Gender Promotion Differences in Economics
Departments in Japan: A Duration Analysis

Ana Maria Takahashi
Shingo Takahashi
November 2014
Discussion Paper No.1429

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANGender Promotion Diﬀerences in Economics
Departments in Japan: A Duration Analysis
Abstract
Using a unique data set from our survey of academic economists in Japan, we
present the ﬁrst detailed study of gender promotion gaps in Japanese academia. The
length of time from initial appointment to promotion to associate professor is greater
for women than men, largely due to women spending more time as lecturer, the lowest
academic rank. The gender gaps in promotions from associate professor to full professor
are more complex. Childless women are promoted faster than childless men. However,since the burden of parenting disproportionately falls on women, this ‘reverse’ gender
gap disappears after a ﬁrst child is born, and women’s time to promotion becomessigniﬁcantly longer than men’s if they have a second child.

Ana Maria Takahashi: Assistant Professor, Graduate School of Economics, Kobe
University, Rokkodai cho 1-1, Nada-ku, Kobe, 6578501, Japan E-mail:takahashi a@econ.kobe-u.ac.jp
Shingo Takahashi: Associate Professor, International University of Japan, Graduate Schoolof International Management, 777 Kokusai-cho, Minami Uonuma-shi, Niigata 949-7277
Japan. Email: staka@iuj.ac.jp

Revised, November 14, 2014

1

Introduction

Studies on gender promotion diﬀerences in academic labor markets reports have found substantial gender promotion diﬀerences in universities in both the US and UK. McDowell etal. (2001) used a sample of economists from the American Economic Association (AEA)
directory and found that female academics have an approximately 12% lower probability
than males of being promoted to full professor after controlling for various characteristics
including publication quality. Ginther and Kahn (2004) also used a sample from the AEA directory and found that the probability of receiving tenure is 13.5% lower for women. Gintherand Hayes (2003), using a sample from the US Survey of Doctorate Recipients (SDR) in thehumanities, showed that being female decreases the probability of achieving tenure by 6.8%.
Ward (2001), using a sample of academics from the UK, showed that the probability that1

a male academic becomes a full professor is 10% higher than a comparable female, after
controlling for numerous personal, job, and human capital characteristics.1
In contrast to the abundance of literature in the US and the UK, there have been few
empirical studies about the gender promotion gap within Japanese universities despite agrowing public interest in gender equality in Japan. In 1999, the Japanese government
enacted the Basic Law for Gender Equal Society. Consequently, in 2000, the Associationof National Universities set out an Action Plan stipulating that each national university
should increase the proportion of female academics to 20% by 2010. In 2008, the Ministryof Education, Sports, Science and Technology (MEXT) announced that selected universities
would receive 6 million yen for each female academic they hire.2 Despite such interestin achieving gender equality in academia, little is known about gender diﬀerences within
Japanese academia. A study conducted by the EPMEWSE3 (2008, 51-52) showed thatwomen on average have lower ranking posts than men after controlling for age. However,the study did not control for any other diﬀerences between male and female academics.
Therefore, a detailed study of gender promotion diﬀerences is called for.
We conducted the ﬁrst detailed study of gender promotion diﬀerences in Japanese
academia by using data that we collected via a mail survey administered in 2008. Ourdata set contains complete information on the year of each promotion, the exact timing of
job mobility, and the types of universities where each respondent previously worked. In
addition, data have personal information such as when respondents married and the ages of
their children. Furthermore, our data set contains detailed information on the publication
record of each academic. Thus, we are able to conduct a duration analysis of promotion while
1

Earlier studies include Kahn (1993) and Broder (1993). Kahn (1993) used an SDR sample in the ﬁeldsof economics and management, and found that women’s ‘hazard’ of promotion is lower than that of men.
Broder (1993) estimated a simultaneous equation model to show the determinants of rank attainment for asample of 362 male and 30 female US academic economists. She obtained a lower predicted rank for females,
although the result was not statistically signiﬁcant.
2Asahi News, October 5, 2008.
3
Japan Inter-Society Liaison Association Committee for Promoting Equal Participation of Men and
Women in Science and Engineering (annex.jsap.or.jp/renrakukai/2007enquete/h19enquete report v2.pdf).

