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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2022. Evaluation of Compensation Data Collected Through the EEO-1 Form. Washington, DC: The National Academies Press. doi: 10.17226/26581.
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Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Evaluation of Compensation Data Collected Through the EEO-1 Form 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 Committee on National Statistics Division of Behavioral and Social Science and Education Consensus Study Report Prepublication Copy - Uncorrected Proofs

NATIONAL ACADEMIES PRESS 500 Fifth Street, NW, Washington, DC 20001 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. International Standard Book Number-13: 978-0-309-XXXXX-X International Standard Book Number-10: 0-309-XXXXX-X Digital Object Identifier: https://doi.org/10.17226/26581 This publication is available from the National Academies Press, 500 Fifth Street, NW, Keck 360, Washington, DC 20001; (800) 624-6242 or (202) 334-3313; http://www.nap.edu. Copyright 2022 by the National Academy of Sciences. National Academies of Sciences, Engineering, and Medicine and National Academies Press and the graphical logos for each are all trademarks of the National Academy of Sciences. All rights reserved. Printed in the United States of America. Suggested citation: National Academies of Sciences, Engineering, and Medicine. 2022. Evaluation of Compensation Data Collected Through the EEO-1 Form. Washington, DC: The National Academies Press. https://doi.org/10.17226/26581. Prepublication Copy - Uncorrected Proofs

The National Academy of Sciences was established in 1863 by an Act of Congress, signed by President Lincoln, as a private, nongovernmental institution to advise the nation on issues related to science and technology. Members are elected by their peers for outstanding contributions to research. Dr. Marcia McNutt is president. The National Academy of Engineering was established in 1964 under the charter of the National Academy of Sciences to bring the practices of engineering to advising the nation. Members are elected by their peers for extraordinary contributions to engineering. Dr. John L. Anderson is president. The National Academy of Medicine (formerly the Institute of Medicine) was established in 1970 under the charter of the National Academy of Sciences to advise the nation on medical and health issues. Members are elected by their peers for distinguished contributions to medicine and health. Dr. Victor J. Dzau is president. The three Academies work together as the National Academies of Sciences, Engineering, and Medicine to provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. The National Academies also encourage education and research, recognize outstanding contributions to knowledge, and increase public understanding in matters of science, engineering, and medicine. Learn more about the National Academies of Sciences, Engineering, and Medicine at www.nationalacademies.org. Prepublication Copy - Uncorrected Proofs

Consensus Study Reports published by the National Academies of Sciences, Engineering, and Medicine document the evidence-based consensus on the study’s statement of task by an authoring committee of experts. Reports typically include findings, conclusions, and recommendations based on information gathered by the committee and the committee’s deliberations. Each report has been subjected to a rigorous and independent peer-review process and it represents the position of the National Academies on the statement of task. Proceedings published by the National Academies of Sciences, Engineering, and Medicine chronicle the presentations and discussions at a workshop, symposium, or other event convened by the National Academies. The statements and opinions contained in proceedings are those of the participants and are not endorsed by other participants, the planning committee, or the National Academies. For information about other products and activities of the National Academies, please visit www.nationalacademies.org/about/whatwedo. Prepublication Copy - Uncorrected Proofs

v 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 JENNIFER PARK, Study Director BRADFORD CHANEY, Senior Program Officer REBECCA KRONE, Senior Program Coordinator ERIC GRIMES, Senior Program Assistant Prepublication Copy - Uncorrected Proofs

vi 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 JANET M. CURRIE, School of Public and International Affairs, Princeton University 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 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 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 JEANNETTE WING, Data Science Institute and Computer Science Department, Columbia University BRIAN HARRIS-KOJETIN, Director MELISSA CHIU, Deputy Director CONSTANCE F. CITRO, Senior Scholar Prepublication Copy - Uncorrected Proofs

vii Preface Pay disparities, or inequality in earnings between women and men, and 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 U.S. 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 between compensation, establishment, and employee characteristics that could be used for enforcement purposes. To access pay data and thereby improve their 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 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 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 (2-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, AFL- CIO); Andrea Wagoner, branch chief, Publications and Analysis Branch) and Jeff Holt (supervisory economist) (both of Division of Occupational Employment Statistics, Bureau of Prepublication Copy - Uncorrected Proofs

viii Labor Statistics, U.S. Department of Labor); Keith A. Bailey (assistant center chief, Longitudinal Employer-Household Dynamics Research, Center for Economic Studies, Census Bureau, U.S. 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, U.S. 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); 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 NORC 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 Welsh (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/FSO/CSSO) 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 committee 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- Prepublication Copy - Uncorrected Proofs

ix 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: Katherine 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, U.S. Department of Agriculture, (retired); Justin McCrary, School of Law, University of Columbia; 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 committee and the National Academies. William M. Rodgers, III, Chair Jennifer Park, Study Director Bradford Chaney, Senior Project 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 Prepublication Copy - Uncorrected Proofs

