Center for American Progress

The Economic Status of Asian American and Pacific Islander Women
Report

The Economic Status of Asian American and Pacific Islander Women

Policymakers must closely examine economic data on the experiences of AAPI women, before and during the coronavirus pandemic, in order to surface key barriers and shed light on effective policy solutions.

Customers and cashiers wear face masks at a supermarket in Quincy, Massachusetts, on March 13, 2020. (Getty/The Boston Globe/Stan Grossfeld)
Customers and cashiers wear face masks at a supermarket in Quincy, Massachusetts, on March 13, 2020. (Getty/The Boston Globe/Stan Grossfeld)

Author’s note: Given data limitations, the author conducted most analyses about Asian Americans, Native Hawaiians, and other Pacific Islanders as one combined group, except where noted. The author uses “AAPI” to refer collectively to people who are Asian, are Native Hawaiian, or belong to other Pacific Islander populations. The author uses “NHOPI” when referring to individuals who identify as Native Hawaiian or Pacific Islander and “Asian” when referring to individuals who identify as Asian or Asian American.

The Asian American, Native Hawaiian, and Pacific Islander (AAPI) community is a vibrant, diverse, and growing segment of the U.S. population. Yet despite the rich cultural diversity within this group—which together speaks more than 100 languages, practices a wide variety of religions, and represents more than 50 ethnic groups—economic narratives often fall short of capturing the income and employment disparities that exist within it, particularly among AAPI women. It is past time to remedy that oversight, which has roots in racial, ethnic, and gender biases.

In 2019, there were an estimated 12.7 million AAPI women—nearly 11.9 million Asian women and almost 803,000 NHOPI women—living in the United States. Together, these women represent almost 3.9 percent of the U.S. population.1 Asians are the fastest-growing racial group in the country, projected to represent 14 percent of the U.S. population by 2065.2 Notably, while Asian women are heavily concentrated in the country’s most populous states—namely California, New York, and Texas—NHOPI women are heavily concentrated in the Western states of California, Hawaii, and Washington. There is rapid population growth for both groups all across the country, particularly in Western and Midwestern states.3 Employment and economic status among members of the AAPI community are also far from uniform: While some AAPI subpopulations are heavily concentrated in higher-wage professional and management occupations, others are heavily concentrated in lower-wage service occupations.4

The coronavirus pandemic—the economic effects of which have disproportionately harmed women of color5—has only reinforced the importance of analyzing disparities along racial, ethnic, and gender lines. Asian women have endured some of the harshest economic effects of this crisis, including shuttered businesses, significant job losses, increased caregiving responsibilities, and much more. While data on NHOPI women are limited, it is likely that these women are experiencing many similar challenges during this crisis. On top of economic distress, AAPI women have experienced alarming surges in racialized violence and harassment fueled by derogatory rhetoric and scapegoating of the AAPI community throughout the pandemic.6 This violence and economic insecurity is not occurring in silos; it is part of the same interwoven story, threatening the safety and security of AAPI women and their families.

Despite the unique experiences of this population, data on AAPI women in the United States are limited and fragmented at best. Intersectional analyses—by race and gender—require more granular data and can often be limited due to small sample sizes that make it difficult to draw reliable conclusions about the experiences of people belonging to distinct communities. This can be especially challenging when assessing data for women belonging to different AAPI subpopulations.

Using the limited available census and U.S. Bureau of Labor Statistics data, this issue brief provides a detailed overview of the economic state of AAPI women—from their labor force participation to their access to paid leave and child care—both before the coronavirus pandemic and, where possible, during the pandemic and economic recovery. By examining the nuances within this frequently overlooked group, policymakers can better craft economic and other policies that are tailored and targeted to specific communities, not just a misleading aggregate.