2

controlling for important time-varying covariates such as the number of young children andmarital status at each point in time during the interval prior to promotion.
The academic labor market in Japan is of interest to researchers for the following reasons.
According to our conversations with several academics, there seems to be some belief that
promotions are granted almost automatically based on age, experience, and education. If
promotions are decided deterministically in line with this belief, then there should be little
room for gender diﬀerences. However, no empirical study to date has tested whether thiscommon wisdom is in fact true. Therefore, whether or not there are gender promotion
diﬀerences in Japanese universities is still an open empirical question.
Second, there are important institutional diﬀerences between Japanese and US universities. Unlike the US, in Japan the ‘up or out’ tenure-track system is almost non-existent,
and the majority of employment contracts are on an unlimited term basis (that is, lifetime
employment). Consequently, we expect to see a very diﬀerent picture of gender promotion
diﬀerences between Japanese and US universities.
The academic labor market is particularly well-suited for the study of gender promotion
diﬀerences due to the presence of well-deﬁned job ranks that are common across universities. Many past empirical studies that used cross-industry samples typically did not havewell-deﬁned ranks that are homogeneous across observations. Consequently, these studies
used proxies for job ranks. For example, Winter-Ebmer and Zweimuller (1997) used skill
requirements as proxies for job ranks. The use of such proxies always leaves the questionof how comparable the job ranks are across observations. Due to well-deﬁned job ranks inacademia, we are able to avoid such problems.
The remainder of the paper is organized as follows: Section 2 presents an overview ofacademic careers in Japan. Section 3 explains our data source, and Section 4 describes theconstruction of each variable. Section 5 presents summary statistics and provides an initial3

look at the data. Section 6 presents our main results. Section 7 concludes with discussionsof the results.

2

Background

There are two types of universities in Japan: public (national, prefectural, and city) andprivate. National universities are funded by the central government, and include prestigious universities such as the University of Tokyo and Kyoto University. Prefectural andcity universities are established by local governments and funded by them and the central
government. Private universities are established by private entities and are largely selfsupporting.
In 2004, the National University Corporatization Law was passed, and it took eﬀect inApril 2006. This law brought signiﬁcant change 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 salary and promotion determination.4 For example, before corporatization, the experience and age 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 1997.5 Lifetime employment requirement no longer applied to faculty at national universities after ‘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
4

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.
5

4

systems, such as the ‘up-or-out’ tenure track system, that could increase the competitivenessof universities. In fact, in 2006, MEXT began to encourage public and private universities toadopt this system by providing ﬁnancial support for introducing and maintaining this system.
However, this tenure system has not spread widely yet, at least in economics departments.
According to the MEXT website6 , in 2014 only three economics departments received thisgrant.7
Therefore, it is not clear how much promotion practices have changed since 2006. However, our analysis must take into account the possibility that the promotion system changed
drastically. For this reasons, our empirical analyses will provide estimation results (1) that
restrict the sample up to 2005, and (2) that use the entire sample up to 2008.
There are four academic ranks in Japan. They are, from the highest to the lowest rank,
full professor (kyouju), associate professor (jun-kyouju), assistant professor (koushi ), and lecturer (joshu or jokyo). Academics typically start their careers either as lecturers or assistantprofessors, then move up the ladder to full professor. However, it is not uncommon for people
to start their careers as associate professors. Some faculty start their academic careers as
full professors after a long non-academic career, often in various ministries. Universities hirethem for the expertise they acquired in their respective ﬁelds. Those who are hired directly
into academia as full professors are not included in our duration analyses.
The lecturer position requires some explanation. Prior to 2007, a lecturer was called ajoshu, the literal translation of which is an ‘assistant’. Despite the name, many joshu conducted research independently, and often taught courses independently. According to Kato
(1996), who investigated the role of joshu using a department level survey, 67 percent of
the surveyed departments in social science ﬁelds answered that joshu were hired to conduct
6

www.jst.go.jp/tenure
Departments can introduce a tenure track system without receiving MEXT ﬁnancial support. Certainly,
more than three economics departments would have introduced tenure track system. However, the paucity
of universities accessing MEXT support indicates that the tenure track system is not wide-spread yet.
75

