The Motherhood Wage Penalty *Maki TAKEUCHI(Yamagata University)
Abstract
The study investigates the relationship between motherhood and mothers’ wages.
Most Japanese women quit their jobs after childbirth; therefore, the gender pay gap inJapan is larger than in other developed countries. Although the relationship between
motherhood and mothers’ wages has been sufficiently investigated in the United States andEurope to resolve the gender pay gap issue, it has been neglected in Japan. After childbirth,mothers experience the following: (1) decline or disturbance of accumulation of humancapital, (2) reduced work eﬀort, (3) trading of higher wage jobs for mother-friendly jobs, or(4) discrimination in the labor market. Among Japanese women aged 20−44 year s, a wage
penalty for mother was approximately 5% per child. Thus, the relationship between
motherhood and wages is not linear and monotonic; women with three or more children
experience higher wage penalties. Furthermore, the most important factor for mot herhood
penalty is long-term unemployment.
Keywords: Motherhood penalty, Human capital, Gender pay gap, Employer discrimination

1. Introduction
Japan is a society in which there is high gender inequality. According to The Global
Gender Gap Report 2017 (World Economic Forum 2017), Japan is listed 114 th out of
144 countries ranked by the Global Gender Gap score. The gaps between women andmen regarding economic participation and political empowerment are factors that
especially contribute to Japan’s low ranking. For example, about 47% of Japanesewomen leave their place of employment after giving childbirth (National Institutionof Population and Social Security Research 2016). In addition, the gender pay gap inJapan is at 25.9% (OECD 2017), which is larger than in other developed countries.
However, the gender pay gap also exists in the United States and Europe, where
women tend to continue participating in the labor market. As measured in 2014,
females in the United States and the United Kingdom earn abo ut 17% less than males*

The study was supported by JSPS KAKENHI Grant Number JP25000001.

―169―

(OECD 2017). The OECD reported that women experience a steep increase in thegender wage gap during childbearing and childrearing years, which indicates thepresence of the so-called “motherhood penalty” (OECD 2012: 169). The same report
revealed that women who work full-time and have children earn significantly lessthan men when compared to childless women. To close the gender pay gap, therelationship between motherhood and mothers’ wages has been thoroughly
investigated in both the United States and Europe, but remains unexamined in Japan.
The purpose of this study is to determine whether the motherhood penalty exists inJapan, and, if so, to examine its causes.

2. Theoretical framework
2.1 Explanation of the motherhood penalty
The motherhood penalty has been a frequently analyzed topic mainly in the UnitedStates (Waldfogel 1997; Budig & England 2001; Gangl & Ziefle 2009; Budig &Hodges 2010; Budig et al. 2012; England et al. 2016). Scholars have explored theextent to which women’s wages are lowered by having children (i.e., the “gross”
motherhood penalty), as well as the reason motherhood results in lowered wages (i.e.,the “net” motherhood penalty). In the United States, women experience an average
wage penalty of 7% per child (Budig & England 2001). The factors leading to thispenalty remain empirically unexplained. However, empirical research has discoveredthat family structure and resources, job characteristics, human capital, unobserved
individual characteristics, and employer discrimination are all circumstances that
affect wages.
Family structure and resources
The family structure includes marital status, the age of the mother at firstchildbirth, and the ages of children in the family. Marriage can increase the family
resources of women through the earnings of a husband, and allows women to reduce
their work eﬀorts while caring for children. This suggests that women who marryhigher-income men are more likely to reduce their work eﬀorts while prolonging
unemployment, which increases the motherhood penalty. In other words, the family
characteristics include both family structure and resources, which are indicators of
the sexual division of labor in the family unit.
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Job characteristics
Women often work part-time to reconcile the responsibilities of work andfamily life. Childcare costs and time constraints often make part -time work an
attractive option for mothers who wish to resolve this conflict, even if it means such
wages are lower than those offered by full-time employment opportunities. However,part-time work rarely offers a transition to full-time employment, and many mothers
continue to work part-time on a long-term basis. Contrary to the theory positing a
trade-oﬀ between high earnings and employment flexibility, it is thought that there isa family-friendly employment link to high earnings. Large firms and public-sector
entities oftentimes offer high or above-average salaries while providing more familyfriendly benefits (e.g., generous maternity and paternity leave, childcare assistance,family allowance, flexible schedules, and reduced working hours) compared to thoseof smaller firms.
Human capital
Women with no (or few) children may have higher amounts of human capital
because they have greater opportunities to be active in the labor market compared to
women with children (especially those who have many). There is both firm -specificand general human capital; a variety of work experiences are used to measure humancapital, including general employment experience, full and part-time experience, andcompany seniority. Budig & England (2001: 206) refer to the number of employment
breaks when discussing this concept, asserting in their study that “continuity may
influence wages – that is, among women with equal years of experience, those withmore continuous experience may have higher earnings.”
Unobserved individual characteristics
Some of the same individual characteristics that are exogenous to bothearnings and fertility may account for the motherhood penalty. Howe ver, thesecharacteristics are not observed in the data. If such unobserved heterogeneity exists,
the correlation between earnings and the presence of children is not causal. For
example, women who care less about their careers may not hesitate to have chi ldren
while taking a career break, which leads to lower wages. Previous studies havediscussed unmeasured heterogeneity (e.g., career aspirations) by using fixed -eﬀects
models. Personal fixed-eﬀects models require panel data that measure variables―171―

