Global COE Hi-Stat Discussion Paper Series 260

Research Unit for Statisticaland Empirical Analysis in Social Sciences (Hi-Stat)

Work-Life Balance and Gender Differences in
Self-Employment Income during
the Start-up Stage in Japan

Hi-Stat Discussion Paper

Hiroyuki Okamuro
Kenta Ikeuchi

November 2012

Hi-Stat
Institute of Economic Research
Hitotsubashi University
2-1 Naka, Kunitatchi Tokyo, 186-8601 Japan
http://gcoe.ier.hit-u.ac.jp

Work–life balance and gender differences in self-employment income during
the start-up stage in Japan

Hiroyuki OKAMURO1, 2* and Kenta IKEUCHI1, 2

1

Hitotsubashi University, Graduate School of Economics, Naka 2-1, Kunitachi Tokyo

186-8601, Japan
2

National Institute of Science and Technology Policy (NISTEP)* Corresponding author: e-mail: okamuro@econ.hit-u.ac.jp; phone: +81-42-5808792

Abstract
This study investigates the determinants of the income levels of newly self-employedworkers compared to those of employees, while focusing on the effects of gender andchildcare. We argue that working mothers with preschool children prefer self-employment
to paid employment, as such arrangements offer better work–life balance. Hence, we predictthat self-employed working mothers earn lower incomes than their male counterparts,
employed counterparts, and female self-employed worker counterparts who do not havepreschool children. Empirical results based on anonymous micro data from Japaneseemployment statistics support our hypotheses.

Keywords: self-employment, income, gender gap, female labor, childcare
JEL classification codes: J13, J16, L26, M13

1

1. Introduction

People switch from employment to self-employment by starting up a new business,and they do this for various reasons. Some workers pursue a business opportunity and aim
to increase their profits or incomes, while others want to enhance personal utility by
improving their work–life balance. The latter pattern is especially distinct among marriedfemale workers with preschool children (Hundley, 2000, 2001). Indeed, a number of studies
suggest that women select self-employment because they want flexible work arrangementsthat allow them to balance career and family (e.g., Lombard, 2001; Macpherson, 1988).
Lucas (1978) and Evans and Jovanovic (1989) each propose basic models ofoccupation choice, in which each worker chooses between paid employment andself-employment, depending on which option will derive a higher expected utility.
Assuming that the personal utility includes both pecuniary and nonpecuniary benefits—suchas flexibility with respect to working location, hours, and intensity—it is reasonable forfemale workers engaged in childcare to prefer self-employment, as it will provide greater
work flexibility, even though they can often expect lower incomes.
Regarding gender equality and childcare support, some major legal and institutionalchanges have been introduced in Japan since the 1990s: the formal introduction of childcare
leave, in April 1992; partial payment of wages and salaries for employees during childcare
leave and a public subsidy for employers that compensates for childcare leave, since
October 1995; the amendment of the Equal Employment Opportunity Law, in April 1999;
the Basic Law for a Gender-equal Society, in June 1999; and the government’s Basic Planfor Gender Equality, in December 2000. Underpinning these changes is a clear trend
regarding the Japanese household structure. According to the Labor Force Survey of the
Ministry of Internal Affairs and Telecommunications, the number of households in which2both husbands and wives work increased drastically in the 1980s, while that in which only
the husbands work decreased. The two structure types balanced in the first half of the 1990s.
Since 1997, the former type has consistently predominated the latter, and the difference intheir numbers has also been growing. However, to the best of our knowledge, few empiricalstudies have been conducted in Japan on the determinants of self-employment income,while focusing on gender differences and considering these institutional changes.
Therefore, this paper investigates the determinants of self-employment income during
the start-up stage of a business, based on anonymous micro data from official Japanesestatistics (Employment Status Survey) captured in three waves (1992, 1997, and 2002),
comparing male and female self-employed workers and female workers in self-employment
and paid employment. We pay special attention to female workers with preschool children
who choose self-employment, comparing their income levels to those of self-employedmale workers and female employees with preschool children under otherwise similar
conditions. Moreover, we examine changes in the determinants of income differences
between these groups during the post-bubble period of the 1990s.
The remainder of this paper is organized as follows. Section 2 provides an overview ofprevious empirical literature on the determinants of self-employment income. Section 3
develops a conceptual framework and presents the hypotheses for empirical analysis.
Section 4 explains the data and estimation models. Section 5 provides descriptive statisticsof the career choices made by male and female employees, as well as the income levels of
self-employed males and females, compared to those of salaried employees. Section 6
reports and discusses the estimation results, and Section 7 concludes this paper.

2. Literature review

3Besides survival and growth, income has been regarded as an important performancemeasure for start-up firms, in both the theoretical and empirical literature 1. Using micro data,previous studies have mainly addressed the effects of owners’ human capital, particularly
formal education and job experience (e.g., Parker and Van Praag, 2006; Robinson andSexton, 1994; Van Praag et al., 2009). With regard to the effect of education on founders’income levels, numerous studies have been executed in developing and developed countries,
as surveyed in the meta-analyses of Van der Sluis et al. (2005) and (2008), respectively. In
essence, previous empirical studies in this line suggest that self-employed workers enjoy ahigher rate of return to formal education than do paid employees, even after controlling for
selectivity and endogeneity bias (Parker, 2009).
Several studies examine gender differences in the performance of new firms (e.g.,
Fairie and Robb, 2009; Robb and Watson, 2012). However, empirical studies on the effectof gender and household structure on self-employment income are relatively scarce, incontrast to the rich literature on the gender gap in wages and salaries among employees (e.g.,
Blau and Kahn, 1997, 2006; Hersch and Stratton, 1997; Stratton, 2001). Using Japanesedata, Genda and Kambayashi (2002) show that, after controlling for age and location effects
and the sample-selection bias, female workers earn significantly less than male workers, andthat this gender difference is larger among self-employed workers than among salaried
employees. However, they do not consider the role of childcare for the lack of related data.
With regard to gender differences in the transition into self-employment, a number ofstudies suggest that women select self-employment because they seek flexible work
arrangements that facilitate a balance of career and family (e.g., Lombard, 2001;
Macpherson, 1988). Georgellis and Wall (2005) examine the factors of transition into
self-employment and find that the determinants significantly differ between male and1This study focuses on the income levels of newly self-employed workers, to facilitate comparability of
returns with employees.

4female employees, suggesting that for women, self-employment is a close substitute forpart-time work. Regarding the effect of childcare on women’s entry into self-employment,
Taniguchi (2002) obtains mixed results while using a longitudinal survey dataset from theUnited States, calling into question the view that self-employment allows working mothers
to balance career and family concerns. Using data from 61 countries, Klyver et al. (2012)
find that overall gender equality is associated with the gender gap in men’s and women’s
self-employment choices.
Hundley (2000) provides the first systematic analysis of the gender gap in
self-employment income versus wage and salary income. He argues that self-employed
women earn less than their male counterparts because they have less energy available todevote to market work. Using a dataset comprising young Americans, he demonstrates that
self-employed women’s incomes decline with marriage, family size (number of children),and housework hours, while self-employed men’s incomes increase with marriage andfamily size. He further finds that this pattern is similar, but less distinct, for male and female
employees, which suggests that in self-employment, the relative incomes of female workers
are more sensitive to family size.
Similarly, Hundley (2001) shows that both the number of young children and thenumber of housework hours negatively correlate with the average hourly earnings ofself-employed women, but not of men. In contrast, Walker (2009) demonstrates that thehousework efforts of self-employed women, rather than their housework hours, contributeto their lower incomes relative to those of their male counterparts.
Previous studies on gender differences in the choice of self-employment andself-employment income have focused on the United States, and little is known about East
Asian countries, including Japan, where institutional environments are quite different fromthose of Western countries. The few exceptions—e.g., Genda and Kambayashi (2002) and5

