82 0.0700 by Contract Size Over $1 Million 4 $2,570,000.00 136 $4,560,000.00 2.33 0.0464 Between $500,000 and $999,999 2 $813,434.00 103 $679,362.00-1.30 0.1980 Between $250,000 and $499,999 10 $316,258.00 202 $353,638.00 1.62 0.1058 Between $50,000 and $249,999 12 $109,138.00 932 $109,656.00 0.03 0.9736 Between $17,500 and $49,999 8 $27,565.00 1,102 $29,263.00 0.52 0.6026 Under $17,500 134 $2,196.90 22,366 $1,762.80-1.63 0.1040 Source: 23 Agency Essex County Contract Files 2002-2004

Analysis of Essex County Procurement and Contracting: Final Report 241 Table K-6c. Mean Difference Tests: Blacks vs. Non-DBEs Black Non-DBEs N Amount N Amount t-stat p-value All Contracts 620 $3,655.40 24,841 $37,632.00 9.10 < 0.0001 by Contract Size Over $1 Million - - 136 $4,560,000.00 - - Between $500,000 and $999,999 - - 103 $679,362.00 - - Between $250,000 and $499,999 - - 202 $353,638.00 - - Between $50,000 and $249,999 10 $123,705.00 932 $109,656.00-0.82 0.4121 Between $17,500 and $49,999 18 $21,584.00 1,102 $29,263.00 1.83 0.0677 Under $17,500 592 $970.12 22,366 $1,762.80 9.53 < 0.0001 Source: 23 Agency Essex County Contract Files 2002-2004 Table K-6d. Mean Difference Tests: Hispanics vs. Non-DBEs Hispanics Non-DBEs N Amount N Amount t-stat p-value All Contracts 144 $208,329.00 24,841 $37,632.00-3.53 0.0004 by Contract Size Over $1 Million 9 $2,300,000.00 136 $4,560,000.00 3.40 0.0012 Between $500,000 and $999,999 7 $750,260.00 103 $679,362.00-1.25 0.2127 Between $250,000 and $499,999 7 $353,126.00 202 $353,638.00 0.02 0.9851 Between $50,000 and $249,999 8 $140,603.00 932 $109,656.00-1.62 0.1056 Between $17,500 and $49,999 7 $25,861.00 1,102 $29,263.00 0.98 0.3295 Under $17,500 106 $2,362.00 22,366 $1,762.80-2.00 0.0460 Source: 23 Agency Essex County Contract Files 2002-2004

Analysis of Essex County Procurement and Contracting: Final Report 242 Table K-6e. Mean Difference Tests: Asians vs. Non-DBEs Asians Non-DBEs N Amount N Amount t-stat p-value All Contracts 64 $213,468.00 24,841 $37,632.00-2.43 0.0150 by Contract Size Over $1 Million 4 $1,790,000.00 136 $4,560,000.00 3.97 0.0009 Between $500,000 and $999,999 5 $628,074.00 103 $679,362.00 0.78 0.4396 Between $250,000 and $499,999 5 $380,845.00 202 $353,638.00-0.84 0.4033 Between $50,000 and $249,999 11 $117,594.00 932 $109,656.00-0.48 0.6278 Between $17,500 and $49,999 2 $29,344.00 1,102 $29,263.00-0.01 0.9902 Under $17,500 37 $2,909.60 22,366 $1,762.80-2.26 0.0238 Source: 23 Agency Essex County Contract Files 2002-2004 Table K-6f. Mean Difference Tests: MBEs vs. Non-DBEs MBEs Non-DBEs N Amount N Amount t-stat p-value All Contracts 723 $42,588.00 24,841 $37,632.00-0.46 0.6491 by Contract Size Over $1 Million 8 $2,250,000.00 136 $4,560,000.00 3.29 0.0021 Between $500,000 and $999,999 6 $726,861.00 103 $679,362.00-0.79 0.4329 Between $250,000 and $499,999 11 $370,463.00 202 $353,638.00-0.76 0.4485 Between $50,000 and $249,999 24 $126,691.00 932 $109,656.00-1.53 0.1267 Between $17,500 and $49,999 20 $27,408.00 1,102 $29,263.00 0.89 0.3712 Under $17,500 654 $1,134.50 22,366 $1,762.80 7.04 < 0.0001 Source: 23 Agency Essex County Contract Files 2002-2004

Analysis of Essex County Procurement and Contracting: Final Report 243 Table K-6g. Mean Difference Tests: Non-profits vs. Non-DBEs Non-profits Non-DBEs N Amount N Amount t-stat p-value All Contracts 1,003 $81,883.00 23,842 $35,801.00-5.38 < 0.0001 by Contract Size Over $1 Million 15 $1,640,000.00 121 $4,920,000.00 5.31 < 0.0001 Between $500,000 and $999,999 24 $717,931.00 80 $666,279.00-1.56 0.1229 Between $250,000 and $499,999 46 $344,948.00 156 $356,200.00 0.94 0.3475 Between $50,000 and $249,999 166 $123,099.00 768 $106,880.00-3.25 0.0014 Between $17,500 and $49,999 86 $32,702.00 1,016 $28,972.00-3.63 0.0003 Under $17,500 666 $1,723.00 21,701 $1,764.00 0.34 0.7354 Source: 23 Agency Essex County Contract Files 2002-2004 Table K-7a. Mean Difference Tests: DBEs vs. Non-DBEs DBEs Non-DBEs N Amount N Amount t-stat p-value All Contracts 1,215 $70,077.00 24,841 $37,632.00-2.34 0.0196 Over $17,500 155 $539,204.00 2,475 $361,770.00-1.71 0.0886 Under $17,500 1,060 $1,478.40 22,366 $1,762.80 3.46 0.0006 Construction 101 $540,127.00 1,238 $283,529.00-2.50 0.0136 Over $17,500 71 $766,581.00 435 $799,191.00 0.19 0.8502 Under $17,500 30 $4,184.90 803 $4,185.10 0.00 0.9998 Professional Services 231 $9,023.90 4,819 $17,590.00 1.94 0.0529 Over $17,500 23 $73,633.00 408 $188,211.00 2.39 0.0173 Under $17,500 208 $1,879.70 4,411 $1,808.20-0.33 0.7411 Supplies and Equipment 146 $178,616.00 4,951 $88,537.00-1.08 0.2793 Over $17,500 38 $678,827.00 1,126 $380,396.00-0.88 0.3771 Under $17,500 108 $2,615.50 3,825 $2,619.60 0.01 0.9912 Source: 23 Agency Essex County Contract Files 2002-2004 Table K-7b. Mean Difference Tests: Females vs. Non-DBEs

Analysis of Essex County Procurement and Contracting: Final Report 244 Females Non-DBEs N Amount N Amount t-stat p-value All Contracts 170 $99,266.00 24,841 $37,632.00-1.82 0.0700 Over $17,500 36 $460,581.00 2,475 $361,770.00-0.66 0.5122 Under $17,500 134 $2,196.90 22,366 $1,762.80-1.63 0.1040 Construction 26 $559,407.00 1,238 $283,529.00-1.36 0.1848 Over $17,500 20 $725,523.00 435 $799,191.00 0.27 0.7904 Under $17,500 6 $5,687.70 803 $4,185.10-0.83 0.4081 Professional Services 51 $6,069.30 4,819 $17,590.00 2.47 0.0139 Over $17,500 3 $76,403.00 408 $188,211.00 2.44 0.0152 Under $17,500 48 $1,673.40 4,411 $1,808.20 0.30 0.7609 Supplies and Equipment 17 $76,751.00 4,951 $88,537.00 0.36 0.7183 Over $17,500 9 $142,734.00 1,126 $380,396.00 3.13 0.0027 Under $17,500 8 $2,520.40 3,825 $2,619.60 0.07 0.9415 Source: 23 Agency Essex County Contract Files 2002-2004

Analysis of Essex County Procurement and Contracting: Final Report 245 Table K-7c. Mean Difference Tests: Blacks vs. Non-DBEs Blacks Non-DBEs N Amount N Amount t-stat p-value All Contracts 620 $3,655.40 24,841 $37,632.00 9.10 < 0.0001 Over $17,500 28 $60,429.00 2,475 $361,770.00 7.98 < 0,0001 Under $17,500 592 $970.12 22,366 $1,762.80 9.53 < 0,0001 Construction 3 $78,435.00 1,238 $283,529.00 3.63 0.0031 Over $17,500 2 $113,714.00 435 $799,191.00 5.65 < 0,0001 Under $17,500 1 $7,876.80 803 $4,185.10 - - Professional Services 24 $20,737.00 4,819 $17,590.00-0.36 0.7228 Over $17,500 8 $54,230.00 408 $188,211.00 2.71 0.0074 Under $17,500 16 $3,990.70 4,411 $1,808.20-1.95 0.0704 Supplies and Equipment 21 $18,951.00 4,951 $88,537.00 4.19 < 0,0001 Over $17,500 6 $61,119.00 1,126 $380,396.00 4.86 < 0,0001 Under $17,500 15 $2,083.70 3,825 $2,619.60 0.54 0.5871 Source: 23 Agency Essex County Contract Files 2002-2004

Analysis of Essex County Procurement and Contracting: Final Report 246 Table K-7d. Mean Difference Tests: Hispanics vs. Non-DBEs Hispanics Non-DBEs N Amount N Amount t-stat p-value All Contracts 144 $208,329.00 24,841 $37,632.00-3.53 0.0004 Over $17,500 38 $782,869.00 2,475 $361,770.00-2.44 0.0191 Under $17,500 106 $2,362.00 22,366 $1,762.80-2.00 0.0460 Construction 42 $683,580.00 1,238 $283,529.00-2.43 0.0189 Over $17,500 32 $895,872.00 435 $799,191.00-0.42 0.6739 Under $17,500 10 $4,245.00 803 $4,185.10-0.04 0.9660 Professional Services 34 $12,020.00 4,819 $17,590.00 0.50 0.6212 Over $17,500 1 $357,434.00 408 $188,211.00 - - Under $17,500 33 $1,552.60 4,411 $1,808.20 0.48 0.6322 Supplies and Equipment 35 $18,159.00 4,951 $88,537.00 3.92 0.0001 Over $17,500 4 $135,745.00 1,126 $380,396.00 2.35 0.0509 Under $17,500 31 $2,987.00 3,825 $2,619.60-0.53 0.5935 Source: 23 Agency Essex County Contract Files 2002-2004

Analysis of Essex County Procurement and Contracting: Final Report 247 Table K-7e. Mean Difference Tests: Asians vs. Non-DBEs Asians Non-DBEs N Amount N Amount t-stat p-value All Contracts 64 $213,468.00 24,841 $37,632.00-2.43 0.0150 Over $17,500 27 $502,011.00 2,475 $361,770.00-1.06 0.2966 Under $17,500 37 $2,909.60 22,366 $1,762.80-2.26 0.0238 Construction 22 $590,698.00 1,238 $283,529.00-1.97 0.0602 Over $17,500 21 $618,017.00 435 $799,191.00 0.92 0.3601 Under $17,500 1 $17,000.00 803 $4,185.10 - Professional Services 25 $11,118.00 4,819 $17,590.00 1.10 0.2758 Over $17,500 4 $56,777.00 408 $188,211.00 2.80 0.0055 Under $17,500 21 $2,421.00 4,411 $1,808.20-0.92 0.3595 Supplies and Equipment 11 $33,866.00 4,951 $88,537.00 1.90 0.0736 Over $17,500 2 $174,417.00 1,126 $380,396.00 0.14 0.8872 Under $17,500 9 $2,632.30 3,825 $2,619.60-0.03 0.9795 Source: 23 Agency Essex County Contract Files 2002-2004

Analysis of Essex County Procurement and Contracting: Final Report 248 Table K-8. Log-Linear Regression Analysis of Contract Amounts with DBE Status Variable Variable Full Model 1 Over $17,500 Under $17,500 Coeff. Est. t-stat. p-value Coeff. Est. t-stat. p-value Coeff. Est. t-stat. p-value Intercept 6.8424 56.11 <.0001 10.7874 60.54 <.0001 6.5907 62.58 <.0001 FY2003 0.1480 4.78 <.0001 0.1887 3.17 0.0016 0.0720 2.84 0.0044 FY2004 0.3018 9.18 <.0001 0.0041 0.07 0.9468 0.2120 7.86 <.0001 New Jersey 0.5332 17.12 <.0001 0.2489 3.38 0.0007 0.2296 9.07 <.0001 New York 0.9742 10.92 <.0001 0.0130 0.08 0.9328 0.7083 9.53 <.0001 Pennsylvania 0.9028 9.56 <.0001 0.3520 1.73 0.0841 0.7587 9.93 <.0001 Construction 2.3376 36.48 <.0001 0.8497 11.98 <.0001 1.1127 17.87 <.0001 Professional Services -0.1046-2.99 0.0028-0.0477-0.70 0.4847 0.0060 0.21 0.8352 Large Size Firm 0.3497 4.25 <.0001 0.6301 5.59 <.0001-0.1132-1.59 0.1120 Medium Size Firm 0.0559 0.52 0.5998-0.0817-0.56 0.5762-0.2010-2.18 0.0290 Missing Size Firm -0.6884-10.99 <.0001-0.2886-3.13 0.0018-0.3906-7.30 <.0001 Established Firm -0.4642-3.89 0.0001-0.0335-0.20 0.8380-0.3881-3.76 0.0002 Pre-911 Firm -0.0641-0.52 0.6037 0.1069 0.64 0.5251-0.1483-1.39 0.1656 Missing Firm Tenure -0.1545-1.19 0.2338 0.2529 1.43 0.1515-0.3123-2.78 0.0054 Nonprofit 1.3219 19.11 <.0001 0.5587 7.04 <.0001-0.0351-0.54 0.5905 DBE -0.0015-0.02 0.9815 0.4105 3.81 0.0001-0.1115-2.12 0.0336 Mean of Dependent 6.7507 11.3335 6.2361 Number of Observation 26049 2629 23419 Adj R-Sq 0.1155 0.1018 0.0446 F Value 227.75 20.86 73.95 p-value <.0001 <.0001 <.0001 Source: Author's estimation using the Essex County Contracts 2002-04

