EMPLOYMENT DISCRIMINATION

Transcription

1 EMPLOYMENT DISCRIMINATION

2 OFCCP: More Powerful than Ever Selected Industry Experience: Aerospace / Industrial Manufacturing Air Transportation Banking, Financial & Investment Services Education / University Electronics Manufacturer Engineering Food Production Grocery Stores Healthcare Services Insurance Services Internet & Software Technologies Logistics Machine Production & Manufacturing Management & Administrative Services Materials Manufacturing Medical Equipment Manufacturing Non-Durable Goods Manufacturing Oil & Gas Production Pharmaceutical Manufacturing Safety Consultant Retail Sales Security Solutions Manufacturing Supply Chain Providers Telecommunications Travel & Tourism Utilities Compensation equity is a stated priority of the current administration. The Lilly Ledbetter Fair Pay Act was the first bill signed by President Obama in 2009 and in 2010 he created the National Equal Pay Task Force combining the resources of the Office of Federal Contract Compliance Programs (OFCCP), the U.S. Equal Employment Opportunity Commission (EEOC), and the Department of Justice (DOJ). OFCCP has been the lead agency dedicated to monitoring fair pay, but has authority only over federal contractors. With the creation of the task force, it is believed that monitoring may eventually extend beyond contractors. Between 2010 and 2013, the OFCCP budget has expanded and staffing has increased by 20-25%. Settlements, both in numbers and in dollars, are up from previous years. The OFCCP is currently designing a compensation data collection tool to help identify companies for further investigation and to provide the data necessary to perform statistical analysis and damages calculations. The one constant is change. In June 2006 the OFCCP retired the long-lived Dubray analyses and specified multiple regression analysis as the primary tool for identifying problematic pay differences between men and women and between members of differing race / ethnicity groups. Their regression specification varies - changing over time and across regional offices. In 2010, they turned to cohort / comparator analyses: they identify two employees in the same job that look alike on paper but with differing pay. The contractor is then asked to explain the pay difference. Cohort analyses do not rely on statistical tests. In February 2013, the OFCCP issued Directive 307, which embraces both the regression and cohort methodologies.

3 About Welch Consulting: OFCCP Experience Each audit is lead by a Ph.D. labor economist who is trained in statistics and has ample experience conducting pay analyses. We use cutting edge techniques to not only make the pay comparisons, but to better understand and refine the data and the analyses. We have successfully worked on over 100 audits across every major industry and in each of the OFCCP s six regional offices. We also have abundant experience with compensation analyses in non-ofccp audits and in class action and private plaintiff litigation. We re here to help. Our purpose is to help federal contractors navigate the seemingly mysterious statistical waters of an OFCCP audit. We are knowledgeable about and experienced with each step of the process. We can help you identify relevant data, perform the statistical analyses, supplement the data and refine the analysis if necessary, prepare data for production, produce tables and reports of results, respond to questions from the OFCCP, verify the OFCCP s calculations, and directly discuss the statistical results with the OFCCP. Other Practices: Employment Discrimination Pay Promotion Hiring Termination Background Checks Other Employment Practices Wage & Hour Issues FLSA Misclassifcation Time Off-the-Clock Meal Periods / Rest Breaks Donning and Doffing Economic Damages Discrimination in Financial Services & Real Estate Commercial Litigation General Statistics Consulting Because the OFCCP methodology is in a constant state of flux, you need to have a qualified statistical expert who is trained to conduct compensation analyses and is experienced with the OFCCP s process. We have that training and experience and we are here to work with you as you defend against an audit.