CONTENTS Summary ....................................................................................................................................................... 1 1. Introduction ............................................................................................................................................. 13 Motivation For The Study ...................................................................................................................... 14 Understanding Pay Disparities For Evidence-Based Policy ............................................................... 14 EEOC Authority And Responsibility For Information Collection ..................................................... 16 Statement Of Task .............................................................................................................................. 20 Approach Of the Panel ........................................................................................................................... 21 Guiding Questions .............................................................................................................................. 21 Method ................................................................................................................................................ 22 Structure Of The Report ..................................................................................................................... 23 Use Of Data For Enforcement By EEOC .............................................................................................. 24 Current Collections Of EEOC Data .................................................................................................... 24 Current Uses of EEO Reports In Enforcement Efforts ....................................................................... 26 Need For Pay Data From Private Employers ......................................................................................... 27 Enhanced Enforcement ....................................................................................................................... 28 Intended Uses Of Component 2 Pay Data .......................................................................................... 28 Summary ................................................................................................................................................ 30 Conclusions And Recommendations ..................................................................................................... 31 2. Design and Implementation Of The EEO-1 Component 2 Instrument ................................................... 32 Background of Component 2 Design ..................................................................................................... 33 2013 National Research Council Report............................................................................................. 37 2015 Sage Computing Pay-Data Study............................................................................................... 39 2016 EEO-1 Pay-Data Collection ....................................................................................................... 40 Special Circumstances Of The EEO-1 Collection ................................................................................. 54 2017 Pause In Component 2 Data Collection ..................................................................................... 55 2019 Court-Ordered Reinstatement Of Component 2 Data Collection .............................................. 56 2019 Compressed Component 2 Data Collection ............................................................................... 57 Possible Effects On Data Quality ........................................................................................................... 58 Summary ................................................................................................................................................ 58 Prepublication Copy - Uncorrected Proofs

Component 2 Instrument Design Decisions And Implementation Experiences To Be Examined ..... 58 Court-Ordered Data Collection ........................................................................................................... 58 Conclusions And Recommendations ..................................................................................................... 60 3. Utility Of Current Concepts And Alternatives........................................................................................ 61 Purpose And Approach Of The Chapter ................................................................................................ 62 Pay Equity Concepts Measured By The Component 2 Instrument ........................................................ 63 Pay ...................................................................................................................................................... 63 Pay Bands ........................................................................................................................................... 64 Occupation .......................................................................................................................................... 70 Hours Worked ..................................................................................................................................... 73 Sex And Gender .................................................................................................................................. 74 Race/Ethnicity..................................................................................................................................... 76 Pay Equity Concepts Not Currently Included ........................................................................................ 78 Other Groups Protected By EEOC ..................................................................................................... 78 Additional Measures Of Individual Characteristics ............................................................................ 79 Summary ................................................................................................................................................ 79 Conclusions And Recommendations ..................................................................................................... 79 4. Do All Eligible Employers Receive And Respond To The Component 2 Instrument? .......................... 82 Introduction ............................................................................................................................................ 83 Method For Evaluating Data Quality ..................................................................................................... 84 Completeness Of EEOC’s Business List ............................................................................................... 86 Response Rates ...................................................................................................................................... 88 By Firm Reporting Method................................................................................................................. 89 By Industry ......................................................................................................................................... 91 By State ............................................................................................................................................... 92 Nonresponse And Effect Of Altering The Collection Period ................................................................ 93 Component 2 Membership Changes Over Time .................................................................................. 101 Merging Establishments Over Time ................................................................................................. 101 By Sample Sources ........................................................................................................................... 102 By Establishment Size ...................................................................................................................... 105 By Industry Sector ............................................................................................................................ 107 Cumulative Effect Of Incomplete Firm Lists And Nonresponse On Representation .......................... 109 Prepublication Copy - Uncorrected Proofs