Analysis of Essex County Procurement and Contracting: Final Report 249 Table K-8a. Log-Linear Regression Analysis of Contract Amounts with DBE Status Variable Variable Construction Professional Services Supplies and Equipment Coeff. Est. t-stat. p-value Coeff. Est. t-stat. p-value Coeff. Est. t-stat. p-value Intercept 8.3868 13.86 <.0001 7.5231 38.36 <.0001 6.8700 31.46 <.0001 FY2003 0.5212 2.91 0.0037 0.0895 1.40 0.1631 0.2142 2.54 0.0112 FY2004 0.2012 1.01 0.312 0.2920 4.20 <.0001 0.1380 1.71 0.0874 New Jersey 1.3369 5.87 <.0001-0.4643-6.22 <.0001 0.9192 7.96 <.0001 New York 0.0694 0.11 0.9126-0.1493-0.94 0.3461 0.8487 3.70 0.0002 Pennsylvania 0.7777 1.17 0.2412 0.0019 0.01 0.9917 0.3664 1.53 0.1249 Large Size Firm 0.9991 1.62 0.1061-0.5583-4.52 <.0001 0.8134 6.33 <.0001 Medium Size Firm 0.7008 1.55 0.1223-0.1008-0.76 0.4489 0.6557 3.05 0.0023 Missing Size Firm 1.5362 4.03 <.0001-0.2544-2.40 0.0162 0.0661 0.61 0.5424 Established Firm -1.6889-2.94 0.0033-0.5802-3.21 0.0014-0.1807-0.95 0.3408 Pre-911 Firm -0.8557-1.44 0.1503-0.1982-1.04 0.2964-0.1727-0.89 0.3715 Missing Firm Tenure -1.9055-2.92 0.0036-0.3098-1.53 0.1265-0.1654-0.82 0.4095 Nonprofit -1.2052-1.59 0.1125-0.3117-1.19 0.2323 0.8434 8.40 <.0001 DBE 1.7304 6.17 <.0001 0.3068 2.32 0.0206 0.4989 2.48 0.013 Mean of Dependent 9.1406 6.7872 7.8759 Number of Observation 1210 5476 5251 Adj R-Sq 0.094 0.0189 0.0397 F Value 10.66 9.14 17.72 p-value <.0001 <.0001 <.0001 Source: Author's estimation using the Essex County Contracts 2002-04 Table K-9. Log-Linear Regression Analysis of Contract Amounts with Race Variables

Analysis of Essex County Procurement and Contracting: Final Report 250 Variable Full Model 2 Over $17,500 Under $17,500 Coeff. Est. t-stat. p-value Coeff. Est. t-stat. p-value Coeff. Est. t-stat. p-value Intercept 6.8326 56.14 <.0001 10.7959 60.74 <.0001 6.5814 62.51 <.0001 FY2003 0.1486 4.81 <.0001 0.1845 3.10 0.0019 0.0732 2.89 0.0038 FY2004 0.3016 9.20 <.0001-0.0074-0.12 0.9039 0.2131 7.90 <.0001 New Jersey 0.5504 17.71 <.0001 0.2736 3.73 0.0002 0.2378 9.40 <.0001 New York 0.9752 10.96 <.0001 0.0057 0.04 0.9708 0.7085 9.54 <.0001 Pennsylvania 0.9029 9.58 <.0001 0.3521 1.73 0.0832 0.7596 9.95 <.0001 Construction 2.2678 35.31 <.0001 0.8002 11.11 <.0001 1.0989 17.65 <.0001 Professional Services -0.1255-3.59 0.0003-0.0312-0.46 0.6472-0.0067-0.23 0.8174 Large Size Firm 0.3716 4.53 <.0001 0.6232 5.54 <.0001-0.0988-1.39 0.1653 Medium Size Firm 0.0630 0.59 0.5532-0.0998-0.68 0.4935-0.1930-2.10 0.0358 Missing Size Firm -0.6795-10.88 <.0001-0.2858-3.10 0.0019-0.3872-7.24 <.0001 Established Firm -0.4696-3.94 <.0001-0.0324-0.20 0.8427-0.3924-3.80 0.0001 Pre-911 Firm -0.1101-0.89 0.3722 0.0747 0.44 0.6565-0.1646-1.54 0.1236 Missing Firm Tenure -0.1454-1.12 0.2615 0.2474 1.41 0.1595-0.3040-2.71 0.0068 Nonprofit 1.3073 18.94 <.0001 0.5408 6.83 <.0001-0.0403-0.62 0.5361 Female 0.8128 4.95 <.0001 0.4319 2.01 0.0444 0.4684 3.25 0.0012 Black -0.6417-7.36 <.0001-0.6007-2.49 0.0128-0.4132-5.94 <.0001 Hispanic 0.6153 3.43 0.0006 0.7479 3.50 0.0005 0.2327 1.44 0.151 Asian 1.5248 5.70 <.0001 0.6059 2.43 0.0152 0.5152 1.88 0.0596 Mean of Dependent 6.7507 11.3335 6.2361 Number of Observation 26049 2629 23419 Adj R-Sq 0.1199 0.1065 0.0465 F Value 198.07 18.41 64.51 p-value <.0001 <.0001 <.0001 Source: Author's estimation using the Essex County Contracts 2002-04 Table K-9a. Log-Linear Regression Analysis of Contract Amounts with Race Variables

Analysis of Essex County Procurement and Contracting: Final Report 251 Variable Construction Professional Services Supplies and Equipment Coeff. Est. t-stat. p-value Coeff. Est. t-stat. p-value Coeff. Est. t-stat. p-value Intercept 8.4197 14.12 <.0001 7.5177 38.39 <.0001 6.9081 31.53 <.0001 FY2003 0.5337 3.02 0.0026 0.0895 1.40 0.162 0.2124 2.51 0.012 FY2004 0.1214 0.61 0.5389 0.2980 4.29 <.0001 0.1423 1.76 0.0782 New Jersey 1.3234 5.91 <.0001-0.4553-6.11 <.0001 0.9094 7.86 <.0001 New York 0.0121 0.02 0.9845-0.1158-0.73 0.4644 0.8409 3.66 0.0003 Pennsylvania 0.7897 1.21 0.2273-0.0023-0.01 0.9898 0.3546 1.49 0.1376 Large Size Firm 1.0324 1.70 0.0903-0.5875-4.77 <.0001 0.7875 6.13 <.0001 Medium Size Firm 0.6535 1.46 0.1442-0.0999-0.75 0.4515 0.6265 2.91 0.0037 Missing Size Firm 1.5173 4.04 <.0001-0.1703-1.59 0.1113 0.0634 0.58 0.5592 Established Firm -1.6692-2.95 0.0033-0.5766-3.19 0.0014-0.1713-0.90 0.3675 Pre-911 Firm -1.0675-1.82 0.0694-0.2060-1.09 0.2771-0.1871-0.97 0.334 Missing Firm Tenure -1.9077-2.97 0.0031-0.3861-1.90 0.0571-0.1903-0.95 0.3435 Nonprofit -1.1635-1.56 0.12-0.3145-1.21 0.2268 0.8398 8.36 <.0001 Female 1.8157 3.42 0.0007 0.0641 0.22 0.8228 1.1370 2.28 0.0226 Black 1.3180 0.88 0.3771 1.8689 5.44 <.0001 0.3931 0.53 0.5937 Hispanic 2.6499 6.00 <.0001-0.2037-0.61 0.5388-0.4272-1.24 0.2159 Asian 2.8315 4.67 <.0001 0.8876 2.16 0.0305 0.7266 1.11 0.2671 Mean of Dependent 9.1406 6.7872 7.8759 Number of Observation 1210 5476 5251 Adj R-Sq 0.12 0.0237 0.0394 F Value 11.31 9.29 14.48 p-value <.0001 <.0001 <.0001 Source: Author's estimation using the Essex County Contracts 2002-04

Analysis of Essex County Procurement and Contracting: Final Report 252 Table K-10. List of Variables in the Regressions Variable Coding FY2003 Dummy variable for fiscal year 2003 FY2004 Dummy variable for fiscal year 2004 New Jersey New York Pennsylvania Large Size Firm Medium Size Firm Missing Size Firm Established Firm Pre-911 Firm Missing Firm Tenure Nonprofit Female Black Hispanic Asian Dummy variable for location in New Jersey Dummy variable for location in New York Dummy variable for location in Pennsylvania Number of employees is greater than 100 and total sales is greater than $1million (50 < Employees < 100 and sales > $500K) or (Employees > 100 and sales < $1 million) or (sales > $1 million and employees < 100) either employees or sales is missing established more 20 years ago established less than 5 years ago established year is missing Nonprofit firms Female owned firms black owned firms Hispanic owned firms Asian owned firms

Analysis of Essex County Procurement and Contracting: Final Report 253 Table K-11. Log-Linear Regression Analysis of Contract Amounts with DBE Status Variable Variable Full Model 1 Over $17,500 Under $17,500 Coeff. Est. t-stat. p-value Coeff. Est. t-stat. p-value Coeff. Est. t-stat. p-value Intercept 6.8424 56.11 <.0001 10.7874 60.54 <.0001 6.5907 62.58 <.0001 FY2003 0.1480 4.78 <.0001 0.1887 3.17 0.0016 0.0720 2.84 0.0044 FY2004 0.3018 9.18 <.0001 0.0041 0.07 0.9468 0.2120 7.86 <.0001 New Jersey 0.5332 17.12 <.0001 0.2489 3.38 0.0007 0.2296 9.07 <.0001 New York 0.9742 10.92 <.0001 0.0130 0.08 0.9328 0.7083 9.53 <.0001 Pennsylvania 0.9028 9.56 <.0001 0.3520 1.73 0.0841 0.7587 9.93 <.0001 Construction 2.3376 36.48 <.0001 0.8497 11.98 <.0001 1.1127 17.87 <.0001 Professional Services -0.1046-2.99 0.0028-0.0477-0.70 0.4847 0.0060 0.21 0.8352 Large Size Firm 0.3497 4.25 <.0001 0.6301 5.59 <.0001-0.1132-1.59 0.1120 Medium Size Firm 0.0559 0.52 0.5998-0.0817-0.56 0.5762-0.2010-2.18 0.0290 Missing Size Firm -0.6884-10.99 <.0001-0.2886-3.13 0.0018-0.3906-7.30 <.0001 Established Firm -0.4642-3.89 0.0001-0.0335-0.20 0.8380-0.3881-3.76 0.0002 Pre-911 Firm -0.0641-0.52 0.6037 0.1069 0.64 0.5251-0.1483-1.39 0.1656 Missing Firm Tenure -0.1545-1.19 0.2338 0.2529 1.43 0.1515-0.3123-2.78 0.0054 Nonprofit 1.3219 19.11 <.0001 0.5587 7.04 <.0001-0.0351-0.54 0.5905 DBE -0.0015-0.02 0.9815 0.4105 3.81 0.0001-0.1115-2.12 0.0336 Mean of Dependent 6.7507 11.3335 6.2361 Number of Observation 26049 2629 23419 Adj R-Sq 0.1155 0.1018 0.0446 F Value 227.75 20.86 73.95 p-value <.0001 <.0001 <.0001 Source: Author's estimation using the Essex County Contracts 2002-04

Analysis of Essex County Procurement and Contracting: Final Report 254 Table K-11a. Log-Linear Regression Analysis of Contract Amounts with DBE Status Variable Variable Construction Professional Services Supplies and Equipment Coeff. Est. t-stat. p-value Coeff. Est. t-stat. p-value Coeff. Est. t-stat. p-value Intercept 8.3868 13.86 <.0001 7.5231 38.36 <.0001 6.8700 31.46 <.0001 FY2003 0.5212 2.91 0.0037 0.0895 1.40 0.1631 0.2142 2.54 0.0112 FY2004 0.2012 1.01 0.312 0.2920 4.20 <.0001 0.1380 1.71 0.0874 New Jersey 1.3369 5.87 <.0001-0.4643-6.22 <.0001 0.9192 7.96 <.0001 New York 0.0694 0.11 0.9126-0.1493-0.94 0.3461 0.8487 3.70 0.0002 Pennsylvania 0.7777 1.17 0.2412 0.0019 0.01 0.9917 0.3664 1.53 0.1249 Large Size Firm 0.9991 1.62 0.1061-0.5583-4.52 <.0001 0.8134 6.33 <.0001 Medium Size Firm 0.7008 1.55 0.1223-0.1008-0.76 0.4489 0.6557 3.05 0.0023 Missing Size Firm 1.5362 4.03 <.0001-0.2544-2.40 0.0162 0.0661 0.61 0.5424 Established Firm -1.6889-2.94 0.0033-0.5802-3.21 0.0014-0.1807-0.95 0.3408 Pre-911 Firm -0.8557-1.44 0.1503-0.1982-1.04 0.2964-0.1727-0.89 0.3715 Missing Firm Tenure -1.9055-2.92 0.0036-0.3098-1.53 0.1265-0.1654-0.82 0.4095 Nonprofit -1.2052-1.59 0.1125-0.3117-1.19 0.2323 0.8434 8.40 <.0001 DBE 1.7304 6.17 <.0001 0.3068 2.32 0.0206 0.4989 2.48 0.013 Mean of Dependent 9.1406 6.7872 7.8759 Number of Observation 1210 5476 5251 Adj R-Sq 0.094 0.0189 0.0397 F Value 10.66 9.14 17.72 p-value <.0001 <.0001 <.0001 Source: Author's estimation using the Essex County Contracts 2002-04 Table K-12. Log-Linear Regression Analysis of Contract Amounts with Race Variables

