Evaluation of Compensation Data Collected Through the EEO-1 Form (2023) / Chapter Skim
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8 Conclusions and Recommendations
8 Conclusions and Recommendations
Pages 291-324
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From page 291... ...
Recent initiatives at federal, state, and private levels aiming to use data to achieve diversity, equity, and inclusion have infused new energy for systemic change. Several of the panel's recommendations may support, complement, and extend upon modernization efforts currently underway at EEOC's Office of Enterprise Data and Analytics.
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From page 292... ...
During charge investigations, Component 2 data could be suitable for calculating raw sex and race/ethnicity pay gaps across and within job categories for establishments that are charged. Specific pay-gap comparisons should be guided by the charge bases (sex, race, color, national origin)
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From page 293... ...
Data issues are numerous and varied in their likely causes. These concerns led the panel to exclude 35 percent of the provided pay data (at the establishment level)
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"Provided pay data" excludes firms with more than 1.4 million employees and Type 6 reports (which did not collect pay data)
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EEOC decided to collect pay data using a categorical measure, with 12 pay bands. These bands are overly wide, leading to a lack of variation in pay and compromising the enforcement utility of Component 2 data, especially with respect to calculating pay gaps at targeted establishments.
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Using established, improved meth ods, other federal agencies have demonstrated that individual-level pay data can substantially reduce respondent burden, increase precision in estimating pay gaps, and protect confidentiality. The Bureau of Labor Statistics' Occupational Employment and Wage Statistics collection is an example.
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From page 297... ...
Aside from job categories, other important and legitimate causes of pay differences, such as education and tenure, are not included in the Component 2 data collection. As described in Chapter 3, without this information, Component 2 data will not contain important information for EEOC enforcement actions and employers' self-assessments.
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From page 298... ...
The panel applied elementary filters to address these deficiencies, but filtering was not as extensive for work hours as for employee counts; and the lack of comparable data from the Component 1 data collection limited the extent of filtering used even for employee counts. Second, full-time, part-time, and part-year employees are all grouped together in Component 2 data based on annual wages, but annual wages reflect different hourly wages for individual employees, and it is unclear how hours worked should be apportioned to calculate hourly pay rates.
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From page 299... ...
For a substantial share of charges, calculating sex and/or race/ethnicity pay gaps and making peer comparisons will be impossible due to few or no workers in relevant SRO cells. In occupations with low sex and race/ ethnicity diversity, such as executives in the Silicon Valley technology sector, it may be impossible to calculate pay gaps for a targeted establishment due to the absence of workers in specific SRO cells.
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From page 300... ...
These are divided into necessary and relatively modest improvements in the short term, and additional improvements for EEOC to implement as part of a broader effort to strengthen Component 2 data and thereby more fully advance the Commission's mission. Although short-term recommendations are presented first, the panel encourages EEOC to act more broadly to improve the Commission's pay-data collection in the future.
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From page 301... ...
The panel advises that all automated data checking be implemented prior to data certification. The panel's analyses suggest that only a small fraction of respondents will need to make these corrections, but the improvement in the quality of EEO-1 reports and their trustworthiness for making regulatory decisions will be dramatically improved.
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From page 302... ...
In addition to the current panel's recommendations for improved data quality, the panel further advises that the two methodological reports prepared by the National Opinion Research Center at the University of Chicago should be consulted closely for future data collections, especially if future collections are based on the legacy EEO-1 reporting format. The panel also recommends that future data-collection efforts be accompanied by pilot studies to assess data quality and fit-for-use issues.
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Furthermore, by eliminating Type 6 reports, consolidated reports could be generated by automatically summing across all submitted establishments in a reporting firm. This would reduce respondent burden and increase data quality, since current respondents must prepare consolidated reports whenever they submit Type 6 reports.
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From page 304... ...
. The response rates and data quality for PEOs was generally superior to that of self-filing, providing a clear advantage to continued PEO filings.
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From page 305... ...
Chapter 4 describes that when compared to external Bureau of Labor Statistics and Census Bureau firm and establishment benchmarks, the EEO-1 response frame and resulting data file have substantial undercoverage of eligible entities, particularly for establishments with fewer than 50 employees. About half of the establishment noncoverage for pay data appears to be a function of firms missing from the respondent frame.
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From page 306... ...
The panel understands this was done to protect the confidentiality of EEO-1 respondents. However, there are several preferred ways to maintain confidentiality that do not impair the ability of authorized users to match and thereby compare and assess data quality and employment and pay trends over time (Federal Committee on Statistical Methdology, 2005)
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From page 307... ...
For pay bands to be useful in detecting pay disparities, an occupation must appear in at least two pay bands within an establishment. Some job categories appear overwhelmingly, but not exclusively, in one pay band: 47 percent of executives are in the top pay band, 51 percent of sales personnel are in the bottom pay band, 45 percent of laborers/helpers are in the bottom pay band, and 58 percent of service workers are in the bottom pay band (Appendix 6-5)
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From page 308... ...
As discussed in Chapter 3, the width of a pay band is approximately 24 log points or about 27 percent, while the raw pay gap by sex is about 22 log points overall, and raw pay gaps for Hispanic and Black workers tend to be somewhat smaller. If EEOC is only focused on the largest pay disparities, then wide pay bands may be less of an issue, but current pay bands will not be useful in many situations.
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From page 309... ...
