Gender salary and promotion gaps in Japanese
academia: Results from science and engineering

Ana Maria Takahashi
Shingo TakahashiThomas MaloneyMay 2015
Discussion Paper No.1522

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANGender salary and promotion gaps in Japanese
academia: Results from science and engineering1
Ana Maria Takahashi†
Shingo Takahashi‡Thomas Maloney§
Abstract
Using original survey data on Japanese academics in science and engineering,
we examined the gender salary and promotion gaps. We found a 6% gendersalary gap after controlling for ranks. This gap was unaﬀected when quality
and quantity of publications were controlled for. In contrast, promotion gap
disappeared when publication variables were controlled for. We failed to ﬁnd
negative eﬀects of marriage and children on women’s salary and promotion,
though a positive sorting into motherhood could conceal such negative eﬀects,
and we provided suggestive evidence for this. Men and women are equally likely
to move to other universities voluntarily, and the salary premiums from thesejob changes are the same for both genders, suggesting that outside job oﬀers
are not responsible for the gender salary gap. Finally, there are substantial
gender diﬀerences in academic labor market dropout rates, which could lead tounderestimation of the gender salary and promotion gaps.
JEL Classification: J7

1.

Introduction

Economic literature on discrimination began to document the gender salary and promotion
gaps in US academia in the 1970s with the works of Katz (1973), Johnson and Staﬀord(1974), Gordon et al. (1974), and Hoﬀman (1976). Despite signiﬁcant progress made by
women in academia since then, research has continued to ﬁnd gender salary and promotion
gaps. Long et al. (1993) found that women in biochemistry are 10% less likely thanmen to be promoted to tenure, while Kahn (1993) found similar results in economics andmanagement. Broder (1993) found a 5-8% salary gap in economics departments in the US,
while Ward (2001) found a 9.4% gender salary gap in economics departments in the UK,
although this salary gap completely disappears when ranks are controlled for.
1

This research was supported by Grant-in-Aid for Young Scientists (B) No.15K17077, and Grant-in-Aid
for Scientiﬁc Research (B) No. 25285094, provided by the Japan Society for the Promotion of Science.
†
Kobe University, Graduate School of Economics‡
International University of Japan, Graduate School of International Relations§
University of Utah, Department of Economics

1

McDowell et al. (2001) showed that women were promoted more slowly than men
in economics departments in the US until the end of the 1980s, but that the promotion
gap disappeared afterward. However, Ginther and Hayes (2003) found that the genderpromotion gap persisted over time, although the gender salary gap within ranks disappearedby 1985. Ginther and Kahn (2009) found a 8% gender promotion gap in the probability ofreceiving tenure in life sciences, but no promotion gap in engineering and physical sciences.
Blackaby et al. (2005) documented a 9% gender salary gap among British economists, but
this gap reduced to 3.5% when ranks were controlled for.
Japan has been noted for its large gender pay gap in the general labor market. Blau
and Kahn (2003) showed that the gender pay gap in Japan was one of the largest among
the OECD countries, with the female-male wage ratio being 0.65 in the late 1990s. Daly etal. (2006) also found a gender salary gap of a similar magnitude in Japan, although the gap
has been steadily declining over time. Kato et al. (2013) showed, by using a personnel data
set for a single ﬁrm, that there is a substantial gender salary gap, but this gap is explained
away by the gender diﬀerences in hours worked.
Although the overall Japanese labor market shows substantial gender inequality insalary, one may expect that academia is an exception. In Japanese academia salary is mostly
determined based on rigid salary tables where experience and educational qualiﬁcations are
the primary determinants of the location on the salary scale. Such rigid salary tables may
ensure gender equality in salary. Moreover, Japan is known for its seniority based promotion
system where experience largely determines promotion in the majority of industries. Given
the near absence of the up-or-out promotion system in Japanese academia, it is reasonable
to expect that the seniority promotion system is used in academia as well. If so, then theseniority based promotion system would shield Japanese academia from a gender promotion
gap.
2However, whether or not there are gender salary and promotion gaps in Japanese
academia is still an open empirical question, since these gaps have not been investigatedwith a similar scrutiny as for the US or UK. Takahashi and Takahashi (2011) is the only
study that documents gender salary gaps in Japanese academia. By using a survey sample
of 337 academic economists, they concluded that there exists a 7% gender salary gap, after
controlling for ranks, the quantity of publications, and various demographic, institutionaland job characteristics. Takahashi and Takahashi (2014) used the same sample to investigate gender promotion gaps. They found a statistically signiﬁcant gender promotion gap
for the ﬁrst promotion spell (from the initial hiring to associate professor). The gendersalary gap for the second promotion spell (from associate professor to full professor) was
more complex. They found that single childless women are promoted more quickly thancomparable men. However, they also found that this ‘reverse’ gender gap disappears after aﬁrst child is born, and women’s time to promotion becomes signiﬁcantly longer than men’s
if they have a second child.
The goal of this study is to investigate if there exist gender salary and promotion gaps
in Japanese academia, and if they exist, to explore the possible reasons for the gaps. We
improve on Takahashi and Takahashi (2011, 2014) by using a much larger data set, andby controlling for both the quantity and quality of research output. Our data come froma survey of academics in science- and engineering-related departments in Japan that we
conducted in 2011. This data set contains 1517 usable observations, and to our knowledge,
this is the largest and most detailed microeconomic data set of Japanese academics thatcurrently exists.
To motivate our study, let us provide an initial look at the gender salary and promotion gaps that can be observed in our data set. Figures 1A and 1B show the average salaryby experience in public universities (national, prefectural, or city universities) and private3

universities, respectively. There are visible gender salary gaps in all the experience categories. Figure 2 shows the Kaplan-Meier survival curves for the duration from the initial
appointment in academia to the full professor position. Here as well, we observe a genderpromotion gap. What could have caused these gaps? This paper will examine the following
three possible causes that have been shown to be important sources of gender salary andpromotion gaps in US and UK academia: (1) gender diﬀerences in academic productivity;(2) gender diﬀerences in household responsibilities, especially due to child rearing; and (3)gender diﬀerences in outside job oﬀers.
Gender diﬀerences in publication productivity are perhaps the ﬁrst potential cause that
one might suspect. In the US and UK, many studies including McDowell et al. (2001),
Ward, (2001), and Ginther and Hayes (2003) have found that female academics have alower rate of publication than males, and that publications are signiﬁcant determinantsof salary and promotion. Regarding Japanese academia, few studies have examined theextent of productivity diﬀerences between genders and how much of the observed gendersalary and promotion gaps can be attributed to such productivity diﬀerences. By using
information on both the quantity and quality of academic publications, we examine theeﬀect of publications on the gender salary and promotion gaps. To preview our results, thegender promotion gap for the latter career spell (duration from the end of lecturer to full
professor) disappeared after controlling for publications. However, the promotion gap forthe early career spell (the duration of lecturer or postdoc) persisted. The gender salarygaps are almost unaﬀected by the inclusion of quality and quantity of publication controls.
Gender diﬀerences in household responsibilities, especially due to child rearing, areanother potential cause that one might suspect. As Becker (1985) theorized, females may
focus more on the family role relative to their male counterparts, which in turn would
reduce the eﬀort allocated to paid work. Although household responsibilities have been4

shown to cause the gender salary gap in the general labor market (Goldin and Polachek,
1987; Korenman and Neumark, 1992), there are only a few studies that examine whether
household responsibilities contribute to the observed gender salary and promotion gaps inacademia. Johnson and Staﬀord (1974) argued that the depreciation of human capitalduring the time out of work might explain a substantial part of the gender salary gap
in academia. Ginther (2004) did not ﬁnd a substantial marriage and motherhood wage
penalty for women in US academia, while Kelly and Grant (2012) did ﬁnd a motherhood
wage penalty, although it was caused by reduced publication due to motherhood.
In this study, we ﬁrst demonstrate that there is a large diﬀerence in the amount ofhousehold work performed by each gender, and that marriage and young children are thecause of this diﬀerence. Thus, to examine if household responsibilities are causing gendersalary and promotion gaps, we examine whether these gaps are concentrated among thefollowing three groups: single childless academics, married academics without children, andmarried academics with children. To preview our results, we failed to show that gendersalary gap is diﬀerent among these three groups. We only found weak evidence of thenegative household duty eﬀects on women’s promotion, but only for the latter career spell.
We argue that there may be a positive sorting into motherhood that conceals the negative
household duty eﬀects, and we provide suggestive evidence for this.
Gender diﬀerences in outside job oﬀers can also cause the gender salary gap, as Boothet al. (2003) theorized in their sticky ﬂoor model. Academics can use outside oﬀers tonegotiate their salaries at their current universities, or they can take the oﬀers to increasesalary. In fact, Blackaby et al. (2005) showed that the driving force of the gender salary
gap in UK academia is the gender diﬀerence in the number of outside job oﬀers.
Although our data do not have outside job oﬀer information, we have the entire historyof each respondent’s job moves, and the reasons for each job move. This can be used to test5the above mentioned possibility, since Booth et al.’s sticky ﬂoor model provides two testablepredictions based on actual job moves. In their model, a ﬁrm oﬀers a guaranteed minimum
wage to a worker provided that he or she acquires the required speciﬁc human capital.
However, if the worker receives an outside oﬀer that exceeds the guaranteed minimum
wage, the ﬁrm matches this oﬀer as long as the oﬀer does not exceed his or her productivity
at the ﬁrm. This explains why gender diﬀerences in the values of outside oﬀers can cause
a gender salary gap among equally productive workers.
If an outside oﬀer exceeds the worker’s productivity at the ﬁrm, the ﬁrm cannot matchthe oﬀer so that the worker moves to another ﬁrm. This means that if the values of outside
oﬀers are higher for men than women on average, (1) men are more likely than equally
productive women to change jobs, and (2) the salary premium for a job move will behigher for men than for equally productive women. These predictions can be tested. To
preview our results, we found that women are as likely as men to move to another job for
positive reasons such as salary increases, and that the wage premium for moving to anotheruniversity for a positive reason is the same for men and women. Thus, gender diﬀerencesin outside job oﬀers do not appear to be the primary reason for the observed gender salarydiﬀerences.