Analysis of Essex County Procurement and Contracting: Final Report 255 Variable Full Model 2 Over $17,500 Under $17,500 Coeff. Est. t-stat. p-value Coeff. Est. t-stat. p-value Coeff. Est. t-stat. p-value Intercept 6.8326 56.14 <.0001 10.7959 60.74 <.0001 6.5814 62.51 <.0001 FY2003 0.1486 4.81 <.0001 0.1845 3.10 0.0019 0.0732 2.89 0.0038 FY2004 0.3016 9.20 <.0001-0.0074-0.12 0.9039 0.2131 7.90 <.0001 New Jersey 0.5504 17.71 <.0001 0.2736 3.73 0.0002 0.2378 9.40 <.0001 New York 0.9752 10.96 <.0001 0.0057 0.04 0.9708 0.7085 9.54 <.0001 Pennsylvania 0.9029 9.58 <.0001 0.3521 1.73 0.0832 0.7596 9.95 <.0001 Construction 2.2678 35.31 <.0001 0.8002 11.11 <.0001 1.0989 17.65 <.0001 Professional Services -0.1255-3.59 0.0003-0.0312-0.46 0.6472-0.0067-0.23 0.8174 Large Size Firm 0.3716 4.53 <.0001 0.6232 5.54 <.0001-0.0988-1.39 0.1653 Medium Size Firm 0.0630 0.59 0.5532-0.0998-0.68 0.4935-0.1930-2.10 0.0358 Missing Size Firm -0.6795-10.88 <.0001-0.2858-3.10 0.0019-0.3872-7.24 <.0001 Established Firm -0.4696-3.94 <.0001-0.0324-0.20 0.8427-0.3924-3.80 0.0001 Pre-911 Firm -0.1101-0.89 0.3722 0.0747 0.44 0.6565-0.1646-1.54 0.1236 Missing Firm Tenure -0.1454-1.12 0.2615 0.2474 1.41 0.1595-0.3040-2.71 0.0068 Nonprofit 1.3073 18.94 <.0001 0.5408 6.83 <.0001-0.0403-0.62 0.5361 Female 0.8128 4.95 <.0001 0.4319 2.01 0.0444 0.4684 3.25 0.0012 Black -0.6417-7.36 <.0001-0.6007-2.49 0.0128-0.4132-5.94 <.0001 Hispanic 0.6153 3.43 0.0006 0.7479 3.50 0.0005 0.2327 1.44 0.151 Asian 1.5248 5.70 <.0001 0.6059 2.43 0.0152 0.5152 1.88 0.0596 Mean of Dependent 6.7507 11.3335 6.2361 Number of Observation 26049 2629 23419 Adj R-Sq 0.1199 0.1065 0.0465 F Value 198.07 18.41 64.51 p-value <.0001 <.0001 <.0001 Source: Author's estimation using the Essex County Contracts 2002-04

Analysis of Essex County Procurement and Contracting: Final Report 256 Table K-12a. Log-Linear Regression Analysis of Contract Amounts with Race Variables Variable Construction Professional Services Supplies and Equipment Coeff. Est. t-stat. p-value Coeff. Est. t-stat. p-value Coeff. Est. t-stat. p-value Intercept 8.4197 14.12 <.0001 7.5177 38.39 <.0001 6.9081 31.53 <.0001 FY2003 0.5337 3.02 0.0026 0.0895 1.40 0.162 0.2124 2.51 0.012 FY2004 0.1214 0.61 0.5389 0.2980 4.29 <.0001 0.1423 1.76 0.0782 New Jersey 1.3234 5.91 <.0001-0.4553-6.11 <.0001 0.9094 7.86 <.0001 New York 0.0121 0.02 0.9845-0.1158-0.73 0.4644 0.8409 3.66 0.0003 Pennsylvania 0.7897 1.21 0.2273-0.0023-0.01 0.9898 0.3546 1.49 0.1376 Large Size Firm 1.0324 1.70 0.0903-0.5875-4.77 <.0001 0.7875 6.13 <.0001 Medium Size Firm 0.6535 1.46 0.1442-0.0999-0.75 0.4515 0.6265 2.91 0.0037 Missing Size Firm 1.5173 4.04 <.0001-0.1703-1.59 0.1113 0.0634 0.58 0.5592 Established Firm -1.6692-2.95 0.0033-0.5766-3.19 0.0014-0.1713-0.90 0.3675 Pre-911 Firm -1.0675-1.82 0.0694-0.2060-1.09 0.2771-0.1871-0.97 0.334 Missing Firm Tenure -1.9077-2.97 0.0031-0.3861-1.90 0.0571-0.1903-0.95 0.3435 Nonprofit -1.1635-1.56 0.12-0.3145-1.21 0.2268 0.8398 8.36 <.0001 Female 1.8157 3.42 0.0007 0.0641 0.22 0.8228 1.1370 2.28 0.0226 Black 1.3180 0.88 0.3771 1.8689 5.44 <.0001 0.3931 0.53 0.5937 Hispanic 2.6499 6.00 <.0001-0.2037-0.61 0.5388-0.4272-1.24 0.2159 Asian 2.8315 4.67 <.0001 0.8876 2.16 0.0305 0.7266 1.11 0.2671 Mean of Dependent 9.1406 6.7872 7.8759 Number of Observation 1210 5476 5251 Adj R-Sq 0.12 0.0237 0.0394 F Value 11.31 9.29 14.48 p-value <.0001 <.0001 <.0001 Source: Author's estimation using the Essex County Contracts 2002-04

Analysis of Essex County Procurement and Contracting: Final Report 257 Table K-13. List of Variables in the Regressions Variable Coding FY2003 Dummy variable for fiscal year 2003 FY2004 Dummy variable for fiscal year 2004 New Jersey New York Pennsylvania Large Size Firm Medium Size Firm Missing Size Firm Established Firm Pre-911 Firm Missing Firm Tenure Nonprofit Female Black Hispanic Asian Dummy variable for location in New Jersey Dummy variable for location in New York Dummy variable for location in Pennsylvania Number of employees is greater than 100 and total sales is greater than $1million (50 < Employees < 100 and sales > $500K) or (Employees > 100 and sales < $1 million) or (sales > $1 million and employees < 100) either emplyees or sales is missing established more 20 years ago established less than 5 years ago established year is missing Nonprofit firms Female owned firms black owned firms hispanic owned firms asian owned firms

APPENDIX L: POLICY SIMULATIONS I. Policy Options To best evaluate how particular policy initiatives might affect the availability and utilization of minority and women-owned businesses by Essex County, we conducted statistical simulations to evaluate the effect five policy options could have on Essex County procurement and contracting. The options we tested are: e-commerce, a local small business development program, an Essex County preference program, an aspirational goal program, and a targeted business development program. Each of these possible strategies provides legally defensible routes by which to remedy the measured disparities identified in this report. An e-commerce solution involves the use of the internet to improve communication with vendors. Aspects of such a program could include: expanding communication to vendors, providing information about various bidding opportunities and outreach on Essex County s procurement process, and allowing vendors to sign up either for e-mail notification of notices and opportunities and/or register as DBEs or other types of specialized businesses. It is expected that e-commerce solutions provide more information, more participation in bidding, and more opportunities for all firms to access the bidding process. Of people who usually have fewer opportunities and information, we assumed many are firms owned by blacks or DBEs. Through e-commerce solutions, these firms would be better able to participate in bidding for and obtaining a contract. In implementing this policy option, one would attempt to register every firm in the marketplace. A local business organization would probably have a list of local businesses with addresses and telephone numbers. New firms who want to become part of the list could register by telephone, in person, by email, or by mail. This effort will require the cooperation and contribution of local authorities. After registration is complete, firms should be identified and grouped by categories such as professional expertise or goods sold. These categories can then be used to notify businesses bout bidding opportunities that apply to their category(s). A local small business program could provide incentives to local vendors to bid on County contracts. Such a program would provide preferences to local SBEs either in the form of bidprice preferences or in the form of award preferences. In the latter case, if two contractors that were equally qualified to perform a service or provide goods one a local SBE and one a non- SBE or non-local firm bid on a contract, the local SBE would receive the contract. This policy approach requires that the type and size or nature of preferences to be used are identified in advance. For example, one type of preference is bid-price. Suppose two equally qualified contractors bid on a contract and were equal in all requirements except price and the local SBE submits a higher bid price than does a non-sbe and/or non-local firm. Under a bidprice preference, the SBE would receive the contract as long as his/her price was not more than a certain percentage above that of the non-sbe/non-local firm. However, it is imperative that the

Analysis of Essex County Procurement and Contracting: Final Report 259 entity setting up the preference program outlines the specific details of the preferences in advance. Using our example, if the entity set the difference in price between preferred and nonpreferred firms at a maximum of 20 percent, the SBE would only receive the contract if its price was no more than 20 percent higher than the non-sbe/non-local firm. If the price difference were greater than 20 percent, then the preference would not apply and the best price would be selected. But, to reiterate, the specific details of any preferences must be laid before the program begins. Under an Essex County (or local) preference program, Essex County would concentrate on hiring firms located in Essex County. Given the concentration of minority and women-owned firms in the county, this is likely to increase their participation in County purchasing. It is also likely to increase the participation of Essex County firms of all backgrounds. This alternative would be similar to the local small business program. It is essential that any preferences are clearly outlined in advance. Thus, if two firms, one local (Essex County-based) and one non-local were equally qualified, the local firm would receive preference over the nonlocal firm and would receive the contract. If the same two firms were equally qualified in all respects except price, and the local firm sets a higher price, the local firm would be preferred if its asking price is not more than a certain percentage above that of the non-local firm. Aspirational goals for specific population groups are often discussed as a response to disparities. This policy option asks: in Essex County, how would contracts and dollars be distributed if we set a 15 percent goal for DBEs? How would it affect just African American or women-owned businesses? One way to implement this policy option is to evaluate the market during the first half of the year. Suppose the target of the program is DBEs. If it appears that 15 percent of contracts and dollars are going toward DBEs, the 15 percent goal is satisfied without taking any special steps and the marketplace is in charge. If DBEs are not receiving 15 percent of contracts or dollars, then during the second half of the year, a government entity might step in and give bidding preferences to DBEs. This is approach is usually applied to different type of contract awards. Then, at the end of the year, for each $100 awarded in public contracts, $15 should have gone to DBEs. Unlike a local small business program that provides incentives to all SBEs, a targeted business development program focuses on businesses owned by specific population groups to help them develop their capacity and become better prepared to compete for public contracts. This policy option involves accessing programs and services that assist targeted business such as: free professional counseling for targeted business owners, programs designed to enhance the success and capacity of women entrepreneurs (if this is one of the targeted populations), computer training to help business owners to take advantage of internet business and resources. II. Simulation Results The following table summarizes the results of the five policy options described above:

Analysis of Essex County Procurement and Contracting: Final Report 260 Table L-1: Summary Table: Additional Contracts, Contracts dollars and number of firms with contracts Policy Options DBE Black # of # of # of Contracts Firms Contract $ # of Contracts Firms Contract $ E-Commerce (1) 89 n.a. 3,485,892 73 n.a. 2,976,427 Local small business development 5,300 1,142 465,334,616 2,941 195 13,663,814 Essex County preference program 860 232 79,000,285 34 13 958,313 Aspirational goal program (2) 457 76 32,034,610 629 60 15,881,703 Targeted business development (3) n.a. n.a. n.a. 19 1 1,292,019 (1) Six different models where performed for this policy option. Model 2 (see methodology section below) was chosen since e- commerce will open a lot of opportunities never before considered (100 percrent bid increase). Additionally, this model uses the existing average contract dollars, which is a realistic assumption. (2) Results for the aspirational goal program found in the column of black firms include black and female firms. Even when the current number of female-owned firms is not larger, its average contract dollar amount is. (3) Four different models were performed for this policy option. Model 3 (see methodology section below) was chosen since it uses the average contract amount among all firms. This is a more realistic scenario because a targeted business development program would be offered to all firms that satisfy the requirements, without considering the type of industry in which they work. An e-commerce program can result in a large share of existing firms bidding on contracts. The amount of awards won through bidding would be $6,462,319 or 9.19 percent of total utilization. However, Table L-1 shows that the e-commerce policy option represents the worst alternative in terms of number of contracts and contract dollars awarded to DBEs. In the case of black firms, it is an intermediate option because it adds 73 contracts, which represent almost 3 million dollars. Alternatively, a local small business development program represents the best policy option both for DBEs and for black firms. This is mainly because among SBE firms, black firms have greater participation (15.82 percent according to a sample survey of 1997 Essex County) than among the total number of firms (0.93 percent). Moreover, DBE firms have a great participation among SBE firms (49.50 percent according to the Essex County database for county contractors), which increases enormously the amount of contracts, firms, and contract dollars for Essex contractors 28. Therefore, this policy option generates the biggest increase in number of firms, contracts, and contract dollars for DBEs and black firms. Further, although Table L-1 shows that the aspirational goal program generates the largest amount of contract dollars for black firms, it should be noted that this amount of money also includes female-owned firms, whereas the local small business development does not. The second best option for DBEs is the Essex County preference program, followed by the aspirational goal development program. For black firms, the second best option is the aspirational goal program followed by the e-commerce policy option. A targeted business development program is a policy that even though it increases utilization among black firms in terms of dollars awarded, contracts awarded, and firms hired, it is not recommended because all the other policy options generate bigger increases. 28 See Table L-3 for details.