during at least two points in time. However, if cross-sectional data is used, then the
results may include unchanging and unmeasured heterogeneities.
Employer discrimination against mothers
Penalties unexplained by the abovementioned mechanisms (and that persist
in fixed-eﬀects models) suggest the existence of an additional mechanism; that is,employer discrimination against mothers. It is difficult to ascertain discrimination by
examining survey data. However, both experimental and audit studies show evidence
to support employer discrimination against mothers regarding callbacks for job
applications, hiring decisions, wage oﬀers, and promotions (Benard & Correll 2010;
Correll et al. 2007).
2.2 Empirical Research Involving the Motherhood Penalty in Japan
With the exception of two studies by Kawaguchi (2005; 2008), there has been
almost no research involving the motherhood penalty in Japan. Kawaguchi’s studies
focused on the Japanese Panel Survey of Consumers (JPSC), but involved longer
panel data in 2008 than in 2005. Kawaguchi (2008) analyzed data from women aged
24-44 by using the JPSC 1993-2003; this analysis found an approximately 24% grossmotherhood penalty using OLS, and around a 5% penalty using fixed -eﬀects.
Kawaguchi’s (2008) research concluded that the main factor of the motherhood
penalty involved career breaks resulting from childbearing. This is because themajority of the penalty can be explained by workplace seniority and the total years of
employment. Our analyses are inferior to Kawaguchi’s (2008) in terms of controllingfor unobserved heterogeneity. However, our analyses have two advantages. That is,
we distinguish between work and unemployment experience in the same manner as
Budig & England (2001), and also clarify the relative importance of each factor of
the motherhood penalty mechanism in Japan.

3. Method
3.1 Data and variables
In this study, we used data from the 2015 Social Stratification and Social Mobility(2015SSM) survey. Data samples involved women who earned wages by working as
company employees. Women who were managers or had obtained executive positions,―172―were self-employed, had family employment, or were enrolled in school were
excluded. Japanese women tend to withdraw from the labor market after childbearing,
but return during middle age. Thus, the wage penalty may diﬀer depending onwhether a mother is in her childrearing years. In order to account for this di ﬀerence,
we prepared two subsamples. One included women aged 20-59 (sample A), and theother included women aged 20-44 (sample B). Sample B was used to analyze thewage gap between childless women and mothers of a younger age group, who tend to
face choices of childbearing (and childrearing) and pregnancy. Out of a total of 4,249
women involved in this study’s data, 1,189 were categorized into sample A, while
703 were categorized into sample B.
This study’s dependent variable was the natural log of hourly wages involvedin each respondent’s current job. We omitted women whose hourly wages appeared to
be data outliers (i.e., those with wages below 100 yen or above 2 0,000 yen per hour).
There were three principal independent variables (i.e., child variables). In our sample
A analysis, the total number of children under 18 years of age or younger was themain independent variable. In alternate specifications, we measur ed children withdummy variables for one child, two children, and three or more children (with “nochildren” as the reference category). The number of children by age (divided into
categories of “3 years of age or younger,” “4-6 years of age,” and “7-18 years ofage”) was the independent variable during our analysis of sample B. Dividing by age
group, we considered the diﬀerence of the motherhood penalty according to the ageof children. Family characteristic variables were used as dummy variables for marit alstatus (i.e., “married,” “divorced or widowed,” (with “never married” as thereference category), and husband’s annual income). These variables represented
family structure and resources.
Measures of human capital included seniority in the present workp lace, total
years of regular employment experience, non-regular employment experience, andunemployment experience. Job characteristics included a dummy variable coded as 1
if the respondent’s current job involved non-regular employment, and dummy
variables for firm sizes of “30-299 people,” “300 people and above,” and “work in
the public sector” (with “1-29 people” as the reference category).
The control variables were age, squared age, and years of education. Our
analyses considered years of education as a control because this variable rarelychanges for women after childbearing in Japan.
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3.2 Models
We used ordinary-least-squares (OLS) regression models to analyze the2015SSM data. Data from the 2015SSM were cross-sectional. Thus, we could not
eliminate bias created by the failure to include controls for unmeasured heterogeneitythat had additive eﬀects. The results of our analysis may therefore leave the e ﬀects of
the unchanging aspects of cognitive aptitude, preferences resulting from earlysocialization, life cycle plans, tastes for aﬄuence, future orientation, and otherunmeasured human capital. Employer discrimination was also not controlled for inthis study. The models capturing the “gross” motherhood penalty included nocontrols other than the child and control variables. Models controlling for family
characteristics, job characteristics, and human capital showed the “net” motherhood
penalty. We determined the mechanism of the motherhood penalty based on the net
eﬀect of child variables by controlling for factors, other than the child variable, thatwere related to the motherhood penalty.