their own research while at the same time serving as teaching and research assistants. In
addition, Kato showed that 19 percent of public universities and 2 percent of private universities answered that joshu were hired to independently teach courses. These results alsomean, however, that 33 percent of departments hired joshu solely as teaching and research
assistants. In fact, research assistant joshu were often doctoral students hired by their own
universities to work part-time.8
In April 2007, MEXT modiﬁed university regulations to separate joshu who independently conduct research and teach from those who are strictly assistants. The former are
now called jokyo, and the latter continued to be called joshu. In our data, nobody was joshu
at the time of survey.
Ideally we should treat only the ‘independent research’ joshu post as the beginning of
one’s academic career. However, our analyses use retrospective information of respondents’
past career, and prior to 2007, we are not able to distinguish ‘independent researcher’ joshu
from ‘research assistants’ joshu. Thus, we have decided to simply treat all joshu as equal andat the beginning of their careers, and we simply refer to both joshu and jokyo as ‘lecturers’.
Kato’s (1996) results that a majority of joshu was hired as independent researchers justiﬁes
our decision. Nonetheless, as a robustness check, we will provide an analysis that discards
the duration of being a lecturer.
Promotion decisions within Japanese universities are made at the department level,
usually by faculty personnel committees. In our survey, we asked who has the greatest
inﬂuence on promotion decisions at the respondent’s university. 66% of the respondents said
it was the faculty personnel committee, 10.5% said that it was the department chair, 6.4%
answered individual faculty members, 4.16% said it was the president of the university, and8

According to a survey conducted by Waseda University in 2007, 53 percent of joshu at Waseda University
oﬃcially worked less than 25 hours per week (Oﬃce of Promotion for Gender Equality, Waseda University,2007). Waseda University, a prestigious private university, hires only its own graduate students to work as
joshu.

6

1.9% said that it was the chairman of the board of trustees.
To our knowledge, there are no rigorous empirical studies about how promotion decisions
are made in Japanese academia. Thus, let us describe what we know from our conversations
with Japanese academics, and from our own experience. For typical permanent positions,
who nominates the candidates for promotion? We know that in some universities, candidates nominate themselves, while in other universities, their colleagues or the chair of thedepartment nominates candidates. In one author’s university, once a nomination is made, afaculty personnel committee writes a recommendation letter to the entire faculty, who theneither approve or reject the promotion at the faculty meeting. There is no requirement tosubmit outside recommendation letters for promotion. However, the author is not aware ofany promotions being denied at his university, and we assume that promotion denials would
also be rare in other universities once the candidate is nominated.
The picture is quite diﬀerent for the universities that adopted up-or-out tenure systems.
Candidates on the tenure track are assessed based on their publications and on outside
recommendation letters. They can be denied promotion, and we heard that there wereactual cases of tenure denials. If candidates are denied promotion, they will have to ﬁndjobs in other universities.
Although few universities have adopted a tenure track system, the use of ﬁxed term
contracts has been increasing rapidly (Yamanoi et al. 2003). Fixed term contracts areusually used for lecturer and assistant professor posts. Although some ﬁxed term positions
allow a limited number of contract renewals, once their contracts expire, these lecturers andassistant professors will have to ﬁnd other jobs.9 Thus, for them, promotion is equivalent
to ﬁnding a permanent position elsewhere. In our sample, 4.6 percent of respondents were
working under ﬁxed term contracts when surveyed.
9

One may be able to apply for a permanent position in the same university if there is an opening. However,some universities do not allow joshu to apply for internal positions.