4 WHAT DATA? The first pass analysis was the OFCCP s self-created trigger test, Is there a difference of 2% or $2,000 (the 2 or 2 test) in any job group? When you failed -- and everybody failed -- the OFCCP requested individual-level pay data for workers under that plan. Under Directive 307, they might simply bypass any trigger test and request the individual level data. This is where the real statistical analyses begin. The first question that most companies ask is What data do I need to send to the OFCCP? The OFCCP has a 16-factor list that provides a starting point, but it is only a starting point. The key task in constructing any analysis is to group together similarly situated employees, i.e. workers that have similar skills, knowledge, abilities, and who perform the same job functions. You know your employees and you know what they do. You know who performs similar tasks. Blindly grouping employees together based on existing data fields can lead to misleading and meaningless results. While we investigate the fields in the data, we also discuss with the client whether these fields group together similar employees. PRELIMINARY ANALYSIS Per the OFCCP s Directive 307, audits may involve both statistical analysis (multiple regression) and non-statistical analysis (cohort / comparator comparisons). Analyses will be run on pay analyses groups, which may be We do the hard work behind the scenes and then present the results to you focusing on everything you need to know. jobs, job groups, grades, or aggregations across these groups of employees. Smaller groups of similarly situated employes will then be examined, perhaps within job titles, followed by comparisons of individual employees, relative to their comparators. Pay analyses are ambitious. Think of all of the determinants that have affected every current worker s pay. Current pay is the product of pay-at-hire and every pay raise since then. How prior experience, skills, and training are valued at the time of hire can change over time and can vary across HR managers. This may involve decisions made over a forty-year period or even longer. Pay raises are affected by the economy, what skills the supervisor values (which can change), historical performance and more recent performance, employee turnover, and many other factors. Trying to capture everything that matters with a limited list of inputs like job title, tenure, and time-in-job misses much. Because of this, it is not surprising that many preliminary analyses show statistically significant pay disparities.

5 WHAT DATA? BEYOND THE PRELIMINARY ANALYSIS While many experts stop at this point, we believe that this is where our true value begins. A statistically significant finding does not mean that discrimination has occurred. But it does mean that further investigation is required and that either the data or the statistical models or the company s pay practices need to be corrected. We utilize known and widely-used statistical techniques and have even developed some of our own to help you identify incorrect data and to identify incomplete and inadequate statistical models. This investigation requires collaboration with the client to determine if there are legitimate, non-discriminatory factors that have been omitted from the analysis and that are incorrectly driving this result. OFCCP FACTOR LIST ID / NAME We excel in helping companies focus on employees and groups of employees that are most likely to identify the incorrect data and illuminate the missing information from the analyses. Once identified, we can incorporate the corrections and any additional data and re-estimate the analyses. We find that most statistically significant preliminary pay disparities can be explained by the legitimate, business-related factors found during our investigation. When the disparities are not eliminated, we can help identify potential pay adjustments, make suggestions for the audit, and recommendations going forward. The case studies on the following pages illustrate how some of these pay comparison diagnostics have been useful in previous engagements. In this short space, we won t show all of the ways that we investigate data, but we show some of the more common techniques. GENDER RACE/ETHNICITY HIRE DATE JOB TITLE EEO-1 CATEGORY AAP JOB GROUP PAY (BASE SALARY OR WAGE) HOURS WORKED OTHER PAY (BONUSES, INCENTIVES, ETC) EDUCATION WORK LOCATION PERFORMANCE RATINGS DEPARTMENT FUNCTION SALARY GRADE (BAND, LEVEL, ETC.) PRIOR WORK EXPERIENCE

