Compensation Analysis 101

Transcription

1 Compensation Analysis 101 Presenter: Daniel Kuang, Ph.D.

2 Overview of Biddle Consulting Group, Inc. Affirmative Action Plan (AAP) Consulting and Fulfillment HR Assessments Custom Test Development & Validation EEO Litigation Consulting /Expert Witness Services Compensation Analysis Publications/Books BCG Institute for Workforce Development Speaking and Training Thousands of AAPs developed each year Audit and compliance assistance myaap 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) High stakes test development Validation studies in response/prevention to litigation 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.) 7,500+ members Free webinars, EEO resources/tools Regular speakers on the national speaking circuit 2

3 Biddle Consulting Group s Institute for Workforce Development (BCGi) BCGi Memberships (free): ~7500+ members / 15,000 HRCI credits to-date Online community Monthly webinars on EEO compliance topics EEO Insight Journal (e-copy) BCGi Platinum Membership (paid) Includes validation/compensation analysis books EEO Tools including those needed to conduct AI analyses EEO Insight Journal (e-copy and hardcopy) Access to the BCGi library of webinars, training materials, and much more 3

4 BCGi Summit Managing Uncertainty: EEO In A New Administration April 6-7, 2017 San Francisco, California 4

5 Contact Information Daniel Kuang, Ph.D. Vice President of Legal and Audit Support Services Biddle Consult Group ext

6 Disclaimer This presentation is not legal advice. We will touch on topics that may have legal ramifications. The presentation is technical in nature: the mechanics and methods of compensation analysis. 6

7 Compensation Series Overview Part 1: Compensation 101 Understanding Context of Compensation Analysis Laws & Regulations Compensation Analysis Logic Basics Statistical Models Part 2: Multiple Regression Compensation Strategies Data Modeling: Multiple Linear Regression Part 3: Advanced Analysis Methods Post OFCCP Directive 307 Analyses Strategies and Recommendations 7

8 PART 1

9 Presentation Overview Laws and Regulations Compensation Analysis on a Budget Analysis Logic 9

10 Laws and Regulations

11 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. 11

12 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. Federal Equal Pay Act (1963) 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. 12

13 Laws & Regulations Lilly Ledbetter Fair Pay Act of 2009 Amends Fair Labor Standards Act (FLSA). EO Pay Transparency Make it illegal to prohibit federal employees from discussing and sharing their own pay information. California Fair Pay Act (SB 358) Not enforced by the OFCCP, but an important law to understand. This amends the California labor codes in many important ways. Importantly, it expands comparisons to individuals in substantially similar jobs. 13

14 Compensation Analysis on a Budget

15 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+ 15

16 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. 16

17 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) 17

18 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? 18

19 Analysis Logic

20 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?

21 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) 21

22 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 22

23 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 23

24 Analysis Logic Less Simple Employee Gender Salary ($) Mary Female 40,000 Bob Male 45,000 Jane Female 46,000 Average Average Female Female Female 40,000 2 $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? 24

25 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 25

26 Quick Recap Laws and Regulations Compensation Analysis on a Budget Analysis Logic 26

27 27

28 PART 2

29 Compensation Series Overview Part 1: Compensation 101 Understanding Context of Compensation Analysis Laws & Regulations Compensation Analysis Logic Part 2: Multiple Regression Basics statistical models Compensation Strategies Data Modeling: Multiple Linear Regression Part 3: Advanced Analysis Methods Post OFCCP Directive 307 Analyses Strategies and Recommendations 29

30 Presentation Overview Statistical Models (2) Statistical Model: t-test Statistical Model: Regression Hands-on Demonstrations 30

31 Statistical Models

32 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) 32

33 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 33

34 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=

35 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 35

36 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=

37 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. 37

38 The Process 38

39 Comp Analysis: 6-Major Pieces There are 6-Major Steps in a Comp analysis 1. Properly defining scope of analysis 2. Data preparation 3. Analysis with and without explanatory factors 4. Evaluate analysis-model validity 5. Interpreting analysis results 6. Computing salary adjustments for hot-spots. We will go through all 6-Steps with a Live Demo 39

40 1-Define Scope of Analysis 1. Define PURPOSE of compensation study 2. Scope Structural a) Organization 3. What are you going to investigate? 4. Understanding Forces/Factors Driving Pay a) Explanatory Factors b) Opportunity, Practice and Policy 5. Understanding Pay Structure and Set SSEGs a) Pay Analysis Groups (PAGs) 40

41 2-Data Preparation 1. Data Concerns a) Missing Data b) Outliers 2. Code Demographics a) Focal (Female, Minority) = 0 b) Reference (Male, White) = 1 3. Dummy Code Categorical Factors 4. Tainted Variables 41