4 Results
4.1 The Gross Motherhood Penalty and its Mechanism
Table 1 presents means for the variables used in the analysis by motherhood status.
Models intended to capture the causal eﬀects begin in Table 2. The gross motherhood
penalty is about 5%. Adding the family characteristic variables to the gross modelslightly decreases the estimated motherhood penalty. The coeﬃcients of marital
status show the married penalty. By examining the average eﬀects of marriage acrosschild statuses, married women earn about 14% less than women who were nevermarried; that is, most women experience wage-loss before becoming mothers. Table
2 shows that job characteristics are clearly part of the explanation of the motherhood
penalty. The net motherhood penalty (controlling for the family and job characteristic
variables shown in Table 1) is about 3%, and the statistical significance of the childvariable weakens. The average eﬀects of non-regular employment across childstatuses (which decrease women’s wages by about 45%) indicates that the wages ofmothers become lower due to changes in employment status after childbearing.
Although the eﬀects of the child variable in the human capital model do not di ﬀer
greatly from the job characteristics model, its coeﬃcient is smaller than both thegross and family characteristics models. Losing human capital reduces the child―174―

penalty from about 5% to 3%.
Table 1. The means for the variables used in the analysis by motherhood status
Hourly wage
Ln hourly wage

Women aged 20-59 (N=1189)
Childless (N=671)
Mother (N=518)
7.097
7.010
(0.521)(0.586)1391.1081306.910(860.326)(790.845)

Women aged 20-44 (N=703)
Childless (N=322)
Mother (N=381)
7.097
6.993
(0.468)(0.593)1347.235
1284.962(676.631)(766.702)

1.838(0.760)1.958(0.773)0.361(0.481)0.461(0.499)0.178(0.383)0.294(0.456)0.480(0.500)0.226(0.419)42.227(12.416)
13.326(1.889)0.191(0.486)0.276(0.504)1.280(0.900)39.977(6.561)
13.129(1.803)30.758(7.155)13.839(2.004)0.257(0.550)0.357(0.546)1.220(0.978)37.139(5.052)
13.113(1.815)0.465(0.499)0.455(0.498)0.080(0.272)214.0835(291.521)

0.056(0.230)0.819(0.386)0.125(0.332)409.073(318.069)

0.798(0.402)0.174(0.380)0.028(0.165)78.494(189.632)

0.060(0.238)0.824(0.381)
0.115(0.320)385.663(261.208)

0.441(0.497)0.521(0.500)0.342(0.475)0.507(0.501)0.277(0.448)0.325(0.469)0.282(0.450)0.116(0.321)0.301(0.459)0.268(0.444)0.307(0.462)0.124(0.329)0.276(0.448)0.292(0.455)0.314(0.465)0.118(0.323)0.315(0.465)0.255(0.436)0.318(0.466)0.113(0.317)6.991(7.482)10.809(10.564)
6.782(8.232)
3.434(5.671)

6.351(6.661)9.807(8.628)5.378(5.618)3.753(4.671)

4.078(4.840)
6.640(6.665)3.075(4.980)0.242(1.037)

5.480(5.645)8.491(7.444)4.845(5.166)
2.882(3.777)

Children Variables
Total number of children
Number of children (dummy)
One child
Two children
Three or more children
Number of children by age
3 years old or younger
4-6 years old
7-18 years old
Age
Education (in years)
Family Characteristics
Marital status
Never married
MarriedDivorced/widowed
Husband's annual income(in million)
Job Characteristics
Non-regular employment
Firm size
1-29 people
30-299 people
300 people and above
Public sector
Human Capital
Seniority in the present workplace(in years)
Total years of regular employment(in years)
Total years of non-regular employment(in years)
Total years of unemployment(in years)―175―