7

3

Data

The data utilized in this project were obtained from a survey we administered via a postal
questionnaire. The original questionnaire (in Japanese), and its English translation are in
the web appendix. The majority of prior studies used publicly available data (Kahn 1993;
McDowell et al. 2001; Ginther and Hayes 2003; Ginther and Kahn 2004), but some studies
undertook independent surveys (Ward 2001). In Japan there are no government or private
statistics on academics that allow detailed promotion analysis, which is why we conducteda survey.
The details of the survey methods can be found in our earlier work (Takahashi and
Takahashi 2011). We collected 4353 names of faculty in economics departments in Japan
from universities websites. Given our ﬁnancial resources and time constraints, we selected1863 individuals and sent questionnaires to their university addresses. Ideally, the selection
method would have been random, but in our case this could have led to a very small female
sample. Therefore, to increase the number of female observations, we selected all the femalesounding names (287 names). The rest (1576 names) were randomly chosen. Questionnaires
were sent from April to June 2008 and participants could reply either by mail or online.
By the end of our survey period, we received 362 responses in all (299 males and 63
females) for a response rate of 19.4%. This response rate is not high but not too low either,when compared to other studies employing mail surveys. For example, Moore et al. (2007)
achieved a response rate of 13%, while Ward (2001) achieved 30%. Assuming that thefemale-sounding names were indeed all women, the response rates for men and women are
19% and 22%, respectively. The response rates are not statistically diﬀerent between genders(p-value=0.19).
The percentage of women in our sample is 16.9%. Based on the statistics provided
by the MEXT Statistics of School Education (Gakkou Kihon Chousa), the percentage of8females in economics departments in Japan was 12.6% in 2007. Thus, we over-sampled
females. Over-sampling of women was purposely done in order to increase the precisionof the estimates. In the labor market discrimination literature, over-sampling of minority
groups is not uncommon. For example, McDowell et al. (2001) and Kahn (1993) used data
which over-sampled females.
The cover letter of the questionnaire listed the three purposes of the survey: (1) analysisof the determinants of wages and promotion, (2) investigation of research, teaching, andadministrative workloads, and (3) gender diﬀerences in wages, promotions, and perceptionsof the work environment. Thus, although we did not present measuring the gender promotion
gap as the sole purpose of the survey, we should worry about respondent bias. For example,
if only those who felt that gender promotion inequality was an important issue participated,
our estimates would be biased. To check for such bias, we asked respondents if they had everfelt discriminated against in terms of work environment, salary, or promotion. 90.58% of the
respondents (88.10% female respondents) replied that they had not felt discriminated against
in promotion decisions. Therefore, we believe that our data is not signiﬁcantly contaminatedby this type of selection bias. However, our results should be interpreted with caution.
In studies of academic labor market that use survey data, full professors are often overrepresented (Blackaby et al. 2005; Moore et al. 2007). However, our data do not seem to havethis type of non-random responses. In our sample, 63% of respondents are full professors.
However, according to the MEXT Statistics of School Education data, 60% of academics
in economics departments in Japan were full professors in 2007. Thus, the diﬀerence isrelatively minor for our sample.1010

In Blackaby et al. (2005) 28.5% of their sample are full professors, while the share of full professors inthe (UK) population is only 18.8% (p.3). Similarly, Moore et al. (2007) have 37.3% full professors in theirsample from the UK, while the percentage of full professors in the population was only 18.8% (p.4-5).

94The constructions of the variables

We utilize hazard function estimation to examine the gender diﬀerences in the following
three promotion spells: (1) time to ﬁrst promotion–the length of time from initial academic
appointment to promotion to associate professor; (2) time to second promotion–from associate professor to full professor; and (3) to have a longer term view of promotion, time fromthe initial appointment to becoming a full professor. Let us explain how the survey questions
were used to compute the duration of these three spells.
The questionnaire asked two sets of questions that can be used to ﬁnd and cross-verify theyear of initial appointment in academia. The ﬁrst set of questions asked about respondents’history of university changes. For each university a respondent worked in the past, the survey
asked the year they were hired (start-year) and the year they left the university (end-year).
The second set of questions asked which year the respondent obtained the following ranks:
full professor (kyouju), associate professor (jun-kyouju), assistant professor (koushi), andlecturer (separately for joshu and jokyo). As noted above, the jokyo position was created in2007. No respondents were joshu when surveyed. All those who entered academia as joshuprior to 2007 and who had not been promoted to assistant professor at the time of survey
were reclassiﬁed as jokyo in 2007.11
The duration of the ﬁrst promotion spell (from initial appointment to associate professor), and the second promotion spell (from associate professor to full professor) are thencomputed using the year of initial entry and the years respondents obtained each academic
rank.
11

There are 5 respondents whose reported years of initial academic ranks came before the reported startyears of their ﬁrst universities. For all the 5 cases, the initial academic rank was joshu, and the years at
which they obtained their second academic ranks corresponded to the start-years of their ﬁrst universities.
Therefore, we judged that their josh positions were likely to be of ‘research assistant type’, and were likely
be part time positions. Thus we treated the start-years of their ﬁrst universities as their years of their initial
appointments. There are two respondents whose years of their initial ranks came after the start-years of theirinitial universities. For them, we used the year of initial academic rank as the beginning of the academic
career, assuming that the inconsistency arose due to incompleteness of responses in the university-yearquestions.