2.

Background

Japan’s four-year university system includes: public universities (national, prefectural, andcity universities), and private universities. National universities are established and fundedby the central government. Prefectural and city universities are established by local governments, but funded by both the local and the central governments. Private universities
are established by private entities, and although they receive public funds to some extent,
they are largely ﬁnancially self-supporting.
6

In 2004, the National University Corporatization Law was passed, and it took eﬀect inApril 2006. This law brought signiﬁcant changes to public universities as the ‘corporatization’ eliminated the public employee status of academics at national universities, and thus
allowed these universities greater freedom in various personnel decisions, including salaryand promotion determination.1 For example, before corporatization, the experience andage necessary to be promoted from an assistant professor to an associate professor in public
universities were set by civil service laws. In addition, according to these laws, national
university employees were considered civil servants and therefore had job security until
they reached retirement age, although it has been possible to hire academics on ﬁxed term
contracts since 1997.2 Lifetime employment requirements no longer applied to faculty atnational universities after the corporatization took eﬀect. The Prefectural and City University Corporatization Law was passed and took eﬀect at the same time. This law, however,
did not force these institutions to corporatize, and instead provided them with the option
to corporatize if they wished to do so.
Corporatization was supposed to facilitate the adoption of new hiring and promotion
systems, such as the ‘up-or-out’ tenure track system that could increase the competitivenessof universities. In fact, in 2006, the Ministry of Education, Culture, Sports, Science andTechnology (MEXT) began to encourage public and private universities to adopt the upor-out tenure system by providing ﬁnancial support for introducing and maintaining thissystem. However, this tenure system has not spread widely yet. According to the MEXT
website3 , only 117 departments in 54 universities received the grant, and these departments
employ only 315 tenure track faculty as of 2014. Therefore, it is not clear how muchpromotion practices have changed since 2006.4
1

For the details of the National University Corporatization Law, see Yamamoto (2004).
In 1997, the Law Regarding the Duration of the Contract of University Employees passed, and allowed
universities to hire academics under ﬁxed term contracts.
3
www.jst.go.jp/tenure
4
Departments can introduce a tenure track system without receiving MEXT ﬁnancial support. Certainly,
2

7

There are four academic ranks in Japanese academia. They are, from the highest to
the lowest rank, full professor (kyouju), associate professor (jun-kyouju), assistant professor(koushi ), and lecturer (jokyo). Although many universities have these four ranks, according
to Japanese education laws, the only ‘oﬃcial ranks’ are full professors, associate professor
and lecturer. It is up to each university to add assistant professor as an extra rank.
As will be detailed later, academics typically start their career as a lecturer, thoughsome academics start as postdocs, or start directly as assistant professors or associate
professors. In addition, there are faculty who start their academic career directly as full
professors after a long non-academic career, often in various ministries.
The total annual salary of academics includes: monthly salary for 12 months, bonuses,
and allowances (i.e., for children less than 20, spouse with income less than a threshold, costof living, teaching at graduate level). In most universities, salary is determined based on arigid pay scale called the salary table. Although currently all universities have the freedom
to set their own salary table, public universities typically follow the guideline salary tableprovided by the National Personnel Authority (NPA).5 Most private universities set theirown salary tables.
A salary table stipulates how salaries should progress depending on academic rank.
Each rank contains a number of divisions (pay scales). The initial division at hiring isdetermined based on age, previous experience, and educational attainment. Before thecorporatization, an academic in public universities used to move up one division annually.
After the reforms, the NPA divided one division into four smaller divisions, so that universities can ﬁne-tune the salary among diﬀerent individuals. Who decides how many divisions
more than 117 departments could have introduced tenure track system. For example, one of the authors’university introduced the tenure track system without this support. However, the paucity of departments
accessing MEXT support indicates that the tenure track system is not widespread yet.
5
NPA (Jinjiin) is a neutral, third-party organization dealing with public employee management. Since
MEXT determines the subsidies to public universities assuming that universities follow the NPA guideline
table, setting salary greater than the NPA’s guideline table is diﬃcult due to budget constraints.

8

a faculty member should move up annually and the criteria used for the decision are in a
black box, presumably depending on each university’s regulations.
Japanese academics typically receive a bonus twice a year. In a few universities, theamount of bonus is set as (Monthly salary including allowances)×K, where K is a predetermined constant set by the university.6 We do not know whether discretionary bonuses
are possible in other universities, or how common they are. If discretionary bonuses are
possible in many universities, a gender salary gap might arise despite the rigid salary tables.
Promotion decisions within Japanese universities are made at the department level,
usually by faculty personnel committees, considering various criteria such as experience,research output and education. However, it is commonly believed that ‘a larger factor than
the research output in determining promotions is the years of experience (OECD 2006,p.78)’.

3.

Data

Data were obtained from a survey we administered via a postal questionnaire in 2011.
Our survey method follows. First, from MEXT’s web page, we obtained an oﬃcial listof all four-year universities in Japan: 86 national, 95 public and 599 private universities.
Second, by checking each university’s website, we collected the names of all faculty insciences- and engineering-related departments: 29,114 names (17,567 from national, 1,487
from prefectural, and 10,060 from private universities).
We sent out 4800 questionnaires. In order to increase the number of female observations,
we ﬁrst selected all the female sounding names (1,122 names). Then, we randomly selected
the rest of the names (3,678). We sent questionnaires to each faculty’s work address, anda reminder was sent two weeks later. As an incentive, we oﬀered 1000 yen (either cash or
6

In one of the authors’ university, K is set equal to 1.375, and there is no room for discretion based on
productivity.

9

a special cash card) to about 500 respondents by lottery. About half of the respondents
participated in the lottery.
We received 1,636 responses for a response rate of 34%. The response rates by gender
are 35% and 28% for males and females, respectively, assuming that all female-sounding
names were in fact females. From the 1636 responses, we dropped 46 observations due
to missing salary information. We dropped observations whose annual salary is less than
1.5 million yen (7 observations), since respondents with such low salary are unlikely to be
fulltime faculty. We also dropped respondents whose age exceeds 65 (42 observations), since
they are likely to be retired academics who were rehired, and since we want to focus on thegender gaps among primary working age academics. Further eliminating observations withmissing variables (20 observations), and observations with clearly inconsistent answers (4observations), we have the usable sample of 1517 observations.
The percentage of females in our ﬁnal sample is 19.6%. Based on the MEXT Statistics
of School Education, the percentage of females in the sciences and engineering related
departments is 6.7%. Our successful oversampling of females will increase the precision of
the estimates.

3.1

Choice of Variables

Table 1 contains all the variables used for our salary regressions and their summary statistics. Promotion analyses will use a subset of these variables. The dependent variable inthe salary equation is the logarithm of the total annual salary which includes the 12-monthsalary plus bonuses and allowances from the current institution. It does not include income from other universities. We have a number of control variables for personal, job,
institutional and human capital characteristics. Let us explain the variables that are notself-explanatory.
Quantity of research output is measured by the number of refereed career articles with10

out distinguishing between single authored and co-authored articles. Quality of research
output is measured by the variable Field top 20 article share. Each respondent reported thenames of the journals in which their top three articles were published. Field top 20 article
share indicates the share of ﬁeld top 20 articles among the three reported articles.7
If female academics are over-represented in low paying universities, the female dummy
will pick up the diﬀerence in university characteristics. To minimize this possibility, we
control for several university characteristics. Universities diﬀer in many dimensions, suchas reputation, research orientation, location, or public or private status. To control for
reputation, the ideal variable would be the departmental ranking. Due to the anonymous
nature of our survey, this is not possible. However, reputation is likely to be correlatedwith research orientation, a characteristic we are able to control for. Our survey asked each
respondent to indicate the weights that their departments place on research, teaching andadministrative duties in promotion decisions.8 We use the weights on research to control
for research orientation. In addition, research orientation is controlled for by including adummy for Ph.D. oﬀering institutions.
Location is also an important determinant of salary, since universities adjust for thecost of living. For example, public universities provide ‘cost of living adjustment allowances’based on the NPA’s university location classiﬁcations. Private universities do not have
explicit cost of living adjustment allowances, but their salaries could reﬂect the cost ofliving. Thus, we include ﬁve large city dummies, Class 1 Cities to Class 5 Cities, based on
NPA’s university location classiﬁcations.9
7

Journal rankings are based on the citation counts, and are derived from journalranking.com.
When such weights are not formally set by the department, we ask respondents to approximate theweights.
9
According to NPA classiﬁcations, Tokyo Metropolitan area is classiﬁed as a Class 1 city, and Osaka isclassiﬁed as a Class 2 city, and so on with decreasingly large cities denoted by class number. At the timeof writing this paper, public universities in class 1 cities provided a cost of living adjustment allowance
equivalent to 18% of the base monthly salary. The survey questionnaire lists all the cities in each location
classiﬁcation, and the respondents were asked to tick their location classiﬁcation.
811

Japanese universities pay allowance for those who have a dependent spouse. This
allowance is paid when the spouse’s annual income is less than 1.3 million yen for public
universities, and less than 1.03 million yen for private universities. Thus, we include adummy for those who are eligible for dependent spouse allowance.
Job mobility variables are created as follows. Our survey asked each respondent toreport their entire history of job moves and the reasons for each move: for a salary increase;
for a better job environment; due to contract expiration; for family reasons; or for ‘otherreasons’. When a respondent chose ‘other reasons’, the survey asked them to write down theactual reasons. The most common ‘other reasons’ were to obtain promotion, and because
they were oﬀered a job.
Respondents were allowed to choose multiple answers. Thus, we reclassiﬁed the moves
into ﬁve categories. A type 1 move is any move that entails a salary increase. A type 2
move is any move that is due to other positive reasons, but that does not entail a salary
increase, namely for a better job environment, for promotion, or because they are oﬀereda job. A type 3 move is a move that is the result of contract expiration, and that does notentail any positive reasons. A type 4 move is a job move that is purely a result of family
reasons. A type 5 is a job move that cannot be classiﬁed in any of the ﬁrst four types.
Regressions will control for the total number of each type of moves.
Table 1 shows that female academics have lower salary, have fewer years of experience,
have fewer publications, are less likely to have managerial positions, are more likely tobe employed under ﬁxed term contracts, and are over-represented in the two lowest ranks.
Female academics are slightly under-represented in research oriented universities. The number of positive job changes (type 1 moves and type 2 moves) is lower for women, while thenumber of negative job moves (type 3 moves and type 4 moves) is greater for women.