Labor force participation

For the past 20 years, Asian women’s civilian labor force participation rate7 has closely resembled the labor force participation rate for women overall.8 Asian men, meanwhile, have participated in the labor force at higher rates than Asian women—a gender disparity mirrored by other race groups.9 (see Figure 1)

Figure 1

Labor force participation rates hit a 20-year low for Asians from March 2020 to April 2020—the worst months of the coronavirus-induced recession—dropping to 55.8 percent for Asian women (a 2.9 percentage point drop) and 71.6 percent for Asian men (a 3.9 percentage point drop). By December 2020, labor force participation rates saw some recovery, reaching 56.9 percent for Asian women and 73 percent for Asian men, but they were still well below the rates recorded in March 2020, before the steepest pandemic effects occurred. Asian women fared slightly worse than women overall, whose labor force participation rate dropped to 56.3 percent in April 2020 (a 2.2 percentage point drop) and was 57.2 percent by December 2020.10

Labor force participation rate estimates for NHOPI women are only available on an annual basis and only for women ages 16 and older, beginning in 2003. In 2020, the labor force participation rate for NHOPI women was 61.5 percent—1.7 percentage points below the rate in 2019.11

Employment

Asian women’s employment reached a 20-year high of 4,827,000 in September 2019, in line with their population growth.12 (see Figure 2) At the onset of the COVID-19 crisis, however, Asian women’s employment dipped by nearly 17 percent from March 2020 to April 2020—a staggering loss of 787,000 jobs that they have yet to fully recover. Although a smaller segment of the population, Asian women experienced a larger drop in their employment during this time than did both women and men overall, who experienced a 15.2 percent drop and a 12.3 percent drop, respectively.13 This almost immediately highlighted the racial and gender disparities baked into the U.S. economy, which have only been further exacerbated by this crisis.

Figure 2

By January 2021, slightly more than 4.3 million Asian women were employed—355,000 less than the number of employed Asian women at the start of the pandemic and 514,000 less than the 20-year high just 16 months prior.

While employment estimates, like labor force participation rate estimates, are limited for NHOPI women, this group’s employment saw a relatively steady incline from 2003 to 2020, with the highest level reported in 2019—an estimated 302,000 NHOPI women employed.14 In 2020, however, this number dropped to 265,000.

Earnings and wage gap

Women’s paid labor—and, of course, their unpaid caregiving labor—has been historically undervalued, as long-standing gender biases and inequalities contribute to the segregation of women, particularly women of color, into low-wage occupations.15 In 2019, more than 1.4 million AAPI women in the labor force worked in jobs that had median hourly earnings below $15 an hour.16 Exacerbating this problem is the stagnant federal minimum wage, which has been frozen at $7.25 per hour since 2009. Due to inflation, this has led to a reduction in the buying power of those earning a minimum wage income.17 AAPI women earning the minimum wage are therefore less able to make ends meet and support their families than they were more than a decade ago.

AAPI women are unique because of the wide differences among AAPI subpopulations. For example, the most common occupations among Indian women are software developers and physicians—two typically high-wage occupations—while the top occupations among Vietnamese women are manicurists and hairdressers, which are typically low-wage occupations.18 Some factors driving these differences among subpopulations may include cultural differences, differences in immigration patterns and generational wealth, and persistent biases around gender, race, ethnicity, nationality, and language. These significant differences in occupations have led to significant disparities in median earnings among AAPI women by subpopulation. For example, while Indian women working full time, year-round had, on average, median annual earnings of $70,000 from 2015 to 2019, that figure was just $36,500 for Vietnamese women.19 (see Figure 3)

Figure 3

Moreover, as a result of intersecting gender and race biases, many AAPI women face a stubborn pay gap compared with white, non-Hispanic men. In 2019, AAPI women working full time, year-round earned, on average, 85 cents for every $1 earned by their white, non-Hispanic male counterparts.20 While this figure reflects a narrower wage gap than those of women overall and white women, it only tells part of the story. Earnings are vastly different among women belonging to different AAPI subgroups. For example, from 2015 and 2019, Taiwanese women out-earned white, non-Hispanic men by 21 cents, on average, while Hmong women earned 40 cents less than white, non-Hispanic men in the same time frame.21 (see Figure 4)

Figure 4

These wage gaps make it harder for AAPI women—including the 55.5 percent of Asian mothers who are primary, sole, or co-breadwinners for their families22—to make ends meet, build wealth, and weather crises like the COVID-19 pandemic. Wage gaps can also lead many people to poverty. While official 2020 poverty data from the U.S. Census Bureau are not yet available, 2019 data show that 7.8 percent of Asian women and 11.6 percent of NHOPI women lived in poverty, compared with 11.5 percent of women overall.23