Analysis of Essex County Procurement and Contracting: Final Report 261 III. Methodology This section describes the details of the results presented above. It includes assumptions, statistical calculations, different scenarios within a policy option, and intermediate results. A. E-commerce solution We used the initial data presented in the table below: Table L-2: E-commerce solution inputs Total number of Total number of % of total awards Bidding success contracts awarded bid awards that were bid rate Total 26,056 526 2.02% 5.23% Non-DBE 24,841 448 1.08% 4.59% DBE 1,215 78 6.42% 16.93% Black 620 5 0.81% 13.97% We tested the effect of an e-commerce program by considering a range of estimates that forecast the effect this program would have on increasing the number of submitted bids and contracts won by black-owned firms and DBEs. We begin with the existing evidence on bids and bid success rates. Out of a total of 26,056 contracts awarded and transactions related to those awards, there were a total of 526 contract awards that came about as a result of Essex County bids 29. For the purposes of this simulation, we also assume the following: Models 1 through 4 a. Models 1 and 2 simulate a 100 percent increase in the number of bids submitted as a result of the program and a contract was awarded for each bid opportunity. b. Models 3 and 4 simulate a 50 percent increase in the number of bids submitted. c. No change in the bid success rate. d. Contract bids are awarded to blacks at same rate as the current bid success rate for blacks. e. Models 1 and 3 use a bid amount of $368,109 and models 2 and 4 use a bid amount of $39,144 the existing averages for these contracts. Models 5 and 6 i. A 100 percent increase in number of possible contract bids 29 The share of contracts that arose from bids about two percent underestimates the actual number of contracts bid, since it does not count bids issued through the State of New Jersey. Moreover, it is based on all contracts, including those for amounts under $17,500 that do not require bids.

Analysis of Essex County Procurement and Contracting: Final Report 262 ii. No change in bid success rates. iii. Contract bids were awarded to blacks in the same proportion as the current percentage of total awards that were bid by blacks multiplied by the current bidding success rate of blacks. iv. The amount of $368,109 is the bid amount used in Model 5 and $39,144 in Model 6. Contract dollar DBE Table L-3: Simulation Results of E-commerce Program Model 1. 100% bid increase $368,109 per bid Model 2. 100% bid increase $39,144 per bid Model 3. 50% bid increase $368,110 per bid Model 4. 50% bid increase $39,145 per bid Model 5. $368,109 per bid Model 6. $39,144 per bid Change $ 32,780,796 $ 3,485,892 $ 16,390,398 $ 1,742,946 $ 4,861,031 $ 516,919 Utilization 11.56% 8.69% 9.95% 8.52% 8.82% 8.40% Black Change $ 27,049,481 $ 2,876,427 $ 13,524,741 $ 1,438,214 $ 257,124 $ 27,342 Utilization 2.87% 0.50% 1.55% 0.36% 0.25% 0.22% Contracts DBE Change 89.05 89.05 44.53 0.45 13.21 13.21 Utilization 5.00% 5.00% 4.83% 4.66% 4.71% 4.71% Black Change 73.48 73.48 36.74 36.74 0.70 0.70 Utilization 2.66% 2.66% 2.52% 2.52% 2.38% 2.38% Source: Essex County Contract Files 2002-2004 Model 2 is the model presented in summary table L-1. B. Local Small Business Program To estimate the impacts of instituting this type of program we assume the following: 1. The total number of contract dollars in Essex County remains unchanged, which means that Essex County is still going to have contracts totaling $465,261,366. 2. All contracts are going to be allocated to local SBE firms whenever possible. 3. The total availability of black firms and DBE firms in Essex County equals the existing counts based on the Dun & Bradstreet measures. To construct this scenario, we took the following steps:

Analysis of Essex County Procurement and Contracting: Final Report 263 Contracts are allocated to black firms and DBE firms according to their participation in the total number of SBE firms: o 15.82 percent of all SBE firms with contracts in Essex County are black. o 49.50 percent of all SBE firms with contracts in Essex County are DBEs. If following this share, the number of possible contracts exceeds the number of available contracts, it is allocated just the number of available contracts for that type of firm (construction, professional services, or others). The number of contracts was obtained applying the following ratios: o Contracts per black firms = 15.96 30 o Contracts per DBE firms = 4.88 Using the Essex County database, we obtained the change in contract dollars which was average dollars per black firm, by type of industry, and multiplying this result by the number of firms. The same procedure was applied to DBE firms. Table L-4: Calculation on Number of firms for Local SBE Preferences Policy Number of firms (1) Availability Max Possible allocation Share by firm type (2) Tot. firms with this option Essex No Essex DBE Black DBE Black DBE Black DBE Black Construction 94 264 138 27 138 27 49.50% 15.82% 131 27 Others 266 986 579 51 579 51 49.50% 15.82% 488 51 Professional ss 937 1198 1383 134 1198 134 49.50% 15.82% 593 133 No codes 262 2075 Total 1559 4523 2100 211 1915 211 1212 211 (1) Data from Data Base Essex County, aggregated by type of firms. (2) Data from Table A.X. Results that appear in Table L-1 are summarized in the last row of the following table: Table L-5: Local Essex County Data for Local Small Business development in Essex County Policy Options DBE Black # of Contracts # of Firms Contract $ # of Contracts # of Firms Contract $ Currently 613 70 25,942,123 427 16 1,156,536 With the Policy Option 5,913 1,212 491,276,739 3,368 211 14,820,350 Change 5,300 1,142 465,334,616 2,941 195 13,663,814 C. Essex County Preference Program To simulate the effects of this policy option, we first aggregated the data by number of firms. Then, we sorted the contracts by NAICS codes and grouped them into three great categories: construction, professional services, and others, and split them by Essex County and non-essex County. The reason for this was that the number of available black and DBE firms was tabulated by type of firms. After that, we allocated all the non-essex County firms that signed contracts 30 Calculated using the summary availability/utilization table L-4.

Analysis of Essex County Procurement and Contracting: Final Report 264 with Essex County to Essex County contractors, according to the availability of black and DBE firms by three types of firms: construction, professional services, and others. The main assumption of this policy scenario is that all the contracts go to Essex County. The next steps were followed to construct this scenario: Contracts were allocated to black firms and DBE firms following the original participation of DBE and black firms by type of industry, among the total number of firms in Essex County. If following this share, the number of possible contracts exceeded the number of available contracts, it was allocated just the number of available contracts for that type of firm (construction, professional services, or others). The number of contracts was obtained applying the following ratios: o Contracts per black firms = 15.96 o Contracts per DBE firms = 4.88 Change in contract dollars was found by obtaining the average dollars per black firm by type of industry from the Essex County database, and multiplying this result by the number of firms. The same procedure was applied to DBE firms. Table L-6: Calculation on Number of firms for Essex County Preferences Policy Number of firms (1) Availability Max possible allocation Share by firm type (2) Additional firms Essex No Essex DBE Black DBE Black DBE Black DBE Black Construction 94 264 200 30 200 30 9.06% 1.36% 24 4 Others 266 986 839 57 839 57 14.17% 0.96% 140 8 Professional ss 937 1198 2005 150 1198 150 11.53% 0.86% 138 17 No codes (3) 257 2075 11.93% 0.93% Total 1,554 4,523 3,044 237 2,237 237 302 29 (1) Data from Data Base Essex County, aggregated by type of firms. (2) Data from Table A.X. Results that appear in Table L-1 are summarized in the last row of the following table: Table L-7: Local Essex County Data for Essex County Preference Program Policy Options DBE Black # of contracts # firms contract $ # of contracts # firms contract $ Currently 613 70 25942123 427 16 1156536 With the Policy Option 1,473 302 104,942,408 461 29 2,114,849 Change 860 232 79,000,285 34 13 958,313 D. Women and Minority-Owned Business Goals

Analysis of Essex County Procurement and Contracting: Final Report 265 We used the initial data presented in the table below: Table L-8: Aspirational Goals Program Inputs # of Firms # of Contracts Average per Contract Contract Dollars Black and women 75 789 25,261 19,930,542 DBE 201 1,215 70,077 85,143,555 Total 6,082 26,056 39,144 1,019,936,064 We assumed the following: The average contract amount remains unchanged. There is a 15 percent increase in contract dollars. This increase in contract dollars is allocated to DBEs and black and/or womenowned firms considering the initial participation in contract dollars that black and/or women-owned firms has in DBE firms. Table L-9: Simulation Results for Women and Minority-Owned Business Policy New policy Change Total contract $ Ave cont $ contracts firms contract $ contracts firms Black and women 35,812,245 25,260.51 1418 135 15,881,703 629 60 DBE 117,178,165 70,077.00 1672 277 32,034,610 457 76 E. Targeted Business Development Program To simulate the effects such a program would have in Essex County, we assumed the following: 1. We identified the top 5 utilized NAICS codes, 31 based on the 2002 to 2004 Essex County Contract file, These five industries were: Table L-10: Industries used to perform the calculations NAICS Description Utilization Percent Total Firms Minority Firms Women Firms Black Firms Hispanic Firms Asian Firms 234990 All Other Heavy Construction $219,209,258 25.49 189 6 8 0 2 1 541330 Professional Services $106,792,133 12.42 1704 133 112 21 22 38 524210 Insurance Agencies and $ 95,176,712 11.07 3013 81 247 8 38 6 Brokerages 813920 Professional Organizations $ 71,748,736 8.34 272 0 3 0 0 0 233320 Commercial and Institutional Building Construction $ 51,484,156 5.99 958 71 77 17 21 8 31 Offices of Physicians (except Mental Health Specialists), the 5 th largest utilized NAICS code, was replaced by the next one, Commercial and Institutional Building Construction, for the targeted business development program.

Analysis of Essex County Procurement and Contracting: Final Report 266 2. We identified 237 black firms out of 25,518 total firms in Essex County, based on Dun & Bradstreet s available lists for Essex County. The proportion of the black firms is 0.93 percent. Because of the development program we assumed that there is an increase of 100 percent in the number of black firms in the top 5 utilized NAICS codes, applying the same proportion of black firms with respect to the total. 3. We used the summary availability/utilization table in the data files of Essex County to calculate that one black firm has 15.96 contracts and the average amount of a contract for black firms is $4,273. The average amount of a contract among all firms is $68,001. We then developed four models to measure the effects of a targeted development program. Option 1: Option 2: Option 3: Option 4: We assumed that the increase in black firms was awarded according to the average of utilization in each NAICS code. For example, in NAICS code 541330, Professional Service, the average of utilization is $62,672 for one firm. The increase of utilization for black firms in Professional Service was found by multiplying the average utilization ($62,672) by the increase of black firms (0.195 firms). We assumed that there is an increase of 19 contracts per black firm and the amount of each additional contract is the average amount for black firms, which is $4,273. We assumed that there is increase of 19 contracts per black firm and the amount of an additional contract is the average contract amount among all total firms or $68,001. There is no black firm in NAICS 234990 All Other Heavy Construction, which is the largest utilized NAICS code. We assume that there is one black firm developed and it will be awarded with the average of All Other Heavy Construction firms, which is $1,159,837. The results of this simulation are outlined below Table L-11: Models for the Targeted Business Development Program Black Policy Options # of # of Contracts Firms Contract $ Option 1 19 1 64,222 Option 2 19 1 81,187 Option 3 19 1 1,292,019 Option 4 23 2 1,183 Source: Essex County Contract Files 2002-2004, Dun & Bradstreet Availability lists for Essex County. It is important to mention that there is no information on the number of DBE firms in NAICS codes. Therefore, it was not possible to simulate a targeted business development scenario for DBE firms.

APPENDIX M: ANALYSIS OF PURCHASING PRACTICES AND PROCEDURES The purchasing decisions of county governments in New Jersey are governed by the State Local Public Contracts Law, NJSA 40A:11-1 et seq. Regulations under this law are promulgated by the Department of Community Affairs, Division of Local Government Services. Relevant regulations may be found at NJAC 5:30-5.1 to 5.5, NJAC 5:30-11.1 to 11.10 and NJAC 5:34-1.1 to 9.7. The Essex County Administrative Code, Article Six, provides that contracting and purchasing decisions shall comply with these sections of State statute and administrative code. The legal requirements specified by State law and regulation are summarized in the Essex County 2003 Purchasing Procedure Manual. An outline of purchasing rules and procedures follows: 1. Small purchases, not requiring formal written bid procedures: For purchases less than $2,625, the using agency submits a request to the Office of Purchasing, which obtains quotes, if necessary. For purchases between $2,625 and $17,500, the following procedures apply: o The using agency submits a purchase requisition to the Office of Purchasing. o The Office of Purchasing secures at least two written quotes. o The using agency recommends an award. o If the using agency recommends an award to a vendor other than the lowest bidder, a written justification must be sent to the Office of Purchasing. 2. Large purchases over $17,500 must follow this formal written bid procedure: No purchase may be subdivided in order to avoid the written bid procedure. Bid specifications are developed by the using agency and submitted to the Office of Purchasing. All vendors must receive the same information. Specifications may not be restrictive. Specifications must provide vendors the opportunity to offer goods of equal or better value. Sole source vendors are not recognized under the law. Favoritism is prohibited. Bids are opened and recorded in a public forum. Bids are reviewed by the Office of Purchasing for compliance with specifications. No late bids are accepted. Bids that comply with specifications are forwarded to the using agency. Bids from vendors who propose unacceptable substitutes of goods or services cannot be considered. The lowest bidder has a prima facie entitlement to a contract.