12

4.

Gender diﬀerences in key characteristics

In this section, we provide a detailed look at the gender diﬀerences in the key characteristics used to examine the reasons for the gender salary and promotion gaps. First, let us
examine the gender diﬀerences in productivity. Figure 3A shows the average number ofcareer articles by experience categories. At any experience, women publish less than men.
Figure 3B presents the average of Field top 20 article share by experience categories. Interestingly, there seem to be no signiﬁcant gender diﬀerences in research quality up to 25 yearsof experience. In fact, women’s research quality seems to be higher for some experience
categories.
Another indicator of research productivity is the amount of competitive grants. Our
survey contains information on the two most important grants at the time of the survey:
their duration in years, the total amount of each grant, and in case of a joint grant, therespondent’s contribution to the grant project in percentage. Annual grant amount is computed as the total amount of grant multiplied by the respondent’s percentage contribution
divided by the duration of the grant project. Figure 4 shows the average Annual grantamount by experience categories. We observe visible gender diﬀerences.
Second, let us look at gender diﬀerences in household responsibility. Our survey asked
information on the percentage of total household work the respondent performs at home, andthe household size (the number of people in the household including the respondent). We
can thus construct a proxy for the amount of household work performed by each respondent
as follows: Household work = (Household size)×(Percentage of household work performed ).
This variable shows the approximate number of family members (including the respondent)
that the respondent takes care of. Thus, if this variable is greater than 1 the respondent is
a net care giver, and if less than 1 the respondent is a net care receiver.

13

Figure 5A shows the average Household work by experience categories. Men’s Household work hovers between 0.5 and 0.6, and thus they are net care receivers. In contrast,
women’s Household work ranges between 1.2 and 1.7, and thus, they are net care givers.
For women, Household work is increasing up to experience 25, and then declines. This increasing trend at the beginning of their careers is likely to capture the increasing householdburden due to marriage and parenting.
A greater amount of household work performed by women would aﬀect their numberof hours worked. Thus, we further examine gender diﬀerences in hours worked. Our survey
contains information on the weekly hours worked during a typical work week, including
hours worked at home and in weekends. Figure 5B presents the average weekly hours
worked by experience categories. For the ﬁrst 15 years of experience, men work longer
hours than females, with the diﬀerence being about 3 hours per week. Gender diﬀerencesin hours worked disappear after 20 years of experience.
What causes the gender diﬀerences in the amount of household work and weekly hours
worked? In Appendix A, we present regression results for Household work and Weekly hours
worked. We found that the gender diﬀerences in household work are present only among(1) married academics with young children, and to a lesser extent in (2) married academics
without young children. Similarly, the gender diﬀerence in the weekly hours worked is foundonly among married academics with young children. These results indicate that children
and marriage are the cause of the gender diﬀerences in household responsibility. Thus, if thehousehold responsibility is the primary source of the the gender salary and promotion gaps,
these gaps should be concentrated among married academics with children, and amongchildless married academics. We test this in the empirical section.
Finally, we examine gender diﬀerences in job mobility patterns. Table 2 provides Tobit
regressions of the job moves. Column 1 shows the results for #Type 1 moves. Female14

coeﬃcient is negative, though it is insigniﬁcant. More importantly, the magnitude of thecoeﬃcient is small, with the marginal eﬀect of Female being only -0.013. Column 2 presents
the results for #Type 2 moves. The coeﬃcient for Female is positive, though the estimatedmarginal eﬀect of female is again very small (0.029). Women are more likely than men
to move purely for contract expiration (Column 3), though the female coeﬃcient is notstatistically signiﬁcant, and the marginal eﬀect is very small (0.017). In sum, there are no
economically and statistically meaningful gender diﬀerences in the job mobility patterns.

5.

Gender salary diﬀerences

Table 3 shows our salary regressions. Model 1 is the most parsimonious model that does notcontrol for ranks. Results show a gender salary gap of about 8.7%. Model 2 adds the rank
variables. This reduces the salary gap to 6.5%, although it remains statistically signiﬁcant.
The relatively small decrease in the estimated gender salary gap implies that the salary gap
that stems from the gender promotion gap is not large. This contrasts with the ﬁndings ofWard (2000) and Ginther and Hayes (2003) that much of the gender salary gap in the UK
and the US academia stems from the gender promotion gap.
As mentioned previously, Japanese universities decide salary based on rigid salary tables. This payment setting method is expected to shield academics from a gender salarygap. Nevertheless, a gap exists in our sample. One interpretation of the result is that theestimated salary gap might capture pay scale diﬀerences among universities, and that there
may be no gender salary gap within each university.
Although we cannot estimate the gender gap for any speciﬁc university, we can examine
if this possibility is true. As public universities mostly follow the guideline salary table set by
the NPA while private universities traditionally set their own salary table, if the estimatedgender salary gap merely reﬂects pay-scale diﬀerences among universities, the gender salary15

gap is likely to be concentrated among private universities. Model 3 and 4 in Table 3
separately estimate the salary regression for public and private universities. The gender
gap is 6.7% in public universities and 6.2% in private universities. Thus, the gender salarygaps are almost equal, which is inconsistent with the aforementioned interpretation.
Now, let us examine if gender diﬀerences in research productivity are responsible forthe gender salary gap. Model 5 in Table 3 adds research quantity and research quality
variables.10 Career articles and its squared term are statistically signiﬁcant. Field top 20
article share does not have a statistically signiﬁcant eﬀect. This does not mean that research
quality is not rewarded in Japanese academia. When we excluded rank variables in an
unreported regression, we found statistically signiﬁcant eﬀects of research quality on salary.
It will also be shown later that research quality is positively associated with the probabilityof promotion. Annual research grant has no signiﬁcant eﬀects. The most important ﬁndingin Model 5 is that the inclusion of productivity variables has little impact on the estimatedgender salary gap. Thus, we can conclude that gender diﬀerences in productivity are notresponsible for the observed gender salary gap.
Next, let us investigate if gender diﬀerences in household responsibility are responsible
for the observed gender salary gaps. Since Household work is the current household responsibility while it is perhaps the cumulative household responsibility that aﬀects salary,
simply controlling for Household work in the regression will not answer the question (i.e.,current Household work might be low because a child has grown up, but there may belasting impacts from household work undertaken when the child was young). However, wehave already shown that marriage and children are the causes of the gender diﬀerences inhousehold responsibility. Thus, children and marriage can capture the negative eﬀects of
10

As for our research quality variable Field top 20 article share, if this variable is indicative of the qualityof the rest of the publications, it is better to use it as a discount factor to compute the quality-adjusted
career publications. However, if it is not indicative of the rest of the publications, it may be better to include
this variable in an additive fashion. We experimented with both methods and we found that additively
incorporating this variable achieves the greatest statistical signiﬁcance. Thus, we use the second method.

16

household responsibilities on salary that were inﬂicted in the past, but that persist overtime. To investigate the eﬀects of cumulative household responsibility on the observed
gender salary gap, we investigate if the gender salary gap is concentrated among childlessmarried academics, and married academics with children.
Model 6 in Table 3 includes the Female×Married interaction as well as the Female×
Total#Children interaction. We use the total number of children instead of the number ofyoung children since children may have long term eﬀects. Note that the model includes rank
variables, so that it does not capture indirect eﬀects of household responsibility throughdelayed promotion. The estimation results show that marriage is associated with a 3.1%
increase in salary for men, while it is associated with a 2.5% increase for women. An increasein the total number of children by one is associated with a 0.9% increase in salary for men
while it is associated with 0.4 % increase in salary for women. The estimated gender salarygaps for (1) single childless academics, (2) married childless academics, and (3) married
academics with one child are 5.1%, 5.7%, and 6.2%, respectively.
Thus, the diﬀerences in the gender salary gaps among the three groups are minor in
magnitude. Moreover, the diﬀerences are statistically insigniﬁcant. Thus, we failed to ﬁnd
evidence that household responsibility is a cause of the gender salary gap. This is a puzzling
result since the marriage and motherhood wage penalties for women are widely documented
in the labor economics literature.11 Should we conclude that there is no marriage andchildren wage penalty for women in Japanese academia?
One interpretation of this result is that this is indeed evidence of the absence of household duty wage penalty for women. Academia is often viewed as a ﬂexible work place, where
faculty have considerable discretion over the setting of their work schedules. This ﬂexibility
might translate into the absence of the household duty wage penalty for women. In addition,
11

For example, see Korenman and Neumark, 1992; Goldin, 1997 and Waldfogel, 1998.

17

a slightly higher gender salary gap for the academics with children may reﬂect the dependent children allowances that universities pay to their employees.12 This allowance is more
likely to be received by men than women, since universities usually have no-double-recipient
policy–when the husband receives the dependent children allowance from his employer (regardless of whether or not his employer is a university), the wife is not qualiﬁed for the
dependent children allowance.
The second interpretation is that there is a household duty wage penalty for women,
but there also is a positive sorting into motherhood–the most productive women self-selectinto motherhood, and this positive sorting might have cancelled out the negative household
duty eﬀects. In fact, Krapf et al. (2014) report such positive sorting eﬀects on academic
publication productivity. Despite the perception that academia is a ﬂexible workplace,
Mason et al. (2013) provided case evidence that women often struggle to balance workand life, especially in the US where there is a pressure to obtain tenure. Even in Japan
where the tenure track system is rare, many early career positions (postdocs and lecturers
in particular) are ﬁxed term positions, which put a similar stress on female academics. In
addition, in science ﬁelds, experiments are often done in a team, and thus ﬂexibility is notalways guaranteed.
Which interpretation is more likely? If there is a positive sorting into motherhood, it
is likely that more productive women decide to have a child at an early stage of their career
because they are perhaps more conﬁdent in their ability to combine children and work,
while less productive women wait longer to have a child perhaps because they see a larger
risk in childbearing or because they may have more tenuous appointments.
Thus, the early child-bearers are likely to have a higher ability and thus a higher salary12

In fact, the estimated child wage premium for men roughly corresponds to the amount of the allowance.
In one of the authors’ university, the annual amount of dependent children allowance is 60 thousand yen,while 0.9% salary premium is equivalent to 76 thousand yen at the male average in our sample.

