COVID-19: a crisis of the female self-employed
Summary
An original research paper, "COVID-19: a crisis of the female self-employed," by Daniel Graeber, Alexander S. Kritikos and Johannes Seebauer, published in the Journal of Population Economics (2021) 34:1141–1187. Using the SOEP-CoV survey of German respondents, the authors report that the self-employed are about 42 percentage points more likely than employees to report gross income losses. They find self-employed women about one-third more likely than self-employed men to experience income losses, with no comparable gap among employees. A Gelbach decomposition attributes the gap largely to women working in industries more severely affected, and the paper reports self-employed women 20 percentage points more likely to be affected by pandemic regulations. It also describes German policy measures such as an emergency aid package of up to e50 billion and ends with its reference list.
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“COVID-19: a crisis of the female self-employed.”
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Journal of Population Economics (2021) 34:1141–1187
https://doi.org/10.1007/s00148-021-00849-y
ORIGINAL PAPER
COVID-19: a crisis of the female self-employed
Daniel Graeber1,2 · Alexander S. Kritikos1,2,3 · Johannes Seebauer1,4
Received: 27 October 2020 / Accepted: 30 March 2021 / Published online: 11 June 2021
© The Author(s) 2021
Abstract
We investigate how the economic consequences of the pandemic and the government-
mandated measures to contain its spread affect the self-employed — particularly
women — in Germany. For our analysis, we use representative, real-time survey data
in which respondents were asked about their situation during the COVID-19 pan-
demic. Our findings indicate that among the self-employed, who generally face a
higher likelihood of income losses due to COVID-19 than employees, women are
about one-third more likely to experience income losses than their male counterparts.
We do not find a comparable gender gap among employees. Our results further sug-
gest that the gender gap among the self-employed is largely explained by the fact that
women disproportionately work in industries that are more severely affected by the
COVID-19 pandemic. Our analysis of potential mechanisms reveals that women are
significantly more likely to be impacted by government-imposed restrictions, e.g.,
the regulation of opening hours. We conclude that future policy measures intending
to mitigate the consequences of such shocks should account for this considerable
variation in economic hardship.
Keywords Self-employed · COVID-19 · Income · Gender ·
Representative real-time survey data · Decomposition methods
JEL Classification J16 · L26 · J31 · J71 · I18
Responsible editor: Klaus F. Zimmermann
Alexander S. Kritikos
akritikos@diw.de
1 DIW Berlin, Mohrenstr. 58, 10117 Berlin, Germany
2 University of Potsdam, Potsdam, Germany
3 GLO, Essen, Germany
4 Freie Universität Berlin, Berlin, Germany
1142 D. Graeber et al.
1 Introduction
The unprecedented shutdown of businesses in specific industries, social distancing
guidelines, and overall insecurity caused by the COVID-19 pandemic resulted in the
temporary halt of major parts of the economy in many countries in 2020, with dire
consequences for these economies (Milani 2021). The service sector, which often
necessitates physical proximity, was particularly affected (Barbieri et al. 2020). At
the same time, the service sector depends more on self-employed individuals than
the manufacturing sector, where the vast majority of workers are employees. In par-
ticular, self-employed women are more likely to work in service industries than
self-employed men: According to the OECD (2017), 91% of self-employed women
and 68% of self-employed men in Germany worked in the service sector in 2016.
The COVID-19 pandemic initiated a public debate as to what extent the female
working population experienced greater income and employment reductions. This
is particularly relevant since women are often the primary caregivers in the family
and, as such, were also confronted with the closure of schools and daycare cen-
ters (Alon et al. 2020). However, the debate revolving around the gender gap and
the impact of the COVID-19 pandemic does not, thus far, differentiate between
forms of employment, although initial descriptive evidence points to stronger neg-
ative effects for self-employed women (see, e.g., Ifo Institute and forsa (2020) for
Germany and Kalenkoski and Pabilonia (2020) for the USA). In this paper, we inves-
tigate whether women in self-employment and among employees are more severely
affected by the economic consequences of the COVID-19 pandemic and associated
non-pharmaceutical interventions (NPI) than men. To the best of our knowledge, we
are the first to explicitly contrast the experience of the self-employed with employees
during the COVID-19 pandemic and, by doing so, to identify where gender disparities
occurred as a consequence of the pandemic.
The particular focus on self-employed individuals is warranted by the increas-
ing importance of self-employment and entrepreneurship for modern economies. For
example, in Germany, around 4.2 million individuals — about ten percent of the
working population — are self-employed, running diverse businesses either with-
out or with additional employees, often micro-businesses with up to 10 employees.
In sum, the self-employed contribute substantially to the economic development in
Germany (Audretsch et al. 2020). It is further important to note that, while there is
still a significant gender gap among the self-employed, the share of women has been
increasing steadily since the turn of the century (Fritsch et al. 2015).
Our study proceeds in three steps. First, we contextualize our analysis on the
comparison between female and male workers in both forms of employment by inves-
tigating the differential impact of the COVID-19 pandemic on the self-employed and
employees. Second, in our main analysis, we examine the gender gap in the effect of
the pandemic on labor market outcomes, focusing on the self-employed. Third, we
provide evidence on potential mechanisms driving the observed gender differences
among the self-employed. For our analysis, we use the Socio-Economic Panel-CoV
(SOEP-CoV), a novel data set sufficiently rich to allow for such a comparison, as it
enables us to control for individual-level heterogeneity to a large extent. SOEP-CoV
surveyed a randomly selected subset of respondents from the SOEP who were asked
COVID-19: a crisis of the female self-employed 1143
to answer a wide array of questions about their economic situation, family situation,
health, the use of public support instruments, as well as attitudes during the early
stages of the COVID-19 pandemic. The SOEP is a representative household panel
in Germany that surveys respondents annually since 1984 (Goebel et al. 2019). By
design, the SOEP-CoV enables us to link individual respondents to their pre-crisis
information. Thus, we can exploit rich information on the respondents, including
their pre-crisis household income, education, household characteristics, personality
traits, and employment experience, among others. Therefore, we are able to ana-
lyze whether individual characteristics that are known to be important determinants
of self-employment influence outcomes during the COVID-19 pandemic (see, e.g.,
Parker 2018).
With this data at hand, we perform multivariate analyses, first comparing the gap
in labor market outcomes between employed and self-employed respondents. We
show that there are significant differences in the influence of the COVID-19 pan-
demic and associated NPIs on the two employment forms: The self-employed are
about 42 percentage points more likely to report losses of gross income than employ-
ees and 30 percentage points more likely to report a reduction in working hours.
Turning to gender differences in the influence of the COVID-19 pandemic, we find
that self-employed women are about one-third more likely than self-employed men
to experience income losses due to the COVID-19 pandemic. We do not find a
comparable gender gap among employees.
We then decompose the gender gap in the probability of income losses among
the self-employed using the Gelbach decomposition (Gelbach 2016), allowing us to
decompose different sets of covariates into their individual contribution to the gender
gap. We show that the gender gaps in the probability of income losses and reductions
in working hours due to the COVID-19 pandemic are driven by the fact that self-
employed women are disproportionately active in industries that are more severely
affected by the COVID-19 pandemic. We do not find such evidence for employees.
Lastly, we provide evidence for a likely channel driving the gender gap among
the self-employed. We find that self-employed women are 20 percentage points more
likely to be affected by regulations adopted during the COVID-19 pandemic.
We show that our results are, once again, driven by the disproportionate sorting
of self-employed women into industries that were more severely restricted by the
NPIs implemented. Moreover, we present evidence that these restrictions mediate
the relationship between industry-sorting and income losses. We also find suggestive
evidence that gendered household production contributes to the gender gap in income
losses. However, this effect is of second order compared to the contribution of the
industry affiliation.
We contribute to the literature in several ways. First, we contrast the gender gap
between employees and self-employed individuals in the labor market during the
early onset of the COVID-19 pandemic. In contrast to related studies relying on the
U.S. Current Population Survey (Fairlie 2020; Kalenkoski and Pabilonia 2020) or
the Canadian Labour Force Survey (Beland et al. 2020), the SOEP-CoV contains
information on earnings losses due to the COVID-19 pandemic. Adams-Prassl et al.
(2020), who collected their own data, are a notable exception in that they do have
information on earnings losses. They do not find gender differences in realized job
1144 D. Graeber et al.
or earnings losses for Germany. While Adams-Prassl et al. (2020) provide impor-
tant initial evidence, they do not distinguish between self-employed individuals and
employees with respect to the gender gap. This is an important distinction since
the labor market in Germany is characterized by stronger rigidities than other coun-
tries, limiting the extent to which firms can cut the wages of their employees (e.g.,
Burda 2016). Furthermore, policy measures taken by the federal government were
mostly aimed at stabilizing the earnings and employment trajectories of employees.
By contrast, self-employed individuals, as residual claimants, are more vulnerable to
economic shocks like the COVID-19 pandemic.
Second, we contribute to the broader literature on gender gaps in labor markets
(e.g., Blau and Kahn 2017; Goldin et al. 2017; Meara et al. 2020) documenting
earnings gaps, which the authors, among other factors, attribute to the selection of
women into occupations or sectors that are associated with lower average wages.
We complement this literature with our finding that the disproportionate representa-
tion of women in certain industries also translates into a gender gap in the impact
of the COVID-19 pandemic. Third, our finding that government-mandated regula-
tions are an important driver of the gender gap in the impact of the pandemic on the
self-employed constitutes novel evidence in the literature.
Lastly, we also contribute to a strand of literature studying the consequences of
the spread of communicable diseases on economic well-being in general (e.g., Karls-
son et al. 2014; Barro et al. 2020; Correia et al. 2020; Velde 2020). These studies
mainly investigate the impact of the 1918 Spanish flu. While providing important
insights, these studies are hampered by limited data due to the historical nature of the
event. In this context, our finding that NPIs have unintended consequences for gen-
der equality implies that this variation in economic suffering needs to be accounted
for when addressing the ongoing COVID-19 pandemic or any future public health
crisis involving communicable diseases of a similar or even greater magnitude.
2 Background: the COVID-19 pandemic, policy measures, and female
self-employment
In this section, we provide a short summary of policy measures enacted in Germany
in the early months of the pandemic, before we relate our study to contemporaneous
research on the impact of COVID-19 on self-employment, as well as on the gender
gap in self-employment.
2.1 Policy measures in the wake of the COVID-19 pandemic
In order to contain SARS-CoV-2, the German government imposed strong restric-
tions beginning in March 2020, shortly before our period of observation. These NPIs
included the closure of schools, daycare centers, restaurants, service companies in
the field of personal hygiene, and most shops — with exceptions for grocery stores.
All public events were canceled and travel was restricted. Meetings in public were
limited to two individuals, while people were required to keep a minimum distance
of 1.5 meters from other people in public spaces (Federal Ministry of Health 2020).
COVID-19: a crisis of the female self-employed 1145
While these measures were certainly sensible from an epidemiological perspective
(e.g., Qiu et al. 2020, Bonacini et al. 2021), more than half of the self-employed
experienced sales and income losses in spring 2020 (Kritikos et al. 2020).
The German government introduced several economic policy measures to mitigate
the economic consequences of the COVID-19 pandemic. The most prominent policy
measure being the expansion of “Kurzarbeit,” the established short-time work com-
pensation scheme where the employment agency covers up to 67% of employees’ net
income.1 As the self-employed are not covered by this instrument, the federal govern-
ment released an emergency aid package of up to e50 billion for the self-employed.