Analysis of Essex County Procurement and Contracting: Final Report 268 o The lowest bidder is entitled to a hearing before the contract is awarded to another vendor. o The lowest bidder can only be denied a contract if, after an administrative hearing, it is determined that: The vendor is not "responsible," i.e., lacks the experience, financial ability, facilities, or moral integrity to fill the contract; or The vendor's bid is in substantial non-compliance with the specifications. Under state law, a contract must be awarded within 60 days of the opening of the bids. o After 60 days, no contract may be entered into unless the vendors agree to extend their prices. If the using agency's recommendation is in compliance with the above rules, the Office of Purchasing issues a Decision Memorandum and a Memorandum of Agreement. o These documents are forwarded to the County Administrator. o The County Administrator submits the contract for the agenda of the next Freeholders Meeting, 25 days in advance of the meeting date. o Purchase requires approval by the Board of Chosen Freeholders. 3. Purchases that are exempt from bidding: Professional services are defined as "services rendered... by a person authorized by law to practice a recognized profession, whose practice is regulated by law..." o Professional services are exempt from bidding under NJSA 40A:11-5. o The County Executive, however, has ordered that three bids be obtained using a "two envelope" system for engineering and architectural services contracts. Under this system, the proposal and the proposed cost are sealed in separate envelopes. This allows for an evaluation of the proposed services that is unbiased by cost considerations. o A professional services contract requires Freeholder resolution, and must be advertised by the Clerk to the Board of Chosen Freeholders. o Using agencies purchase professional services by preparing and filing documents with the County Administrator and request action by the Board of Chosen Freeholders. Extraordinary Unspecifiable Services (EUS) are defined as services "which are specialized and qualitative in nature requiring expertise, extensive training, and proven reputation in the field of endeavor. o State regulations specify that the application of this provision must be construed narrowly in favor of open competitive bidding. o EUS purchases follow the same procedures as those for professional services. Contracts entered into with federal, state or local government.

Analysis of Essex County Procurement and Contracting: Final Report 269 4. Other Exempt Purchases: The table lists other types of purchases that are exempt from competitive bidding procedures: The printing of legal briefs, records, and appendices to be used in any legal proceeding in which the contracting unit may be a party The furnishing of tax maps for the contracting unit The printing of bonds and related material Insurance Purchases related to the operation of a restaurant by any non-profit historical society or at any historical preservation site Equipment repair services and parts, when the service is an EUS The purchase of perishable food as a subsistence supply Any work by handicapped persons employed by a sheltered workshop The marketing of recyclable materials Purchase of electricity or services related to electrical transmission The printing of municipal ordinances Wastewater treatment services Expenses for travel and conferences Purchases from a public utility subject to the jurisdiction of the Board of Public Utilities or the Federal Energy Regulatory Commission The publishing of legal notices Goods and services related to an election Library and educational goods and services The purchase of equipment used in confidential investigations, with the approval of the Attorney General The acquisition of artifacts of other items of unique, intrinsic artistic or historical character Inspections undertaken pursuant to the State Uniform Construction Code Purchase of steam or electricity from a cogeneration facility Certain towing and storage contracts Purchases of goods and services at rates set by the Universal Service Fund Water supply services Support for proprietary computer software The management or operation of an airport 5. Procedural exemptions from formal bid procedures: If a bid has been advertised twice, and less than two bids have been received, then a contract may be negotiated without further bidding. If a) at least three quotations on materials have been received, and b) the materials are covered under a State contract, and c) the lowest bid is at least 10 percent lower than the price under the State contract, then the contract may be awarded by resolution without further negotiation. 6. Emergency Purchases: Department heads who violate emergency procedures are personally responsible for the expended funds. Department heads must submit emergency forms with the following elements:

Analysis of Essex County Procurement and Contracting: Final Report 270 o A description of the emergency. An emergency cannot be something that could have been reasonably foreseen. o The time of the occurrence of the emergency o The need for invoking emergency procedures o The cost If the Office of Purchasing determines that an emergency exists, the Office will notify the County Administrator and direct the Department to proceed. Freeholder involvement: o At the next Freeholder meeting, the County Administrator will advise the Freeholders of the emergency action. o Payment requires approval by the Board of Chosen Freeholders. No expenditure may be made when such expenditures shall exceed the available budgeted appropriation. Emergency purchases shall exist for a limited duration only. 7. Change Orders: General Work cannot be changed in such a way as to nullify the effect of competitive bidding. A change in the number of units may be changed only if unit prices were sought in the original specifications and included in the contract. A change may not be made in the quality or character of work provided. A change order cannot be used for upward price adjustments. A change order cannot change the cost by more than 20 percent. A change order cannot be issued if it is reasonably possible to execute a new contract. Availability of funds must be certified by the Chief Financial Officer. A Freeholder resolution must occur before the execution of an order. 8. Change Orders: Professional Services and EUS The change must be within the scope of activities of the original contract. The 20 percent limitation does not apply to professional services and EUS. If the change involves work that is not within the original scope of services, then the Freeholders must approve the change. 9. Change Orders for Construction: Criteria Change orders for construction projects are limited to unforeseeable problems, which would result in substantial cost changes if public bids were required. Change orders for construction projects cannot be made if the change materially changes the scope of work or if a new bid would be possible without unduly disrupting work. 10. Procedures for change orders for construction projects when Public Works is the project manager:

Analysis of Essex County Procurement and Contracting: Final Report 271 The contractor must notify the Director of Public Works of the proposed change in contract. The Director instructs the contractor to prepare a proposal. The Director reviews the proposal to ensure that: o The work is not in the original contract. o The work is within the overall scope of the project. o The work is required immediately. o Unit costs are in the original proposal, or are reasonable. o The incremental cost will not cause the total cost of the project to exceed the engineering project cost projections. Public Works creates a change order form. The Director forwards the form to the Office of Purchasing. If the cost of the change is not agreed to, the Director may recommend that a Construction Change Directive be issued. o A Construction Change Directive would direct the contractor to make the change subject to further negotiation of cost. If the Office of Purchasing approves the request, then Public Works notifies the contractor, and work proceeds. The County Administrator notifies the Freeholders of the change. The change requires Freeholder approval. 11. Procedures for change orders for construction projects when a construction management firm (CMF) is the project manager: The contractor must notify the CMF of the need for a change. The CMF reviews the proposal using the five criteria cited in point 10. The CMF forwards recommendations to the Director of Public Works. Procedures then follow the steps outlined in point 10. 12. Exemptions for construction changes: Minor site modifications are exempt from Freeholder approval. Examples include additional fill stone, modifications of footings, and additional rock blasting. Minor site modifications may result in only minor cost increases. 13. Federal and state supply schedules: A contracting unit may purchase goods and services without bid under federal and state supply schedules.

Analysis of Essex County Procurement and Contracting: Final Report 272 State supply schedules include contracts for goods or services entered into on behalf of the State, by the Division of Purchase and Property in the Department of the Treasury. A contracting unit may also use the federal supply schedules of the General Services Administration for the purchase of reprographic equipment and services. 14. Aggregation Aggregation refers to the purchase of goods or services that are used by multiple departments. When different departments use similar goods or services, the Office of Purchasing generally purchases these goods or services on behalf of the entire county. Aggregation is not required for food, conferences, hats, t-shirts, gifts, or uniforms. Aggregation is also not required when the cost of a purchase is less than $2,625. However in these cases, the director of the department must provide an explanation and justification of the expense to the Office of Purchasing

APPENDIX N: ANECDOTAL EVIDENCE ANALYSIS In addition to the availability and utilization data that was collected for this study, we also gave critical consideration to information and feedback that we received from several sources: Comments from Three Public Forums Buyer Interviews Feedback and Recommendations from Disparity Study Commissioners Web Survey Responses This appendix describes the comments, feedback, and data we acquired regarding vendor interest and access, County purchasing practices, and County leaders concerns and input. We believe that this information, taken together with the availability and utilization data, will produce a complete picture of the reality of minority and woman-owned business access to Essex County contracting and purchasing. Comments from Public Forums Activity Location Attendance Testimony Forum 1 Essex County College 44 14 June 20, 2005 West Caldwell Forum 2 Hall of Records 48 25 June 27, 2005 Newark Forum 3 Essex County College 54 23 June 25, 2005 Newark Total 146 62 The Disparity Study Commissioners listened to commentary and requests from more than 60 citizens mostly business owners who offered nearly 100 specific comments, recommendations, and concerns about their interests and experiences in seeking access to County government buyers. The most frequently lodged complaint from these participants was the lack of access to information and the inability to receive communication from County agencies regarding purchasing and bidding opportunities. The Commissioners heard this complaint from citizens attending all three forums. Another complaint heard from citizens attending all three forums was that despite their deliberate efforts to pursue contracts or bid opportunities, they did not get any response to their inquiries. These complaints suggest that access and outreach are critical missing ingredients in the County purchasing process. According to the comments heard at the public forums, not only does the County fail to solicit business from the open market, there is little to no response from County personnel when vendors actively seek out business opportunities. It also became evident that vendors in the greater Essex County market region believe that County business goes to the same vendors all the time. This complaint was another heard from

Analysis of Essex County Procurement and Contracting: Final Report 274 citizens at all three public forums. In some cases, testimony concerning this practice was quite vehement and compelling. The following table presents a summary of all the comments received at the public forums. Table N-1. Public Forum Testimony ISSUE FORUM 1 FORUM 2 FORUM 3 No. of Comments Low bidder not winning the project or contract X 1 Open the process for small purchases or purchase orders XXX X 4 Business is run by wholesale suppliers or manufacturers X 1 representatives Unable to secure any work at all, despite efforts to identify and/or bid X XXX XXXX 8 on opportunities (no response to inquiries) No access to information or communication about purchasing and XX XXXXX XXX 13 bidding opportunities XXX County should implement outreach to notify M/WBEs about contract XX 2 opportunities Even with access to bonding or demonstrated ability to produce very X X 2 large contracts, firms still cannot get Essex County contracts Our materials cost more than larger non-mbes X 1 Have necessary equipment but still can t get work X X X 3 New requirements make qualifying for bids more difficult X 1 (e.g., requiring newer year models) Want access to County buyers XXX X 4 Create MBE bidders list X XX 3 Use internet technology to get on a bidder list and/or receive X 1 notification about upcoming bids Vietnam Veteran status X 1 Need help understanding purchasing/bidding process XX X 3 Access to special or emergency purchases X 1 Access to professional service contracts X XX 3 (e.g., legal, investment banking, accounting, architectural design, insurance) The same companies win bids all the time XX X XXXX 7 Want access to names of general contractors bidding on County X 1 contracts Stop graft and corruption in the form of kickbacks in contract bids X X 2 Want more access to sub-contracts XX 2 County does not follow through to insure that MBEs get work and/or X XX 3 get full pay for jobs won Peer review for engineering projects to open access; review X X 2 specifications to ensure realistic requirements Vendors with connections get the contracts X XX 3 M/WBE certification and/or RFP paperwork is onerous XXX XX 5 Implement partnering and/or a mentoring program, race neutral X X 2 program, low-income area program Monitor bidding activity to ensure that named MBEs are not just for XXX 3 show; and that they deliver the entire job not a 10% buyout Do not punish firms that lodge complaints X 1 Need financing or bonding help XXXX 4 Project sizes are too large for many M/WBEs X 1 Provide support for MBE sub-contractors (treat them as primes, e.g., NY program), including contractor training programs XXX 3

Analysis of Essex County Procurement and Contracting: Final Report 275 ISSUE FORUM 1 FORUM 2 FORUM 3 No. of Comments M/WBE contracts will contribute to job growth in the County; County XXXX 4 should ensure diverse workforces among the contractors For social services contracts, follow-up to ensure that County s clients X 1 get jobs and that vendors who cannot produce jobs do not get contracts renewed Hold unions accountable for minority participation XX 2 Total Number of Comments 98 Buyer Interviews Interviews were conducted with the designated Buyers from each of the County s 23 agencies, as well as the County s authorized Purchasing Agent, for a total of 24 interviews. None of the interviewees hold the title Buyer although one holds the title Director of Purchasing. Rather, these individuals are responsible for implementing, managing, and/or facilitating the purchasing needs for their respective department/agency. Their positions include such titles as administrative secretary, secretarial assistant, clerk or clerk typist, budget analyst, business manager, chief financial officer, and director or executive director. For more than half of them, buying is not their only responsibility (their procurement task comes with the job ), and about a third of them come to their position with only minimal formal training, much of which was acquired decades ago. Consequently, depending on their formal role and/or title, their knowledge and involvement in the purchasing process varies widely. There are five or six salient points to be taken from the information shared by the County s buying agents. First, regardless of the County s purchasing practices and policies, those who are currently responsible for making the buying decisions are either neutral, or positively disposed toward doing business with minorities and women business owners (M/WBEs). This suggests that whatever changes may be generated and deployed throughout County agencies to improve opportunities for M/WBEs will likely face minimal resistance, and may even be welcomed in some quarters. That said, it is clear that carefully delivered training will be required to raise the level of awareness, sensitivity, and purchasing expertise relative to M/WBE procurement practices. Second, the process implemented in 2003 seems to provide a strong access point for M/WBE participation, in that when notices for bids are announced in local papers, those notices also go to the Freeholders offices, and the County s Affirmative Action office. This practice might be adjusted to ensure that these offices have the time and opportunity to recommend M/WBE vendors who should be notified about the upcoming bid. The more troubling news comes pursuant to the more systemic components of the County s purchasing practices, and the way in which those factors work together to produce barriers to M/WBEs. One such factor is the division of purchasing categories, and how those categories are filled. Specifically, items that would be considered small purchases and might be most amenable to M/WBE procurement are subject to the bid process at either the State or County level. The purchasing guidelines in place since 2003 require that all of the County s acquisitions for a

