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Closing racial and gender gaps in the labor market could bring big benefits to Ninth District states

Addressing labor disparities could add billions of dollars to state economies

August 23, 2021


Tyler Boesch Data Scientist, Community Development and Engagement
Ben Horowitz Senior Policy Analyst, Community Development and Engagement
Kim-Eng Ky Senior Data Scientist, Community Development and Engagement (former)
Vanessa Palmer Data Director, Center for Indian Country Development
Closing racial and gender gaps in the labor market could bring big benefits to Ninth District states, key image
kali9/Getty Images

Article Highlights

  • Greater equity in the economy would add to state economic output
  • Opportunities for shared economic gains exist at multiple points in the labor market
  • New simulation tool estimates economic impact of job market disparities
Closing racial and gender gaps in the labor market could bring big benefits to Ninth District states

Expanding opportunities for workers to pursue education, find employment, earn higher wages, and clock more hours would boost state economies. In a simulation by researchers from across the Federal Reserve System, eliminating racial/ethnic (identified hereafter as racial) and gender gaps in labor market outcomes increased economic output in each of the six states in the Ninth Federal Reserve District by one quarter to one third.

The public can explore these results using a new, interactive data tool that reports how much the labor-related part of each state’s gross domestic product (GDP), or total economic output, might increase if racial and gender gaps in the labor market were closed. The simulation is adapted from the methodology in a recent working paper by economists at the Federal Reserve Bank of San Francisco, who used national survey data to estimate the economic benefit to the U.S. economy from reducing racial and gender gaps in the labor market. To build on this work, a team of additional researchers from across the Federal Reserve System extended the San Francisco Fed’s approach to gauge potential gains at a state level. The simulation leverages 15 years of U.S. Census Bureau data to estimate the economic implications of disparities in four key labor-related measures and highlights the economic stakes for creating a more equitable future. (For more information on the methodology used to create the tool, see the sidebar below.)

In the Federal Reserve’s Ninth District—which we at the Federal Reserve Bank of Minneapolis serve, and which covers Minnesota, Montana, North Dakota, South Dakota, 26 counties in northwestern Wisconsin, and the Upper Peninsula of Michigan—the simulation estimates that state-level economic gains from closing racial and gender gaps in labor market measures over 2005–2019 would have ranged from $3.9 billion per year in South Dakota to $64 billion per year in Michigan. Differences in the scale of gains across states largely stem from differences in population size and makeup; the smaller or less racially and ethnically diverse the state, the lower the dollar amount of potential economic gains. Nevertheless, each of the gains is appreciable within the context of each state’s economy. While the simulation does not formally estimate the impacts of adjusting particular policies, the takeaway is clear: when individuals are able to fully participate in the labor market, the economy as a whole benefits.


The researchers divided each state’s population into race, gender, and race-gender groups and simulated the increased labor-related output that would result if every group had at least the same average labor-related measures as a comparison group that has historically faced the fewest systemic barriers in the job market. For example, the simulation of closing gender gaps compared women’s labor-related measures in each state to those of men of the same race, and the simulation of closing racial gaps compared the labor-related measures of each racial and ethnic group to those of same-gender whites in the same state. (If a group’s value for a given measure was higher than that of its comparison group—for example, in cases where women’s educational attainment in a state was higher than men’s—the measure was left unchanged.)

Development of the tool required taking a simple approach to defining race, which is described, along with other particulars, in the tool’s detailed methods page. (General information about the tool can be found on the FAQs page.)

The simulation does not account for complex interactions that can occur among labor market measures. For example, with labor market barriers removed, in some households the increase in one adult’s labor force participation could be offset by a decrease in another’s, in cases when there’s a need to accommodate child care or other household responsibilities.

In addition, the diversity of economic stories within racial and ethnic categories is important to note. For example, immigrants from different parts of the globe may face different economic conditions and opportunities, often tied to the circumstances that led to their immigration, and recent work by the Minneapolis Fed shows the variation in Native Americans’ labor market experiences across urban and rural geographies.

Increased output from increasing the employment-to-population ratio

One labor market measure that the tool includes is the employment-to-population ratio, defined as the share of the civilian working-age population in a geographic area that is currently employed. Among Ninth District states, this measure is often lower among Black and Native American individuals as compared to white, Asian, and Latino/a individuals in the same state, as well as lower among women relative to men. According to the simulation, if employment-to-population ratio gaps by race and gender had been closed over 2005–2019, economic outputs would have increased by nearly $1 billion annually in the less-populous states of Montana, North Dakota, and South Dakota; $6 billion annually in Minnesota and Wisconsin; and $16 billion annually in Michigan.