This program supported the self-employed facing strong losses in revenues with lump
sum payments of up to e15,000. The use of this payment was limited to covering
fixed operating costs and temporarily increased the subjective survival probability
(Block et al. 2020). In addition, the self-employed received easier access to unem-
ployment benefits “Arbeitslosengeld 2” (Federal Ministry for Economic Affairs and
Energy 2020).
2.2 Related research on self-employment
Crisis-related research on self-employment has received considerable attention (see,
e.g., Doern et al. 2019). On the one hand, a large part of this literature focuses on
the question of how individuals decide about venturing new businesses in reaction
to a crisis (see, e.g., Siemer 2014) and, on the other hand, the crisis management
of existing businesses (see, e.g., Davidsson and Gordon 2016). Much less is known
about the magnitude of the impact of crises on the self-employed; existing research
is often based on qualitative interviews with retrospective questions (see, e.g., Doern
2016).
In contrast to other crises, the COVID-19 pandemic affects nearly the entire self-
employed population, as is documented in contemporaneous research, all of which
shows that self-employed individuals suffered significantly from the consequences of
the COVID-19 pandemic.2 For the UK, Blundell and Machin (2020) show that three
out of four self-employed individuals report a reduced work load. While they pro-
vide important evidence on the impact of the COVID-19 pandemic on self-employed
individuals, they do not consider gender differences in their analysis. Fairlie (2020)
documents that the activity of business owners in the USA plummeted by 3.3 mil-
lion, or 22%, during the early stages of the COVID-19 pandemic. Fairlie (2020) also
documents considerable race and gender differences in the effects of the COVID-19
pandemic on the number of active small businesses. In contrast to our study, Fairlie
(2020) does not have information on income losses. Kalenkoski and Pabilonia (2020),
1 Under this scheme, employers send their employees into short-time work where the Federal Employment
Office subsidizes a large portion of the wage costs pertaining to those contractual working hours that
employees are not working. This instrument allows employers to keep their workforce through the crisis
while protecting employees from losing their jobs, and from major wage losses, see also Cahuc (2019).
2 There are also various studies investigating the effects of the COVID-19 pandemic on overall employment
(Forsythe et al. 2020; Chetty et al. 2020; Cajner et al. 2020; Juranek et al. 2020; Coibion et al. 2020;
Adams-Prassl et al. 2020).
1146 D. Graeber et al.
who focus on unincorporated self-employed in the USA, find that self-employed
individuals are about 57 percentage points less likely to be employed in April 2020,
compared to February. The authors, like Fairlie (2020), also do not have information
on income. Kalenkoski and Pabilonia (2020) likewise document gender differences in
the effects of the COVID-19 pandemic on self-employed individuals. Lastly, Beland
et al. (2020) report an activity decline of 14.8% for incorporated and 10.1% for unin-
corporated entities in Canada. They also find gender differences in the impact on
COVID-19 on employment and hours, yet do not analyze this differential impact, nor
do they have information on income.
In summary, we expand the analysis of gender differences in the effect of the
COVID-19 pandemic on self-employed individuals in two important ways: First, we
have information on income losses, in addition to income information from 2019.
Second, we provide important evidence that it is the sorting of women into industries
that are more strongly affected by the pandemic and associated NPIs that drives the
observed gender differences among the self-employed.
Lastly, our study also relates to the literature on gender gaps in self-employment.
In most countries, fewer women than men are self-employed (Elam et al. 2019).
While the share of women in self-employment was as low as 25% at the turn of
the century in Germany (Fritsch et al. 2015), it increased continously to nearly 35%
in 2017 (Günther and Marder-Puch 2019). This development was also aided by
the active promotion of self-employment via start-up subsidies (see, e.g., Caliendo
and Künn 2015). The literature documents a variety of reasons for the still exist-
ing gender gap in self-employment, ranging from differences in the intergenerational
transfer of human capital (see, e.g., Georgellis and Wall 2005), differing influ-
ences of age (see, e.g., Leoni and Falk 2010), differing risk attitudes (Caliendo
et al. 2014), self-confidence (see, e.g., Koellinger et al. 2013), or the willing-
ness to compete (see, e.g., Bönte and Piegeler 2013), while there is also sub-
stantial heterogeneity in employment decisions both among women and between
women and men (Patrick et al. 2016). Certainly, these differences may inform
the implications of our findings for the development of female self-employment
post-pandemic.
3 Data
In this section we briefly describe our data set and discuss the outcome variables used
in the analysis. We then provide descriptive statistics of these outcome variables.
3.1 SOEP-CoV
For our analysis, we use a unique data source to estimate the effect of the COVID-
19 pandemic on the self-employed. The SOEP-CoV survey was launched in April
2020 to investigate the socio-economic consequences of the COVID-19 pandemic
in Germany. In the first part of this special survey, respondents, interviewed in nine
waves between April and July 2020, were asked about their economic status, family
situation, health information, and attitudes during the COVID-19 pandemic (Kühne
COVID-19: a crisis of the female self-employed 1147
et al. 2020). Importantly, the SOEP-CoV questionnaire includes a set of questions
targeting self-employed individuals.
What makes the SOEP-CoV particularly useful, is its integration into the SOEP.
The Socio-Economic Panel (SOEP) is a representative, longitudinal survey of house-
holds in Germany that started 1984 and is administered to households and the
households’ members on a yearly basis since then.3 As of 2020, the SOEP includes
approximately 20,000 households with more than 30,000 adult household mem-
bers. The SOEP contains information on the households and its members’ economic
situation, education, and attitudes, among other things (Goebel et al. 2019).
The respondents surveyed in the SOEP-CoV are a random subset of the SOEP
population. Thus, it combines the wealth of longitudinal, pre-pandemic informa-
tion from the SOEP with a wide array of questions that are related specifically
to the COVID-19 pandemic. These unique features make the SOEP-CoV the ideal
data set to analyze our research questions. For our analysis, we focus on individu-
als who are either gainfully employed (part- and full-time) or self-employed. We do
not consider self-employed individuals who identified as helping family members in
2019.
3.2 Outcome variables
In our analysis, we investigate the differential influence of the COVID-19 pandemic
by self-employment status and gender. We focus on the likelihood of experiencing
a decrease in income (gross earnings), working hours, and working at least partially
from home due to the COVID-19 pandemic. In addition, we also have information on
the magnitude of losses of monthly income and reductions in weekly working hours.
These outcomes jointly determine how individuals have experienced the COVID-19
crisis to a significant degree and allow for examining differences between employ-
ees and the self-employed. Importantly, the questions on income losses, reductions in
working hours, and remote work are framed causally. That is, respondents are explic-
itly asked whether, and to what extent, income and hours worked have changed due
to the pandemic. Similarly, they are asked whether they are working from home due
to the pandemic, either in part or completely.
While employees are partially protected from income losses in the short-run, when
they have fixed employment contracts, this does not apply to the self-employed.
The main mechanisms through which employees can face changes in income and
working hours are job losses and participation of their employer in short-time work
schemes. Furthermore, employees and self-employed individuals may select into
different industries. To the extent that these industries are hit by the crisis to vary-
ing degrees, the likelihood of reductions in incomes and working hours will differ.
The same argument applies to gender differences. To the extent that women select
into different industries and occupations than men, along with the extent to which
these are differently affected by the pandemic, its effect on income and hours will
be different. Finally, the potential for working remotely vastly differs across sectors
3 We use the SOEPv35. DOI: 10.5684/soep-core.v35. In addition, we use the preliminary data of the SOEP
for 2019.
1148 D. Graeber et al.
and jobs (von Gaudecker et al. 2020; Alipour et al. 2020; Dingel and Neiman 2020).
While front-line workers continue to be potentially exposed to the virus throughout
the pandemic, it is more easily possible for individuals in office jobs to do their work
partly, if not completely, from home. By contrast, the arts and entertainment industry,
where remote work is nearly non-existent, came to an almost complete halt. Thus, in
our main analysis, we shed light on the heterogeneous influence of the COVID-19
pandemic on these core outcomes, which shape the experience of the workforce dur-
ing the COVID-19 pandemic. Other variables used in the analysis are described in
Table 5 in Appendix 1.
3.3 Descriptive statistics on outcomes at the extensive margin
Tables 6, 7, and 8 in Appendix 1 show summary statistics for our analysis sample. The
sample is restricted to those individuals for whom the full set of control variables used
is available. Importantly, they describe how self-employed individuals were affected
by the pandemic in comparison to employees with respect to our outcomes of interest,
and how these experiences differ by gender in both employment forms. Figure 1a–c
illustrate these differences. The probability of facing reductions in income and work-
ing hours is considerably larger among the self-employed than among employees.
Around 55% of self-employed individuals report a decline in income and around 50%
in working hours, while this is the case for only 13% of employees with respect to
income and 20% of them with respect to working hours. A drop in demand directly
affects the income and workload of self-employed individuals, whereas income and
working hours of employees are affected by a sales decrease in their firms only if
they are sent into short-time work or laid off. While job losses following the initial
COVID-19 pandemic lockdown are rare in Germany, at least when compared to the
experience of other countries (Adams-Prassl et al. 2020), the instrument of short-time
work is used extensively.4 Although the difference is notably smaller, remote work as
a direct consequence of the pandemic is also more common among the self-employed
(with 46%) than among employees (39%).
Figure 1 also shows striking patterns of gender differences in the outcome
variables. Most notably, there is a significant gender gap within the group of self-
employed individuals: 63% of self-employed women faced income losses as opposed
to 47% of their male counterparts. At the same time, 54% of self-employed women
and 46% of self-employed men reduced their working hours. With respect to remote
work, the gender gap is smaller and, in fact, inverts with men being more likely to
work from home than women.
These gender gaps, however, are not replicated among employees. Here, the gen-
der difference in the probability of income losses amounts to roughly two percentage
points and inverts. The gender gap in the probability of working from home is
similar in magnitude to that of the self-employed. Thus, there is a significant self-
employment gap in the outcomes of interest with sizeable gender differences that are
concentrated among the self-employed.
4 See Table 11 in Appendix 1 and Section 4.1 for a discussion of job loss due to the pandemic.
COVID-19: a crisis of the female self-employed 1149
Fig. 1 Gender comparison of
.7
raw differences in probabilities
Prob. of income decrease due to COVID-19
of labor market outcomes. Note: .6
a–c display the raw differences
in the probability of labor .5
market outcomes over
employment status and gender, .4
respectively. Vertical bars
correspond to 95% confidence .3
intervals. The stars next to the
.2
respective employment group
indicate whether the mean
.1
differences by gender within the
groups are statistically 0
significant and read *p < 0.10, Employees* Self-employed***
**p < 0.05, ***p < 0.01. Male Female
Details are displayed in
Tables 6, 7, and 8 in Appendix 1 (a) Probability of income decrease
.7
Prob. of working time decrease due to COVID-19
.6
.5
.4
.3
.2
.1
0
Employees Self-employed
Male Female
(b) Probability of working time decrease
.7
Prob. of working from home due to COVID-19
.6
.5
.4
.3
.2
.1
0
Employees*** Self-employed
Male Female
(c) Probability of remote work
1150 D. Graeber et al.
3.4 Descriptive statistics on decreases in income and hours at the intensive
margin
We also provide descriptive evidence on the magnitude of decreases in income and
working hours among the self-employed, beginning with the magnitude of losses
in monthly earnings.5 Figure 2 displays the boxplots for monthly absolute income
losses for all self-employed individuals as well as separately for women and men.
