Walking on Eggshells. Effective Management of Internal Pay Equity. CUPA-HR New York Chapter

Transcription

1 CUPA-HR New York Chapter Walking on Eggshells Effective Management of Internal Pay Equity Moshe Mayefsky Senior Consultant Megan Werner Associate Consultant Copyright 2018 by The Segal Group, Inc. All rights reserved.

2 Learning Objectives and Key Takeaways Background: The What and Why Statistical Analysis: A How To Guide Next Steps: Your To Do List Questions 1

3 The What and Why So What is Pay Equity? Pay Equality Equal pay for equal work Equality in rights, opportunities and status vs. Pay Equity Ensuring impartiality, fairness, and unbiased and unprejudiced compensation policies and practices Goal of eliminating systemic discrimination in pay setting (e.g., gender or race) 2

4 The What and Why Acknowledging the Pay Gap Gender/Ethnicity Median Weekly Earnings by Race/Ethnicity and Gender, 2017 Men Women $1,207 $690 $710 $603 $657 $971 $795 $903 $941 $770 92% 90% 84% 87% 76% Hispanic or Latino African American White Asian All Races Source: U.S. Bureau of Labor Statistics, Median weekly earnings of full-time wage and salary workers by selected characteristics, Annual Averages 3

5 The What and Why State and Federal Pay Related Legislation Has Become Prominent Note: Outlier states without Equal Pay legislation include Alabama, Mississippi, and North Carolina 4

6 The What and Why State and City Pay Laws Have Been Growing Aggressively Massachusetts Salary History Ban Affirmative Defenses Self-Evaluation Defense New Jersey Protected categories under NJLAD Comparisons across all operations/facilities Retaliation provisions Philadelphia Prohibits salary history inquiries; also applies to current employees seeking new position New York City Illegal Salary Inquiries Illegal Salary History Consideration California Employer Pay Scale Disclosures Salary History Ban 5

7 The What and Why Federal Enforcers Also Have Gained Power EEOC no longer needs a complaint to investigate and the OFCCP only needs a federal contract 6

8 The What and Why Top Executives Catching On Pay is an outcome of an un-level playing field. Until you level that playing field, you're going get that same outcome. Ellen J. Kullman, Former CEO, DuPont You can t be a great CEO and say that I m not committed to gender equality today. Marc Benioff, CEO, Salesforce It allows us to continue to attract the kinds of talent and the kinds of great people that help us build this brand. Kevin Johnson, CEO, Starbucks 7

9 8 Walking on Eggshells Statistical Analysis: A How To Guide

10 Internal Pay Equity Statistical Analysis What a Statistical Analysis Can Do For You Identify potential systemic issues and determine follow-up actions Increase employee engagement Self-evaluation defense Avoid brand damage Investigate employee claims Litigation defense, regulatory compliance 9

11 Internal Pay Equity Statistical Analysis Case Studies Situation #1 A-OK University conducted a proactive internal pay equity analysis proactively for the faculty population The objective of the study was to test the hypothesis that males and females are paid similarly for similar roles Situation #2 Unfair University (the U ) conducted a reactive internal pay equity analysis as a result of a complaint from a faculty member The objective of the study was to test each school at the institution to see if the individual claimant was an outlier 10

12 Internal Pay Equity Statistical Analysis Layman s Guide to the Analysis Steps for Conducting a Pay Equity Analysis 1. Gather Data: Collect and clean necessary census data 2. Descriptive Statistics: Identify the issue and quantify the gap 3. Correlation Matrix: Measure the strength of relationship between variables 4. Multivariate Regression: Predict pay considering the effects of all variables with strong relationships 5. Draw Conclusions: Systemic issues and individual outlier examination 6. Next Steps: Identify additional analyses, review, action items 11

13 Internal Pay Equity Statistical Analysis Layman s Guide to the Analysis Descriptive Statistics provide a high level portrayal of pay for the employee population based on years of service, job classification, etc. Salary Count Average Std Dev Female 275 $65,000 $5,000 Male 250 $75,000 $15,000 Years of Service Average Std Dev Observations: Males earn $10,000 more, on average, but also have 3 more years of service 12

14 Internal Pay Equity Statistical Analysis Layman s Guide to the Analysis Correlation Matrix provides guidance to the art of regression modeling Annualized Salary Annualized Salary 1.00 Years of Service Age Years of Service Age Gender Ethnicity Gender Ethnicity

15 Internal Pay Equity Statistical Analysis Layman s Guide to the Analysis Multivariate Regression statistically describes the relationship between compensation and multiple factors that determine it Key Components R 2 P-Values 14

16 Internal Pay Equity Statistical Analysis Layman s Guide to the Analysis Overall Explanatory Power To determine the explanatory power of the overall model, we assess the R 2 value using the following statistical conventional standards 30% 70% Moderate 0% 30% Weak 70% 100% Strong 15

17 Internal Pay Equity Statistical Analysis Layman s Guide to the Analysis Predictive Power of Each Variable The p-value is the probability of obtaining a result at least as extreme as what was actually observed (e.g., 0.05 equals 1 in 20 likelihood) Less than 0.01 P-value Interpretation Very strong relationship Greater than 0.01 and Less than 0.05 Greater than 0.05 and Less than 0.1 Greater than 0.1 Strong relationship Little to no relationship No relationship 16