Analysis of Essex County Procurement and Contracting: Final Report 276 particular kind of product represent a real threshold to determine whether that item is in fact a small purchase. For example, one department s annual requirements for ballpoint pens might amount to $200, and could be fulfilled with three phone quotes for the lowest price. That department s needs, however, are now added to the needs of all other County departments, thus raising the value of that purchase to $3,000 or more, and requiring three written quotes to find the lowest price. However, if that purchase request is bundled with a request for staples, paper clips, binders, etc. (as is often the case), the dollar value of the request for all those items has now been raised to well over $17,500 (for all departments needing the same things). It must now be acquired through a formal bid, whether through the County or through the State. A vendor must now be able and willing to meet all of the County s purchasing needs for a plethora of items, rather than just ball point pens. Meeting these levels of demand are quite achievable by most M/WBEs who are in the appropriate business. The difficulty in doing business with the County emerges when this purchasing category guideline is compounded with another of the systemic issues in the County s procurement practices that of having a fairly entrenched vendor base. The buyers acknowledgement that their department has used the same vendor for years makes it more difficult for M/WBEs to begin doing business with the County. (This is corroborated by M/WBEs who have attempted to sell to County agencies). It does not seem to matter whether the items purchased are goods or services, and whether the item is subject to the bid process (i.e., large purchases) or not (professional services, EUS, other purchases). In order to overcome this barrier, an M/WBE firm must successfully bid and win a contract with the County or the State to provide these kinds of office supplies which they will generally win along with several other vendors. Then, County buyers have to elect to contact them (the M/WBE firm), as opposed to other winning vendors (e.g., those they ve been doing business with for years ), in order to fulfill a purchase order for the desired items. The practice of using vendors who have won State contracts to sell things like office supplies and automobiles has proven to be a barrier to many M/WBEs. Specifically, bidding and winning these contracts requires completion of an extraordinary amount of paperwork, not to mention financial expenditures to fulfill bonding, insurance, and registration fee requirements. Small businesses have long complained about the burden of paperwork; and the burden for M/WBEs is typically multiplied by their need to prove M/WBE or disadvantaged status. Moreover, companies with strong cash flows or substantive income streams can afford to have someone on staff manage these requirements, whereas most M/WBE owners must address these requirements themselves or make these tasks add-ons for staff hired to handle other responsibilities. The County s practice of using entrenched vendors seems to be complicated by several factors. First, the buyers decisions to turn to known vendors seems to occur by default. According to them, their tendency is to use tried and true vendors, which automatically cuts out any new vendors who can provide the same or better products and services. Further, once vendors are successful in bidding and winning State and County contracts, they are capable of building on their success, in order to win repeat bids. This is particularly true for those contracts that do not require low-bid status in order to win, i.e., professional services, EUS purchases, or other purchases. The practice of using entrenched vendors is exacerbated by the practice of doing business with vendors who are politically connected, either through social, professional, or

Analysis of Essex County Procurement and Contracting: Final Report 277 campaign connections, or more egregiously, through campaign contributions. Here, minority or woman-owned status is far less relevant than whether a vendor is willing, on some level or another, to pay to play. Feedback and Recommendations from Disparity Study Commissioners On August 2, 2005, a six-hour retreat was convened during which the Commissioners were provided with findings from our quantitative and qualitative research. Specifically, the Commissioners were provided with findings from the analysis of contract files, residential lending patterns, and discrimination regression analyses, as well as results from interviews with the County s purchasing personnel and the comments from the public forum participants. In addition to quantitative and qualitative data, Commissioners received a substantive briefing on the legal precedents that focused on arguments made, accepted, and rejected in the relevant courts, as well options for the redress of findings of discrimination, and how those options are perceived in the courts. Commissioners were then presented with a menu of race and gender neutral, as well as race and gender conscious remedies for expanding opportunities for minority and women-owned businesses (See Exhibit N-1). They were also presented with a brief proposal for principles upon which they might base the County s policy for creating a minority and woman-owned business program (See Exhibit N-2). All of this information was presented for the purpose of engaging the Commissioners in a discussion about developing policy options that the County may pursue, relative to addressing the prospect of any discrimination against minority and/or women-owned businesses that might be uncovered during the course of the disparity study. After all the material was presented, the Commissioners were guided through the process of beginning to select of policy options. The first activity required the Commissioners to individually identify their own preferences for addressing any disparities that may be uncovered in the study process. They were asked to think about all of the data that had been put forward during the proceedings earlier in the day and produce three to five recommendations that they believed would be most responsive to the concerns that arose for them, personally. After they each selected three to five programming solutions, the Commissioners were divided into four small workgroups, in order to organize their preferences based on the Pareto analysis (the 80/20 rule suggesting that 80 percent of any set of results stem from 20 percent of associated efforts). They discussed their preferences, in comparison to those of their fellow Commissioners and devised a list that represented their best thinking. Then, as a group, they prioritized their recommendations into one list and assigned points to each of the identified preferences, with the most popular preference receiving the greatest number of points. After assigning points to their preferences, they were then asked to associate those preferences with one or more of the six policy area categories: Access

Analysis of Essex County Procurement and Contracting: Final Report 278 Outreach Technical Assistance Financial Assistance Monitoring Penalties and Rewards Exhibit N-1. Description of M/WBE Program Options in Order of Commissioners Preferences 1. E-commerce Solutions. These include centralized bidder registration process, bid rotations, small contract award rotations, industry-specific bidder outreach. Race/Gender Neutral (Carrot) 2. Small Local Business Enterprise Program. It provides subcontracting goals and/or prime contract set-asides for small local businesses; local defined by location of principal office or where a significant number of employees are domiciled. Race/Gender Neutral (Carrot) 3. Revolving Working Capital Fund. It provides a working capital financing vehicle for small government contractors on government contracts; funded by banks and other private sector credit sources. Race/Gender Neutral (Carrot) 4. Technical Assistance Referral Network. It provides an assessment of new small business vendors regarding technical assistance needs and issues referrals to existing community resources that provide such assistance. Race/Gender Neutral (Carrot) 5. Procurement Process Reform. It includes re-packaging of smaller bid packages, multiprime contracts, restrictive contract specification review, expedited payment of invoices, and subcontract bid depositary. Race/Gender Neutral (Carrot) 6. Good Faith Efforts. Prime contractors are required to submit documentation of good faith efforts to subcontract with M/WBE firms. In the event that the prime contractors fail to submit such documentation, they are disqualified from bidding for a contract. The lowest responsible bidder that has demonstrated good faith efforts to subcontract to M/WBE firms is awarded the contract. Race/Gender Conscious 7. Prompt Payment Provisions. These require government purchasers and prime contractors to promptly pay supplier invoices to avoid penalties and interest charges. Race/Gender Neutral (Stick) 8. Targeted Solicitation or Bidding Rotation Preference. It is typically used for smaller contracts that do not require open competition (e.g., only three bids or price quotes are required). The purchasing officer is required to solicit at least one or two M/WBE firms to bid. Race/Gender Conscious

Analysis of Essex County Procurement and Contracting: Final Report 279 9. Purchasing Personnel Evaluation Criteria for Supplier Diversity. This requires annual review of government buyers performance in conducting outreach to attract new bidders and to achieve greater diversity in contract awards as a condition of receiving salary increases and promotions. Race/Gender Neutral (Stick) 10. Pilot Programs. They encourage joint ventures and teaming agreements among M/WBEs. Race/Gender Neutral (Carrot) 11. Promotion of Best Practices Principles. Encourage the corporate community to adopt internal audit procedures that identify and remove procurement barriers and discriminatory practices that disproportionately affect minority suppliers. Race/Gender Neutral (Carrot) 12. Advertising in Local Papers. This was proposed by the Commissioners. It is likely a response to complaints from the public that they are never notified or informed of bidding opportunities. Race/Gender Neutral (Carrot) 13. Mandatory M/WBE Subcontracting Goals Program. On any given contract, prime contractor bidders are required to subcontract a certain percentage of the overall contract dollar amount to certified M/WBE firms. Typically, this type of program includes a goal waiver process whereby prime contractors can request waivers or reductions in subcontracting goal requirements due to a lack of M/WBE subcontractor availability or unreasonable price. Prime contractors not obtaining a waiver and not satisfying the minimum subcontracting goal are disqualified from the contract award on the basis of being non-responsive to contract specifications. Race/Gender Conscious 14. Certification Process. This was proposed by the Commissioners. The specific references from two work groups were certification process and train and certification for minority business. It is likely that this refers to a certification process that verifies race and gender status and a training process for County staff and/or minority and women business owners to support the implementation of a certification process. Race/Gender Conscious 15. Price Preferences. The bids of M/WBE prime bidders are discounted by a certain percentage (typically between 5 percent and 10 percent) for purposes of ranking the lowest responsive bidder. However, if the contract is awarded to the M/WBE, it is awarded at the price actually bid. The U.S. Department of Defense has used this approach. However, this kind of program is frequently criticized for stereotyping M/WBE firms as more costly and less competitive. It also means that contracts awarded to M/WBE firms will result in the taxpayers paying a premium for goods and services. Race/Gender Conscious 16. Mentor Program. This is ostensibly a component of the suggestion for creating capacity development programs (On-the-Job-Training Demonstration Projects; Mentor-Protégé Programs, and Manufacturing Distributorship Supplier Development Programs). Race/Gender Neutral (Carrot) 17. Evaluation Preferences. Proposals submitted by M/WBE professional services firms are awarded bonus evaluation points. In the alternative, prime proposals submitted by non-

Analysis of Essex County Procurement and Contracting: Final Report 280 minority professional services firms are given bonus evaluation points on a basis proportionate to the level of participation by M/WBE firms on their team. Race/Gender Conscious 18. Linked Deposit Programs. Financial institutions seeking government deposits are required to participate in lending programs that increase access to capital for M/WBE firms. In the alternative, such institutions are evaluated and ranked based, in part, upon criteria regarding their lending patterns to M/WBE firms. Race/Gender Neutral (Carrot) 19. Wrap-Around Bonding and Insurance Programs. A government unit purchases umbrella bonding and insurance policies to provide coverage to all of its contractors at a standard rate. Race/Gender Neutral (Carrot) 20. Buyers Public Forum. This was proposed by the Commissioners. It is likely a response to requests from the public for more access and the need for outreach to M/WBEs. There was a specific recommendation in the public comments and from buyers for a small business expo, which would ostensibly become a buyers public forum. Race/Gender Neutral (Carrot) 21. Identify MBE Manufacturing Distributors. This was proposed by the Commissioners. The specific reference was identify manufacture distrib. (mbe). The program was identified with policies related to access, outreach, and monitoring. There was no further explanation. Race/Gender Conscious 22. Monitoring. This was proposed by the Commissioners. The recommendation was identified with policies related to access, outreach, and monitoring. There was no further explanation. Race/Gender Neutral (Stick) 23. Legislative Body to Monitor M/WBE initiatives. This was proposed by the Commissioners. The specific reference was, legislative body to monitor implementation and adherence. As no points were associated with this recommendation, nor was it associated with any of the suggested policy options, it may be unnecessary to incorporate it within a simulation analysis, except to insure that the study findings present strategies that address this very critical concern. Race/Gender Neutral (Stick)

Analysis of Essex County Procurement and Contracting: Final Report 281 Exhibit N-2. Proposed Policy Principles for an Essex County M/WBE Program 1. Ensure Open and Transparent Process and Practices All efforts and results should be documented and made available for public review. Small business/m/wbe guidelines should be widely adopted and made public. 2. Outreach Training should be provided throughout the process internally for County buying representatives and administrators and externally for prospective prime vendors and sub-contractors. Workshops, seminars, and forums should be presented regularly. Establish clear paths of communication both internally among purchasing representatives and externally for vendors and sub-contractors. 3. Efficiency: Avoid Economic Distortions Ensure that proposed solutions reflect most cost-effective practices over the long term, keeping in mind the good of the public at large. 4. Accountability and Consistency Ensure that accountability is present both internally and externally. Establish appropriate levels of responsibility and authority internally among purchasing representatives and externally among primes and sub-contractors. 5. Interdependency Acknowledge linked consequences of economic, social, and environmental responses to expanding access to minority and women-owned businesses. 6. Equity: Equal Opportunities for M/WBEs to Participate in the Economy 7. Focus on the Achievable; Build in Quick, Early Wins 8. Look for Ways to Encourage Private Sector Investment Banks and Major Corporate Vendors to be Mentors and Joint Venture Partners 9. Successful Leadership Requires Focus, Drive, and Simplicity Do not take a shotgun approach. Rather, invest adequate and appropriate energy on a few, solid initiatives; build a track record, then do add-ons. 10. Build an Evaluation and Monitoring Process into the Program from the Beginning

Analysis of Essex County Procurement and Contracting: Final Report 282 The first five policy areas outlined prior to Exhibit N1 were identified using the feedback received in the buyers interviews and public forums. The research associate added the last issue, Penalties and Rewards, to support the monitoring process. In short, if the County decides to implement a monitoring mechanism, it would be best supported with a system of rewards and/or penalties. Otherwise, the results of monitoring outcomes may not be reinforced as originally intended. After the groups ranked their combined preferences for programming, using Pareto analysis, the Commissioners used the paired comparison process to further sort and rate their preferences. The paired comparison process required each small group to rate each of their preferences against all the other preferences that they had identified. As they compared each recommendation against all the others, they assigned 1, 2, or 3 points to their most preferred recommendation, in accordance to the strength of that selection over its associated recommendation. After each of the four groups produced the results of their guided choices, the research associate combined all of their responses into one list, so that the points assigned to each recommendation by each of the groups were combined to show the preferences of the entire Commission. The results of their recommendations are reflected Table N-2. With respect to the six areas of policy concern, the activity that rose to the top of the list was for monitoring, followed by access. Outreach and technical assistance also received serious consideration. Rewards and penalties and financial assistance received the lowest degrees of focus. It is interesting to note that the top ten most preferred remedies include only two race and gender conscious programs, No. 6 - Good Faith Efforts Sub-contracting and No. 8 - Targeted Solicitations. All other remedies selected in the top ten preferences for a County program are race and gender neutral.