The median and mean of monthly income losses due to the COVID-19 pandemic
are e1500 and e3021 for all self-employed individuals, respectively. Self-employed
men experience higher absolute income losses, with median income losses of e2000,
compared to e1000 for women. The corresponding means are e4741 and e1945 for
self-employed men and women, respectively.
To measure relative losses, we relate the magnitude of income losses to 2019
earnings by dividing the absolute monthly losses in gross earnings by the monthly
gross earnings of the previous year. However, since intra-year changes in income are
frequent among the self-employed, the following results should be interpreted with
some caution.6
The results for relative income losses are shown in Fig. 3. Figure 3a displays the
boxplot for all self-employed individuals. The median and mean of relative income
losses among all self-employed individuals are 0.77 and 1.54, respectively. Figure 3b
displays the boxplot for self-employed men and women. The median is 0.79 for self-
employed women and 0.69 for men. Thus, in contrast to absolute losses, this suggests
that the relative income losses tend to be larger for women. However, a formal median
comparison indicates that we cannot reject equality of medians for self-employed
men and women.
Turning to the reduction of weekly working hours due to COVID-19, we find that
the median and mean absolute decreases are 15 and 18.07 hrs, respectively.7 The cor-
responding distribution is displayed in Fig. 4a. Figure 4b shows that the median and
mean reduction of working hours for self-employed men are 19 and 18.60 hrs, respec-
tively. The corresponding figures for self-employed women are slightly smaller, with
a median of 15 and a mean of 17.61 hrs. Yet again, formal tests of equality across
groups do not allow us to reject the hypothesis of no differences.
Lastly, we focus on relative reductions in weekly working hours. We divide the
decrease in weekly working hours due to COVID-19 by the actual weekly work-
ing hours of the previous year. The distributions are depicted in Fig. 5.8 Figure 5a
5 Note that the question on the precise amount of income losses was not included in the first of the nine
waves of the SOEP-COV. Since this happens to be the wave with the largest number of interviewees, we
are left with 104 self-employed individuals who reported income losses. We report both median and mean
losses, but consider the median a superior statistic of centrality in this context, given that the distribution
of income losses is, as expected, strongly right skewed (Sorgner et al. 2017).
6 Compared to the previous analysis, we lose 23 observations. The final sample includes 81 observa-
tions. Moreover, we do not adjust for inflation. However, first projections indicate that the inflation rate
is approximately close to zero for 2020. Lastly, we partly observe individuals in different months, i.e., we
are not able to account for seasonality. This possibly introduces some additional measurement error.
7 We have information on reductions in working hours for all waves of the SOEP-CoV.
8 For the figures, we dropped a single observation with a relative reduction of 10.
COVID-19: a crisis of the female self-employed 1151
8,000 8,000
Monthly loss of gross earnings due to COVID-19
Monthly loss of gross earnings due to COVID-19
6,000 6,000
4,000
4,000
2,000
2,000
0
0
Male Female
(a) (b)
Fig. 2 The distributions of absolute monthly losses in gross earnings among self-employed individuals.
Note: a and b display boxplots for monthly income losses among all self-employed individuals as well as
self-employed men and women. The large red diamond indicates the median. The upper and lower ends of
the box display the range between the 25th and 75th percentiles. The whiskers span all data points within
1.5 inter-quartile range of the nearer quartile. Small blue dots indicate observations outside the whiskers.
a All. b Gender differences
displays the respective distribution for all self-employed individuals. The median and
mean are 0.6 and 0.78, respectively. Figure 5b displays the corresponding gender-
specific distributions. For self-employed men, the median and mean of relative
working hours reductions are 0.5 and 0.77. For self-employed women, these figures
are 0.63 and 0.79, respectively. Once again, the differences between men and women
are not statistically significant.
5 5
Rel. change of gross earnings due to COVID-19
Rel. change of gross earnings due to COVID-19
4 4
3
3
2
2
1
1
0
0
Male Female
(a) (b)
Fig. 3 The distributions of monthly relative losses in income (gross earnings) among self-employed indi-
viduals. Note: a and b display boxplots for relative monthly income losses among all self-employed
individuals as well as self-employed men and women. The large red diamond indicates the median. The
upper and lower end of the box display the range between the 25th and 75th percentiles. The whiskers span
all data points within 1.5 inter-quartile range of the nearer quartile. Small blue dots indicate observations
outside the whiskers. a All. b Gender differences
1152 D. Graeber et al.
Reduction weekly working hours due to COVID-19
50 50
Reduction weekly working hours due to COVID-19
40 40
30
30
20
20
10
10
0
0
Male Female
(a) (b)
Fig. 4 The distributions of the reduction in weekly working hours among the self-employed. Note: a and
b display boxplots for reductions in weekly working hours among all self-employed individuals as well as
self-employed men and women. The large red diamond indicates the median. The upper and lower end of
the box display the range between the 25th and 75th percentiles. The whiskers span all data points within
1.5 inter-quartile range of the nearer quartile. Small blue dots indicate observations outside the whiskers.
a All. b Gender differences
4 Multivariate analysis
Our descriptive results in the previous section show that the crisis following the
COVID-19 pandemic impacts the female self-employed considerably more than all
other groups. In this section, we perform multivariate analyses to better understand
how these differences emerge.
Rel. reduction weekly working hours due to COVID-19
Rel. reduction weekly working hours due to COVID-19
4 4
3 3
2
2
1
1
0
0
Male Female
(a) (b)
Fig. 5 The distributions of relative reductions in weekly working hours among the self-employed. Note: a
and b display boxplots for relative reductions in weekly working hours among all self-employed individu-
als as well as self-employed men and women. The large red diamond indicates the median. The upper and
lower end of the box display the range between the 25th and 75th percentiles. The whiskers span all data
points within 1.5 inter-quartile range of the nearer quartile. Small blue dots indicate observations outside
the whiskers. a All. b Gender differences
COVID-19: a crisis of the female self-employed 1153
4.1 Comparison of the self-employed and employees
To put the analysis of the gender gap among the self-employed into a larger con-
text, we start with a comparison of all self-employed individuals with employees.
Table 1 shows the results of a regression of indicators for a decrease in income, a
decrease in working hours, and working from home, respectively, on an indicator for
self-employment. While the odd-numbered columns only include state indicators as
well as week indicators, the even columns expand the set of controls to include our
complete set of controls.9 With only state and week fixed effects as controls, self-
employed individuals are 42 percentage points more likely to have experienced an
income loss and 30 percentage points more likely to have experienced a reduction in
working hours compared to employees. Self-employed individuals are also about six
percentage points more likely to work from home.
The comparison of odd-numbered with even-numbered columns of Table 1 reveals
that individual-level and household-level characteristics explain very little of the
differences between self-employed individuals and employees with respect to the
probability of income losses and hours reductions. The coefficient on the indicator for
self-employment remains almost unchanged when adding controls (compare column
(1) to column (2) and column (3) to column (4), respectively). Having a migra-
tion background appears to significantly increase the probability of suffering income
losses and hours reductions, while a higher household income has the opposite effect.
That is consistent with the finding of Fairlie (2020), who also finds a racial gap in
how the self-employed are hit by the COVID-19 pandemic. By contrast, the probabil-
ity of working from home seems to be explained by the added controls: Individuals
from more affluent households are more likely to be working from home during the
pandemic, likely a result of selection into jobs that are more easily done from home
(e.g., office jobs; see Alipour et al. 2020). Similarly, better-educated individuals are
significantly more likely to work from home, so are parents.
To pin down the relevance of industry fixed effects, Table 9 in Appendix 1 displays
the R-squared alongside the coefficients on the self-employment indicator for the
unrestricted models in Table 1, both with and without the inclusion of industry fixed
effects. The R-squared increases substantially once industry effects are accounted for,
implying that industry-variation contributes significantly to explaining the respec-
tive outcomes.10 However, differential selection into industries adds rather little to
describing the overall differences between employees and the self-employed, as evi-
denced by the marginal changes in the self-employment gap once industry fixed
effects are accounted for.11
9 Note that the estimates displayed in odd columns somewhat differ from the raw self-employment gap
due to the inclusion of state and time fixed effects. The inclusion of these fixed effects is important since
individuals in the sample were interviewed at different stages of the pandemic. Over time, the incidence
of, for example, working from home changed. Thus, an accurate reflection of a self-employment (gender)
gap requires that the dynamic of the pandemic is accounted for.
10 Once industry effects are added, the R-squared increases by around 50% for the probability of working
from home while nearly doubling for the probability of facing a decrease in income and hours, respectively.
11 For a more detailed analysis on the differences in the estimates of industry fixed effects, see Graeber
et al. (2021).
1154 D. Graeber et al.
In summary, it seems that the differential impact of the COVID-19 pandemic
between employees and the self-employed with respect to income and working hours
is neither primarily driven by differences in individual- and household-level charac-
teristics nor by selection into different industries, but by differences in the association
of these factors with the respective outcomes. The pandemic shock hit the self-
employed uniformly harder. This seems plausible as employees are often shielded
from job and income losses by employment contracts and job protection legislation,
while such mechanisms do not exist for the self-employed. By contrast, individual-
and household-level characteristics can nearly fully account for differences in the
likelihood of working from home between self-employed and employed individuals.
Thus far, we focus our analysis on the population of (self-)employed individuals
in 2020. However, employees may have lost their job over the course of the pan-
demic and self-employed individuals may have terminated their business. To account
for this, we look at the working population of 2019 and investigate whether individ-
uals who were self-employed in 2019 differ from those who were employees with
respect to the probability of changes in income, changes in working hours, and job
loss. The latter is defined as the proportion of individuals who transitioned into non-
employment between 2019 and 2020 and who respond that this transition was due to
the COVID-19 pandemic. The results are shown in Table 11 in Appendix 1. Overall,
1.7% of those working in 2019 are non-employed in 2020 because of the pandemic.
Importantly, self-employed individuals are 1.2 percentage points more likely to have
terminated their business than employees are to have lost their job, although this dif-
ference is not statistically significant. Note as well that the reported results for income
and working hours changes slightly differ from those in Table 1. This is explained
by the focus on the employment status of 2019, rather than 2020 in Table 11 in
Appendix 1. Differences result from two sources: First, employees surveyed in 2019
may have become self-employed between the times of the interview in 2019 and
2020, and vice versa. Second, individuals who were not in employment at the time of
the interview in 2019 may have founded a business prior to the time of the interview
in 2020. However, the differences in the reported results between Table 1 and Table
11 in Appendix 1 are minor.
