Who Is the “She” in the Pandemic “She-Cession”?
Summary
A research article by Andrew Taeho Kim, Matt Erickson, Yurong Zhang and ChangHwan Kim of the Department of Sociology, University of Kansas, published in Population Research and Policy Review (2022) 41:1325–1358. Using the 2017 to 2020 monthly Current Population Survey and a difference-in-difference design, the authors examine how COVID-19 affected at-work status among prime-working-age people grouped by gender, marital status, parenthood and education. The abstract reports that married women without school-aged children did not experience a relative decline in employment compared with married fathers. It states that less-educated never-married childless women and never-married mothers were the women most adversely affected during 2020. The authors describe marriage as a form of within-family insurance they call the added caregiver effect.
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Population Research and Policy Review (2022) 41:1325–1358
https://doi.org/10.1007/s11113-021-09693-0
ORIGINAL RESEARCH
Who is the “She” in the Pandemic “She‑Cession”? Variation
in COVID‑19 Labor Market Outcomes by Gender and Family
Status
Andrew Taeho Kim1 · Matt Erickson1 · Yurong Zhang1 · ChangHwan Kim1
Received: 3 June 2021 / Accepted: 7 December 2021 / Published online: 29 January 2022
© The Author(s), under exclusive licence to Springer Nature B.V. 2021
Abstract
The sharp decline in employment after the COVID-19 lockdown was not uniformly
felt across demographic groups. Utilizing the 2017 to 2020 monthly Current Popula-
tion Survey and using a difference-in-difference design, we investigate the varying
impacts of COVID-19 on at-work status among the prime-working-age population,
accounting for typical seasonal fluctuations in employment. The target population is
grouped by gender, marital status, parenthood, and level of education. Our results
uncover complex variations by gender, marital status, and children’s age. Contrary
to popular belief, married women without school-aged children did not experience
a relative decline in employment compared to married fathers. A majority of dis-
advantages in employment that married women experienced are accounted for by
controlling for typical seasonal fluctuations. The women whose employment was
most distinctively adversely affected by COVID-19 during 2020 were less-educated
never-married childless women and never-married mothers. Less-educated men who
were not currently married also experienced a disadvantage in employment rela-
tive to equally educated married fathers. These findings imply that, during the pan-
demic recession, marriage offered a form of within-family insurance that we call the
“added caregiver effect.” The further implications of these findings are discussed.
Keywords COVID-19 · Employment · Gender · Marital status · Parenthood
* Andrew Taeho Kim
andrew_taeho@ku.edu
Matt Erickson
matt.erickson@ku.edu
Yurong Zhang
yurongzhang@ku.edu
ChangHwan Kim
chkim@ku.edu
1
Department of Sociology, University of Kansas, Lawrence, USA
1 3
Vol.:(0123456789)
1326 A. T. Kim et al.
Introduction
As state and local governments instituted lockdowns to restrain the spread of
COVID-19 in the spring of 2020, a pandemic-imposed recession began. The
employment-to-population ratio in the U.S. plummeted from 59.9% in March to
51.3% in April, an 8.6-percentage-point drop.1 As of December, the employment-to-
population ratio (57.4%) had not recovered to the pre-lockdown level. However, the
decline in employment was not felt uniformly across demographic groups. Unlike
in previous recessions, during which men tended to be more likely to lose their jobs
than women (Goodman et al., 1993; Hoynes et al., 2012), more women than men
lost jobs during the pandemic recession (Alon et al., 2020b; Landivar et al., 2020).
Because of this, some news media outlets have labeled the 2020 recession a “She-
cession”2, citing academic papers (Alon et al. 2020a, 2020b).
Researchers have worried that women’s unemployment after the pandemic might
further stall progress toward gender equality in employment or even reverse it (Alon
et al., 2020a; Petts et al., 2021). The unique nature of the 2020 pandemic recession
and its implications for future work arrangements, however, requires refined assess-
ments of which groups are most negatively affected. Physical distancing and restric-
tions on opening hours affected service industries in which women are more concen-
trated than men (Cajner et al., 2020; Montenovo et al., 2020). Women’s employment
appeared to be slower to recover after April, the lockdown month, than men’s (Alon
et al., 2020b). However, women are also more likely to work in education-related
occupations and industries that are sensitive to seasonal fluctuation (Raley et al.,
2006). A failure to take into account these seasonal variations could lead to an over-
estimation of recession-related women’s unemployment during the 2020 summer
months.
The lockdown and remote working bring in a whole new set of policy-related
issues such as childcare (Petts et al., 2021; Sevilla & Smith, 2021), home schooling
(Averett, 2021; Petts et al., 2021), out-of-school programs (Fornaro et al., 2021),
sick leave (Jelliffe et al., 2021), and unemployment insurance (Ganong et al., 2020).
The continuation of employment by teleworking became tightly entangled with
the burden of childcare (Barkowski et al., 2020). A new burden of home school-
ing for parents because of school closures (Averett, 2021; Petts et al., 2021) was
unevenly distributed between genders. However, the adverse impact of such conflict
may not have been constant across all mothers. A survey reported that partnered par-
ents’ divisions of housework and childcare became more equal after the pandemic
began (Carlson et al., 2020). This implies that, during the pandemic recession, mar-
riage may have shielded women from voluntary unemployment due to the burden of
childcare.
1
U.S. Bureau of Labor Statistics’ Employment-population ratio: https://www.bls.gov/charts/emplo
yment-situation/employment-population-ratio.htm Accessed on February 20, 2021.
2
E.g., New York Times on May 9, 2020: https://www.nytimes.com/2020/05/09/us/unemployment-coron
avirus-women.html; Bloomberg on December 30, 2020: https://www.bloomberg.com/opinion/articles/
2020-12-30/covid-she-cession-the-pandemic-doesn-t-have-to-hurt-women-s-jobs-most.
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Who is the “She” in the Pandemic “She‑Cession”? Variation in… 1327
The shielding effect of marriage may also have varied across levels of education.
The tasks of many highly educated workers can be done remotely, while those of
less-educated workers tend to require physical contact and in-person presence (Mon-
tenovo et al., 2020). Highly educated workers tend to have jobs with paid sick leave,
while less-educated workers who became victims of the pandemic at a higher rate
typically lack this benefit (Jelliffe et al., 2021). The combination of the increased
burden of child care at home and the continuing requirement of in-person contact at
work could potentially have nullified the shielding effect of marriage for less-edu-
cated mothers. Given that variation in employment outcomes across demographic
groups during the pandemic was larger among the less educated than among the
highly educated (Kim et al., 2021), a failure to consider possible differentiated out-
comes across levels of education among working mothers risks oversimplifying the
nature of the pandemic recession and hindering policy responses.
In this study, we investigate variation across demographic groups in the impact of
COVID-19 on at-work status. We aim to understand the peculiarity of the COVID-
19 related employment crisis by identifying which refined groups were the most
adversely affected. To this end, we classify the prime-working-age population into
fifteen groups–seven for men and eight for women–depending on gender, marital
status, and parenthood. We further compare outcomes among these groups across
levels of education. To account for typical seasonal fluctuations and pandemic-
related employment declines common to all groups, we use a difference-in-dif-
ference design, utilizing data from each monthly current population survey (CPS)
sample from 2017 to 2020. Our results demonstrate that the detrimental effects of
COVID-19 on employment were much too complex for the simple “she-cession”
label. A majority of disadvantages in employment that married women experienced
are accounted for by controlling for typical seasonal fluctuations. The women whose
employment was most distinctively adversely affected by COVID-19 during 2020
were less-educated never-married childless women and never-married mothers.
These findings imply that marriage has offered a form of within-family insurance
during the pandemic recession that we call the “added caregiver effect.” Given that
teleworking is likely to stay in the foreseeable future, this study contributes to the
literature not only by uncovering which groups were more negatively affected by the
pandemic recession but also by shedding new light on the role of family in employ-
ment, which will help with proper policy development in the era of remote working.