Industries and occupations

A Center for American Progress analysis of essential industries and occupations found that an estimated 27 percent of employed AAPI women are essential workers.24 And of these AAPI women essential workers, about 3 in 4 are essential workers in either health care or food services.25

Overall, the top industries among AAPI women range from health care and education to restaurant and food services.26 (see Table 1)

Table 1

The outsize impact of the coronavirus recession on AAPI women and other women of color is due in large part to the fact that women of color disproportionately work in service sector industries—nearly all of which have hemorrhaged jobs throughout the pandemic as businesses with high customer interaction were shuttered and/or adjusted to limit operations in an attempt to slow the spread of COVID-19.27 Nearly half a million AAPI women work in restaurants and other food services as well as nail salons and other personal care services—industries that, combined, employ about 1 in 10 AAPI working women.28 (see Table 1)

In addition, AAPI women have been on the front lines during this crisis, working in health care as registered nurses and personal care aides as well as in the service sector as cashiers and retail salespersons.29 (see Table 2)

Table 2

As described earlier, there is also significant occupational diversity within the AAPI community: While the top job for Chinese women is accountant or auditor, it is elementary or middle school teacher for Japanese women and registered nurse for Filipino and Korean women.30

Unemployment

Analysis by the National Women’s Law Center found that a striking 44 percent of Asian women over the age of 16 who lost their jobs during the pandemic were out of work for at least six months as of December 2020—the highest rate among women of any racial group.31 By January 2021, both Asian and Hispanic women had higher unemployment rates than did their male counterparts, further demonstrating that women of color continue to be left behind by the recovery.32

The economic effects of the pandemic on women of all races have been on stark display since March 2020. (see Figure 5) For Asian women ages 20 and older, the unemployment rate spiked from 3.0 percent in February 2020 to 16.4 percent in May 2020.33 And while the unemployment rate has begun to rebound, Figure 5 makes clear not only that women overall have yet to fully recover but also that Asian women continue to struggle more than white women, despite the fact that their unemployment rates were lower than or on par with white women before the pandemic. In January 2021, the unemployment rate for Asian women jumped again—from 6.6 percent in December 2020 to 7.9 percent—indicating that the economic impacts of the coronavirus recession are still far from over.34

Figure 5

The unemployment rate is a measure of the unemployed as a percentage of the labor force and thus does not count individuals who stopped looking for work altogether. Assessment of monthly civilian labor force levels indicates that there were 224,000 fewer Asian women in the labor force in January 2021 than there were in February 2020, before the pandemic.35

Unfortunately, monthly unemployment data for NHOPI are not available, and an annual analysis does not provide the granular insight needed to assess trends during the COVID-19 crisis.

Access to paid leave and child care

Access to paid leave and child care is essential to economic security; yet the United States is the only industrialized nation in the world without a guarantee of any paid leave for workers.36 This failure places a heavy economic burden on women, who are more likely to shoulder unpaid caregiving responsibilities—such as caring for children, parents, and extended family—in addition to handling household needs and more. In the absence of comprehensive paid family and medical leave, women, including AAPI women, often must accommodate caregiving and other unpaid obligations by reducing work hours, thereby lowering total earnings, or by leaving the labor force altogether. The Family and Medical Leave Act (FMLA) of 1993 guarantees eligible workers 12 weeks of leave, or 26 weeks of leave to care for military service members; but this leave is unpaid. Just 53 percent of Asian workers are eligible for the FMLA, which is slightly lower than the eligibility rates for many other groups.37

Women are also disproportionately affected when child care is inaccessible, similarly risking jobs and career mobility to manage competing responsibilities. Half of Asian women live in child care deserts—areas where licensed child care supply is too low to effectively serve children and families—and child care access is a strong predictor of maternal employment.38 The COVID-19 pandemic is only worsening America’s child care deserts, which could prevent many women from reentering the labor force, thus deepening overall income inequality over time.39

Conclusion

The coronavirus pandemic has halted economic progress for the average worker. However, job losses have been particularly devastating in many of the industries that disproportionately employ AAPI women. While the wage gap between AAPI women and white, non-Hispanic men appears narrower than most other wage gaps, this statistic hides a more complicated, disparate reality when disaggregated across subpopulations. AAPI women’s labor—just like that of women overall—is consistently undervalued, lacking targeted policy solutions to meet the challenges of modern work and caregiving responsibilities.