In the United States, unpaid household responsibilities—such as caretaking, housework, and operational tasks—fall disproportionately to women and are not captured in measured employment statistics, a reality that is reflected in lower levels of paid employment among women and fewer hours of paid work compared to men. To the extent that some of these responsibilities would be shifted to men in the thought experiment at the heart of this article, thus lowering men’s economic output, our calculations of the gains from closing gender gaps are an overestimate. For households with children, the simulated results underscore the role that accessible, affordable child care plays in enabling parents to pursue employment opportunities.

Boosts through hourly earnings

The 2005–2019 data show that in the Ninth District, men of a given race almost always earn a higher average hourly wage than women of the same race in each state. Out of 30 combinations of state and race categories (for example, comparing Asian men and Asian women in South Dakota), men’s reported average hourly earnings were higher than same-race women’s in all but two.

Racial gaps in hourly earnings are also apparent. Asian men are the highest earners in half of the Ninth District’s six states, with white men higher or equal elsewhere. In every state, Native American women or women of color are the group taking home the lowest hourly pay.

Hourly earnings further vary by states’ economic landscapes. In North Dakota and South Dakota—rural states with economies focused on the agricultural and energy sectors—average hourly earnings are lower overall relative to neighboring states, but highest for white male workers; in more industrialized states, male Asian and white workers have the highest average hourly wages.

But what if these gaps didn’t exist? Earnings gaps that the simulation explores closing over the 2005–2019 period are large, ranging from an $8 per hour (47 percent) gap between Native American women and white men in Montana to a $12 per hour (63 percent) gap between Latina women and white men in Minnesota. Due to the size of the disparities and the number of workers affected, the simulation shows that in each state, steps to eliminate racial and gender gaps in average hourly earnings could have increased GDP by billions to tens of billions of dollars annually.

Gains from expanding access to paid work hours

For all Ninth District states and racial categories, the data show that men report working more paid hours per week, on average, than women. Further, in all but one of the 30 state-race combinations analyzed, the average weekly hours across all racial categories of men is at least 40—a common benchmark for full-time work in the United States—and across all racial categories of women is less than 40. Among women, gaps in average weekly hours worked by race are minimal or nonexistent. In contrast, among men, gaps by race are generally three to four hours per week, and as much as ten per week in one state.

According to the simulation, total gains that could have resulted from closing these racial and gender gaps in average weekly hours worked are $1 billion annually in the rural states of Montana, North Dakota, and South Dakota; over $7 billion annually in Minnesota and Wisconsin; and $13 billion annually in Michigan.

Benefits from increased educational attainment

People with more than a high school education can more easily access higher-wage jobs. That makes the existing racial and gender gaps in the share of workers holding at least a bachelor’s degree sobering: among Ninth District workers, gaps in college-degree attainment between white men and the race-gender group with the lowest attainment range from 16 percentage points in North Dakota to 24 percentage points in Minnesota.

Closing these educational attainment gaps results in simulated per-state gains of up to $1.4 billion annually through increased hourly earnings. These gains are in addition to other benefits to states when individuals attend college, such as increased civic participation and improved health outcomes, that are not captured by the simulation.

Gaps can be addressed

More equitable participation in the labor market translates to healthier, more secure lives for individuals and families. It also enhances economic and social stability for communities: outcomes of a more equitable economy could be increased tax revenue and homeownership, among other benefits.

The Federal Reserve’s analysis and interactive tool demonstrate that closing racial and gender gaps both in the type of work and how it is compensated (reflected in educational attainment and hourly earnings) and the amount of work (reflected in employment rates and hours worked) that labor force participants can access would lead to shared economic gain.

Shifts at state and local levels can effect powerful economic change. This thought experiment offers an entry point for conversations among community stakeholders who are exploring the causes of—and solutions to—labor market disparities. Across the Ninth District, measures to close these gaps can be a means of enhancing prosperity for all.

Tyler Boesch
Data Scientist, Community Development and Engagement
Tyler Boesch analyzes data, develops visualizations, and creates statistical models to help the Community Development and Engagement team understand issues affecting low- and moderate-income communities. Before joining the Bank, he was a graduate research assistant with the University of Minnesota Center for Urban and Regional Affairs.
Ben Horowitz
Senior Policy Analyst, Community Development and Engagement
Ben Horowitz writes about policies and programs impacting affordable housing, early childhood development, and investments in low- and moderate-income communities.
Vanessa Palmer
Data Director, Center for Indian Country Development

Vanessa Palmer is the data director for the Minneapolis Fed’s Center for Indian Country Development (CICD), where she leads efforts to collect, harmonize, and sustainably manage research-ready data in support of economic self-determination in Indian Country. In addition, she uses statistical tools and data visualization to support CICD’s applied research work.