Literature Review
Two Types of Economic Crises and Gender in Previous Literature
The economic impact of a crisis on men and women’s labor-market outcomes
depends on the nature of the crisis. In general, men and women are not evenly dis-
tributed across occupations and industries, so they experience distinct economic cir-
cumstances when social events hit different economic segments. Typical economic
recessions in the United States tend to lead to greater job losses for men than for
women (Redbird & Grusky, 2016). When banks stopped investing during the Great
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1328 A. T. Kim et al.
Recession, the male-dominated industries of manufacturing and construction were
among those hit hardest, while the female-dominated education and health services
sector suffered significantly less or even gained jobs (Borbely, 2011; Dunne & Fee,
2010; Goodman & Mance, 2011; Hout et al., 2011). Although men were hit harder
by job loss than women during the Great Recession, women’s employment recov-
ered more slowly following the recession than men’s did, largely because of large
job cuts in the disproportionately female public sector during the post-recession
period (Hartmann et al., 2014; Laird, 2017).
In the case of natural disasters such as hurricanes, female-dominated industries
have typically been harder-hit (Fothergill, 1998; Morrow, 1999; Vigdor, 2008; Will-
inger & Knight, 2012). In addition, when disasters create an additional caregiving
burden within families, that burden is likely to fall on women (Peek & Fothergill,
2008; Morrow & Enarson, 1996). Because lower-income women are more likely to
rely on extended-family networks for caregiving, these women face a more intense
increase in caregiving demands when disasters make these networks inaccessible
(Peek & Fothergill, 2008).
In labor-market terms, the COVID-19 pandemic shares qualities with both nat-
ural disasters and economic recessions, without fitting neatly into either category.
A typical recession, by definition, consists of a prolonged period during which the
national economy experiences no growth (Hout et al., 2011). In contrast with the
slow burn of a typical recession, however, the effect of COVID-19 was more like
an explosion: Because of the lockdowns imposed early in the pandemic, economic
activities and employment plunged essentially overnight. Additionally, the pandemic
downturn was set apart from previous recessions in that the lockdown necessitated
the disruption of many childcare and education arrangements, including the highly
unusual closure of public schools that in some areas stretched on for many months.
The immediate onset of the economic downturn and the sudden care burden borne
by families were more typical of a natural disaster than a usual recession. Because of
this, we would expect some differences in the labor-market effects of the pandemic
recession, compared with a typical recession.
During a regular recession, a spouse or partner can serve as within-family insur-
ance by stepping into the labor market in the event of a layoff, sickness of family
members, or increased childcare. This so-called “added worker effect” was widely
observed during previous economic recessions as women entered the labor force
to maintain their families’ income when their partners lost employment (Lundberg,
1985; Birinci, 2019; Pruitt & Turner, 2020; Blundell et al., 2016; Sahin et al., 2010).
During the pandemic recession, however, we might expect important differences in
the way such within-family insurance might function. One possibility is that families
with children would not benefit from such insurance due to the increased childcare
load. Individuals who may otherwise have been able to increase their labor supply to
compensate for a spouse’s layoff might have had their time and energy occupied by
childcare, making it impossible for them to increase their work hours or take a new
job (Alon et al., 2020b). However, another possibility is that within-family insurance
did play a role during this pandemic because “stay-at-home” work arrangements
enabled some partnered parents to deal with the sudden childcare need, thus, pre-
venting the other partner from exiting paid work, or facilitating the other partner’s
13
Who is the “She” in the Pandemic “She‑Cession”? Variation in… 1329
employment opportunities. For single parents, on the other hand, the entirety of this
burden fell on only one individual, unless other family members or informal caregiv-
ers were available. During the pandemic recession, spouses and partners may have
functioned as a shock absorber for parents, providing some degree of protection
from the shock of the increased care burden, rather than functioning as labor-market
insurance in the event of a job loss.
Further, partners may have functioned as pandemic shock absorbers in other
ways, including for individuals without children. The wide-sweeping response to the
pandemic altered many people’s daily lives in ways beyond just work and childcare.
Lockdowns also forced the closure of restaurants, cleaning businesses, and other
services used to outsource labor that would otherwise be performed in the home.
At the same time, lockdowns and distancing guidelines radically transformed many
people’s social lives, limiting or even eliminating in-person social contact with
people outside one’s own household. These changes affected individuals without
children just as they affected parents, and a spouse or partner might have provided
childless individuals emotional support and daily care that made the seismic effect
of the pandemic response more endurable. Research on the psychological effects of
marriage suggests it generally has a positive effect on mental well-being, including
by providing a source of social and financial support, as well as by conferring emo-
tional benefits such as self-efficacy (Waite, 1995; Williams et al., 2010). The social
support provided by a spouse or partner can have a stress-buffering effect for mar-
ried or partnered individuals, regardless of whether they have children (Carlson &
Kail, 2018; Cohen & Wills, 1985). Because of this, married or partnered individuals
may have been able to withstand the stress of the pandemic with less negative effect
on their well-being relative to single individuals.
However, the stress-buffering benefits of marriage may not be uniform across
socioeconomic strata. Carlson and Kail (2018) found that marriage provided bet-
ter protection against depressive symptoms for lower-SES individuals than it did for
higher-SES individuals. This difference was mediated in part by social support from
friends and family: Higher-SES individuals tended to report higher levels of social
support regardless of whether they were married, whereas lower-SES individuals
tended to report very low levels of social support if unmarried but enjoyed a boost
of social support when married. For higher-SES individuals, the social support pro-
vided by marriage might effectively just take the place of social support previously
provided by other friends and family, whereas for lower-SES individuals marriage
(or partnership) might provide a unique source of social support (Carlson & Kail,
2018; Sarkisian & Gerstel, 2008). For all these reasons, spouses and partners may
have functioned as within-family insurance providing some protection against the
stress caused by the pandemic’s changes to everyday life, regardless of one’s parent-
hood status, and this protection may have been most valuable for lower-SES individ-
uals. If the stress of the pandemic had differential effects on well-being across lines
of parenthood status and SES, it may have had differential effects on labor-market
participation across those lines, as well.
Given these potential differential effects of the pandemic, we propose that dur-
ing the pandemic recession, spouses and partners may have functioned as within-
family labor-market insurance in a way different than that previously identified in
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1330 A. T. Kim et al.
the economic literature: by serving as an added caregiver rather than as an added
worker. This “added caregiver effect”—the presence of a spouse or partner to pro-
vide care in the form of parenting or in the form of social support for one’s part-
ner—may have left couples better equipped to maintain their labor force participa-
tion during the pandemic relative to unmarried or unpartnered individuals.
Differential Labor‑Market Effects of COVID‑19 by Gender, Marital Status,
Parenthood Status, and Education
Gender
Previous recessions have resulted in heavier job losses for men. Research on the
initial pandemic-related spike in unemployment in April 2020, however, indicates
a sharp contrast with previous economic downturns, as more women lost jobs
than men (Alon et al., 2020b; Landivar et al., 2020). This gender gap in employ-
ment remained stable through at least June, though researchers using different data
sources arrived at slightly different estimates of the extent of the gap (Adams-Prassl
et al., 2020; Cajner et al., 2020; Montenovo et al., 2020).
As with past economic downturns, a likely explanation for this gender dispar-
ity is variation in impacts by industry, combined with industrial gender segregation.
However, because the 2020 downturn was precipitated by a pandemic, the most
affected industries were different from those in a typical recession. Industries involv-
ing a great deal of face-to-face interaction have been hard-hit by job losses (Cajner
et al., 2020; Montenovo et al., 2020). These service-oriented industries have dispro-
portionately female workforces, unlike the male-dominated industries of construc-
tion and manufacturing that are typically most affected by recessions (Alon et al.,
2020b). Industries in which high proportions of workers are able to work remotely,
on the other hand, have been relatively sheltered from job losses (Adams-Prassl
et al., 2020; Papanikolaou & Schmidt, 2020). Men are more likely to work in occu-
pations in which at least 50% of workers report being able to work remotely, though
conclusions on gender differences in ability to telecommute may vary based on the
threshold used to define an occupation as “telecommutable” (Alon et al., 2020a;
Montenovo et al., 2020).