Given these factors, the economic status of AAPI women is less than certain. It is essential that AAPI women and the subgroups that exist within this community are part of the broader economic narrative as the country seeks to recover from the pandemic and resultant recession. Policymakers must ensure that both immediate recovery efforts and future economic policies do not leave AAPI women behind; and this starts by understanding the diversity within this rapidly growing population.

Robin Bleiweis is a research associate for women’s economic security with the Women’s Initiative at the Center for American Progress.

The author would like to thank Diana Boesch, Lorena Roque, and Areeba Haider for their insights and invaluable research assistance on this issue brief.

To find the latest CAP resources on the coronavirus, visit our coronavirus resource page.

Endnotes

  1. All figures are for each “race alone or in combination.” See U.S. Census Bureau, “Annual Estimates of the Resident Population by Sex, Race, and Hispanic Origin for the United States: April 1, 2010 to July 1, 2019,” at https://www.census.gov/newsroom/press-kits/2020/population-estimates-detailed.html (last accessed March 2021).
  2. Pew Research Center, “Modern Immigration Wave Brings 59 Million to U.S., Driving Population Growth and Change Through 2065: Views of Immigration’s Impact on U.S. Society Mixed,” September 28, 2015, available at https://www.pewresearch.org/hispanic/2015/09/28/modern-immigration-wave-brings-59-million-to-u-s-driving-population-growth-and-change-through-2065/.
  3. Without controlling for overall state population size, the author found that Asian women’s population grew fastest in North Dakota, South Dakota, Indiana, and Nebraska, while NHOPI women’s population grew fastest in North Dakota, Iowa, Arkansas, and Oklahoma. Author’s analysis is based on U.S. Census Bureau, “Annual State Resident Population Estimates for 6 Race Groups (5 Race Alone Groups and Two or More Races) by Age, Sex, and Hispanic Origin: April 1, 2010 to July 1, 2019,” available at https://www.census.gov/data/tables/time-series/demo/popest/2010s-state-detail.html (last accessed March 2021).
  4. Author’s findings are based on analysis of employed workers by sex, race, employment, and occupation, using data from Steven Ruggles and others, “Integrated Public Use Microdata Series, U.S. Census Data for Social, Economic, and Health Research, 2019 American Community Survey: 1-year estimates: Version 10.0” (Minneapolis: Minnesota Population Center, 2021), available at https://usa.ipums.org/usa/.
  5. Diana Boesch and Shilpa Phadke, “When Women Lose All the Jobs: Essential Actions for a Gender-Equitable Recovery” (Washington: Center for American Progress, 2021), available at https://americanprogress.org/issues/women/reports/2021/02/01/495209/women-lose-jobs-essential-actions-gender-equitable-recovery/.
  6. Mina Kim and Diane Lim, “Seeing Asian Women to Better Understand the Pandemic ‘She-cession’,” Economist Mom, August 2020, available at https://economistmom.com/2020/08/19/seeing-asian-women-to-better-understand-the-pandemic-she-cession/. See also, Jada Chin, “Covid fueled anti-Asian racism. Now elderly Asian Americans are being attacked,” The Washington Post, February 9, 2021, available at https://www.washingtonpost.com/nation/2021/02/09/attacks-asian-american-elderly-/; Erin Donaghue, “2,120 hate incidents against Asian Americans reported during coronavirus pandemic,” CBS News, July 2, 2020, available at https://www.cbsnews.com/news/anti-asian-american-hate-incidents-up-racism/.
  7. The labor force participation rate represents the percentage of people who are working or actively looking for work.
  8. U.S. Bureau of Labor Statistics, “(Unadj) Labor Force Participation Rate – 20 yrs. & over, Asian women,” available at https://beta.bls.gov/dataViewer/view/timeseries/LNU01332267 (last accessed March 2021). Findings for Asian women are not seasonally adjusted. See also, “(Seas) Labor Force Participation Rate – 20 yrs. & over, Women,” available at https://beta.bls.gov/dataViewer/view/timeseries/LNS11300026 (last accessed March 2021).
