Our Pay Equity Analysis:
Introduction:
On June 25, 2021, the Biden-Harris Administration issued an executive order to strengthen and advance diversity, equity, inclusion, and accessibility (DEIA) in the Federal workforce. Included in the executive order is a focus on evaluating and improving pay equity for Federal employees from different social and cultural backgrounds. The heads of each agency have been tasked to collect, use, and analyze demographic data and trends related to diversity to evaluate several aspects of the agency including pay and compensation.
To support the goal of achieving pay equity in in the Federal workforce, GovStrive has developed a solution to evaluate pay in federal agencies and identify key areas for improving pay equity using federal data and cutting-edge methods from the social and data sciences. Our solution is rigorous and can be customized for analyses of pay equity in specific federal agencies using human resources (HR) data. The results of our solution can be used by agencies to inform action plans that improve pay equity for employees from different social and cultural backgrounds.
GovStrive’s Approach to Evaluating Pay Equity in Federal Agencies
Our approach to analyzing pay equity is informed by the recent research from federal, non-profit, and academic organizations. Research has found unexplained pay gaps for private and public sector workers from different demographic categories including sex, marital status, parenthood, race, ethnicity, veteran status, and disability status. These unexplained gaps suggest that pay equity should be further evaluated and improved to promote fair compensation across demographics for similar positions and work.
Drawing from prior research, we performed a pay equity analysis comparing the federal sector to the private sector. We used a sample of data from the U.S. Census Bureau American Community Survey (ACS). The sample represents the 2021 U.S. workforce, including citizens between the ages of 27 – 65 who were full-time employees working at least 40 hours a week with incomes from $15,100 to $180,000. Our analysis included seven demographic variables: sex, race, ethnicity, marital status, number of children in the home, disability status, and veteran status. Additionally, five known factors that affect compensation were used in this analysis to isolate and more accurately estimate pay differences by demographics. These controlling factors were location (measured using region and metropolitan status), educational attainment, years of experience (approximated using age), and occupation (measured using occupational families).
Executive Order on Diversity, Equity, Inclusion, and Accessibility in the Federal Workforce: June 25, 2021
- The Director of OPM will work with agencies to review and revise job classification and compensation practices.
- Agencies will be prohibited from seeking or relying on previous salary information.
- Agencies that follow compensation practices other than the one established under Title 5 must review and revise job compensation practices to be more equitable.
- The President will receive a report concerning any changes to government and agency-specific compensation practices.
Results
Our analyses found that many demographics of interest, even after accounting for location, occupation, educational attainment, and years of experience, were still significant in determining differences in pay.
Sex
With respect to workers’ sex, female employees were paid less than male employees. In the private sector, female workers were paid an average of 8.47% less than male workers. In the Federal sector, female workers were paid an average of 6.89% less than their male counterparts. These results align with findings from OPM (2023) and GAO (2020), which reported pay inequities between female and male employees in the Federal workforce.
Figure 3. Income Differences by Sex.
Marital Status
In the private sector, non-married workers made an average of 5.88% less than married workers. Similarly, non-married Federal workers made an average of 3.76% less than their married counterparts. This finding aligns with research from the Federal Reserve Bank of St. Louis (2020), which reported that married workers earn more than non-married workers.
Figure 4. Income Differences by Martial Status.
Children at Home
In the private sector, workers who have at least one child at home made an average of 0.72% more than workers who do not have children at home. In the public sector, workers who have at least one child at home made 0.91% more than workers who do not have children at home. These outcomes are consistent with findings from the Pew Research Center (2023), which found that having children increases income on average, particularly for fathers.
Figure 5. Income Differences after One Child.
Disability Status
In the private sector, workers with disabilities made an average of 3.11% less than workers without disabilities. The pay gap was slightly larger in the Federal sector, as workers with disabilities made 3.52% less than workers without disabilities. Similar to these findings, United States Census Bureau (2019) reported that year-round workers with disabilities earn 87 cents for every dollar earned by workers without disabilities. This gap can vary across occupations, and is small or non-existent for many occupations.
Figure 6. Income Differences by Disability Status.
Ethnicity
In the private sector, Hispanic workers made an average of 2.44% less than those who were not Hispanic. This difference was smaller in the federal sector, where Hispanic workers made 0.78% less than their non-Hispanic counterparts. These findings were similar to those reported by Pew Research Center (2016) and the U.S. Department of Labor (2020), which found that Hispanic workers on average made less than non-Hispanic workers.
Figure 8. Income Differences by Ethnicity.
Veteran Status
In the private sector, workers who are veterans made 0.78% more than their non-veteran counterparts. In the federal sector, workers who are veterans made 1.74% more than their non-veteran counterparts, although this difference was not statistically significant. The effect being a veteran has on pay is debated across different research institutions, which aligns with the lack of statistical significance found in this study. These findings align most closely with research conducted by Pew Research Center (2019) where households headed by veterans tended to have higher incomes than those not headed by veterans.
Figure 7. Income Differences by Veteran Status.