Gender differences in industrial distribution also intersect with month-to-month
economic fluctuations. Such monthly variations are associated with natural events,
such as weather, or institutional schedules, such as school calendars, holidays, and
the timing of tax collection (Geremew & Gourio, 2018). Construction has a strong
seasonal component; the employment rate is very low during winter. Outside of
construction, the end-of-the-year holiday season typically brings the peak of U.S.
GDP, as well as the temporary expansion of jobs in retail sale (Barsky & Miron,
1989; Miron, 1996). Another related industrial seasonality is the increase of manu-
facturing activities around October, in preparation for the holiday season (Barsky
& Miron, 1989; Miron, 1996). Tourism and hospitality have business spikes during
summer and traditional holiday seasons (Kelliher, 1989). Among all these monthly
fluctuations, schools closing during summer seems to have the greatest impact on
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Who is the “She” in the Pandemic “She‑Cession”? Variation in… 1331
women’s employment because the majority of primary and secondary teachers are
women (U.S. Department of Labor, 2017). Therefore, conclusions about the gen-
dered effect of any major social crisis could be biased without accounting for the
monthly variation of industries.
Given these previous findings, we test the following two hypotheses:
Hypothesis 1a Overall, during the pandemic months of 2020 (April–December),
monthly employment outcomes were worse for women than for men.
Hypothesis 1b This between-gender difference in employment outcomes will
no longer be significant after controlling for current or most recent industry and
occupation.
Parenthood Status
Using different data sources, researchers have found that the mediating effect of
industry does not explain all of the gender gap in employment declines early in the
pandemic downturn (Cajner et al., 2020; Montenovo et al.,2020). A commonly pro-
posed mechanism for this unexplained portion of the gender gap is the increased
care burden for mothers during the pandemic, due to school closures and the loss
of childcare arrangements (Collins et al., 2021; Petts et al., 2021). Given gendered
cultural beliefs about responsibility for parenting (Blair-Loy, 2003; Stone, 2007), we
would expect the resulting additional childcare burden to fall primarily on mothers.
Several researchers have studied whether increased parenting burdens might have
led to a reduction in mothers’ labor supply during the pandemic. Collins et al. (2021)
found that the gender gap in weekly work hours increased by between 1 and 2 h for
employed married parents of children younger than 13 during the initial months of
the pandemic. However, another study that adjusted for seasonal trends in previous
years found no significant difference between parents of children under 13 and par-
ents of older children in terms of employment, being at work, or work hours in the
initial months of the pandemic (Barkowski et al., 2020).
Employment changes during the initial lockdown period in the U.S. were driven
largely by layoffs and not by voluntary quits, so it is unlikely that childcare burdens
explain a large portion of the initial drop in employment during spring 2020 (Dias
et al., 2020). However, as the increased childcare burden for many parents stretched
on for many months, it may have had an effect later in the pandemic, especially in
the fall as schools in many parts of the country continued to operate virtually all
or some of the time. The frustration some women may have felt at their partners’
lack of childcare support may have led to increased strain over time (Calarco et al.,
2020). Or, as happened during the Great Recession, a change in the gendered divi-
sion of labor at home sparked by an external shock may have gradually faded over
time as familiar gendered patterns returned (Berik & Kongar, 2013). Shorter school
days have been found to create a barrier for maternal employment (Graves, 2013;
Berthelon et al., 2015; Ruppanner et al., 2019). For parents of school-aged children,
school is the main source of child care (Capizzano et al., 2002; Laughlin, 2010).
Tedeschi (2020) found an association between states’ rates of school closures and
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1332 A. T. Kim et al.
distance learning and the work outcomes of mothers of school-aged children as the
2020-21 school year began.
An additional proposed explanation for inequality in the pandemic labor market
by gender and parenthood status is a fatherhood premium in avoiding post-pandemic
layoffs. The gendered cultural model of the male breadwinner may lead employers
to favor fathers over other workers in employment decisions, including whom to lay
off during downturns (Correll et al., 2007). Dias et al. (2020) found that fathers had
the lowest likelihood of being laid off by April 2020, compared with mothers and
men or women without children.
Given these proposed mechanisms, we test the following hypotheses:
Hypothesis 2a During the pandemic months of 2020 (April–December), season-
ally adjusted employment outcomes were better for fathers than for mothers, women
without children, or men without children.
Hypothesis 2b During the final four months of 2020 (September–December),
coinciding with the beginning of the 2020–2021 school year, seasonally adjusted
monthly employment outcomes were worse for mothers of school-aged children
(6–17) than for other gender and parenthood status groups.
Marital Status
Other theory and findings suggest that the effects of increased parental burdens
might not affect all mothers in the same way. Although parents did face an unprec-
edented childcare and educational burden, those parents who shifted to full-time
teleworking spent more time at home and less time at external workplaces during
the pandemic, giving them more flexibility to balance paid work and childcare than
they may have had previously (Lyttelton et al., 2020). It may be that the increase
in remote working during the pandemic led to increased childcare participation by
men, as they spent more time at home than ever before (Carlson et al., 2020; Alon
et al., 2020a). One survey found evidence that partnered parents’ divisions of house-
work and childcare became more even after the pandemic began, though the gen-
der gap widened among couples in which the father was the sole or primary earner
(Carlson et al., 2020). Research on the gender division of labor among teleworking
parents prior to the pandemic has found that teleworking may narrow the gender gap
in time spent on childcare (Lyttelton et al., 2020).
To this point, much of the sociological research on the gendered effects of the
pandemic lockdown and recession has focused on partnered or married parents
(Collins et al., 2021; Petts et al., 2021; Carlson et al., 2020; Calarco et al., 2020;
Landivar et al., 2020). However, if remote working has prompted men to assume a
greater share of the childcare burden among partnered parents, this suggests that the
largest increased childcare burden at the onset of COVID-19 fell on single moth-
ers rather than partnered mothers. Compared with partnered mothers, single moth-
ers likely lacked flexibility in adapting to the loss of childcare due to the pandemic
(Alon et al., 2020a). To the extent that women’s labor-market outcomes may have
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Who is the “She” in the Pandemic “She‑Cession”? Variation in… 1333
been affected by increased childcare demands due to the pandemic, that effect may
have been strongest among single mothers relative to married or partnered mothers.
Hypothesis 3 During the pandemic months of 2020 (April–December), employment
outcomes were worse for single mothers than for married mothers.
Education
Just as the pandemic, by its nature, has had a greater effect on female-dominated
industries, it has also had a greater effect on industries predominantly employing
less-educated workers. Occupational segregation by gender has been much more
persistent over time among individuals without college degrees than among college
graduates (England, 2010). Further, job quality and employment security can vary
within the same occupation and industry. Across all age, gender, and racial groups,
increases in unemployment early in the pandemic were greater among individuals
without college degrees (Kim et al., 2021; Moen et al., 2020). Workers in sectors
characterized by face-to-face contact disproportionately do not have college degrees;
workers with college degrees, on the other hand, are more likely to work in tele-
commutable occupations (Montenovo et al., 2020). Thus, mothers without college
degrees were probably more likely than those with college degrees to lose their job
involuntarily due to the pandemic. In the event that less-educated mothers were not
laid off, they likely had less flexibility in adapting to increased childcare burdens
given their inability to do their job remotely (Montenovo et al., 2020). Additionally,
the spouses of married women without college degrees are themselves likely not to
have graduated from college (Schwartz & Mare, 2005), and therefore, less likely to
be working remotely during the pandemic and able to assume some of the increased
childcare burden. Given all this, differences in labor-market outcomes across gender
and family status may be starker among individuals without college degrees than
among college-educated individuals.
Hypothesis 4 Disparities in employment outcomes across demographic groups were
greater among individuals without college degrees than among college graduates.