Size: Differential Coverage Of Firms And Establishments By The Component 1 And Component 2 Instruments ........................................................................................................................................... 111 Industry: Differential Coverage Of Firms And Establishments By The Component 1 And Component 2 Instruments .................................................................................................................... 114 Size: Component 2 Data Versus External Benchmarks .................................................................... 118 Industry: Component 2 Data Versus External Benchmarks ............................................................. 119 Summary .............................................................................................................................................. 120 Conclusions And Recommendations ................................................................................................... 121 5. Measurement Quality ............................................................................................................................ 122 Introduction .......................................................................................................................................... 123 Chapter Overview ............................................................................................................................. 123 Notation ............................................................................................................................................ 125 Internal Inconsistency And Extreme Values ........................................................................................ 126 Internal Inconsistencies..................................................................................................................... 126 Extreme Values ................................................................................................................................. 128 Comparisons Between Components 1 And 2 Data .............................................................................. 130 Comparisons Between 2017 And 2018 Component 2 Data ................................................................. 136 Filtering Data To Improve Usability .................................................................................................... 142 Determining An Appropriate Filter .................................................................................................. 142 Quality Indicators After Filtering ..................................................................................................... 146 Summary .............................................................................................................................................. 154 Conclusions And Recommendations ................................................................................................... 156 Chapter Appendices ............................................................................................................................. 157 Appendix 5-1 Percent Of Data Present For Hours Worked And Employment In Srop Cells .............. 157 6. Are Component 2 Pay Data Useful For Examining National Pay Differences? ................................... 165 Objective .............................................................................................................................................. 165 Approach .............................................................................................................................................. 166 Findings................................................................................................................................................ 168 Comparison 1: ACS Data And “EEO’d” ACS Data ......................................................................... 168 Comparison 2: Component 2 Data And EEO’d ACS Data .............................................................. 179 Comparison 3: ACS Data And EEO’d ACS: Adding Controls For Education And Age ................. 183 Comparison 4: Component 2 Data Adding Establishment Fixed Effects ......................................... 184 Summary .............................................................................................................................................. 184 Prepublication Copy - Uncorrected Proofs

Conclusions And Recommendations ................................................................................................... 186 7. Are Component 2 Pay Data Useful For Investigating Individual Establishments And Local Labor Markets?.................................................................................................................................................... 200 Objective .............................................................................................................................................. 200 Analytical Approach ............................................................................................................................ 201 Data And Measures .............................................................................................................................. 201 Calculating Pay Gaps ........................................................................................................................ 202 Pay Bands ......................................................................................................................................... 202 Adjusted Versus Unadjusted Gaps ................................................................................................... 205 Hours Worked ................................................................................................................................... 205 Job Categories ................................................................................................................................... 205 Results .................................................................................................................................................. 206 Local Labor-Market Profile: Silicon Valley Technology Sector ...................................................... 206 Targeted Analysis: Profiling Four Establishments ........................................................................... 212 Wage Distributions And Outlier Analysis ........................................................................................ 216 Comparison Of Target Establishment Pay Gaps To Local Labor-Market Averages........................ 219 Discussion ............................................................................................................................................ 220 Implications For Suitability Of Component 2 Data For Intended Enforcement Uses ...................... 221 Summary .............................................................................................................................................. 223 Conclusions And Recommendations ................................................................................................... 224 Chapter Appendices ............................................................................................................................. 225 8. Conclusions And Recommendations .................................................................................................... 228 Overall Assessment .............................................................................................................................. 228 Value Of Data As Collected ............................................................................................................. 228 Identifying Eligible Filers ................................................................................................................. 231 Measurement Concerns..................................................................................................................... 231 Appropriate Use ................................................................................................................................... 233 Improvements Necessary In The Short Term ...................................................................................... 235 Address Likely Sources Of Error...................................................................................................... 235 Address Measurement Gaps ............................................................................................................. 240 Broaden And Strengthen Data Collection And Analysis ..................................................................... 242 Policy Case For Implementing Change................................................................................................ 247 Prepublication Copy - Uncorrected Proofs

