Disparate Disruptions: Intersectional COVID-19 Employment Effects by Age, Gender, Education, and Race/Ethnicity

Abstract

These are unprecedented times, as the COVID-19 pandemic disrupts public health, social interaction, and employment attachments. Evidence to date has been about broad shifts in unemployment rates as a percent of the labor force. We draw on monthly Current Population Survey data to examine subpopulation changes in employment states across the life course, from January through April 2020. COVID-19 downturns produced disparate life-course impacts. There are increases in unemployment and being out of the workforce at all ages, but especially among young adults, with young women most at risk. Intersectional analyses document conjoint life-course vulnerabilities by gender, educational attainment, and race/ethnicity. For example, Black men aged 20–29 with a college degree experienced a 12.4 percentage point increase in being not in the labor force for other reasons (NILF-other). Individuals with less than a college degree in their 50s and 60s were more likely to become unemployed, regardless of race. And more non-college-educated Asian men in their 60s and 70s reported being retired (6.6 and 8.9 percentage point increases, respectively). Repercussions from the pandemic may well challenge assumptions and possibilities for older adults’ working longer.

In 1972, psychologist Angus Campbell and political scientist Phillip E. Converse teamed up to edit a book, The Human Meaning of Social Change. Their theme was that an “impressive change in rates, while a critical datum in itself, can be interpreted in very different ways when questions of human meaning of the change come to be asked” (Campbell & Converse, 1972, p. 6). For instance, they pointed out, “…there is little in the way of systematic information on historically significant populations…” (p. 7).

Today once again, “the face of the nation, and indeed the planet, is being remade,” (p. 7) but at warp-speed, across days, weeks, and months, not years. COVID-19 is producing a health and economic upheaval upending conventional assumptions about employment and security, but also exacerbating disparities in who is working. We know its labor-market effects are not felt evenly across the United States. And yet, like Campbell and Converse a half century ago, today we have no “systematic information” in terms of the pandemic’s effects on employment across historically disadvantaged groups at different ages and life-course stages.

In this research brief, we examine disparities in the employment effects of COVID-19 across intersecting subgroups in the United States. We use 10-year age groups as a rough index of life-course stage (Mortimer & Moen, 2016). Historically, these life stages have been broadly conceived as roughly isomorphic with age as follows: People in their 20s are moving into employment in what is commonly characterized as “emergent” adulthood (Arnett, 2000). Those in their 30s are increasingly in the labor market. Forty-somethings are most apt to be building families and careers, even as some in their 50s are reaching greater seniority or facing age discrimination. Individuals in their 60s are moving to and through work and retirement along different paths; those in their 70s and 80s are mostly retired.

We have previously argued that a new life stage is evolving, what we call “encore” adulthood, in the space opening up between conventional “prime” working years and the increasingly delayed frailties associated with old age (Moen, 2016a, b; Moen & Flood, 2013). Like the emerging adult life stage at the early end of the life course, we see encore adulthood as a time of risk but also of possibility (Moen, 2016a, b). Existing evidence prior to the COVID-19 pandemic is clear; more women and men in these encore years—their 50s, 60s, and 70s—are working longer (Fasbender, Wang, Voltmer, & Deller, 2016; Fisher, Ryan, Sonnega, & Naudé, 2016; Fry, 2019; Moen & Flood, 2013). Are they also at greater or lesser risk of unemployment and dropping out of the labor force due to COVID-19? And how does this differ by gender, education, and race/ethnicity, in combination with being in the conventional retirement decade of the 60s or being what is commonly referred to as an older worker in one’s 50s?

Our evidence shows that COVID-related forces have produced greatest risk for women and men in their 20s without a college degree. We also find there is more risk across the life course than is evident in traditional unemployment statistics. Specifically, in addition to adding to the unemployment rolls, the effects of COVID-19 are also causing larger shares of the population to be out of the labor force for “other” reasons, beyond disability and (self-defined) retirement reasons. We also find the pandemic-precipitated rise in both unemployment and being out of the workforce accentuates existing inequalities in who is not employed by gender, race/ethnicity, educational level, and age/life-course stage. Our intersectional approach (see below) also reveals less understood inequalities by race/ethnicity in combination with gender, age, and education.

DATA AND METHODS

We examine disparities in employment states for women and men in different age groups during the period when the nation was hit by both health and economic repercussions of the COVID-19 pandemic. To further understand disparities in its effects, we use intersectional analysis (Collins, 2015; Romero, 2018), charting the interconnecting experiences of women and men at different ages, with and without a college degree, and among different races/ethnicities immediately prior to and during the early months of the pandemic in the United States, from January to April 2020.

We analyze monthly labor force data from the Current Population Survey prepared by IPUMS (Flood, King, Rodgers, Ruggles, & Warren, 2020). The nationally representative sample we use in our analysis includes repeated cross-sections of individuals aged twenty and older from January 2020 through April 2020. Current Population Survey interviews occur each month during the week that contains the 19th, which explains why there is a spike in unemployment in April rather than in March, despite initial unemployment claims hitting record highs in the last 2 weeks of March. Individuals identifying as Native American or multiracial are omitted from our analyses of multi-layered intersections given small cell sizes. Table 1 provides a description of the sample.

Table 1.

Descriptive Statistics of the Sample

	Women		Men
	Observations	Weighted Percent	Observations	Weighted Percent
White	121,355	62.71	114,519	63.54
Black	17,908	12.56	13,792	11.20
Hispanic	21,453	15.51	19,836	16.44
Asian	10,498	6.64	9,276	6.22
Native American	1,707	0.76	1,525	0.74
Multiracial	2,612	1.82	2,388	1.86
20–29	24,986	17.31	25,369	18.61
30–39	29,429	17.42	28,086	18.36
40–49	27,669	16.07	25,712	16.56
50–59	30,588	16.96	28,381	17.34
60–69	31,133	16.12	27,851	15.49
70+	31,728	16.12	25,937	13.64
No college degree	111,881	63.37	105,883	65.50
College or above	63,652	36.63	55,453	34.50
Retireda	11,283	22.00	8,368	17.26
Disableda	2,567	5.27	2,306	5.34
NILF-othera	5,695	13.34	2,267	5.83
Unemployeda	832	2.02	1,062	2.66
Self-employeda	324	0.62	645	1.41
Part-time, economic/unknowna	727	1.71	522	1.27
Part-time, noneconomica	3,825	8.20	2,009	4.67
Full-timea	18,784	41.42	19,966	49.27
Full-time, long hoursa	2,507	5.42	5,577	12.30

Note. NILF-other = not in the labor force for other reasons.

^aObservations and weighted percent in January 2020.

Our main variable of interest is employment status. We identify nine distinct states of employment (Moen, Flood, & Wang, 2020). These are: retired, disabled, not in the labor force for other reasons (NILF-other), unemployed, self-employed, part-time for economic reasons/unknown, part-time for noneconomic reasons, full-time, and full-time long hours (50+ hr per week). We see dramatic changes in and pay particular attention to the proportions of people who are unemployed or NILF-other (e.g., in school, caregiving), given that the COVID-19 pandemic and subsequent public health policies led to significant reductions in economic activity and employment opportunities. To be clear, when we discuss the proportion of people unemployed in a particular subgroup, say men in their 50s, we report the fraction of that entire subsample (all men in their 50s) that is unemployed as opposed to reporting the official unemployment rate, which indicates the fraction of the sample in the labor force who are unemployed.

RESULTS

Our analyses show considerable intersectional variation by age, gender, education, and race/ethnicity in the effects of the COVID-19 pandemic on employment. We illustrate the variation with a series of tables and figures.