Methodology and Research Design
Data
We use the monthly CPS from January to December 2017–2020 from the Integrated
Public Use Microdata Series (IPUMS) (Flood et al., 2020). The sample is limited
to the prime-working-age population, ages 25–54.3 We further limit our sample to
those not in school and exclude individuals who are currently serving in the military.
3
We assessed the same analyses using slightly different age ranges such as 25–49 and 18–64. Results
did not differ significantly.
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1334 A. T. Kim et al.
Because of the rotating sample nature of CPS, one person can appear more than
once in our cross-sectional sample, which can induce a downward bias in the esti-
mation of standard errors. To address this issue, we use person weights and obtain
robust, clustered standard errors using individual IDs in all analyses.
Dependent Variables
Our primary dependent variable is a dichotomous measure of being at work last
week (1) or being not at work (0). “Not at work” includes individuals who had
a job but were not at work, as well as those who were unemployed or not in the
labor force. Those who were unemployed because of temporary layoffs due to the
COVID-19 lockdown should be classified as unemployed. However, a substantial
portion of such individuals were miscategorized as employed but not at work (U.S.
Bureau of Labor Statistics, 2020). Unemployment rates among those in the labor
force can be biased because of this misclassification. We focus on the probability
of being at-work, or the employment-to-population ratio, because it is not affected
by this COVID-19-related misclassification and, thus, is the most stable measure of
employment.4
Main Independent Variables: Disaggregated Groups by Gender, Marital Status,
and Parenthood
Based on gender, marital status, and parenthood status, we classify respondents
into seven male groups and eight female groups: (1) married fathers with youngest
children ages 18+; (2) married fathers with youngest children ages 6–17; (3) mar-
ried fathers with youngest children ages 0–5; (4) married men without children; (5)
never-married men; (6) cohabiting men; (7) separated/widowed/divorced men; (8)
married mothers with youngest children ages 18+; (9) married mothers with young-
est children ages 6–17; (10) married mothers with youngest children ages 0–5; (11)
married women without children; (12) never-married mothers; (13) never-married
women without children; (14) cohabiting women; and (15) separated/widowed/
divorced women, setting (1) married fathers with youngest children ages 18+ as
the reference group. We separate never-married singles and separated/widowed/
divorced primarily because of their age differences. Among prime working-age indi-
viduals, women’s labor force participation tends to decrease in their 30s and early
40s (Goldin & Mitchell, 2017). Considering that the average age for never-married
singles and separated/widowed/divorced are the mid-30s and mid-40s, respectively,
putting those two groups in the same category could cloud the estimates. Married
parents are classified according to the age of the youngest co-resident child, with
married fathers and mothers each divided into three groups depending on whether
4
We assessed the same sets of analyses using dichotomous indicators of being in the labor force or
being employed. Our conclusion holds.
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Who is the “She” in the Pandemic “She‑Cession”? Variation in… 1335
their youngest child was of preschool age, school age, or an adult.5 We do not sep-
arate never-married fathers from never-married childless men because the sample
size of the former group is too small. For the same reason, we divide cohabiting and
separated/widowed/divorced only by gender, without accounting for parenthood.
See Appendix Table 2 for descriptive statistics for the 15 groups.
Analytic Strategy
To explore the differentiated impact of COVID-19, we use a difference-in-difference
design. First, we explore monthly changes in at-work status during 2020 compared
to January of that year, which constitute the first difference. Next, we compare these
monthly changes across fifteen groups (the second difference). We assess Eq. (1) for
monthly data as follows:
Dec. 15 15
Dec.
∑ ∑ ∑ ∑ ∑
y= 𝛽m M m + 𝛿g Gg + 𝛾m,g (Mm × Gg ) + 𝜋X + 𝛼 + 𝜀, (1)
m=Feb. g=2 m=Feb. g=2
where M is a vector of month dummy variables that differentiate February through
December from January. 𝛽m captures the proportion at work relative to January in
month m in a given year. G is a vector of fourteen dummy variables that differenti-
ate the sample by marital and parenthood status. Our coefficient of interest 𝛾m,g is a
difference-in-difference (DID) estimate treating the month as a treatment, showing
the added disadvantage of group g relative to married fathers with youngest children
ages 18+ in proportion at work vs. January in month m.
A problem of Eq. (1) is that the assumption of a common trend (Wing et al.,
2018) may not hold across the fifteen groups. Women are less likely to be at work
during the summer than men. Parenting school-age children will affect summer
employment as well. The failure to account for variations in seasonal fluctuation
could result in an overestimation of the relative reduction in at-work status among
women. To address this concern, we cancel out the variation in seasonal fluctuation
across groups by introducing an additional difference point, the difference between
2020 and the previous years. That is, we assess a difference-in-difference-in-differ-
ence model as shown in Eq. (2):
5
The results were almost identical when we changed the criteria to the presence of any school-aged chil-
dren rather than using the age of the youngest child.
13
1336 A. T. Kim et al.
Dec. Dec.
∑ ∑
y= 𝛽m Mm + 𝜂C + 𝛿m (Mm × C)
m=Feb. m=Feb.
15 Dec. 15 15
∑ ∑ ∑ ∑
+ 𝛾g Gg + 𝜔m,g (Mm × Gg ) + 𝜃g (C × Gg )
g=2 m=Feb. g=2 g=2 , (2)
Dec. 15
∑ ∑
+ 𝜓m,g (Mm × C × Gg )
m=Feb. g=2
∑
+ 𝜋X + 𝛼 + 𝜀
where C is a dummy indicator that differentiates between 2020 and the non-pan-
demic years of 2017–2019. Our main coefficient of interest is 𝜓m,g, a seasonally
adjusted DID estimate. It compares the effect of the year 2020 (i.e., the pandemic
year) on changes in the dependent variable across months (m) for group g relative to
married fathers with youngest children ages 18+. Throughout the paper, we call 𝛾m,g
of Eq. (1) “DID in 2020” and 𝜓m,g of Eq. (2) ‘Seasonally adjusted DID’.
Control Variables
All multivariable analyses control for age, age squared, levels of education, race,
marital status, citizenship status, state fixed effects, metro status, and number of
children in the household. Levels of education differentiate between less than high
school, high school graduates, some college, bachelor’s degree holders, and gradu-
ate degree holders. Race variables distinguish whites, blacks, Asians, Hispanics and
other non-Hispanic origin. The citizenship status variable consists of categories of
citizen, naturalized citizen, and non-citizen. State fixed effects control for state-level
variations in the strength of economic lockdowns and other unobserved state-level
heterogeneity. The metro indicator identifies whether the respondent is in a metro
area or not. In models controlling for occupation and industry, we use nine catego-
ries of occupation and thirteen categories of industry.6 For those who are not cur-
rently employed, we apply their previous occupation and industry information.
Empirical Results
Before examining differences across detailed groups, let’s explore overall changes
in at-work status for men and women. The at-work rate in January 2020 was
85.5% for men and 73.2% for women. In April, soon after the onset of lockdowns,
the rates plummeted to 71.2% for men and 58.3% for women (see Appendix
Table 2 for details). Figure 1A shows changes in the proportion at work relative
to January in 2017–2019 and 2020 by gender. The changes are net of the control
variables specified above. Both genders experienced a substantial drop in at-work
rate in April 2020 and subsequent months (dotted gray line). The magnitude of
6
When detailed 635-category occupation and 280-category industry are used instead of broad catego-
ries, the results are nearly identical with what we report here.
13
Who is the “She” in the Pandemic “She‑Cession”? Variation in… 1337
Fig. 1 Gender differences in proportion at work vs. January, 2017–2020. A shows the difference at-work
rates from January. B shows 𝛾m,g from equation (1) and 𝜓m,g from Eq. (2) in month m. g is replaced with a
dummy variable indicating women. Control variables include age, age squared, levels of education, race,
citizenship status, state, metro status, number of children in the household and marital status. Model 2 of
b controls for occupation and industry. Shaded area indicate 95% confidence intervals
the initial drop in the lockdown month of April is similar between genders. Dur-
ing the reopening period from May to August, however, men’s at-work proportion
seemed to recover at a faster pace than women’s.
