BLS WORKING PAPERS

U.S. Department of Labor

U.S. Bureau of Labor Statistics

Office of Productivity and Technology

Differential Initial Impacts of COVID-19 on the Employment and Hours of the Self-employed

Charlene Marie Kalenkoski, Texas Tech University and IZA

Sabrina Wulff Pabilonia, U.S. Bureau of Labor Statistics

Working Paper 528

September 2020
Differential Initial Impacts of COVID-19 on the Employment and Hours of the Self-employed

Charlene Marie Kalenkoski, Ph.D., CFP®

Texas Tech University and IZA

Sabrina Wulff Pabilonia, Ph.D.

U.S. Bureau of Labor Statistics

September 2020

Abstract:

This study examines the initial impact of COVID-19 on the employment and hours of 

unincorporated self-employed workers using data from the Current Population Survey. Randomeffects and difference-in-difference-in-differences models are estimated and differential impacts 

by gender, marital status, and parental status are examined. Although employment and hours 

decreased for all groups due to the response to the health threat posed by the pandemic

differential impacts by gender, marital status, and parental status exist. Married women were 

less likely to be working than married men, while single women were more likely to be working 

than single men. However, fathers of school-age children who remained employed were 

working reduced hours compared to men without children. Remote work and working in an 

essential industry mitigated some of the negative effects on employment and hours.

Keywords: COVID-19, coronavirus, self-employment, entrepreneurship, gender, remote work

JEL codes: D1, J1, J16, J2, J23

Contact Information: Charlene Marie Kalenkoski, Department of Personal Financial Planning, 

Texas Tech University, 1301 Akron Avenue, Box 41210, Lubbock, TX 79409-1210 (email: 

charlene.kalenkoski@ttu.edu); Sabrina Wulff Pabilonia, U.S. Bureau of Labor Statistics, 2 

Massachusetts Ave. NE, Rm. 2180, Washington, DC 20212 (e-mail: 

Pabilonia.Sabrina@bls.gov). 

Acknowledgements: Charlene Marie Kalenkoski acknowledges research grant funding from the

IZA Emergency Research Thrust for this project. We thank Daniel Hamermesh, Peter Meyer, 

and Jay Stewart for helpful suggestions. We thank Thomas Korankye for research assistance.

All views expressed in this paper are those of the authors and do not necessarily reflect the views 

or policies of the U.S. Bureau of Labor Statistics.
1

1. Introduction

The government shutdowns of businesses and schools in response to the COVID-19 

pandemic have led to serious disruptions in work, schooling, and family life around the 

world. However, the ability of many Americans to work at home has dampened the resulting 

economic crisis (Barrero, Bloom, and Davis 2020; Bick, Blandin, and Mertens 2020; 

Brynjolfsson, Horton, Ozimek, Rock, Sharma, and Ye 2020; Montenovo, Jiang, Rojoas, 

Schmutte, Simon, Weinberg, and Wing 2020). This paper focuses on self-employed workers 

because they were already more likely to be working from home pre-pandemic than wage and 

salary workers and they are also more likely to have greater flexibility in scheduling their work

hours than wage and salary workers (U.S. Bureau of Labor Statistics, 2019). According to the 

2018 American Time Use Survey (ATUS), which showed time use one to two years prior to the 

shutdowns, 51 percent of unincorporated self-employed workers in the United States worked at 

home on their main job on an average day, while only 21 percent of wage and salary workers did 

so (U.S. Bureau of Labor Statistics 2019). Therefore, the effects of the shutdowns on selfemployed workers who could work at home were likely to be less than those of wage and salary 

workers. 

However, self-employed workers who worked from home were also affected by school 

and day-care shutdowns, with children now being thrust into their work environment. These 

shutdowns probably affected female self-employed parents more than male self-employed 

parents because of gender norms within the home that result in women doing the majority of 

child care (Burda, Hamermesh, and Weil 2008; Sent and van Staveren 2019; Sevilla and Smith 

2020). In addition, because women are more likely than men to become self-employed to better 

balance work and family demands (Budig 2006; Gimenez-Nadal, Molina, and Ortega 2012), 

there may be differential impacts by marital and parental status. 
2

Another issue is that the shutdowns affected “essential” and “nonessential” industries 

differently. Thus, one’s own industry as well as the employment status and industry of a spouse 

also may impact a self-employed worker’s employment and hours as families juggle household 

and child-care responsibilities. However, if a mother’s spouse loses his job, the mother may 

work extra hours to compensate for the lost income while the father cares for the children or vice 

versa.

The magnitude of the overall effect of the U.S. response to the health threat posed by the 

pandemic on the total number of self-employed workers is large. U.S. Bureau of Labor Statistics 

news releases (2020a and 2020b) state that between February and April 2020, the number of 

unincorporated self-employed workers fell by 12 percent, not seasonally adjusted, from 9.3 

million workers to 8.2 million workers. These figures include those who reported being 

employed but absent, which rose substantially over the pandemic for all groups of workers. 

Over the same period, Fairlie (2020b) found that the number of actively working unincorporated 

self-employed workers fell by 28 percent. 

This paper examines the early impacts of the response to the COVID-19 pandemic on the 

employment and hours of unincorporated self-employed workers using monthly panel data from 

the Current Population Survey (CPS) for February–May of 2020 for those initially self-employed

and at work in February 2020. Differential impacts by gender, marital and parental status are 

examined.1 February 2020 is considered a normal month and March, April, and May of 2020 are 

months affected by the COVID-19 shutdowns. Social distancing policies and shutdowns began 

in March, were widespread by April, and began being relaxed in some locations in May. The 

school closures for primary and secondary students occurred for the most part after the March 

 

1 Parental status is defined as there being a child in the household. 
3

CPS reference week. All states had adopted some form of social distancing measures by March 

23rd (Adolph, Amano, Bang-Jensen, Fullman, and Wilkerson 2020). Given the sequence of 

events, the negative effects of the shutdowns should be larger in April than in March and smaller 

in May than in April.

To determine how the employment and hours of unincorporated self-employed workers 

were affected differently across the months as the nature of the shutdowns changed, indicators 

for month are included as explanatory variables in the random-effects models. Separate models 

are estimated by marital status and gender. To determine the differential impacts for different 

groups of self-employed workers, the month variables are interacted with parental status, 

whether one’s industry is deemed “essential,” whether one’s occupation could plausibly allow 

remote work, and whether there are extra adults in the household. 

While these month dummies and interactions in the random-effects models can be treated 

as exogenous regressors, as the shutdowns are a natural experiment and a host of control 

variables also are included to control for heterogeneity, any remaining unobserved heterogeneity

could cause omitted variable bias. Therefore, we also estimate difference-in-difference-indifferences (DDD) models which net out these potentially omitted variables. For these models, 

respondents to the February and April CPS in 2019 comprise the control group and respondents 

from the February and April CPS in 2020 comprise the treatment group. Treatment is assumed 

to occur in April 2020.

The results show that, although the shutdowns decreased employment and hours for all 

groups of self-employed workers, there were differential effects by gender, marital status, and 

parental status. Effects were larger for April than for March, as expected, and the loosening of 

restrictions in May did not yet have much of a moderating effect. Married women were less 
4

likely to be working than married men, while single women were more likely to be working than 

single men. However, fathers of school-age children who remained employed were working 

reduced hours compared to men without children. Having a job that could possibly be done 

remotely or working in an essential industry mitigated some of the negative labor market effects 

of the shutdowns. Having an extra adult in the household had additional negative effects on 

employment.

2. Related Literature

This paper contributes to several strands of literature, including the burgeoning literature 

on the labor-market effects of COVID-19 in general (see Brodeur, Gray, Islam, and Bhuiyan

2020 for a review). More specifically, this paper is related to the literature on the effects of the 

business cycle on the unincorporated self-employed, the effects of economic downturns in 

general, and the unprecedented effects of the simultaneous health shock on households and 

families. 

The unincorporated self-employed have unique characteristics. One is their tendency to 

remain self-employed for relatively short durations, which is dependent on macroeconomic 

conditions (Rissman 2006). In addition, compared to the incorporated self-employed, they tend 

to engage in work activities that demand relatively low levels of cognitive skills and high levels 

of manual coordination (Levine and Rubinstein 2017). Although many of the self-employed 

may do some work from their home, a significant portion of unincorporated self-employed work 

in construction, including small, home-construction activities whose services were in lower 

demand during the COVID-19 pandemic while households were social distancing (Hipple and 

Hammond 2016). Because they can control their work hours to a greater extent than wage and 
5

salary workers, self-employed parents may have more flexibility to work reduced hours rather 

than stopping work altogether to provide more child care.