4.2 Gender differences among the self-employed
As discussed in Section 3.3, we observe considerable gender differences in the prob-
ability of income declines among the self-employed. Section 4.1 further reveals that
self-employed individuals are, in general, much more likely to suffer income losses
than employees. Turning to our core analysis, we investigate how self-employed as
well as employed women are affected by the COVID-19 pandemic in comparison to
their male counterparts. We apply the Gelbach (2016) decomposition to further ana-
lyze the gender differences with respect to the likelihood of a decline in income due
to the COVID-19 pandemic. This decomposition reveals the individual contributions
of covariates to the change in the gender gap, thus assigning each covariate-bundle
a proportion of the overall contribution. Importantly, it is not path dependent, as this
decomposition is, unlike sequential covariate addition, invariant to the sequence in
which we would usually insert the covariates to gauge the stability of the coefficient
COVID-19: a crisis of the female self-employed 1155
Table 1 Restricted and unrestricted models for differences in the likelihood that income or working hours
decreased or individuals are working from home between employees and self-employed respondents
(1) (2) (3) (4) (5) (6)
Income Income Working Working Remote Remote
hours hours work work
Self-employed 0.418*** 0.421*** 0.301*** 0.302*** 0.061** 0.021
(0.029) (0.031) (0.029) (0.031) (0.030) (0.032)
Demographics
Gender: female 0.019 0.022 −0.013
(0.013) (0.016) (0.017)
Age 0.006 −0.003 −0.005
(0.005) (0.005) (0.005)
Age squared 0.000 0.000 0.000
(0.000) (0.000) (0.000)
Migration background 0.040** 0.040** −0.026
(0.016) (0.019) (0.019)
Big 5
Extraversion 0.000 0.008 −0.001
(0.006) (0.007) (0.008)
Conscientiousness −0.010 −0.018** 0.001
(0.007) (0.008) (0.008)
Openness to experience 0.010 0.006 0.025***
(0.006) (0.007) (0.008)
Neuroticism −0.004 0.001 −0.008
(0.006) (0.007) (0.007)
Agreeableness 0.004 −0.004 0.002
(0.006) (0.007) (0.008)
Household context
HH size 0.006 0.011 −0.008
(0.007) (0.008) (0.009)
Married 0.021 0.016 −0.021
(0.015) (0.017) (0.018)
School child or younger 0.007 −0.004 0.049**
(0.018) (0.021) (0.022)
Log of HH net income −0.039** −0.034* 0.098***
(0.016) (0.018) (0.020)
Education (ref. low)
Intermediate education 0.031 0.023 0.073***
(0.019) (0.022) (0.020)
High education 0.011 −0.005 0.293***
(0.021) (0.024) (0.024)
Unemployment experience 0.000 0.005* −0.005**
1156 D. Graeber et al.
Table 1 (continued)
(1) (2) (3) (4) (5) (6)
Income Income Working Working Remote Remote
hours hours work work
(0.003) (0.003) (0.002)
Mean of outcome 0.169 0.169 0.222 0.222 0.395 0.395
Observations 3531 3531 3518 3518 3533 3533
R2 0.11 0.23 0.05 0.13 0.03 0.31
The table displays models with and without controls for differences between self-employed and employ-
ees. All models include state and week fixed effects. Columns (1), (3), and (5) display results for the
models without controls. Columns (2), (4), and (6) display results for the models with controls. The
unrestricted models also include NACE 2 fixed effects. Standard errors are robust and in parentheses.
*p < 0.10, **p < 0.05, ***p < 0.01
of interest. In our analysis, the Gelbach decomposition answers the question of how
much of the change in the gender gap can be attributed to different variables in the
set of controls as we move from the base specification, the restricted model, to the
full specification that includes all controls, the unrestricted model (for more details
on the methodology, see Appendix 2).
In our sample of self-employed individuals, we observe a gender gap of 17.4
percentage points in the likelihood of experiencing an income loss in our restricted
model. This can be inferred from column (1) in Table 2.12 Relative to self-employed
men, self-employed women are about one-third more likely to experience an income
loss because of the COVID-19 pandemic. As discussed in Section 3.3 and confirmed
in Table 13 in Appendix 1, there is no comparable gender gap among employees. In
our unrestricted model in column (2) of Table 2, the gender gap decreases to 8.1 per-
centage points and is statistically indistinguishable from zero. This outcome implies
that our controls can explain about 9.3 percentage points, or 53.4%, of the initial
gender gap.13
The largest share of the gender gap in income losses is explained by the fact that
women are over-represented in industries in which individuals are more likely to
experience income losses. This is seen in Fig. 6a, which displays the results of the
Gelbach decomposition: 9.2 percentage points, or 98.8% of the total change, can be
explained by NACE fixed effects.14 Demographic characteristics, particularly age,
explain as much as 33.8% of the total change in the gender gap between the unre-
stricted and restricted models. Other groups of characteristics add nearly nothing to
the total change in the gender gap.
12 Once again, the estimates displayed in the restricted models somewhat differ from the raw gender gap
due to the inclusion of state and time fixed effects.
13 The corresponding analysis of the magnitude of earnings losses are shown in Graeber et al. (2021). Since
sample sizes decrease considerably, the analysis suffers from imprecision. Effect sizes still confirm our
main conclusions, even for the changes at the intensive margin.
14 Detailed results of the Gelbach decomposition are depicted in Table 10 in Appendix 1.
COVID-19: a crisis of the female self-employed 1157
Table 2 Restricted and unrestricted models for the likelihood that income or working hours decreased or
individuals are working from home among self-employed individuals
(1) (2) (3) (4) (5) (6)
Income Income Working Working Remote Remote
hours hours work work
Gender: female 0.174*** 0.081 0.068 −0.051 −0.017 −0.040
(0.058) (0.073) (0.060) (0.073) (0.057) (0.069)
Demographics
Age 0.027 0.007 −0.042**
(0.019) (0.020) (0.021)
Age squared −0.000* 0.000 0.000*
(0.000) (0.000) (0.000)
Migration background 0.064 0.120 −0.117
(0.110) (0.099) (0.085)
Big 5
Extraversion 0.011 0.067* 0.046
(0.040) (0.037) (0.037)
Conscientiousness −0.031 −0.058 0.033
(0.039) (0.038) (0.037)
Openness to experience 0.066* 0.051 0.058*
(0.038) (0.036) (0.034)
Neuroticism −0.031 −0.003 −0.013
(0.036) (0.039) (0.035)
Agreeableness −0.040 −0.067* −0.032
(0.035) (0.034) (0.033)
Household context
HH size −0.061 −0.076** 0.092***
(0.039) (0.036) (0.033)
Married 0.037 −0.010 0.026
(0.073) (0.078) (0.071)
School child or younger 0.045 0.211** −0.018
(0.103) (0.094) (0.101)
Log of HH net income −0.026 0.100* −0.146***
(0.058) (0.058) (0.052)
Education (ref. low)
Intermediate education −0.102 0.074 −0.108
(0.125) (0.114) (0.112)
High education −0.149 −0.026 0.057
(0.132) (0.120) (0.119)
Unemployment experience −0.026** 0.001 −0.013
(0.012) (0.010) (0.011)
Mean of outcome 0.552 0.552 0.495 0.495 0.457 0.457
1158 D. Graeber et al.
Table 2 (continued)
(1) (2) (3) (4) (5) (6)
Income Income Working Working Remote Remote
hours hours work work
Observations 310 310 309 309 311 311
R2 0.13 0.41 0.09 0.40 0.16 0.47
The table displays restricted and unrestricted models underlying the Gelbach decomposition. All models
include state and week fixed effects. Columns (1), (3), and (5) display results for the restricted models.
Columns (2), (4), and (6) display results for the unrestricted models. The unrestricted models also include
NACE 2 fixed effects. Standard errors are robust and in parentheses. *p < 0.10, **p < 0.05, ***p < 0.01
Thus, the industry-specific likelihood of an income loss is positively associated
with the share of women in the respective industry. In Fig. 7, we display binned scat-
ter plots for the association between the respective industry-specific fixed effects in
the likelihood of an income loss and the share of women for self-employed individ-
uals and employees, respectively.15 We observe a positive association between the
industry fixed effects and the share of women in the respective industries. The OLS
coefficient for the underlying relationship implies that a 10 percentage point higher
share of women in a given industry is associated with an increase in the likelihood of
experiencing an income loss of about 5.6 percentage points.
Moreover, the results in columns (3) and (5) of Table 2 do not support the notion
of a gender gap in the likelihood of a decline in working hours and working from
home.1617 However, the change in the OLS coefficient for the indicator for being
female between the restricted and unrestricted model and Fig. 6b suggests an eco-
nomically significant change in the likelihood of a decline in working hours of about
11.9 percentage points. This is more than fully accounted for by the fact that, again,
women are disproportionately represented in those industries, which are hit the hard-
est by the COVID-19 pandemic. In addition, Fig. 7c suggests a positive association
between the share of women across industries and the likelihood of experiencing a
decline in working hours in these industries. This constitutes evidence that the indus-
try affiliation moderates the relationship between the likelihood of a decline in work-
ing hours and the gender of self-employed respondents, while there is no evidence
15 In Fig. 7, we calculate the share of women in the respective industries over the complete working sample,
i.e. we do not distinguish between self-employed and employed individuals.
16 Note that the possibility to work from home has a moderating effect on the likelihood of an income and
working hours decline. If we include the indicator for working from home in the unrestricted models, the
coefficients suggest that working from home is associated with a 13.72 (p = 0.063) and 15.62 (p = 0.041)
percentage point lower likelihood of a decline in income or working hours, respectively. However, the
Gelbach decomposition suggests that working from home because of the COVID-19 pandemic does not
contribute to the gender difference in these two outcomes. These results are available upon request.
17 There might also exist initial gender differences in the standard workload among the self-employed.
Therefore, we also examined whether including actual weekly working hours of the previous year alters
the estimated gender gaps in a meaningful way, which is not the case.
COVID-19: a crisis of the female self-employed 1159
Fig. 6 Gelbach decomposition 30
of the gender gap in labor market
outcomes among self-employed
respondents. Note: a-c display
20
the Gelbach decomposition of
the gender gap in the likelihood
of an income reduction, a Percentage points
reduction in working time, and 9.29 9.18
10
working from home among
self-employed respondents. Red 3.14
bars indicate 95% confidence -0.11 -0.21 0.15
0
intervals based on robust -2.86
standard errors. a Likelihood of
income decline. b Likelihood of
decline in working time. -10
c Likelihood of remote work Tot. change Demogr. NACE Big 5 HH context UE Education
Characteristics
(a)
30
20
Percentage points 11.85 12.14
10
0.65 -0.01 0.39
0 -1.01 -0.31
-10
Tot. change Demogr. NACE Big 5 HH context UE Education
Characteristics
(b)
30
20
Percentage points
10
2.23 1.75 1.59
0.02 -0.51 0.14
0 -0.76
-10
Tot. change Demogr. NACE Big 5 HH context UE Education
Characteristics
(c)
1160 D. Graeber et al.
.7 .7
Industry fixed effect Industry fixed effect
.5 .5
.3 .3
.1 .1
-.1 Coef. = 0.564** (0.221) -.1 Coef. = 0.042 (0.163)
.2 .4 .6 .8 1 0 .2 .4 .6 .8 1
Share female Share female
(a) (b)
.7 .7
Industry fixed effect Industry fixed effect
.5 .5
.3 .3
.1 .1
-.1 Coef. = 0.252 (0.254) -.1 Coef. = 0.024 (0.144)
.2 .4 .6 .8 1 0 .2 .4 .6 .8 1
Share female Share female
(c) (d)
.7 .7
Industry fixed effect Industry fixed effect
.5 .5
.3 .3
.1
.1
-.1 Coef. = 0.057 (0.156)
-.1 Coef. = 0.086 (0.280)
.2 .4 .6 .8 1 0 .2 .4 .6 .8 1
Share female Share female
(e) (f)
Fig. 7 The association between industry specific fixed effects for the probability of an income or working
time decrease as well as for the probability of working from home and the share of women in the respective
industry. Note: a–f display the association between industry specific fixed effects and the share of women
in the respective industry for the working population in 2020. The fixed effects stem from a regression of
our three outcomes on industry indicators, respectively. The share of women corresponds to the share of
women in the respective industry in our working sample. Both figures correspond to a binned scatterplot.