When we track the change in at-work rates in 2017–2019 (solid black line),
distinct gendered monthly fluctuations in at-work status emerge. Women show a
much larger drop in at-work status during the summer months relative to Janu-
ary (− 0.022 in June, − 0.043 in July, and − 0.021 in August). While men show
a drop in summer, the magnitude is not comparable. A failure to account for this
13
1338 A. T. Kim et al.
Fig. 2 Change in proportion at work compared to January, 2017–2020. Control variables include age,
age squared, levels of education, race, citizenship status, state, metro status, number of children in the
household and survey year for 2017–2019. Shaded area indicate 95% confidence intervals
seasonal fluctuation could result in a misinterpretation of the pre-existing gender
gap as the effect of the pandemic.
Indeed, Fig. 1B shows the importance of adjusting for seasonal fluctuations.
Without the adjustment for seasonal fluctuation, DID in 2020 shows a larger gender
gap during the summer months of 2020, reaching − 0.034 in July. When we adjust
for seasonal fluctuations from years prior in Model 1, the gap is reduced to − 0.001
and statistically insignificant. The statistically significant women’s disadvantage in
employment right after the lockdown is mostly attributable to seasonal fluctuations.
When schools reopened in September, however, the seasonally adjusted gender gap
emerged. Hypothesis 1a is only partially supported. Further, the additional female
disadvantage specific to 2020 is fully accounted for by occupation and industry in
Model 2, which supports Hypothesis 1b. The next question is whether these patterns
vary across marital status and parenthood.
Changes in At‑Work Status: 2017–2019 vs. 2020
Figure 2 shows the proportion at work relative to January in 2017–2019 and 2020
for fifteen demographic groups. For 2017–2019, we add controls for survey year on
top of the other control variables. For men, regardless of marital and parenthood
status, there was no distinct fluctuation across calendar months in 2017–2019. For
13
Who is the “She” in the Pandemic “She‑Cession”? Variation in… 1339
women, seasonal fluctuation in at-work status differed by marital status and parent-
hood. Across groups, three distinct patterns emerge. First, before the pandemic, all
groups of married women experienced a drop in the probability of being at work
during the summer. Even married childless women experienced a drop in employ-
ment. Second, never-married and cohabiting women, regardless of motherhood, did
not show noticeable seasonal fluctuation in employment in 2017–2019.
Third, never-married men and women suffered the most from the pandemic-
imposed recession. Never-married mothers’ at-work status decreased by 22.0 per-
centage points in April 2020 compared to January. Surprisingly, never-married men
experienced the second-largest drop (19.3 percentage points) after never-married
mothers.
Another noteworthy point from Fig. 2 is that the month of the lowest at-work rate
in 2020 was not necessarily April for married women. Their at-work rate in July was
similar to that in April or even lower. Together, these results suggest the effect of
COVID-19 varied across gender, marital and parenthood status.
DID Estimates in At‑Work Status
Next, we examine the extent to which the effect of COVID-19 differed by gender,
marital and parenthood status. First, we look at monthly changes relative to mar-
ried fathers with youngest children ages 18+ in 2020 using DID estimates from Eq.
(1), which controls for demographic covariates and education. Second, we adjust
the DID estimates to account for monthly fluctuations observed in prior years
(2017–2019), resulting in the Seasonally adjusted DID estimates from Eq. (2). Fig-
ure 3A presents the result. The differences between the two estimates quantify the
extent to which the failure to account for the seasonal fluctuation biases the relative
(dis)advantage of a given group.
Out of the 14 groups that we compared to married fathers with youngest chil-
dren ages 18+, 9 groups experienced relative disadvantage in at least 1 month after
adjusting for seasonal fluctuations: (1) married fathers with youngest children ages
0–5; (2) married men without children; (3) never-married men; (4) cohabiting men;
(5) separated/divorced/widowed men; (6) married mothers with youngest children
age 6–17; (7) married women without children; (8) never-married mothers; and (9)
never-married women without children. Five groups did not experience statistically
significant disadvantages in employment throughout the year 2020: (1) married
fathers with youngest children age 6–17; (2) married mothers with youngest chil-
dren age 18+; (3) married mothers with youngest children age 0–5; (4) cohabiting
women; and (5) separated/divorced/widowed women. For disadvantaged groups, the
timing and the magnitude of disadvantage vary.
Never-married mothers were the only group who experienced a substantial dis-
advantage for the entire year after the lockdown. For never-married and other
not-married women, the seasonal adjustment did not alter estimates of their rela-
tive disadvantage. Beginning in April (− 0.090), never-married mothers continu-
ously had the largest disadvantage in every month, statistically significant in every
month except November, throughout the entire year. It should be emphasized that
13
1340 A. T. Kim et al.
Fig. 3 Disadvantage relative to married fathers with youngest children ages 18+ in proportion at work
vs. January. Control variables include age, age squared, levels of education, race, citizenship status, state
fixed effects, metro status, and number of children in the household. Shaded area indicate 95% confi-
dence intervals based on robust standard errors
the disadvantages shown in Fig. 3A are added disadvantages on top of the drop in
employment experienced by the reference group, married fathers with youngest
children age 18+. Childless never-married women were the second most negatively
affected group by the lockdown. They experienced a notable disadvantage in at-
work status from April to September. These findings support Hypothesis 3 strongly.
13
Who is the “She” in the Pandemic “She‑Cession”? Variation in… 1341
Unlike for never-married women, the seasonal adjustment made noticeable
changes in estimations for married women. Especially during the summer months,
the magnitude of relative disadvantage for married women was reduced after sea-
sonal adjustment. The reduction in relative disadvantage is particularly large for
married mothers of school-aged children (youngest age 6–17), although the disad-
vantage for this group is still statistically significant. This result does not fully sup-
port Hypothesis 2a. The timing of the worst disadvantage for this group was not dur-
ing the months right after the lockdown. Instead, it was during the school reopening
months in fall 2020. Their seasonally adjusted-DID estimate compared to married
fathers of school-aged children was − 0.049 in September, which indicates that the
at-work rate for married mothers of school-aged children declined by an additional
4.9 percentage points compared to married fathers with school-aged children even
after factoring in seasonal fluctuations. These findings offer support for Hypothesis
2b. Because school is likely the main source of childcare for these families (Capiz-
zano et al., 2002; Laughlin, 2010), the partial school opening in fall 2020 seems to
have imposed an extra burden on married mothers of school-aged children. In the
case of married mothers of preschool-aged children and married mothers of adult
children, no statistically significant disadvantage relative to married fathers is evi-
dent after seasonal adjustment. This implies that the relatively larger drop in at-work
status for married women during summer 2020 was mostly attributable to typical
gendered monthly fluctuations in at-work status across calendar months.
An interesting and unexpected finding from Fig. 3 is that not-currently married
men and married men without children were also relatively disadvantaged compared
to married fathers. During the months right after the lockdown, the relative disad-
vantages of never-married men were larger than those of any married female groups.
Similar to married childless women, married childless men showed a mild disadvan-
tage relative to married fathers. Cohabiting men and separated/divorced/windowed
men experienced statistically significant negative impacts in some months. Their
disadvantages look even larger (although statistically insignificantly different) com-
pared to their female counterparts.
The complexity of the effects of COVID-19 by gender, marital status and parent-
hood could be related to the differences in occupational and industrial distributions
across these groups. Gendered seasonal fluctuations are also germane to gendered
occupational and industrial distributions (Raley et al., 2006). Industries such as eat-
ing and drinking places, retail trade, and services, in which women are concentrated,
suffered the most in April right after the lockdown (See Appendix Figs. 7 and 8 for
the changes in at-work status across months by industry and by occupation). At the
same time, as in other recessions, employment in the male-dominated construction
sector declined as much as that of retail trade. In terms of occupations, while low-
skilled occupations were hit hard, high-skilled occupations such as business- and
finance-related occupations and computer/engineering/science occupations showed
hardly any changes between 2017–2019 and 2020.