Age and Gender Disparities

Table 2 shows percentage point changes in employment states by gender and age for April 2020 compared to January 2020. Across all age groups, much of the job loss from January to April occurs in the form of exits from full-time work and long-hour work. For women and men across the age spectrum, there are also reductions in working part-time for noneconomic reasons, but little change in self-employment, being out of the workforce because of a disability, or being self-defined as retired. There are sharp increases in unemployment and being out of the workforce for reasons other than retirement or disability (hence NILF-other). Accordingly, we focus below on trends in unemployment and NILF-other for different subgroups at different stages of the life course, as operationalized by age.

Table 2.

Percentage Point Changes in Employment Status From January to April 2020

Women
	20–29	30–39	40–49	50–59	60–69	70+
Retired	0.2	0.1	−0.4	−0.3	−0.4	0.8
Disabled	−0.1	−0.2	0.3	−1.2	−0.6	−0.1
NILF-other	6.6	3.4	2.7	4.2	1.2	0.3
Unemployed	10.2	6.8	6.4	7.4	4.6	1.5
Self-employed	0.3	0.2	0.2	0.7	0.4	0.2
Part-time, economic/unknown	−0.1	0.5	0.8	0.6	0.8	0.4
Part-time, noneconomic	−4.8	−3.6	−3.2	−3.2	−3.8	−1.9
Full-time	−10.2	−6.1	−5.7	−6.6	−2.1	−0.9
Full-time, long hours	−2.0	−1.1	−1.2	−1.5	0.0	−0.3
Men
	20–29	30–39	40–49	50–59	60–69	70+
Retired	0.0	0.3	−0.4	−0.7	0.5	2.7
Disabled	−0.3	−0.2	0.3	−0.1	−1.6	−0.6
NILF-other	6.2	3.6	2.5	2.9	1.4	−0.2
Unemployed	8.5	6.8	6.4	6.2	4.1	1.7
Self-employed	0.1	0.2	0.5	0.3	0.1	0.2
Part-time, economic/unknown	0.5	0.0	0.3	0.7	0.8	0.4
Part-time, noneconomic	−3.8	−1.2	−0.6	−0.7	−2.1	−2.7
Full-time	−8.9	−6.1	−6.1	−6.0	−1.7	−1.2
Full-time, long hours	−2.2	−3.3	−2.8	−2.6	−1.5	−0.3

Note. NILF-other = not in the labor force for other reasons.

Figure 1 captures monthly proportions of men and women across age groups who are unemployed and NILF-other from January to April 2020. The percentage of people unemployed rises slightly from January to March, followed by a spike in April corresponding to when the economic effects from the pandemic were in full force. Emerging adults in their 20s saw the largest increases, with young women experiencing a 10.2-point increase (from 4 to 14.2%), and young men an 8.5-point increase in those reporting they are unemployed.

Figure 1.

Percent unemployed and not in the labor force (NILF) for other reasons. (a) Women, Unemployed; (b) Men, Unemployed; (c) Women, NILF for other reasons; (d) Men, NILF for other reasons.

Age-graded disparities became more pronounced following the pandemic. Specifically, following the large increase (Table 2) in unemployment for those in their 20s, gains in unemployment are progressively less sharp with age. Still, those in their 60s report significant increases. Despite the fact that many in this age group continue to define themselves as retired (over a third of men; over two in five women in Table 2), there is a 4.6 percentage point increase in unemployment for women in their 60s, and a 4.1 percentage point increase for men in this age group as a result of COVID-19’s impacts. Men and women in their 30s, 40s, and 50s fall between those in their 20s and 60s in terms of the share unemployed due to the pandemic, with

We also see considerable increases by April in those out of the labor force for “other” reasons across age groups from the 20s through the 50s. Women and men in their 20s are the most likely to experience an increase in NILF-other (a 6.6-point increase for women and 6.2-point increase for men). Declining proportions of those in their 20s who are out of the labor force for “other” reasons cite “school” as the reason for being out of the workforce (Appendix Tables D and E). And greater proportions give an “other” reason, something we return to in the discussion.

Greater Vulnerability of Those Without a College Degree

Table 3 shows disparities in employment participation by age, gender, and education in January, prior to COVID-19. Note that, even before the COVID-19 shock, those with a college degree were more likely to work and less likely to be unemployed or NILF-other. This holds true for both women and men across age groups. Figures 2 and 3 illustrate how a college degree is protective against COVID-driven dislocations. The numbers underlying these figures are in Tables 4 and 5 for women and men, respectively. These figures also illustrate intersecting age and education differences in the effects of COVID-19. Women and men in their 20s without a college degree are the most apt to experience an increase in both unemployment and being NILF-other from January to April. There is a minimal difference by college/no college education in COVID-driven unemployment effects for women in their 60s (increases in unemployment of 4.8 and 4.2 percentage points for women without and with a college degree, respectively). A college education is somewhat protective for men in their 60s, with non-college degree men reporting a 4.5 point increase in unemployment, compared to only a 3.2 point increase for college-degree men in their 60s. For men without a college degree who are younger than age 60, the percentage point increase in unemployment is 1.5 to 2 times higher compared to those with a college education, a 3- to 4-point educational gap in upturns in unemployment across the age spectrum from 20 to 60. In light of age bias (Gordon & Arvey, 2006), high levels of increased unemployment due to COVID-19 for women and men in their 50s without a college degree (8.3 and 7.6 percentage point increases for women and men, respectively) portend future difficulties in reemployment for these less-educated older workers as well as for even older workers in their 60s and 70s.

Table 3.

Percent in Each Employment State in January 2020, By Gender, Age, and Education

Women
		20–29	30–39	40–49	50–59	60–69	70+
No college degree	Retired	0.4	0.9	1.3	7.5	44.2	83.7
	Disabled	2.6	4.4	7.2	14.0	11.6	4.4
	NILF-other	27.3	24.8	19.0	13.1	7.1	2.2
	Unemployed	4.7	3.5	2.7	1.8	1.1	0.3
	Self-employed	0.1	0.8	0.9	0.7	0.6	0.1
	Part-time, economic/unknown	3.6	2.3	1.8	1.6	0.7	0.5
	Part-time, noneconomic	14.1	8.8	7.7	6.9	8.0	3.8
	Full-time	44.4	50.2	53.7	49.1	24.3	4.5
	Full-time, long hours	2.8	4.3	5.6	5.4	2.5	0.5
College or above	Retired	0.4	0.8	1.3	7.0	42.1	80.8
	Disabled	0.2	0.8	1.3	2.9	3.3	1.5
	NILF-other	13.3	14.5	13.8	8.2	4.7	1.6
	Unemployed	2.4	1.4	1.8	1.4	1.0	0.5
	Self-employed	0.3	0.7	1.2	1.0	1.3	0.4
	Part-time, economic/unknown	2.1	1.4	1.6	1.6	1.7	0.9
	Part-time, noneconomic	7.5	7.3	8.6	8.5	10.5	6.0
	Full-time	63.8	63.0	58.4	58.0	30.0	6.9
	Full-time, long hours	9.9	10.1	12.2	11.4	5.5	1.4
Men
		20–29	30–39	40–49	50–59	60–69	70+
No college degree	Retired	0.3	0.4	1.4	5.9	35.3	79.7
	Disabled	4.0	4.5	6.9	11.7	13.7	4.1
	NILF-other	18.1	6.3	5.0	4.1	2.8	1.7
	Unemployed	5.9	4.1	2.9	2.6	1.4	0.4
	Self-employed	0.8	1.6	1.7	2.2	1.6	0.8
	Part-time, economic/unknown	2.6	1.9	1.2	0.8	1.0	0.7
	Part-time, noneconomic	9.6	3.2	2.3	2.7	5.5	4.3
	Full-time	52.2	63.9	62.9	55.4	32.2	6.9
	Full-time, long hours	6.6	14.0	15.7	14.6	6.7	1.4
College or above	Retired	0.6	0.6	1.4	4.1	34.8	72.8
	Disabled	0.9	0.6	1.0	1.9	2.4	1.4
	NILF-other	11.8	2.9	2.8	2.3	1.5	1.4
	Unemployed	3.5	1.3	1.8	1.7	1.7	0.6
	Self-employed	0.5	1.1	1.4	1.8	1.8	1.6
	Part-time, economic/unknown	1.6	0.5	0.6	0.8	1.2	0.7
	Part-time, noneconomic	6.3	1.9	1.6	2.9	6.9	7.9
	Full-time	64.0	70.0	65.7	62.1	35.8	10.3
	Full-time, long hours	10.8	21.1	23.7	22.2	13.9	3.1

Note. NILF-other = not in the labor force for other reasons.