10In the empirical analysis, data are split into one-year increments to allow for time varying
variables. The ﬁrst time varying variable is the private university dummy (public university
as the excluded category). This variable was constructed from the question on the startand end-years of employment at all the universities where respondents had worked. This
question also asked respondents to identify the type of university (public or private) that
had employed them, enabling us to create a time varying private university dummy. In casethe university type is missing, we include a dummy indicating that it is missing.
The second time varying variable is the marriage dummy. We asked respondents whatyear they married, enabling us to construct a marriage dummy that takes 1 only for the years
during which they were married. The third (set) of time varying variables are the children
variables. We asked the ages of all the respondents children at the time of the survey. Using
this information, we constructed variables that show the number of children who fall into
two age brackets, 0–5 and 6–18.
The regressions control for respondents’ age at the beginning of their academic careers.12
We control for ﬁnal academic degrees by including a dummy for the master’s degree, anda dummy for PhDs from foreign universities, with PhDs from Japanese universities as the
excluded category. We control for cohort dummies for those who entered the academic
labor market in the 1980s, 1990s, 2000-2005, and 2006 onward. The cohort that enteredacademia starting in 2006 is expected to capture the possible eﬀects of the national andpublic university ‘corporatization’ that began that year. The 2000-2005 cohort dummy
captures the possible eﬀect of the 2000 Action Plan that stipulates that national universities
should increase the number of female academics.
In the 1990s, there was a signiﬁcant convergence in the percentage of male and female
PhD graduates entering academia.13 The 1990s cohort dummy captures the eﬀects of possible
12

This variable was constructed as current age minus academic experience, where academic experience wascomputed as 2008 minus the year of initial appointment
13
See Takahashi and Takahashi (2011).

11

changes in the labor market conditions that caused this convergence. The cohort dummy forthe 1980s captures the eﬀects of speciﬁc labor market conditions at that time, for example,
the enactment of the 1985 Law of Equal Employment Opportunity of Men and Women.
We also control for research productivity. In the questionnaire, we asked respondents forthe total number of refereed articles and books they had published to date.14 We converted
the number of career publications into yearly publication rates by dividing the number ofpublications by years of academic employment. This conversion assumes that publication
rates are constant over time, an assumption that is likely erroneous, but since promotions
were decided in the past, converting publication into an annual rate is necessary, and failingto control for measures of productivity could confound gender diﬀerences in productivity
with gender diﬀerences in promotion.

5

Summary statistics and an initial look at the data

We eliminated from the analysis the observations for which we cannot determine the years
respondents entered academia (6 observations). Respondents who entered academia directly
as full professors were dropped from the analysis (22 observations). Respondents who enteredacademia in 2008 (the year of survey) were dropped because a duration that is shorter than
one is treated as zero in our one-year increment data (4 observations). Four other observations were dropped due to incompleteness or inconsistency in their responses. Eliminatingthese observations, we have the ﬁnal sample of 326 observations.
Although the full sample contains 326 observations, this is the number of observations
available only for the analysis of the spell from initial appointment to becoming full professor. The available samples are diﬀerent depending on the promotion levels and on sample
restrictions. Table 3 summarizes sample sizes for diﬀerent intervals and for diﬀerent sample
14

In the survey, we separately asked the number of single-author articles and co-authored articles. In
computing the total number of career articles, we divided the number of co-authored articles by 2 assumingthat the majority of co-authored articles are written by two authors

12

restrictions. Appendix A details the reasons for the variations.
Table 1 shows the summary statistics for women and men in the largest sample used inthis study. Because some of the variables are time varying, we show the summary statistics
at the time of promotion to full professor, or at 2008 if respondents have not been promoted
to full professor. There are 56 female observations, approximately 17 percent of the sample.
The average age at initial appointment is 31.2 for men and 32.2 for women. As compared tomale academics, greater percentages of females entered academia in the more recent cohorts(see cohorts from 1990s onward). Both Annual refereed article rate and Annual book rate arehigher for female academics. While this may reﬂect a woman’s shorter academic career if thelife-cycle publication proﬁle is concave, we did not ﬁnd evidence of concavity in our data.15
32 percent of men and 37 percent of women’s ﬁnal degree is a master’s degree. Thus, women
have lower academic qualiﬁcations on average, which may contribute to slower promotion.
The bottom half of the table also provides the summary statistics at the time of survey(2008) to provide another picture of the respondents. Note that these variables are notused for the analyses because the regressions use retrospective variables. The average ageof respondents is 50 for men and 43 for women. 64 percent of men and only 42 percent of
women are full professors. Thus, women are under-represented in the highest academic rank
and over-represented in all the lower ranks.
Table 2 presents respondents rank at initial appointment. There appear to be somegender diﬀerences in initial rank assignment. 13.7 percent of men and 9 percent of women
began their careers as associate professors. The percentage of women whose initial rank
was lecturer is slightly lower (23.2 percent) than that of men (29.6 percent). We will later
investigate if there are gender gaps in the initial rank assignment.
Let us now show the Kaplan-Meier survival estimates that use the restricted sample
15

We regressed career publications on academic experience and its squared term. However, the squaredexperience did not have a statistically signiﬁcant eﬀect.