18 Internal Pay Equity Statistical Analysis Sample Analysis Output Statistically Significant Variables Weight p-value Base amount (all faculty) $67,000 p<.001 Years of Service Variables Years since degree prior to XYZ 400 p=.022 Years at XYZ prior to current rank (500) p=.038 Years in current rank 300 p=.001 Rank Variables Assistant Professor 1 0 Baseline Associate Professor 1 8,900 p<.001 Professor 1 27,800 p<.001 College Variables College of Arts and Sciences 2 0 Baseline College of Education 2 4,800 p=.009 College of Business 2 47,100 p<.001 Statistically Insignificant Variables Weight p-value Gender p=.272 Non-minority p=.650 A faculty member s salary can be reasonably predicted based on years of service, rank, and college alone. Neither gender nor race/ethnicity was a significant predictor of pay. For Example: An associate professor in the College of Arts and Sciences, with 6 years between receiving his/her degree and date of hire, and 10 years in current rank, would have a predicted salary of $81,300. $67,000 + ($400 x 6) + ($300 x 10) + $8,900 = $81,300 1 Rank was coded as follows: 1=Currently holds rank, 0=Does not hold rank 2 College variables were coded as follows: 1=Member of college, 0=Member of another college 3 Gender was coded as follows: 1=Male, 0=Female 4 Minority defined as: Asian, Black/African American, Hispanic, Other, and Two or more races 17

19 Internal Pay Equity Statistical Analysis Case Study #1 Standard Errors from Expected Expected Salary vs. Standard Errors From Expected Salary Male Female -2 $60,000 $65,000 $70,000 $75,000 $80,000 $85,000 Expected Salary Results of the Study Primary Drivers: Faculty rank Years in rank Age at hire Accounted for 90% of the differences in pay between faculty members Gender was statistically insignificant Results were communicated widely at faculty orientation 18

20 Internal Pay Equity Statistical Analysis Case Study #2 Results of the Study Systemic inequities between males and females were initially found at the claimant's school When examining outliers, however, many were found to have had a similar characteristic, which led to additional data collection and reruns With the model re-run, gender was a statistically insignificant variable Further, the claimant s salary was found to be in line with predictive model shared with legal team for litigation defense 19

21 20 Walking on Eggshells Your To Do List

22 Your To Do List Getting Started 1. Imperatives: Understand the organization s current mindset on pay equity, as well as external and internal imperatives to address internal pay equity 2. Build Urgency: Assess the readiness of your organization to take on pay equity build urgency around the need for the study, the extent to which the organization is walking on eggshells 3. Team: Assemble the individuals within the organization who will have input for the identification of disparities and addressing the outcomes from the analysis Outside the organization: Determine with General Counsel whether attorneyclient privilege may be warranted for the study 4. Plan: Develop an ABC Plan (actions, budgetary constraints and communications). Assign accountabilities and reserve budget for a pay equity study 21

23 Your To Do List continued Partnered Analysis 1. Collect and Clean Data: Scrub and manage data to maintain accuracy 2. Descriptive Statistics: Analyze the population in categories to identify areas of focus and variables as potential pay drivers 3. Correlation Matrix: Measure the strength of relationship between variables given sufficient sample sizes 4. Multivariate Regression: Predict pay considering the effects of all variables with strong relationships 5. Analyze and Assess: Use analyses to identify and draw conclusions on systemic internal pay disparities, if any, as well as individual outlier examination 22

24 Your To Do List continued Managing Results 1. Develop Action Steps to Address Issues: Determine approach to address the identified disparities 2. Examine Outliers and Additional Data: Identify potential additional data fields for the study, based on trends found in outliers 3. Analyze Impact: Address policies, practices, and/or areas of concern identified as problematic. Estimate cost of addressing inequities from outlier population 4. Remediate and Communicate: Based on the ABC Plan, determine approach to address the issues and communicate results 23

25 Questions and Contact Information Moshe Mayefsky Megan Werner

26 Appendix 25

27 Normal Distribution One standard deviation 68% of data 95% of data 99.7% of data

28 Descriptive Statistics Distributions and Probability Plots Distribution of Actual Salaries Probability Plot of Actual Salaries Expected Actual Salary $200K $160K $120K $80K <40K 90K 95K >140K $40K

29 Descriptive Statistics Distributions and Probability Plots Distribution of Log Salaries Probability Plot of Log Salaries Expected Log Salary < >11.85K

30 Sample Output Scorecard for Department Heads $350,000 $300,000 $250,000 $200,000 $150,000 $100,000 $50,000 $0 Employee A Employee B Employee C Employee D Employee E Employee F Employee G Employee H Employee I Employee J Actual Pay $140,000 $150,000 $152,000 $157,000 $175,000 $198,000 $218,000 $231,000 $237,000 $360,000 Predicted Pay $164,583 $146,148 $152,676 $143,351 $187,499 $198,938 $209,044 $273,864 $205,477 $290,100 Perf. Factor Perf. Factor 2 Yes No No No No No Yes Yes No Yes Perf. Factor 3 20% 20% 20% 25% 40% 80% 60% 80% 60% 85% External Market Range 25 th 75 th +1 SE Predicted Pay -1 SE Actual Pay 29