6 Influential Observations We can identify the pay observations in the analysis that have the biggest impact on the gender or race-based pay differences. The purpose is to identify data errors and missing pay determinants and to show that these statistically significant and potentially problematic results are often very fragile. We also want to know when they are not fragile. CASE STUDY In the table below, we show the impact of successively removing employees from the analysis. Results may be fragile. >> In this actual audit, we began with an OFCCP-style preliminary analysis that resulted in a 6.2% pay difference adverse to women. The 6.2% was equivalent to 2.75 standard deviation units. Differences of two or more standard deviations are often considered statistically significant and this is the standard used by the OFCCP. Given the number of women, the 6.2% pay difference, and average pay, the OFCCP would have asked for $2,100,000 in damages if the data had been submitted as we received it. data errors & missing inputs In the table below, we can look to the far right columns and see that the difference falls to 2.48 standard deviations when the first employee is removed. It then falls to 2.23 and then to 1.96 when the next two employees are removed. We can t just remove employees from the comparison, but we can investigate whether we are missing something about these highly influential employees. It is also interesting and informative that in a comparison involving nearly 500 workers and a damage calculation of over $2 million, the significant result hinges upon only three workers. It is not robust and we can see that a few misclassified employees or a few data errors can create the appearance of a problem when one does not actually exist. Spending a few hours correcting the data can pay huge dividends when compared to the $2,100,000 the OFCCP demands. In this case the company identified five workers as being incorrectly coded as full-time when they were really part-time. After this simple data correction was made, the potentially problematic pay difference observed above fell below two standard deviations and the audit was closed. We would highly recommend finding these data anomalies prior to submitting the individual-level data to the OFCCP so as to eliminate several costly rounds of data corrections with the OFCCP. Know your data before you submit it.

7 Influential Groups of Employees We can also identify potentially problematic groups of employees, i.e., those that make the biggest contribution to a given pay differential. These groups may be jobs or grades, which often highlights groups of employees that are not actually similar. The groups may also be departments or locations, which may identify special circumstances or problematic areas. In an audit for a telecommunications provider, we ran an OFCCP-style preliminary regression analysis and found a pay difference of 3.33% adverse to women. This difference was equivalent to 2.26 standard deviation units, i.e., it was statistically significant. OFCCP would have computed damages of $295,000 if the data we received had been submitted to them. special circumstances In performing our regression diagnostics we noticed that most of the difference came from a single job. This is shown in the graph below. Here we see that Job Title 2 accounted for 72.8% of the total pay difference. After further discussions with the company we discovered that the functions carried out by one group of workers had been outsourced. Workers in that job could look for other work or they could stay with the company. But if they stayed, they would need to move to a new job in a lower paying grade. CASE STUDY This case study reveals that a single job accounted for 72.8% of the total pay difference. Under closer inspection, we found that circumstances affecting that single job explained the seemingly unfavorable result. << Their actual pay, however, would not be reduced. These workers were red-circled, i.e., their pay was above the maximum pay for their new grade. When this fact was incorporated into the analyses, the pay difference fell to 1.14% and 1.03 standard deviation units. The audit was closed favorably.

8 Importance of Communication A hospital submitted individual-level data to the OFCCP believing that OFCCP would find pay to be both gender and race neutral. OFCCP did find that pay differences were gender-neutral, but that both Hispanic ($2.01 and 4.13 standard deviation units) and white ($1.20 and 3.76 standard deviation units) registered nurses, RN s, were paid statistically significantly less than Asian RN s (the preferred group in their analyses). OFCCP computed damages at $2,300,000. CASE STUDY We have found that it s important to have discussions with your HR personnel so that we understand the factors that determine pay at your company and the data that captures this information. Every company is unique as is their data. >> We were engaged at that point. We were able to replicate the OFCCP s results and began discussions with the company regarding the data. It was soon discovered that the RN time observed in the data was not only incomplete but that they were only recording time since the last renewal date in the state. Time worked as an RN in other states or prior to the last renewal was not considered. They believed that RN time was the only factor affecting pay for RN s, but hoped that the errors in the data would not lead to a result like the one above. Let s talk. Faced with a $2.3 million dollar back-pay calculation, they began entering correct RN time data from their personnel files. The difference in the time since license is presented in the graph below. Time as an RN in the original data is lower than actual RN time for all three race groups, but it is most pronounced for Asians (understated by 86% as compared to 72% for whites and 58% for Hispanics). When the RN time was corrected and the regressions were re-estimated, the pay differences fell to $0.81 (0.66 standard deviation units) for Hispanics and to $0.14 (0.17 standard deviation units) for whites. Neither of these differences is statistically significant, the OFCCP was satisfied with the explanation, and the audit was quickly closed. You know your data and you know your employees. It is important that we work together to ensure that the data and the analyses are accurate and meaningful. B-8

