Consensus Study Report
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This study was supported by the U.S. Equal Employment Opportunity Commission through agreement #45310020S0036. Support of the work of the Committee on National Statistics is provided by a consortium of federal agencies through a grant from the National Science Foundation (award number SES-1560294) and several individual contracts. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the organizations or agencies that provided support for the project.
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Suggested citation: National Academies of Sciences, Engineering, and Medicine. 2023. Evaluation of Compensation Data Collected Through the EEO-1 Form. Washington, DC: The National Academies Press. https://doi.org/10.17226/26581.
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PANEL TO EVALUATE THE QUALITY OF COMPENSATION DATA COLLECTED FROM U.S. EMPLOYERS BY THE U.S. EQUAL EMPLOYMENT OPPORTUNITY COMMISSION THROUGH THE EEO-1 FORM
WILLIAM M. RODGERS III (Chair), Federal Reserve Bank of St. Louis
M.V. LEE BADGETT, University of Massachusetts
PAUL P. BIEMER, RTI International
LISA CATANZARITE, UNITE-LA and Association of Chamber of Commerce Executives
SIWEI CHENG, New York University
REBECCA DIXON, National Employment Law Project
LISETTE GARCIA, Penn State University
CLAUDIA GOLDIN, Harvard University
JUDITH K. HELLERSTEIN, University of Maryland
ELIZABETH HIRSH, University of British Columbia
KRISTEN M. OLSON, University of Nebraska-Lincoln
DONALD TOMASKOVIC-DEVEY, University of Massachusetts
VALERIE RAWLSTON WILSON, Economic Policy Institute
Staff
JENNIFER PARK, Study Director
BRADFORD CHANEY, Senior Program Officer
REBECCA KRONE, Senior Program Coordinator
ERIC GRIMES, Senior Program Assistant
COMMITTEE ON NATIONAL STATISTICS
ROBERT M. GROVES (Chair), Office of the Provost, Georgetown University
LAWRENCE D. BOBO, Department of Sociology, Harvard University
ANNE C. CASE, School of Public and International Affairs, Princeton University, Emeritus
MICK P. COUPER, Institute for Social Research, University of Michigan
DIANA FARRELL, JPMorgan Chase Institute, Washington, DC
ROBERT GOERGE, Chapin Hall at the University of Chicago
ERICA L. GROSHEN, School of Industrial and Labor Relations, Cornell University
DANIEL E. HO, Stanford Law School, Stanford University
HILARY HOYNES, Goldman School of Public Policy, University of California, Berkeley
DANIEL KIFER, Department of Computer Science and Engineering, The Pennsylvania State University
SHARON LOHR, School of Mathematical and Statistical Sciences, Arizona State University, Emerita
JEROME P. REITER, Department of Statistical Science, Duke University
NELA RICHARDSON, ADP Research Institute, Roseland, NJ
JUDITH A. SELTZER, Department of Sociology, University of California, Los Angeles
C. MATTHEW SNIPP, School of the Humanities and Sciences, Stanford University
ELIZABETH A. STUART, Department of Mental Health, Johns Hopkins Bloomberg School of Public Health
Staff
MELISSA CHIU, Director
BRIAN HARRIS-KOJETIN, Senior Scholar
CONSTANCE F. CITRO, Senior Scholar
Preface
Pay disparities—or inequality in earnings between women, men, and those among race/ethnicity groups—are well-documented in national statistics. While differences in pay can be attributed to differences in workers’ education, work experience, or occupation, these factors fail to fully explain sex and race/ethnicity pay gaps.
Differences in pay based on sex and race/ethnicity have been outlawed by the federal government for almost 60 years. The Equal Employment Opportunity Commission (EEOC) has statutory authority to enforce pay equity. However, prior to the data collection that is the focus of the panel’s study, there were no other sources of federal data from employers describing the relationship among compensation, establishment, and employee characteristics that could be used for enforcement purposes. To access pay data and thereby improve its ability to address pay disparities, EEOC needed to expand its data-collection activities to include measures of pay.