13

up to 2005. Figure 1A plots the survival curves for the ﬁrst promotion–from the initial
appointment to associate professor, separately for males and females. Women’s survival
curve lies slightly above that of men, indicating that women may be promoted more slowly
than men. Figure 1B plots the survival curves for the second promotion–from associate
professor to full professor. Interestingly, women’s survival curve lies slightly below that ofmen, indicating that female academics may be promoted more quickly than male academics.
Finally, Figure 1C shows the survival curves from the initial appointment to full professor.
Women’s survival function lies slightly above that of men, but the diﬀerence seems to be
fairly small.

6
6.1

Empirical results

The gender diﬀerences in the initial rank at hiringAs described in Table 2, there is considerable variation in initial academic rank. Before
presenting our duration analyses, it is useful to see whether there are any gender diﬀerences
in initial rank assignment. Table 4 column 1 shows an ordered probit regression where thedependent variable takes the value 3 if the initial rank is associate professor, 2 if it is assistantprofessor, and 1 if it is lecturer. The regression controls for age at hiring, and includes adummy indicating if the respondents were married when hired.
The female coeﬃcient is negative, but is insigniﬁcant. The marginal eﬀects of Female forthe probability of being hired as an associate professor, an assistant professor, and a lecturer
are approximately -3%, -5%, and 8%, respectively. Thus, women are more likely to be hired
as lecturers than men. However, all the marginal eﬀects are not statistically signiﬁcant.
Columns 2 and 3 show the multinomial probit results. The marginal eﬀects of Female
on starting their careers as associate professors, assistant professors, and lecturers are -3%,
0.6%, and 4%, respectively. Thus, women are more likely to start their careers as lecturers,
though the estimated gender gaps are not statistically signiﬁcant.
14

6.2

Hazard function analysis for the ﬁrst promotion

Now we show the result of Weibull hazard function estimation for the ﬁrst promotion spell:the spell from the initial appointment to associate professor. As noted earlier, Japanese
academia underwent a signiﬁcant change in 2006 when national universitas were ‘corporatized’. Thus, the promotion patterns might have changed since then. Therefore, we run
regressions on two diﬀerent samples, one ending in 2005 and the other one ending in 2008.
Table 5 Models 1, 2 and 3 show the estimated hazard ratios for the restricted sample up to2005.
Model 1 is the most parsimonious model and controls only for age at hiring. In thismodel, females’ hazard ratio is about half that of males, and is statistically signiﬁcantly
diﬀerent from 1 at the 5 percent level. Model 2 contains all other explanatory variables.
The female hazard ratio is almost unchanged, and it is statistically signiﬁcant even at the 1
percent level. The hazard ratio for Age at hiring is greater than one, indicating that those
who enter into academia at an older age are promoted more quickly. Those whose ﬁnal
degree is a master’s degree have lower hazard of promotion than those who have PhDs fromJapanese universities. Having a PhD from a foreign university does not have a signiﬁcanteﬀect on promotion. The children variables do not show signiﬁcant eﬀects either. Promotion
is slower at private universities.
Model 3 includes interactions between Female and the children variables, as well as
an interaction between Female and Married, to capture the possibility that children andmarriage have more negative impacts on promotion for women. However, the hazard ratio
for Female is almost unchanged, although its statistical signiﬁcance decreased. Unexpectedly,
the numbers of children in all age categories are positively associated with the likelihood of
promotion, both for males and females. The eﬀects are even greater for female academics.