18

than late child-bearers. In Appendix B we examined this possibility. More speciﬁcally, we
divided academics with at least one child into: (A) those who had their ﬁrst child within
one year prior to the beginning of their academic career; (B) those who had their ﬁrst childwithin three years of academic career; and (C) those who had their ﬁrst child after three
years of academic career. Having the ﬁrst child one year prior to the academic career could
indicate that they might have successfully negotiated and delayed the start of the job after
receiving a job oﬀer. This may indicate high productivity. Having the ﬁrst child within
three years of academic experience would also indicate high productivity.13
Thus, we compared the salary of the early child-bearers (group A and B) with the thelate child-bearers (group C). As Table B.2 in Appendix B indicates, for female academics,
group A has a salary premium of 14.8%, and group B has a salary premium of 5.2% ascompared to the group C. Such large salary premiums for having children early may partly
reﬂect university diﬀerences–more productive women are hired by better paying universities,
and at the same time they are likely to give birth at early stage. We found no such pattern
for men. The salary premium for female early child-bearers is consistent with the hypothesisthat there exists a positive sorting into motherhood. Thus, we are inclined to support the
hypothesis that the negative household duty eﬀects is concealed by the positive motherhood
sorting.
Finally, we examine if the observed gender salary diﬀerences may stem from the gender
diﬀerences in the values of outside job oﬀers. If this is the case, then the wage premium frompositive job moves must be higher for men than women. Model 7 in Table 3 includes the job
mobility variables as well as the interactions between these variables and Female. #Type 1
moves (moves due to salary increases) has a positive coeﬃcient but it is insigniﬁcant. #Type
13

Some academics gave birth earlier than one year prior to the beginning of their career. They are likely
to be the ones who worked in the non-academic sector ﬁrst, and then moved to academia. Thus, we do notinclude them in the ‘early child-bearers’ groups.

19

2 moves (other positive moves) has a negative but small and insigniﬁcant coeﬃcient. Not
surprisingly, #Type 3 moves (contract expiration only) has a negative coeﬃcient. Ourinterest is the interaction term between Female and #Type 1 moves. If the gender gap
stems from the gender diﬀerences in outside job oﬀers, this interaction term should havea negative coeﬃcient. However, the interaction coeﬃcient is positive, though it is notstatistically signiﬁcant. Thus, the above hypothesis was not supported by the data. Let us
also look at the result for other positive moves,#Type 2 moves. The coeﬃcient for #Type
2 moves×Female interaction is negative, but it is small and insigniﬁcant.
In sum, we did not ﬁnd evidence that salary premium for a positive job move is higher
for men. Combining this result with the earlier ﬁnding that there are no diﬀerences in thenumber of ‘positive’ job moves, we can conclude that gender diﬀerences in outside job oﬀers
are not responsible for the gender salary gap in Japanese academia.

6.

Gender promotion gaps

6.1

Typical patterns of academic career

Now, we turn our attention to the gender promotion gap. First, it is worth describing thetypical patterns of academic careers in our sample. Table 4 tabulates the rank at initialhiring in academia. 70.1% of academics started their career from the lowest instructor rank
(lecturer), but a few academics start their career from even a lower position (postdoc). A
few respondents started their career directly as assistant professors or associate professors.
5.3% of respondents started their career directly as full professors, and they are excluded
from our promotion analysis since for them the duration to promotion is zero.
The bottom half of Table 4 tabulates what rank respondents received immediately
after their early career positions (lecturer or postdoc). About one third of the respondents
became assistant professors and about one third skipped assistant professorship and became20

associate professors. The rest were still lecturers at the time of survey. Thus, a considerable
percentage of respondents have skipped the assistant professor position.14
In this study, we analyze the following two separate promotion spells: the early career
spell, which is the duration of being either a lecturer or a postdoc; and the latter career spell,which is the duration from the end of early career spell to the promotion to full professor.
Note that the latter career spell can start either in an assistant professor or in an associate
professor position (since some respondents skipped the assistant professor positions). Whena respondent started their career directly as an assistant professor or associate professor,
the year of initial hire is set as the start of the latter career spell.
There are two reasons for why we separated the promotion spells in this way. First,
Takahashi and Takahashi (2014) indicated in their analysis of promotion in economics departments in Japan that the gender promotion gap is concentrated in the very early career.
Thus, we suspect that the gender promotion gap may be more problematic for the early
career spell in our data as well. Second, both of the early career positions (lecturer andpostdoc) are often unstable ﬁxed term positions, and thus getting out of these positions is
one of the most important milestones of a career in academia in Japan.
Graph 6A shows the Kaplan Meier survival function estimation of the duration of theearly career spell. We observe visible gender diﬀerences in the survival probability. Graph
6B shows the Kaplan Meier survival function of the duration of the latter career spell. For
this spell, gender diﬀerences in the probability of promotion are much less pronounced.
In the following hazard function analyses, we split the data in one year increments toaccommodate time-varying variables. As we know the exact timing of each job change andthe type of university the respondent worked for in the past, the private university dummy
is now time-varying taking the value 1 only for the time the respondent is working at a14This could be because the assistant professor rank is optional according to education law in Japan, andthus, the distinction between assistant and associate professor becomes blurred.

21

private university. We converted the number of career articles into a yearly publication
rate. This construction assumes that publication rate is constant over time. This is likely
erroneous, but since promotion is decided in the past, converting it into an annual rate isnecessary.
As described already, there is a considerable variation in the initial academic rank.
Thus, before presenting our hazard function analyses, it is useful to investigate whether
there are gender diﬀerences in the initial rank assignment. We ran a probit model thatpredicts the probability of whether an academic’s initial appointment is as a lecturer ora postdoc as opposed to an assistant professor or an associate professor. Results are in
Table 5. We do not observe a gender gap in the probability that one starts their career as
a lecturer or postdoc.

6.2

Hazard function estimations

Now, we present our hazard function analyses. To allow possible duration dependence, we
employ a Weibull baseline hazard. Table 6 presents the results. Column 1 includes ourbaseline results for the early career spell. The female coeﬃcient is negative and signiﬁcantat the 5% level. Column 2 presents our baseline results for the latter career spell. The
female coeﬃcient is negative and statistically signiﬁcant.
Column 3 and 4 include controls for annual publication rate and the publication quality.
Let us ﬁrst look at the results for the early career spell (Column 3). The publication rateand its squared term have statistically signiﬁcant coeﬃcients. Field top 20 article share
does not have a signiﬁcant eﬀect for this spell. The inclusion of these productivity controls
slightly reduced the female coeﬃcient, though the coeﬃcient remains negative and highly
signiﬁcant.
Now, let us add publication rate and publication quality to our analysis of the latter
career spell (Column 4). The publication rate and its squared term have statistically sig22

niﬁcant coeﬃcients. Field top 20 article share now has a statistically signiﬁcant coeﬃcient.
Most importantly, the female coeﬃcient is reduced substantially, and it is no longer statistically signiﬁcant. Thus, gender diﬀerences in publication seem to be the primary source ofthe gender promotion gap for the latter career spell.
We now proceed to examine if the gender promotion gap simply reﬂects gender differences in household responsibility. If this is the case, then the gender promotion gap
should be concentrated among married childless academics and among married academics
with children. To test this, we include Female×Married interaction as well the interactions
between Female and the number of young children. The number of young children is separated in two age categories (age 0 to 5 and age 6 to 18). In addition, children variables aretime-varying.15
Let us ﬁrst look at the results for the early career spell (Column 5). Inclusion of theseinteraction variables does not change the female coeﬃcient much, though the statistical signiﬁcance is reduced, and now it is only marginally signiﬁcant (pval=0.156). The coeﬃcientsfor all the interaction terms are negative, although none of them are statistically signiﬁcant.
Most importantly, based on the results in Column 5, the magnitude of the genderpromotion gaps within diﬀerent marriage×children cells are not diﬀerent for this spell.
Graphs 7A to 7C show the estimated survival probabilities for single childless academics,
married childless academics, and married academics with one child. We observe visible
gender promotion gaps for all the three groups. These gender promotion gaps are notappreciably diﬀerent among these three groups.
Now, we take a look at the latter career spell (Column 6). None of the coeﬃcients forthe interaction terms are statistically signiﬁcant. However, the estimated magnitudes of
15

The questionnaire asked the age of each child at the time of survey. Based on this information, we areable to compute the number of children that falls into a particular age category at a particular point in
time.

23

gender promotion gaps among diﬀerent marriage-children cells are meaningfully diﬀerent.
Graph 8A to 8C show the estimated survival probability for single childless academics,
married childless academics, and married academics with one child. Graph 8A shows asizable promotion ‘advantage’ for single childless women over single childless men. The
expected duration to promotion is 14.3 years for men while it is only 11.8 years for women.
Graph 8B and 8C shows, however, that this ‘reverse promotion gap’ completely vanishes
for married academics with or without children, and it seems that men and women arepromoted at a fairly equal rate.
As a robustness check, we re-estimated the hazard function for the latter career spell,
separately for single childless academics, married childless academics, and married academics with young children in unreported regressions.16 Although the estimated female
coeﬃcients were not statistically signiﬁcant, the pattern of the gender gap conﬁrms ouranalysis in the previous paragraph: there is a reverse promotion gap among single childless academics, but that the reverse promotion gap vanishes for married academics with orwithout a child.
Thus, we have only weak evidence of the negative eﬀects of household duties on female
promotion–the disappearance of the ‘reverse gender promotion gap’ for married academics
with and without a child in the latter career spell. However, as we discussed in the salary
analysis, a positive sorting into motherhood might be concealing such negative eﬀects.
The above analyses presented us with a puzzling pattern of the gender promotion gap:a gender promotion gap exists in the early career spell, but not in the latter career spell.
Furthermore, we even found a ‘reverse promotion gap’ in the latter career spell among single
childless academics. We note that the same pattern, including the existence of a reverse
16

In these regressions, single academics are deﬁned as those who were never married during the latter
career spell. Childless married academics were deﬁned as those who were married at least one period during
the spell, but never had a child during the period. Married academics with young children are deﬁned as
those who were married at least one year during the spell, and had at least one child aged between 0 to 18
during that spell.

