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1 Compensation Analysis of 3 Presenters: Daniel Kuang, Ph.D. John Piatt

2 Overview of Biddle Consulting Group, Inc. Affirmative Action Plan (AAP) Consulting and Fulfillment HR Assessments EEO Litigation Consulting /Expert Witness Services Compensation Analysis Publications/Books BCG Institute for Workforce Development Nation-Wide Speaking and Training Thousands of AAPs developed each year Audit and compliance assistance AutoAAP Enterprise software AutoGOJA online job analysis system TVAP test validation & analysis program CritiCall pre-employment testing for 911 operators OPAC pre-employment testing for admin professionals Video Situational Assessments (General and Nursing) Over 200+ cases in EEO/AA (both plaintiff and defense) Focus on disparate impact/validation cases Proactive and litigation/enforcement pay equity studies COMPare compensation analysis software EEO Insight : Leading EEO Compliance Journal Adverse Impact (3 rd ed.) / Compensation (1 st ed.) 4,000+ members Free webinars, EEO resources/tools Regular speakers on the national speaking circuit

3 Biddle Consulting Group Institute for Workforce Development (BCGi) BCGi Standard Membership (free) Online community Monthly webinars on EEO compliance topics EEO Insight Journal (e-copy) BCGi Platinum Membership Fully interactive online community Includes validation/compensation analysis books EEO Tools including validation surveys and AI calculator EEO Insight Journal (e-copy and hardcopy) Members only webinars, training and much more

4 Daniel Kuang, Ph.D. Vice President of Legal and Audit Support Services Biddle Consult Group ext. 145 John Piatt Director of EEO/AA Operations & Customer Services Biddle Consult Group ext

5 Compensation Series Overview Part 1: Compensation 101 Understanding Context of Compensation Analysis Basics statistical models Compensation Strategies Part 2: Multiple Regression Data Modeling: Multiple Linear Regression Part 3: Advanced Analysis Methods Advanced Data Modeling Post OFCCP Directive 307 Analyses 5

6 Presentation Overview Laws and Regulations Compensation Analysis on a Budget Analysis Logic Statistical Model: t-test Statistical Model: Regression Strategies and Recommendations 6

7 Laws and Regulations

8 Laws & Regulations Executive Order According to 41 CFR (b)(3), contractors must evaluate their compensation system(s) to determine whether there are gender-, race-, or ethnicitybased disparities. According to 41 CFR , the employer s wage schedules must not be related to or based on the sex of the employee. 8

9 Laws & Regulations Title VII of the 1964 Civil Rights Act It shall be an unlawful employment practice for an employer to fail or refuse to hire or to discharge any individual, or otherwise to discriminate against any individual with respect to his compensation... because of such individual's race, color, religion, sex, or national origin. Lilly Ledbetter Fair Pay Act of 2009 Amends Title VII, the ADEA, ADA, and the Rehabilitation Act of 1973 to clarify discriminatory compensation decisions/practices are unlawful and the discrimination occurs each time the compensation is paid. 9

10 Compensation Analysis on a Budget

11 Compensation Analysis on a Budget Proactive Analysis Can Dramatically Reduce Damages OFCCP generally begins with a make-whole relief calculation which typically includes: Current adjustments Back-pay (for two years) Interest (from the beginning of the enforcement period through the signing of the conciliation agreement) Benefits When you proactively identify problems, you have the option to just make current adjustments The difference in financial impact (cost) between the OFCCP finding issues v proactively finding them yourself can sometimes be 10X+ 11

12 Compensation Analysis on a Budget Technological advancements has dramatically reduced the cost for performing analyses Analyses can oftentimes be conducted for a fraction of what they cost just a few years ago Of course... running the analyses is just one portion of the cost, what about the cost of fixing the identified issues? The cost for completely fixing the identified issues can often dwarf the cost for running the analyses... but there is another option... allocate a fixed amount of available funds then address the issues in priority of legal exposure. 12

13 Compensation Analysis on a Budget Creating a fixed-pool of funds to address problem areas. Benefits: It avoids the need for a blank-check It increases the likelihood of receiving approval for the project because now the total costs are known The amount of available funds can be determined based on: 1) budgetary constraints, and 2) a company s level of risk aversion/tolerance Choose to focus on either: the job titles with the largest exposure the most under-paid employees (regardless of job title) 13

14 Compensation Analysis on a Budget Risk Tolerance/Aversion Continuum Conduct No Analyses: It s Better We Don t Know Identify and Completely Address All Problems Extremely Risk Tolerant Conduct Analyses: Limited Budget to Fix Issues Issues Will Take Many Years to Address... But Exposure Will be Reduced Extremely Risk Averse Conduct Analyses: Moderate Budget to Fix Issues Issues Will Take a Few Years to Address... But Exposure Will be Reduced Moving in the right direction... but how long are you comfortable with the exposure? 14

15 Analysis Logic

16 Analysis Logic We understand why it is important to analyze compensation data Ensure fair-pay and equity Stay in compliance with federal laws and regulations What are you looking for? How do you investigate pay disparity?