Figure 2.

Percent unemployed by educational attainment. (a) Women, no college degree; (b) Women, college and above; (c) Men, no college degree; (d) Men, college and above.

Figure 3.

Percent not in the labor force (NILF) for other reasons by educational attainment. (a) Women, no college degree; (b) Women, college and above; (c) Men, no college degree; (d) Men, college and above.

Table 4.

Percentage Point Changes in Employment Status From January to April 2020, Women

Women Without a College Degree
	20–29	30–39	40–49	50–59	60–69	70+
Retired	0.2	0.1	−0.3	−0.3	−1.2	0.8
Disabled	−0.3	−0.1	0.2	−1.5	−0.6	0.0
NILF-other	7.9	4.4	4.2	4.8	1.3	0.0
Unemployed	11.5	9.0	8.1	8.3	4.8	1.6
Self-employed	0.4	0.2	0.4	0.7	0.7	0.2
Part-time, economic/unknown	−0.4	0.5	1.1	0.9	1.2	0.5
Part-time, noneconomic	−6.2	−4.8	−3.4	−3.3	−3.6	−1.6
Full-time	−11.7	−8.1	−9.2	−8.4	−2.6	−1.2
Full-time, long hours	−1.4	−1.2	−1.2	−1.1	0.0	−0.2
Women With a College Degree or Above
	20–29	30–39	40–49	50–59	60–69	70+
Retired	0.1	0.0	−0.5	−0.3	1.2	0.9
Disabled	0.2	−0.3	0.6	−0.6	−0.4	−0.1
NILF-other	4.4	2.6	1.2	3.4	1.0	1.1
Unemployed	7.8	4.5	4.4	5.8	4.2	1.1
Self-employed	0.1	0.2	−0.1	0.6	−0.3	0.2
Part-time, economic/unknown	0.4	0.6	0.5	0.2	0.0	0.3
Part-time, noneconomic	−1.9	−2.3	−2.9	−3.2	−4.4	−2.8
Full-time	−7.5	−4.1	−1.7	−3.8	−1.2	−0.2
Full-time, long hours	−3.5	−1.2	−1.5	−2.1	−0.1	−0.6

Note. NILF-other = not in the labor force for other reasons.

Table 5.

Percentage Point Changes in Employment Status From January to April 2020, Men

Men Without a College Degree
	20–29	30–39	40–49	50–59	60–69	70+
Retired	0.0	0.5	−0.4	−1.2	2.1	2.3
Disabled	−0.3	−0.4	0.6	−0.2	−2.4	−0.8
NILF-other	7.2	4.4	3.3	3.2	1.5	−0.4
Unemployed	9.5	8.4	8.2	7.6	4.5	1.7
Self-employed	0.0	0.2	0.8	0.2	−0.1	0.3
Part-time, economic/unknown	0.6	−0.2	0.2	0.9	0.9	0.0
Part-time, noneconomic	−4.6	−1.9	−0.7	−0.5	−2.1	−2.4
Full-time	−9.9	−8.1	−9.6	−7.2	−3.8	−0.7
Full-time, long hours	−2.5	−2.9	−2.4	−2.8	−0.7	−0.1
Men With a College Degree or Above
	20–29	30–39	40–49	50–59	60–69	70+
Retired	0.0	0.0	−0.4	0.2	−2.4	3.3
Disabled	−0.3	0.2	−0.1	0.1	0.2	−0.2
NILF-other	3.7	2.5	1.0	2.2	1.1	0.0
Unemployed	5.6	4.7	3.4	3.6	3.2	1.8
Self-employed	0.5	0.3	0.0	0.3	0.5	−0.1
Part-time, economic/unknown	0.2	0.2	0.5	0.3	0.5	1.0
Part-time, noneconomic	−1.6	−0.2	−0.3	−1.1	−2.2	−3.1
Full-time	−6.7	−3.6	−0.4	−3.5	2.2	−2.2
Full-time, long hours	−1.5	−4.2	−3.6	−2.2	−3.0	−0.6

Note. NILF-other = not in the labor force for other reasons.

Greater Vulnerability by Race/Ethnicity

Figures 4–7 and Appendix Tables A–C highlight similarities and disparities along racial/ethnic lines of COVID-19 effects on employment. General patterns of increases in unemployment and NILF-other are similar for all groups over the first 4 months of 2020 (Figures 4–7). But recall the different starting points, with Black women and men, particularly those in their 20s and 30s with less than a college degree, having a higher chance of being unemployed before the pandemic (Appendix Table A).

Figure 4.

Percent of women unemployed by age and race. (a) Women aged 20–29; (b) Women aged 30–39; (c) Women aged 40–49; (d) Women aged 50–59; (e) Women aged 60–69; (f) Women age 70+.

Figure 7.

Percent of men not in the labor force (NILF) for other reasons by age and race. (a) Men aged 20–29; (b) Men aged 30–39; (c) Men aged 40–49; (d) Men aged 50–59; (e) Men aged 60–69; (f) Men age 70+.

Figure 5.

Percent of men unemployed by age and race. (a) Men aged 20–29; (b) Men aged 30–39; (c) Men aged 40–49; (d) Men aged 50–59; (e) Men aged 60–69; (f) Men age 70+.

Figure 6.

Percent of women not in the labor force (NILF) for other reasons by age and race. (a) Women aged 20–29; (b) Women aged 30–39; (c) Women aged 40–49; (d) Women aged 50–59; (e) Women aged 60–69; (f) Women age 70+.

Asian and Hispanic women are most likely to move to NILF-other after the COVID pandemic, followed by White and Black women (Appendix Table B). In fact, Asian women in their 30s, 40s, and 50s without a college degree have the highest levels of NILF-other by April (Appendix Table B), possibly taking on or focusing exclusively on family-care responsibilities (Appendix Tables D and E).

Educational attainment plays a large role in racial disparities, with Blacks and Hispanics less likely and Asians and Whites the most likely to have a college degree. For women in their 20s, the increase in unemployment appears to be a more function of race and ethnicity than education. Black women without a college degree in their 20s have a 12.4 percentage point increase in unemployment, even as unemployment of Hispanic women in this age/education subgroup rose by 11.1 points and that of Asian women 10.4 points (compared to a 12.4 percentage point increase by White women). But there are differences by both education and race/ethnicity for men in their 20s. Black and Hispanic men without a college degree in their 20s experience an 11.4 percentage point increase in unemployment, with less-educated Asian men report an astounding 15.8 increase. These increases are in contrast to White men in this age/education subgroup who experienced an 8.8 percentage point increase.

Asian men without a college degree seem especially vulnerable, with unemployment rate changes from January to April, ranging from 15.8 percentage points in the 20s to 12.4 in the 60s. More generally, across the age spectrum and within each race/ethnic subgroup, men and women without a college degree experience higher unemployment as a result of the pandemic compared to counterparts with a college degree (Appendix Tables F and G).

However, some with college degrees are also at risk. Variations for college-educated women in their 20s are highest for Hispanic women (11.4 percentage point increase) and lowest for Black women (2.1 percentage point increase). Black and Hispanic men in their 20s with a college degree have higher spikes in unemployment (8.2 and 12.7 percentage points, respectively, compared to only a 3.9 increase for college-educated Asian men and a 4.2 increase for college-educated White men). College-educated Hispanic women and men in their 20s move from the lowest likelihood of unemployment in January to the highest by April. And, except for those in their 30s, this continues to be the case across the life course. College-educated Hispanic men in their 40s have effectively no unemployment in January; this rises to 9.7% by April, even as women in this subgroup experience a 7 percentage point increase. This trend persists for older individuals, with a 9-point increase in unemployment for college-educated Hispanic women in their 50s and 60s. Hispanic men with college degrees in their 50s have a 5.9 percentage point increase and those in their 60s a 6.4-point increase in unemployment.