24

promotion gap, was found in Takahashi and Takahashi (2014), who studied the genderpromotion gaps in economics departments in Japan. Given this, we are inclined to believethat such a pattern is not merely due to statistical noise.
Then, what could explain such a pattern? One possible explanation is that womenmight have higher academic labor market dropout rates as compared to men during theearly career spell, and only the most productive women survive into the later career spell.
Such a ‘survivor bias’ could lead to an underestimation of the gender promotion gap in thelatter career spell, and even make women appear to promote faster than men. Note againthat the early career positions are often ﬁxed term positions, and thus, a promotion is often
equivalent to receiving a permanent position elsewhere after the contract expires. Thus, agreater dropout rate for women could occur if there is gender discrimination in hiring into
a permanent academic position.
Let us examine if there are gender diﬀerences in academic labor market dropout rates.
For this purpose, we obtained the 2010 MEXT statistics of university faculty (Kyoin Kojin
Chosa), which contain information, although fairly basic, about the entire faculty in Japan,such as age, experience, the name of universities, departments they belong to, whether they
quit the university within one year prior to the survey, and the reason for the quit. Basedon this data set, we computed the annual academic labor market dropout rates in scienceand engineering ﬁelds.17
Figure 9A shows the overall annual academic labor market dropout rates; the male
dropout rate is 1.4 % while the female dropout rate is as much as 3.8%. Figure 9B shows
the breakdown by ranks. The dropout rates are higher for the lower academic positions
both for men and women, but women’s dropout rates are signiﬁcantly higher than those
17

Reasons for quit are classiﬁed as follows: (1) retirement, (2) mental sickness, (3) other sickness, (4)death, (5) move to a research institute, (6) move to a non-academic job, (7) move to another university,(8) entered a university as a student, (9) family reasons, and (10) other reasons. Academic labor market
dropouts are deﬁned as quits due to reasons (2), (3), (6), (8), (9), and (10).

25

of men for all the positions, and especially for the lower academic ranks. Note that these
are annual dropout rates, so over the course of several years, a considerable number ofwomen would drop out of the academic labor market. Given this, we cannot rule out thepossibility that gender diﬀerences in the academic labor market dropout rates are causing
the promotion patterns we observe.
Nevertheless, we admit that this is just one possible explanation. Another possible
explanation is employer learning. For example, employers might be more uncertain about
women’s productivity than men’s upon hiring, which might make women’s promotion slower
in their early career. However, as the employers learn the ability of women more precisely
over time, women’s speed of promotion would catch up with that of men’s, eliminating thegender promotion gap for the latter career spell.

7.

Discussion and Conclusion

Despite the growing interest in gender equality in Japanese academia, there have been very
few studies that analyzed the gender salary and promotion gaps. This study contributes
to the literature by providing detailed analyses of gender salary and promotion gaps inJapanese academia using original survey data.
We found that there is a gender salary gap of 6% after controlling for ranks and various
other characteristics. When quality and quantity of publication are additionally controlled,
this gender salary gap was almost unchanged, indicating that gender diﬀerences in publication productivity are not responsible for the gender salary gap.
The results for the promotion analyses were quite diﬀerent, however. The gender promotion gap for the latter career spell disappeared when quality and quantity of publication
were controlled for. Thus, the gender diﬀerences in publication productivity appear to be
the main source of the gender promotion gap for this spell. This result also indicates that26

publications aﬀect wage mainly through promotion. We note however that, for the early
career spell, a sizable gender promotion gap remained even after controlling for publications.
This raises the question of why gender promotion gap is present only in the early career
spell. We provided two possible explanations. One of these explanations suggests thatwomen’s greater academic labor market dropout rates in the early career spell, which might
be a result of discriminatory hiring into permanent positions, and the resulting ‘survivor
bias’ might be concealing a gender promotion gap in the latter career spell. If there is sucha survivor bias, we are also underestimating the gender salary gap. We admit however, that
other explanations can be possible, such as employer learning.
As for the analysis of the eﬀects of household duties on the gender salary gap, theestimated gap was not appreciably diﬀerent among diﬀerent marriage×children cells, thus
failing to show evidence that household duties are responsible for the gender salary gap.
We only found weak evidence of negative household duty eﬀects for promotion in the latter
career spell. One possible explanation for the lack of evidence is that there may be a positive
sorting into motherhood that is concealing the negative household duty eﬀects.
Finally, we found no evidence that gender diﬀerences in the value of outside job oﬀers
are responsible for the estimated gender salary gap, despite the fact that there are signiﬁcant
university-to-university job changes for both male and female academics. Perhaps, Japanese
academics are not using outside job oﬀers to negotiate salary.
There are a number of limitations in this study. The most important limitation is that
we failed to control for university ﬁxed eﬀects. Thus, we are not able to eliminate thepossibility that the estimated gender salary and promotion gaps reﬂect the situation where
women are disproportionately appointed in low-paying and slow-promoting universities.
The incompleteness in our publication variables should be acknowledged as well. Theselimitations could result in our overstating the gender salary and promotion gaps.
27

It is fair to say, however, that the results of this study are a reminder that genderequality in salary and promotion in Japanese academia cannot be assumed simply because of
the existence of rigid salary tables and the possible existence of a seniority based promotion.
While more studies are deﬁnitely needed to ﬁrmly establish the existence or the absence ofgender salary and promotion gaps, we believe that this study have substantially contributed
to the understanding of the state of gender inequality in Japanese academia.

28References
Becker, Gary, S., 1985, “Human Capital, Eﬀort, and the Sexual Division of Labor”, Journalof Labor Economics, 3(1), pp. S33-S58
Blackaby, David; Booth, Alison, L. and Frank, Jeﬀ, 2005, “Outside Oﬀers and the Gender
Pay Gap: Empirical Evidence from the UK Academic Labor Market”, Economic
Journal, 115(February), pp. F81-107
Blau, Francine D. and Kahn, Lawrence M., 2003, “Understanding International Diﬀerencesin the Gender Pay Gap”, NBER Working Paper 8200
Border, Ivy, E., 1993, “Professional Achievements and Gender Diﬀerences among Academic
Economists”, Economic Inquiry 31, pp. 116-127
Booth, Allison, Francesconi, M. and Frank, J., 2003, “A Sticky Floors Model of Promotion,
Pay and Gender”, European Economic Review, 47(2) pp. 295-322
Daly, Anne; Xin, Meng; Kawaguchi, Akira and Mumford, Karen, 2006, “The Gender Wage
Gap in Four Countries”, Economic Record, 82(257), pp. 165176
Ginther, Donna K. and Hayes, Kathy J., 2003, “Gender Diﬀerences in Salary and Promotion
for the Faculty in the Humanities 1977-95”, Journal of Human Resources 38(1), pp.
34-73
Ginther, Donna K., 2004, “Why Women Earn Less: Economic Explanations for the Gender
Salary Gap in Science” AWIS Magazine, 33(1), 610.
Ginther, Donna K. and Kahn, Shulamit, 2004, “Women in Economics: Moving up or Fallingoﬀ the Academic Career Ladder?”, Journal of Economic Perspectives 18(3), pp. 193214
Ginther, Donna K. and Kahn, Shulamit 2009, Does Science Promote Women? Evidence
from Academia 1973-2001 in Science and Engineering Careers in the United States,
Chicago, IL: University of Chicago Press for NBER
Goldin Claudia and Polachek Solomon, 1987, “Residual Diﬀerences by Sex: Perspectives
on the Gender Gap in Earnings” , American Economic Review, 77(2) Papers andProceedings, pp. 143-151
Goldin, Claudia, 1997, “Career and Family: College Women Look to the Past.” In Francine
Blau and Ronald Ehrenberg, eds. Gender and Family Issues in the Workplace. New
York: Russell Sage
Gordon, Nancy M.; Morton, Thomas E. and Braden, Ina C., 1974, “Faculty Salaries: Is
There Discrimination by Sex, Race, and Discipline?”, American Economic Review,
64(3), pp. 419-427
Hoﬀman, Emily P., 1976, “Faculty Salaries: Is There Discrimination by Sex, Race, andDiscipline? Additional Evidence” American Economic Review, 66(1), pp.196-98
Kahn, Shulamit, 1993, “Gender Diﬀerences in Academic Career Paths in Economists”,
American Economic Review 83(2), pp. 52-56
29