Analysis of Essex County Procurement and Contracting: Final Report 283 Table N-2. Compiled Results from Pareto Analysis and Paired Comparison Analysis Rank Order Policy Areas Point Value 1 Monitoring 56 2 Access 50 3 Outreach 31 4 Technical Assistance 29 5 Rewards/Penalties 16 6 Financial Assistance 10 Rank Order Program Options Preference Point Value Type Pareto Paired Total Comp 1 E-Commerce Solutions RG-Nc 28 45 73 2 Small Local Business Enterprise Program RG-Nc 9 39 48 3 Revolving Working Capital RG-Nc 16 24 40 4 Technical Assistance RG-Nc 8 29 37 5 Procurement Process Reform RG-Nc 10 18 28 6 Good Faith Efforts Sub-contracting RG-C 8 17 25 7 Prompt Payment RG-Ns 6 17 23 8 Targeted Solicitation RG-C 5 17 22 9 Purchasing Personnel Evaluation RG-Ns 5 16 21 10 Pilot Program RG-Nc 5 15 20 10 Promotion of Best Practices Principles RG-Nc 6 14 20 11 Advertising in Local Papers RG-Nc 4 14 18 12 Mandatory M/WBE Program RG-C 11 6 17 13 Certification Process RG-C 6 8 14 14 Price Preference RG-C 4 9 13 15 Mentor Program RG-Nc 5 7 12 16 Evaluation Preference RG-C 2 8 10 17 Linked Deposit Program RG-Nc 7 2 9 18 Wrap Around Insurance/Bonding Program RG-Nc 5 5 19 Buyers Public Forum RG-Nc 4 4 20 Identify MBE Manufacturing Distributors RG-C 3 3 21 Monitoring RG-Ns 3 3 22 Legislative Body to Monitor M/WBE initiatives RG-Ns Preference Type: RG-Nc = Race and Gender Neutral Carrot RG-Ns = Race and Gender Neutral Stick RG-C = Race and Gender Conscious

Analysis of Essex County Procurement and Contracting: Final Report 284 Table N-3. Demographics of County Agency Buyers Personal Characteristics Race/Ethnicity White 16 Hispanic 5 African American 3 Gender Male 9 Female 9 Age Median 53 years old Range 33 to 65 years old Education/Training Highest Level of Education Completed High School 6 Some College 8 College Degree Graduate + Graduate Studies 6 Degree 4 Academic Background/Training Business/Administration/Finance/Accounting 11 Secretarial Training 3 Law/Public Administration 2 Liberal Arts 5 Former Essex County Freeholder 3 Professional Experience Is Purchasing Your Primary Responsibility? Yes 11 No 13 Years in Current Position Median 7 years Range 2 months to 25 years Less than 3 years 8 people 4 to 9 years 8 10 to 25 years 8 Years Experience in Previous Positions Median 15 years Range 8 to 31 years

Analysis of Essex County Procurement and Contracting: Final Report 285 Table N-4. Current and Past Titles of County Agency Buyers (in alphabetical order) Current Job Titles of County Agency Buyers Assistant Secretary Budget Analyst Business Manager Chief Financial Officer Chief, Administrative Services Clerk Confidential Assistant Deputy Executive Director Officer Principal Clerk Typist Purchasing Agents Scheduler Secretarial Assistant Senior Program Analyst Supervising Clerk Previous Job Titles of County Agency Buyers Account Clerk Accounts Payable Clerk Administrative Assistant Administrative Secretary Assistant Controller Assistant Statistician Assistant Vice President Associate Director Associate Registrar Attorney Bank Teller Bursar Chief Analyst Clerk Analyst Customer Service Representative Deputy Clerk, Finance & Grants Director Director of Management & Budgets District Manager Executive Director Freeholder Junior Accountant Key Punch Operator Legal Stenographer Management Administration Manager Manager of Local Services Office Manager Principal Accounting Clerk Principal Clerk Typist Purchasing Agent Receptionist Senior Administrative Analyst Senior Clerk Stenographer Senior Clerk Typist Senior Data Entry Operator Supervising Accountant Supervising Clerk Supervisor, Casework U.S. Army

Analysis of Essex County Procurement and Contracting: Final Report 286 Table N-5. Products/Services Purchased for County Departments and/or their Constituents Accounting and Legal Services Animal Control Architectural & Engineering Services Auto Parts Books, Magazines, Journals Bullet Proof Vests Cars and/or Vans Clothing Construction Contractors DNA/Drug Testing Services Entertainment Agents Facilities Maintenance Food Services Furniture Handcuffs Holsters Ink Cartridges Instructors Landscaping Linens Medical Supplies & Services Office Equipment Office Supplies Paper PC Hardware Pepper Spray Pharmaceuticals and Related Services Porta-Potties Print Ads Public Relations Services Rental Space Snow Blowers Sports Equipment Storage Textbooks Ticket Booths Tickets (printing) Tractors Training Transportation Services Veterinarian Services Weapons X-Ray Services Table N-6. Description of Essex County Departments Size of 24 Departments/Agencies/Institutions in this Study Number of Employees Median: 34 employees Range: 6 to 1030 employees 6 to 35 employees 13 departments 130 to 419 6 519 to 1030+ 5 Size of Department s Purchasing Budget Median: $850,000 Range: $8,000 to $40 million $8,000 to $31,000 5 departments $150,000 to $850,000 5 $1.3 to 7.2 million 7 $19 to 40 million 2

Analysis of Essex County Procurement and Contracting: Final Report 287 Table N-7. Purchasing Categories by Percentage of Purchases Made Per Category Size of Purchase Type of Purchase Professional Services Less than or Equal to $2,625 (exempt from low bid requirements) 50 to 95 percent 9 departments 45 to 99 percent 5 departments 25 to 45 percent 3 departments 2 to 10 percent 5 departments 5 to 10 percent 5 departments Extraordinary Unspecified Services (exempt from low bid requirements) $2,625 to $17,500 50 percent or more 3 departments 85 to 90 percent 1 department 20 to 45 percent 4 departments 20 to 40 percent 3 departments 5 to 15 percent 7 departments Less than or equal to 5 percent 2 departments Larger Purchase (Over $17,500) State Contracts 50 percent 1 department 55 to 95 percent 6 departments 20 to 45 percent 4 departments 5 to 30 percent 3 departments 10 to 10 percent 5 departments Other Purchases 50 percent 1 department 5 percent 2 departments

Analysis of Essex County Procurement and Contracting: Final Report 288 Tables N-8 through N-20 outline buyers responses to questions asked during their interviews. The responses included in the tables do not represent 100 percent of the answers received. Table N-8. Can you recommend vendors for various purchasing categories? Purchasing Categories Can Recommend Vendors? YES NO Size of Purchase Small Purchases (Less than or Equal to $2,625) 6 12 $2,625 to 17,500 8 5 Large Purchases (Over $17,500) 3 3 Type of Purchase Professional Services 6 4 EUS purchases 3 2 State Contracts - 7 Other Purchases 1 1 Table N-9. How Are Vendors Selected for Product Purchases? Recommend vendors based on our experience with them. Whoever gets the job done quickest with the fewest errors. For [this equipment], I check to see if we have a vendor. I have the requestor find a vendor selling the item. They prepare the specs, and I'll prepare a requisition with those specs, and submit it to Purchasing. I don't often have time to look for three vendors, which Purchasing likes us to do. If I don't send up three vendors, Purchasing may ask for more detail or additional specs. We talk to other counties; see who they do business with. I select from Purchasing Office bid list. Almost all purchasing is done from that list. We use the State contract for all our small purchases. I only buy through the County office supply contract.

Analysis of Essex County Procurement and Contracting: Final Report 289 Table N-10. How Are Best Possible Vendors Selected for Professional Services? Three other counties recommended the trainer for our training program. I know we've dealt with one doctor for several years. The second doctor (a female) came in on a bid process. The vendor that I recommended for this contract (an MBE), I ve known for 25 years. We have a service cleaning contract that was extended on a 'no bid' because they hire disabled workers. Most often, they're recommended to the Exec. Director by someone at the County; then we interview them. E.g., for our audits: they submitted a cost proposal, they came in about $6000 lower than our existing vendor. They were qualified with (RMA) experience; they won the contract. When these are awarded, they're likely to be renewed. It s usually with whoever has done work with us in the past. For our services, our team [was] suggested by the County of Essex. They suggested the Bond Counsel and the attorneys; [our] current engineer was selected thru the RFP process. For the (X) audit, we use (X) - they've had the contract for the last 20 yrs. The only time we bid it out, we got two respondents: (X) and ADP. For the (Y) audit (an EUS award - State-mandated initiative), we use (Z). We've only used this one vendor since I've been here. We used another vendor in 1984-1985, but then went back to (Z). In NJ, the companies that do this kind of audit must be a Registered Municipal Accountant (RMA); there may be 20 such firms. (Z) probably does about 2/3 of the towns in the County. I heard somewhere that they had Minority status. We use the DOT/DEP point system to determine vendor standards. [It depends on the work, what qualifications are applied.] The County Counsel and the Deputy Counsel selects our vendors; I don't know how the arbitrators, medical examiners, etc. get selected. I just pay the bills. With exception of legal services, (that's all County Counsel's call); architectural and engineering firms are selected by Public Works through an RFP process. The majority of all other professionals hired would result from the RFP issue by Purchasing with specifications and conditions written by the using Agency. Purchasing does the logistics.

Analysis of Essex County Procurement and Contracting: Final Report 290 Table N-11. How were things done between 1996 and 2002? (Prior to the tenure of County Executive DiVencenzo s tenure) 11 Respondents: The process has not changed very much 4 Respondents: Now, all purchasing activity goes before the Freeholders for their approval Before, we could just pick the phone up and call a vendor. The C.E. is interested in getting bids, despite our department's exemption. He wants to make sure you're getting the best price. The only thing that might have changed is the contracts - contracts get renewed yearly now. The C.E. is putting an emphasis in diversity. But these are things I believed in too. When I got here, there were no Hispanic vendors. In Essex County, how could you not have Hispanic vendors? I know he (C.E.) wants to change things. He's trying to do business with Essex County vendors. What we're doing now is following the RFP process. We are definitely bidding all our supplies & equipment, not just calling any Tom, Dick or Harry. And we re purchasing from the State contract, if we re not putting items out for bid. Well, before (2002), no one bothered to interpret the law correctly, so the aggregates have changed. Everybody was going out contracting for their own department because no one knew. Now we must get more than one quote; we need to know more vendors who can quote on items; they (Purchasing) won't just accept one quote. The most dramatic change: no professional services were ever bid out; we could award professional service contracts without ever bidding. The Board of Chosen Freeholders (not the County Executive) was established as the governing body. Previously, everyone acted as though the C.E. was the governing body. Now, all purchasing decisions go before the Freeholders. Also, the bid threshold now relates to aggregation by commodity not by vendor. Formerly, we aggregated purchases by vendor, e.g., they would purchase up to $17,500.00 with Vendor A; then go to Vendor B. And, we now operate under guidance of a Purchasing Manual and a Competitive Contracting Manual.

Analysis of Essex County Procurement and Contracting: Final Report 291 Table N-12. Is under-bidding a big problem? 14 Respondents Under-bidding is not a problem I know the bills often go over the originally agreed upon amount. It always has to go to the Freeholders so they can extend the contract amounts. For construction bids - it s a major problem. They (construction contractors) know where the flaws are in our specifications & designs; and they know how to exploit the flaws. So we established a four-layer change order approval process. This assures that the change was not in the original scope of work; that it s cost effective; and that it s necessary. Public Works has gotten better at managing change orders. Now that every single change order goes before the Freeholders for approval, buyers are discouraged from using this as a means to give contracts out to 'friends.' We don't accept change orders under ordinary circumstances. If change orders are required, they need Board approval. Table N-13. Is there a formal complaint process for when vendors want to challenge an award decision? Yes No/Don t Know 10 Responses 10 Responses Yes. I think this process is outlined in our contract boilerplate. The appeal process is directed to the head of the Agency as well as County Admin. After bids are received, we can talk to everybody about the work; before the bids are in, we can't talk to anybody. Yes. Written communications about our purchasing decisions go to all bidding vendors. They call their lawyer if they want to challenge our decision. The Purchasing Agent makes the final decision. They have a right to a hearing; the County Administrator is the Hearing Officer. They provide a written opinion; that opinion stands unless its taken to court. How many complaints per year? None/None known of Maybe 6 per year 15 Respondents 1 Respondent

Analysis of Essex County Procurement and Contracting: Final Report 292 Table N-14. Questions about Vendor Payments Are vendor payments timely? Yes Don t know 15 Responses 3 Responses They're much better now than they used to be. Sometimes we couldn't place ads due to unpaid bills. Bills are more timely now. Yes. The only reason why they wouldn't be is if the vendor doesn't return the ORIGINAL voucher. Yes, now they are. They weren't before the implementation of the Contract Division's new procedures. Yes. We only get complaints from contractors because many work as Subs. How are payment schedules determined? Payments are made upon delivery; leases are paid monthly. Vendors submit invoices; we process payments after completion of a job. Partial payments can be made on contracts, e.g., psychiatric services; payments are made after doctors visits with (subject) patients. Payment schedules are determined by the contract process. Some contracts are cost reimbursement; some are fee for services. Are there ever provisions made for start-up payments? No 14 Responses Yes. If the bid is won, we could set up a purchase order bid as a contract, and then we could issue a partial payment. This has to be agreed before the work gets started. Rarely. That practice was stopped because some vendors were being paid for nothing. If it s done now, it s only for new programs, e.g., start-up funds for Juvenile Justice contracts (eg, $ [sic] for computers for a new vendor to monitor tracking ankle bracelets). Yes; sometimes we're required to pre-pay on a contract. No. The bid price must be all-inclusive. It s a violation of State Law to make up-front payments on a P.O.; for contracts, there are provisions in the law to allow for partial payments.