  9. Findings for Asian women and men are based on analysis of U.S. Bureau of Labor Statistics, “(Unadj) Labor Force Participation Rate – 20 yrs. & over, Asian women”; U.S. Bureau of Labor Statistics, “(Unadj) Labor Force Participation Rate – 20 yrs. & over, Asian men,” available at https://beta.bls.gov/dataViewer/view/timeseries/LNU01332266 (last accessed March 2021). See also, series IDs LNS11300028, LNS11300029, LNS11300031, LNS11300032, LNS11300034, and LNS11300035. Findings for Asian women and men are not seasonally adjusted.
  10. U.S. Bureau of Labor Statistics, “(Seas) Labor Force Participation Rate – 20 yrs. & over, Women.”
  11. U.S. Bureau of Labor Statistics, “(unadj) Civilian labor force Participation rate, Women, Native Hawaiian or Other Pacific Islander only,” available at https://beta.bls.gov/dataViewer/view/timeseries/LNU01335645 (last accessed March 2021). Findings for NHOPI women are not seasonally adjusted.
  12. Author’s findings are based on analysis of U.S. Bureau of Labor Statistics, “(Unadj) Employment Level – 20 yrs. & over, Asian Women,” available at https://beta.bls.gov/dataViewer/view/timeseries/LNU02032267 (last accessed March 2021). Findings for Asian women are not seasonally adjusted.
  13. Author’s calculations are based on analysis of U.S. Bureau of Labor Statistics, “(Seas) Employment Level – 20 yrs. & over, Women,” available at https://beta.bls.gov/dataViewer/view/timeseries/LNS12000026 (last accessed March 2021); and U.S. Bureau of Labor Statistics “(Seas) Employment Level – 20 yrs. & over, Men,” available at https://beta.bls.gov/dataViewer/view/timeseries/LNS12000025 (last accessed March 2021).
  14. Author’s findings are based on U.S. Bureau of Labor Statistics, “(unadj) Employed – Native Hawaiian or Other Pacific Islander, women,” available at https://beta.bls.gov/dataViewer/view/timeseries/LNU02035645 (last accessed March 2021). Findings for NHOPI women are not seasonally adjusted.
  15. Sarah Jane Glynn, “An Unequal Division of Labor: How Equitable Workplace Policies Would Benefit Working Mothers” (Washington: Center for American Progress, 2018), available at https://americanprogress.org/issues/women/reports/2018/05/18/450972/unequal-division-labor/.
  16. Median hourly wages for occupations were determined using U.S. Bureau of Labor Statistics, “May 2019 National Occupational Employment and Wage Estimates United States,” available at https://www.bls.gov/oes/current/oes_nat.htm (last accessed March 2021). Author’s calculations are based on Sarah Flood and others, “Integrated Public Use Microdata Series, 2020 Current Population Survey Data for Social, Economic, and Health Research: Version 8.0 (dataset), Annual Social and Economic Supplement” (Minneapolis: Minnesota Population Center, 2021), available at https://cps.ipums.org/cps/. The majority of Current Population Survey (CPS) occupation codes were matched to Bureau of Labor Statistics occupation codes using the crosswalk available at U.S. Bureau of Labor Statistics, “Classifications and Crosswalks,” available at https://www.bls.gov/emp/documentation/crosswalks.htm (last accessed March 2021). However, given Census Bureau alterations to CPS occupation codes beginning with its 2020 survey, several codes could not be matched on the current crosswalk, which has yet to be updated. In these instances, the author used her own discretion to manually match occupation codes based on job title and other factors. Full appendix on file.
  17. David Cooper, “Raising the federal minimum wage to $15 by 2024 would lift pay for nearly 40 million workers” (Washington: Economic Policy Institute, 2019), available at https://www.epi.org/publication/raising-the-federal-minimum-wage-to-15-by-2024-would-lift-pay-for-nearly-40-million-workers/.