Turning to those who are NILF-other, both proportions and changes in them vary by gender and age as well as by education and race/ethnicity. In January 2020, over a fourth of women in their 20s who were not college graduates reported being NILF-other, ranging from 25.3% of Blacks, 25.8% of Whites, 28.9% of Hispanics to 47% of Asians. Considerably fewer of those with college degrees (10.4% Whites to 26.4% Asians) were NILF-other. By April, there was no appreciable change in the proportion of Asian women in their 20s who were NILF-other but this rose by 10 percentage points for Hispanic women in their 20s without a college degree and almost 10 points for Hispanic women with a college degree. The largest increase was for Asian women in their 30s without a college degree, increasing from 28% to 41.1%.

The story is different for men. Black and Asian men in their 20s are especially likely to be NILF-other compared to their White and Hispanic counterparts, even if they have a college degree (Appendix Table B). Black men aged 20–29 with a college degree or above experience a 12.5 percentage point increase in the proportion NILF-other, far outpacing their Hispanic, White, and Asian counterparts (13.3% in January vs. 25.8% in April; see Appendix Table B). By contrast, Hispanic young men without a college degree report a large COVID-driven increase (10.8 percentage points) in being out of the workforce (increasing from 13.0% to 23.8%), Hispanic men with a college degree in their 30s increased from negligible proportions NILF-other (0.9%) to over 1 in 10 (11.4%).

At the other end of the life course, we also find disparities in the effects of COVID-19 on unemployment and leaving the workforce by age/gender/education/racial intersections. Asian men in their 50s and 60s without a college degree are at highest risk of exiting full-time jobs and moving into unemployment (13.2 and 12.4 percentage point increases in the 50s and 60s, respectively, see Appendix Table G).

Overall, we see little increase in retirement as a result of the COVID pandemic, at least at its onset (we have data only through April 2020). But there is one subgroup exception: Asian men in their 60s and 70s without a college degree experience increases in the proportion reporting being retired (6.6 and 8.9 percentage point increases, respectively—Appendix Table G. By contrast, there is a smaller percentage point increase in being retired for non-college degree Black (6.6%) and Hispanic (3.01%) men in April compared to January.

DISCUSSION

These findings underscore the different and unequal effects of COVID-19 on employment status. They also illustrate both enduring and new inequalities, as well as the importance of and need for additional intersectional analyses across overlapping social categories. First, COVID-19 employment effects are unevenly distributed across the life course as well as by other social markers. Those in their 20s are particularly at risk. Nevertheless, even women and men in their 50s and 60s experience marked increases in unemployment.

Individuals with college degrees have been somewhat protected from COVID-driven unemployment effects, a protection compounded by various combinations of gender and race/ethnicity. The most vulnerable to unemployment increases have been young women and men without a college degree, especially Hispanic and Black women, and Asian and Hispanic men. But college degrees are also unevenly distributed, with Asians the most apt to be college-educated at every age/life stage, followed by Whites (data not shown). Yet, Asian men in their 30s, 40s, 50s, and 60s who did not graduate from college report remarkable increases in unemployment.

This points to something of paradox with regard to Asians; Asians are the most educated subgroup in the United States. Asians holding college degrees have the lowest unemployment rates prior to COVID-19 and are the most protected against the unemployment ravages of this pandemic. But simultaneously, Asian men without a college degree experience its most deleterious effects. What is clearly needed are studies of life-course pathways of different subgroups of Asians—paths to a college education, and to the cumulative employment advantages/disadvantages ensuing from getting or failing to obtain a college degree.

A second finding is that the effects of a COVID economy extend beyond increased unemployment; some people in the United States are also moving out of the workforce for “other” reasons. NILF-other increases by 10 percentage points for Hispanic men and women in their 20s without a college degree, and for Black and Hispanic young (20s) women with college degrees. Why are so many in their 20s moving to NILF-other as a result of this economic shock? One explanation could be that working college students are losing their jobs, and as a consequence, see themselves as out of the labor force and just in college. However, the proportions saying they are NILF-other because they are in school decreases between January and April (Appendix Tables D and E). Neither is caring for family members a likely explanation, as the proportions citing that reason declined slightly or stayed roughly the same by April.

It could be that many of these emerging adults have little experience with layoffs, and see themselves as suddenly out of the labor force precisely because they have lost their jobs and see little hope of getting a new one in this COVID economy. They might thus not see themselves as both not employed and looking for work, the definition of being unemployed. It is possible that they moved back home, and living with one’s parents makes it easier to just be “out of the labor force.” The proportions of those in their 20s who describe themselves as “household heads” declined somewhat from January to April 2020, even as the proportions who describe themselves as a “child” of the household head increased (data available from authors). This relationship between employment change and household change needs further examination.

Another group with large proportions moving out of the workforce for other reasons are women, especially Hispanic and Asian women. These could be those in “nonessential” jobs that are not conducive to remote working arrangements mandated by most governors in response to the health crisis and hence laid off. Instead of actively seeking another job when there are few positions available, they could be turning to the “job” they already have at home. The fact that women are historically the informal care providers of society suggests they may well be moving toward an exclusive focus on providing family care—to preschoolers and school-agers suddenly spending all their time at home. Or they could be caring for older parents or partners with health conditions rendering them especially vulnerable to COVID-19. Clearly, research is needed on why the proportions of women moving out of the workforce, not because they are disabled or retired but for “other” reasons, differ by race and ethnicity.

We find considerable increases, in every age group/stage of the life course, in those NILF-other not providing a reason for it (Appendix Tables D and E). In other words, they report being out of the workforce not because of retirement or disability but for other reasons (NILF-other) and then, when given a list of possible explanations (such as being in school or caring for family members), they report it is for some “other” reason. This suggests they may be laid off but not actively looking for work. If they were looking for work, they would have fallen into the “unemployed” category. Research is clearly needed to unpack the increases in this catch-all NILF-category and how it varies within and across subgroups throughout the life course.

A third key finding is, as the figures show, the pandemic has widened the age spread in both unemployment and NILF-other for women and men. The spike in unemployment among emerging adults may reflect their lack of any experience/tenure advantage in what are most probably entry-level jobs and the first to go with the COVID outbreak. Still, many encore adult women and men in their 50s and 60s also find themselves at considerable risk of being unemployed. These differential age/life stage impacts offer a key arena for future research. Conversely, there is little change in the proportions describing themselves as self-employed, or of not working due to retirement or disability, at least in this 4-month period. The “retirement” pathway may shift over subsequent months, but the major changes in employment status through April were between working, being unemployed, and being NILF-other.

These unprecedented times have enormous implications for individual work lives, labor-market policies and practices, and public safety nets. They may also challenge how we measure employment and unemployment opportunities and constraints (Hyde & Dingermans, 2017; Voss, Snih, Li, Hung, Richards, 2020). Our findings on NILF-other suggests the need for thinking about and studying who at different ages and life stages are essentially a type of discouraged worker, dropping out of the workforce in the face of the COVID downturn and thus not captured in unemployment statistics.