The regression coefficients stem from an OLS regression of the industry fixed effects on the share of
women in the respective industries. Robust standard errors are in parentheses. *p < 0.10, **p < 0.05,
***p < 0.01. a Income decline for self-employed individuals. b Income decline for employees. c Working
time decline for self-employed individuals. d Working time decline for employees. e Remote work for
self-employed individuals. f Remote work for employees
COVID-19: a crisis of the female self-employed 1161
for such a relationship for the probability of working from home. We also do not find
support for such a relationship among employees.
In Table 12 in Appendix 1, we display the five industries with the highest and
lowest shares of women, respectively. The industries with the highest share of women
include, for example, the hospitality sector and personal services — industries that
were hit particularly hard by the COVID-19 crisis.18 For each of these industries we
also show the associated industry fixed effect from a regression of the likelihood of an
income loss on state and week indicators as well as industry indicators. The average
share of women for these industries in our sample is 82.57% and the average estimate
of the fixed effects is 0.41.19 Conversely, the average share of women in the five
industries with the lowest shares of women in our sample is 25.68% and the average
fixed effect for these industries is 0.19.20 Thus, the contribution of industry fixed
effects to the likelihood of suffering income losses due to the COVID-19 pandemic
is largest in industries where women are overrepresented.
5 Potential mechanisms
In this section, we investigate potential mechanisms driving our results. Note that the
gendered industry effects presented in Section 4.2 encompass a variety of factors:
Not only do they suggest the existence of direct effects of the pandemic that impact
industries to varying degrees, i.e., through government-imposed restrictions, but also
the importance of other NPIs, such as the closure of schools or day-care centers, and
the indirect effects these have on income or hours worked, i.e., through changes in the
intra-household allocation of time. Therefore, the overall contribution of the industry
fixed effects is the product of the strength of the selection into industries as well as
the association of the respective industry with the respective outcome.
In the following, we further characterize these relationships. We investigate to
what extent direct regulations, or shortages in supply or demand, drive the dispropor-
tionate impact of the COVID-19 pandemic on self-employed women. We then test
whether gendered specialization in home production might have contributed to the differ-
ential impact of the COVID-19 pandemic among self-employed women and men.
5.1 Business-related distortions due to the COVID-19 pandemic
In the SOEP-CoV questionnaire, self-employed respondents were asked whether they
have been affected by several events in the wake of the COVID-19 pandemic and
18 In our sample, the industries with the highest shares of women are, in decreasing order, “Other personal
service activities,” “Social work activities without accommodation,” “Retail trade, except of motor vehicles
and motorcycles,” “Accommodation,” and “Human health activities.” A detailed breakdown of industries
is limited by sample size restrictions. In Table 12 in Appendix 1, we only display industries with at least
ten observations.
19 The reference category is the agricultural sector.
20 The five industries with the lowest shares of females are, in increasing order, “Land transport and
transport via pipelines,” “Printing and reproduction of recorded media,” “Specialized construction activ-
ities,” “Computer programming, consultancy and related activities,” and “Manufacture of machinery and
equipment n.e.c.”
1162 D. Graeber et al.
associated NPIs. Of these, we focus on events that might have detrimental effects on
the self-employed respondents’ income or working time. These are “being affected
by regulations, e.g. opening hours” (restrictions), “suppliers are not able to deliver
parts or products to perform business” (supply), and “customers are cancelling ser-
vices or orders” (demand). We apply the Gelbach decomposition to decompose the
gender gap in the likelihood that the self-employed respondents report to have been
affected by these events. Table 3 displays the restricted and unrestricted model for
these three events.21
We find that self-employed women are 20.2 percentage points more likely than
their male counterparts to state that they are affected by rules or restrictions. We
do not find such differences for the supply of intermediate goods or for demand
shortages. In Fig. 8, we show detailed Gelbach decompositions of the gender gap
for business-related events. The Gelbach decomposition in Fig, 8, along with the
results in Table 3, provide evidence that it is, once again, the disproportionate rep-
resentation of women in industries most affected by the pandemic that explains the
differential effects.22 Our full set of covariates explains about 15 percentage points of
this gender gap, with about 9 percentage points thereof attributable to industry fixed
effects. While the total change of the gender gap between the restricted and unre-
stricted model is significant at the five percent level of significance, the contribution
of industry fixed effects is significant at the ten percent level of significance.
Moreover, we find that government-imposed restrictions contribute significantly
to the gender gap in the likelihood of an income decline. This is shown in Fig. 9,
where we include indicators for the three business-related events in the wake of the
COVID-19 pandemic in the Gelbach decomposition of the gender gap in income
losses.23 Among the three business-related events considered, being affected by rules
and restrictions due to the COVID-19 pandemic is the only relevant contributor to
the gender gap in income loss. As depicted in Fig. 9, rules and restrictions account
for 4.5 percentage points of the total change of 10.3 percentage points.24 At the same
time, the contribution of industry fixed effects is considerably attenuated from 9.2 to
7.1 and is significant at the 10% level of significance, suggesting that government-
imposed restrictions disproportionately affect industries in which women are over-
represented and that those restrictions contribute to positively to the likelihood of an
income decline.
5.2 Household income and household chores
As noted previously, direct regulations of businesses are not the only government
interventions that can potentially affect labor market outcomes of self-employed indi-
viduals. Other NPIs include the closure of schools and child-care facilities, which
21 See Table 6 and Table 7 in Appendix 1 for summary statistics on the dependent variables used in this
section.
22 The detailed results for the Gelbach decomposition are depicted in Table 14 in Appendix 1.
23 For the sake of brevity, we consolidate all other characteristics in the category “Remainder.”
24 Detailed results for the Gelbach decomposition are displayed in Table 15 in Appendix 1.
COVID-19: a crisis of the female self-employed 1163
Table 3 Restricted and unrestricted models for the likelihood that a business was affected by the respective
event
(1) (2) (3) (4) (5) (6)
Restrictions Restrictions Supply Supply Demand Demand
Gender: female 0.202*** 0.051 −0.027 −0.057 0.052 −0.007
(0.058) (0.068) (0.041) (0.048) (0.059) (0.073)
Demographics
Age −0.005 0.028** 0.022
(0.019) (0.013) (0.019)
Age squared 0.000 −0.000** −0.000*
(0.000) (0.000) (0.000)
Migration background 0.092 0.014 0.032
(0.090) (0.075) (0.097)
Big 5
Extraversion 0.039 −0.004 0.039
(0.037) (0.029) (0.039)
Conscientiousness −0.025 0.021 −0.046
(0.036) (0.024) (0.039)
Openness −0.030 −0.009 0.055
(0.037) (0.027) (0.038)
Neuroticism 0.064* −0.001 0.001
(0.035) (0.024) (0.039)
Agreeableness 0.037 −0.038 −0.017
(0.035) (0.026) (0.037)
Household context
HH size −0.001 0.024 −0.035
(0.032) (0.027) (0.040)
Married −0.019 −0.058 −0.041
(0.073) (0.056) (0.079)
School child or younger −0.091 −0.099 −0.038
(0.096) (0.078) (0.108)
Log of HH net income −0.057 0.015 0.018
(0.057) (0.044) (0.060)
Education (ref. low)
Intermediate education −0.110 −0.147 −0.112
(0.105) (0.098) (0.116)
High education −0.054 −0.132 −0.100
(0.108) (0.103) (0.120)
Unemployment experience −0.016 −0.011** −0.021**
(0.011) (0.005) (0.009)
1164 D. Graeber et al.
Table 3 (continued)
(1) (2) (3) (4) (5) (6)
Restrictions Restrictions Supply Supply Demand Demand
Mean of outcome 0.457 0.457 0.122 0.122 0.434 0.434
Observations 311 311 311 311 311 311
R2 0.13 0.46 0.05 0.31 0.09 0.38
The table displays restricted and unrestricted models underlying the Gelbach decomposition for business
events. All models include state and week fixed effects. Columns (1), (3), and (5) display results for the
restricted models. Columns (2), (4), and (6) display results for the unrestricted models. The unrestricted
models also include NACE 2 fixed effects. Standard errors are robust and in parentheses. *p < 0.10,
**p < 0.05, ***p < 0.01
may also contribute to the observed gender gap. Assume that households maxi-
mize income subject to a time constraint. Further, assume decreasing returns and
comparative advantages in household and market production, respectively. Under
these conditions, both spouses would participate in the labor force in normal times.
However, their respective contributions to the household income would be deter-
mined by their relative productivity in home and market production (e.g., Weiss 1993;
Bertrand et al. 2015). In this class of models, the partner who is relatively more
productive at home production tends to spend more time with household chores or
childcare. At the same time, their spouse spends more time in market production,
where they are hypothesized to be relatively more productive, and thus earn a higher
income.25
Given these assumptions, households need to re-optimize if, for instance, child-
care facilities close. Under these circumstances, it is likely that the partner with the
higher relative productivity in home production reduces time in market production
while the other partner increases hours worked, ceteris paribus. One implication of
this simplified model is that, if women tend to be the partner who is relatively more
productive in home production, we would observe a gender gap in income and time
decreases as a consequence of NPIs reducing the share of home production that can
be outsourced, i.e., the closure of childcare facilities.26 So far, we have accounted for
this by controlling for the presence of children and household size.
We now test this prediction by including an individual’s earnings, relative to the
overall earnings of the household, in our models. The concept is focal in the litera-
ture on gender norms (e.g., Bertrand et al. 2015; Foster and Stratton 2021). For each
respondent, we know the partner from 2018. Thus, we are able to link the partners’
earnings from 2019 to each respondent. Then, we calculate the relative earnings of
each individual within each of these household pairs. Note that not every individual
in our data has a partner. In such cases, the relative earnings for this observation is
25 At this point, we abstract from gender norms, which could also explain the gendered response to a
closure of childcare facilities.
26 We are very grateful to an anonymous referee who suggested the discussion of intra-household
dynamics.
COVID-19: a crisis of the female self-employed 1165
Fig. 8 Gelbach decomposition 30
of the gender gap in business-
related events. Note: a–c display
the Gelbach decomposition of
20
the gender gap in the likelihood
Percentage points
of various business-related 15.08
events. Red bars indicate 95%
confidence intervals and are 8.91
10
based on robust standard errors.
4.60
a Rules or restrictions. b Supply
1.67
of intermediate products c 0
0.03 0.17 -0.31
Demand shortage
-10
Tot. change Demogr. NACE Big 5 HH context UE Education
Characteristics
(a)
30
20
Percentage points
10 2.99
2.21 2.20
-0.22 0.11 -0.16
0 -1.16
-10
Tot. change Demogr. NACE Big 5 HH context UE Education
Characteristics
(b)
30
20
Percentage points
10
5.92
4.44
2.07
-0.23 -0.45 0.22 -0.13
0
-10
Tot. change Demogr. NACE Big 5 HH context UE Education
Characteristics
(c)
1166 D. Graeber et al.
Fig. 9 Contribution of the 30
business-related events to the
gender gap in the likelihood of
an income decline. Note: a–c
20
display the importance of
various business-related events
for the gender gap in the Percentage points
10.32
likelihood of an income decline.