Prior research on the effects of macroeconomic conditions on the unincorporated selfemployed in the United States finds that their total hours are procyclical (Carrington, McCue, 

and Pierce 1996; Pabilonia 2014); however, higher unemployment rates are associated with an 

increase in entry rates, often due to a lack of alternatives (Fairlie 2013; Fairlie and Fossen 2019), 

even at potentially reduced hours. To date, only Fairlie (2020a; 2020b) has examined effects of 

the COVID-19 pandemic on this group of workers in the U.S. Using the CPS and examining 

small business owners (many of whom are classified as unincorporated self-employed workers), 

Fairlie (2020a) found that between February and April 2020, the number of working business 

owners dropped by 22 percent. In addition, African-American, immigrant, and female business 

owners were especially hard hit by the shutdown of nonessential activities. In further work, 

Fairlie (2020b) examined the partial rebound in May 2020 as businesses reopened, resulting in a 

net 15 percent decline between February and May 2020. Over the same period, but for Canadian 

self-employed workers, Beland et al. (2020) document a 10.1 percent decrease in ownership of 

unincorporated entities. They also find a substantial decrease in ownership and aggregate hours 

for women, immigrants, and less-educated people. 

In married households, members of a couple jointly decide how much time to devote to

market work, household production, and their children, which may depend on relative income, 

social norms, productivity differences in time inputs, and bargaining power (Schoonbroodt 

2018). As a result of the closure of schools and child-care facilities in response to the COVID19 pandemic, there was an increased demand for household child care. In a married family, this 

increased responsibility could be shared. In a single-parent family, the burden likely fell
6

completely on the parent unless there was an extra adult in the household such as an unmarried 

partner, grandparent, aunt, or college student (informal care coming from outside the household 

was discouraged due to calls for social distancing). Although women spend more time caring for 

children than do men even among dual-earner couples (Alon, Doepke, Olmstead-Rumsey, and 

Tertilt 2020), Aguiar, Hurst, and Karabarbounis (2013) found that during the Great Recession,

men reallocated time to child care as the unemployment rate increased, while women increased 

their housework time. More recently, Pabilonia and Vernon (2020) find that, when working 

remotely, fathers shift some of the reduction in their commute time to primary child care, while 

there is no change in primary child-care time for mothers. Some of that increase in time is during 

typical working hours. There is prior evidence from time-use surveys that a reduction in workrelated activities leads to men shifting relatively more daily hours toward their children. In 

addition, in the Great Recession, when male-dominated sectors such as manufacturing and 

construction were especially hard-hit, there was evidence that women worked more hours on 

weekends to compensate for lost income (Morrill and Pabilonia 2015).

Concurrent research on the early effects of the pandemic on the labor market finds that 

women, particularly those with young children, are more affected than men on average

(Montenovo, Jiang, Rojoas, Schmutte, Simon, Weinberg, and Wing 2020; Zamarro, Perez-Arce, 

and Prados 2020). This is partly due to women’s employment being concentrated in serviceoriented sectors of the economy classified as “nonessential” (Alon, Doepke, Olmstead-Rumsey, 

and Tertilt 2020). However, it is also due to the increase in child-care responsibilities as schools 

and child-care facilities closed, affecting parents’ ability to work outside (and sometimes inside) 

the home. Sevilla and Smith (2020), however, found a drop in the gender child-care gap in the 

U.K., as furloughed men picked up some of the increase in household-provided child care. 
7

Using the CPS and focusing on couples, Heggeness (forthcoming) compares labor market effects 

in states with early and late school closures. She found that mothers in early closure states were 

53 percent more likely than mothers in late closure states to be employed but absent from work. 

Of those remaining active at their job, she found that mothers in early closure states had higher 

average weekly hours than mothers in late closure states, while fathers worked fewer hours. 

Descriptive analyses based on the Understanding Coronavirus in America Tracking Survey

indicate that 33 percent of working mothers in two-parent households provided all of the care for 

children while schools were closed in early April, while only ten percent of males provided all of 

the care (University of Southern California Center for Economic and Social Research 2020;

Zamarro, Perez-Arce, and Prados 2020). Roughly twenty-five percent of parents shared care. 

3. Data

This paper examines changes in the employment and work hours of unincorporated selfemployed workers, using data from the CPS basic monthly files for February–May of 2020 for 

those initially self-employed in February 2020. February 2020 is considered a normal month and 

March, April, and May of 2020 were affected by the COVID-19 shutdowns. The school closures 

for primary and secondary students occurred for the most part after the March CPS reference 

week. The reference week typically includes the 12th of the month and ended in March on the

14th. The World Health Organization (WHO) did not announce the pandemic until March 11th, 

although media coverage picked up in early March after several cases were identified in 

Washington State and people had already started to change their behavior in response to the 
8

news reports. Therefore, the effects are expected to be smaller in March than in April. If the reopenings were effective, the effects might be smaller in May than in April as well.2

 

The CPS interviews a panel of individuals/households for four months, then does not 

interview them for eight months, then re-interviews them again for four months. Each month

there are eight rotation groups of households. Those households which are in their first or fifth 

month in the sample plausibly can be followed each month from February to May while those in 

their second and sixth month in the sample can be followed from February to April, and so forth.

Thus, each subsequent month, the sample of potential continuers falls (approximately 75 percent 

in the second month of the panel, 50 percent in the third month, and 25 percent in the fourth 

month). However, in any given month, a household may also choose not to respond. For 

example, there may be a response in February and in April, but not in March and May for an 

individual interviewed for the first time in February.

The sample for this analysis includes civilian adults aged 18 and older who were 

unincorporated self-employed workers on their main job and at work in February 2020.

3

 The 

analysis follows them from February through May 2020 (for the random-effects models) and 

from February to April 2020 (for the DDD models).4 The unbalanced panel includes 3,403, 

2,302, 1,515, and 776 individuals in the months of February, March, April, and May,

respectively. For the DDD analyses, we use a balanced panel of individuals who were selfemployed and at work in February 2019 or 2020 and subsequently interviewed in April of 2019 

or 2020 (excluding March). Comparing the same months across 2019 and 2020 controls for 

 

2 The May CPS reference week was March 8th through 14th. The April CPS reference week was April 12th through 

18th. The May CPS reference week was May 10 through 16th.

3 We drop a small number of workers who can be matched on CPS identifying variables (HHRID HHRID2 

PULINENO) but do not match on age and sex.

4 May is not included in the DDD models, because there was a different treatment in May as the country began 

reopening. 
9

seasonal differences. In 2019, our sample includes 1,510 individuals. In 2020, our sample 

includes 1,515 individuals. 

A general concern about the CPS data collected during the pandemic has been a spike in 

those reporting employed but absent for “other reasons.” Respondents who reported not working 

due to efforts to contain the spread of COVID-19 should have been classified as unemployed on 

temporary layoff, but many were misclassified as employed but absent (U.S. Bureau of Labor 

Statistics 2020b and 2020c). For this reason, this analysis focuses on the change in employment 

status for those who were self-employed and at work in February, i.e. those with positive hours. 

Additional information included in the analysis concerns the plausibility that an 

individual’s job (or their spouse’s job, if applicable) can be done entirely remotely. This is 

referred to in the analysis as a remote job. In addition, information about whether an individual 

(or spouse, if applicable) worked in an essential industry is used. The remote-job variable is 

based on Dingel and Neiman (2020) who measured the feasibility of a job being done entirely at 

home based upon job tasks reported in the Occupational Information Network (O*NET) surveys,

with some additional tweaks to match the change from the 2010 Census codes to the 2018

Census codes in the 2020 CPS.5In most cases, the remote-job variable takes a value of 0 for not 

being able to be done remotely, and 1 for being able to be done entirely remotely. However, in 

several cases, only part of any occupation in the CPS could be classified as being done remotely 

and so the value reflects the share employed in the occupation who can work remotely. The 

essential industry variable is based upon Delaware’s nonessential closed business criteria, which 

is reported at the 4-digit NAICS level (Delaware Division of Public Health, Coronavirus 

 

5 Additional research by Bick, Blandin, and Mertens (2020) using the Real-Time Population Survey (a CPS-like 

questionnaire) shows that about 72 percent of workers (or 35.2 percent of the workforce) who could work at home 

as defined by Dingel and Neiman (2020) actually worked entirely at home in May 2020.
10

Response 2020). For three detailed CPS industries (Charter Bus Industry, Cable and Other 

Subscription Programming, and Real Estate), the September 2019 Quarterly Census of 

Employment and Wages (QCEW) is used to record the nonessential employment share.