42 3-Analyze Data 1. t-test 2. Multiple Linear Regression a) classic Regression b) Pay Analysis Groups (PAGs) 3. Cohort Analysis 42

43 4-Evaluate Model/Analysis Validity 1. Inter-Variable Correlation Matrix 2. Multicollinearity 3. Interactions 4. Model Explanatory Power (R 2 ) 5. Regression Beta-Weight 43

44 5-Interpret Results 1. Identify Statistical Significance 2. Interpret Model Stats 44

45 6-Compute Pay Adjustments 1. IF NECESSARY Compute Pay Adjustment 45

46 Live Demonstration in Excel 46

47 Quick Recap Statistical Model: t-test Statistical Model: Regression 6-Steps in Compensation Analysis 47

48 48

49 PART 3

50 Compensation Series Overview Part 1: Compensation 101 Understanding Context of Compensation Analysis Laws & Regulations Compensation Analysis Logic Part 2: Multiple Regression Basics statistical models Compensation Strategies Data Modeling: Multiple Linear Regression Part 3: Advanced Analysis Methods Post OFCCP Directive 307 Analyses Strategies and Recommendations 50

51 Presentation Overview OFCCP Directive 307 Directive 307 Summary (Pre/Post) Opportunity Analysis Pay Analysis Group Advanced Data Modeling Strategies and Recommendations 51

52 Pre/Post 307 Comparisons 52

53 Directive 307 Summary Summary of OFCCP Compensation Investigation Procedures Compliance Officer (CO) will: 1. Conduct preliminary analysis of summary data 2. Conduct an analysis of individual employee-level data 3. Determine the approach from a range of investigations and analytical tools 4. Consider all employment practices that may lead to compensation disparities 5. Develop pay analysis groups 6. Investigate systemic, small group, and individual discrimination 7. Review and test factors before accepting factors for analysis 8. Conduct onsite investigation, offsite analysis, and refinement of the model 53

54 Pre 307 v Post 307 Pre 307 Framework /flow-chart approach Crippling and Handicapped Post 307 Not Framework /flow-chart approach No Constraints/Limitation Any Source Impacting Pay is Open To Scrutiny 54

55 PRE 307: Framework Trigger Test No DONE Data Request Analysis t-test No DONE Analysis Cohort No DONE Analysis Regression No DONE CA/Damage $$$ 55

56 POST 307: Framework Trigger Test No Data Request Analysis t-test Analysis Regression No Analysis Cohort No CA/Damage $$$ 56

57 POST 307: Framework Trigger Test Data Request t-test ANALYSIS Regression Analysis Cohort PAGs Opportunity CA/Damage $$$ 57

58 Game Changers Why has OFCCP experienced so little success? Example Please Dir 307 Changes this in their favor Opportunity Analysis PAGs 58

59 Post 307: Opportunity Analysis Game Change #1 OFCCP will Investigate Anything Impacting Pay Examples (non-exhaustive) Promotions Hires Shifts Contingency Pay Bonus Assignments Claims/Theories (non-exhaustive) Glass Ceiling Glass Walls Funneling Stove-piping/Silo 59

60 Post 307: Pay Analysis Group Game Changer #2 Classic SSEG defense Less Effective Less Relevant Statistically Aggregate SSEGs A new battle of grouping analysis groups Why is this a game changer? 60

61 Post 307: Pay Analysis Group Pre 307, OFCCP s hands were bound and cannot win!!! Problems/Issues Pre Post Small sample size/not analyzable Small statistical power Post 307, OFCCP has the necessary tools to win!!! 61

62 Post 307: Pay Analysis Group Simple Regression Job Title Location SSEG Admin 1 Houston 1 Tampa 2 Admin 2 Phoenix 3 Tampa 4 Manager 1 Dallas 5 Houston 6 Phoenix 7 Tampa 8 62

63 Post 307: Pay Analysis Group Aggregated Regression PAG Job Title Location SSEG PAG Admin 1 Houston 1 1 Tampa 2 1 Admin 2 Phoenix 3 2 Tampa 4 2 Manager 1 Dallas 5 3 Houston 6 3 Phoenix 7 3 Tampa

64 Directive 307 Structured Organizations/Institutions 64

65 Structured Organizations/Institutions Are Uniquely Easy Targets for OFCCP Structured: Opportunity Analysis Pay Analysis Group (PAG) Horizontal Jobs across organization/college (Prof. by Depts). Vertical o Similarly Situated/Same Jobs Levels (e.g. Prof Level Assistant, Associate, Ten.) o Similarly Situated 65

66 Structured Organizations/Institutions Are Uniquely Different In Compensation Job variety Similar to a small country Every Job Has Different Forces Time in Company is not the same Time in Job is not the same Performance factors are different Performance Objective are different 66