In 2012, EEOC requested the National Research Council to recommend how EEOC should collect compensation data from private employers. In 2015, EEOC asked Sage Computing for further recommendations. In 2016, EEOC began to collect pay data using an expanded EEO-1 form. The pay-data collection was stopped in 2017 by the Office of Management and Budget (OMB) due to concerns about employer burden, but the historic employment form of EEO-1, known as Component 1, was permitted to continue. In 2018, the National Women’s Law Center successfully sued EEOC and OMB to continue collection of pay data, known as Component 2. Accordingly, pay-data collection began in 2019 for reporting years 2017 and 2018.
In 2020, EEOC asked the National Academies of Sciences, Engineering, and Medicine to examine the quality of Component 2 data for its intended use and provide recommendations for future data collections. In response to this request, the National Academies appointed a panel under the Committee on National Statistics to conduct this task. Thirteen scholars representing a broad array of disciplines—labor economics, sociology, statistics, survey design and methodology, employment law, race and gender equality studies, and diversity and inclusion evaluation—were included on the panel. We thank Robert Lattimer for his service on the panel (resigned as of August 18, 2021).
Panel meetings were held from February 2021 to January 2022. The period coincided with the COVID-19 pandemic and the national shutdown that occurred in response. As a result, the panel completed the entirety of its work remotely. This introduced new challenges and opportunities for the panel’s work. For example, the panel met more frequently (24 meetings rather than six) for shorter sessions (two-hour web sessions rather than day-long in-person meetings).
This meeting model required greater participation from the panel to plan and moderate sessions. However, the model was well-suited to receiving input from outside experts during open panel meetings. These inputs, reflecting various expertise and perspectives, were essential to the panel’s review. Accordingly, we thank Charlotte Burrows (chair, EEOC); Chris Haffer (chief data officer, EEOC); Rashida Dorsey (then director of Office of Enterprise Data and Analytics’ Data Development and Information Products Division, EEOC); David Fortney (cofounder, Fortney & Scott, LLC); Adam P. Romero (deputy director of executive programs) and Janette Wipper (chief counsel) (both of the California Department of Fair Employment and Housing); Emily Martin (vice president for education and workplace justice, National Women’s Law Center); Joi Olivia Chaney (executive director, Washington Bureau; senior vice president, policy and advocacy, National Urban League); Yona Rozen (associate general counsel, American Federation of Labor and Congress of Industrial Organizations); Andrea Wagoner (branch chief, Publications and Analysis Branch) and Jeff Holt (supervisory economist) (both of Division of Occupational Employment Statistics, Bureau of Labor Statistics, Department of Labor); Keith A. Bailey (assistant center chief, Longitudinal Employer-Household Dynamics Research, Center for Economic Studies, Census Bureau, Department of Commerce); Hakan Aykan (director) and Nathan Adams (economist) (both of Research and Analytic Services, Office of General Counsel, EEOC); Marla Stern-Knowlton (systemic supervisor, EEOC); Robert M. LaJeunesse (director of enforcement) and Edo Navot (labor economist) (both of the Office of Federal Contract Compliance Programs, Department of Labor); Jason Keller (assistant director) and Robert Parrilli (division manager) (both of Equal Pay Act of Illinois, Illinois Department of Labor); Lynn A.
Clements (director, audit and HR services, Berkshire Associates); Karen Minicozzi (human resource information consultant); Doug Tapp (human capital consultant, Deloitte Consulting LLP); Valentin Estevez (advisory council member, National Industry Liaison Group; senior managing director, Welch Consulting); and Anthony Kaylin (chair, National Industry Liaison Group; vice president, American Society of Employers).
We also thank Elizabeth Fox-Solomon (chief of staff, EEOC) and Kimberly Essary (deputy chief data officer, EEOC) for their constructive partnership throughout this endeavor.