4. Descriptive Statistics: Labor Market Differences by Gender, Marital Status, and 

Parental Status

Figure 1 shows the decline in employment by gender for unincorporated self-employed 

workers who were at work in February 2020.6 Of these self-employed workers, over 86 percent

were still at work in March, with a negligible gender difference. This is not unexpected, as 

shutdowns had not occurred widely until after the March CPS reference week. However, in 

April, only 65 percent of the men and 58 percent of the women remained at work. Thus, while 

both men and women among the self-employed suffered reduced employment in April, the 

shutdown had a statistically significant larger effect on women.7 In May, given the partial reopenings, about 74 percent of self-employed men and just over 61 percent of women were 

working.8 Thus, men appear to be bouncing back to a greater degree than women. This may be 

due to gender roles, where the man is expected to be the breadwinner in the family (Allred 2018; 

Betrand, Kamenica, and Pan 2015), and the fact that schools and many child-care facilities had 

not yet re-opened. 

 

6 CPS final weights are used in the descriptive analyses.

7 T-tests for all the employment and hours differences by gender, couple status and parental status reported in this 

descriptive section are statistically significant at the 10 percent level unless otherwise stated.

8 Although our main analyses examine whether self-employed workers are doing any work in subsequent months

compared to self-employment in February, some of the self-employed reported that they had transitioned into wageand-salary employment. Of those actively working in March, 5 percent of men and 4 percent of women switched to 

wage-and-salary employment. In April, 6 percent of men and 7 percent of women switched to wage-and-salary 

employment. By May, 11 percent of men and 16 percent of women switched to wage-and-salary employment. The 

latter finding is not statistically significantly different.
11

Figure 2 shows the decline in the average weekly hours worked by gender for

unincorporated self-employed workers who were at work in February 2020. In February, selfemployed men worked approximately 37 hours per week and women worked approximately 30 

hours per week. In March, hours declined to 32 hours per week for men, on average, and to 26

hours per week for women. In April, hours fell even further, to 23 hours per week for men and 

16 hours for women. In May, hours started to bounce back for men (back to 28 hours per week) 

but there was little change for women.

Figure 3 shows how gender and parental status are related to employment for married

individuals. Married couples can trade off housework and child-care tasks with their partner and 

so individuals in these households have greater flexibility than those in single-parent households, 

all else equal. Again, we see a decline in employment for everyone from February through April 

and an increase from April to May. However, the declines are much larger for women,

especially those with children (though the difference between women with children and those 

without children is not statistically significant at conventional levels). Having children does not

make any difference for men’s employment.

Figure 4 shows how gender and parental status are related to employment for single 

individuals. These individuals do not necessarily have a partner to help with household tasks 

such as caring for children.9 Single men had larger declines in employment from February 

through April than married men. There is an especially large drop for single fathers with 

household children in April. However, single fathers experienced a large increase in 

employment in May, getting them almost to the same employment level as single men without 

children. Single women also experienced a drop in employment in March and April, with a 

 

9 Approximately 19 percent of single individuals are living with an unmarried partner.
12

slightly larger drop for single mothers than for non-parents in April (though the results were not 

statistically significant at conventional levels). However, single women, with or without 

children, did not experience a rebound in employment in May as married individuals or single 

men did.

Figure 5 shows how gender and parental status are related to average weekly hours for 

married individuals by month. In February, men without children worked approximately 37

hours, while men with children worked over 40 hours. However, in April, men worked only 25 

hours, regardless of parental status. Women worked substantially less than men in all months, 

and women with children worked fewer hours than women without children, although the latter 

differences were not statistically significantly in April and May. Women with children were the 

only group not to rebound at all in May.

Figure 6 shows how single workers’ average weekly hours were affected, by parental 

status and gender. Theirs is a similar story to that for married workers, but there is a huge drop 

in hours for single fathers in April compared to married fathers. Single fathers in April have an 

even lower number of work hours, on average, than single mothers, though the difference is not 

statistically significant. However, single fathers rebound in May, while single mothers do not. 

Appendix Table A1 provides greater detail about the descriptive statistics of the sample,

including a breakdown by the presence and age of children given the different amounts of 

supervision and help with online schooling that were necessary during the shutdowns. 

5. Models Used to Show Initial COVID-19 Impacts

Two types of models are estimated to examine the initial differential impacts of the 

COVID-19 pandemic shutdowns (a natural experiment) on the employment and work hours of 

the unincorporated self-employed. The first type of models, random-effects models, exploit the 
13

richness of the data to examine how employment and hours changed as shutdowns began to 

occur in March, were more widespread in April, and partial re-openings began in May. Month 

dummy variables capture the effects of the shutdown and reopening, and they are interacted with 

parental status, industry type (essential or not), occupation type (remote work possible or not), 

and number of extra household adults to determine whether the effects differ by the presence of 

children, work situation of the respondent, and the presence of extra adults. The random-effects 

models are estimated separately for respondents who are married and those who are single, and 

also for men and women separately, as married couples may be better able to juggle 

responsibilities than singles, and there are different cultural expectations for men and women. 

Models that pool men and women are also presented. These include a gender dummy and a 

gender dummy interacted with the month variables.

The second type of models, difference-in-difference-in-differences (DDD) models, do not 

examine the evolution of employment changes as the shutdowns began, became complete, and 

then began being rescinded. Instead, it considers the change from February to April as a single 

“treatment” and examines the effect of this treatment on employment and hours. While these 

models do not make full use of the richness of the data as the random-effects models do, they are 

preferred when there is a concern about omitted variable bias due to unobserved heterogeneity. 

Although we do not expect this to be an issue given our extensive set of controls, we also 

estimate DDD models because they difference out time-invariant unobserved heterogeneity. 

5.1 Random-Effects Models

We estimate several random-effects models as follows:

(1) Yit = β0 + β1Wi + β2Mt + β3Wi*Mt + γXi + μi + εit
14

where Yit is one of three outcomes examined for individual i in year t. The first is employment 

status, a variable which takes a value of 1 if the respondent was employed and at work during the 

reference week and 0 otherwise.

10 The second is hours worked last week. The third is hours 

worked conditional on being employed and at work. Wi is a vector of key regressors measured 

in February 2020 (to avoid changes potentially caused by the treatment/shutdowns) that include 

dummies for gender (for those analyses that pool men and women), any household child age <6, 

any household child age 6–17, respondent’s job is a remote job, respondent’s job is in an 

essential industry, and a continuous measure of the number of extra adults (besides self and 

partner) in the household. Mt is a vector of month dummy variables for March, April, and May 

of 2020. Wi*Mt are the interactions between the key regressors included in Wi and month. The 

matrix Xi includes additional control variables measured as of February 2020. These include age 

and age squared and indicators for education (high school degree, some college, bachelor’s 

degree, advanced degree), race (African-American, other race), Hispanic ethnicity, cohabitation 

status, immigrant status, living in an MSA, state of residence, own industry, spouse’s industry,

and indicators for whether a respondent’s spouse is employed, in a remote job, and in an 

essential industry. μi is the unobserved, person-specific effect, assumed to be uncorrelated with 

the other included regressors, and εit is the random noise error term. The coefficients β0, β1, β2, 

β3, and the vector γ are to be estimated. The key coefficients are β2 and β3, as these give the level 

and interaction effects of the treatment (i.e. shutdowns). The models control for clustering by 

household, because in some cases both the respondent and spouse are self-employed. 