Harada (2003)—do not consider the effects of household structure and childcare; thus, one
contribution of the current study is that it extends the representative empirical research by
Hundley (2000) to the Japanese labor market and thus applies it to the Japanese context. In
addition, we focus more directly on the role of childcare than does Hundley (2000), bycomparing the income levels of self-employed women with preschool children to those of
their male counterparts and to those of salaried female employees with preschool children.
It is also noteworthy that we target the founders of start-up firms (i.e., within five years ofoperation), rather than self-employed workers in general, to consider explicitly changes toself-employment that are driven by their devotion to childcare.
Moreover,moststudies—including

those

of

Hundley(2000,

2001)—usecross-sectional data from a specific year or a short period. In contrast, a major advantage ofthe current study is that its dataset covers a relatively long period, in three cohorts. As
mentioned, there were many legal improvements in Japan with regard to gender equality
and childcare support during the 1990s. By using a dataset of three different cohorts,
including one in the 1990s, we can consider the effects of these institutional changes on
female workers in self-employment and paid employment, as well as gender differences inself-employment income 2.
Thus, the current study offers a number of contributions to the existing literature. First,
although our research concept is based on Hundley’s work, we focus more directly on theeffect of childcare; we do so by comparing income levels, both between male and female
self-employed workers with preschool children and between self-employed female workers
with preschool children and those in paid employment. Second, our analysis targets thefounders of new start-ups (i.e., newly self-employed workers), rather than self-employed2

Previous studies empirically investigate the effects of the Equal Employment Opportunity Law, which was
enforced in 1986 and strengthened in 1999, on gender wage gap (Abe, 2010), as well as the career
opportunities and choices of female workers (Abe, 2011; Cannings and Lazonick, 1994). However, to the bestof our knowledge, no empirical studies have addressed the impact of these legal improvements on
self-employment in Japan.

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workers in general. Thus, we also explore in detail the relationship between occupation
choice decision and childbirth/childcare by female employees, while considering the timingof quitting a job or switching to self-employment. Third, we estimate changes in the effects
of gender and childcare on relative income levels across three different survey waves, inwhich the macroeconomic conditions, employment system, and institutions for genderequality and work–life balance changed considerably in Japan. Finally, to the best of ourknowledge, this is the first study on this topic to use representative micro data from Japan,
and so it may facilitate interesting comparisons to other developed countries.

3. Conceptual framework and hypotheses

As mentioned, we focus on differences in income between self-employed male andfemale workers, both with preschool children, and between self-employed female workers
with preschool children and those in paid employment, while taking into consideration theeffect of childcare. These research questions are in line with those of Hundley (2000, 2001).
Before examining the factors that contribute to gender differences in self-employment
income, we explore the patterns of occupation choice (i.e., continuing as an employee,
quitting a job, or switching to self-employment) in Japan, while comparing male and female
employees and focusing on the effect of preschool children. Several studies suggest thatwomen select self-employment because they seek flexible work that will facilitate life–workbalance (e.g., Lombard, 2001; Macpherson, 1988). However, most working mothers inJapan choose to retire from the labor market to concentrate on childcare, rather than switch
to self-employment. Therefore, it is important to determine, by using statistical data, howmany female employees with preschool children quit their jobs to devote their efforts tochildcare, how many switch to self-employment, and when they decide to make these7changes. We examine these descriptive statistics later, in Section 5.
Hundley (2000) argues that gender differences in self-employment income areobserved because married women and men enter self-employment for different reasons:“self-employment enables married women to devote considerable effort to householdproduction while continuing in market work, albeit with lower earnings. This contrasts withthe perspective provided by studies of self-employed men, which see self-employment as avehicle for entrepreneurship enabling the capture of rewards for extra work and innovation”(p. 96). Thus, the income gap between male and female self-employed workers could be
attributed to their different commitments to household and market work.
Based on the Labor Force Survey of Japan’s Ministry of Internal Affairs andTelecommunications, a distinctive feature of the Japanese labor market is the M-form curvein a plot of change in the female labor force participation with age: it decreases
considerably as women reach their thirties, as they retire from the labor market to devote
themselves to childcare, then increases again after some years of devotion to childcare(Figure 1). In contrast, the labor force participation ratio of the male population is almost
consistently 100%, from the late twenties to the early fifties.
The M-form curve has been changing since the 1970s, as the female labor force
participation as a whole has gradually increased; however, such patterns persist, anddistinguish Japan from other developed countries. Moreover, using micro data from thesame statistics used in this study, Nakamura and Ueda (1999) find that access to childcare
support and short working hours help married women continue in their work. This suggests
that, in Japan, childcare seriously affects the careers of female workers, but not of maleworkers, although the gender gap in labor force participation has been decreasing.
Indeed, according to a survey of the Ministry of Health, Labor, and Welfare in 2001,
around 70% of working mothers retire from their jobs with the birth of their first child. Of
8those who find new jobs later, 65% work on a part-time basis (Cabinet Office, 2004).
Moreover, another survey by the same ministry in 2002 reveals that 64% of mothers (butonly 0.3% of fathers) in paid employment took childcare leave. According to this survey,
the burden of childcare is unequally shared between the parents: 86% of working mothers(and almost 100% of nonworking mothers) usually take care of their children, mainly bythemselves, while the sharing of housework and childcare with working fathers does not
significantly differ between working and nonworking mothers. Hence, we expect that,among the self-employed, female workers are more likely to devote themselves to childcare
and thus earn lower incomes than their male counterparts.
We can also argue that female self-employed workers with preschool children earnlower incomes than those without preschool children, because the latter may have chosen
self-employment to pursue business opportunities, while the former likely did so to devote
themselves more to childcare. Similar arguments can also be applied to comparisons ofself-employed female workers with preschool children and those in paid employment: thelatter tend to be more career-oriented, while the former tend to be more family-oriented, andthus devote less effort to market work. Therefore, we propose the following hypotheses:
H1: Female self-employed workers with preschool children tend to earn lower incomes thantheir male counterparts.
H2: Female self-employed workers with preschool children tend to earn lower incomes thanother female self-employed workers.
H3: Female self-employed workers with preschool children tend to earn lower incomes thanfemale workers with preschool children in paid employment.
Finally, involvement in childcare may differentially affect gender income differencesamong the self-employed and among salaried employees. Female workers have acomparative advantage in childcare: they can adjust their working hours and intensity more9

flexibly and according to their childcare needs when self-employed than when in paid
employment. Thus, we can predict that the gender income difference in self-employment
increases with the number of preschool children, to a larger extent than among employees;
this gives rise to our fourth hypothesis:
H4: Male/female income differences among self-employed workers are more sensitive tothe number of preschool children than those among employees.
Further, we will compare the estimation results of the same models across the 1992,
1997, and 2002 survey cohorts, using each cohort subsample separately. In this way, we can
examine if, and to what extent, the gender gap among self-employed workers and the gap
between female workers in self-employment and paid employment changed along withchanges in the economic and institutional environment.
As mentioned, several legal and institutional changes were introduced in Japan in the
1990s, regarding gender equality and childcare support (e.g., childcare leave andcompensation). Such improvements may have led to more female employees staying at
work during and after childcare, rather than turning to self-employment. However, suchchanges may not have affected the income levels of self-employed female workers,
especially those with preschool children, because improvements in gender equality andchildcare support targeted only salaried employees. To the best of our knowledge, few
empirical studies have been made on determinants of self-employment income, considering
gender differences in childcare, and institutional changes; this study aims to fill thisresearch gap.