  18. Author lists “manicurists” as shorthand for “manicurists and pedicurists” as well as “hairdressers” as shorthand for “hairdressers, hairstylists, and cosmetologists,” all based on American Community Survey occupation codes and categories. Author’s findings are based on analysis of employed workers by sex, race, employment, and occupation using data from Steven Ruggles and others, “Integrated Public Use Microdata Series, U.S. Census Data for Social, Economic, and Health Research, 2015-2019 American Community Survey: 5-year estimates: Version 10.0” (Minneapolis: Minnesota Population Center, 2021), available at https://usa.ipums.org/usa/. Indian women reflect those who selected “Asian Indian” as their race.
  19. Author’s findings for AAPI subgroups are based on Steven Ruggles and others, “Integrated Public Use Microdata Series, U.S. Census Data for Social, Economic, and Health Research, 2015-2019 American Community Survey: 5-year estimates: Version 10.0.” Indian women reflect those who selected “Asian Indian” as their race.
  20. The gender wage gap was calculated by finding the ratio of AAPI women’s median earnings to white, non-Hispanic men’s median earnings—for full-time, year-round workers. Author’s calculations are based on Sarah Flood and others, “Integrated Public Use Microdata Series, 2020 Current Population Survey Data for Social, Economic, and Health Research: Version 8.0 (dataset), Annual Social and Economic Supplement.”
  21. Author’s calculations are based on Steven Ruggles and others, “Integrated Public Use Microdata Series, U.S. Census Data for Social, Economic, and Health Research, 2015-2019 American Community Survey: 5-year estimates: Version 10.0.” White, non-Hispanic men had median annual earnings of $58,000 from 2015 to 2019.
  22. CAP and Jeff Chapman’s analysis of Sarah Flood and others, “Integrated Public Use Microdata Series, Current Population Survey: Version 7.0” (Minneapolis: Minnesota Population Center, 2020), available at https://doi.org/10.18128/D030.V7.0.
  23. Author’s calculations are based on the official poverty measure using the 2020 Current Population Survey’s Annual Social and Economic Supplement. See Sarah Flood and others, “Integrated Public Use Microdata Series, 2020 Current Population Survey Data for Social, Economic, and Health Research: Version 8.0 (dataset), Annual Social and Economic Supplement.”
  24. Estimates define “essential” using American Community Survey (ACS) industry and occupational codes and federal guidance, which is limited in scope and does not include, for example, teachers or child care workers, whose work has been deemed essential by some states and localities but not by the federal government. Therefore, given the exclusion of certain occupations disproportionately held by women—particularly women of color—these are likely underestimates. For more on methodology and an appendix of industry and occupation codes, see Areeba Haider, “Congress Must Strengthen SNAP To Support Essential Workers During the Coronavirus Crisis,” Center for American Progress, June 11, 2020, available at https://americanprogress.org/issues/poverty/news/2020/06/11/486187/congress-must-strengthen-snap-support-essential-workers-coronavirus-crisis/. Author’s calculations are based on analysis of Steven Ruggles and others, “Integrated Public Use Microdata Series, U.S. Census Data for Social, Economic, and Health Research, 2019 American Community Survey: 1-year estimates: Version 10.0.” Data are for people employed in 2018.
  25. Ibid. Fifty-one percent of AAPI women essential workers were employed in the health care industry—defined for the purposes of this analysis as work in offices of physicians; outpatient care centers; home health care services; general medical and surgical hospitals, and specialty hospitals; psychiatric and substance abuse hospitals; nursing care facilities; residential care facilities; and other health care services. Twenty-four percent of AAPI women essential workers were employed in food services—defined for the purposes of this analysis as work in sugar and confectionery products; fruit and vegetable preserving and specialty food manufacturing; retail bakeries; bakeries and tortilla manufacturing; seafood and other miscellaneous foods; beverage manufacturing; community food and housing, and emergency services; restaurants and other food services; drinking places, alcoholic beverages; and not specified food industries.