5.2 Difference-in-Difference-in-Differences Models

 

10 Logit or probit random-effects models would be appropriate due to the dichotomous nature of the first dependent 

variable. However, the models would not converge. Robust standard errors adjust for heteroscedasticity.
15

In order to identify the causal effects of the shutdowns following the WHO 

announcement of the COVID-19 pandemic on March 11th, we estimate DDD models where the 

control group includes those sampled in both February and April 2019 and the treated group 

includes those sampled in both February and April 2020. We examine the initial differential 

effects of the shutdowns on employment and hours in April by estimating several models of the 

following form:

(2) Yit = β0 + β1Aprilt + β2Yeari + β3hi + β4Aprilt*hi + β5Yeari*hi + β6Aprilt*Yeari +

β7hi*Aprilt*Yeari + γXit + εit

where Yit is one of three outcomes for individual i in year t: (a) employment status, (b) hours 

worked last week, or (c) hours worked last week conditional on being employed and at work. An 

Aprilt dummy is included to control for the second time period. Yeari equals 1 if the individual 

is in the treated group (interviewed in 2020) and 0 otherwise. hi is an indicator variable for a 

particular subgroup of self-employed workers (i.e., females, parents, those with a remote job, 

those in an essential industry, and those with extra adults in the household) who may be 

differentially affected by the shutdowns. Our models allow only one differential to be explored 

at a time. The product of Aprilt and Yeari, equals 1 in April 2020 (when the COVID-19

shutdowns were complete) and 0 otherwise. The vector Xit includes the individual, spatial, and 

job characteristics controls specified above, which improves the model precision, and εit is the 

error term.11 The effect of COVID-19 for those not in the subgroup of interest, non-parents for 

example, is β6. The differential effect for those in the subgroup of interest, parents for example, 

 

11 An alternative model including individual fixed effects could also be estimated. Results are similar and available 

upon request.
16

is β7. We estimate these models with ordinary least squares and clustering by household-year 

pair cluster.

6. Initial Differential Impacts of COVID-19

6.1 Random-Effects Models Results

Table 1 shows the results from the random-effects models for employment.12 Looking at 

the “All” columns, it can be seen that employment was lower for everyone in March, April, and 

May compared to February. In April 2020, the unincorporated self-employed were much less 

likely to still be employed and at work than in February (married individuals were 48 percentage 

points less likely while single individuals were 57 percentage points less likely). However, the 

negative effects appear to be smaller in May, as businesses reopened, than in April, except for 

single women. Again examining the “All” columns, we can see that, compared to married men, 

married women were 7 percentage points less likely to be employed in April than in February 

and 11 percentage points less likely to be employed in May than in February. However, 

compared to single men, single women were 11 percentage points more likely to be employed in 

April than in February. 

 Now looking at the other columns that show results by marital status and gender, it is 

seen that having young children increases the probability that married men are employed in May 

compared to February. Having school-age children increases the probability that they are 

employed in March compared to February. On the other hand, single men with school-age

children are less likely to be employed in April compared to February. Having a remote job 

increases April employment for married men and for both single men and women, relative to 

 

12 Summary statistics for the estimated models are included in Appendix Tables A2 and A3. Across months in 2020, 

demographics are similar, suggesting that the results should not suffer from nonresponse bias due to any differential 

reduction in nonresponse. Samples in February 2019 and 2020 indicate that our control group and treatment group 

are fairly similar.
17

February. Being in an “essential industry” increases the probability of being employed for 

married men in March, for all in April, and for all singles in May, compared to February. It also 

increases the probability for married women in May.

Having an extra adult in the household (not a spouse, but perhaps a young adult child or 

parent) decreases the probability that married women are employed in May compared to 

February. It also decreases the probability that single men are employed in March through May

compared to February. One plausible explanation for the negative effects are that the extra adult 

may require care that had previously been provided by the market. A related explanation may be 

that the respondent did not want to return to work to prevent exposure of the extra adult to 

COVID-19. In addition, the extra adult may have been ill with COVID-19 and thus everyone 

was quarantined. 

Table 2 shows the results from the random-effects models for hours worked. Work hours 

were reduced substantially for all groups in April and May compared to February, with the 

effects larger in April than May (hours were also slightly reduced in March). Single women 

experienced smaller reductions than single men, however, in March and April. Married women 

experienced a larger reduction than married men in May compared to February. Married men 

with young children worked more hours in May than in February, compared to men without 

young children, while married men with school-age children worked fewer hours in May than in 

February compared to married men without school-age children. Single men with school-age

children worked fewer hours in April than in February compared to single men without schoolage children. 

Being in a remote job increased only the hours of work of single women in April 

compared to February. This also appears to be true for May as the coefficient is similar but 
18

estimated imprecisely. Working in an essential industry increased hours in April and May 

compared to February for all groups. 

For married women, having an extra adult in the household reduced their hours of work 

in April compared to February. Although estimated imprecisely, this may also have been the 

case for May compared to February, as the size of the coefficient is similar. Again, this negative 

impact may be because the extra adult was at risk for COVID-19. For single men and women, 

having an extra adult in the household decreased their work hours in April compared to 

February, and possibly also for May. 

Table 3 shows the impact of the COVID shutdowns on hours worked by those remaining 

employed and at work. These estimates show that most of the impacts of COVID on hours are 

coming through the extensive margin (i.e., the reduction in employment). However, there are 

still some impacts on the intensive margin. There are reductions in hours for everyone still 

working in April compared to February and in May for married women. In addition, there is an 

additional 4 to 5-hour reduction in working hours in May for those with school-aged children

compared to non-parents (estimates are similar in Table 2). For single women, having a remote 

job also moderates the impact of the shutdowns in April on the intensive margin.

6.2 Difference-in-Difference-in-Differences Models Results

Table 4 shows the results from the DDD models for employment. Each panel is a 

separate regression. Panel A shows the effects of the COVID shutdowns on several groups of 

self-employed workers, all married, married men, married women, all single, single men, and 

single women. Compared to the random-effects models, the effects appear somewhat smaller in 

magnitude, with married individuals being 23 percentage points less likely and single individuals 

being 24 percentage points less likely to be employed and at work due to COVID. In Panel B, 
19

we examine whether there are different group effects by gender. Among married individuals, 

women were less likely to be employed and at work due to COVID than men, but the estimate 

was imprecise. However, among single individuals, the reductions in employment were larger 

for men than for women. We found this with the random-effects models as well. Also similar to 

the estimates from the random-effects models, single fathers of school-age children (Panel E) 

were significantly less likely to be employed in April 2020 due to COVID compared to single 

men without school-age children (a 17-percentage-point difference). Having a remote job does 

not have a protective effect on the probability of employment as hypothesized. More important 

appears to be working in an essential industry. Working in an essential industry compared to a 

non-essential industry substantially increased the probability of being employed for all groups

with the exception of single women, 16 percentage points for married men, 32 percentage points 

for married women, and 23 percentage points for single men. Having an extra adult in the 

household decreases the probability of being employed during the COVID shutdowns by 8

percentage points for single men.

Table 5 shows the results from the DDD models for hours worked last week. The effect 

of the shutdowns in April 2020 is a reduction of 11 hours per week, on average, which is smaller 

than what we found using the random-effects model. We do not find statistically significant 

gender differences in the reduction in hours due to COVID, although the coefficient in the 

singles regression is positive as in the random-effects model. Hours for single fathers of schoolage children are disproportionately negatively affected compared to men without school-age 

children (almost 10 fewer hours for fathers, which is slightly larger than what we found using the 

random-effects model). 
20

Again, as in the previous table, we do not find that having a remote job changes the effect 

of COVID on hours. However, working in an essential industry has a statistically significant 

positive effect on hours for married individuals, with married women working 13.5 hours more 

than married women not in an essential industry and married men working 9.8 hours more. 

Thus, being in an essential industry reduces the effects of the shutdowns on married individuals 

substantially. In addition, being in an essential industry reduces the effects of the shutdowns on 

the hours of single men. Having an extra adult in the household increased the hours single 

women worked compared to single women without an extra adult in the household, but the result 

is imprecise. 

Table 6 presents the results from the DDD models for hours worked by those remaining 

employed and at work. These estimates show that most of the effects of COVID on hours are 

coming through the extensive margin (i.e., the reduction in employment). However, even those 

remaining employed and at work worked fewer hours, with married and single individuals 

working 5 fewer hours as a result of the shutdowns. We did not find significant gender 

differences in the effects. However, we did find that having a remote job mitigated the negative 

effects of the shutdowns for single women on the intensive margin. Working in an essential 

industry had a protective effect on hours for married individuals.