Kato, Takao; Kawaguchi, Daiji and Owan, Hideo, 2013, “Dynamics of the Gender Gap in
the Workplace: An econometric case study of a large Japanese ﬁrm”, RETI Discussion
Paper May 13-E-038
Katz, David, A., 1973, “Faculty Salaries, Promotions, and Productivity at a Large University”, American Economic Review, 63(3), pp. 469-477
Kelly, Kimberly, and Linda Grant, 2012, “Penalties and Premiums: The Impact of Gender,
Marriage, and Parenthood, on Faculty Salaries in STEM and non-STEM Fields”
Social Studies of Science, Vol. 42, pp. 872899
Korenman, Sanders D. and Neumark, David, 1992, “Marriage, Motherhood, and Wages”
Journal of Human Resources, pp. 233-255
Krapf, Matthias; Heinrich W. Ursprung; and Christian Zimmermann, 2014, “Parenthoodand Productivity of Highly Skilled Labor: Evidence from the Groves of Academe,”
Working Papers 2014-1, Federal Reserve Bank of St. Louis
Johnson, George E. and Staﬀord, Frank P., 1974, ”Lifetime Earnings in a Professional Labor
Market: Academic Economists”, Journal of Political Economy, 82(3), pp. 549-569
Long, Scott, J.; Allison, Paul, D. and McGinnis, Robert, 1993, “Rank Advancement in
Academic Careers: Sex Diﬀerences and the Eﬀects of Productivity”, American Sociological Review 58(5), pp. 703-722
Mason, Mary Ann; Nicholas H. Wolﬁnger, and Marc Goulden, 2013. “Do Babies Matter?
Gender and Family in the Ivory Tower”, New Brunswick: Rutgers Uniiversity Press
McDowell, John. M.; Singell Jr., Larry, D., and Jr. Ziliak, James, P., 2001, “Genderand Promotion in the Economics Profession”, Industrial and Labor Relations Review
54(2), pp. 224-244
OECD, 2006, “OECD Thematic Review of Tertiary Education: Country Background Report of Japan”, Higher Education Bureau, Ministry of Education, Culture, Sports
and Technology, Japan
Takahashi, Ana Maria and Shingo Takahashi, 2011, “Gender Salary Diﬀerences in Economics Departments in Japan.” Economics of Education Review, 30(6), December,
pp. 1306-1319
Takahashi, Ana Maria and Takahashi, Shingo, 2014, “Gender Promotion Diﬀerences in
Economics Departments in Japan: A Duration Analysis”, Discussion Paper 1429,
November, Kobe University
Ward, Melanie E., 2001, “Gender and Promotion in the Academic Profession”, Scottish
Journal of Political Economy 48(3), pp. 283-302
Waldfogel, Jane, 1998, “Understanding the “Family Gap” in Pay for Women with Children”,
The Journal of Economic Perspectives, Vol. 12, No. 1, Winter, pp. 137-156
Yamamoto, Kiyoshi, 2004 “Corporatization of National Universities in Japan: Revolution
for Governance or Rhetoric for Downsizing?”, Financial Accountability and Management, 20(2), May, 0267-4424, pp. 153-18130

Figure 1: Experience-salary proﬁles1200

B: Private Universities

400

400

600

salary in 10,000yen
600
800

salary in 10,000 yen
800
1000

1000

A: Public Universities

0

10

20
30
40
Academic experience
Male

50

0

Female

10

20
30
40
Academic experience
Male

50

FemaleFigure 2: Duration from the initial appointment to the full professor position.

0.00

0.25

0.50

0.75

1.00

Kaplan−Meier survival estimates

0

10
20
30
Duration from initial appointment to full professor
Male

31

Female40

Table 1: Summary statistics for salary regressions# Obs
Annual salary (10,000 yen)FemaleFull professor
Associate professor
Assistant professor
Lecturer(will be the excluded category)
Academic experience
Non academic experience
Private University
Department oﬀers PhD# Career articles
Field top 20 article share
Annual amount of grant(in 10,000 yen)
Educ=MA and below(will be excluded category)
Educ= PhD from Japan
Educ= PhD from abroad
Weight on researchfor promotion decision
Teach graduate classes#MA supervisees#PhD supervisees

Males
=1221

Females=296

859.5(239.6)0719.6(221.9)10.430(0.495)0.313(0.464)0.0385(0.192)0.219(0.414)16.30(10.94)3.368(7.003)0.274(0.446)0.906(0.292)60.070(64.792)
0.422(0.397)281.616(1531.654)
0.075(0.263)0.906(0.292)0.0197(0.139)53.11(22.68)0.744(0.436)
3.627(3.719)0.771(1.616)0.179(0.384)0.301(0.459)0.115(0.319)0.405(0.492)
11.85(9.674)2.567(5.305)0.389(0.488)0.861(0.346)32.128(35.543)
0.413(0.389)145.681(465.615)
0.088(0.284)0.892(0.311)0.0203(0.141)
48.05(22.55)0.568(0.496)
2.260(2.713)0.446(0.990)#Subjects taught/year=0#Subjects taught/year=1-3(will be excluded category)#Subjects taught/year=4-6#Subjects taught/year≥7#Sessions taught/week
Eligible for spousal allowance
Fixed term contract
Have a managerial position
Initial rank=Full professor
MarriedTotal#Kids#Type 1 moves#Type 2 moves#Type 3 moves#Type 4 moves#Type 5 moves
Class 1 cities
Class 2 cities
Class 3 cities
Class 4 cities
Class 5 cities
Smaller cities(will be the excluded category)
Department ﬁeld dummies

Males

Females

0.106(0.308)0.347(0.476)0.442(0.497)0.105(0.306)2.305(1.469)0.258(0.438)0.153(0.360)0.120(0.325)0.0590(0.236)0.843(0.364)1.314(1.097)0.0753(0.302)0.295(0.598)0.0852(0.328)0.0205(0.153)0.00573(0.0857)
0.0958(0.294)0.0663(0.249)0.138(0.345)0.184(0.388)0.130(0.337)0.516(0.500)Yes

0.199(0.400)0.375(0.485)0.294(0.456)0.132(0.339)2.330(1.737)0.0169(0.129)0.253(0.436)
0.0372(0.189)0.0270(0.162)0.669(0.471)0.649(0.908)0.0574(0.261)0.253(0.487)0.145(0.431)
0.0439(0.236)0.0135(0.116)0.145(0.353)0.0811(0.273)0.132(0.339)0.223(0.417)0.132(0.339)0.419(0.494)
Yes

Inside the brackets are standard deviations. Department ﬁeld dummies are: mechanical engineering, material engineering, bio-engineering, electronics, civil engineering, engineering in business ﬁeld, engineeringin other social science, physics and mathematics, chemistry, bioscience, astronomy, and ‘other’ ﬁeld.

32

Figure 3: Experience-research productivity proﬁle
B: Field Top 20 Share profile00

Field Top 20 Share.2.4

Number of career publications
50

100

.6

A: Career publication profile

0

10

20
30
40
Academic experience
Male

50

0

10

Female20
30
40
Academic Experience
Male

50

FemaleFigure 4: Experience-annual amount of grant proﬁle0

The amount of grant
200
400

600

The amount of grant (in 10,000 yen)

0

10

20
30
Academic experience
Male

33

Female4050

Figure 5: Household work and hours worked
B: Weekly hours worked profile

0

40

45

.5

Household work
1

Hours worked per week
50
55

1.5

60

2

65

A: Household work

0

10

20
30
40
Academic experience
Males

50

0

Females

10

20
30
40
Academic experience
Males

34

Females50

Table 2: Determinants of job mobility (Tobit regressions)
Dept var=#Type 1 moves(Salary increase)#Type 2 moves(Other positive moves)#Type 3 moves(Contract expiration
only)

Female-0.335(0.328)-0.115(0.328)0.045(0.045)-0.001(0.001)-0.042*(0.025)0.554(0.533)-10.574(0.000)-0.034(0.031)0.207(0.338)-4.057***(0.793)
Yes
1,517

0.128
(0.159)0.156(0.175)0.158***(0.024)-0.004***(0.001)-0.057***(0.013)0.507**(0.254)0.654(0.468)-0.006(0.014)0.409**(0.169)-2.883***(0.380)
Yes
1,517

0.212
(0.252)0.012(0.279)-0.002(0.041)-0.001(0.001)-0.013(0.021)0.317(0.447)0.709(0.821)-0.155***(0.042)0.669**(0.301)-2.711***(0.633)Yes
1,517-0.013(0.0120)0.029(0.038)0.013(0.0169)

MarriedAcademic experience(Academic experience)2
Non academic experience
Have Phd from Japan
Have PhD from abroad
Annual publication rate
Field top 20 article shareCons
Dept ﬁeld dummies# Obs
Marginal eﬀect of Female

Inside brackets are robust standard errors. *,**,***= signiﬁcant at 10%, 5%, and 1% levels.

35

Table 3: Salary regression

FemaleModel 1Model 2Model 3
Pub univ

Model 4
Priv univ

Model 5Model 6

Model 7-0.087***(0.012)0.025***(0.002)-0.000***(0.000)0.007***(0.001)-0.064***(0.011)0.304***(0.020)0.179***(0.017)0.084***(0.022)0.018***(0.002)-0.000***(0.000)0.004***(0.001)-0.067***(0.011)0.294***(0.019)0.175***(0.016)
0.067***(0.023)0.018***(0.002)-0.000***(0.000)0.006***(0.001)-0.064***(0.022)0.364***(0.050)0.214***(0.046)0.133***(0.046)0.017***(0.003)-0.000***(0.000)0.000(0.002)0.055***(0.013)0.038***(0.011)0.047***(0.011)0.010(0.027)-0.061***(0.011)0.282***(0.020)0.170***(0.017)0.081***(0.022)0.017***(0.002)-0.000***(0.000)0.004***(0.001)0.072***(0.016)-0.013***(0.004)0.014(0.011)-0.002(0.002)0.037***(0.011)-0.051**(0.020)0.279***(0.020)0.169***(0.017)0.079***(0.022)0.017***(0.002)-0.000***(0.000)0.003***(0.001)0.071***(0.016)-0.013***(0.004)0.014(0.011)-0.002(0.002)0.031**(0.013)-0.006(0.026)0.009**(0.004)-0.005(0.012)-0.059***(0.012)0.277***(0.021)0.167***(0.017)0.078***(0.022)0.017***(0.002)-0.000***(0.000)0.004***(0.001)0.070***(0.016)-0.013***(0.004)0.016(0.011)-0.002(0.002)0.030**(0.012)

Full Professor
Associate Professor
Assistant Professor
Academic experience
Academic experience2
Non academic experience#Career articles/100(#Career articles/100)2
Field top 20 article share
Annual grant amount/1000
MarriedMarried×Female
Total#Kids
Total#Kids×Female#Type 1 moves(salary increase)#Type 1 moves×Female

0.007*(0.004)0.016(0.012)0.021(0.032)-0.001(0.006)-0.003(0.018)-0.037***(0.014)-0.009(0.032)-0.015(0.027)0.021(0.044)0.008(0.052)0.066(0.076)#Type 2 moves(other positive reasons)#Type 2 moves×Female#Type 3 moves(contract expiration only)#Type 3 moves×Female#Type 4 moves(family reasons only)#Type 4 moves×Female#Type 5 moves(other reasons)#Type 5 moves×Female