4. Data and empirical models

We use anonymous micro data from the Employment Status Survey of Japan, in three10

waves (1992, 1997, and 2002) 3. This statistical survey targets a random sample of 1 million
adults (i.e., those older than 15) in Japan, or approximately 1% of the entire population. For
academic use, the data of 80% of the individuals in the sample are made available asanonymous data, in that almost all location information has been stripped out.
Individual-level data elements—such as age, income, and family size—are categorized into
groups so that no individual can be identified.
Because this study’s main objective is to analyze the determinants of income level
among newly self-employed individuals and salaried employees, we exclude someindividuals from our dataset. First, we exclude from the analysis those who are unemployed
or students, and any other individual who is not in the labor market. Second, by focusing
particularly on those who are newly self-employed, rather than on those who have been
self-employed for some time, we also exclude self-employed workers who have been in
their business for more than five years. Third, for clarity of comparison, we exclude
self-employed workers with no previous job experience, as well as salaried employees who
were previously self-employed. Thus, our final dataset comprises workers who had been
self-employed for fewer than five years and salaried employees who had not beenpreviously self-employed. The entire three-cohort sample includes approximately 1.2
million people.
We estimate the incomes of both newly self-employed workers and employees.
Because information in our dataset on annual income is not available as absolute values, but
only as categories (i.e., interval-coded), we employ interval regression (see Wooldridge
2010, pp. 783–785), rather than ordinary least squares (OLS) regressions 4 . A major

3This official statistical survey has been carried out every five years since 1956 by Japan’s Statistical Officeof the Ministry of Internal Affairs and Telecommunications. Currently, micro data of these three survey years
are available as anonymous data for academic and educational purposes.
4
As a robustness check, we also conduct an ordered Probit estimation, which is often employed for the
analysis of categorical data that feature intervals. Despite several similarities among these estimation models,a major difference is that the cut points are parameters used to estimate for the ordered Probit model, while11advantage of this estimation compared to ordered Probit estimation is that the estimatedparameters contain the partial effects, as in OLS regressions.
Suppose that individual i’s unobserved annual income is determined by(1)

I i = z i γ + vi ,where z is a vector of independent variables, 𝛾 is a vector of their coefficients, and vi is anerror term. Assuming that the error term has a standard normal distribution—i.e.
𝜈𝑖 ~N(0, 𝜎 2 ) —we estimate 𝛄 and its variance 𝜎 2 . We define the observed ordinal variable

Incomei, which represents worker i’s income level, as
Incomei = 0 if
Incomei = 1 if…….
Incomei = J if 𝐼𝑖 > 𝑘𝐽 ,where I is unobservable individual income and 𝑘1 < 𝑘2 < ⋯ < 𝑘𝐽 denote the known
interval limits; we obtain the conditional probabilities Pr(Incomei = j | z) for j = 0, 1, … J.
The log likelihood for a random draw i is(2),
for j = 1, 2, …, J. The parameters 𝛄 and 𝜎 are estimated to maximize the log-likelihood
function:(3)

with interval regression, the interval endpoints are given. We provide the results of ordered Probit estimationin Appendix Table A2.

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The vector of independent variables, z, includes a dummy that takes the value of 1 forfemales, and 0 otherwise (Fem); the number of preschool children (under six years of age)
(Child); and their interaction term (Fem_Child). In addition, a dummy for new
self-employment (Self) is included, which takes the value of 1 if the worker has become
self-employed within the preceding five years, and 0 otherwise. To test our hypotheses, it isnecessary to estimate not only the main effects on the income level, but also the interaction
effects of being self-employed (Self), gender (Fem), and the number of preschool children(Child). We therefore include all possible interaction terms of Self with Fem and Child(Self_Fem, Self_Child, and Self_Fem_Child).
Independent variables also comprise some control variables for individual-level
characteristics: education level (a dummy for university graduates, UnivGrad), age(dummies for 10 age groups), and household and family types (a dummy for each ofhousehold head, HeadHH; his or her spouse, Spouse; and being married, Marriage). Thereis also a control variable for regional characteristics (a dummy for the largest agglomeration
regions 5, Metro), and survey year (cohort) dummies are also included in the model.
Further, to control for differences in the characteristics of previous and current jobs
among self-employed workers, we include the interaction terms of the self-employment
dummy and the dummies for firm age (from the second to the fifth year since start-up),
employed years in the previous job (nine categories), employment status in the previous job(five types), firm size of the previous job (11 categories), the industry dummies for 10
sectors, and the dummy for the same sector in the current and previous jobs. In somespecifications, we also include dummy variables for working days and hours (11 categories),
to control for the effects of working hours on income level. Detailed definitions and thespecifications of the variables are provided in Table A1 in the Appendix.
5

Regional characteristics are available only as a distinction between a location in one of the three largest
agglomeration regions around Tokyo, Osaka, or Nagoya, and any other locations.

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The switch from salaried employment to self-employment does not randomly occur,
but it is rather endogenous. People select self-employment for various reasons. As
mentioned, female employees with preschool children may prefer self-employment to paid
employment, to achieve a better balance of career and childcare; if this is the case, theself-selection bias of female workers with preschool children is not negligible in our sample.
However, we examine how much income level differs, for example, between female
workers with preschool children in self-employment and salaried employment, notcontrolling for the preference for self-employment, but rather including it. This is because
we argue that female self-employed workers with preschool children prefer flexible work at
a low income, so that they can devote themselves to childcare.
Each employed worker with a preschool child has three occupational choices: remain
an employed worker, quit the job (i.e., retire from the labor market) and devotehimself/herself to childcare, and switch to self-employment to balance work and childcare.
In Japan, most female employees quit their jobs around the birth of the first child, while
only a small portion start up their businesses; those who remain employees after childbirth
are assumed to have relatively good opportunities for a business career (and thus a highopportunity cost associated with a devotion to childcare). In this sense, the wages of female
employees with preschool children in our sample may be positively biased, compared totheir self-employed counterparts.
However, as we describe in detail in the following section, most female employees do
not face such an occupational choice, but consider with childbirth whether to continue orquit their jobs, and choose between paid employment and self-employment after a few years,
when the first child goes to kindergarten. Therefore, most of those who choose selfemployment are expected to have a business career opportunity comparable to those who
continue their careers as employed workers, rather than to those who quit their jobs at
14

childbirth. Therefore, we need not go beyond controlling for personal ability with thevariables for age, formal education, and job experience in estimating the relative income
levels of female self-employed workers with preschool children.

5. Descriptive statistics on career choice and income level

Before presenting and discussing the estimation results regarding income level, we
demonstrate in this section some descriptive statistics pertaining to the career choices of
employees, with special attention on female employees with preschool children. We areespecially interested in how many choose self-employment as opposed to retirement, andwhen they make this career choice. Such a detailed description is important, because mostfemale employees in Japan opt to retire from the labor market rather than start up a new
business after childbirth, and also because they make this choice at different times.
Table 1 shows the changes in employment status during the preceding year (as apercentage of all observations), according to the age of the first child in the household when
the survey was conducted. Most employees have no preschool children, which is regarded
as the baseline. The upper rows show the data of the pooled sample, with the data for eachsurvey cohort displayed below. The table also contrasts descriptive statistics for male andfemale employees.
For example, of the total (pooled) sample, 34.2% (27.5%) of the female employeeschose to leave the labor market completely, just before (after) the birth of the first child andduring the year preceding the survey; meanwhile, only 0.1% of female employees inpregnancy or with newborn babies became self-employed during the same period 6 .

6The age of the first child in Table 1 is the age when the survey was conducted. Since the survey asks for thechange in occupation status during the preceding year or the tenure of the current occupation, the fact that afemale employee had a newborn child (i.e., younger than one year) when the survey was conducted means that

15

However, when they continued to work as employees until the first child became one year
or older, they mostly continued to work further as employees. It is noteworthy that thepropensity for female employees to become self-employed slightly increased to 0.3% after
the child became one year old, and increased considerably to 0.6% when the child became
three years old (i.e., the age to enter kindergarten). This evidence suggests that theoccupation choices of female workers are strongly contingent upon childcare, and that mostdelay becoming self-employed until their children have grown enough to go to a nursery or
kindergarten.
In contrast, among male workers, the ratio of changes in employment status does notvary with the age of the first child. Most male employees (more than 99%) continued to be
employees in the year preceding the survey. Thus, their occupation choices are notcontingent upon childcare.
In comparing these statistics across the survey cohorts, we notice that the ratio of
female employees who leave their jobs around childbirth clearly increased between 1997
and 2002. The increase in the ratio of employees who quit their jobs or started up new
businesses can also be observed among male workers, but much less distinctively thanamong female workers.
The descriptive statistics presented here suggest that, unlike with male workers, female
workers’ occupation choices are strongly contingent upon childcare, but the decision to
become self-employed is made at a stage later than that to leave employment altogether todevote themselves to childcare.
Table 2 first compares the average rates (%) of starting up a new business (becoming
self-employed) during the five years preceding the survey, between genders and between
those with and without preschool children. The second part of the table shows the average

she left the occupation or started up a new business during her pregnancy.