7. Conclusion

The initial impacts of COVID-19 on the employment and hours worked of the 

unincorporated self-employed indicate differential effects by gender, marital status, and parental 

status. The negative effects were largest in April 2020, with a small rebound in May 2020. As a 

result of the response to the COVID-19 pandemic, married women were less likely to be 

employed and at work than married men, while single women were more likely to be employed 
21

and at work than single men. Both single and married fathers with school-age children spent 

fewer hours working than men without children, suggesting that they may have been spending 

more time on household or child-care responsibilities. Negative effects were mitigated if they 

worked in an essential industry. Only minimal evidence of mitigation through having a remote 

job was found (random-effects model only). Having an extra adult in the household had 

additional negative effects on employment for single males and additional negative effects on 

hours for all groups with the exception of married men.

The stronger rebound in work for married men than for married women suggests that 

self-employed women may remain out of the labor force longer as men resume their role as the 

family breadwinner and women maintain their primary role as caregiver, especially as schools 

are engaging in online rather than in-person learning. This could have serious long-term 

negative implications for female labor force participation, the gender wage gap, and household 

income.
22

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25

Fig.1 Unincorporated self-employed who were at work in 2020

Note: All workers were self-employed and at work in February 2020. For males, N =

2,056, 1,366, 861, 442 for consecutive months. For females, N = 1,347, 936, 654, 334 

for consecutive months.

Source: Current Population Survey
26

Fig.2 Average weekly hours worked by the unincorporated self-employed in 2020

Note: All workers were self-employed and at work in February 2020. For males, N =

2,056, 1,366, 861, 442 for consecutive months. For females, N = 1,347, 936, 654, 334

for consecutive months.

Source: Current Population Survey
27

Fig.3 Unincorporated self-employed who were at work (married individuals, by parental 

status)

Note: All workers were self-employed and at work in February 2020. For males, N 

=1,334, 905, 573, and 287. For females, N = 841, 573, 417, and 214.

Source: Current Population Survey
28

Fig.4 Unincorporated self-employed who were at work (single individuals, by parental 

status)

Note: All workers were self-employed and at work in February 2020. For males, N =722,

461, 288, and 155. For females, N = 506, 363, 237, and 120.

Source: Current Population Survey
29

Fig.5 Average weekly hours worked by married individuals, by parental status

Note: All workers were self-employed and at work in February 2020. For males, N 

=1,334, 905, 573, and 287. For females, N = 841, 573, 417, and 214.

Source: Current Population Survey
30

Fig.6 Average weekly hours worked by single individuals, by parental status

Note: All workers were self-employed and at work in February 2020. For males, N =722,

461, 288, and 155. For females, N = 506, 363, 237, and 120.

Source: Current Population Survey
31

Table 1. Differential Effects of COVID on Employment Status (Random-Effects Models)

Married Single

All Men Women All Men Women

Female -0.01 -0.00

(0.01) (0.01)

Child age<6 -0.01 -0.02** 0.00 -0.02 -0.00 -0.03

(0.01) (0.01) (0.02) (0.01) (0.02) (0.03)

Child age 6-17 -0.01 -0.01 0.01 -0.01 -0.02 0.01

(0.01) (0.01) (0.01) (0.01) (0.01) (0.02)

Remote job -0.01 0.00 -0.03**-0.02**-0.01 -0.05***

(0.01) (0.01) (0.01) (0.01) (0.01) (0.02)

Essential industry -0.02**-0.01 -0.04**-0.01 0.04 -0.04**

(0.01) (0.02) (0.02) (0.01) (0.03) (0.02)

Extra adults 0.01* 0.01** 0.01 0.01* 0.00 0.01**

(0.00) (0.00) (0.01) (0.00) (0.01) (0.01)

March -0.15***-0.21***-0.13***-0.16***-0.11***-0.17***

(0.03) (0.04) (0.03) (0.03) (0.04) (0.04)

April -0.48***-0.47***-0.54***-0.57***-0.56***-0.50***

(0.04) (0.06) (0.05) (0.05) (0.07) (0.06)

May -0.31***-0.29***-0.41***-0.49***-0.42***-0.56***

(0.05) (0.07) (0.07) (0.07) (0.08) (0.08)

March X Female -0.03 0.03

(0.02) (0.03)

April X Female -0.07** 0.11**

(0.03) (0.04)

May X Female -0.11*** 0.02

(0.04) (0.06)

March X Child age<6 -0.02 0.01 -0.06 0.02 0.00 0.02

(0.03) (0.03) (0.05) (0.05) (0.08) (0.06)

April X Child age<6 -0.05 -0.04 -0.06 0.09 0.15 0.01

(0.04) (0.05) (0.06) (0.07) (0.10) (0.10)

May X Child age<6 0.03 0.11*-0.05 0.07 0.11 -0.01

(0.05) (0.06) (0.08) (0.09) (0.13) (0.12)

March X Child age 6-17 0.06*** 0.07*** 0.04 -0.01 -0.00 -0.00

(0.02) (0.02) (0.03) (0.03) (0.05) (0.04)

April X Child age 6-17 0.01 0.03 -0.02 -0.10*-0.15*-0.06

(0.03) (0.04) (0.05) (0.06) (0.08) (0.07)

May X Child age 6-17 0.01 0.00 0.01 0.03 -0.03 0.08

(0.04) (0.05) (0.07) (0.07) (0.12) (0.09)

March X Remote job -0.02 0.00 -0.03 0.01 -0.01 0.05

(0.02) (0.02) (0.03) (0.03) (0.04) (0.04)

April X Remote job 0.07** 0.08** 0.06 0.11** 0.10* 0.13**

(0.03) (0.04) (0.05) (0.04) (0.06) (0.07)

May X Remote job 0.02 -0.03 0.07 0.08 -0.01 0.18**

(0.04) (0.06) (0.07) (0.06) (0.08) (0.08)

March X Essential industry 0.03 0.08**-0.02 -0.00 -0.04 0.02

(0.02) (0.03) (0.03) (0.03) (0.04) (0.04)

April X Essential industry 0.23*** 0.20*** 0.26*** 0.24*** 0.26*** 0.20***

(0.04) (0.06) (0.05) (0.05) (0.07) (0.06)

May X Essential industry 0.10** 0.06 0.11* 0.27*** 0.23*** 0.31***

(0.05) (0.07) (0.06) (0.06) (0.09) (0.08)
32

Table 1 Continued. Differential Effects of COVID on Employment Status (Random-Effects Models)

Married Single

All Men Women All Men Women

March X Extra adults 0.01 0.02 -0.01 -0.01 -0.03* 0.01

(0.01) (0.01) (0.02) (0.01) (0.02) (0.01)

April X Extra adults -0.01 -0.01 -0.03 -0.04**-0.09*** 0.03

(0.02) (0.03) (0.04) (0.02) (0.02) (0.03)

May X Extra adults -0.04 -0.01 -0.10*-0.07**-0.07*-0.04

(0.03) (0.04) (0.06) (0.03) (0.04) (0.04)

Observations 5,144 3,099 2,045 2,852 1,626 1,226

Between R2 0.24 0.21 0.31 0.27 0.30 0.35

Notes: Standard errors in parentheses are clustered by household. Control variables also include a quadratic in age, 

education, race, Hispanic ethnicity, immigrant status, own major industry, spouse major industry, MSA status, and 

state fixed effects. Controls in married regressions include spouse employed, spouse major industry, spouse remote 

job, and spouse essential industry. Control variables in single regressions include cohabiter. The all regressions 

include female. * p < 0.10, ** p < 0.05, *** p < 0.01

Source: Current Population Survey, February – May 2020
33

Table 2. Differential Effects of COVID on Hours (Random-Effects Models)

Married Single

All Men Women All Men Women

Female -10.585***-3.027***

(0.846) (1.126)

Child age<6 -2.947***-1.897 -5.662*** 0.579 3.824 -2.268

(1.049) (1.223) (1.843) (1.954) (2.852) (2.774)

Child age 6-17 -0.654 0.365 -1.437 -0.770 -1.264 -0.171

(0.819) (1.016) (1.305) (1.145) (1.620) (1.859)

Remote job 0.597 1.490 -0.573 2.347* 1.596 2.776

(0.894) (1.196) (1.361) (1.257) (1.528) (2.017)