The panel recommends that these issues be examined further by field testing and cognitive interviews with respondents, as well as through internal discussions at EEOC about the timeliness of data for enforcement purposes. The panel also notes that the Chamber of Commerce Foundation and the T3 Innovation Network have launched the Jobs and Employment Data Exchange (JEDx)
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From page 310... ...
report recommended EEOC to use of pay bands rather than individual-level pay data under the dual assumptions that pay bands would reduce reporting burden and address confidentiality concerns. However, the panel finds that pay bands are not the appropriate solution for managing respondent burden or confidentiality in pay-data collections given the availability of established, improved methods of pay-data collection and disclosure avoidance.
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From page 311... ...
Full Fair Labor Time/ Job Title Box 5 W-2 Standards Act Hours Weeks Part-Time Person (Write In) Earnings Status Worked Worked Status Gender Ethnicity Race 1 65,000 Exempt 45 FT Male Hispanic or White Latino 2 35,000 Exempt 52 PT Female Not Hispanic Black or Latino 3 36,000 Non-exempt 2,080 52 FT Male Not Hispanic Asian or Latino 4 24,000 Non-exempt 1,463 40 PT Non-binary Hispanic or American Indian Latino or Alaska Native 311
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From page 312... ...
Field testing should estimate respondent burden relative to alternative methods and assess confidentiality protections to be applied. In the panel's judgment, to improve accuracy and comparability of data, EEOC should stop using legacy EEOC job categories, which are neither directly comparable to other federal occupational data nor sufficiently detailed for analysis of similarly situated employees.
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From page 313... ...
To limit respondent burden, EEOC should explore established, improved data systems for occupational coding of individual-level job titles, such as those used by the Bureau of Labor Statistics' Occupational Employ ment Wage Statistics collection. EEOC anticipated that employers may not track hours worked for employees exempt from the Fair Labor Standards Act.
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From page 314... ...
RECOMMENDATION 3-10: EEOC should explore the measurement of pay gaps for additional groups protected under its authority or policy equities, including persons age 40 and older, persons with dis abilities, and veterans. To do this robustly while minimizing respondent burden, other federal data collections measuring pay of these groups, such as the American Community Survey, may be instructive.
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. One role of a recognized statistical unit would be to advise on the sharing of EEO-1 pay data with employers in a way that informs employer selfassessment while appropriately addressing confidentiality concerns.
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, while targeted investigations for enforcement purposes proceed as a separate data activity. POLICY CASE FOR IMPLEMENTING CHANGE EEO-1 pay data could be an essential resource to advance EEOC's mission (Box 8-2)
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EEO-1 pay data. The statistical and survey methods needed to accomplish these recommendations are well-established among federal data collections.
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• Instrument redesign • Consult with state of Illinois as it develops its individual-level data collection strategy
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From page 319... ...
and federal data-collection agencies to explore measures and methods used (e.g., American Community Survey [ACS]
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Therefore, the panel advises EEOC to adopt a forward-looking approach to anticipate, test, and thereby provide the most relevant measurement of pay, work, and protected groups. Good Government Several of the panel's recommendations, if implemented, are anticipated to reduce respondent burden and cost, and add value to stakeholders.
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From page 321... ...
• The federal standard offers solutions for reporting race/ ethnicity data in a combined format 3-6 • Explore ways to collect • Within EEOC's • Work with other federal data on employees' sex, mandate agencies to identify gender identity, and sexual appropriate measures and orientation • Reflects changing methods society • Develop and test measures • Allows greater precision • Instrument redesign • Allows comparison to external benchmarks continued
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From page 322... ...
322 COMPENSATION DATA COLLECTED THROUGH THE EEO-1 FORM TABLE 8-3 Continued Strategy or Policy Recommendation Rationale Mechanism 3-10 • Enable comparisons of pay • Protected under • Examine other federal for persons age 40 and older, current statutory data collections measuring persons with disabilities, and authorities or pay of these groups, such veterans policy equities as ACS • Develop and test measures • Instrument redesign 3-2 • Require PEOs to submit data • Improve • Instrument redesign separately for each firm they accuracy of PEO represent submissions • Require employing firms • Ensure certification to certify PEO submissions is appropriate before filing TABLE 8-4 Recommendations to Use Good Government and Statistical Practices Strategy or Policy MechaRecommendation Rationale nism 2-1 • Combine Components 1 • Reduce burden • Instrument redesign and 2 • Reduce measurement error 2-2 • Eliminate Type 6 report • Increase completeness • Instrument redesign option, mandating Type 8 of data for enforce reports from all establish- ment ments in multi-establishment firms • Reduce measurement error in consolidated • Eliminate consolidated reports reports (Type 2) and replace with automated calculation • Reduce respondent burden
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From page 323... ...
CONCLUSIONS AND RECOMMENDATIONS 323 TABLE 8-4 Continued Strategy or Policy MechaRecommendation Rationale nism 5-2 • Prior to future Component • Reduce reporting error • Field test 2 data collection, conduct a field test • Reduce burden • Cognitive interviews • Investigate sources • Instrument redesign of error in employee count and hours worked data • Potentially make hours-worked report adjacent to employee count • Investigate function ing of new survey questions • Use cognitive interviewing to determine employer understanding of questions, difficulties in responding, and strategy of obtain ing data to report to EEOC 3-1 • Implement a standard • Improve data compa- • Instrument redesign reporting period rability • Reduce respondent burden 8-1 • Provide filers with a method • Improve data quality • Consistent with ongo to download and review and assist with self- ing EEOC data-mod responses before submission assessment ernization efforts • (Currently provided for the Component 1 instrument but not for Component 2) continued
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From page 324... ...
4-3 • Use consistent and unique • Facilitates data merges, • Assign to professional firm and establishment and therefore data research organization identifiers checking and trend under contract analysis, by authorized monitored by technical users staff (such as OEDA)
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