Table 2. The average motherhood penalties per child+ Familycharacteristics

Gross
Total number of children
Age
Age2
Education (in years)-0.052 **(0.017)0.043 ***
(0.012)0.000 **
(0.000)0.086 ***
(0.008)

Marital status (ref; never married)
MarriedDivorced/widowed
Husband's annual income(in million)
Non-regular employment
Firm size (ref; 1-29 people)
30-299 people
300 people and above
Public sector
Seniority in the present workplace(in years)
Total years of regular employment(in years)
Total years of non-regular employment(in years)
Total years of unemployment(in years)
Intercept

5.047 ***
(0.241)+ Job characteristics+ Human capital-0.042 *(0.020)0.041 **
(0.012)0.000 **
(0.000)0.083 ***
(0.009)-0.030 †(0.017)0.030 **
(0.011)0.000 **
(0.000)0.054 ***
(0.008)-0.031 †(0.017)0.025 *
(0.011)0.000(0.000)0.051 ***
(0.008)-0.142 *(0.063)0.021(0.061)0.000 †
(0.000)-0.072(0.057)0.032(0.056)0.000 *
(0.000)-0.456 ***(0.029)-0.084(0.055)0.037(0.054)0.000 **
(0.000)-0.318 ***(0.038)0.057 †
(0.035)0.171 ***
(0.034)0.226 ***
(0.053)0.060 †
(0.035)0.173 ***
(0.034)0.193 ***
(0.053)0.007 **
(0.002)0.000(0.005)-0.009 †(0.005)-0.011 *(0.005)5.816 ***
(0.218)5.127 ***
5.766 ***
(0.244)(0.217)
N
1189
R2
0.100
0.107
0.303
0.328
Note: Women aged 20-59. OLS models. Numbers in parentheses are robust standard errors. † p < .10, * p < .05, ** p < .01, *** p < .001.

Table 3 shows measurements regarding the presence of children with threedummy variables, with each being relative to the reference category of no children.
We determined whether measuring “motherhood” with a continuous variable counting
the total number of children obscured nonlinear or non-monotonic relationships.
Table 3 shows that the gross penalty is about 12% for one child, 13% for twochildren, and 20% for three or more children. There is large gap between having one
or two children and three or more children; there is an additional 7% for having threeor more children. Controlling for the family characteristics and all job char acteristic
variables, the penalties are 8% for one child and 10% for two children, but thepenalty for three or more children becomes statistically insignificant. As with the―176―models using the number of children as a continuous variable, the addition of job
variables reduces the penalty.
Table 3. Determining whether the relationships between motherhood and wages
are linear or monotonic
One childControl variables in models

Two children

Gross-0.121(0.040)+ Family characteristics-0.106(0.042)+ Job characteristics-0.080**-0.130(0.049)**-0.117(0.051)**-0.097(0.038)+ Human capitalThree or more children**-0.201(0.097)**-0.186*†(0.098)**-0.152(0.044)(0.095)-0.059-0.067-0.114(0.038)(0.044)(0.100)1189

N

Note: Women aged 20-59. OLS models. Numbers in parentheses are robust standard errors.
†
p < .10, * p < .05, ** p < .01, *** p < .001.

Moreover, looking at the human capital model, all penalties lose statisticalsignificance and decrease to about half (from 12% to 6%, 13% to 7%, and 20% to
11% for each child status). These results imply that the loss of human capital during
unemployment or working non-regularly is an important factor of the motherhood
penalty mechanism. In addition, women with three or more children experience agreater penalty because they are more likely to take long employment breaks, making
it diﬃcult to return to the labor market as regular workers.
We analyzed the mechanism of the motherhood penalty in more detail byfocusing on variables that could change after childbearing (i.e., non-regular
employment, firm size, seniority in the present workplace, total years of regular
employment,

total

years

of

non-regular

employment,

and

total

years

of

unemployment). Table 4 shows a comparison of the magnitude of each variable in thechanges of the child penalty by setting the family characteristics model (see tables 2
and 3) as a baseline model. The values in table 4 are gaps between the coe ﬃcient of
the child variable in the family characteristics model and the one in each model. For
example, when the coeﬃcient of the total number of children is -0.031 in the modelthat added the non-regular employment variable to the family characteristics model,
the magnitude is (-0.03) - (-0.04) = 0.01 (this is the value at the top of the left
column). The results show that non-regular employment, regular employment―177―experience, and unemployment experience are key factors of the motherhood penalty.
Regardless of the type of the child variable, the total years of unemployment
especially reduce the child penalties and reduce their statistical significance.
Table 4. Comparison of the magnitude of each variablein the changes of the child penalty
Gross, family
characteristics + nonregular employment
Total number of children
One child
Two children
Three or more children

