Analysis of Essex County Procurement and Contracting: Final Report

Transcription

1 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 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

2 TABLE OF CONTENTS INTRODUCTION... 4 METHODOLOGY... 6 DATA COLLECTION... 6 GEOGRAPHIC MARKETPLACE AVAILABILITY AND UTILIZATION ANALYSIS PRESENCE OF DISCRIMINATION ANALYSIS DISPARITY STUDY FINDINGS AND CONCLUSIONS DISPARITY STUDY RECOMMENDATIONS & POLICY RECOMMENDATIONS INSTITUTIONAL REDESIGN ACCOUNTABILITY PROTOCOLS RACE NEUTRAL AND RACE CONSCIOUS PROGRAMS APPENDICES APPENDIX A: GLOSSARY OF KEY TERMS APPENDIX B: LEGAL ANALYSIS I. U.S. SUPREME COURT PRECEDENTS II. CONTROLLING LOCAL PRECEDENTS III. OVERVIEW OF OTHER KEY LOWER COURT DECISIONS IV. GUIDANCE ON DISPARITY STUDY METHODOLOGY AND PROGRAM DEVELOPMENT FROM RELEVANT COURT DECISIONS APPENDIX C: ASSUMPTIONS APPENDIX D: DESCRIPTION OF CONTRACTS AND DATA SOURCES I. FILES FROM THE ORIGINAL 19 AGENCIES II. FILES FROM FOUR ADDITIONAL AGENCIES III. DBE LIST CREATION APPENDIX E: SUMMARY OF ALL CONTRACTS ANALYZED APPENDIX F: ANALYSIS OF MINORITY OR ETHNIC GROUPS APPENDIX G: GEOGRAPHIC MARKETPLACE APPENDIX H: AVAILABILITY ANALYSIS APPENDIX I: UTILIZATION ANALYSIS APPENDIX J: PASSIVE DISCRIMINATION I. EMPLOYMENT II. SIZE AND CAPACITY OF BUSINESS III. CREDIT MARKETS APPENDIX K: STATISTICAL DISPARITY ANALYSIS I. BIDS

3 II. CONTRACTS III. MEASURES OF DISCRIMINATION APPENDIX L: POLICY SIMULATIONS I. POLICY OPTIONS II. SIMULATION RESULTS III. METHODOLOGY APPENDIX M: ANALYSIS OF PURCHASING PRACTICES AND PROCEDURES APPENDIX N: ANECDOTAL EVIDENCE ANALYSIS COMMENTS FROM PUBLIC FORUMS BUYER INTERVIEWS FEEDBACK AND RECOMMENDATIONS FROM DISPARITY STUDY COMMISSIONERS REFERENCES

4 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 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?

5 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.

6 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:

7 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 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 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.

8 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, ,563 11,260 3,877 $723,763, 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, $25,831, Utilities Authority $63,096, Vocational School 6,967 6,967 1,045 $17,523, Improvement Authority 8,525 5, $294,804, Subtotal 26,953 $1,125,019, Intergovernmental Transfers 897 $105,069, Non-Profits 1,003 $82,129, Subtotal excluding 25,053 $937,821, intergovernmental transfers Geographic Market Area of 13,116 $891,892, Counties Information secured on SIC or NAICS codes Construction 832 $377,636, Professional Services 5,050 $442,734, Supplies and Equipment 5,097 $39,630, All Other 2,137 $31,891, Total Used in Availability - 10,979 $860,001, Utilization Analysis DBEs 1,215 $85,143, MBEs 723 $30,790, Females 170 $16,875, African Americans 620 $2,266, Hispanics 144 $29,999, Asians 64 $13,661, 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.

9 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

10 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 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 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.

11 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 = 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 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 is the latest available version of the SMOBE/SWOBE report.

12 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 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.

13 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 percent for DBEs, percent for MBEs, 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 percent of all available firms. The DBE availability rates for the three service areas are: percent for construction-related services, 40.51percent for professional services, and 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.

14 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.

15 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 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.

16 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, 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 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.

17 Analysis of Essex County Procurement and Contracting: Final Report 17 under $17,500 yielded an overall DBE availability rate of percent. The availability rate for the three service areas are 7 percent for construction, percent for professional services, and 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 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.

18 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.

19 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 ed to vendors for whom the County had 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.

20 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

21 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 percent of available firms and about 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.