Essential industry -1.480 1.756 -4.101***-0.005 2.877 -2.751

(1.101) (1.583) (1.534) (1.378) (2.045) (1.993)

Extra adults 1.571*** 1.668*** 1.906** 0.091 -0.867 1.682**

(0.508) (0.574) (0.897) (0.475) (0.578) (0.858)

March -4.942***-4.804***-4.889***-5.395***-4.622**-2.140

(1.098) (1.548) (1.226) (1.442) (1.880) (1.807)

April -20.145***-18.966***-19.114***-20.464***-18.906***-18.868***

(1.690) (2.462) (1.792) (2.067) (2.512) (2.522)

May -12.816***-10.836***-17.416***-14.490***-13.839***-15.723***

(2.200) (2.999) (2.852) (2.946) (4.028) (3.041)

March X Female -0.253 3.785***

(0.839) (1.224)

April X Female 1.784 4.593***

(1.170) (1.711)

May X Female -2.936* 0.502

(1.519) (2.418)

March X Child age<6 -0.171 1.541 -2.929 -2.228 -0.607 -3.194

(1.150) (1.453) (1.809) (2.286) (3.254) (3.209)

April X Child age<6 -1.590 -1.381 -1.537 3.450 4.146 1.806

(1.625) (2.238) (2.188) (2.822) (3.807) (4.420)

May X Child age<6 2.513 4.949* 0.138 4.068 5.097 2.504

(2.021) (2.830) (2.519) (3.128) (4.386) (4.774)

March X Child age 6-17 0.121 0.433 -0.461 0.041 0.405 0.047

(0.966) (1.209) (1.386) (1.491) (2.301) (1.981)

April X Child age 6-17 -1.286 -2.222 0.025 -4.802**-7.468**-1.910

(1.403) (1.806) (1.873) (2.208) (3.361) (2.871)

May X Child age 6-17 -3.029*-5.534**-0.084 -4.702*-6.334 -3.308

(1.764) (2.232) (2.341) (2.778) (4.968) (4.166)

March X Remote job -0.576 -0.537 -0.239 -0.990 -0.029 -1.378

(0.907) (1.189) (1.297) (1.269) (1.745) (1.825)

April X Remote job 2.017 1.539 2.751 2.378 0.523 5.326**

(1.265) (1.736) (1.816) (1.817) (2.559) (2.603)

May X Remote job 0.064 -2.959 3.343 1.503 -1.275 5.320

(1.694) (2.298) (2.483) (2.417) (3.387) (3.418)

March X Essential industry 1.377 0.619 2.045 -0.562 -1.822 0.411

(1.037) (1.553) (1.269) (1.278) (1.821) (1.844)

April X Essential industry 10.963*** 9.669*** 12.016*** 8.436*** 9.082*** 8.095***

(1.488) (2.348) (1.732) (1.786) (2.631) (2.539)

May X Essential industry 7.027*** 5.764** 7.625*** 6.428*** 7.192* 5.921*

(1.888) (2.856) (2.392) (2.493) (3.838) (3.417)
34

Table 2 Continued. Differential Effects of COVID on Hours (Random-Effects Models)

Married Single

All Men Women All Men Women

March X Extra adults -0.130 0.099 -0.648 -0.608 -0.994 -0.130

(0.616) (0.730) (1.030) (0.510) (0.640) (0.616)

April X Extra adults -1.629*-0.728 -3.293**-0.882 -2.399**-1.629*

(0.989) (1.119) (1.615) (0.905) (1.055) (0.989)

May X Extra adults -0.749 0.466 -3.165 -1.575*-1.537 -0.749

(1.454) (1.523) (2.578) (0.909) (1.148) (1.454)

Observations 5,144 3,099 2,045 2,852 1,626 1,226

Between R

2 0.22 0.20 0.20 0.16 0.24 0.22

Notes: Standard errors in parentheses are clustered by household. Control variables also include a quadratic in age, 

education, race, Hispanic ethnicity, immigrant status, own major industry, spouse major industry, MSA status, and 

state fixed effects. Controls in married regressions include spouse employed, spouse major industry, spouse remote 

job, and spouse essential industry. Control variables in single regressions include cohabiter. The all regressions 

include female. * p < 0.10, ** p < 0.05, *** p < 0.01

Source: Current Population Survey, February – May 2020
35

Table 3. Differential Effects of COVID on Hours for those Remaining Employed (Random-Effects Models)

Married Single

All Men Women All Men Women

Female -10.561***-2.757**

(0.826) (1.101)

Child age<6 -2.943***-1.574 -6.034*** 1.012 3.963 -1.313

(1.035) (1.182) (1.858) (1.967) (2.850) (2.769)

Child age 6-17 -0.926 0.210 -1.901 -0.808 -1.148 -0.602

(0.808) (0.998) (1.315) (1.141) (1.585) (1.840)

Remote job 0.855 1.548 -0.116 3.003** 1.788 4.023**

(0.878) (1.165) (1.369) (1.235) (1.510) (1.977)

Essential industry -0.801 2.480 -3.226** 0.067 1.934 -2.111

(1.066) (1.520) (1.551) (1.339) (1.994) (1.987)

Extra adults 1.341*** 1.388** 1.705*-0.123 -1.027* 1.342

(0.504) (0.567) (0.903) (0.466) (0.556) (0.855)

March -0.084 1.474 -1.629 -1.695 -1.500 1.280

(0.897) (1.237) (1.047) (1.383) (1.754) (1.883)

April -7.653***-6.847***-7.243***-6.550***-2.704 -12.485***

(1.880) (2.631) (2.078) (2.177) (2.404) (3.284)

May -3.474 -2.187 -7.864**-0.308 -0.107 -4.068

(2.192) (2.590) (3.235) (2.979) (4.005) (3.502)

March X Female -0.703 2.860**

(0.739) (1.122)

April X Female 1.714 -0.862

(1.120) (1.768)

May X Female -2.008 -1.629

(1.533) (2.560)

March X Child age<6 -0.227 1.127 -2.705 -2.847 -0.905 -3.573

(1.049) (1.297) (1.763) (2.069) (2.653) (3.191)

April X Child age<6 0.046 0.024 -0.017 0.178 -4.372 4.010

(1.519) (1.807) (2.593) (2.900) (3.903) (4.148)

May X Child age<6 2.853* 3.725* 1.400 0.873 -3.704 3.819

(1.669) (2.058) (2.649) (2.689) (3.863) (4.937)

March X Child age 6-17 -1.011 -1.279 -0.650 0.792 1.004 0.822

(0.848) (1.053) (1.277) (1.250) (1.673) (1.815)

April X Child age 6-17 -1.287 -2.131 -0.090 -1.293 -2.678 0.387

(1.320) (1.563) (2.072) (2.361) (3.887) (2.923)

May X Child age 6-17 -3.371**-5.362***-0.585 -4.790**-1.874 -5.940

(1.620) (1.877) (2.405) (2.397) (4.322) (4.750)

March X Remote job -0.412 -0.904 0.316 -1.136 -0.046 -1.840

(0.793) (1.009) (1.213) (1.154) (1.503) (1.781)

April X Remote job -1.167 -2.341 0.585 0.901 -3.042 5.524**

(1.227) (1.562) (2.011) (1.863) (2.503) (2.795)

May X Remote job -1.215 -4.215** 2.614 0.559 -2.150 3.814

(1.599) (1.988) (2.654) (2.592) (3.378) (3.661)

March X Essential industry 0.285 -1.579 1.832 0.021 -0.965 0.622

(0.873) (1.213) (1.166) (1.204) (1.660) (1.829)

April X Essential industry 6.013*** 5.495** 6.782*** 3.953** 2.405 6.555**

(1.648) (2.448) (1.970) (1.889) (2.758) (2.785)

May X Essential industry 4.048** 3.920 4.188*-1.076 -0.367 -0.007

(1.827) (2.428) (2.532) (2.448) (3.869) (3.403)
36

Table 3 Continued. Differential Effects of COVID on Hours for those Remaining Employed (Random-Effects 

Models)

Married Single

All Men Women All Men Women

March X Extra adults -0.342 -0.261 -0.506 -0.271 -0.308 -0.210

(0.583) (0.671) (0.996) (0.465) (0.576) (0.764)