The evidence from this study is most dramatic in demonstrating COVID-driven impacts on the labor-market attachments of emerging adults, most likely in entry-level jobs. But equally important for research and policy are the COVID consequences for older workers in their 50s, 60s, and 70s. Will discrimination in hiring and retention be accentuated as older adults are seen as having greater health risks than younger workers and job applicants? We have previously described (Moen, 2016a,b) a new stage, one rife with risks but also possibilities for encore engagement in paid and unpaid work. Will the COVID pandemic render many older adults discouraged about finding such options, eventually pushing them out of work permanently? Will jobs or community service opportunities in this encore life stage of the 50s, 60s, and 70s become even further constrained depending on one’s gender, education, and race/ethnicity? Even before COVID-19, there was insufficient understanding of factors that predict—and produce disparities in—older adults’ participation in paid work. Repercussions from the pandemic may well contest assumptions, possibilities, and policies promoting working longer (McNamara & Williamson, 2013; Munnell & Sass, 2008) as older adults are seen as more vulnerable to COVID-19 and possibly at greater health risk in the workplace. But this is a story in progress, requiring additional research as the pandemic and its labor market and policy consequences continue to unfold.

Appendix Table A. Percent Unemployed by Education, Gender, Age, and Race

Women
		No College				College+
		January	February	March	April	January	February	March	April
20–29	White	4.0	2.8	4.0	16.4	1.7	1.7	2.0	10.0
	Black	7.9	7.0	7.4	17.4	5.7	3.3	0.7	7.8
	Hispanic	3.9	3.9	5.1	15.0	3.0	3.0	5.4	14.4
	Asian	3.3	1.9	5.8	13.7	2.8	3.8	1.4	8.9
30–39	White	2.6	2.6	2.5	12.3	0.9	1.0	1.7	5.9
	Black	5.3	5.8	6.6	13.7	2.0	2.5	1.7	8.3
	Hispanic	4.6	3.2	4.6	11.9	1.4	0.7	0.3	4.5
	Asian	0.4	1.2	2.1	9.5	3.3	1.9	5.2	5.2
40–49	White	2.0	2.2	2.1	9.5	1.7	1.7	2.2	6.0
	Black	4.9	2.6	1.8	10.6	1.8	0.9	1.4	6.2
	Hispanic	3.0	3.9	3.4	11.7	2.6	3.5	4.9	9.6
	Asian	3.1	3.0	5.8	17.4	1.5	3.3	2.5	4.1
50–59	White	1.4	1.3	1.9	8.6	1.3	0.9	2.0	6.6
	Black	3.0	1.3	4.4	9.8	2.3	1.1	2.0	6.4
	Hispanic	2.5	2.6	3.7	13.9	1.1	1.1	1.6	10.1
	Asian	0.6	0.0	1.6	16.7	1.3	0.9	2.0	9.5
60–69	White	0.9	0.9	1.1	5.9	0.7	0.9	1.2	5.5
	Black	1.3	0.8	1.9	6.0	2.5	2.2	0.8	2.4
	Hispanic	1.5	1.7	1.1	5.6	0.0	4.2	3.4	9.2
	Asian	2.0	0.0	2.1	8.3	3.2	1.4	1.5	1.7
70+	White	0.3	0.2	0.3	1.6	0.4	0.5	0.3	1.7
	Black	0.3	0.9	0.5	2.2	0.1	1.1	0.0	1.2
	Hispanic	0.0	0.2	0.4	3.1	1.8	0.0	0.0	1.4
	Asian	0.0	0.0	0.0	0.1	1.2	0.0	0.0	0.9
Men
20–29	White	5.4	5.0	5.2	14.2	3.5	2.7	2.9	8.3
	Black	9.6	7.9	11.2	16.5	3.8	7.0	1.2	12.1
	Hispanic	5.1	3.9	7.1	16.5	1.6	1.2	6.1	14.3
	Asian	2.5	3.9	4.9	18.3	5.2	2.6	4.5	7.0
30–39	White	3.3	4.2	4.1	11.7	1.2	1.7	2.2	5.9
	Black	7.6	7.9	5.8	11.3	1.8	3.1	3.0	9.6
	Hispanic	4.1	3.7	4.3	13.7	1.3	0.7	1.0	4.1
	Asian	2.2	2.5	4.9	17.7	1.5	1.9	1.4	5.4
40–49	White	2.0	2.2	2.9	8.9	1.7	1.7	1.8	4.9
	Black	3.3	7.4	6.0	12.9	2.5	0.1	2.1	4.0
	Hispanic	4.3	4.3	5.0	13.7	0.0	0.2	2.4	9.7
	Asian	0.9	0.0	2.8	11.5	3.1	1.7	0.9	3.9
50–59	White	2.3	2.3	2.0	9.3	1.9	2.1	2.5	5.2
	Black	3.1	2.6	4.4	9.9	0.7	0.6	1.6	3.8
	Hispanic	3.9	4.2	4.6	12.5	1.3	2.3	2.1	7.3
	Asian	0.3	2.0	4.2	13.6	1.9	1.0	2.6	5.8
60–69	White	1.5	2.1	2.0	5.3	1.6	1.4	1.5	4.6
	Black	0.5	1.1	2.0	5.1	1.8	1.1	0.0	3.5
	Hispanic	1.9	2.2	3.5	8.9	3.1	1.1	1.2	9.5
	Asian	1.9	0.1	1.6	14.2	1.9	1.1	3.2	6.3
70+	White	0.3	0.2	0.5	2.1	0.5	0.8	0.6	2.4
	Black	0.0	1.2	1.3	2.8	1.3	1.6	1.9	2.1
	Hispanic	2.0	1.4	0.9	2.5	3.9	0.0	0.0	3.0
	Asian	0.0	0.0	0.0	1.7	0.0	1.3	4.0	1.7

Appendix Table B. Percent NILF for Other Reasons by Gender, Age, and Race

Women
		No College				College+
		January	February	March	April	January	February	March	April
20–29	White	25.8	26.4	29.0	33.7	10.4	10.9	11.4	13.9
	Black	25.3	26.3	29.8	31.4	12.4	17.6	20.0	23.1
	Hispanic	28.9	28.8	32.0	38.9	14.1	11.8	19.7	23.6
	Asian	44.7	39.6	40.7	43.3	26.4	21.6	24.4	25.8
30–39	White	22.5	22.1	24.1	25.1	12.6	12.4	13.0	15.6
	Black	17.5	17.1	16.6	19.7	9.1	8.2	8.6	12.7
	Hispanic	32.6	32.5	34.5	39.8	15.9	18.6	17.7	19.8
	Asian	28.0	33.3	33.5	41.1	27.8	26.3	26.7	27.7
40–49	White	15.0	17.1	16.7	19.6	12.1	11.7	12.0	14.0
	Black	12.8	14.0	10.6	20.2	9.4	6.2	9.6	11.3
	Hispanic	29.0	27.8	30.4	31.6	14.7	11.8	11.4	14.1
	Asian	24.1	21.0	21.4	29.5	26.2	26.1	24.9	24.0
50–59	White	11.3	11.7	11.9	14.1	6.9	7.8	7.5	10.5
	Black	7.2	9.2	8.0	14.9	5.1	6.7	5.8	8.0
	Hispanic	20.9	22.2	23.9	28.6	11.2	9.6	9.5	18.3
	Asian	21.6	22.6	22.0	27.1	20.0	19.5	17.6	19.9
60–69	White	5.4	5.5	5.5	7.5	4.1	3.4	3.3	4.8
	Black	5.6	4.5	4.5	4.7	5.2	2.9	3.7	6.0
	Hispanic	16.4	13.0	11.7	13.7	6.6	8.5	5.1	10.4
	Asian	11.9	11.9	19.9	19.0	9.5	5.2	8.7	10.9
70+	White	1.4	1.3	1.5	1.5	1.1	1.0	1.1	2.4
	Black	2.7	3.1	5.4	3.9	0.3	2.3	1.0	2.2
	Hispanic	5.5	5.9	4.7	5.5	3.1	4.6	7.9	7.3
	Asian	7.7	5.6	7.2	5.1	10.1	3.0	1.6	6.2
Men
20–29	White	17.0	16.1	17.9	22.4	8.4	8.2	8.7	11.6
	Black	22.9	22.4	22.2	28.7	13.3	7.7	10.1	25.8
	Hispanic	13.0	16.3	18.5	23.8	18.3	15.4	11.8	16.2
	Asian	41.9	43.8	44.3	49.8	20.1	17.4	17.2	23.1
30–39	White	5.1	5.6	5.1	8.4	2.1	2.5	2.7	3.8
	Black	10.1	11.9	11.8	19.7	5.1	3.4	5.3	8.5
	Hispanic	5.6	5.6	7.1	9.4	0.9	2.9	3.5	11.4
	Asian	9.8	11.4	11.8	18.0	6.4	4.9	4.2	6.1
40–49	White	3.9	4.6	5.0	7.3	3.1	2.3	2.6	2.8
	Black	11.1	10.6	8.2	13.4	3.1	4.1	3.3	5.7
	Hispanic	4.3	5.4	5.7	6.8	1.0	1.0	1.3	5.4
	Asian	7.0	5.8	6.2	12.5	2.4	1.1	3.8	6.3
50–59	White	4.0	3.6	3.6	6.0	2.1	2.1	2.6	4.3
	Black	6.8	7.9	6.5	9.5	1.8	1.4	1.1	4.5
	Hispanic	2.5	4.1	4.0	9.0	3.1	1.2	2.2	3.4
	Asian	4.0	6.1	9.4	11.4	3.1	3.5	6.7	7.1
60–69	White	2.0	2.3	2.4	3.3	1.4	1.1	1.6	2.1
	Black	4.9	3.8	5.4	7.0	4.8	4.7	2.6	5.8
	Hispanic	4.1	4.0	2.7	6.0	0.2	3.2	2.7	4.4
	Asian	3.7	3.7	3.3	7.3	1.8	3.0	3.8	2.9
70+	White	1.4	1.1	1.0	1.3	1.1	1.1	1.0	1.4
	Black	3.9	1.9	2.8	1.4	6.6	0.5	0.1	3.0
	Hispanic	1.1	2.9	1.9	1.1	0.0	3.3	6.1	2.5
	Asian	2.7	2.2	3.0	1.4	2.6	4.7	1.6	1.4