Table continues to next page36

Table 3 continued

Private university
Department oﬀers PhD
Have PhD from Japan
Have PhD from abroad(Excluded=MA and below)
Weight on researchfor promotion (%)
Fixed term position
Have a managerial position
Teach graduate classes#MA supervisees#PhD supervisees#Subjects taught/year=0#Subjects taught/year=4-6#Subjects taught/year≥7(Excluded=#subject=1-3)#Sessions taught/week
Eligible for spousal allowance
Initial rank =Full ProfClass 1 cities
Class 2 cities
Class 3 cities
Class 4 cities
Class 5 cities(Excluded=smaller cities)
Cons
Dept ﬁeld dummies#Obs
R squared

Model 1Model 20.155***(0.012)0.049***(0.016)
0.046***(0.018)0.031(0.030)0.0003(0.0002)-0.066***(0.014)0.088***(0.013)0.095***(0.013)0.005***(0.001)0.010***(0.004)-0.052***(0.015)0.021**(0.010)0.002(0.017)0.008**(0.004)-0.027***(0.010)0.157***(0.035)0.103***(0.015)0.110***(0.017)
0.096***(0.012)0.074***(0.012)0.047***(0.013)6.027***(0.032)
Yes
1,517
0.753

0.144***(0.011)
0.042***(0.015)0.019(0.016)-0.009(0.026)0.001***(0.000)-0.054***(0.014)0.047***(0.012)0.016(0.014)0.004***(0.001)0.005*(0.003)-0.030**(0.014)0.003(0.010)-0.019(0.016)0.002(0.003)-0.019**(0.010)0.102***(0.031)
0.098***(0.014)0.103***(0.016)
0.093***(0.011)0.078***(0.011)0.040***(0.011)6.070***(0.030)
Yes
1,517
0.795

Model 30.026
(0.016)0.027(0.018)0.005(0.027)0.000*(0.000)-0.033**(0.013)0.047***(0.013)-0.001(0.013)-0.000(0.001)0.007**(0.003)-0.043***(0.015)0.009(0.009)0.008(0.015)0.003(0.004)-0.023***(0.009)0.046(0.032)0.122***(0.016)
0.094***(0.019)0.078***(0.011)0.084***(0.010)0.021*(0.011)
6.112***(0.031)
Yes
1,068
0.828

Model 4Model 5Model 6

Model 70.046*(0.026)-0.008(0.030)-0.083(0.062)0.001**(0.000)-0.095***(0.034)0.039(0.025)0.018(0.032)0.012***(0.002)0.010(0.012)0.001(0.047)-0.014(0.026)-0.050(0.031)-0.002(0.006)-0.004(0.025)0.157***(0.049)0.064**(0.027)0.102***(0.031)0.104***(0.028)0.067**(0.034)0.081**(0.032)6.207***(0.061)
Yes
449
0.727

0.152***(0.011)
0.041***(0.015)0.014(0.016)-0.017(0.026)0.001***(0.000)-0.054***(0.014)0.046***(0.012)0.013(0.014)0.003**(0.001)0.004*(0.002)-0.028**(0.014)0.007(0.010)-0.016(0.016)0.003(0.003)-0.019**(0.010)0.105***(0.031)
0.097***(0.014)0.099***(0.016)0.090***(0.011)0.073***(0.011)0.034***(0.011)
6.061***(0.030)
Yes
1,517
0.799

0.151***(0.011)
0.042***(0.015)0.015(0.016)-0.014(0.026)0.001***(0.000)-0.055***(0.014)0.045***(0.012)0.012(0.014)0.003**(0.001)0.005*(0.002)-0.029**(0.014)0.007(0.010)-0.015(0.016)0.003(0.003)-0.022**(0.010)0.105***(0.031)
0.099***(0.014)0.100***(0.016)
0.091***(0.011)0.073***(0.011)0.034***(0.011)
6.058***(0.030)
Yes
1,517
0.799

0.152***(0.011)0.040***(0.015)0.016(0.016)-0.010(0.026)0.001***(0.000)-0.053***(0.014)0.046***(0.012)0.013(0.014)0.003**(0.001)0.004*(0.002)-0.028**(0.014)0.006(0.010)-0.018(0.016)0.004(0.003)-0.021**(0.010)0.104***(0.031)
0.098***(0.014)0.101***(0.016)0.088***(0.011)0.074***(0.011)0.032***(0.011)
6.062***(0.030)
Yes
1,517
0.801

Inside brackets are robust standard errors. *,**,***= signiﬁcant at 10%, 5%, and 1% levels.

37

Table 4: Promotion patterns
Rank at initial hiringin academia

# Obs

%

Included in promotion
regressions

Full professor
Associate professor
Assistant professor
Lecturer
Postdoc
Promotion history missing
Total

80
140
122
1063
99
13
1517

5.3%
9.2%
8.0%
70.1%
6.5%
0.9%
100%

NO
YES
YES
YES
YES
NO

Next rank after the early career positions (i.e., lecturer or postdoc)
Associate professor
Assistant professor
Still a lecturer at the time of survey
Total

390
381
391
1,162

33.6%
32.8%
33.7%
100%

Inside brackets are robust standard errors. *,**,***= signiﬁcant at 10%, 5%, and 1% levels.

Figure 6: Kaplan Meier survival curves

0.25

0.50

0.75

1.00

B: The latter career spell

0.00

0.00

0.25

0.50

0.75

1.00

A: The early career spell

0

10

20
analysis time
Male

30

40

0

Female10

20
analysis time

Male

30
Female

The early career spell is the duration of lecturer and postdoc. The latter career spell is the duration fromthe end of the early career spell to full professor.

38

Table 5: The rank at initial hiring = lecturer or postdoc (probit regression)
Variables

Coeﬃcients

Female0.017(0.107)-0.117***(0.009)-0.648**(0.309)-0.222(0.171)1.278***(0.187)Yes
1,424

Non academic career
Educ=Phd from abroad
Educ=Phd from JapanCons
Department specialization dummies#Obs
Marginal eﬀect of female

0.004(0.0258)

Inside brackets are robust standard errors. *,**,***= signiﬁcant at 10%, 5%, and 1% levels.

39

Table 6: Hazard function analysis of promotions

Baseline

FemaleNon academic experience
Educ=PhD from Japan
Educ=PhD from abroad(Excluded=MA and below)
Private university

Productivity

Early
career(1)

Lattercareer(2)

Early
career(3)

Lattercareer(4)

Early
career(5)

Lattercareer(6)-0.475***(0.145)0.006(0.015)1.281***(0.178)2.290***(0.344)0.645***(0.109)-0.351*(0.189)0.056***(0.012)0.176(0.200)0.005(0.308)0.390***(0.103)0.213**(0.096)0.438*(0.224)-0.373***(0.136)0.002(0.016)
1.108***(0.174)2.123***(0.339)0.739***(0.109)0.177***(0.039)-0.008***(0.003)0.010(0.119)0.148(0.094)-0.172(0.191)0.047***(0.011)-0.049(0.176)-0.164(0.274)0.622***(0.103)0.194***(0.042)-0.006**(0.003)0.482***(0.136)
0.396**(0.194)-4.888***(0.243)0.431***(0.030)1118
767-7.042***(0.364)0.828***(0.036)978479-5.300***(0.250)0.474***(0.030)1118
767-7.932***(0.373)0.898***(0.036)
978479-0.300(0.210)0.002(0.016)
1.112***(0.175)2.103***(0.345)0.748***(0.109)0.174***(0.038)-0.007***(0.002)0.018(0.120)0.104(0.113)0.063(0.285)0.050(0.055)-0.199(0.191)0.076(0.061)-0.168(0.194)-5.290***(0.248)0.464***(0.031)
1118
767

0.435
(0.330)0.045***(0.011)
0.007(0.173)-0.098(0.276)0.625***(0.102)0.183***(0.043)-0.006**(0.003)0.493***(0.138)0.454*(0.249)-0.590(0.464)-0.261**(0.103)-0.658(0.528)0.171***(0.050)0.092(0.236)-7.813***(0.399)0.855***(0.038)
978479

Annual publication rate
Annual publication rate2
Field top 20 article share
MarriedMarried×Female#Kids age 0-5#Kids age 0-5×Female#Kids age 6-18
#Kids age 6-18×FemaleCons
Log(α)#Subjects#FailuresHousehold responsibility

Inside brackets are cluster robust standard errors. *,**,***= signiﬁcant at 10%, 5%, and 1% levels.

40

Figure 7: Early career spell: Estimated survival probabilities

Survival Prob
0 .2 .4 .6 .8 1

B: Married childless academics

Survival Prob
0 .2 .4 .6 .8 1

A: Single childless academics

0

10

20
30
experience
Males

40

50

0

Females

Survival Prob
0 .2 .4 .6 .8 1

10

20
30
experience
Males

40

20
30
experience
Males

C: Married academics with 1 child

0

10

50

Females41

40
Females50

Figure 8: Latter career spell: Estimated survival probabilities

Survival Prob
0 .2 .4 .6 .8 1

B: Married childless academics

Survival Prob
0 .2 .4 .6 .8 1

A: Single childless academics

0

10

20
30
experience
Males

40

50

0

Females

Survival Prob
0 .2 .4 .6 .8 1

10

20
30
experience
Males

40

20
30
experience
Males

C: Married academics with 1 child

0

10

50

Females42

40
Females50

Figure 9: Academic labor market dropout rates in science and engineering ﬁelds in 2010

A: Annual academic labor market
dropout rates in science andengineering in 2010:
Overall
0.04
0.035
0.03
0.025
0.02
0.015
0.01
0.005
0
Males

Females

B: Annual academic labor market
dropout rates in science andengineering in 2010:
By rank
0.07
Dropout rates

0.06
0.05
0.04
0.03

Males

0.02

Females

0.01
0Lecturers(Jokyo or
Joshu)

Assistantprofessors

Associate
professors

Fullprofessors

Statistics are based on 2010 MEXT Kyouin Kojin Chousa.