17 Analysis Logic Q: What are we looking for in a comp analysis? A: We are looking for difference in pay. Examples: Is there difference in pay between men v. women? Is there difference in pay between whites v. minorities? Is there difference in pay between individual race groups (e.g. whites v. Asian) 17

18 Analysis Logic Very Simple Simplest Case: 1 to 1 comparison Employee Gender Salary ($) Mary Female 40,000 Bob Male 45,000 How confident are you that this difference is not due to chance alone? Very confident! Probability=100% that there is a $5,000 difference Male is paid more than Female 18

19 Analysis Logic Less Simple Less Simple Case: 2 to 1 comparison Employee Gender Salary ($) Mary Female 40,000 Bob Male 45,000 Jane Female 46,000 How do you determine if there is difference in pay between Male and Females now? Mary < Bob < Jane Answer: Compare Average Group Salary 19

20 Analysis Logic Less Simple Employee Gender Salary ($) Mary Female 40,000 Bob Male 45,000 Jane Female 46,000 Average Average Female Female Female = = 40, $43,000 46,000 Average Average Male Male Male = = 45,000 1 $45,000 Average Female $43,000 < Average Male $45,000 How confident are you that this difference is not due to chance alone? 20

21 Analysis Logic Statistical Significance When is differences in group averages meaningful and statistically significant? Statistical significance testing: Evaluates group difference and determines: Probably of observing the difference given the data Whether the difference is due to chance alone Events that would occur with p 0.05 are statistically significant. Probability of less than 1 in 20 random trials 21

22 Statistical Models

23 t-test The t-test is a powerful and simple method to test for differences in pay between any two (2) groups The t-test on tests for simple differences. It does not take into account explanatory factors that may impact compensation (e.g., tenure, performance, education). Multiple Regression is a more advanced and generalized model of t-test. Multiple Regression tests for between group differences after controlling for explanatory factors 23

24 t-test Example 1: t-test perspective Group Salary($) Male 35,000 Male 35,000 Male 35,000 Male 40,000 Male 40,000 Male 40,000 Female 40,000 Female 40,000 Female 40,000 Female 55,000 Female 55,000 Female 55,000 Average Salary Male=$37,500 Female=$47,500 Simple Mean Group Difference=$10,000 t=2.83, p=

25 Multiple Regression Multiple Regression is a more advanced and generalized model of t-test. Multiple Regression tests for between group differences after controlling for explanatory factors 25

26 Multiple Regression Example 1: Regression perspective Group Salary($) Tenure Male 35,000 1 Male 35,000 1 Male 35,000 1 Male 40,000 2 Male 40,000 2 Male 40,000 2 Female 40,000 2 Female 40,000 2 Female 40,000 2 Female 55,000 3 Female 55,000 3 Female 55,000 3 Adjust Mean Group Difference=$0.00 p=

27 Multiple Regression Typically, Multiple Regression requires 2-semesters of graduate-level instruction. The actual underlying math and proper methodology is well beyond the scope of our webinar. Setting up and running a regression is EASY. Proper set-up and interpretation is DIFFICULT. Multiple Regression is iterative You must specify a valid model for analysis. It is very easy to mis-specify a model and draw incorrect conclusions: false positives or false negatives Please ask for help if you are unsure. 27

28 Strategies and Recommendations 28

29 Strategies and Recommendations Policy/Personnel-Based Step 1: Audit Current Pay Documentation Practices Verify sufficient documentation exists to clearly support compensation decisions. Focus primarily on rationale behind starting pay and performance-based specifics. Step 2: Develop Specific Criteria for Compensation Decisions Develop objective and measurable guidelines for compensation decisions and apply them consistently. For example: establish (narrow) starting salary ranges for specific positions. Step 3: Review Compensation Decisions Establish third-party internal review process for compensation decisions (e.g., starting salary, yearly increases, etc.)... review should be conducted by personnel with knowledge of identified issues. 29

30 Strategies and Recommendations Policy/Personnel-Based (cont.) Step 4: Revise Document Retention Practices as Necessary Maintain records regarding compensation decisions to ensure data/evidence is available in the event of future litigation. Step 5: Train Supervisors and Managers Train all supervisors and managers regarding new policies/procedures. Step 6: Conduct Periodic Statistical Analysis of Compensation Data Proactively determine whether pay disparities exist. Once identified, make adjustments to eliminate unexplained disparities (only make adjustments after a statistical and cohort-level review have been conducted) 30