Enforce Pay Equity In The Workplace ............................................................................................. 248 Account For A Changing Society ..................................................................................................... 248 Good Government ............................................................................................................................ 248 References ................................................................................................................................................. 254 Appendices................................................................................................................................................ 265 A Biosketches Of Panel Members And Project Staff ................................................................................ 265 Panel Members ..................................................................................................................................... 265 Project Staff.......................................................................................................................................... 269 B Data-Handling Procedures Used For Original Data Analysis................................................................ 270 C Data Storage, Security, And Management Procedures .......................................................................... 272 Data Storage And Security Procedures ................................................................................................ 272 File Structure ........................................................................................................................................ 272 Data Issues ........................................................................................................................................... 273 Documentation ..................................................................................................................................... 274 D Biosketches Of Contracted Project Staff ............................................................................................... 275 Prepublication Copy - Uncorrected Proofs

BOXES, FIGURES, TABLES, CHAPTER APPENDICES Boxes BOX 1-1 Chapter 1 Highlights, 13 BOX 1-2 Statement of Task, 21 BOX 2-1 Chapter 2 Highlights, 32 BOX 3-1 Chapter 3 Highlights, 61 BOX 3-2 Transition of OEWS Pay Data Collection from Pay Bands to Individual-Level Data, 69 BOX 4-1 Chapter 4 Highlights, 83 BOX 5-1 Chapter 5 Highlights, 123 BOX 5-2 Rules Used to Define Red, Orange, and Green Flags for the Outlier Analysis for SROPs, Establishments, and Firms, 130 BOX 6-1 Chapter 6 Highlights, 166 BOX 7-1 Chapter 7 Highlights, 201 BOX 8-1 Highlights of Chapter 8, 229 BOX 8-2 EEOC Mission and Vision Statements, 248 Figures FIGURE 2-1 EEO-1 Component 2 report types that employers must complete. 42 FIGURE 2-2 EEO-1 Component 1 instrument, page 1, 2016. 44 FIGURE 2-3 EEO-1 Component 2 instrument Section D (online version), 2016. 46 FIGURE 2-4 EEO-1 Component 2 instrument data upload form (Example). 49 FIGURE 3-1 Percentage of employees in each pay band, by EEO-1 job category. 67 FIGURE 3-2 California Department of Fair Housing and Employment pay-data collection on- line instrument for reporting year 2020: exemplar hours form. 74 FIGURE 4-1 Component 2 firm response rates by NAICS code, 2017 and 2018. 92 FIGURE 4-2 Component 2 response rates by state, 2017 and 2018. 93 FIGURE 4-3 Cumulative response rate by date of collection, component 2, 2017 and 2018. 95 FIGURE 5-1 Top three quintiles for intercomponent Average Absolute Relative Difference (ARD) for number of employees in an SRO cell by year and firm characteristic (excludes inconsistent zero cells). 135 FIGURE 5-2 Quintiles of Component 2 average Absolute Relative Difference (ARD) when comparing 2017 and 2018 for number of employees, by administration mode and establishment size. 141 FIGURE 5-3 Quintiles for average Component 2 average Absolute Relative Difference (ARD) when comparing 2017 and 2018 for hours per employee, by administration mode and establishment size. 142 FIGURE 5-4 Comparison of 2017 establishment sizes in Components 1 and 2 data prior to filtering to remove outliers. 144 FIGURE 5-5 Comparison of 2017 Component 1 and 2 establishment sizes after filtering to remove outliers. 147 FIGURE 5-6 Comparison of establishment sizes in 2017 to 2018 Component 2 data sizes prior to filtering to remove outliers. 150 Prepublication Copy - Uncorrected Proofs