15

While children can increase the probability of promotion through motivation eﬀects16 ,a more likely explanation is that children capture unobserved job diﬀerences. For example,
suppose that there are two types of employers–one type promotes automatically while theother promotes on merit. Further suppose that the automatic type promotes employees more
quickly on average than the merit type. Then, academics in the former type of universities
are more likely to have a child during the ﬁrst promotion interval, while academics in thelatter type of university may wait until they have met their promotion criteria, hence causing
children to be positively associated with promotion probability. In fact, as will be shown
later, children have negative eﬀects on the second promotion.
Models 4, 5 and 6 simply re-estimate Models 1, 2 and 3 by using the full sample up to
2008. To capture the possible ‘structural change’ starting in 2006, we include the interaction
between Female and the dummy indicating that the observations are after 2006.17 All themodels exhibit qualitatively similar results as the previous models, although the statisticalsigniﬁcance of the female hazard ratio for Models 6 increased. All of the hazard ratios for theinteraction between Female and After 2006 are greater than 1, although they are statisticallyinsigniﬁcant.
What are the magnitudes of the estimated gender promotion gaps? Figure 2A shows
the survival curves for women and men, based on Model 2.18 As can be seen, there is a fairly
large gender promotion gap. At 5 years of experience, the probability of not being promoted
is 31.6 percent for men, while it is 51.7 percent for women. The expected time to promotion
is 4.1 years for men, compared to 6.2 years for women, a diﬀerence of approximately 2 years.
Figure 2B compares the survival curves of males who entered in academia at age 31 (themean), and of males who entered in academia at age 39 (the 95th percentile). Although those16

see Krapf et al. (2014) for a discussion.
One may expect that policy change would aﬀect all the coeﬃcients, not just gender. However, given thelack of observations, we decided to capture only the structural change with respect to gender.
18
We computed for those who are married without children, have PhDs from Japanese universities, andentered academia in the 1990s. Other variables are held at the average.
17

16

who enter at age 31 have a higher survival probability as compared to those who enteredat age 39, the diﬀerence appears to be minor. In fact, the expected time to promotion is
4.1 years if entering at age 31 while it is 4.6 years if entering at age 39. Thus, while age isapparently factored into promotions, experience is a more important factor.

6.3

Discarding the duration of lecturer from the ﬁrst promotion

As discussed earlier, some lecturers (joshu) are part-time research assistants who are graduate
students. Ideally, we would not place these ‘research-assistant type’ lecturers on the ﬁrst rungof the academic career ladder, a place that should be occupied only by full-time researchers
and lecturers with considerable discretion. However, we are not able to distinguish between
these two groups prior to 2007. This ambiguity could lead to an under-estimation of thegender promotion gap. For example, suppose that labor market discrimination operates
through the decision to appoint PhD course students to ‘research assistant type’ lecturer
positions. If male students are more likely to be appointed as ‘research assistant type’
lecturers, including the years spent as a research assistant in the ﬁrst promotion interval will
make the gender promotion gap appear smaller.
To test this possibility, we now re-estimate all the models by discarding the time spent as
a lecturer from the initial promotion interval, that is, we treat the year a respondent became
an assistant professor as his or her initial appointment in academia. Table 6 shows the results.
The size of our sample is reduced either because some respondents were lecturers at the timeof survey, skipped the assistant professor position, or because some of them became assistantprofessors in the year of censoring.19 Although all the female hazard ratios are less than 1,
they are much closer to 1 than the earlier results, and all of them are statistically insigniﬁcant.
Thus, the estimated gender promotion gap decreased. The fact that the promotion gap issigniﬁcantly reduced indicates that the gender gap in the ﬁrst promotion is largely due to19

Although all of the respondents who were lecturers at the time of the survey were jokyo, we excluded
them for consistency.

17

women remaining lecturers for longer periods, and perhaps partly due to the possibility wefound earlier that women are more likely than men to start their careers as lecturers.