Note. NILF-other = not in the labor force.

Appendix Table C. Number of Observations by Gender, Race, Age, and Education

Women
		No College Degree		College or Above
		Observations	Weighted Percent	Observations	Weighted Percent
White	20–29	9,386	5.83	5,518	3.56
	30–39	8,963	4.59	9,814	5.27
	40–49	9,139	4.75	8,332	4.61
	50–59	12,996	6.66	8,252	4.51
	60–69	15,259	7.48	8,230	4.09
	70+	18,383	8.92	7,083	3.59
	Total	74,126	38.24	47,229	25.63
Black	20–29	2,188	1.98	640	0.59
	30–39	2,008	1.58	1,057	0.84
	40–49	1,939	1.35	1,077	0.81
	50–59	2,194	1.42	1,019	0.70
	60–69	2,359	1.44	840	0.55
	70+	2,097	1.25	490	0.29
	Total	12,785	9.02	5,123	3.77
Hispanic	20–29	3,682	3.00	870	0.69
	30–39	3,387	2.61	1,136	0.84
	40–49	3,403	2.41	990	0.71
	50–59	2,938	2.03	709	0.50
	60–69	1,906	1.35	453	0.30
	70+	1,700	1.15	279	0.21
	Total	17,016	12.55	4,437	3.26
Asian	20–29	766	0.52	977	0.74
	30–39	662	0.40	1,511	1.04
	40–49	781	0.45	1,277	0.83
	50–59	887	0.51	919	0.58
	60–69	829	0.49	631	0.43
	70+	862	0.52	396	0.25
	Total	4,787	2.89	5,711	3.88
Native American	20–29	247	0.12	25	0.02
	30–39	307	0.14	36	0.02
	40–49	203	0.10	74	0.04
	50–59	258	0.11	67	0.03
	60–69	227	0.08	56	0.02
	70+	176	0.07	31	0.02
	Total	1,418	0.62	289	0.15
Men
		No College Degree		College or Above
		Observations	Weighted Percent	Observations	Weighted Percent
White	20–29	11,277	7.19	4,366	3.03
	30–39	10,560	5.78	8,074	4.73
	40–49	10,098	5.75	6,897	4.14
	50–59	12,860	7.26	7,381	4.47
	60–69	13,794	7.18	7,864	4.35
	70+	13,136	6.54	8,212	4.31
	Total	71,725	39.71	42,794	25.04
Black	20–29	2,092	2.11	375	0.46
	30–39	1,618	1.57	708	0.67
	40–49	1,623	1.33	719	0.61
	50–59	2,005	1.46	606	0.49
	60–69	1,889	1.30	462	0.34
	70+	1,382	0.88	313	0.21
	Total	10,609	8.64	3,183	2.76
Hispanic	20–29	3,972	3.51	588	0.55
	30–39	3,567	3.23	795	0.70
	40–49	3,231	2.77	708	0.64
	50–59	2,655	2.10	630	0.51
	60–69	1,779	1.32	407	0.32
	70+	1,227	0.89	277	0.21
	Total	16,431	13.82	3,405	2.93
Asian	20–29	905	0.62	893	0.70
	30–39	627	0.41	1,322	1.00
	40–49	609	0.38	1,157	0.87
	50–59	802	0.45	821	0.56
	60–69	618	0.37	513	0.34
	70+	546	0.33	463	0.32
	Total	4,107	2.57	5,169	3.77
Native American	20–29	256	0.14	28	0.03
	30–39	246	0.13	38	0.02
	40–49	183	0.09	63	0.03
	50–59	253	0.12	37	0.02
	60–69	217	0.08	45	0.02
	70+	127	0.05	32	0.01
	Total	1,282	0.62	243	0.13

Appendix Table D. Reason for not Being in the Labor Force in January if NILF for Other Reasons

Women
		20–29	30–39	40–49	50–59	60–69	70+
No college degree	Disabled	1.1	2.0	3.3	7.8	9.1	7.9
	Ill	0.5	0.9	2.8	3.4	4.0	1.9
	In school	44.5	7.9	5.0	2.1	2.3	4.9
	Taking care of house/family	47.2	85.0	83.1	79.3	66.2	53.2
	Other	6.6	4.2	5.3	5.4	5.6	13.3
	Blank	0.2	0.1	0.4	2.1	12.7	18.9
College or above	Disabled	0.9	0.0	0.8	3.3	5.5	3.9
	Ill	0.3	0.7	1.1	3.0	3.5	0.0
	In school	48.1	9.9	5.7	3.0	0.5	29.5
	Taking care of house/family	35.8	84.4	88.1	83.9	69.8	30.0
	Other	14.9	4.4	4.1	6.3	7.1	11.8
	Blank	0.0	0.6	0.3	0.4	13.7	24.8
Men
		20–29	30–39	40–49	50–59	60–69	70+
No college degree	Disabled	2.5	13.8	15.9	23.4	23.6	26.8
	Ill	2.6	4.1	2.5	11.1	6.2	6.1
	In school	68.1	21.3	13.5	9.4	1.1	6.8
	Taking care of house/family	9.8	29.0	30.4	25.5	19.1	14.2
	Other	17.0	31.7	36.9	27.5	27.1	19.1
	Blank	0.0	0.0	0.9	3.0	22.9	27.0
College or above	Disabled	0.6	1.6	5.2	11.3	17.9	21.3
	Ill	0.0	2.6	4.6	3.1	5.6	0.0
	In school	74.3	34.9	15.2	10.4	3.1	3.9
	Taking care of house/family	7.1	25.6	38.9	32.0	16.9	18.2
	Other	18.0	35.3	33.2	36.7	13.8	4.4
	Blank	0.0	0.0	2.8	6.5	42.7	52.2

Note. NILF-other = not in the labor force.