16

income (millions of yen) 7 for each gender, divided between the employees and theself-employed, and within each group, those with and without preschool children.
The average figures in this table suggest that male workers are more likely to become
self-employed than their female counterparts, regardless of whether or not they havepreschool children. The table also shows that, for both male and female employees, thosewith preschool children are more likely to become self-employed than those without
preschool children, but the difference between these groups is larger for female than formale workers.
Regarding average income, Table 2 shows that 1) male workers earn more than female
workers, among both employees and the self-employed; 2) male workers earn more thanfemale workers, whether with or without preschool children; 3) for both male and female
workers, salaried employees earn more than those who are self-employed; and 4) amongfemale workers, both for employees and the self-employed, those with preschool children
earn less than those without preschool children, but this is not the case for males who are
self-employed. These findings regarding income level are consistent with the hypotheses
presented in the previous section; in the next section, we examine them more precisely,
using multiple regression models.
Figures 2a to 2d compare the distribution of annual income between genders, inmillions of yen, as measured from the midpoint of each income category. For example, thefirst interval (0, 0.5) denotes the income category between JPY 0 (no income, including
deficit) and JPY 0.5 million. Figures 2a and 2b show the income distribution of employedworkers without and with preschool children, while Figures 2c and 2d show those of newly
self-employed workers without and with preschool children, respectively. The genderincome difference is distinct in these figures, but it is noteworthy that newly self-employed7

As mentioned, we could obtain only income data that were broken into categories, not absolute values.
Therefore, when calculating average income, we use the median of each category.

17

women with preschool children concentrate in the lowest-income categories, and thus mostof them earn less than JPY1 million per year.
Table 3 compares the distribution of sectors in which newly self-employed workers run
their own businesses, between male and female workers and between those with andwithout preschool children within each gender. It is noteworthy that female self-employedworkers concentrate in retail, restaurant, and consumer services, but also in business andprofessional services, compared to self-employed men. Moreover, large portions ofself-employed women with preschool children work in retail (36%) and professional
services (20%), while only 4% work in the restaurant sector. Unfortunately, a more detailed
breakdown of sectors from the micro data is not possible.
One may expect that self-employed women, especially those with preschool children,
are more likely to run businesses with their husbands as co-founders or co-owners,
compared to female employees. However, according to Table 4, the occupation types of thehusbands show a similar distribution between female employees and self-employed workers,
while the ratio of self-employed husbands is higher among wives in paid employment thanin self-employment. Moreover, in both groups, wives with preschool children are more
likely to have husbands who work as employees than those without; therefore, we may
assume that newly self-employed female workers, especially those with preschool children,
are more likely to be supported by husbands who are in (relatively highly) paid
employment.
However, Figure 3a does not provide evidence that the husbands of self-employed
women earn more than those of female employees—nor do we see that the husbands ofself-employed female workers with preschool children have higher incomes than those
without preschool children. Thus, we cannot argue that the lower incomes of self-employed
women, especially of those with preschool children, are supported and supplemented by18

their husbands’ higher incomes. This does not change, even when we observe the
distribution of household income instead of that of husbands’ income (Figure 3b).

6. Estimation results and discussion

Table 5 presents the estimation results of the interval regression model for the pooled
sample of the surveys in 1992, 1997, and 2002. The dependent variable is the natural
logarithm of income level, ln(Income). We use the data in logarithm, given the income
distribution of the pooled sample and because we can estimate the effects of independent
dummy variables as percentage-changes in income 8. Model 1 controls for demographic
variables such as age and education of workers, and it includes survey cohort dummies.
Model 2 additionally includes some controlling variables for previous and current jobs.
Models 3 and 4 control for working days and hours, and thus estimate proxies for theireffects on efficiency or productivity.
Each of the self-employment dummy (Self), the female dummy (Female), and thenumber of preschool children (Child) has statistically significant negative effects on income
in all models. Each of the interactive terms has significant coefficients in the same sign inall models, except for Fem_Child. Therefore, the estimation results of the interval
regression models are considerably robust.
We can calculate relative income differences among the types of workers, directly fromthe regression coefficients, because they indicate the marginal effects of independent

8

In this section, we provide the results with ln(Income) as the dependent variable, but we conduct also theordered Probit estimations and the interval regression using the income level (without logarithm) as thedependent variable. The results of these estimations, especially those of the ordered Probit model, are quite
similar to those of the interval regression analysis. We report in Appendix Table A2 the results of ordered
Probit estimations.

19variables in percentage 9. For example, in Model 1, the male–female income difference ofself-employed workers without preschool children (29.1%: sum of the coefficients of Fem
and Self_Fem = –0.291) is significantly smaller than that of employees without preschool
children (48.4%: coefficient of Fem = –0.484). At the same time, among female workers
without preschool children, the income difference between the self-employed andemployees (52.8%: sum of the coefficients of Self and Self_Fem = –0.528) is alsosignificantly smaller than that among male workers without preschool children (72.1%:
coefficient of Self = –0.721) 10. We summarize in Table 6 the calculation of marginal effects
as income gaps. For example, the gender income gap among newly self-employed workers
without preschool children is 29.1% in Model 1, but it increases to 61.0% with the first (thesecond row) and to 92.9% (the third row) with the second preschool child. Moreover, thegender income gap of 61.0% for newly self-employed workers is reduced to 24.3% when
we control for the characteristics of current and previous jobs and working days and hours(the second row in Model 4).
Based on these results, we can test our hypotheses. First, Hypothesis 1 is supported,because the sum of the coefficients of Fem, Self_Fem, Fem_Child, and Self_Fem_Child isnegative in all models (the second row in the case of one child, the third row in the case of
two children). Second, Hypothesis 2 is supported, because the sum of the coefficients ofChild, Self_Child, Fem_Child, and Self_Fem_Child is negative in all models (the 13th row).
Third, Hypothesis 3 is supported, because the sum of the coefficients of Self, Self_Fem,
Self_Child, and Self_Fem_Child is negative in all models (the 8th and 9th rows). Fourth,9𝑑𝑌

By differentiating the equation 𝑙𝑛𝑌 = 𝑎0 + 𝑎1 𝑋 with regard to Y, we obtain

1

𝑌

= 𝑎1

𝑑𝑋
𝑑𝑌

, and thus

= 𝑎1 𝑑𝑋.
The income difference between female workers in self-employment and paid employment can be calculatedby differentiating the estimated model with regard to Self, while the comparable income difference for maleworkers is obtained by making Fem take a value of 0 and then differentiating the estimated model with regard
to Self. The resulting difference-in-differences equals the sum of the coefficient of Self_Fem (0.224) and the
product of the coefficient of Self_Fem_Child and the number of preschool children (–0.285*Child).
𝑌

10

20

Hypothesis 4 is supported, because the coefficient of Self_Fem_Child is significantly
negative in all models (Table 5).
As suggested previously, in Models 3 and 4, we consider the effect of working days
and/or hours by including in the set of independent variables dummies for working days andweekly working hours. This means that in these models, the coefficients of the othervariables can be interpreted as the effects on productivity, rather than on income itself.
While the results of Models 3 and 4 are generally similar to those of Model 2, thecoefficients of the former models are mostly smaller than those of the latter. These results
indicate that the income gap between men and women, between self-employed andemployed workers, and between workers with and without preschool children become
smaller when we control for working days and hours. Therefore, these results suggest thatfemale workers and newly self-employed people work less than their aforementioned
counterparts.
With regard to the control variables, we obtained the following results, which aremostly consistent with our expectations 11 : University graduates, married workers, andinhabitants of the largest cities tend to earn higher incomes than their counterparts.
Additionally, heads of households are more likely to earn higher incomes than their spouses
and other family members 12. Income levels peak in the fifties. The level of experience in thecurrent business (i.e., years of operation since start-up) has a positive effect, but the tenurein the former job has a negative effect on self-employment income 13 . Self-employedworkers with managerial experience (executive officers in their previous job) tend to earn
11