0.01 (†)
0.03 (*)
0.01 (*)
0.02

Gross, family
characteristics +firm size
0.00 (**)
0.00 (**)-0.00 (**)
-0.07 (*)

Gross, family
characteristics +seniority in thepresent workplace
0.00 (*)
0.01 (*)
0.00 (*)
0.02 (†)

Gross, family
Gross, family
Gross, family
characteristics + total characteristics + total characteristics + total
years of regular
years of non-regular
years ofemployment
employment
unemployment
Total number of children
0.01 (†)-0.01 (*)
0.02(**)
One child
0.07
-0.01
0.08
(*)
Two children
0.08
0.00
0.09
(†)
Three or more children
0.12
0.00
0.11
Note: Women aged 20-59(N =1189). Marks in parentheses are the statistical significance of eachchildren variable in the each model.
†
p < .10, * p < .05, ** p < .01, *** p < .001.

4.2 The wage penalty for mothers in the younger age group
Here, we examine whether motherhood penalties diﬀer according to the ageof children. Table 5 presents a nearly 7% gross motherhood penalty for children
between 7-18 years of age, but not for other age groups. This result may reflect a
situation of mother employment in Japan in which there is a selection for working
mothers with children of 3 years of age or younger. Most mothers with children of 3
years of age or younger do not participate in the labor market. Thus, only mothers
who earn high wages remain in the labor market. The gross motherhood penalty forhaving children aged 7-18 years is explained by job characteristics and human capital.
The net motherhood penalty by controlling for the job characteristic variables isabout 3%, and not statistically significant.
―178―

Table 5. Average motherhood penalties according to the age of children+ Familycharacteristics

Gross
Number of children by age
3 years old or younger
4-6 years old
7-18 years old
Age
Age2
Education (in years)

0.012(0.059)-0.072(0.054)-0.072 **(0.027)0.085 **
(0.031)-0.001 *(0.000)0.072 ***
(0.010)Marital status (ref; never married)
MarriedDivorced/widowed
Husband's annual income(in million)
Non-regular employment
Firm size (ref; 1-29 people)
30-299 people
300 people and above
Public sector
Seniority in the present workplace(in years)
Total years of regular employment(in years)
Total years of non-regular employment(in years)
Total years of unemployment(in years)
Intercept

4.560 ***
(0.508)+ Job characteristics+ Human capital

0.019(0.060)-0.069(0.055)-0.075 **(0.029)0.092 **
(0.031)-0.001 **(0.000)0.068 ***
(0.011)-0.052(0.057)-0.016(0.049)-0.034(0.024)0.101 ***
(0.028)-0.001 **(0.000)0.042 ***
(0.010)-0.071(0.057)-0.015(0.050)0.005(0.026)0.100 **
(0.027)-0.001 **(0.000)0.031(0.012)-0.134(0.090)0.025(0.083)0.000(0.000)-0.104(0.082)0.019(0.070)0.000 †
(0.000)-0.458 ***(0.037)-0.124(0.082)0.033(0.068)0.000 †
(0.000)-0.305 ***(0.052)0.067(0.046)0.205 ***
(0.044)0.203 **
(0.069)0.078 †
(0.045)0.207 ***
(0.044)0.185 **
(0.068)0.005(0.004)-0.011(0.009)-0.025 **(0.009)-0.046 ***(0.012)4.840 ***
(0.439)4.523 ***
4.800 ***
(0.511)(0.446)
N
703
R2
0.107
0.107
0.314
0.353
Note: Women aged 20-44. OLS models. Numbers in parentheses are robust standard errors. † p < .10, * p < .05, ** p < .01, *** p < .001.

5. Conclusion
We found a motherhood wage penalty of approximately 5% per child among Japanesewomen. The relationship between motherhood and wage is not linear or monotonic;
women with three or more children experienced a greater wage penalty. The mostimportant factor behind the mechanism of the motherhood penalty is long -term
unemployment. This result suggests that preventing human capital from decreasing is―179―

important for easing motherhood penalties. It is also possible that employer
discrimination, which prolongs the unemployment of mothers, is the most significant
mechanism of the motherhood penalty. The result of the wage penalty for mothers
among the younger age group infers a sample selection bias. To clarify themotherhood penalty in Japan, not only verification with various data, but dealing
with selection is also necessary.

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