Appendix Table E. Reason for not Being in the Labor Force in April if NILF for Other Reasons

Women
		20–29	30–39	40–49	50–59	60–69	70+
No college degree	Disabled	1.1	1.4	3.7	5.1	6.3	6.7
	Ill	0.8	0.9	1.7	1.8	1.9	3.8
	In school	43.3	6.0	3.7	2.1	1.1	0.7
	Taking care of house/family	40.5	80.7	77.7	72.8	64.8	51.3
	Other	14.1	10.4	13.1	17.0	20.4	20.9
	Blank	0.2	0.6	0.1	1.1	5.5	16.6
College or above	Disabled	1.9	0.1	1.9	1.7	4.9	6.7
	Ill	0.0	0.3	1.6	1.6	1.6	0.0
	In school	44.2	9.9	4.2	3.3	0.6	0.7
	Taking care of house/family	34.3	77.0	80.9	70.4	49.0	41.9
	Other	19.6	11.9	10.5	22.0	33.3	34.7
	Blank	0.0	0.8	0.9	1.0	10.7	16.0
Men
		20–29	30–39	40–49	50–59	60–69	70+
No college degree	Disabled	2.5	5.1	6.0	12.6	16.8	5.6
	Ill	1.7	3.0	2.9	4.7	3.1	5.1
	In school	58.1	12.9	7.9	7.9	3.1	2.1
	Taking care of house/family	10.8	29.4	36.1	22.0	16.8	4.6
	Other	26.7	48.7	46.9	50.4	48.1	53.4
	Blank	0.2	0.9	0.4	2.4	12.1	29.2
College or above	Disabled	0.8	0.7	6.7	5.8	4.7	13.8
	Ill	2.9	3.8	3.7	4.4	0.0	9.7
	In school	52.0	21.7	6.6	5.3	2.3	0.0
	Taking care of house/family	8.8	22.3	32.1	24.9	23.2	12.5
	Other	34.4	51.5	49.1	57.3	48.4	42.2
	Blank	1.0	0.0	1.8	2.2	21.4	21.7

Note. NILF = not in the labor force.

Appendix Table F. Percentage Point Changes in Employment Status, January to April, by Race, Women

Women Without a College Degree
		20–29	30–39	40–49	50–59	60–69	70+
Women With a College Degree or Above
		20–29	30–39	40–49	50–59	60–69	70+
White women	Retired	0.2	0.3	0.1	0.1	−1.9	0.0
	Disabled	0.2	−0.2	0.4	−2.0	−1.3	0.3
	NILF-other	7.9	2.6	4.6	2.8	2.1	0.1
	Unemployed	12.4	9.7	7.5	7.2	5.0	1.3
	Self-employed	0.3	0.0	0.2	0.8	0.5	0.2
	Part-time, economic/unknown	0.0	1.1	0.8	0.9	1.3	0.5
	Part-time, noneconomic	−7.6	−4.6	−3.8	−3.6	−3.9	−1.8
	Full-time	−11.1	−8.0	−8.2	−5.1	−2.1	−0.3
	Full-time, long hours	−2.4	−0.9	−1.7	−0.9	0.2	−0.3
Black women	Retired	1.2	−0.4	−0.7	−0.3	−1.5	2.2
	Disabled	0.1	2.9	1.0	0.7	1.8	−1.1
	NILF-other	6.1	2.1	7.4	7.7	−0.9	1.2
	Unemployed	9.5	8.4	5.8	6.8	4.8	1.9
	Self-employed	0.3	−0.7	0.3	1.3	1.0	0.0
	Part-time, economic/unknown	−1.6	−2.3	1.8	0.0	0.5	0.6
	Part-time, noneconomic	−0.7	−3.2	−2.1	−2.7	−1.2	−0.8
	Full-time	−14.4	−4.5	−13.1	−13.0	−2.9	−3.5
	Full-time, long hours	−0.5	−2.2	−0.4	−0.6	−1.6	−0.5
Hispanic women	Retired	0.0	0.2	−0.3	−0.8	1.8	4.9
	Disabled	−0.7	−1.7	0.0	−2.4	0.8	−1.2
	NILF-other	10.0	7.1	2.6	7.6	−2.7	0.0
	Unemployed	11.0	7.3	8.7	11.5	4.0	3.1
	Self-employed	0.6	1.0	0.7	−0.1	0.0	0.2
	Part-time, economic/unknown	−0.4	1.4	1.1	0.9	1.9	0.5
	Part-time, noneconomic	−7.3	−5.8	−3.8	−2.1	−3.4	−1.8
	Full-time	−12.8	−8.6	−8.3	−13.6	−3.0	−6.2
	Full-time, long hours	−0.5	−1.1	−0.6	−0.9	0.5	0.5
Asian women	Retired	−2.6	0.1	−2.6	−0.8	−2.9	8.6
	Disabled	−1.8	0.1	−3.6	0.8	1.9	−3.0
	NILF-other	−1.4	13.1	5.4	5.5	7.1	−2.6
	Unemployed	10.5	9.1	14.2	16.1	6.3	0.1
	Self-employed	0.7	2.0	0.6	0.7	3.3	0.0
	Part-time, economic/unknown	2.2	1.0	1.2	4.2	−0.5	0.0
	Part-time, noneconomic	−2.6	−8.6	−0.7	−1.8	−7.3	−1.0
	Full-time	−5.2	−13.9	−11.8	−21.4	−8.3	−2.2
	Full-time, long hours	0.2	−3.0	−2.8	−3.3	0.4	0.0
Women With a College Degree or Above
		20–29	30–39	40–49	50–59	60–69	70+
White women	Retired	0.0	0.1	−0.2	0.6	1.2	0.5
	Disabled	0.3	−0.1	0.1	−0.3	0.5	−0.1
	NILF-other	3.5	3.0	1.9	3.6	0.8	1.3
	Unemployed	8.2	4.9	4.4	5.3	4.8	1.3
	Self-employed	0.2	0.4	0.1	0.8	0.0	0.0
	Part-time, economic/unknown	1.1	1.0	0.3	−0.1	0.4	0.4
	Part-time, noneconomic	−2.1	−3.0	−3.7	−3.4	−4.8	−2.7
	Full-time	−7.9	−3.7	−0.6	−3.5	−2.8	−0.3
	Full-time, long hours	−3.3	−2.5	−2.3	−2.9	−0.1	−0.4
Black Women	Retired	0.3	0.8	−2.7	−3.2	4.8	−0.2
	Disabled	0.1	−1.2	1.6	−0.3	−2.3	−1.5
	NILF-other	10.8	3.6	1.9	2.8	0.8	1.9
	Unemployed	2.1	6.3	4.3	4.1	−0.1	1.1
	Self-employed	−0.5	−1.0	−1.7	−0.7	−0.8	0.0
	Part-time, economic/unknown	0.8	−0.5	0.6	1.3	−1.1	0.0
	Part-time, noneconomic	−1.0	−1.1	0.0	−1.4	−2.5	−4.3
	Full-time	−4.9	−4.9	−2.3	−5.3	0.1	3.9
	Full-time, long hours	−7.7	−2.0	−1.7	2.7	1.0	−0.9
Hispanic Women	Retired	0.0	−0.6	−0.8	−1.0	−6.4	3.8
	Disabled	0.1	0.6	1.2	−2.3	−4.8	−1.3
	NILF-other	9.5	3.9	−0.7	7.1	3.9	4.3
	Unemployed	11.4	3.1	7.0	9.0	9.2	−0.5
	Self-employed	0.0	−0.1	0.4	2.0	0.0	2.8
	Part-time, economic/unknown	−1.3	−0.5	−0.9	−0.2	0.9	0.5
	Part-time, noneconomic	−0.8	−0.3	−1.7	−2.7	−3.2	−4.2
	Full-time	−14.4	−8.9	−5.6	−10.7	−0.9	−4.0
	Full-time, long hours	−4.4	2.7	1.1	−1.2	1.5	−1.5
Asian Women	Retired	0.5	−0.7	0.6	−3.3	1.7	6.1
	Disabled	0.0	−0.3	0.5	1.0	−2.9	3.1
	NILF-other	−0.6	−0.2	−2.3	−0.1	1.3	−4.0
	Unemployed	6.0	1.9	2.6	8.1	−1.5	−0.3
	Self-employed	0.0	0.7	−0.3	0.7	−2.0	0.0
	Part-time, economic/unknown	−0.9	−0.3	2.0	1.9	−3.3	0.0
	Part-time, noneconomic	−1.6	−0.7	−2.0	−3.8	−3.1	−1.2
	Full-time	−2.9	−2.3	−1.8	−2.4	11.8	−1.7
	Full-time, long hours	−0.5	1.9	0.6	−2.0	−1.9	−2.1

Note. NILF-other = not in the labor force for other reasons.