April X Extra adults -0.736 -0.112 -1.744 0.785 -0.622 1.549

(0.992) (1.003) (1.704) (0.978) (1.648) (1.182)

May X Extra adults 1.224 1.691 0.208 -0.065 -0.056 -0.593

(1.453) (1.359) (2.662) (0.795) (1.076) (1.608)

Observations 4,482 2,773 1,709 2,446 1,389 1,057

Between R

2 0.18 0.16 0.16 0.12 0.18 0.19

Notes: Standard errors in parentheses are clustered by household. Control variables also include a quadratic in age, 

education, race, Hispanic ethnicity, immigrant status, own major industry, spouse major industry, MSA status, and 

state fixed effects. Controls in married regressions include spouse employed, spouse major industry, spouse remote 

job, and spouse essential industry. Control variables in single regressions include cohabiter. The all regressions 

include female. * p < 0.10, ** p < 0.05, *** p < 0.01

Source: Current Population Survey, February – May 2020
37

Table 4. Differential Effects of COVID on Employment Status (Difference-in-Difference-in-Differences Models)

MARRIED SINGLE

Panel ALL

(N = 3,888)

MEN

(N = 2,242) 

WOMEN

(N = 1,646)

ALL

(N = 2,162)

MEN

(N = 1,236)

WOMEN

(N = 926)

A COVID -0.23*** -0.21*** -0.26*** -0.24*** -0.29*** -0.18***

(0.02) (0.02) (0.03) (0.03) (0.04) (0.04)

COVID -0.21*** -0.29***

B (0.02) (0.03)

COVID X Female -0.06 0.10*

(0.03) (0.05)

COVID -0.19*** -0.21*** -0.26*** -0.17***

C (0.03) (0.04) (0.04) (0.05)

COVID X Child age<18 -0.03 -0.12* -0.12 -0.04

(0.04) (0.06) (0.09) (0.09)

COVID -0.20*** -0.25*** -0.30*** -0.18***

D (0.02) (0.03) (0.04) (0.04)

COVID X Child age<6 -0.06 -0.08 0.12 -0.08

(0.06) (0.08) (0.12) (0.14)

COVID -0.19*** -0.24*** -0.26*** -0.17***

E (0.03) (0.04) (0.04) (0.05)

COVID X Child age 6-17 -0.04 -0.07 -0.17* -0.03

(0.04) (0.06) (0.09) (0.10)

COVID -0.22*** -0.24*** -0.29*** -0.22***

F (0.03) (0.05) (0.05) (0.06)

COVID X Remote job 0.05 -0.03 -0.01 0.08

(0.05) (0.06) (0.08) (0.09)

COVID -0.34*** -0.43*** -0.46*** -0.24***

G (0.06) (0.04) (0.08) (0.06)

COVID X Essential industry 0.16** 0.32*** 0.23*** 0.11

(0.07) (0.06) (0.09) (0.08)

COVID -0.21*** -0.27*** -0.23*** -0.20***

H (0.02) (0.03) (0.04) (0.05)

COVID X Extra adults 0.01 0.03 -0.08*** 0.03

(0.03) (0.05) (0.03) (0.04)

Notes: Each panel is a separate regression. Standard errors in parentheses are clustered by household-year. Control variables in all regressions also include a 

quadratic in age, education, race, Hispanic ethnicity, immigrant status, own major industry, MSA status, state, month and year fixed effects. Controls in married 

regressions include spouse employed, spouse major industry, spouse remote job, and spouse essential industry. Control variables in single regressions include 

cohabiter. The all regressions include female. Regressions include interactions of the subgroup with month and year. * p < 0.10, ** p < 0.05, *** p < 0.01 Source: 

Current Population Survey, February and April 2019–2020
38

Table 5. Differential Effects of COVID on Hours (Difference-in-Difference-in-Differences Models)

MARRIED SINGLE

Panel ALL

(N = 3,888)

MEN

(N = 2,242) 

WOMEN

(N = 1,646)

ALL

(N = 2,162)

MEN

(N = 1,236)

WOMEN

(N = 926)

A COVID -11.26*** -10.92*** -11.73*** -10.52*** -11.53*** -9.30***

(0.88) (1.10) (1.25) (1.17) (1.61) (1.75)

COVID -10.92*** -11.53***

B (1.09) (1.58)

COVID X Female -0.81 2.23

(1.50) (2.31)

COVID -10.47*** -10.74*** -9.88*** -8.88***

C (1.44) (1.68) (1.82) (2.07)

COVID X Child age<18 -1.24 -2.28 -8.17** -1.41

(2.23) (2.50) (3.82) (3.87)

COVID -10.58*** -11.16*** -11.66*** -9.28***

D (1.21) (1.38) (1.69) (1.81)

COVID X Child age<6 -1.90 -3.23 1.82 -0.42

(2.90) (3.18) (5.07) (6.43)

COVID -10.20*** -11.14*** -9.83*** -9.00***

E (1.36) (1.54) (1.76) (2.05)

COVID X Child age 6-17 -2.10 -1.68 -10.11** -1.44

(2.32) (2.63) (4.18) (3.99)

COVID -11.20*** -12.32*** -11.48*** -11.77***

F (1.42) (1.94) (1.99) (2.44)

COVID X Remote job 0.79 1.13 -0.21 5.57

(2.31) (2.60) (3.61) (3.61)

COVID -19.19*** -18.46*** -17.27*** -10.64***

G (3.03) (1.70) (3.07) (2.44)

COVID X Essential industry 9.81*** 13.48*** 7.58** 2.79

(3.25) (2.41) (3.61) (3.47)

COVID -10.33*** -10.78*** -10.40*** -11.28***

H (1.25) (1.39) (1.83) (2.00)

COVID X Extra adults -1.57 -3.18 -1.71 3.11

(1.45) (2.25) (1.41) (1.95)

Notes: Each panel is a separate regression. Standard errors in parentheses are clustered by household-year. Control variables in all regressions also include a 

quadratic in age, education, race, Hispanic ethnicity, immigrant status, own major industry, MSA status, state, month and year fixed effects. Controls in married 

regressions include spouse employed, spouse major industry, spouse remote job, and spouse essential industry. Control variables in single regressions include 

cohabiter. The all regressions include female. Regressions include interactions of the subgroup with month and year. * p < 0.10, ** p < 0.05, *** p < 0.01 Source: 

Current Population Survey, February and April 2019–2020
39

Table 6. Differential Effects of COVID on Hours of those Remaining Employed (Difference-in-Difference-in-Differences Models)

MARRIED SINGLE

Panel ALL

(N = 3,042)

MEN

(N = 1,858) 

WOMEN

(N = 1,184)

ALL

(N = 1,624)

MEN

(N = 932)

WOMEN

(N = 692)

A COVID -5.05*** -4.79*** -5.45*** -5.04*** -3.92** -6.51***

(0.86) (1.03) (1.30) (1.15) (1.57) (1.73)

COVID -4.79*** -3.92**

B (1.02) (1.54)

COVID X Female -0.66 -2.59

(1.49) (2.25)

COVID -5.48*** -5.31*** -3.16* -7.02***

C (1.40) (1.73) (1.77) (2.07)

COVID X Child age<18 1.34 -0.35 -3.97 1.85

(2.07) (2.64) (3.88) (3.79)

COVID -4.98*** -5.21*** -3.32** -6.96***

D (1.16) (1.42) (1.66) (1.79)

COVID X Child age<6 1.07 -1.38 -6.99 4.70

(2.55) (3.52) (4.88) (6.54)

COVID -5.15*** -5.01*** -3.39** -7.08***

E (1.30) (1.60) (1.71) (2.06)

COVID X Child age 6-17 0.77 -1.23 -3.59 2.38

(2.14) (2.77) (4.61) (3.82)

COVID -3.76*** -5.49*** -3.55* -9.47***

F (1.35) (2.08) (1.88) (2.58)

COVID X Remote job -3.15 -0.12 -0.93 6.37*

(2.14) (2.76) (3.63) (3.57)

COVID -12.40*** -9.48*** -4.85 -8.46***

G (3.00) (2.02) (3.54) (2.60)

COVID X Essential industry 8.73*** 6.48** 1.11 3.35

(3.20) (2.62) (3.98) (3.50)

COVID -3.97*** -4.78*** -4.89*** -8.00***

H (1.17) (1.45) (1.81) (2.06)