We omitted these results from the tables to save space; they are available from the authors upon request.
With regard to household structure, we did not control for the co-residence of working mothers with theirown parents or parents-in-law. Previous studies suggest that such arrangements may strongly affect occupation
choice and the work–life balance of female workers who have preschool children (Ogawa and Ermisch, 1996;
Sasaki, 2002).
13
The latter result is contrary to our expectation and also inconsistent with the results in the literature. The
effect of experience in a previous job becomes negative and significant when it exceeds 20 years, which may
imply that long duration of experience suppresses innovation and flexibility of thinking, and thus lowers
income.
1221

higher incomes, while those in nonregular employment earn lower incomes than those withother employment statuses in their previous job. Self-employed workers earn higher
incomes when they start a business in the same industry sector as their previous work, thanfor any other sectors. Finally, the longer they work, the higher their income becomes.
These estimation results are mostly consistent with those of previous studies,
especially those of Hundley (2000, 2001). The effects of gender, self-employment, and thenumber of preschool children on income level are in general similar between the UnitedStates and Japan, despite there being major differences in the institutional factors pertaining
to their respective female labor markets. Unfortunately, however, an exact comparison is notpossible, given the different estimation models and controls involved. Moreover, in Hundley(2000), the baseline is an unmarried female worker who is compared to a married female
worker with a child, while children are not limited to those of preschool age.
As mentioned, the sample in the current study comprises workers who have been in
self-employment for less than five years. With regard to the effect of childcare, we
implicitly assume that several female workers switch from paid to self-employment after
the birth of the first child, to obtain greater flexibility in work. However, by definition, oursample may also include self-employed workers who started up their businesses before thebirth of their first child. In this regard, we may argue that self-employed women withpreschool children tend to prefer, all else being equal, more flexibility in work to higher
income, as such arrangements allow them to devote themselves to childcare, regardless ofwhether they switched to self-employment before or after the childbirth. Nevertheless, some
empirical checks would need to be in place.
To cope with this problem empirically, we check whether self-employed workers
started up their businesses before or after the childbirth—indeed, we find bothpatterns—and include in the estimation a dummy variable that takes the value of 1 if they22

started up before childbirth and 0 otherwise, and its interactive term with the female dummy.
We use this interactive term to take the gender difference in childcare explicitly into
consideration. The results in Table 7 are quite similar to those in Table 5, while the newvariables do not show significant effects on income level. These results suggest that
self-employed women with preschool children tend to select the work flexibility that
self-employment offers, even if doing so means a lower income, regardless of the timing of
the childbirth.
We apply another robustness check by using a question item from the survey regarding
the reason for leaving the previous job. Here we pay special attention to two particularreasons—namely, “marriage or childcare” and “to pursue economic opportunity”—andinclude the related dummy variables and interactive terms with the female dummy and thenumber of preschool children. Table 8 shows that the results do not change considerablywith these new variables, and that only the interactive terms of the reason “marriage orchildcare” and the female dummy and/or the number of preschool children are negative andsignificant. These results suggest that the reason for starting up a business significantly
affects income level and that, specifically, self-employed women with preschool children
earn lower incomes when they leave their previous jobs to start up a business and devote
themselves more to childcare 14.
Finally, we check the structural change in the time series across the three survey
cohorts of 1992, 1997, and 2002. We generate estimation results through the models that
include the interactive terms of the major independent variables (Self, Female, Children,
Self_Fem, Self_Child, Fem_Child, and Self_Fem_Child) with cohort dummies for 1997 and2002, taking the 1992 cohort as the baseline reference (Table 9). For clarification, we
14

We implicitly assume that female self-employed workers intentionally accept a lower income in order toattain better work–life balance. However, in fact, we cannot clearly distinguish between intended andunintended low income from self-employment. The latter case reflects business failures, and Figures 3a and 3b
suggest that such cases may not be negligible.

23

calculate the marginal effects of the variables for each cohort and its differences between
1992 and 1997, as well as those between 1992 and 2002. To save space, we report inTable 8 only the marginal effects based on the estimation results of Model 1 in Table 9,which do not control for working days and hours.
Table 10 provides the following evidence. First, the gender income gap significantly
decreased for workers without preschool children and for employees with preschool
children, especially between 1992 and 1997, but not for newly self-employed workers withpreschool children. Second, the income gap between employees and newly self-employedworkers without preschool children significantly changed between 1992 and 2002, for bothmale and female workers. Third, the negative effects of the number of preschool children on
income significantly decreased for both male and female employees, but not for
self-employed women. At least the first and the third pieces of evidence suggest that theinstitutional and policy changes in the 1990s with regard to childcare leave and genderequality in fact contributed to a reduction in the gender income gap among employees andsupporting employees with preschool children 15, but that they did not change the income
levels of self-employed women with preschool children relative to their male counterparts,
other female self-employed workers, or female workers with preschool children in paid
employment 16.

7. Concluding remarks

15

Abe (2010) finds no evidence that the Equal Employment Opportunity Law contributed to reductions in thegender wage gap among university graduates. Differences between her results and those of the current study
may be attributed to their use of different datasets, estimation methods, observation periods, and units ofanalysis (i.e., aggregated versus micro data).
16
It is noteworthy that we did not directly analyze the effects of legal, institutional, or macroeconomicchanges during the observation period, but estimated changes across three survey cohorts. To address thisissue more explicitly, future research may employ different estimation approaches, such as a semiparametricdifference-in-differences estimation (Abadie, 2005).

24

In this paper, we investigated the determinants of self-employment income level in theearly stages (i.e., within five years) of becoming self-employed. We used anonymous micro
data from Japan’s Employment Status Survey, focusing in particular on gender differencesamong self-employed workers and on the difference between self-employed and salaried
female workers, while considering the effect of childcare. We argued that female workers
with preschool children give priority to work flexibility, and thus their incomes tend to belower than those of their male counterparts and female workers who do not have any
preschool children. This is because they devote less time and effort to market work andmore to housework and childcare than do the members of the other groups. Moreover, we
also predicted that male–female income differences among self-employed workers are moresensitive to the number of preschool children than are those among employees.
We tested our hypotheses using interval regression models and obtained results that
support those hypotheses. We found that female self-employed workers with preschool
children tend to earn lower incomes than their male counterparts, other female
self-employed workers, and female workers with preschool children in paid employment.
Moreover, gender income differences among self-employed workers are more sensitive tothe number of preschool children than among those of employees. Regarding self-employedworkers, we checked the robustness of these results by controlling for working days andhours and using additional information on the timing of childbirth and the reasons forleaving the previous job. Finally, in comparing the results from different cohorts, we saw
significant differences in the changes in gender income gap between self-employed andemployed workers with preschool children, reflecting institutional and legal changes in the
1990s regarding gender equality and childcare support.
This paper has some shortcomings that can be largely attributed to data constraints.
First, precise estimations are impossible, because most variables are available only as
25categories, rather than as absolute values—although we tried to obtain proxy estimations
through interval regression. Second, although we used micro data from the three different
cohorts, panel data analysis was not possible, because the sample of workers in each wasdifferent from the other two. Despite these problems, this study contributes to the existing
literature as the first in-depth empirical study of self-employment income using Japanese
micro data and comparing three different cohorts, while in particular focusing on the role ofchildcare in decision-making vis-à-vis employment.
On the whole, our findings suggest that self-employment may provide working
mothers with preschool children with work flexibility and an opportunity to achieve better
work–life balance. In this respect, policymakers should be more aware of this role ofself-employment as an occupational choice, when designing support policy that addresses
work–life balance and childcare.

26

Acknowledgments
This study was supported by a Grant-in-Aid for Scientific Research (A) (No. 20243018)
from the Japan Society for the Promotion of Science; the authors appreciate this financial
support. The authors are also grateful to the National Statistics Center (NSTAC) for
providing them with anonymous micro data from the Employment Status Survey. An earlyversion of this paper was presented at a NSTAC Workshop at the Institute of Statistical
Mathematics in Tokyo, Japan, and the 25th Research in Entrepreneurship and Small
Business (RENT) Conference in Bodo, Norway, both in November 2011; the 10th
Interdisciplinary European Conference on Entrepreneurship Research (IECER) in
Regensburg, Germany, in February 2012; and the 2012 Annual Spring Meeting of theJapanese Economic Association (JEA) in Sapporo, Japan, in June 2012. The authors thankthe participants of these conferences for their helpful comments and suggestions. Anyremaining omissions or errors are the authors’ own responsibility.

