No Pay Data, No Pay Gap Benchmarks. Conduct a Pay Equity Audit Now

Under the stewardship of Trump-appointee Janet Dhillon, the EEOC is rolling back federal pay reporting requirements. This returns the United States to the back of class of developed countries in terms of pay reporting. This rollback also contradicts the Commissions earlier finding that these data are “necessary” to the Commission’s ability to efficiently address pay discrimination, a central tenant of its mandate. In addition, by ceasing to collect pay and hours worked data, the EEOC is depriving employers of the ability to compare their pay distributions to averages drawn from the same industry, geography, firm size, and other relevant categories.

In 2016, the EEOC reported: “the EEOC expects to periodically publish reports on pay disparities by race, sex, industry, occupational groupings, and Metropolitan Statistical Area (MSA)” based on these data. Average pay differences between employees of different genders and/or race/ethnicities may have a legitimate explanation. Often such explanations are common to comparable employers. Without the aggregate data the EEOC had offered to publish, it is more difficult for an employer to know where their pay gaps stand relative to comparable firms. The absence of such comparisons makes it all the more imperative that employers conduct their own pay equity audits. A pay equity audit is an analytical tool that seeks to explain internal differences in pay across the workforce in terms of justifiable business factors.

After commissioning a study from the National Academy of Sciences and conducting a more focused evaluation under a pilot study, the EEOC received approval under the Paperwork Reduction Act to collect aggregate W-2 income data and total hours worked from private employers with at least 100 employees. This collection (referred to as Component 2 of the EEO-1 Report) expanded the EEO-1 Report, which those employers already filed annually. That existing EEO-1 Report is referred to as the Component 1 collection, and counted employees by their gender and race/ethnicity across 10 broad job categories. Component 2 further breaks down these employee counts by 12 pay bands, based on W-2 reported income. Consequently, Component 2 makes Component 1 redundant as the information from Component 1 can be extracted by summing the pay band employee counts from the Component 2 collection.

In August 2017, the Trump administration’s White House Office of Management and Budget (OMB) issued a stay, halting Component 2 data collection before it had started. In 2019, the EEOC went ahead with its Component 1 collection. At the same time, groups advocating for the rights of women and Latinos won a court reversal of that illegal OMB stay. For the first time in its history, the US joined many other developed nations in gathering pay data for the purposes of addressing pay discrimination and providing industry benchmarks.

With the court decision, the EEOC collected pay and hours data for the 2017 and 2018 reporting years. Yet in September 2019, as this Component 2 collection was ramping up, Commission Chair Dhillon gave notice that the EEOC would not pursue further collection of income and hours worked data for reporting years 2019 through 2021, despite the Commission’s earlier determination that such data are “necessary for the enforcement of Title VII, the EPA, and Executive Order 11246.” Enforcement of these anti-discrimination laws is an essential component of the EEOC’s mandate.

The Commission’s reversal relies on the EEOC’s new calculation of the burden that producing any EEO-1 report data placed on employers, the so-called “Revised Burden Estimate” (RBE). Since the passage of the 1995 Paperwork Reduction Act (PRA), the EEOC has had to submit burden estimates for Component 1 of the EEO-1 Report to the OMB for approval. Adjusted for the number of anticipated respondents and aligned with current wages, those prior burden estimates range from $26.1 million to $45.9 million. In contrast, the EEOC’s RBE is over $297 million per year for the Component 1 collection alone. Despite the essential nature of pay and hours data, the Commission argued that given the enormity of its new RBE for simply reporting gender and race/ethnicity representation by job category, that it would only collect that information (i.e., Component 1). That is, the crux of the EEOC’s argument against collecting pay data is not based on the cost of collecting pay data at all.

Nor is the EEOC’s argument even based on the cost to employers of identifying gender, race/ethnicity and job position of its employees. Buried in the details of the EEOC’s argument is the assumption it takes an average of 8 hours per establishment with fewer than 50 employees to simply count the total number of employees, regardless of gender, race/ethnicity, or job category. According to the EEOC’s new burden calculation, it takes 8 hours for an employer to answer the question: “How many employees work at this specific address?” This single assumption contributes 70 percent of the RBE. Perversely, this is more than 10 times longer than the EEOC’s burden calculation assumes it takes for the employer to provide an accounting by gender, race/ethnicity, and job category for all employees across all establishments in the firm.

Beyond the absence of comparison pay distributions to guide employers and the pay discrimination enforcement implications and, a further consequence of the abandonment of the federal collection of pay data is that states and municipalities are moving to fill the gap. As the EEOC removes guideposts, the regulatory thicket grows. It is increasingly important that employers conduct their own, preemptive pay equity audits.

To learn more about achieving pay equity, and to receive a free pay gap risk assessment, click here.

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No Pay Data, No Pay Gap Benchmarks. Conduct a Pay Equity Audit Now
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No Pay Data, No Pay Gap Benchmarks. Conduct a Pay Equity Audit Now
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Pay equity audits are even more essential as the EEOC backs away from informing employers of where they stand relative to peers.
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Trusaic
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