The nature of the panel’s charge also presented new challenges and opportunities. The panel engaged in original data analysis to examine the quality of the Component 2 data. To conduct these analyses at the direction of the panel, the National Academies contracted with RTI International. We thank Dan Liao (senior research statistician and program manager); Sahar Zangeneh (research statistician); Jennifer J. Unangst (research statistician); John David Bunker, Jr. (statistician); and Philip Lee (research statistician) of RTI International for their extraordinary support. We thank Karen Grigorian (vice president and project director) and Lance Selfa (principal research scientist) of the National Opinion Research Center at the University of Chicago for facilitating knowledge transfer regarding the Component 2 data collection.
For assistance in managing the contract award process at the National Academies, we thank Kevin Hale (director of procurement services and sub-award administration), Dorothy Yee (manager and sub-award administrator), Madeline Welch (procurement specialist), and Elizabeth J. Molyé (senior contract manager) of the Office of the Chief Financial Officer. For assistance in ensuring appropriate data security controls were included in the contract language, we thank Marc Gold (deputy general counsel) and Mattie Cohan (associate general counsel), both of the Office of General Counsel.
Additionally, the EEO-1 data files examined for the panel’s analysis are controlled-use, requiring special care to protect against unauthorized access. Indeed, neither the National Academies project staff nor panel members had access to the data files examined. We thank the National Academies staff Enita A. Williams (director of program security) and Ross MacIsaac (information systems security manager) of the Office of Program Security for their assistance in ensuring appropriate data security.
For conducting their work within these extraordinary circumstances and requirements, we give special thanks to the study panel, who devoted exceptional time, thought, and energy to this endeavor.
A number of staff members of the National Academies made significant contributions to the report. Patricia Brick (former staff) contributed to early management of this project. Eric Grimes made sure that panel meetings ran smoothly; he and Rebecca Krone assisted in preparing the manuscript,
and otherwise provided key administrative and logistical support; Kirsten Sampson Snyder managed the report review and production process; and Brian Harris-Kojetin, director of the Committee on National Statistics, and Melissa Chiu, deputy director of the Committee on National Statistics, provided valuable guidance and oversight. We also thank Susan Debad for her exceptional editing of the report.
This Consensus Study Report was reviewed in draft form by individuals chosen for their diverse perspectives and technical expertise. The purpose of this independent review is to provide candid and constructive comments that will assist the National Academies in making each published report as sound as possible and to ensure that each report meets the institutional standards for quality, objectivity, evidence, and responsiveness to the study charge. The review comments and draft manuscript remain confidential to protect the integrity of the deliberative process.
We thank the following individuals for their review of this report: Katharine G. Abraham, Department of Economics and Joint Program in Survey Methodology, University of Maryland; Francine D. Blau, Industrial and Labor Relations, Cornell University; Kevin F. Hallock, Office of the President, University of Richmond; Nicole Mason, Office of the President and Chief Executive Officer, Institute for Women’s Policy Research; Jaki McCarthy, National Agricultural Statistics Service, Department of Agriculture (retired); Justin McCrary, School of Law, Columbia University; G. Roger King, Senior Labor and Employment Counsel, HR Policy Association; Ani Huang, Senior Vice President, HR Policy Association; and Lincoln Quillian, Department of Sociology, Northwestern University.
Although the reviewers listed above provided many constructive comments and suggestions, they were not asked to endorse the conclusions or recommendations of this report, nor did they see the final draft before its release. The review of this report was overseen by Erica Groshen, School of Industrial and Labor Relations, Cornell University, and Kenneth W. Wachter, Demography and Statistics, University of California, Berkeley (emeritus). They were responsible for making certain that an independent examination of this report was carried out in accordance with the standards of the National Academies and that all review comments were carefully considered. Responsibility for the final content rests entirely with the authoring panel and the National Academies.