31 Strategies and Recommendations Step 1: Create pivot tables (as initial investigation) Need for action liability! Grand Difference Potential JOBCODE/JOBTITLE Data Female Male Total Difference (%) Liability ($) 1 ADMINISTRATIVE SUPPORT Count of GENDER Average of Salary $12.08 $14.52 $12.80 $ % $152, Average of Time in Company Average of Performance Average of Time in Job CUST SERV REP 1 Count of GENDER Average of Salary $11.29 $13.25 $12.94 $ % $244, Average of Time in Company Average of Performance Average of Time in Job CUST SERV REP 2 Count of GENDER Average of Salary $14.29 $14.35 $14.31 $ % $14, Average of Time in Company Average of Performance Average of Time in Job DEPARTMENT MANAGER Count of GENDER Average of Salary $15.97 $17.42 $16.92 $ % $60, Average of Time in Company Average of Performance Average of Time in Job SUPERVISOR - CUSTOMER SERVICE Count of GENDER Average of Salary $23.70 $23.70 $23.70 $ % $0.00 Average of Time in Company Average of Performance Average of Time in Job 4.9 Copyright Biddle -4.1 Consulting Group, Inc. Note: Copyright 1. Potential 2013 Liability Biddle Consulting = "Make-Whole Group Relief" = Difference ($) x 2080 (hours) x 2 (years) x # impacted x 1.25 (benefits + interest) 31

32 Strategies and Recommendations Step 2: Conduct statistical regression analyses (if differences are identified in initial review) Step 3: Prioritize your efforts (focus on the low-hanging fruit i.e., a statistically significant difference with a large number of employees Step 4: Conduct cohort review (i.e., a file-by-file review to identify why differences remain starting salary, education, prior salary, quantity or quality of previous experience) Starting salary is often the culprit... But the question is why are the starting salaries different and do you have the information necessary to justify the difference? Step 5: Make changes where differences cannot be justified statistically or by cohort review (must use regression analyses to identify the amount needed for each impacted individual) 32

33 Strategies and Recommendations Cohort Review (Example 1) Sample Cohort Analysis Ordered by Salary (Descending) Name Gender Salary ($) Time in Co. (Years) Avg. Perform. Scores (3 years) Educ. (Years) Steve Randall M $57, Chris Avery M $52, Leigh Barrows F $51, Danielle Yoko F $51, Mike Freeman M $51, Frank Viola M $50, John Smith M $50, Frank Robison M $49, John Cameron M $49, Mike Stevens M $48, Shelli Jackson F $48, Desiree Laub F $47, Dan Bostick M $43, Nina Ling F $42, Heather Monte F $42, Shana Larris F $40, Nancy Tramel F $40,

34 Strategies and Recommendations Cohort Review (Example 2) Sample Cohort Analysis Ordered by Time in Company (Descending) Name Gender Salary ($) Time in Co. (Years) Avg. Perform. Scores (3 years) Educ. (Years) Mike Freeman M $51, Shana Larris F $40, Leigh Barrows F $51, Frank Robison M $49, Danielle Yoko F $51, Mike Stevens M $48, John Cameron M $49, Sarah Norris F $47, Dan Bostick M $43, Desiree Laub F $47, Frank Viola M $50, John Smith M $50, Nancy Tramel F $40, Heather Monte F $42, Chris Avery M $52, Nina Ling F $42,

35 Strategies and Recommendations Impact of Starting Salary (Example 1) Longitudinal Impact of $4,000 Difference in Starting Salaries (Assuming a Constant 4% Yearly Increase) Year Mike Salary ($) Stephanie Pay Disparity ($) Starting $40, $36, $4, $46, $42, $4, $56, $51, $5, $69, $62, $6, $84, $75, $8, $102, $92, $10, $124, $112, $12, Accumulated difference over 30 years: $224,

36 Strategies and Recommendations Impact of Starting Salary (Example 2) Longitudinal Impact of $4,000 Difference in Starting Salaries (Assuming: 4% Yearly Increase for Mike / 5% Yearly Increase for Stephanie) Year Salary ($) Mike Stephanie Pay Disparity ($) Starting $40, $36, $4, $41, $37, $3, $43, $39, $3, $44, $41, $3, $46, $43, $3, $48, $45, $2, $50, $48, $2, $52, $50, $1, $54, $53, $1, $56, $55, $1, $59, $58, $ $61, $61, $ $64, $64,

37 Quick Recap Laws and Regulations Compensation Analysis on a Budget Analysis Logic Statistical Model: t-test Statistical Model: Regression Strategies and Recommendations 37

38 38

39 Daniel Kuang, Ph.D. John Piatt Biddle Consulting Group's Institute for Workforce Development (BCGi) 39

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