10
7.14
We summarize the residual
4.52
characteristics in the category
“Remainder.” Red bars indicate
0 -1.34
95% confidence intervals and
are based on robust standard
errors. a Rules or restrictions. b
Supply of intermediate products. -10
c Demand shortage Tot. change NACE Restrictions Remainder
Characteristics
(a)
30
20
Percentage points
9.19 9.14
10
-0.05 0.10
0
-10
Tot. change NACE Supply Remainder
Characteristics
(b)
30
20
Percentage points
8.95 8.32
10
1.97
0 -1.34
-10
Tot. change NACE Demand Remainder
Characteristics
(c)
COVID-19: a crisis of the female self-employed 1167
100% or 0%. We account for these single households via the inclusion of an indica-
tor for having a partner in 2018. If an individual did not work in 2019, we impute
zero earnings. We then include relative earnings in the Gelbach decomposition. If the
conjecture above is true, we would expect that women are more likely to have lower
relative earnings and relative earnings would be negatively associated with the inci-
dence of a decrease in working time, income, or the likelihood of working from
home.
With respect to the likelihood of income reductions we find some evidence for
the first part of the conjecture. That is, the results indicate that households optimize
and exploit comparative advantages. Table 4 displays the restricted and unrestricted
model for our outcome variables. In addition to the standard set of controls, we now
include the individual’s share of household earnings in 2019. In addition, all models
include an indicator for the presence of a partner. For the likelihood of an income
decline due to the COVID-19 pandemic, the earnings share of the individual is sig-
nificant at the ten percent level of significance. The point estimate suggests that a ten
percent increase in the individual’s earnings share is associated with a 2.6 percent-
age points reduction in the likelihood of an income reduction. Similarly, the Gelbach
decomposition in Fig. 10a suggests that women account for a smaller share of the
total household earnings, on average, and that the share of household earnings is neg-
atively associated with the likelihood of an income reduction due to the COVID-19
pandemic. This relationship accounts for 25.8% of the total change in the gender gap.
However, the estimate is not very precisely estimated, meaning we cannot reject the
hypothesis that this contribution is not different from zero (p = 0.104). However,
it is worth emphasizing that the gender gap almost completely vanishes as soon as
we account for relative earnings (compare column (2) of Table 2 to column (2) of
Table 4).
With respect to the likelihood of a reduction in working hours or the incidence
of working from home, we find no evidence for a significant association with the
individuals’ earnings share within the household. The Gelbach decompositions in
Figs. 10b and c likewise do not provide an indication that the relative income posi-
tion contributes to explaining the gender gap. One interpretation of these findings is
that a negative association would appear only for outcomes that translate directly into
material well-being. For working time, this is not clear a priori. For self-employed
individuals, there are various possible circumstances where working time reductions
do not necessarily translate into reduced earnings. With respect to the incidence
of working from home, other factors are likely more relevant, i.e., the extent to
which the job of the self-employed individual or their partner can be performed
remotely.27
27 We abstract from leisure in this analysis since we assume that individuals shift their time from market
production to household chores.
1168 D. Graeber et al.
Fig. 10 Gelbach decomposition 30
of the gender gap in labor
market outcomes among 25
self-employed respondents,
20
testing for specialization in the
household context.
Percentage points
15 13.14
Note: a–c display the Gelbach 11.40
decomposition of the gender gap 10
in the likelihood of an income
5 3.39
reduction, a reduction in 2.38
1.09
0.15
working time, and working from 0 -0.47
home among self-employed -4.79
respondents. Red bars indicate -5
95% confidence intervals based
-10
on robust standard errors.
a Reduction of income. Tot. changeDemog. NACE Big 5 HH context UE Educ. Rel. inc.
Characteristics
b Reduction in weekly working
hours. c Remote work (a)
30
25
20
Percentage points
14.94
15
12.66
10
5
1.24
0.24 0.08 -0.19
0 -0.49
-3.17
-5
-10
Tot. changeDemog. NACE Big 5 HH context UE Educ. Rel. inc.
Characteristics
(b)
30
20
Percentage points
10
3.12 3.15 3.10
0.60
0 -0.63 -0.90 -0.34
-1.87
-10
Tot. changeDemog. NACE Big 5 HH context UE Educ. Rel. inc.
Characteristics
(c)
COVID-19: a crisis of the female self-employed 1169
Table 4 Restricted and unrestricted models for differences in likelihood that income or working hours
decreased, accounting for relative income differences
(1) (2) (3) (4) (5) (6)
Income Income Working Working Remote Remote
hours hours work work
Gender: female 0.154** 0.022 0.066 −0.054 0.002 −0.029
(0.067) (0.086) (0.070) (0.088) (0.066) (0.077)
Demographics
Age 0.056* 0.016 −0.031
(0.032) (0.033) (0.034)
Age squared −0.001* 0.000 0.000
(0.000) (0.000) (0.000)
Migration background 0.018 0.025 −0.207*
(0.131) (0.114) (0.113)
Big 5
Extraversion 0.044 0.054 0.052
(0.047) (0.045) (0.044)
Conscientiousness −0.040 −0.016 −0.016
(0.046) (0.045) (0.045)
Openness to experience 0.055 0.035 0.048
(0.048) (0.046) (0.041)
Neuroticism −0.062 −0.042 −0.015
(0.042) (0.044) (0.040)
Agreeableness −0.087** −0.073* −0.023
(0.043) (0.043) (0.041)
Household context
HH size −0.072 −0.065 0.100***
(0.050) (0.043) (0.036)
Married 0.072 −0.012 0.028
(0.124) (0.151) (0.117)
School child or younger 0.056 0.247** 0.078
(0.124) (0.110) (0.124)
Log of HH net income −0.064 −0.127** −0.127**
(0.069) (0.066) (0.064)
Education (ref. low)
Intermediate education 0.019 0.090 −0.049
(0.146) (0.137) (0.137)
High education −0.033 0.064 0.065
(0.161) (0.142) (0.149)
Unemployment experience −0.025 0.013 −0.048***
(0.020) (0.021) (0.017)
1170 D. Graeber et al.
Table 4 (continued)
(1) (2) (3) (4) (5) (6)
Income Income Working Working Remote Remote
hours hours work work
Income share −0.260* −0.002 0.143
(0.135) (0.156) (0.136)
Mean of outcome 0.561 0.561 0.496 0.496 0.496 0.496
Observations 239 239 238 238 238 238
R2 0.17 0.50 0.13 0.48 0.13 0.48
The table displays restricted and unrestricted models underlying the Gelbach decomposition. All models
include state and week fixed effects as well as indicators for having a partner. Columns (1), (3), and (5)
display results for the restricted models. Columns (2), (4), and (6) display results for the unrestricted
models. The unrestricted models also include NACE 2 fixed effects. Standard errors are robust and in
parentheses. *p < 0.10, **p < 0.05, ***p < 0.01
6 Conclusion
We analyze how the economic shock related to SARS-CoV-2 affects the self-
employed in comparison to employees, and focus in particular on the female working
population. We show that the 4.2 million self-employed men and women are 42 per-
centage points more likely to experience an income loss than employees and that
they have a 30 percentage points higher chance of experiencing a decrease in work-
ing hours. This differential impact on the two employment forms cannot be explained
by differences in individual-level characteristics or selection into different industries.
The self-employed are more likely to suffer income losses and reductions in working
hours throughout.
Unlike for self-employed workers, employees’ wages and working hours in Ger-
many are more rigid than in comparable countries. In addition, to prevent mass
layoffs, the German government expanded “Kurzarbeit,” its well-established short-
time work scheme that allows for temporary reductions in the wages and hours of
employees. Indeed, the fraction of employees who experience income losses is pro-
portional to the fraction of employees in short-time work schemes (Kritikos et al.
2020). Thus, it appears that the labor market impact of the COVID-19 pandemic was
mitigated by “Kurzarbeit”.
Among the self-employed, we find that women are about one-third more likely
to face income losses due to the COVID-19 pandemic than men. We do not find
COVID-19: a crisis of the female self-employed 1171
a comparable gender gap among employees, which is likely a result of labor mar-
ket rigidities. Our results reveal that the largest share of gender differences among
the self-employed is attributable to the fact that self-employed women work dispro-
portionately often in industries that are more severely affected by the COVID-19
pandemic than men. This is supported by the observable gender gap in the extent
to which self-employed individuals were affected by government-imposed restric-
tions, such as the regulation of opening hours. We provide evidence that this directly
translates into gender differences in income losses. Moreover, we find suggestive
evidence that gendered household production also contributes to the gender gap in
income losses. Still, this is of second order compared to the contribution of industry
effects.
Our study has important policy implications that may be applicable to policy
responses to the further development of the current pandemic or for future pan-
demics (Petrovan et al. 2020). We show that the self-employed, in particular women,
are hit significantly harder by this systemic shock than other parts of the work-
ing population, which is, in part, a direct consequence of policy measures enacted
to contain the spread of the virus. This outcome should also be seen in the con-
text of the slowly increasing willingness of women to enter self-employment. If
self-employed women feel less supported by policy measures during such a sys-
temic shock than female employees, society risks that they will start turning away
from this employment form. Thus, the gender gap in self-employment may widen
again. This could negatively affect growth, notably in parts of the economy that
depend strongly on female self-employment. The design of policy measures intend-
ing to mitigate negative economic shocks in the ongoing or in comparable future
crisis situations, should, therefore, account for this variation in economic hard-
ship. Given our finding that government-imposed restrictions are a factor through
which this unequal impact of the pandemic emerges, targeted policies that restore
gender equity seem particularly relevant. Similarly, given our finding that the self-
employed are disproportionately affected by the COVID-19 pandemic, policy makers
may consider different measures aimed at supporting them. However, every such
policy measure involves the risk of moral hazard. That is, it provides incentives
for self-employed individuals to engage in risky behavior in a way it would not
occur in the absence of support schemes. On the other hand, the detrimental effect
of the COVID-19 pandemic on the self-employed is not the result of individual
decision-making, rather it is a systematic and unexpected shock, and in part a direct
consequence of government regulation. More generally, any support scheme for
the self-employed may create both negative and positive externalities, which are to
be weighted against each other. For instance, self-employment and entrepreneur-
ship are shown to have a positive effect on growth (Stoica et al. 2020). As such,
support schemes which successfully retain the propensity to remain self-employed
through the crisis have the potential to facilitate recovery after the COVID-19
pandemic.
1172 D. Graeber et al.
Appendix 1. Additional tables
Table 5 Variable descriptions
(1) (2) (3)
Variable Description Year of origin
Income (gross) decrease Indicator reflecting decrease of monthly 2020
gross income due to the COVID-19 pan-
demic.
Working hour decrease Indicator reflecting decrease of weekly 2020
working hours due to the COVID-19 pan-
demic.
Income loss Exact amount of lost income due to the 2020
COVID-19 pandemic.
Number of working hour Number of weekly working hours decreased 2020
decrease due to the COVID-19 pandemic.
Remote work Indicator reflecting working from home due 2020
to the COVID-19 pandemic.