Among all industries and occupations, education-related industries/occupa-
tions were the only sectors demonstrating a deep reduction in employment dur-
ing the summer months routinely prior to 2020. Workers in education-related sec-
tors reduced their labor supply substantially during the summer. The at-work rate
13
1342 A. T. Kim et al.
Fig. 4 Seasonally adjusted disadvantage relative to married fathers with youngest child ages 18+ in pro-
portion at work vs. January by levels of education. Control variables include age, age squared, levels of
education, race, citizenship status, state fixed effects, metro status, and number of children in the house-
hold. Shaded area indicate 95% confidence intervals
declined by 33.5% in July for the education service industry during 2017–2019.
Relative to the other groups in our analysis, married mothers of school-aged chil-
dren were the most concentrated in education sectors. Around one fifth of married
mothers of school-aged children worked in education sectors, compared with only
one twentieth of married fathers. In Fig. 3B, we examine whether industrial and
13
Who is the “She” in the Pandemic “She‑Cession”? Variation in… 1343
occupational compositions could account for the differentiated impact of the lock-
down across groups.
The relative disadvantages of all married women and of married childless men
are fully explained away by industry and occupation. The reason why married moth-
ers of school-aged children bore a larger negative shock is that they were more con-
centrated in industries and occupations that either fluctuate seasonally or were hit
harder than other sectors during the pandemic recession.
Unlike that of married women, the disadvantages of all five not-married groups—
never-married men, cohabiting men, separated/divorced/widowed men, childless
never-married women, and never-married mothers—remain statistically significant
net of occupation and industry. This finding suggests that marriage operates as a
buffer rather than an amplifier when it comes to pandemic-related job losses.
Differences by Levels of Education
To explore whether differentiated disadvantages across groups vary further depend-
ing on levels of education, we divide groups by whether they have a bachelor’s
degree (the highly educated) or not (the less-educated). Figure 4 shows the results.
The contrast between the less educated and the highly educated is striking. Among
those with Bachelor’s degrees, no group is statistically more disadvantaged com-
pared to married fathers with youngest children age 18+ either before or after the
control of occupation and industry. Even never-married mothers did not seem to
suffer from relative disadvantages when they were college educated, at least during
the months right after the lockdown and before the school reopening in Fall. These
results imply that among the college educated, gender, marital status, and parent-
hood were not associated with relative disadvantages in employment.
However, the likelihood of employment for the less educated does vary statisti-
cally significantly depending on gender, marital status, and parenthood. As shown
in Fig. 4A, all less-educated groups, except married mothers of non-school-age chil-
dren, experienced exacerbated disadvantage after the lockdown, although the period
and the magnitude of the disadvantages varied across groups. The elevated disad-
vantages of never-married men, cohabiting men, separated/divorced/widowed men,
never-married childless women, and never-married mothers evident in Fig. 3 were
almost completely driven by disadvantages among the less-educated.
Less-educated childless never-married women and never-married mothers were
substantially more likely to lose their employment after the lockdown. Interestingly,
less-educated married fathers with young (age 0–5) children and married childless
men also experienced an exacerbated drop in employment after the lockdown. Sur-
prisingly, less-educated married mothers with young (age 0–5) children or adult
children did not experience more negative drops in employment compared to mar-
ried fathers with adult children. Overall, these findings support Hypothesis 4. Dis-
parities in employment outcomes across groups were greater among the less edu-
cated than among the highly educated.
13
1344 A. T. Kim et al.
Occupation and industry can explain only part of the relative disadvantages for
the less educated.7 The exacerbated disadvantages for less-educated groups remain
statistically significant in Fig. 4B. In particular, the disadvantages of less-educated
never-married childless women and never-married mothers remain astonishingly
high after the control of occupation and industry.
In sum, the groups that were the most severely disadvantaged for a prolonged
period after the initial lockdown were less-educated never-married childless women
and never-married mothers. Less-educated married mothers of school-aged children,
less-educated married men without children, and less-educated not-currently mar-
ried men also experienced relative disadvantages compared to similarly less-edu-
cated married fathers.
Work Hours, Earnings, and Earnings Inequality
Changes in labor-market outcomes affected by COVID-19 may have gone beyond
employment status. In this section, we briefly examine changes in work hours, earn-
ings, and earnings inequality among those who maintained at-work status after the
lockdown. Figure 5 presents DID estimates for work hours and earnings, applying
the same model specification we used in the analyses of at-work status.8
For both weekly work hours and weekly earnings, we find surprisingly null sea-
sonally adjusted DID estimates for all groups. No group showed substantial or sta-
tistically significant disadvantages compared to married fathers in terms of work
hours or earnings. Never-married women and mothers, the most negatively affected
groups in terms of at-work status, did not experience more reduction in work hours
and earnings than other groups. While there were distinct variations in the effect of
COVID-19 by marital and parenthood status in terms of being at work, we find no
evidence that work hours or earnings among those who stayed at work were affected
differently by gender, marital status, or parenthood. We checked whether the results
differed when the groups were further divided by education, finding no such out-
comes. To check whether the pandemic recession affected part-time and full-time
employment status differently across groups, we limit our sample to those who were
employed and then estimate the changes in the likelihood of part-time employment,
finding no variation across the 15 groups.
For an additional robustness check, we explore whether those who lost jobs dur-
ing the pandemic recession were those who were employed part time before the
pandemic. This analysis requires longitudinal data. Exploiting the rotation group of
7
Less-educated mothers with youngest children age 18+ seem to have been advantaged in October and
November before controlling for occupation and industry, but the advantages disappear with the controls
of occupation and industry in Fig. 4. We checked whether distinct industrial or occupational distributions
could account for their advantages, but did not find such evidence. This group seems to have been more
likely than other groups to have been protected when they worked in government and health-related sec-
tors, but such advantages were observed only in November. We suspect that the November advantage
might be a short-term data fluctuation rather than a reflection of a systematic advantage.
8
For weekly earnings, we limit our sample to respondents with positive earnings in the outgoing rota-
tion group of the CPS.
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Who is the “She” in the Pandemic “She‑Cession”? Variation in… 1345
Fig. 5 Seasonally adjusted relative (dis)advantage in weekly work hours and weekly earnings compared
to married fathers among employed. Note: Samples are limited to those employed in the month of survey.
Control variables include age, age squared, levels of education, race, citizenship status, state fixed effects,
metro status, and number of children in the household. Shaded area indicate 95% confidence intervals
the CPS, we construct a new mini-panel dataset where the same individuals appear
in two adjacent years. The sample is limited to those who appeared in consecutive
years in the same month. With these data, we investigated whether employment sta-
tus measured before the pandemic could explain away the employment disadvan-
tages among those without a BA, finding no such effect.
13
1346 A. T. Kim et al.
Fig. 6 Variance of log weekly earnings, 2017–2020. Samples are limited to positive earners among those
in outgoing rotation group. Earnings weight is applied
Last but not least, we examine changes in inequality of weekly earnings. Figure 6
shows the variance of log weekly earnings across months in 2017–2020. Overall
patterns across calendar months did not differ noticeably between 2020 and the pre-
vious years. We also cross-checked changes in inequality of equivalized household
income (results not shown here), finding that the pattern is similar to that of indi-
vidual earnings.
Discussion and Conclusion
Who is “She” in the “She‑Cession”?
Utilizing 2017–2020 CPS monthly data, we investigated variation in the effects of
COVID-19 on at-work status across gender, marital status, and parenthood. Table 1
summarizes our findings by the most refined grouping. There are several noteworthy
findings. First, the impression that the COVID-19-related recession is a “she-ces-
sion” is misleading. There is substantial variation among female groups. Not-cur-
rently married male groups were more disadvantaged than married women without
school-aged children.
Second, age of children matters. The employment of married women of school-
aged children was more negatively impacted by COVID-19 than that of married
fathers even after adjusting for seasonal fluctuation. The negative impact on this
group was more pronounced during the school reopening period from September
to October. The relatively larger negative impact on married women of school-aged
children was associated with their unique occupational and industrial distribution.