  26. Beginning with 2018 data, ACS industry codes were adjusted and do not entirely match up with ACS industry codes used from 2013 to 2017. Therefore, for simplicity in analysis, the author used 1-year estimates rather than 5-year estimates. More granular analyses—for example, by AAPI subgroup or for NHOPI women and Asian women separately—were limited by the smaller sample sizes available under 1-year estimates. Author’s calculations are based on analysis of Steven Ruggles and others, “Integrated Public Use Microdata Series U.S. Census Data for Social, Economic, and Health Research, 2019 American Community Survey: 1-year estimates: Version 10.0.”
  27. Jocelyn Frye, “On the Frontlines at Work and at Home: The Disproportionate Economic Effects of the Coronavirus Pandemic on Women of Color” (Washington: Center for American Progress, 2020), available at https://americanprogress.org/issues/women/reports/2020/04/23/483846/frontlines-work-home/.
  28. Author’s calculations are based on an analysis of employed workers by sex, race, employment, and industry using data from Steven Ruggles and others, “Integrated Public Use Microdata Series U.S. Census Data for Social, Economic, and Health Research, 2019 American Community Survey: 1-year estimates: Version 10.0.”
  29. Author’s calculations are based on an analysis of employed workers by sex, race, employment, and occupation using data from Steven Ruggles and others, “Integrated Public Use Microdata Series, U.S. Census Data for Social, Economic, and Health Research, 2015-2019 American Community Survey: 5-year estimates: Version 10.0.”
  30. Ibid.
  31. Claire Lampen, “All the Jobs Lost Last Month Reportedly Belonged to Women,” The Cut, January 10, 2021, available at https://www.thecut.com/2021/01/all-jobs-lost-in-december-2020-were-held-by-women-report.html.
  32. Author’s findings for Asian women and men are based on analysis of U.S. Bureau of Labor Statistics, “(Unadj.) Unemployment Level – 20 yrs. & over.” Findings for Hispanic women and men are based on analysis of U.S. Bureau of Labor Statistics, “(Seas.) Unemployment Level – 20 yrs. & over,” available at https://data.bls.gov/PDQWeb/ln (last accessed March 2021). See also, series IDs LNU04032267, LNU04032266, LNS14000035, and LNS14000034. Findings for Asian women and men are not seasonally adjusted.
  33. U.S. Bureau of Labor Statistics, “(Unadj.) Unemployment Level – 20 yrs. & over, Asian women.” Findings for Asian women are not seasonally adjusted.
  34. Ibid.
  35. Authors calculations are based on analysis of U.S. Bureau of Labor Statistics, “(Unadj) Civilian Labor Force Level 20 yrs. & over, Asian women,” available at https://beta.bls.gov/dataViewer/view/timeseries/LNU01032267 (last accessed March 2021). Findings for Asian women are not seasonally adjusted.
  36. Diana Boesch, “Quick Facts About Paid Family and Medical Leave,” Center for American Progress, February 5, 2021, available at https://americanprogress.org/issues/women/news/2021/02/05/495504/quick-facts-paid-family-medical-leave/.
  37. Scott Brown and others, “Employee and Worksite Perspectives of the FMLA: Who Is Eligible?” (Rockville, MD: Abt Associates, 2020, available at https://www.dol.gov/sites/dolgov/files/OASP/evaluation/pdf/WHD_FMLA2018PB1WhoIsEligible_StudyBrief_Aug2020.pdf. Analysis by race and gender were unavailable due to sample size limitations.
  38. Rasheed Malik and others, “America’s Child Care Deserts in 2018” (Washington: Center for American Progress, 2018), available at https://americanprogress.org/issues/early-childhood/reports/2018/12/06/461643/americas-child-care-deserts-2018/.
  39. Rasheed Malik and others, “The Coronavirus Will Make Child Care Deserts Worse and Exacerbate Inequality,” Center for American Progress, June 22, 2020, available at https://americanprogress.org/issues/early-childhood/reports/2020/06/22/486433/coronavirus-will-make-child-care-deserts-worse-exacerbate-inequality/.

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Authors

Robin Bleiweis

Research Associate, Women’s Economic Security

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