Appendix Table G. Percentage Point Changes in Employment Status, January to April, by Race, Men

Men Without a College Degree
		20–29	30–39	40–49	50–59	60–69	70+
White men	Retired	0.1	0.8	−0.7	−1.2	2.5	1.4
	Disabled	0.2	0.3	0.9	−0.7	−2.5	−0.5
	NILF-other	5.4	3.2	3.3	2.0	1.3	−0.1
	Unemployed	8.8	8.4	6.9	7.0	3.8	1.8
	Self-employed	0.0	0.1	0.8	0.0	−0.1	0.6
	Part-time, economic/unknown	0.0	−0.3	0.2	0.9	1.0	−0.1
	Part-time, noneconomic	−4.4	−1.6	−0.6	−0.6	−2.7	−2.3
	Full-time	−7.9	−6.5	−8.4	−5.2	−2.8	−0.6
	Full-time, long hours	−2.3	−4.2	−2.4	−2.3	−0.4	−0.1
Black men	Retired	−0.8	0.0	0.2	−1.8	1.3	7.1
	Disabled	−1.1	−2.9	1.4	2.4	−2.1	−3.3
	NILF-other	5.8	9.6	2.3	2.7	2.1	−2.5
	Unemployed	6.9	3.6	9.6	6.8	4.6	2.8
	Self-employed	−0.4	−0.9	1.7	0.7	0.0	−0.3
	Part-time, economic/unknown	1.2	2.5	−0.3	0.5	1.8	0.2
	Part-time, noneconomic	−4.7	−0.9	−0.9	0.6	−1.2	−2.6
	Full-time	−4.3	−7.8	−15.9	−8.8	−3.6	−2.1
	Full-time, long hours	−2.7	−3.2	1.9	−3.2	−2.9	0.7
Hispanic men	Retired	0.1	0.2	−0.5	−0.1	−1.5	2.8
	Disabled	−1.2	−0.2	−0.2	0.0	0.0	0.4
	NILF-other	10.8	3.8	2.6	6.5	1.9	0.1
	Unemployed	11.4	9.6	9.5	8.6	7.0	0.5
	Self-employed	0.2	0.5	0.6	0.3	−0.5	−0.6
	Part-time, economic/unknown	0.4	−1.0	0.3	1.1	−0.4	0.6
	Part-time, noneconomic	−2.5	−1.9	−0.6	−1.4	0.5	−3.7
	Full-time	−16.9	−10.7	−8.1	−9.9	−6.2	1.6
	Full-time, long hours	−2.3	−0.1	−3.6	−5.0	−0.8	−1.5
Asian men	Retired	−0.6	−0.5	−0.5	−3.9	6.6	8.9
	Disabled	1.0	−1.3	0.8	1.4	−1.4	−4.4
	NILF-other	7.8	8.2	5.6	7.4	3.6	−1.4
	Unemployed	15.8	15.6	10.6	13.2	12.4	1.7
	Self-employed	0.0	2.7	−1.1	2.0	2.3	0.0
	Part-time, economic/unknown	3.0	0.7	4.1	2.4	0.7	0.0
	Part-time, noneconomic	−11.2	−5.6	−1.1	0.0	−3.6	−2.8
	Full-time	−12.9	−15.8	−12.6	−22.0	−20.8	−2.9
	Full-time, long hours	−2.9	−4.0	−5.9	−0.6	0.3	0.8
Men With a College Degree or Above
		20–29	30–39	40–49	50–59	60–69	70+
White men	Retired	0.0	0.2	0.0	0.1	−1.8	2.8
	Disabled	−0.3	0.4	−0.5	0.3	0.0	−0.3
	NILF-other	3.2	1.7	−0.3	2.2	0.7	0.3
	Unemployed	4.8	4.7	3.2	3.3	3.0	2.0
	Self-employed	0.4	0.6	−0.4	0.3	0.5	0.0
	Part-time, economic/unknown	0.2	0.5	0.8	0.0	0.5	1.0
	Part-time, noneconomic	−2.2	0.4	−0.6	−0.7	−2.4	−3.3
	Full-time	−6.0	−3.4	2.0	−4.5	2.5	−2.1
	Full-time, long hours	−0.1	−5.1	−4.1	−1.1	−2.8	−0.5
Black men	Retired	1.0	0.3	−2.2	−0.3	−6.6	−0.8
	Disabled	−1.1	−0.1	0.8	0.4	2.3	−0.3
	NILF-other	12.4	3.4	2.6	2.7	1.0	−3.6
	Unemployed	8.2	7.8	1.5	3.1	1.7	0.7
	Self-employed	0.1	−0.6	1.5	−1.7	0.0	0.0
	Part-time, economic/unknown	1.8	−0.2	0.5	1.5	2.4	3.6
	Part-time, noneconomic	−0.5	0.1	−0.9	−2.0	−2.4	−1.0
	Full-time	−18.2	−1.1	4.6	1.4	6.6	−1.8
	Full-time, long hours	−3.7	−9.5	−8.4	−5.2	−5.1	3.2
Hispanic men	Retired	0.0	0.0	−1.8	1.5	−3.0	6.0
	Disabled	−1.0	−1.1	0.8	−1.5	−0.9	0.9
	NILF-other	−2.1	10.5	4.4	0.3	4.2	2.5
	Unemployed	12.7	2.8	9.7	5.9	6.4	−0.9
	Self-employed	0.9	0.4	1.4	3.3	1.6	−1.0
	Part-time, economic/unknown	−1.7	−1.2	−1.4	0.7	2.3	0.0
	Part-time, noneconomic	−0.9	−0.7	−0.8	−2.3	0.2	0.3
	Full-time	−8.2	−11.2	−9.1	−1.1	−4.8	−2.0
	Full-time, long hours	0.4	0.5	−3.2	−6.8	−6.1	−5.8
Asian men	Retired	−0.4	−0.8	0.3	1.3	−1.8	9.0
	Disabled	0.4	0.2	0.5	0.7	0.5	−0.1
	NILF-other	3.0	−0.3	3.9	4.0	1.1	−1.2
	Unemployed	1.8	3.9	0.8	3.9	4.3	1.7
	Self-employed	1.4	−0.3	0.0	−0.1	0.0	−0.4
	Part-time, economic/unknown	0.5	0.2	1.1	0.5	−2.5	0.0
	Part-time, noneconomic	−1.3	−1.4	1.9	−2.4	−2.1	−3.3
	Full-time	1.2	1.6	−8.3	−6.3	1.0	−5.8
	Full-time, long hours	−6.6	−3.1	−0.2	−1.5	−0.6	0.1

Note. NILF-other = not in the labor force for other reasons.

FUNDING

Funding support for this research was provided by the National Science Foundation, grant #1850914, Phyllis Moen and Sarah Flood co-principal investigators; the Life Course Center (1P30AG066613-01), funded through a grant from the National Institute on Aging; and the Minnesota Population Center (P2CH041023) and IPUMS CPS (R01HD067258), both funded through grants from the Eunice Kennedy Shriver National Institute for Child Health and Human Development (NICHD).