William M. Rodgers, III, Chair
Jennifer Park, Study Director
Bradford Chaney, Senior Program Officer
Panel to Evaluate the Quality of Compensation Data
Collected from U.S. Employers by the U.S. Equal Employment
Opportunity Commission Through the EEO-1 Form
Contents
Understanding Pay Disparities for Evidence-Based Policy
EEOC Authority and Responsibility for Information Collection
Use of Data for Enforcement by EEOC
Current Collections of EEOC Data
Current Uses of EEO Reports in Enforcement Efforts
Need for Pay Data from Private Employers
2. Design and Implementation of the EEO-1 Component 2 Instrument
Background of Component 2 Design
2013 National Research Council Report
2015 Sage Computing Pay-Data Study
2016 EEO-1 Pay-Data Collection
Special Circumstances of the EEO-1 Collection
2017 Pause in Component 2 Data Collection
2019 Court-Ordered Reinstatement of Component 2 Data Collection
2019 Compressed Component 2 Data Collection
Possible Effects on Data Quality
Component 2 Instrument Design Decisions and Implementation Experiences to Be Examined
3. Utility of Current Concepts and Alternatives
Purpose and Approach of the Chapter
Pay-Equity Concepts Measured by the Component 2 Instrument
Pay-Equity Concepts Not Currently Included
Other Groups Protected by EEOC
Additional Measures of Individual Characteristics
Conclusions and Recommendations
4. Do All Eligible Employers Receive and Respond to the Component 2 Instrument?
Method for Evaluating Data Quality
Nonresponse and Effect of Altering the Collection Period
Component 2 Membership Changes Over Time
Merging Establishments Over Time
Cumulative Effect of Incomplete Firm Lists and Nonresponse on Representation
Size: Component 2 Data Versus External Benchmarks
Industry: Component 2 Data Versus External Benchmarks
Conclusions and Recommendations
Internal Inconsistency and Extreme Values
Comparisons Between Components 1 and 2 Data
Comparisons Between 2017 and 2018 Component 2 Data
Filtering Data to Improve Usability
Determining an Appropriate Filter
Quality Indicators After Filtering
Conclusion and Recommendations
6. Are Component 2 Pay Data Useful for Examining National Pay Differences?
Comparison 1: ACS Data and “EEO’d” ACS Data
Comparison 2: Component 2 Data and EEO’d ACS Data
Comparison 3: ACS Data and EEO’d ACS: Adding Controls for Education and Age
Comparison 4: Component 2 Data Adding Establishment Fixed Effects
Adjusted Versus Unadjusted Gaps
Local Labor-Market Profile: Silicon Valley Technology Sector
Targeted Analysis: Profiling Four Establishments
Wage Distributions and Outlier Analysis
Comparison of Target Establishment Pay Gaps to Local Labor-Market Averages
Implications for Suitability of Component 2 Data for Intended Enforcement Uses
8. Conclusions and Recommendations
Improvements Necessary in the Short Term
Address Likely Sources of Error
Broaden and Strengthen Data Collection and Analysis
Policy Case for Implementing Change
Enforce Pay Equity in the Workplace
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Boxes, Figures, Tables, Chapter Appendixes
BOXES
3-2 Transition of OEWS Pay-Data Collection from Pay Bands to Individual-Level Data
8-2 EEOC Mission and Vision Statements
FIGURES
S-1 Anticipated total eligible firms and establishments and available pay data, 2018 Component 2
2-1 EEO-1 Component 2 report types that employers must complete
2-3 EEO-1 Component 2 instrument Section D (online version), 2016
2-4 EEO-1 Component 2 instrument data-upload form (example)
3-1 Percentage of employees in each pay band, by EEO-1 job category
4-1 Component 2 firm response rates by NAICS code, 2017 and 2018
4-2 Component 2 response rates by state, 2017 and 2018