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31

Figure 1: Labor Force Participation Ratio (%) in Japan, by Age (2000)
100
90
80
70
60
50

Male

40

Female3020
100Source: Census of Population 2000

32

Figure 2a: Income Distribution of Employees without Preschool Children(%, millions of yen)
25
20
15
10
5
0

Male

FemaleFigure 2b: Income Distribution of Employees with Preschool Children(%, millions of yen)
30
25
20
15
10
5
0

Male

Female33

Figure 2c: Income Distribution of Self-employed Workers without Preschool Children(%, millions of yen)
35
30
25
20
15
10
5
0

Male

FemaleFigure 2d: Income Distribution of Self-employed Workers with Preschool Children(%, millions of yen)
45
40
35
30
25
20
15
10
5
0

Male

Female34

Figure 3a: Distribution of Husbands' Annual Income (%, millions of yen),
by Types of Female Worker
30
25
20
15
10
5
0

Employees: without children

Employees: 1 or more children

Newly self-employed: without children

Newly self-employed: 1 or more children

35Figure 3b: Distribution of Household Annual Income (%, millions of yen),
by Types of Female Worker
20
18
16
14
12
10
8
6
4
2
0

Employees: without children

Employees: 1 or more children

Newly self-employed: without children

Newly self-employed: 1 or more children

36

Table 1: Changes in Employment Status of the Sample Employees during thePreceding Year
Male

Total

Age of the first childContinuingemployees(%)

Changeto selfemployed(%)

Changeto jobless
(%)

0

99.3

0.3

0.5

1

99.3

0.3

2

99.3

3
4
5
No preschool children
1992

Age of the first childAge of the first childAge of the first child

No preschool children

Continuing
employees(%)

Changeto selfemployed (%)

Changeto jobless(%)# of obs.

9,703

63.8

0.1

36.2

6,022

0.4

10,409

68.8

0.1

31.1

5,707

0.2

0.5

11,137

92.5

0.2

7.3

5,045

99.3

0.2

0.5

11,507

95.0

0.2

4.8

5,290

99.2

0.3

0.5

11,240

95.1

0.5

4.5

5,368

99.3

0.2

0.5

11,553

96.2

0.3

3.5

6,153

98.1

0.2

1.7

579,441

97.1

0.1

2.8

485,481

0

99.3

0.4

0.4

3,284

68.4

0.1

31.5

1,929

99.4

0.3

0.3

3,675

72.7

0.1

27.2

2,005

2

99.5

0.2

0.4

3,957

92.8

0.1

7.1

1,885

3

99.5

0.1

0.4

4,173

95.9

0.1

4.0

1,954

4

99.3

0.4

0.4

4,236

95.9

0.4

3.7

2,072

5

99.3

0.3

0.4

4,336

97.2

0.2

2.6

2,358

98.9

0.2

0.9

203,296

98.1

0.1

1.8

171,834

0

99.5

0.2

0.3

3,297

68.6

0.1

31.3

1,810

1

99.4

0.3

0.3

3,442

72.4

0.1

27.6

1,673

2

99.4

0.2

0.4

3,775

93.9

0.4

5.7

1,601

3

99.5

0.3

0.2

3,907

95.8

0.5

3.7

1,696

4

99.4

0.3

0.3

3,656

96.9

0.5

2.6

1,649

5

99.5

0.2

0.3

3,865

96.7

0.4

2.9

2,012

98.6

0.2

1.1

198,992

97.9

0.1

2.0

162,362

0

99.1

0.2

0.7

3,122

56.0

0.0

44.0

2,283

1

99.1

0.2

0.8

3,292

62.1

0.0

37.8

2,029

2

99.0

0.3

0.7

3,405

90.7

0.2

9.1

1,559

3

98.9

0.1

1.0

3,427

93.0

0.1

7.0

1,640

4

99.0

0.1

0.9

3,348

92.2

0.5

7.3

1,647

5

99.0

0.2

0.8

3,352

94.3

0.2

5.5

1,783

96.6

0.2

3.1

177,153

95.0

0.1

4.9

151,285

No preschool children
2002

# of
obs.

1

No preschool children
1997

Female37

Table 2: Descriptive Statistics of the Ratio of Newly Self-employed and the Average
Annual Income of Male and Female Workers
Ratio of newly self-employed (%)
Total
Total

Male

Female

1.5

2.1

0.9

No preschool children

1.5

2.0

0.8

1 or more preschool children

1.9

2.2

1.3

Average annual income(millions of yen)
Total

Male

Female

Total

3.6

4.8

2.0

Employees

3.6

4.9

2.0

No preschool children

3.6

4.9

2.1

1 or more preschool children

3.8

4.8

1.9

2.4

2.7

1.4

No preschool children

2.3

2.6

1.5

1 or more preschool children

2.7

3.2

0.9

Newly self-employed

Construction

Manufacturing

Wholesale

RetailRestaurant

Consumer service

Professional service

Business service

OthersTable 3: Sector-based Structure of Newly Self-employed Workers (%)

All

8.4

5.0

2.4

12.8

9.6

5.2

10.0

7.9

38.8

Male

11.1

5.8

3.0

10.3

7.1

2.5

7.4

6.5

46.3

Female

0.4

2.6

0.9

20.1

17.0

13.0

17.5

12.0

16.7

without preschool children

9.5

5.9

3.0

10.1

6.8

2.6

7.5

7.1

47.5

1 or more preschool children

19.0

4.9

2.9

11.6

8.3

2.1

6.8

3.9

40.6

without preschool children

0.4

2.7

0.8

17.1

19.4

13.6

16.9

12.2

16.8

1 or more preschool children

0.1

1.6

1.1

36.3

4.1

9.6

20.4

10.4

16.3

Male

Female38

Table 4: Types of Female Workers and Occupation Types of Their Husbands(%)

Husband’s occupation type

Types of female workers

Self-

FamilyUnEmployee

employed

workeremployed

without children

19.1

0.8

72.8

7.3

1 or more children

9.9

2.7

86.2

1.2

Newly

without children

13.1

2.0

73.1

11.8

self-employed

1 or more children

5.9

0.7

93.1

0.3

18.0

1.0

74.3

6.7

Employees

Total

Table 5: Estimation Results of Interval Regression Models (Pooled Sample)
Dependent variable: ln(Income)ModelsSelf

FemaleChildren

Self_Fem

Self_Child

Fem_Child

Self_Fem_Childln(_sigma)

Controls for demographics

[1]

[2]

[3]

[4]

–0.721***–1.008***–0.450***–0.643***[0.00851][0.0317][0.00791][0.0300]–0.484***–0.476***–0.387***–0.380***[0.00183][0.00184][0.00158][0.00159]–0.0247***–0.0225***–0.0280***–0.0261***[0.00126][0.00126][0.00117][0.00117]

0.193***

0.149***

0.218***

0.245***[0.0174][0.0203][0.0160][0.0189]0.120***

0.0567***0.0601***0.0260*[0.0128][0.0125][0.0120][0.0121]–0.0288***–0.0330***

0.0155***0.0129***[0.00291][0.00294][0.00239][0.00241]–0.290***–0.204***–0.162***–0.122***[0.0286][0.0285][0.0253][0.0254]–0.424***–0.436***–0.582***–0.595***[0.000835][0.000860][0.000930][0.000958]

Yes

Yes

Yes

Yes

Controls for current and previous jobs

Yes

Working day/hour dummies
Cohort dummies
Number of observations

Yes
Yes

Yes

Yes

Yes

Yes

Yes

1,230,234

1,165,494

1,227,298

1,162,991

Robust standard errors in brackets. * p < 0.05, ** p < 0.01, *** p < 0.001.