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Who is the “She” in the Pandemic “She‑Cession”? Variation in… 1347
Table 1 Relative (dis)advantage compared to married fathers in proportion at work, setting January 2020
as a reference point
Group Month in 2020
Apr May Jun Jul Aug Sep Oct Nov Dec
I. Highly educated (Ref = Equally educated married fathers with youngest child ages 18+)
Married father, youngest 6–17 + + △ △ △ △ △ △ △
Married father, youngest 0–5 + △ △ △ △ △ △ △ △
Married childless men + △ △ △ △ △ △ △ △
Single men △ △ △ △ △ △ △ △ △
Cohabiting men + △ △ △ △ △ △ △ △
Sep/Div/Wid men △ △ △ △ △ △ △ △ △
Married mother, youngest 18+ △ △ △ △ △ △ △ △ △
Married mother, youngest 6–17 △ △ △ △ △ △ △ △ △
Married mother, youngest 0–5 + △ △ △ △ △ △ △ △
Married childless women △ △ △ △ △ △ △ △ △
Single mothers △ △ △ △ △ △ △ △ △
Single childless women + △ △ △ △ △ △ △ △
Cohabiting women △ △ △ △ △ △ △ △ △
Sep/Div/Wid women △ △ △ △ △ △ △ △ △
II. Less educated (Ref = Equally educated married fathers with youngest child ages 18+)
Married father, youngest 6–17 △ △ △ △ △ △ △ △ △
Married father, youngest 0–5 − △ △ − − △ △ △ △
Married childless men △ △ − △ − − △ △ △
Single men − − △ △ − △ △ △ △
Cohabiting men − − △ △ − − △ △ △
Sep/Div/Wid men △ △ △ △ − − − △ △
Married mother, youngest 18+ △ △ △ △ △ △ △ + △
Married mother, youngest 6–17 △ − △ △ − − − △ △
Married mother, youngest 0–5 △ △ △ △ △ △ △ △ △
Married childless women △ △ △ △ △ − △ △ △
Single mothers − − − − − − − △ −
Single childless women − − − − − − − − −
Cohabiting women − △ − △ △ △ △ △ △
Sep/Div/Wid women △ △ △ △ △ − △ △ △
Results are based on Fig. 4A. + indicates relative advantage compared to married fathers that is statisti-
cally significant at least .05 alpha level and − indicates relative disadvantage. △ means statistically non-
significant
There are more married women working in education sectors, which are sensitive to
seasonal fluctuations and school reopenings.
Third, never-married childless women and never-married mothers were the
two groups who bore the most negative impacts of COVID-19. For a prolonged
period after the initial lockdown, never-married women and mothers were sub-
stantially and statistically significantly more disadvantaged than other groups.
13
1348 A. T. Kim et al.
Never-married men were relatively more disadvantaged compared to married
fathers as well. However, unlike never-married women and mothers, their disad-
vantages lasted for a relatively short period right after the lockdown.
Fourth, when we disaggregate the demographic groups further by education,
no groups, including never-married women and mothers, experienced a greater
relative drop in employment than married fathers when they had a bachelor’s
degree. Variation across demographic groups was observed only among the less-
educated. The employment rates of all less-educated never-married singles—
including not-currently married men, never-married childless women, and never-
married mothers—were more negatively affected by the pandemic for a prolonged
period. Differences in occupational and industrial distributions only partially
accounted for the larger relative disadvantages for these groups. Among mar-
ried individuals, less-educated married men without children and less-educated
married mothers of school-aged children were also disadvantaged compared to
equally educated married fathers.
Fifth, the variation in the negative impact of COVID-19 on labor-market out-
comes across demographic groups was primarily in employment. Among those
who stayed employed, the differences in changes in work hours and earnings
across groups were at most minor.
Some may wonder if the observed variation across marital status and education
is a reflection of the racial composition of these groups, as there is a noticeable
racial gap in marriage rates (Bloome & Ang, 2020), and blacks and Hispanics
are over-represented among unmarried mothers in our sample. As a robustness
check, we reassessed our models by dropping one racial/ethnic minority group
at a time (results not shown here), finding that the considerable disadvantage of
never-married singles, especially among the less-educated, did not reflect differ-
ences in racial composition.
Overall, our results clearly demonstrate that the impacts of the pandemic
recession on labor-market outcomes were quite heterogeneous and complex.
Contrary to the perception of a she-cession, married women without school-aged
children were not worse off compared to married fathers. The unique features
of the pandemic recession are found elsewhere. Less-educated never-married
women, regardless of motherhood, were distinctively more negatively affected by
COVID-19 than other groups. The amplified burden of childcare at the onset of
the lockdown and school closings may have taken a heavy toll on the employment
of never-married mothers. Their lack of flexibility in adapting to the increased
load of childcare may have driven less-educated never-married mothers away
from their employment. The pandemic did not only impose unprecedented child-
care and educational burdens on parents; it also increased the burden of self-care
for all. Many individuals without partners or spouses lacked the luxury of either
remote working or shared caregiving responsibilities. Marriage and partnership
seem to have provided a buffer in dealing with the pandemic’s negative shocks.
These results raise new research questions on the role of marriage during the pan-
demic recession and in the new teleworking culture.
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Who is the “She” in the Pandemic “She‑Cession”? Variation in… 1349
Within‑Family Insurance and the Added Caregiver Effect
In the past, family has tended to offer a buffer against economic shocks such as
recessions, a phenomenon called within-family insurance (Alon et al., 2020b; Sahin
et al., 2010). Existing literature on within-family insurance centers on the “added
worker effect,” where the decrease or loss in one spouse’s earnings leads to an
increase in the probability of working or the absolute number of hours worked by
the other spouse (Stephens, 2002; Juhn & Potter, 2007). The assumption is that to
maximize family utility, one spouse’s displacement promotes the other spouse’s
labor-market behavior to compensate for the lost income. While we see both the
support for (Sahin et al., 2010) and limitations (Landivar et al., 2020) of such an
explanation, within-family insurance in a pandemic recession may drastically differ.
Reduction in employment is a common trait of all recessions. The heightened
burden of childcare, children’s education, and self-care, as well as the introduction
of physical and social distancing, were the peculiarities of the pandemic recession.
Sociological studies on the gendered impact of the pandemic have focused on mar-
ried couples because school-closing and stay-at-home orders increased the child-
care burden for couples (Collins et al., 2021; Petts et al., 2021; Carlson et al., 2020;
Calarco et al., 2020; Landivar et al., 2020). However, there is an additional form of
care that was affected by the pandemic: self-care. Previous studies showed that the
social support provided by a spouse or partner can work as a stress absorber (Carl-
son & Kail, 2018; Cohen & Wills, 1985). Partnered couples offer both physical and
emotional care services to each other. The reduction in, or complete lack of, social
activities as a result of physical and social distancing may have jeopardized emo-
tional self-care, especially for those who rely on social networks outside families.
Our results indicate that marriage and partnership may have still worked as
within-family insurance during the pandemic recession. The main mechanism for
within-family insurance during the pandemic, however, would not be the “added
worker effect.” Unlike in previous recessions, unemployment benefits were extended
by the federal Coronavirus Aid, Relief, and Economic Security Act (CARES) in
2020, which increased benefits, extended the benefit period, and included previously
uncovered workers. As a result, the incentive of providing an added worker became
lower.
Instead, the “added caregiver effect” seems to have been the main mechanism
of within-family insurance during the pandemic recession. Here, the responsibili-
ties of caregiving include both childcare and self-care. We do not argue that mar-
riage necessarily reduces the burden of childcare for women during the reces-
sion. Instead, we point out that a partner in the same household can at least offer
caregiving service when necessary, providing a flexibility never-married singles
cannot enjoy (Petts et al., 2021). The value of such flexibility was particularly
high during the pandemic because the requirement of physical distancing made
it hard to find a reliable emergency caregiver outside the family. If teleworking
remains common after the pandemic recession has ended, we argue that the added
caregiver effect may become a permanent feature of labor markets to some extent.