67 Advanced Data Modeling Opportunity Analysis 67

68 Opportunity Analysis 4. Consider All Employment Practices that May Lead to Compensation Disparities Including, but not limited to: o o o Starting salary, base pay, non-base pay, and other practices Access to opportunities affecting compensation (such as higher paying positions, work assignment, training, preferred shift work, access to overtime, pay increases, incentives, higher commissions or desirable sales territories, etc.) Policies and practices that result in a Glass Ceiling o Funneling into high/low paying positions Important Note: This is a game changer. Many of these are not compensation analyses... They evaluate the distribution of opportunities that impact compensation... this is a very different analytical approach. 68

69 Opportunity Analysis 4. Consider All Employment Practices that May Lead to Compensation Disparities (cont.) Examples of analyses of the distribution of opportunities: Invited to Training Not Invited to Training Men Women Good Sales Territory * Not Good Sales Territory White Minority Each will result in a probability or standard deviation that will be interpreted as they normally are within a typical adverse impact analysis. * Good v Not Good territories will need to be objectively defined. 69

70 Opportunity Analysis 4. Consider All Employment Practices that May Lead to Compensation Disparities (cont.) Regression analyses allow for an investigation of the included variables Interactions can be calculated at the same time the original compensation analyses are conducted Interactions occur when predictor variables operate differently for different groups Example(s) include: o o Each additional performance appraisal point equates to $1000/men but only $750/women Each additional year of tenure equates to $1500/whites but only $1000/minorities 70

71 Opportunity Analysis Interactions $140,000 $120,000 $100,000 Min Wht $80,000 $60,000 $40,000 $20,000 $ Tenure 71

72 Opportunity Analysis % 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% Minority White Linear (Minority) Linear (White) 30.00% 20.00% 10.00% 0.00% Production I Associate Manager Supervisor Admin-I Admin-II Admin-II Regional Admin-II National Admin-II HQ Copyright 2015 Biddle 1Consulting Group Executive 72

73 Advanced Data Modeling PAG 73

74 Aggregated Regression Categorical factors Qualitatively unique and different categories Example: Location, Job Title, Division, etc. Statistically Control for Categorical Factors Aggregated Regression Aggregate/Pool pay differences at each SSEG across categorical factors Powerful Tool for OFCCP Game Changer Old tricks (divide & hide/slice & dice) cannot hide true pay disparity 74

75 Aggregated Regression Old Tricks??? Example Please 75

76 Aggregated Regression What s the new tool? Pay Analysis Groups (PAG) How do you Control for categorical factors? Dummy Coding What are dummy codes? Coding scheme of 0 s and 1 s to unique identify levels/categories of a factors. Create k-1 dummy codes o k=number of levels/categories of a factor 76

77 Aggregated Regression Dummy Code??? Example Please 77

78 Warning: Invalid Model Specif. It is EASY to misspecify your model. Understand what you are rolling up Overly broad PAGs are most likely incorrect One-size fits all models for large and broad organizations do not reflect reality o e.g., is TIC, TIJ, Perf, etc. and salary same for all jobs? To establish a claim of pay discrimination, the charging party must present evidence to support a valid model. 78

79 Warning: Invalid Model Specif. One-size DOES NOT fit all Job Title Location TIC TIJ Perf. Admin 1 Houston Tampa Admin 2 Phoenix Tampa Manager 1 Dallas Houston Phoenix Tampa 79

80 Warning: Invalid Model Specif. One-size DOES fit all Job Title Location TIC TIJ Perf. Retail Associate Atlanta Dallas Houston Los Angeles New York Phoenix Seattle Tampa 80

81 Warning: Invalid Model Specif. To establish a claim of pay discrimination, the charging party must present evidence to support a valid model. We experience many misspecified models from claimants expert statisticians. 81

82 Strategies and Recommendations 82

83 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. 83

84 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) 84

85 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 $ % $0.00 Average of Time in Company Average of Performance Average of Time in Job 4.9 Copyright Biddle -4.1 Consulting Group, Inc. Note: Copyright 1. Potential 2015 Liability Biddle Consulting = "Make-Whole Group Relief" = Difference ($) x 2080 (hours) x 2 (years) x # impacted x 1.25 (benefits + interest) 85

86 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) 86

87 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,

88 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,

89 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,

90 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,

91 Quick Recap OFCCP Directive 307 Directive 307 Summary (Pre/Post) Opportunity Analysis Pay Analysis Group Directive 307 & Ed/Research Inst. Advanced Data Modeling Strategies and Recommendations 91

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93 Daniel Kuang, Ph.D. Vice President of Legal and Audit Support Services Biddle Consult Group ext