39

Table 6: Estimated Marginal Effects (Pooled Sample; 1%/100 Change in Income)Models

[1]

[2]

[3]

[4]

Female/male gap within:
[1]
[2]
[3]
[4]
[5]
[6]

Newly self-employed without preschool children
Newly self-employed with 1 preschool child
Newly self-employed with 2 preschool children
Employees without preschool children
Employees with 1 preschool child
Employees with 2 preschool children–0.291***–0.610***–0.929***–0.484***–0.513***–0.541***[0.017][0.027][0.052][0.002][0.003][0.006]–0.528***–0.698***–0.868***–0.721***–0.601***–0.481***[0.015][0.024][0.047][0.009][0.012][0.023]–0.224***–0.053***0.095***–0.025***[0.025][0.003][0.013][0.001]–0.327***–0.564***–0.802***–0.476***–0.509***–0.542***[0.020][0.028][0.053][0.002][0.003][0.006]–0.169***–0.315***–0.462***–0.387***–0.371***–0.356***[0.016][0.024][0.046][0.002][0.003][0.005]–0.232***–0.334***–0.436***–0.450***–0.390***–0.330***[0.014][0.021][0.041][0.008][0.011][0.022]–0.114***–0.012***0.032**–0.028***[0.022][0.002][0.012][0.001]–0.135***–0.243***–0.352***–0.380***–0.367***–0.354***[0.019][0.026][0.048][0.002][0.003][0.005]–0.109***–0.013***0.000–0.026***[0.022][0.002][0.012][0.001]

Newly self-employed/employee gap within:
[7]
[8]
[9]
[10]
[11]
[12]

Female without preschool children
Female with 1 preschool childFemale with 2 preschool children
Male without preschool children
Male with 1 preschool childMale with 2 preschool children
Effects of the number of preschool children within:

[13]
[14]
[15]
[16]

Female self-employed
Female employees
Male newly self-employed
Male employees–0.203***–0.056***
0.034**–0.022***[0.026][0.003][0.012][0.001]

Control variables:
[17]
[18]
[19]
[20]

Demographics
Current and previous job
Working day/hour dummies
Cohort dummies

Yes

Yes

Yes
Yes
Yes

Robust standard errors in brackets. * p < 0.05, ** p < 0.01, *** p < 0.001.

40

Yes
Yes
Yes

Yes
Yes
Yes
Yes

Table 7: Estimation Results Controlling for the Timing of Childbirth (Pooled Sample)
Dependent variable: ln(Income)Models

[1]

[2]

[3]

[4]

–1.008***–1.006***–0.747***–0.643***[0.0317][0.0317][0.0301][0.0301]–0.476***–0.476***–0.430***–0.380***[0.00184][0.00184][0.00167][0.00159]–0.0225*** –0.0224***–0.0242***–0.0261***[0.00126][0.00126][0.00120][0.00117]

0.149***

0.149***

0.239***

0.245***[0.0203][0.0203][0.0192][0.0189]

0.0567***0.0465*0.0404*0.025[0.0125][0.0187][0.0179][0.0181]–0.0330*** –0.0329***–0.0010.0129***[0.00294][0.00294][0.00264][0.00241]–0.204***–0.203***–0.213***–0.122***[0.0285][0.0361][0.0339][0.0321]0.027–0.0080.001[0.0353][0.0339][0.0344]

0.002

0.120

0.004

[0.0793][0.0747][0.0727]–0.436***–0.436***–0.530***–0.595***[0.000860][0.000860][0.000885][0.000958]

Controls for demographics

Yes

Yes

Yes

Yes

Controls for current and previous jobs

Yes

Yes

Yes

Yes

Self

FemaleChildren

Self_Fem

Self_Child

Fem_Child

Self_Fem_Child

Startup before Childbirth

Female × Startup before Childbirthln(_sigma)

Working day dummies

Yes

Working day/hour dummies
Cohort dummies
Number of observations

Yes
Yes

Yes

Yes

Yes

1,165,494

1,165,268

1,163,864

1,162,784

Robust standard errors in brackets. * p < 0.05, ** p < 0.01, *** p < 0.001.

41

Table 8: Effects of the Reasons for Job Turnover (Pooled Sample)
Dependent variable: ln(Income)ModelsSelf
FemaleChildren
Self_Fem
Self_Child
Fem_Child
Self_Fem_Child
Male × Turnover for marriage orchildcare

[1]

[2]

[3]

[4]

[5]

–1.008***–0.984***–1.000***–1.007***–1.007***[0.0317][0.0313][0.0316][0.0317][0.0317]–0.476***–0.481***–0.476***–0.476***–0.476***[0.00184][0.00182][0.00184][0.00184][0.00184]–0.0225***–0.0227***–0.0225***–0.0225***[0.00126][0.00126][0.00126][0.00126]

0.149***

0.203***

0.222***

0.153***

0.154***[0.0203][0.0205][0.0211][0.0204][0.0204]

0.0567***

0.0531***

0.0548***0.0543**[0.0125][0.0125][0.0126][0.0169]–0.0330***–0.0330***–0.0330***–0.0330***[0.00294][0.00294][0.00294][0.00294]–0.204***–0.0822**–0.0987**–0.068[0.0285][0.0298][0.0373][0.0457]

0.264

0.264

[0.164][0.165]–0.271***–0.313***[0.0607][0.0695]

Female × Turnover for marriage
or childcare

0.219

0.195

[0.132][0.132]–0.471***–0.441***[0.0328][0.0352]

Male × Preschool children ×
Turnover for marriage orchildcare
Female × Preschool children ×
Turnover for marriage orchildcare
Male × Preschool children ×
Turnover to pursue economic
opportunities
Female × Preschool children ×
Turnover to pursue economic
opportunitiesln(sigma)–0.436***–0.436***–0.436***–0.436***–0.436***

Controls for demographics[0.000860]
Yes[0.000860]
Yes[0.000860]
Yes[0.000860]
Yes[0.000860]
Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

1,165,471

1,165,471

Controls for current and previous
jobs
Cohort dummies

0.001[0.0316]–0.126[0.0949]

Number of observations

1,165,494
1,165,599
1,165,471
Robust standard errors in brackets. * p < 0.05, ** p < 0.01, *** p < 0.001.

42

Table 9: Differences among Cohorts (Interval Regression Models)
Dependent variable: ln(Income)
[1]

[2]

[3]

[4]

Self

–0.590***[0.014]–0.952***[0.051]–0.353***[0.013]–0.593***[0.048]

Female–0.496***[0.003]–0.487***[0.003]–0.420***[0.003]–0.413***[0.003]

Children–0.033***[0.002]–0.030***[0.002]–0.034***[0.002]–0.032***[0.002]

Self_Fem

0.183***[0.029]0.106**[0.034]0.219***[0.026]0.211***[0.031]

Self_Child

0.047*[0.019]0.012[0.018]0.018[0.018]–0.002[0.018]

Fem_Child–0.040***[0.005]–0.045***[0.005]0.003[0.004]–0.001[0.004]

Self_Fem_Child–0.251***[0.048]–0.159***[0.047]–0.173***[0.041]–0.127**[0.041]

Y97

Self–0.162***[0.020]–0.075[0.075]–0.117***[0.019]–0.081[0.071]×Female0.025***[0.004]0.024***[0.004]0.063***[0.004]0.062***[0.004]

Children

0.016***[0.003]0.014***[0.003]0.012***[0.003]0.011***[0.003]

Self_Fem–0.060[0.041]–0.026[0.047]–0.039[0.037]0.010[0.044]

Self_Child

0.101***[0.030]0.057*[0.028]0.051[0.028]0.026[0.028]

Fem_Child

0.010[0.007]0.013[0.007]0.011*[0.006]0.014*[0.006]

Self_Fem_Child–0.057[0.068]–0.051[0.067]0.047[0.059]0.045[0.059]Y02

Self–0.260***[0.021]–0.091[0.079]–0.203***[0.019]–0.074[0.074]×Female0.018***[0.005]0.015**[0.005]0.052***[0.004]0.050***[0.004]

Children0.013***[0.003]0.013***[0.003]0.008**[0.003]0.008**[0.003]

Self_Fem

0.100*[0.044]0.178***[0.052]0.039[0.041]0.103*[0.048]

Self_Child

0.123***[0.033]0.081*[0.033]0.073*[0.031]

0.052[0.031]

Fem_Child

0.030***[0.007]0.032***[0.007]0.034***[0.006]

0.036***[0.006]

Self_Fem_Child–0.058[0.071]–0.106[0.072]–0.016[0.064]–0.038[0.066]–0.427***[0.001]–0.438***[0.001]–0.586***[0.001]–0.600***[0.001]ln(sigma)
Cohort dummies ×Controls for
demographics
Cohort dummies ×Controls for current
and previous jobs
Cohort dummies ×
Working day/hour
dummies

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

1,227,298

1,162,991

Number of
1,230,234

1,165,494

observations
Robust standard errors in brackets. * p < 0.05, ** p < 0.01, *** p < 0.001.