FIGURE 5-7 Comparison of establishment sizes in 2017 to 2018 component 2 data after filtering to remove outliers. 151 FIGURE 5-8 Comparison of 2017 to 2018 Component 2 total employee hours worked before and after filtering to remove outliers. 154 FIGURE 6-1 Schematic description of the four comparisons. 168 FIGURE 6-2 Basic pay differentials in ACS data by sex and race/ethnicity (natural log). 171 FIGURE 6-3 Intersectional pay differentials from white males in the ACS (natural log. 172 FIGURE 6-4 Pay differentials by sex and race/ethnicity in the ACS with sequential controls (natural log). 174 FIGURE 6-5 Pay differentials by sex and race/ethnicity in the ACS with EEO’d occupations (natural log). 176 FIGURE 6-6 Pay differentials by sex and race/ethnicity in the ACS with EEO’d occupations and pay bands (natural log). 179 FIGURE 6-7 Basic pay differentials in Component 2 data by sex and race/ethnicity (natural log). 181 FIGURE 6-8 Intersectional pay differentials in Component 2 data (natural log). 182 FIGURE 6-9 Pay differentials by sex and race/ethnicity in Component 2 data with sequential controls (natural log). 183 FIGURE 7-1 Annual pay gap in percent relative to white men, by job category, for men. 211 FIGURE 7-2 Annual pay gap in percent relative to white men, by job category, for women. 211 FIGURE 7-3 Ratio (logged) of sex and race/ethnicity groups’ pay as compared with white men, by job category. 218 FIGURE 8-1 Anticipated total eligible firms and establishments and available pay data, 2018 Component 2. 231 Tables TABLE 2-1 Federal Data Collections with Pay and Demographic Measures, 34 TABLE 2-2 Previous Measurement Recommendations and Decisions, 36 TABLE 2-3 Reporting Periods Used for Establishments When Completing EEO-1 Data Forms, 51 TABLE 3-1 Pay Bands in EEOC Component 2 Collection, 2017–2018, 65 TABLE 3-2 Percent Employed by Pay Band, Component 2 Data, 2018, 68 TABLE 4-1 EEO-1 Component 2 Filing Universe Frame Development, 88 TABLE 4-2 Component 2 Firm Response Rates, by Source of Firm, 91 TABLE 4-3 2017 Characteristics of Component 2 Establishments, by Completion Date (Count), 98 TABLE 4-4 2017 Characteristics of Component 2 Establishments, by Completion Date (Percent), 99 TABLE 4-5 2018 Characteristics of Component 2 Establishments, by Completion Date (Count), 100 TABLE 4-6 2018 Characteristics of Component 2 Establishments, by Completion Date (Percent), 101 TABLE 4-7 Characteristics of Component 2 Establishments, by Year, 105 TABLE 4-8 Years of Appearance of Component 2 Establishments, by Establishment Size, 107 TABLE 4-9 Years of Appearance of Component 2 Establishments, by Industry, 109 Prepublication Copy - Uncorrected Proofs

TABLE 4-10 Number of Firms in EEO-1 Data and Census BDS: 2017 and 2018, 113 TABLE 4-11 Number of Establishments, by Data Source: 2017 and 2018, 114 TABLE 4-12 Number of Firms, by Industry and Data Source: 2017 and 2018, 116 TABLE 4-13 Number of Establishments, by Industry and Data Source: 2017 and 2018, 117 TABLE 5-1 Percent of SROP Cells with Missing Data on Hours Worked or Number of Employees, 129 TABLE 5-2 Percentage of Firms, Establishments, and Cells by Hours-Worked-per-Employee Flag Status, Administration Mode, and Size for 2017, Component 2, 131 TABLE 5-3 Percent of SROs with Inconsistent Zero Numbers of Employees When Comparing 2018 Component 1 and Component 2, 133 TABLE 5-4 Mean Intercomponent Relative Differences (RD) for Number of Employees in an SRO Cell by Administration Mode, Size, and Year, 134 TABLE 5-5 Average Indexes of Inconsistency When Comparing Component 1 and Component 2 Responses, by Administration Mode, Size, and Year, 137 TABLE 5-6 Percent of Component 2 SROs Having Non-Zero Employees in One or Both Years by Administration Mode, Size, and Year, 139 TABLE 5-7 Average Component 2 Relative Differences When Comparing 2017 and 2018 for Number of Employees (RD(E)) and Average Hours Worked (RD(H/E)) by Administration Mode and Size, 140 TABLE 5-8 Component 2 Average Indexes of Inconsistency When Comparing 2017 and 2018 for Number of Employees per SROP, by Administration Mode and Establishment Size, 143 TABLE 5-9 Comparison of 2017 Component 1 to Component 2 Distributions for Number of Establishments and Total Number of Employees Before and After Filtering to Remove Outliers, by Size, 146 TABLE 5-10 Mean Relative Difference (RD) When Comparing Components 1 and 2 for Number of Employees in an SRO Cell Before and After Filtering to Remove Outliers, by Administration Mode, Size, and Year, 148 TABLE 5-11 Average Intercomponent Indexes of Inconsistency Before and After Filtering, by Administration Mode, Size, and Year, 149 TABLE 5-12 Component 2 Average Relative Differences and Indexes of Inconsistency When Comparing 2017 and 2018 for Number of Employees by Administration Mode and Size Before and After Filtering to Remove Outliers, 152 TABLE 5-13 Component 2 Average Relative Differences and Indexes of Inconsistency When Comparing 2017 and 2018 Data for Hours Worked per Employee Before and After Filtering to Remove Outliers, by Administration Mode and Size, 153 TABLE 7-1 Number of Pay Bands Used per SRO cell, by Selected Employer Characteristics, 204 TABLE 7-2 Number of Pay Bands Used per Selected Employee Characteristics, 205 TABLE 7-3 Summary Statistics of EEOC Population by Race/Ethnicity, Sex, and Occupation: Silicon Valley Technology Sector, 2018, 209 TABLE 7-4 Comparisons of Pay Gaps Across Selected Establishments, by Race/Ethnicity, Sex and Occupation: Silicon Valley Technology Sector, 2018, 215 TABLE 8-1 Example of Possible EEO-1 Pay-Data Collection Form Obtaining Individual- Level Data (Spreadsheet Version), 244 TABLE 8-2 Recommendations to Enforce Pay Equity in the Workplace, 250 Prepublication Copy - Uncorrected Proofs