43Appendix A: Determinants of household responsibility
and weekly hours worked.
Table A.1 Columns 1 and 2 show Household work regression results.18 Column 1 is thesimplest model that does not control for gender diﬀerentiated eﬀects of marriage and young
children. Household work is greater for female by 0.8. Column 2 includes the interaction
between Female and Married, as well as the interactions between Female and young children variables. Gender diﬀerences in household responsibility are present only among: (1)married childless academics, and to a greater extent (2) married academics with young
children. Thus, it is the presence of young children and the husband that cause gender
diﬀerences in household responsibility.
Table A.1 Columns 3 and 4 show Weekly hours worked regression results.19 Column 3
does not control for gender diﬀerentiated eﬀects of marriage and young children. Women
work 1.48 hours less than men per week, but this diﬀerence is statistically insigniﬁcant.
Column 4 includes the interaction between Female and Married, as well as the interactions
between Female and young children variables. Gender diﬀerence in weekly hours worked
are present only among married academics with children aged 0 to 5. We observe nogender diﬀerences in weekly hours worked among single childless academics, among married
childless academics, and among married academics with children aged 6 years and older.
This results indicate that it is younger children that cause gender diﬀerences in weekly
hours worked. Combining all the results, it is reasonable to conclude that it is marriage
and young children that cause gender diﬀerences in household responsibility.

18

The number of observations is smaller than that for the salary regression due to missing Household
Work information.
19
The number of observations is smaller than that for the salary regression due to missing Weekly hours
worked information.

44

Table A.1: Determinants of Household responsibility and Weekly hours worked.
Dept var=

FemaleMarriedHousehold work
(1)

(2)

(3)

(4)

0.835***(0.037)-0.061(0.046)-0.032(0.057)-0.415***(0.047)0.831***(0.073)0.151***(0.031)0.570***(0.068)0.037*(0.022)0.696***(0.050)0.031**(0.016)0.000(0.002)-0.002(0.002)-0.012(0.057)0.013(0.047)-0.007(0.061)-0.032(0.042)-0.128***(0.047)-0.024(0.094)-0.023(0.045)-0.043(0.030)-0.167***(0.046)0.013(0.010)0.000(0.001)-0.136***(0.031)-0.011(0.038)0.009**(0.004)0.002(0.008)0.006(0.034)0.986***(0.072)
1,495
0.534

-1.475(1.087)-2.249*(1.358)1.083(1.951)-1.514(1.616)-1.026(2.518)1.791*(1.080)-8.504***(2.354)1.098(0.764)-1.488(1.734)-0.182(0.532)-0.144**(0.059)-0.010(0.075)-1.780(1.984)-2.355(1.640)-1.933(2.086)0.054(1.459)0.709(1.609)-2.120(3.283)-1.547(1.543)1.054(1.048)2.270(1.588)0.368(0.327)-0.011(0.018)0.246(1.056)-0.888(1.334)0.443***(0.125)0.986***(0.286)-0.382(1.172)62.214***(2.475)
1,488
0.054

Married×Female#Kids age 0-50.241***(0.035)#Kids age 0-5×Female#Kids age 6-18

0.135***(0.025)#Kids age 6-18×Female#People in the household
including respondent
Academic experience
Non academic experience
Full professor
Associate professor
Assistant professor
Have a managerial position
Educ=Phd from Japan
Educ=Phd from abroad#Subject taught/year =0#Subject taught/year =4-6
#Subject taught /year≥7(Excluded=#subject=3)#Sessions taught per week
Weight on researchfor promotion (%)
Eligible for spousal allowance
Teach graduate classes# MA supervisees#PhD supervisees
Fixed term position
Constant#Obs
R-squared

Weekly hours worked-0.000(0.018)0.001(0.002)-0.002(0.003)-0.074(0.067)-0.007(0.056)0.012(0.071)-0.032(0.050)-0.131**(0.055)0.005(0.111)
0.021(0.053)-0.035(0.036)-0.129**(0.055)0.015(0.011)0.000(0.001)-0.205***(0.036)-0.009(0.045)0.009**(0.004)0.003(0.010)-0.009(0.040)0.716***(0.084)1,495
0.352 45

0.602
(1.034)0.850(0.741)-0.125(0.530)-0.147**(0.059)-0.008(0.075)-1.838(1.986)-2.305(1.644)-1.887(2.092)0.041(1.465)0.689(1.615)-2.048(3.295)-1.706(1.549)0.973(1.052)2.021(1.594)0.391(0.328)-0.012(0.018)0.431(1.054)-0.748(1.338)0.437***(0.125)0.969***(0.288)-0.389(1.177)63.073***(2.445)
1,488
0.043

Inside brackets are robust standard errors. *,**,***= signiﬁcant at 10%, 5%, and 1% levels.

Appendix B: Timing of the birth of the ﬁrst child andsalary.
If more productive women self-select into motherhood, there should be a productivity difference in salary between women who gave birth at an early stage of their career and women
who gave birth at a late stage of their career. Figure B.1 shows the histogram of the timingof birth relative to the start of one’s career. Time zero indicates the start of one’s career.
Minus one indicates one year prior to the start of one’s career. For both male and female
academics, the histograms appear to peak at three years of academic experience, then decline. Child birth before the start of academic career is much less common for both maleand female academics.
We divided academics with at least one child into: (A) those who had their ﬁrst childwithin one year prior to the beginning of their academic career; (B) those who had theirﬁrst child within three years of academic career; and (C) those who had their ﬁrst childafter three years of academic career. Having a child one year prior to the academic career(Group A) could indicate that they might have successfully negotiated and delayed thestart of the job after receiving a job oﬀer. This may indicate high productivity. Having theﬁrst child in the ﬁrst three years of career would also indicate high productivity (Group B).
Group C is the late child-bearer group. In addition, we added a dummy for those who had
their ﬁrst child more than one year prior to the beginning of their career. They are likely
to be the ones who initially worked in the non-academic sector. Thus, we do not include
them in the ‘early child-bearer’ groups.
We added dummy variables for these categories, as well as the interactions between
Female and these dummies to the salary regression. Table B.1 shows the regression results.
Note that the excluded category is those who do not have a child. However, what we areinterested to see is if the salary for early child-bearers is higher than that of late child46

bearers. In the case of men in Group A, the wage premium is computed as the coeﬃcientfor the Group A dummy minus the coeﬃcient for the Group C dummy. For women, theinteraction terms are taken into account.
Table B.2 shows the early child-bearers wage premiums. For women, Group A has a
14.8% higher salary than Group C, and Group B has a 5.2% higher salary than Group
C. Wage premium for having a child early is consistent with the hypothesis that there isa positive sorting into motherhood. Having said so, the large salary premium for having
children early would in part reﬂect the university diﬀerences–more productive women are
hired by better paying universities, and at the same time, are likely to give birth at an earlystage of their career. In contrast, we observe no such wage premium for men.

47

Figure B.1: The timing of birth relative to the start of the academic career

0

2

Percent
4
6

8

10

Males

−40

−20
0
Academic experience at the birth of the first child

20

0Percent
5

10Females

−30

−20

−10
0
Acacemic experience at the birth of the first child

48

10

20

Table B.1: Salary and the timing of the birth of the ﬁrst child: Dept var=Log(Annual
salary)Female-0.051**(0.020)0.272***(0.020)0.168***(0.016)0.081***(0.022)0.017***(0.002)-0.000***(0.000)0.002*(0.001)
0.069***(0.016)-0.013***(0.004)0.015(0.011)-0.002(0.002)0.034**(0.014)-0.020(0.028)0.008(0.006)-0.053***(0.020)

Full Professor
Associate Professor
Assitant Professor
Academic experience
Academic experience2
Non academic experience#Career publication/100(#Career publication/100)2
Field top 20 article share
Annual grant amount(in 10 million yen)
MarriedFemale×Married
Total#Kids
Total#Kids×Female
Timing of ﬁrst birth relative tothe beginning of the academic career
Group A: Within 1 year prior to
the beginning
Above×Female
Group B: Within 3 years ofacademic career
Above×Female
Group C: More than 3 years ofacademic career
Above×Female
More than 1 year prior the beginning
Above×Female

Fixed term position
Have a managerial position
Private university
Department oﬀers PhD
Educ=PhD from Japan
Educ=PhD from Abroad
Weight on researchfor promotion (%)
Teach graduate classes# MA supervisees#PhD supervisees#Subject taught/year =0#Subject taught/year =4-6
#Subject taught /year≥7(Excluded=#subject=3)#Sessions taught per week
Eligible for spousal allowance
Initial rank =Full Prof

-0.032(0.028)0.262***(0.091)-0.015(0.019)0.149***(0.044)-0.013(0.016)
0.095**(0.041)
0.056***(0.021)
0.029(0.061)

Class 1 cities
Class 2 cities
Class 3 cities
Class 4 cities
Class 4 cities
Cons
#Obs
R squared

Inside brackets are robust standard errors. *,**,***= signiﬁcant at 10%, 5%, and 1% levels.

49-0.054***(0.013)0.044***(0.012)0.149***(0.011)
0.042***(0.014)0.014(0.016)-0.013(0.026)0.000***(0.000)0.011(0.014)0.003**(0.001)0.004*(0.002)-0.029**(0.014)0.007(0.009)-0.017(0.016)0.004(0.003)-0.022**(0.010)0.100***(0.031)
0.097***(0.014)0.098***(0.016)0.089***(0.011)0.074***(0.011)0.034***(0.011)
6.062***(0.030)
1,517
0.804

Table B.2: Salary premium for having a child early: Comparison group= Group C (academics who had their ﬁrst child after 3 years of academic experience)
Timing of the birth of the ﬁrst childrelative to the beginning of academic career

Males

Females

Group A: Within 1 year prior to the beginningGroup B: Within 3 years of academic career-0.019-0.0020.148**
0.052**

Inside brackets are robust standard errors. *,**,***= signiﬁcant at 10%, 5%, and 1% levels.

50