Race
In the private and federal sectors, non-white workers made significantly less than white workers. The largest differences in pay by race were observed in the private sector, where non-White workers made an average of 1.76% to 5.10% less than white workers. In the federal sector, non-white workers made an average of 0.77% to 4.59% less than white workers. Differences in pay for federal workers of two or more races were not statistically significant. These findings align with research conducted by the Department of Labor (2020), which found that Black, Native American/American Indian, and Multiracial workers all have lower average weekly incomes compared to White workers. It also agrees with the National Women’s Law Center (2023) who found that Asian American, Native Hawaiian, and Pacific Islander (AANHPI) women who worked full-time, year-round in 2021 were paid 92 cents for every dollar made by a white, non-Hispanic man.
Figure 9. Income Differences by Race.
Our findings illustrated that the children at home, disability status, and the Asian race category were found to have a larger unexplained pay gap than those found in the private sector. Two demographic categories were found to have insignificant pay gaps: veteran status and the two or more races category, and the rest of the demographics of interest in the federal sector have smaller unexplained gaps than those in the private sector. Demographics with smaller pay gaps include sex, marital status, ethnicity, and all but two race categories (i.e., not including Asian and two or more races). Although many of the unexplained pay gaps were shown to be smaller in the federal sector, the presence of any unexplained pay gaps means we still have work to do to achieve pay equity for people of different backgrounds. Using this background knowledge and agency specific data, GovStrive can not only investigate the areas where there are pay inequities for each agency but can investigate the causes behind the gaps that are found.
Pay Equity Analysis
Discovery Phase
Within agencies, our customized pay equity analyses use workforce roster data to detect any pay inequities that may be present in demographics of interest. These areas of interest may include gender, race, ethnicity, marital status, etc., while accounting for known compensation factors such as location, grade, step, occupation, educational attainment, and years of service.
Pay equity analyses can be performed using regression analyses and decomposition analyses. Agency leaders can then use the results of the analyses to determine where there are potential pay inequities which can be further explored in the Explanation Phase.
Explanation Phase
The Explanation Phase of GovStrive’s Pay Equity Analysis aims to find possible reasons as to why pay inequities found in the Discovery Phase are present in the agency data. Were these pay inequities found at the start of employee careers or were they developed over time? This can be investigated using methods such as cohort analyses and qualitative studies via interviews and focus groups surrounding current practices and promotion policies.
Agency leaders can then make action plans or policy adjustments to align with the current goals of the federal workforce when it comes to equitable pay.
References
Bennett, Jesse. (2019, December 9). Veteran households in U.S. are economically better off than those of non-veterans. https://www.pewresearch.org/short-reads/2019/12/09/veteran-households-in-u-s-are-economically-better-off-than-those-of-non-veterans/
Cheeseman Day, Jennifer and Taylor, Danielle. (2019, March 21). In Most Occupations, Workers With or Without Disabilities Earn About the Same. United States Census Bureau. https://www.census.gov/library/stories/2019/03/do-people-with-disabilities-earn-equal-pay.html
Vandenbroucke, Guillaume and Peake, Makenzie. (2020, September 21). Taking a Closer Look at Marital Status and the Earnings Gap. Federal Reserve Bank of St. Louis. https://www.stlouisfed.org/on-the-economy/2020/september/taking-closer-look-marital-status-earnings-gap
Kochhar, Rakesh. (2023, March 01). The Enduring Grip of the Gender Pay Gap. PEW Research Center. https://www.pewresearch.org/social-trends/2023/03/01/the-enduring-grip-of-the-gender-pay-gap/
National Women’s Law Center. (2023, March 29). Some Asian American, Native Hawaiian, and Pacific Islander Women Lose Over $1 Million Over a Lifetime to the Racist and Sexist Wage Gap. National Women’s Law Center. https://nwlc.org/resource/aanhpi-wage-gap/
Office of Federal Contract Compliance Programs. (2020, July). Earning Disparities by Race and Ethnicity. U.S. Department of Labor. https://www.dol.gov/agencies/ofccp/about/data/earnings/race-and-ethnicity
Patten, Eileen. (2016, July 1). Racial, Gender Wage Gaps Persist in U.S. Despite Some Progress. Pew Research Center. https://www.pewresearch.org/short-reads/2016/07/01/racial-gender-wage-gaps-persist-in-u-s-despite-some-progress/
Ruggles, Steven; Flood, Sarah; Sobek, Matthew; Backman, Daniel; Chen, Annie; Cooper, Grace; Richards, Stephanie; Rogers, Renae; and Schouweiler, Megan. IPUMS USA: Version 14.0 [dataset]. Minneapolis, MN: IPUMS, 2023. https://doi.org/10.18128/D010.V14.0
The White House. (2021, June 25). Executive Order on Diversity, Equity, Inclusion, and Accessibility in the Federal Workforce. The White House. https://www.whitehouse.gov/briefing-room/presidential-actions/2021/06/25/executive-order-on-diversity-equity-inclusion-and-accessibility-in-the-federal-workforce/
United States Government Accountability Office. (2020, December 03). Gender Pay Differences: The Pay Gap for Federal Workers Has Continued to Narrow, but Better Quality Data on Promotions Are Needed. GAO. https://www.gao.gov/assets/gao-21-67.pdf
U.S. Office of Personnel Management. (2023, May 10). Release: OPM Releases Proposed Regulations to Prohibit Use of Previous Salary History. OPM. https://www.opm.gov/news/releases/2023/04/opm-releases-proposed-regulations-to-prohibit-use-of-previous-salary-history/