9 Outliers We were approached to conduct an audit of Ph.D. level scientists for a large national defense employer. Upon running the preliminary OFCCP-style analyses, we found statistically significant pay differences between men and women. We compared what the scientists were earning to what the statistical model said they should earn, given the factors in the regression analysis. This comparison is presented visually in the graph below. The points represent salary and tenure observations and the line is the regression fitted relationship between pay and tenure. We quickly noted that the fit of the regression was good for most of the scientists (the points are close to the line), but that a select group was clustered above the regression line, i.e., they did not fit the model. We reported the results back to the company and they quickly identified these as their distinguished scientists, i.e., those that had won major awards and directed large teams and were paid accordingly. This information was not in the original data production. We asked for a list of all distinguished scientists and incorporated the data into the analysis. This minor supplement eliminated the pay differences found in the preliminary comparisons. CASE STUDY This case study illustrates how the inclusion of a previously overlooked detail, distinguished status, explained pay differences in the preliminary data. << B-9

10 Compensation Analysis Variable List for Transactional Databases This is a general list of factors that we have found useful in audits. It is overinclusive in that most companies don t track or even utilize all of this information. As you are probably better aware of the determinants of pay at your company, we look for your guidance in deciding which factors are relevant. If there are additional relevant factors that are not included on this list, we would ask that you provide them as well. 1 2 Identification & Personal Info 1. Employee ID 2. Name 3. Race/Ethnicity 4. Gender 5. Date-of-Birth Employment Status & Action Related Info 1. Action code and action reason 2. Employment status (active, terminated, on-leave, etc.) 3. Regular or temporary status 4. Full-time/Part-time status 5. Original and most recent hire date 6. Adjusted service date 7. Termination date(s) 3 4 Pay Information 1. Base Pay 2. Pay Frequency 3. Currency 4. Where relevant, other forms of compensation including bonuses, awards, over-time, commissions, stock options, and restricted stock. Organizational Structure 1. Company or Subsidiary 2. Division or Organization 3. Business Unit 4. Department 5. Supervisor ID 6. Location information such as area, region, district, & store / branch / office / site.

11 Getting the whole picture means looking at all the parts CASE STUDY Job Information 1. Job Code/Job Title 2. Job or Pay Grade/Level 3. FLSA Status 4. Job Family, Job Sub-Family, Job Function, or other aggregations of job codes 5. EEO-1 Job Category (nine categories Officials & Managers, Professionals, etc.) 6. AAP Job Group 7. Census Occupation Code 8. Job Date or Time-in-Job 9. Grade Date or Time-in-Grade Performance & Qualifications 1. Performance Ratings 2. Educational Attainment including degree/level, degree date, discipline, and school 3. Certifications or licenses received or training completed 4. Years of prior relevant experience (or prior work histories) and years of relevant experience with the company 5. Any objective performance measures (sales, etc.) Other Factors Relevant to Pay (that are not listed above) Examples are: acquisition information, store volume, etc. SUPPLEMENTAL LIST Ideally, we would like to have all available information about an employee s work experience prior to working with your company and information learned during the interview process. This might include (but is not limited to): I. Detailed Prior Work Experience Information 1. Job Title/Occupation 2. Job Responsibilities 3. Management Level/Supervisory Responsibilities 4. Company/Industry 5. Relevant Experience 6. Dates of Employment 7. Starting and Ending Base Pay 8. Other Compensation (bonuses, commissions, tips, awards, etc.) whether earned any and, if so, how much (annualized amounts). 9. Termination Date and Reason 10. Dates of Leaves-of-Absence II. Other Information Learned During the Interview Process 1. Scores from any test(s) administered 2. Salary Requested by Applicant 3. Additional Qualifications

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