43

Table 10: Comparison of Marginal Effects between Cohorts(interval regression models without controlling for current and previous jobs and working days and hours: 1%/100 change in income)
Y92

Y97

Y02

Diff.: Y97–Y92

Diff.: Y02–Y92

Female/male gap within:[1]

Newly self–employed without preschool children–0.313***[0.029]–0.348***[0.029]–0.195***[0.034]–0.035[0.041]0.118**[0.044][2]

Newly self–employed with 1 preschool child–0.604***[0.046]–0.686***[0.045]–0.514***[0.049]–0.082[0.064]0.090[0.067][3]

Newly self–employed with 2 preschool children–0.895***[0.089]–1.023***[0.088]–0.832***[0.095]–0.128[0.125]0.063[0.130][4]

Employees without preschool children–0.496***[0.003]–0.472***[0.003]–0.478***[0.003]0.025***[0.004]0.018***[0.005][5]

Employees with 1 preschool child–0.536***[0.005]–0.501***[0.006]–0.488***[0.006]0.035***[0.008]0.048***[0.008][6]

Employees with 2 preschool children–0.576***[0.009]–0.530***[0.010]–0.498***[0.011]0.045**[0.014]0.078***[0.014]

Newly self-employed/employee gap within:[7]

Female without preschool children–0.407***[0.025]–0.628***[0.025]–0.567***[0.030]–0.221***[0.035]–0.161***[0.039][8]

Female with 1 preschool child–0.610***[0.042]–0.787***[0.040]–0.705***[0.042]–0.177**[0.058]–0.095[0.059][9]Female with 2 preschool children–0.814***[0.082]–0.947***[0.078]–0.843***[0.081]–0.133[0.114]–0.029[0.116][10]

Male without preschool children–0.590***[0.014]–0.751***[0.015]–0.850***[0.016]–0.162***[0.020]–0.260***[0.021][11]

Male with 1 preschool child–0.542***[0.018]–0.603***[0.021]–0.679***[0.026]–0.060*[0.028]–0.137***[0.031][12]

Male with 2 preschool children–0.495***[0.034]–0.454***[0.042]–0.509***[0.050]0.041[0.054]–0.014[0.061]

Effects of the number of preschool children within:[13]

Female newly self-employed–0.276***[0.044]–0.206***[0.042]–0.167***[0.045]0.070[0.061]

0.109[0.063][14]

Female employees–0.073***[0.004]–0.046***[0.005]–0.029***[0.005]0.026***[0.007]0.043***[0.007][15]

Male newly self-employed

0.015[0.019]0.132***[0.023]0.151***[0.027]0.117***[0.030]0.136***[0.033][16]

Male employees–0.033***[0.002]–0.017***[0.002]–0.020***[0.002]0.016***[0.003]0.013***[0.003]

Robust standard errors in brackets. * p < 0.05, ** p < 0.01, *** p < 0.001.

44

Appendix
Table A1: Definition of Variables
Variable

Definition

IncomeIncome levels of workers: ordinal variable in 12 categories (less than JPY0.5 million, JPY0.5–0.99
million, JPY1–1.49 million, JPY1.5–1.99 million, JPY2–2.49 million, JPY2.5–2.99 million, JPY3–3.99
million, JPY4–4.99 million, JPY5–6.99 million, JPY7−9.99 million, JPY10–14.99 million, and JPY15
million+)Self-employment dummy variable: a value of 1 for self-employed workers within five years since
start-up, and 0 for employees
Female dummy variable: a value of 1 for female and 0 for male workers

Self
Fem
Child

Number of preschool children (up to five years old)

UnivGradDummy variable: a value of 1 one for university graduates, and 0 otherwise

Age
HeadHH

Worker’s age in 10 groups (15–24 / 25–29 / 30–34 / 35–39 / 40–44* / 45–49 / 50–54 /55–59 /60–64 /65+), each of them corresponding to a dummy variable
Dummy variable: a value of 1 for heads of households, and 0 otherwise

SpouseDummy variable: a value of 1 for spouses of the household heads, and 0 otherwise

Marriage

Dummy variable: a value of 1 if the worker is married, and 0 otherwise

Metro

Dummy variable: a value of 1 for the inhabitants of the three largest cities in Japan (Tokyo, Osaka, or
Nagoya), and 0 otherwise
Set of year dummies: a value of 1 for each survey year (1992* / 1997 / 2002), and 0 otherwiseYear
Duration
PrevTenure

Set of five dummy variables: a value of one for each group of business duration since start-up (1* / 2 / 3 /4 / 5 years), and 0 otherwise
Set of nine dummy variables: a value of 1 for each group of experience in the previous job (less than 1
year / 1–2 years / 3–4 / 5–9* / 10–14 / 15–19 / 20–24 / 25–29 / 30+ years), and 0 otherwise

PrevPosition

Set of five dummy variables: a value of 1 for each category of previous job position (regular workers* /nonregular (part-time) workers / executive officers / self-employed / others), and 0 otherwise

PrevSize

Set of 11 dummy variables: a value of 1 for each employee size group of the previous job (1–4* / 5–9 /10–19 / 20–29 / 30–49 / 50–99 / 100–299 / 30–499 / 500–999 / 1,000+ employees / public agency), and 0
otherwise
Dummy variable: a value of 1 if the business sector of the self-employed is the same as in the previous
job, and 0 otherwise
Set of 10 dummy variables: a value of 1 for each sector (construction / manufacturing / wholesale / retail/ restaurant / consumer service / business service / medical service / educational service / others*), and 0
otherwise
Set of six dummy variables: one for each category of working days (working fewer than 50 days ayear* / 50–99 / 100–149 / 150–199 / 200–249 / 250 days or more), and 0 otherwise

SameSector
SectorWorkingdays
Workingdays/hours

Set of 11 dummy variables: one for each category of working days or hours (nonregular worker working
fewer than 50 days a year* / 50–99 / 100–149 / 150+ days / regular worker working fewer than 15 hours
a week / 15–21 hours a week / 22–34 / 43–45 / 46–48 / 49–59 / 60 hours+ a week), and 0 otherwise* The names of the control variables do not appear in the estimation results tables.

45

Table A2: Estimation Results of Ordered Probit Models (Pooled Sample)
Dependent variable: Income
ModelsSelf

FemaleChildren

Self_Fem

Self_Child

Fem_Child

Self_Fem_Child

Controls for demographics

[1]

[2]

[3]

[4]

–1.106***–1.507***–1.248***–1.153***[0.0125][0.0466][0.0487][0.0519]–0.738***–0.737***–0.730***–0.693***[0.00274][0.00278][0.00280][0.00287]–0.0527***–0.0488***–0.0521***–0.0557***[0.00211][0.00215][0.00220][0.00224]

0.395***

0.346***

0.487***

0.512***[0.0246][0.0296][0.0308][0.0324]

0.189***

0.0975***0.0739***0.0565**[0.0193][0.0191][0.0202][0.0214]0.00329–0.00413

0.0324***0.0504***[0.00413][0.00422][0.00422][0.00416]–0.403***–0.287***–0.277***–0.203***[0.0399][0.0404][0.0424][0.0429]

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Controls for current and previous jobs
Working day dummies

Yes

Working day/hour dummies
Cohort dummies
Number of observations

Yes
Yes

Yes

Yes

Yes

1,230,234

1,165,494

1,164,074

1,162,991

Robust standard errors in brackets. * p < 0.05, ** p < 0.01, *** p < 0.001.

46