With teleworking, the frequency at which parents work at home while their chil-
dren are present will rise. If childcare becomes less accessible, the role of the
13
1350 A. T. Kim et al.
added caregiver will be enhanced. Thus, as teleworking becomes a part of the
New Normal, reliable and easily accessible childcare services will become even
more important than before, especially for single mothers.
The fact that the employment gap between married mothers and never-mar-
ried mothers was larger among those without bachelor’s degrees suggests that the
shock-absorbing effect of an added caregiver is especially larger among the less-
educated. Given that other resources are more limited for less-educated individu-
als than for those with college degrees, the benefits of within-family insurance
seem to have been more valuable for the less-educated. Previous research sug-
gests that the social support provided by a spouse is more beneficial to the well-
being of lower-SES individuals because, relative to higher-SES individuals, they
tend to lack other sources of social support outside of marriage (Carlson & Kail,
2018). The radical limitations on social interaction resulting from lockdowns and
distancing guidelines may have had an especially harmful effect, then, on unpart-
nered lower-SES individuals, because they lacked social and emotional support
relative to partnered low-SES individuals or higher-SES individuals regardless
of marital or partnership status. The pandemic may have been especially socially
isolating for unpartnered low-SES individuals. For low-SES individuals without
children, in particular, the added caregiver effect may be understood as the insur-
ance provided by the presence of a partner or spouse who can perform not just
household care work such as cooking and cleaning but also emotional and social
care work for their partner during times of widespread stress or social isolation,
such as a pandemic emergency. We propose that such an effect contributed to
the labor-market disadvantages we observe among less-educated never-married
women during the pandemic recession in particular, regardless of parenthood
status.
This study investigated variations in the impact of COVID-19 on at-work status
across demographic groups, contributing to the literature by exposing much more
complicated effects of the pandemic across demographic groups and suggesting a
new theoretical explanation of within-family insurance. This study is not without
limitations. First, the analyses presented here are by no means causal. While we
find it crucial to account for seasonal fluctuations in assessing disadvantages across
our groups of interest, our goal is to estimate relative (dis)advantage more precisely
rather than to explore the causal mechanisms of the pandemic recession. Second,
our estimates are of changes in at-work status among the working-age population,
not within-individual change due to the pandemic. Although the CPS’s outgoing
rotation sample design does make it possible to create mini-panel data, the utility
of such a design is limited in this case because a given individual is followed only 4
months in a given year. Third, we are unable to test alternative explanations, such as
the possibility that never-married singles were more concentrated in jobs that were
vulnerable to the pandemic recession in a way that could not be fully accounted for
by the control of occupation, industry, and part-time employment. Fourth, although
we suggest a new theoretical argument about within-family insurance, this study
does not empirically examine how family dynamics affected gendered labor supply
during the pandemic recession. Whether marriage and partnership will continue to
play a more prominent role in the labor market in the era of teleworking, and, if so,
13
Who is the “She” in the Pandemic “She‑Cession”? Variation in… 1351
what policies can be developed to make a more supportive community for never-
married singles are topics for future research.
Appendix
See Table 2 and Figs. 7 and 8
Fig. 7 Proportion at work vs. January by industry, 2017–2020. Control variables include gender, age, age
squared, levels of education, race, citizenship status, state fixed effects, metro status, marital status, num-
ber of children in the household and survey year for 2017–2019. Vertical bars indicate 95% confidence
intervals based on robust standard errors
13
1352 A. T. Kim et al.
Fig. 8 Proportion at work vs. January by occupation, 2017–2020. Control variables include gender, age,
age squared, levels of education, race, citizenship status, state fixed effects, metro status, marital status,
number of children in the household and survey year for 2017–2019. Vertical bars indicate 95% confi-
dence intervals based on robust standard errors
13
Table 2 Descriptive statistics
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)
Married Married Married Married Never Cohabit Sep/ Married Married Married Married Never Never Cohabit Sep/
Father Father Father Men Married Men Div/ Mother Mother Mother Women Married Married Women Div/
18+year 6–17 year ≤5yr old No child Men Wid 18+year 6–17 year ≤5yr old No child Mother Women Wid
old old old old
Child Child Child Men Child Child Child No child Women
At work (%)
January 86.5 91.4 92.6 87.6 76.2 86.1 79.5 70.6 73.4 61.7 77.3 73.3 78.4 77.9 74.7
2020
April 74.2 80.1 79.9 75.6 57.5 70.7 64.5 58.7 59.2 49.4 63.4 52.0 62.3 60.3 59.2
2020
Mean age 48.9 44.0 36.4 40.8 34.5 36.6 43.7 48.7 42.8 34.5 41.2 36.3 34.5 36.2 43.6
Education (%)
Who is the “She” in the Pandemic “She‑Cession”? Variation in…
Less 13.0 10.5 9.0 6.9 9.9 10.6 10.8 11.3 8.8 7.3 5.1 14.6 5.8 8.2 9.9
than
high
school
High 34.7 25.6 23.2 27.8 34.3 34.0 36.3 30.2 20.5 17.6 22.8 36.5 22.8 26.9 27.7
school
gradu-
ate
Some 25.0 23.5 23.0 24.0 24.8 27.3 28.5 27.9 24.4 23.0 24.5 33.7 23.2 28.7 32.1
col-
lege
Bach- 18.0 24.2 27.0 26.6 23.0 20.3 17.2 20.1 28.3 31.2 29.3 10.9 31.8 24.4 19.5
elor’s
degree
1353
13
Table 2 (continued)
1354
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)
Married Married Married Married Never Cohabit Sep/ Married Married Married Married Never Never Cohabit Sep/
13
Father Father Father Men Married Men Div/ Mother Mother Mother Women Married Married Women Div/
18+year 6–17 year ≤5yr old No child Men Wid 18+year 6–17 year ≤5yr old No child Mother Women Wid
old old old old
Child Child Child Men Child Child Child No child Women
Gradu- 9.3 16.1 17.7 14.7 8.0 7.8 7.1 10.6 18.0 21.0 18.2 4.4 16.4 11.8 10.9
ate
degree
Race (%)
White 58.9 59.9 58.8 65.2 51.5 60.1 62.0 58.8 60.0 60.3 67.3 26.2 52.1 63.1 56.0
Black 9.5 7.6 8.2 9.4 18.3 13.2 13.9 8.9 7.1 7.2 8.5 43.0 20.9 10.5 17.5
Hispanic 23.5 22.5 21.8 15.7 20.5 20.5 18.5 22.1 21.4 20.1 13.4 25.6 16.3 19.0 19.9
Others 8.1 10.0 11.2 9.6 9.6 6.1 5.6 10.1 11.5 12.4 10.8 5.2 10.7 7.4 6.6
Non- 13.5 13.8 16.7 12.0 9.5 8.9 8.1 12.8 13.8 16.2 10.2 9.3 6.8 7.8 8.2
citizen
(%)
Total No. 50,487 204,751 165,643 159,996 234,767 95,365 86,870 65,788 224,812 168,919 173,218 61,148 136,706 94,241 132,873
of Obs.
Average 1052 4266 3451 3333 4891 1987 1810 1371 4684 3519 3609 1274 2848 1963 2768
N per
month
Unique 12,687 45,449 38,284 40,496 60,922 25,108 22,624 16,256 50,089 39,283 43,087 16,350 37,738 24,909 34,287
indi-
viduals
Sep/Div/Wid indicate separated, divorced, or widowed. Because of the rotation panel design of the CPS, the same individual can appear multiple times. For married indi-
viduals, child’s age is the youngest one’s A. T. Kim et al.
Who is the “She” in the Pandemic “She‑Cession”? Variation in… 1355
Funding The authors have no relevant financial or non-financial interests to disclose.
Data Availability All datasets used for analyses are available from https://cps.ipums.org/cps/
Declarations
Conflict of interest The authors have no conflicts of interest to declare that are relevant to the content of
this article.
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