Age Difference between survey year and birth year. Pre-2020
Female Indicator for being female. Pre-2020
Migration background Indicator for having a direct or indirect migration Pre-2020
background.
Openness to experience Second factor of a principal component analysis of 2019
the items of the BIG 5-inventory.
Conscientiousness Third factor of a principal component analysis of the 2019
items of the BIG 5-inventory.
Extraversion First factor of a principal component analysis of the 2019
items of the BIG 5-inventory.
Agreeableness Fifth factor of a principal component analysis of the 2019
items of the BIG 5-inventory.
Neuroticism Fourth factor of a principal component analysis of the 2019
items of the BIG 5-inventory.
Household size Number of household members. 2019
Household net income Monthly household net income in 2015 Euros. If 2019
information is missing, we imputed the information
by plugging in the mean for each education x child
presence x self-employment status-cell.
Married Indicator for being married. 2019
School child or younger Indicator reflecting the presence of a child of school 2020
age or younger.
Basic school leaving Indicator for categories 0 “in school” to 1c “basic Last available
degree vocational education” according to the Comparative information in
Analysis of Social Mobility in Industrial Nations seven years pre
(CASMIN)-scale. 2020
COVID-19: a crisis of the female self-employed 1173
Table 5 (continued)
(1) (2) (3)
Variable Description Year of origin
Intermediate school leav- Indicator for categories 2b “intermediate general Last available
ing degree qualification” to 2c voc “vocational maturity certifi- information in
cate” according to the CASMIN-scale. seven years pre
2020
Tertiary school leaving Indicator for categories 3a “lower tertiary education” Last available
degree or 3b “higher tertiary education” according to the information in
CASMIN-scale. seven years pre
2020
Unemployment Generated unemployment experience from 2018
experience “pgen.dta” of the SOEP v.35.
NACE 2 code Two-digit NACE Industry – Sector. Missing values, 2019
e.g., due to unemployment in 2019, are coded as a
separate category.
Subject to regulation Indicator reflecting whether the self-employed indi- 2020
vidual’s business was subject to regulations to contain
COVID-19, e.g., regulation of opening hours.
Supply problems Indicator reflecting whether the self-employed indi- 2020
vidual’s business suffered from shortages of interme-
diate goods.
Demand problems Indicator reflecting whether the self-employed indi- 2020
vidual’s business suffered from cancellation of their
services and goods, i.e., demand shortage.
The table provides information on variables and their year of origin
Table 6 Summary statistics
(1) (2) (3) (4) (5)
Self-employed Individuals Employees Individuals P-value of (1)–(3)
Income (gross) decrease 0.552 310 0.132 3221 0.000
Working hour decrease 0.495 309 0.196 3209 0.000
Remote work 0.457 311 0.390 3222 0.021
Demographics
Age 53.791 311 47.034 3222 0.000
(11.154) (10.533)
Female 0.498 311 0.611 3222 0.000
Migration background 0.164 311 0.205 3222 0.086
1174 D. Graeber et al.
Table 6 (continued)
(1) (2) (3) (4) (5)
Self-employed Individuals Employees Individuals P-value of (1)–(3)
Personality traits
Openness to experience 0.317 311 −0.032 3222 0.000
(1.010) (0.975)
Conscientiousness 0.099 311 0.076 3222 0.664
(0.928) (0.919)
Extraversion 0.092 311 0.015 3222 0.196
(0.967) (1.019)
Agreeableness −0.005 311 −0.088 3222 0.159
(1.009) (0.989)
Neuroticism −0.127 311 −0.051 3222 0.188
(0.954) (0.973)
Household context
Household size 2.617 311 2.815 3222 0.017
(1.427) (1.386)
Household net income (e) 4619.53 311 3826.88 3222 0.000
(4482.76) (1970.61)
Married 0.624 311 0.585 3222
School child or younger 0.354 311 0.468 3222 0.000
Education (ref. basic)
Intermediate 0.379 311 0.493 3222 0.000
Tertiary 0.514 311 0.348 3222 0.000
Unemployment experience 0.876 311 0.882 3222 0.968
Revenue-reducing events in the wake of COVID-19
Subject to regulation 0.457 311
Supply problems 0.122 311
Demand problems 0.434 311
The table displays mean and standard deviations, in parentheses, for self-employed and gainfully
employed individuals
COVID-19: a crisis of the female self-employed 1175
Table 7 Summary statistics for self-employed individuals
(1) (2) (3) (4) (5)
Female Individuals Male Individuals P-value of (1)–(3)
Income (gross) decrease 0.632 155 0.471 155 0.004
Working hour decrease 0.536 153 0.455 156 0.156
Remote work 0.432 155 0.481 156 0.392
Demographics
Age 52.245 155 55.327 156 0.015
(10.230) (11.835)
Female 1.000 155 0.000 156 .
Migration background 0.155 155 0.173 156 0.665
Personality traits
Openness to experience 0.232 155 0.403 156 0.135
(1.015) (1.001)
Conscientiousness 0.144 155 0.055 156 0.397
(0.939) (0.918)
Extraversion 0.235 155 −0.050 156 0.009
(0.835) (1.066)
Agreeableness 0.199 155 −0.207 156 0.000
(0.941) (1.036)
Neuroticism 0.042 155 −0.296 156 0.002
(0.970) (0.910)
Household context
Household size 2.626 155 2.609 156 0.917
(1.378) (1.479)
Household net income (e) 4374.67 155 4862.82 156 0.338
(5021.36) (3875.48)
Married 0.613 155 0.635 156
School child or younger 0.355 155 0.353 156 0.967
Education (ref. basic)
Intermediate 0.413 155 0.346 156 0.226
Tertiary 0.484 155 0.545 156 0.283
Unemployment experience 0.868 155 0.883 156 0.965
Revenue-reducing events in the wake of COVID-19
Subject to regulation 0.561 155 0.353 156 0.000
Supply problems 0.110 155 0.135 156 0.504
Demand problems 0.458 155 0.410 156 0.397
The table displays mean and standard deviations, in parentheses, for self-employed individuals
1176 D. Graeber et al.
Table 8 Summary statistics for employees
(1) (2) (3) (4) (5)
Female Individuals Male Individuals P-value of (1)–(3)
Income (gross) decrease 0.123 1969 0.146 1252 0.063
Working hour decrease 0.205 1959 0.182 1250 0.121
Remote work 0.369 1970 0.423 1252 0.002
Demographics
Age 47.141 1970 46.866 1252 0.470
(10.063) (11.235)
Female 1.000 1970 0.000 1252 .
Migration background 0.197 1970 0.216 1252 0.193
Personality traits
Openness to experience −0.082 1970 0.046 1252 0.000
(0.993) (0.942)
Conscientiousness 0.164 1970 −0.063 1252 0.000
(0.904) (0.925)
Extraversion 0.110 1970 −0.136 1252 0.000
(1.002) (1.026)
Agreeableness 0.036 1970 −0.282 1252 0.000
(0.965) (0.997)
Neuroticism 0.100 1970 −0.289 1252 0.000
(0.985) (0.905)
Household context
Household size 2.875 1970 2.720 1252 0.002
(1.354) (1.432)
Household net income (e) 3763.45 1970 3926.69 1252 0.022
(1936.66) (2019.63)
Married 0.580 1970 0.593 1252
School child or younger 0.491 1970 0.431 1252 0.001
Education (ref. basic)
Intermediate 0.535 1970 0.427 1252 0.000
Tertiary 0.327 1970 0.382 1252 0.001
Unemployment experience 0.985 1970 0.719 1252 0.004
The table displays mean and standard deviations, in parentheses, for gainfully employed individuals
COVID-19: a crisis of the female self-employed 1177
Table 9 Relevance of industry fixed effects in Table 1
(1) (2) (3)
Income Working hours Remote work
Model without industry fixed effects Self-employed 0.434*** 0.316*** 0.014
(0.029) (0.030) (0.031)
R2 0.12 0.07 0.21
Unrestricted model Self-employed 0.421*** 0.302*** 0.021
(0.031) (0.031) (0.032)
R2 0.23 0.13 0.31
The table displays the coefficient estimates and R-squared of the unrestricted models in columns (2), (4),
and (6) of Table 1 with and without the inclusion of industry fixed effects. Corresponding robust standard
errors are in parentheses. *p < 0.10, **p < 0.05, ***p < 0.01
Table 10 Detailed results for the Gelbach decomposition of the gender gap among self-employed
individuals
(1) (2) (3)
Income Working hours Remote work
Total change 0.093* 0.119** 0.022
(0.049) (0.049) (0.051)
Demographics 0.031* 0.007 0.018
(0.017) (0.014) (0.018)
NACE 0.092** 0.121*** 0.000
(0.045) (0.043) (0.041)
Big 5 −0.029 −0.010 −0.005
(0.023) (0.026) (0.024)
Household context −0.001 −0.003 0.016
(0.012) (0.014) (0.016)
Unemployment experience −0.002 0.000 0.001
(0.007) (0.001) (0.004)
Education 0.001 0.004 −0.008
(0.006) (0.006) (0.010)
The table displays the detailed results of the Gelbach decomposition of the gender gap among self-
employed individuals. Columns (1), (2), and (3) display the results for the likelihood of an income decline,
decline in working hours and working from home. The total change corresponds to the change in the
gender gap between the restricted and the unrestricted models. The remaining rows show the contribu-
tion of the respective groups of covariates to the total change. Corresponding robust standard errors are in
parentheses. *p < 0.10, **p < 0.05, ***p < 0.01
1178 D. Graeber et al.
Table 11 Restricted and unrestricted models for differences in the likelihood that income or working hours
decreased or that individuals transitioned into non-employment between employees and self-employed
respondents, conditional on the employment status in 2019
(1) (2) (3) (4) (5) (6)
Income Income Working Working Job Job
hours hours loss loss
Self-employed 0.366*** 0.364*** 0.266*** 0.267*** 0.012 −0.007
(0.031) (0.033) (0.031) (0.033) (0.009) (0.018)
Demographics
Gender: female 0.015 0.021 0.007
(0.014) (0.016) (0.005)
Age 0.001 −0.003 −0.003*
(0.005) (0.006) (0.002)
Age squared 0.000 0.000 0.000*
(0.000) (0.000) (0.000)
Migration background 0.037** 0.042** 0.008
(0.017) (0.020) (0.007)
Big 5
Extraversion 0.005 0.011 0.005**
(0.006) (0.007) (0.002)
Conscientiousness −0.008 −0.022*** −0.001
(0.007) (0.008) (0.002)
Openness to experience 0.010 0.005 0.002
(0.006) (0.008) (0.002)
Neuroticism −0.003 0.002 0.002
(0.006) (0.008) (0.002)
Agreeableness 0.001 −0.005 0.003
(0.006) (0.007) (0.002)
Household context
HH size 0.009 0.015* 0.001
(0.008) (0.009) (0.003)
Married 0.015 0.014 0.005
(0.016) (0.018) (0.006)
School child or younger 0.014 −0.005 0.000
(0.019) (0.021) (0.007)
Log of HH net income −0.044*** −0.042** −0.009
(0.017) (0.019) (0.006)
Education (ref. low)
Intermediate education 0.045** 0.023 −0.006
(0.019) (0.023) (0.008)
High education 0.031 0.001 −0.001
(0.022) (0.025) (0.009)
COVID-19: a crisis of the female self-employed 1179
Table 11 (continued)
(1) (2) (3) (4) (5) (6)
Income Income Working Working Job Job
hours hours loss loss
Unemployment experience 0.000 0.007* 0.004**
(0.003) (0.003) (0.002)
Mean of outcome 0.168 0.168 0.219 0.219 0.017 0.017
Observations 3348 3348 3334 3334 3661 3661
R2 0.08 0.22 0.04 0.13 0.01 0.05
The table displays models with and without controls for differences between self-employed and employ-
ees. All models include state and week fixed effects. Columns (1), (3), and (5) display results for the
models without controls. Columns (2), (4), and (6) display results for the models with controls. The
unrestricted models also include NACE 2 fixed effects. Standard errors are robust and in parentheses.