4-3 Cumulative response rate by date of collection, Component 2, 2017 and 2018
5-5 Comparison of 2017 Component 1 and 2 establishment sizes after filtering to remove outliers
6-1 Schematic description of the four comparisons
6-2 Basic pay differentials in ACS data by sex and race/ethnicity (natural log)
6-3 Intersectional pay differentials from White males in the ACS (natural log)
6-4 Pay differentials by sex and race/ethnicity in the ACS with sequential controls (natural log)
6-5 Pay differentials by sex and race/ethnicity in the ACS with EEO’d occupations (natural log)
6-7 Basic pay differentials in Component 2 data by sex and race/ethnicity (natural log)
6-8 Intersectional pay differentials in Component 2 data (natural log)
7-1 Annual pay gap in percent relative to White men, by job category, for men
7-2 Annual pay gap in percent relative to White men, by job category, for women
7-3 Ratio (logged) of sex and race/ethnicity groups’ pay as compared with White men, by job category
8-1 Anticipated total eligible firms and establishments and available pay data, 2018 Component 2
TABLES
2-1 Federal Data Collections with Pay and Demographic Measures
2-2 Previous Measurement Recommendations and Decisions
2-3 Reporting Periods Used for Establishments When Completing EEO-1 Data Forms
3-1 Pay Bands in EEOC Component 2 Collection, 2017–2018
3-2 Percent Employed by Pay Band, Component 2 Data, 2018
4-1 EEO-1 Component 2 Filing Universe Frame Development
4-2 Component 2 Firm Response Rates, by Source of Firm
4-3 2017 Characteristics of Component 2 Establishments, by Completion Date (Count)
4-4 2017 Characteristics of Component 2 Establishments, by Completion Date (Percent)
4-5 2018 Characteristics of Component 2 Establishments, by Completion Date (Count)
4-6 2018 Characteristics of Component 2 Establishments, by Completion Date (Percent)
4-7 Characteristics of Component 2 Establishments, by Year
4-8 Years of Appearance of Component 2 Establishments, by Establishment Size
4-9 Years of Appearance of Component 2 Establishments, by Industry
4-10 Number of Firms in EEO-1 Data and Census Bureau BDS: 2017 and 2018
4-11 Number of Establishments, by Data Source: 2017 and 2018
4-12 Number of Firms, by Industry and Data Source: 2017 and 2018
4-13 Number of Establishments, by Industry and Data Source: 2017 and 2018
5-1 Percent of SROP Cells with Missing Data on Hours Worked or Number of Employees
7-1 Number of Pay Bands Used per SRO Cell, by Selected Employer Characteristics
7-2 Number of Pay Bands Used per Selected Employee Characteristics
8-2 Recommendations to Enforce Pay Equity in the Workplace
8-3 Recommendations to Account for a Changing Society
8-4 Recommendations to Use Good Government and Statistical Practices
CHAPTER APPENDIXES
APPENDIX 5-1 Percent of Data Present for Hours Worked and Employment in SROP Cells
APPENDIX 5-2 Percentage of Firms, Establishments, and Cells with Each Flag Status (2018)
APPENDIX 5-3 Technical Memorandum Describing Merging Datasets for Data Quality Assessment
APPENDIX 6-1 EEO-1 Component 2 Adjustments
APPENDIX BOX 6-1 Sequence of Steps Followed to Create Analysis File for Regressions
APPENDIX 6-2 Number of Employees, by Selected Employee and Establishment Characteristics: 2018
APPENDIX 6-3 Hours Worked in Thousands, by Employee and Establishment Characteristics: 2018
APPENDIX 6-4 Key Summary Statistics
APPENDIX 6-5 Percentage of Employees in Each Job Category Who Are in Each Pay Band
APPENDIX 6-6 Regression Results Using Detailed ACS Earnings Information and SOC Codes
APPENDIX 6-7 Regression Results Using ACS Detailed Earnings Information and EEO-1 Job Categories