TABLE 8-3 Recommendations to Account for a Changing Society, 251 TABLE 8-4 Recommendations to Use Good Government and Statistical Practices, 252 Chapter Appendices APPENDIX 5-1 Percent of Data Present for Hours Worked and Employment in SROP Cells, 158 APPENDIX 5-2 Percentage of Firms, Establishments, and Cells with Each Flag Status (2018), 159 APPENDIX 5-3 Technical Memorandum Describing Merging Datasets for Data Quality Assessment, 160 APPENDIX 5-4 Inconsistent Zeros for Number of Employees Comparing Component 2 Data for 2017 and 2018 at the SRO Level, 165 APPENDIX 6-1 EEO-1 Component 2 Adjustments, 188 APPENDIX BOX 6-1 Sequence of Steps Followed to Create Analysis File for Regressions, 189 APPENDIX 6-2 Number of Employees, By Selected Employee and Establishment Characteristics: 2018, 191 APPENDIX 6-3 Hours Worked in Thousands, by Employee and Establishment Characteristics: 2018, 193 APPENDIX 6-4 Key Summary Statistics, 195 APPENDIX 6-5 Percentage of Employees in Each Job Category Who Are in Each Pay Band, 196 APPENDIX 6-6 Regression Results Using Detailed ACS Earnings Information and SOC Codes, 197 APPENDIX 6-7 Regression Results Using ACS Detailed Earnings Information and EEO-1 Job Categories, 198 APPENDIX 6-8 Regression Results Using Fully EEO’d ACS Data—Pay-Band Earnings and EEO-1 Job Categories, 199 APPENDIX 6-9 2018 EEO-1 Component 2 Data Regression Results, 200 APPENDIX 7-1 NAICS Codes Used for Selecting High Tech Sample, 226 APPENDIX 7-2 Zip Codes and Cities Included to Focus on Silicon Valley, 227 APPENDIX 7-3 Silicon Valley Tech Sector (2018) Summary Statistics Prior to and After Data Filtering to Remove Outliers, 228 Prepublication Copy - Uncorrected Proofs

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Evaluation of Compensation Data Collected Through the EEO-1 Form Get This Book
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The U.S. Equal Employment Opportunity Commission (EEOC) expanded EEO-1 data collection for reporting years 2017 to 2018 in an effort to improve its ability to investigate and address pay disparities between women and men and between different racial and ethnic groups. These pay disparities are well documented in national statistics. For example, the U.S. Census Bureau (2021) found that Black and Hispanic women earned only 63 percent and 55 percent as much, respectively, of what non-Hispanic White men earned.

Evaluation of Compensation Data Collected Through the EEO-1 Form examines the quality of pay data collected using the EEO-1 form and provides recommendations for future data collection efforts. The report finds that there is value in the expanded EEO-1 data, which are unique among federal surveys by providing employee pay, occupation, and demographic data at the employer level. Nonetheless, both short-term and longer-term improvements are recommended to address significant concerns in employer coverage, conceptual definitions, data measurement, and collection protocols. If implemented, these recommendations could improve the breadth and strength of EEOC data for addressing pay equity, potentially reduce employer burden, and better support employer self-assessment.

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