*p < 0.10, **p < 0.05, ***p < 0.01
Table 12 The share of women and industry fixed effects for income losses
Rank NACE code Description Share female FE estimate
(1) (2) (3) (4) (5)
High share 1 96 Other personal service activities 0.857 0.480**
women
(0.236)
2 88 Social work activities without accom- 0.832 0.124
modation
(0.242)
3 47 Retail trade, except of motor vehicles 0.818 0.775***
and motorcycles
(0.222)
4 55 Accommodation 0.818 0.283
(0.242)
5 86 Human health activities 0.803 0.405*
(0.208)
Low share 1 49 Land transport and transport via pipelines 0.189 0.463
women
(0.334)
2 18 Printing and reproduction of recorded 0.235 −0.425*
media
(0.234)
1180 D. Graeber et al.
Table 12 (continued)
Rank NACE code Description Share female FE estimate
(1) (2) (3) (4) (5)
3 43 Specialized construction activities 0.273 0.093
(0.249)
4 62 Computer programming, consultancy 0.290 0.098
and related activities
(0.246)
5 28 Manufacture of machinery and equip- 0.297 0.738***
ment n.e.c.
(0.218)
The table displays the share of women and the associated income loss fixed effects for the industries
with the highest share and lowest share of women. For the table, we display only industries with at least
ten observations. Column (1) displays the rank within each panel. Columns (2) and (3) display the two-
digit NACE code and the description, respectively. Column (3) displays the share of women within each
occupation in our full sample. Column (5) displays industry fixed effect estimates, which stem from a
regression of the likelihood of an income loss on state and week indicators as well as industry indicators,
along with robust standard errors in parentheses. The reference industry is “Crop and animal production,
hunting and related service activities.” *p < 0.10, **p < 0.05, ***p < 0.01
Table 13 Restricted and unrestricted models for the likelihood that income and working hours decreased
or individuals are working from home among employees
(1) (2) (3) (4) (5) (6)
Income Income Working Working Remote Remote
hours hours work work
Gender: female −0.022* 0.014 0.021 0.026 −0.048*** −0.009
(0.012) (0.013) (0.014) (0.016) (0.018) (0.018)
Demographics
Age −0.004 −0.008 0.000
(0.005) (0.006) (0.006)
Age squared 0.000 0.000 0.000
(0.000) (0.000) (0.000)
Migration background 0.041** 0.031 −0.020
(0.016) (0.019) (0.019)
Big 5
Extraversion −0.002 0.005 −0.007
(0.006) (0.007) (0.008)
Conscientiousness 0.007 −0.014* −0.003
(0.006) (0.008) (0.008)
Openness to experience −0.010 0.002 0.026***
(0.007) (0.008) (0.008)
COVID-19: a crisis of the female self-employed 1181
Table 13 (continued)
(1) (2) (3) (4) (5) (6)
Income Income Working Working Remote Remote
hours hours work work
Neuroticism −0.005 −0.002 −0.009
(0.006) (0.008) (0.008)
Agreeableness 0.000 −0.005 0.005
(0.006) (0.007) (0.008)
Household context
HH size 0.009 0.016* −0.019**
(0.007) (0.008) (0.009)
Married 0.021 0.027 −0.031*
(0.015) (0.018) (0.019)
School child or younger 0.014 −0.014 0.049**
(0.018) (0.021) (0.023)
Log of HH net income −0.028* −0.044** 0.151***
(0.016) (0.019) (0.020)
Education (ref. low)
Intermediate education 0.035* 0.016 0.069***
(0.019) (0.023) (0.020)
High education 0.018 −0.008 0.283***
(0.021) (0.025) (0.025)
Unemployment experience 0.003 0.005* −0.002
(0.003) (0.003) (0.002)
Mean of outcome 0.132 0.132 0.196 0.196 0.390 0.390
Observations 3221 3221 3209 3209 3222 3222
R2 0.01 0.17 0.01 0.10 0.03 0.34
The table displays restricted and unrestricted models underlying the Gelbach decomposition. All models
include state and week fixed effects. Columns (1), (3) and (5) display results for the restricted models.
Columns (2), (4) and (6) display results for the unrestricted models. The unrestricted models also include
NACE 2 fixed effects. Standard errors are robust and in parentheses. *p < 0.10, **p < 0.05, ***p < 0.01
1182 D. Graeber et al.
Table 14 Detailed results for the Gelbach decomposition of the gender gap in potential mechanisms
among self-employed individuals
(1) (2) (3)
Restrictions Supply Demand
Total change 0.151*** 0.030 0.059
(0.049) (0.033) (0.050)
Demographics 0.017 0.022** 0.044**
(0.015) (0.011) (0.018)
NACE 0.089* 0.022 0.021
(0.046) (0.028) (0.043)
Big 5 0.046** −0.012 −0.002
(0.022) (0.017) (0.025)
Household context 0.000 −0.002 −0.004
(0.010) (0.007) (0.011)
Unemployment experience 0.002 0.001 0.002
(0.006) (0.003) (0.007)
Education −0.003 −0.002 −0.001
(0.005) (0.006) (0.005)
The table displays the detailed results of the Gelbach decomposition of the gender gap in potential mech-
anisms among self-employed individuals. Columns (1), (2), and (3) display the results for the likelihood
of an income decline, decline in working hours and working from home. The total change corresponds to
the change in the gender gap between the restricted and the unrestricted models. The remaining rows show
the contribution of the respective groups of covariates to the total change. Corresponding robust standard
errors are in parentheses. *p < 0.10, **p < 0.05, ***p < 0.01
Table 15 Detailed results for the Gelbach decomposition of the gender gap in the likelihood of an income
decline among self-employed individuals, including business-related events as an explanatory variable
(1) (2) (3)
Restrictions Supply Demand
Total change 0.103** 0.092* 0.089*
(0.049) (0.049) (0.051)
NACE 0.071* 0.091** 0.083**
(0.043) (0.045) (0.042)
Event 0.045** −0.001 0.020
(0.018) (0.003) (0.022)
Remainder −0.013 0.001 −0.013
(0.029) (0.030) (0.025)
The table displays the detailed results of the Gelbach decomposition of the gender gap in the likelihood of
an income decline among self-employed individuals. Columns (1), (2), and (3) display the results including
and indicator whether respondents state their business has been affected by restrictions or policies, supply
or demand shortages in the wake of the COVID-19 pandemic, respectively. The total change corresponds
to the change in the gender gap between the restricted and the unrestricted models. The remaining charac-
teristics are included in the group “Remainder.” Corresponding robust standard errors are in parentheses.
*p < 0.10, **p < 0.05, ***p<0.01
COVID-19: a crisis of the female self-employed 1183
Appendix 2. Derivation of the Gelbach decomposition
Assume two sets of variables, X,; and X9, with k, and ky variables each.28 The
population linear relationship is given by:
Y= Xf, + X2f2. +€ (1)
We label the components of the OLS estimator that correspond to the variables in
Xj, and X92, pi will and bo, respectively.
Thus, we obtain:
y = XB" + Xobp +e (2)
Now let us consider the coefficient on X; from a base specification that completely
ignores the variables in X2. We denote this estimator phase — = (xX; TX 1)7 'xTy T
The Gelbach (2016) decomposition answers the question of how mut, of the
change in X coefficients can be attributed to different variables in X2 as we move
from the base specification that has no X2 covariates to the full specification that
includes both X, and all X2 covariates. In the context of our analysis, X; would
refer to a gender indicator, and X>2 to the full set of control variables. Throughout
the analysis, we partial out week and state fixed effects. The decomposition links
the estimates of the base and full specification on X, through the following identity,
which is obtained by pre-multiplying both sides of Eq. 2 by (X 1X 1) 1X H and using
the orthogonality of the fitted residuals to the columns of X\:
phase = BI 4 (XTX) TXT Xap (3)
Re-writing the above identity and defining the change in the coefficient on the
pea se — py ull
gender dummy between the base and the full model as é= , one obtains
Il _
5 = phase — BI"! — (XTX) TXT Xo py, (4)
which corresponds to the omitted variable bias formula.
Let X2x be the column of observations on the kth covariate in X> and let 62x be
the estimated coefficient on X2,; in the full specification, then
ky
A 4 A
5 = SUX X1) XT Xa bor. (5)
k=1
since the omitted variables bias formula is linear in its ky components.
From there, the practical implementation of the decomposition follows naturally:
Estimate the full model to obtain Bo.
Estimate the vector of coefficients on Xj in a set of OLS regressions with each
of the k2 covariates X2,% as dependent variable. This yields (X 1X 4 1 X2x.
3. Multiply (x 1X 1X 1 Xxx by Box to obtain ok which is the component esti-
mated to be due to each variable k.
Ne
28This exposition borrows heavily from the one given in Gelbach (2016).
Q Springer
1184 D. Graeber et al.
The set of covariates we include in our Gelbach decomposition, i.e., X2 , are:
• Demographics: second-order polynomial in age, indicator for a migration back-
ground,
• NACE codes (2019): indicators for the two-digit NACE codes,
• Big 5 (2019): openness to experience, conscientiousness, extraversion, agree-
ableness, and neuroticism,
• Household context (2019): household size, indicators for being married, presence
of school children (or younger) in the household, the logarithm of household net
income (2019/18) and
• unemployment experience (2018).
Acknowledgement We would like to thank the editor-in-chief of this journal, Klaus F. Zimmermann,
three anonymous referees, Charlene Kalenkoski, Johannes König, Madeleine Meurer, Julia Schmieder,
and Diemo Urbig, as well as the participants of the workshop on “Economic Effects of the COVID-19
2020 Pandemic on Entrepreneurship and Small Firms” for very valuable comments.
Funding Open Access funding enabled and organized by Projekt DEAL. The survey data used in this
study are part of the project “SOEP-CoV The Spread of the Coronavirus in Germany: Socio-Economic
Factors and Consequences,” funded by the German Federal Ministry of Education and Research. The data
can be accessed via the research data center of the SOEP.
Daniel Graeber received funding from the German Federal Ministry of Education and Research via
the project “SOEP-CoV The Spread of the Coronavirus in Germany: Socio-Economic Factors and Con-
sequences” as well as the Deutsche Forschungsgemeinschaft via the project “Mental Health Dynamics
of Migrants - An Analysis of Factors for Resilience and Vulnerability along Family and Personality
Dimensions (project number 415809395).”
Alexander Kritikos received funding from the research grant “INNOMSME” (project number:
01UI1802), provided by the German Federal Ministry of Education and Research.
Declarations
Conflict of Interest The authors declare that they have no conflict of interest.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons
licence, and indicate if changes were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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