Analysis of Essex County Procurement and Contracting: Final Report 293 Table N-15. Questions about Vendor Qualifications Please list specific qualifications that all vendors must have in order to bid on contracts with your agency. Contract Boilerplate Documents 5 Responses State Business Registration 6 Responses Certain licenses, certifications, permits 4 Responses Track record/referral 4 Responses Financial criteria 4 Responses o I think it s all politics. Law firms change with the administration - unless firms deal with administrations across party lines. o Bids must be Responsive - meaning 'everything that's asked for is there;' and bids must be Responsible - meaning the vendor proves capable of doing the job - very subjective. In Bid documents (for construction) - they must be registered through the Contractors Registration Act - a copy of the Business Registration; stockholders' disclosures; they must also have the Affirmative Action statement; and a bid guarantee, and performance bond, if required (this is a financial payment that assures entry into contract - a 10 percent payment not to exceed $20k [sic]; this guarantee is a certified check or bid bond that is returned if the contract is completed satisfactorily). What specific qualifications are required for the main (or predominant) type of product or service for which your agency contracts? None /Not Applicable (to our purchases) 12 Responses For goods and services, if a vendor bids on an item at the lowest bid price they can get the bid. Qualifications are non-existent for products, other than this. [Any necessary qualifications are noted in] the section of the contract boilerplate that requires the vendor to discuss prior work experience. We retain the right to reject all bids. They describe their prior projects; we make assessments based on their experience.

Analysis of Essex County Procurement and Contracting: Final Report 294 Table N-16. Questions about Minority and Women-Owned Business Enterprises List any significant barriers you believe exist that might prevent or limit the ability of MBEs and/or WBEs from Bidding on contracts with your agency Don t know of any barriers 10 Responses o Not really. I do business with a WBE. I needed specially printed forms, and the WBE happened to be the low bidder. I also buy furniture from an MBE. o Bids are placed in the newspaper. Vendors can call in for information/specs. We mail them the info. They mail back their bids. I don't know of any barriers. It s who you know or It s politics 2 Respondents o It doesn't matter if you're minority. Barriers are everywhere. Barriers are Green. This is a political business. Why would you help someone that hasn't helped you? If you're not on this list, you don t get the business. Very few or no M/WBEs in this business 2 Respondents o They (M/WBEs) may not want to be involved in this business because it's such a small market. Companies that do this (printing poll books) are probably working in only 3-4 counties. o The primary constraint is that there are no minority-owned facilities. Haulers have total access. Also, the nature of the market has been to absorb smaller operations. Some are now merging. Stringent requirements/large capacity requirements/ structural requirements 5 Respondents o Social services agencies may have certain organizational issues, e.g., insurance requirements; 501(c)3 status. Some church groups may have difficulty completing affirmative action forms. o Maybe the large scope of the contracts; the insurance or surety performance bond requirements. These are the barriers that I'm aware of. o Perhaps bonding is a problem for smaller firms. o The Local Public Contracts Law is the biggest barrier! We have no ability to select any of our bidders. For example, we can't go to the Ironbound to buy electrical supplies; we have to go to thru the Bid process, wherein Ironbound retailers may not be competitive, even if they entered the bid. ALSO, size, and their ability to provide volume and timing, e.g., 3000 clothing bags. And, we require bonding at the value of the contract. Bonding is sometimes prohibitive, but it s necessary because sometimes it may take up to 6 months to get rid of a poor vendor. o Familiarity and comprehension of the bid documents; Insurance; Securing a performance bond; Compliance with State law - e.g., construction contractor's registration & business registration. Also, most construction contractors may not have the time/resources to come [sic] knowledgeable about what we do.

Analysis of Essex County Procurement and Contracting: Final Report 295 Winning contracts with your Agency Don t know / There are no barriers 13 Responses If they don t bid, they can t win 2 Respondents Pricing/Supplier Costs (M/WBEs can t achieve lowest bid status) o When bids come in, if we don't pick the lowest bidder, we have to justify our decision. Maybe they omit something that we need in their bid response. Maybe they're not able to furnish the specific item, but something comparable. o They need to know how much to charge for products and services. This requires a lot of research. o Costs are always going to be a driving factor. This is subjective; I think our process is fair. o I think the Low-Bid rule can get in the way of using good minority firms. Low bidders don't always provide the best service or product. What are your beliefs about Minority and Women Business Enterprises? No opinion 3 Respondents Our department does business with M/WBEs 6 Respondents M/WBEs should be included in the process 5 Respondents Whoever can provide the goods and services we need should be able to (don t care whether they re M/WBEs) 6 Respondents o Our activity is statutory. Whoever is in the business of providing these services is welcome here. o I wouldn't know. I don't get into that. What I do is submit the bids. I don't see who gets sent information, or who receives it. I just see the end product. I don't know if there are M/WBEs bidding in the process. At (another dept.) the question of M/WBE bidding didn't come up. o They (M/WBEs) seem to stay away from it due to the (noted barriers). And, out-ofpocket start-up costs can sometimes be prohibitive. We can't pay for services that haven't been rendered, so we can't absorb start-up costs. o Their past history with our process keeps them from staying engaged in the process

Analysis of Essex County Procurement and Contracting: Final Report 296 Table N-17. Questions about Facilitating M/WBE Participation in the County Purchasing Process Do you have a list of MWBE firms from which to select prospective bidders and/or vendors? No/Don t Know Yes We used to 21 Responses 2 Responses 1 Response A few years ago, we coded M/WBEs. That fell apart about the mid-1980's. We had several set-aside programs. Our department is preparing to update and recreate the entire list We just completed a mailing to our MBEs, and we included updates to the Community College Contracts Law. Is there any verification process for identifying M/WBEs? Not Applicable to our department At one time, there was a self-classification process. Yes; we use State-certified vendors. 21 Respondents No. As we don't have set-asides, awards are not based on that qualification. Is there an M/WBE Program (e.g. Outreach, Mailing, etc.)? Don t Know No 12 Respondents 2 Respondents I think so 3 Respondents Yes (hopefully so) 4 Respondents

Analysis of Essex County Procurement and Contracting: Final Report 297 Table N-18. In your view, is there anything that can be added to, or taken away from, the current process to change the current reality for M/WBE firms? Added to the process Provide M/WBEs with a Purchasing Manual that describes our process Use larger print when advertising bids Improve methods for insuring that M/WBEs are legitimate (e.g., certification) Ask for quotes from more M/WBEs in the Small Purchases and RFP procedures Do more outreach; develop a list of M/WBEs Establish goals for purchasing from M/WBEs; Perhaps a Point System, with M/WBEs getting extra points in determining responsive bids Create/reinstate a Set-Aside Program Try to help M/WBEs to address the problem of acquiring bid bonds Provide more control to departments regarding how their purchasing dollars are spent Give M/WBEs more information so that they can participate in the process Taken away from the process Be mindful of project labor agreements; they might end up placing M/WBEs at a disadvantage Reduce insurance requirements Reduce the size of the contracts Table N-19. What advice would you offer to M/WBE firms that are interested in participating in the County s purchasing program? Become educated about our process Be proactive; market yourselves; get in to see the buyers for your products/services Get onto State contracts Be responsive bidders; follow instructions in the bid process Deliver good quality products and services Sharpen your pencils insure that your prices are competitive Attend Freeholders meetings, as well as workshops and other outreach gatherings Form joint ventures Stay in the process; continue to submit bids, and don t be afraid to challenge our decisions

Analysis of Essex County Procurement and Contracting: Final Report 298 Table N-20. Is there any other information that is important to know about the purchasing process? I believe there are vendors with better offers, ideas, and equipment. I don t know if purchasing knows them. I don t know how far they go to find lower priced vendors. If they had a business expo, I think that would open a lot of possibilities. I have a comment. I get reviewed by the State's AA/Contract Compliance person from the NJ Treasury Dept, regarding my compliance with the Local Public Contracts laws. They do the check-offs re "appropriate language" in my contracts. This has nothing to do with the actual hiring and sub-contracting practices! Therefore, it s a waste of time if there's no real oversight. There is no change to the end result or reality in the (Agency's) practices. We're 'graded' on the quality of our paperwork - not on the actual reality. If they (the State) sent out investigators to 'count the bodies' and look at hiring/promotion practices - that might make a difference. This effort is a good thing. Hopefully, we'll put together a list of companies that we can do business with. The fact that this is going on, and its being publicized, showing that we're making a concerted effort, this should help us do better. We should create a Reference Guide for M/WBEs, broken down by vendor type. Our hands are tied by what I think is a stupid law (Local Public Contracts). It s guided by mistrust - despite our checkered past. I don't care who gives (a product or service) to me, as long as I get what I need. We'd feel more like a neighbor (in this community) if we could buy from local vendors. Our Affirmative Action and Freeholders offices should be more directly involved in this process. When bid advertisements are sent out - copies go to the Freeholder Clerk, the Affirmative Action Office, and the Inspector General s Office. Affirmative Action can help to identify companies to bid on those opportunities.

Analysis of Essex County Procurement and Contracting: Final Report 299 Essex County, New Jersey Procurement Officers Interview Form To the Respondent: Thank you for taking the time to speak with me about the purchasing process and activity in your agency. I ll be asking questions about your background, about this Department, and about the purchasing and contracting procedures in general, and as they relate to MWBE [stet] vendors. This shouldn t take longer than 45 minutes. Your answers will be considered anonymously. In other words, I ll only be sharing the information that you give to me without attaching your name, and without using it in a way that can identify you, personally. Consequently, I hope you ll be willing to answer these questions with as much detail and candor as possible. Do you have any questions of me before we begin? About the Survey Respondent 1. Department: 2. Name: 3. Job Title: 4. How long in this role? 7. Highest Level of Education completed: 5. Is Purchasing your primary responsibility? Yes No High School 6. Other responsibilities: Some College Associates Degree Bachelor Degree Some Graduate Studies Graduate Degree Post-Graduate Studies 8. What was your primary area of study? 9. Could you briefly recount your four most recent job titles in this or other organizations, prior to taking on this role? Job Title Organization/Agency # Years 10. Year Born? FOR INTERVIEWER USE ONLY: Race: Wht Blk Hisp AsA Oth Gend: M F

Analysis of Essex County Procurement and Contracting: Final Report 300 About the County Agency/Department 11. How would you describe the primary mission of this Department? 12. No. Employees? 13. Approx. Annual Budget for this Dept.? 14. Approx. Value of Annul Purchases for this Dept.? 15. What would you say are the predominant products and/or services purchased for this Agency and/or its constituents? About the Contracting Process 16. What percentage of this Department s purchases are comprised of the following types of bids, and what is the level of your ability to select prospective vendors: Types of Bids % of purchases Can you recommend vendors? Yes No Small purchases under $2625 Small purchases between $2625 and $17,500 Large Purchases (acquired thru formal bid process; low bids prevail) Professional Services (exempt from bid process) Extraordinary Unspecified Services (exempt) Contracts with federal, state or local gov ts. (exempt) Other purchases that are exempt from bid process, e.g. tax maps, perishable foods, equipment repair, certain towing and/or storage contracts, etc. Describe: 17. How do you select vendors that you recommend for any of the above types of bids?

Analysis of Essex County Procurement and Contracting: Final Report 301 18. How is the best possible vendor selected for professional services contracts? 19. How were things done between 1996 and 2002? 20. How problematic is the practice of under-bidding? How do you manage your budgets against that practice? 21. Is there a formal complaint process for when vendors want to challenge an award decision? Yes No Please describe. 22. No. of complaints/year? 23. Can you describe the nature or type of complaints that you are aware of? 24. Would you say that vendor payments are timely? Yes No 25. How are payment schedules determined? 26. Are there ever provisions made for start-up payments? Yes No Please describe: 27. Please list specific qualifications that all vendors must have in order to bid on contracts with your agency. 28. What specific qualifications are required for the main (or predominant) type of product or service for which your agency contracts?

Analysis of Essex County Procurement and Contracting: Final Report 302 29. List any significant barriers you believe exist that might prevent or limit the ability of MBEs and/or WBEs from: a) Bidding on contracts with your agency b) Winning contracts with your agency 30. What are your beliefs about the participation of MWBE firms in the purchasing process? 31. In your view, is there anything that can be added to, or taken away from the current process to change the current reality for MWBE firms? 32. What advice would you offer to MWBE firms that are interested in participating in the County s purchasing program? 33. Do you have a list of MWBE firms from which to select prospective bidders and/or vendors? Yes No 34. How often is your MWBE list updated? 35. Is there any verification process? Yes No 36. Is there an MWBE Program (e.g., Outreach, Mailing, etc.)? Yes No 37. Is there any other information that is important to know about the purchasing process?

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