COVID X Extra adults -2.24 -2.20 1.86 2.20

(1.42) (2.46) (1.89) (1.76)

Notes: Each panel is a separate regression. Standard errors in parentheses are clustered by household-year. Control variables in all regressions also include a 

quadratic in age, education, race, Hispanic ethnicity, immigrant status, own major industry, MSA status, state, month and year fixed effects. Controls in married 

regressions include spouse employed, spouse major industry, spouse remote job, and spouse essential industry. Control variables in single regressions include 

cohabiter. The all regressions include female. Regressions include interactions of the subgroup with month and year.* p < 0.10, ** p < 0.05, *** p < 0.01 Source: 

Current Population Survey, February and April 2019–2020
40

Appendix A

Table A1. Mean Employment and Hours in 2020 by Marital and Parental Status

Sample February March April May

Panel A. Employed at Work

Married

Males 1.00 0.88 0.70 0.75

No children 1.00 0.85 0.70 0.76

Child age<6 1.00 0.91 0.66 0.81

Child age 6–17 1.00 0.92 0.69 0.73

Females 1.00 0.86 0.56 0.61

No children 1.00 0.86 0.59 0.67

Child age<6 1.00 0.76 0.48 0.57

Child age 6–17 1.00 0.87 0.54 0.59

Single

Males 1.00 0.84 0.56 0.71

No children 1.00 0.85 0.60 0.72

Child age<6 1.00 0.85 0.61 0.94

Child age 6–17 1.00 0.79 0.37 0.67

Females 1.00 0.87 0.62 0.60

No children 1.00 0.86 0.65 0.62

Child age<6 1.00 0.89 0.60 0.55

Child age 6–17 1.00 0.90 0.57 0.64

Panel B. Average Weekly Hours

Married

Males 38.55 33.84 25.30 29.00

No children 37.02 32.07 25.48 29.26

Child age<6 39.18 35.57 24.54 31.41

Child age 6–17 40.52 35.94 24.10 27.84

Females 30.04 24.90 15.52 15.93

No children 31.13 26.22 16.48 17.62

Child age<6 25.79 18.01 11.89 13.12

Child age 6–17 29.45 24.64 15.34 15.40

Single

Males 35.54 28.39 19.77 25.87

No children 35.52 28.72 21.51 27.55

Child age<6 37.82 30.62 18.81 27.75

Child age 6–17 36.00 26.72 11.15 19.17

Females 30.85 28.14 17.54 17.69

No children 31.38 27.91 17.73 19.30

Child age<6 25.57 24.81 20.49 15.30

Child age 6–17 30.60 29.66 17.21 15.67

Observations 3,403 2,302 1,515 776

Note: CPS final weights used. Sample restricted to those who were unincorporated selfemployed and at work in February.

Source: Current Population Surveys
41

Table A2. Means for Random Effects Sample (2020)

Variable February March April May

Employed at work 1.00 0.87 0.62 0.68

Hours on main job 34.65 29.60 20.32 22.96

Female 0.39 0.40 0.43 0.44

Age 49.44 49.36 49.33 48.91

High school degree 0.27 0.27 0.25 0.27

Some college 0.28 0.28 0.29 0.29

Bachelor’s degree 0.23 0.24 0.24 0.24

Advanced degree 0.12 0.12 0.13 0.13

Black 0.08 0.08 0.07 0.07

Other race 0.07 0.07 0.08 0.07

Hispanic 0.18 0.18 0.18 0.20

Any child age<6 0.14 0.14 0.15 0.16

Any child age 6–17 0.30 0.31 0.31 0.32

Married 0.62 0.62 0.64 0.63

Number of extra HH adults 0.53 0.54 0.51 0.52

Cohabiter 0.06 0.06 0.06 0.07

Immigrant 0.22 0.22 0.22 0.21

Remote Job 0.38 0.39 0.40 0.42

Essential Industry 0.68 0.68 0.67 0.66

Own Industry

Agriculture and mining 0.07 0.07 0.07 0.06

Construction 0.18 0.17 0.16 0.16

Manufacturing 0.03 0.03 0.03 0.03

Trade, transportation, and utilities 0.14 0.14 0.14 0.13

Information 0.02 0.02 0.02 0.02

Financial activities 0.08 0.08 0.08 0.10

Professional and business services 0.20 0.20 0.20 0.20

Educational and health services 0.11 0.11 0.12 0.12

Leisure and hospitality 0.07 0.07 0.08 0.08

Other services/Public administration 0.11 0.11 0.10 0.08

Spouse – Employed 0.47 0.46 0.48 0.48

Spouse - Remote job 0.22 0.22 0.24 0.25

Spouse - Essential industry 0.34 0.34 0.36 0.35

Spouse Industry 

Agriculture and mining 0.03 0.03 0.03 0.04

Construction 0.04 0.03 0.03 0.04

Manufacturing 0.04 0.04 0.04 0.04

Trade, transportation, and utilities 0.08 0.08 0.09 0.08

Information 0.01 0.01 0.02 0.01

Financial activities 0.04 0.04 0.04 0.05

Professional and business services 0.06 0.07 0.08 0.08

Educational and health services 0.10 0.10 0.09 0.08

Leisure and hospitality 0.03 0.02 0.02 0.03

Other services/Public administration 0.05 0.05 0.05 0.05

Observations 3,403 2,302 1,515 776

Note: CPS final weights used. Sample restricted to those who were unincorporated self-employed and at work in 

February.

Source: Current Population Surveys
42

Table A3. Means for Difference-in-Difference-in-Differences Sample

Variable Feb 2019 April 2019 Feb 2020 April 2020

Employed at work 1.00 0.90 1.00 0.62

Hours on main job 34.66 32.99 34.36 20.32

COVID 0 0 0 1

Female 0.42 0.41 0.44 0.43

Age 49.36 49.37 49.70 49.33

High school degree 0.26 0.26 0.25 0.25

Some college 0.28 0.28 0.29 0.29

Bachelor’s degree 0.23 0.23 0.24 0.24

Advanced degree 0.12 0.12 0.13 0.13

Black 0.07 0.06 0.07 0.07

Other race 0.09 0.09 0.07 0.08

Hispanic 0.18 0.17 0.16 0.18

Any child age<6 0.16 0.16 0.15 0.15

Any child age 6–17 0.33 0.33 0.31 0.31

Married 0.61 0.61 0.64 0.64

Number of extra HH adults 0.52 0.52 0.50 0.51

Cohabiter 0.07 0.07 0.06 0.06

Immigrant 0.22 0.22 0.21 0.22

Remote Job 0.41 0.41 0.40 0.40

Essential Industry 0.69 0.69 0.67 0.67

Own Industry

Agriculture and mining 0.08 0.08 0.07 0.07

Construction 0.16 0.16 0.16 0.16

Manufacturing 0.03 0.03 0.03 0.03

Trade, transportation, and utilities 0.12 0.12 0.14 0.14

Information 0.01 0.01 0.02 0.02

Financial activities 0.07 0.07 0.08 0.08

Professional and business services 0.21 0.21 0.19 0.20

Educational and health services 0.12 0.12 0.12 0.12

Leisure and hospitality 0.06 0.06 0.08 0.08

Other services/Public administration 0.13 0.12 0.10 0.10

Spouse – Employed 0.47 0.47 0.48 0.48

Spouse - Remote job 0.23 0.23 0.24 0.24

Spouse - Essential industry 0.35 0.35 0.36 0.36

Spouse Industry 

Agriculture and mining 0.04 0.04 0.03 0.03

Construction 0.03 0.03 0.03 0.03

Manufacturing 0.03 0.03 0.04 0.04

Trade, transportation, and utilities 0.07 0.07 0.09 0.09

Information 0.01 0.01 0.02 0.02

Financial activities 0.04 0.04 0.04 0.04

Professional and business services 0.06 0.06 0.08 0.08

Educational and health services 0.11 0.11 0.09 0.09

Leisure and hospitality 0.02 0.02 0.02 0.02

Other services/Public administration 0.06 0.06 0.05 0.05

Observations 1,510 1,510 1,515 1,515

Note: CPS final weights used. Some differences are due to different weights. Restricted to respondents who could be 

matched between February and April and who were working in February.

Source: Current Population Surveys, 2019–2020