6.4

Hazard function analysis for the second promotion

Now, we provide the Weibull hazard regression results for the second promotion: the spellfrom promotion to associate professor to becoming a full professor. Models 1, 2, and 3of Table 7 show the results that use the restricted sample up to 2005. The results are
signiﬁcantly diﬀerent from the analysis of the ﬁrst promotion. For Models 1 and 2, thehazard ratios for Female are nearly 1, and they are not statistically signiﬁcant. According
to Model 2, having one child age 0 to 5 would decrease the hazard of promotion by themultiplicative factor of 0.74, and it is statistically signiﬁcant. The eﬀect of children ages 6
to 18 on promotion is small and statistically insigniﬁcant. Interestingly, second promotion
is faster in private universities, a result opposite to the ﬁrst promotion analysis.
Model 3 includes interactions between Female and the children variables, as well as theinteraction between Female and Married. Children age 0 to 5 reduce the hazard of promotion
both for women and men, although the eﬀect is much larger for women and is signiﬁcant
only for them (p-val=0.06). Children age 6 to 18 also decrease the hazard of promotion bothfor men and women. Again, the eﬀect is larger for women and is signiﬁcant only for them(p-val=0.06). Interestingly, single women and married women without children have greater
hazard of promotion than their male counterparts.
Models 4 through 6 re-estimate Models 1 through 3 by using the unrestricted sample up
to 2008. These models simply conﬁrm the results of Models 1 to 3. In sum, for the second
promotion, childless women are promoted more quickly than men. The expected promotion
interval for a childless woman is 9.6 years, while it is 11.8 years for their male counterparts.
However, when we take the eﬀects of children into account, this ‘reverse’ promotion gap
completely disappears with one child, and women’s time to promotion becomes signiﬁcantly
18

longer with two children. Figure 3 shows the survival probabilities separately for: (1) married
men with one child, (2) married women with one child, and (3) childless married women.
The graphs assume that the child was born in the third year of the spell. As can be seen, awoman with one child has a higher probability of not been promoted until the child becomes
6 years old. After that, the survival probabilities of male and female academics with one
child are almost identical. Note also that a married woman without children has a much
lower survival probability than a married man with one child.
Figure 4 shows the survival probability of married men and women with two children.
We assume that the ﬁrst child was born in the third year, and the second child was born inthe sixth year of the spell. As can be seen, the survival curve for women lies much abovethat for men. The expected duration for men in this case is 11.3 years while it is 14.4
years for women. Thus, although childless women have faster promotions than their malecounterparts, the negative eﬀects of children that disproportionately fall on women maketheir promotion eventually slower than males.

6.5

Hazard function estimation from the initial appointment tofull professor

Now, we provide the analysis for the spell from initial appointment to becoming full professor
to take a longer term view of promotion. Table 8 Models 1 to 3 show the estimation resultsthat use the restricted sample up to 2005. Models 1 and 2 show that the hazard ratios for
Female are about half that for males, although a signiﬁcant level is achieved only for Model
2. When we include the interaction terms between Female and the children variables, as wellas the interaction between Female and Married, the hazard ratio for Female becomes muchcloser to 1, and is no longer statistically signiﬁcant. This conﬁrms our previous ﬁndings thatthe female academics (both single and married) have slower ﬁrst promotions, but that single
women have faster second promotions, so that there is no gender promotion gap.
19

Models 4 through 6 use the unrestricted sample up to 2008. The results are qualitatively
and quantitatively similar to Models 1 to 3. Overall, the results are consistent with ourprevious analyses about ﬁrst and second promotions.

6.6

Probit promotion analyses

Small sample size is a concern for this study given that our results could be sensitive torandomness in the data. Thus, we provide additional and simpler analyses of promotion
duration using probit models to see if we could conﬁrm our hazard function results.
First, we analyze the ﬁrst promotion. As shown in the Kaplan-Meier survival graph(Figure 1A), nearly 50 percent of male academics are promoted to associate professor within
three years. Thus, we estimate the probability of being promoted to associate professor
within 3, 4, and 5 years, separately. Note that the regressions do not include observations
that are censored before these threshold experiences. We analyze only the restricted sample
up to 2005.
Table 9 shows the results. Columns 1 to 3 are the simple models that do not control
for the gender diﬀerentiated eﬀects of children and marriage. The gender coeﬃcients arenegative and signiﬁcant for promotion within 3 years and within 5 years. The estimatedeﬀects are relatively large. The marginal eﬀect of Female coeﬃcient for promotion within
3 years is a 16.7 percentage point reduction in the promotion probability. For promotion
within 4 years, the gender coeﬃcient is small and insigniﬁcant, indicating that the genderpromotion gap estimates are perhaps sensitive to the randomness in the data.
Columns 4 to 6 show the models that control for the gender diﬀerentiated eﬀects ofchildren and marriage. The female coeﬃcients are fairly large for promotion within 3 years
and 5 years, although the statistical signiﬁcance decreased. The marginal eﬀect is -0.21 incase of promotion within 3 years. Again, the female coeﬃcient is small and insigniﬁcant for
promotion within 4 years. However, the results generally conﬁrm our hazard function results
20that there is a gender promotion gap for the initial promotion.
Next, we re-examine the gender promotion gap for the second promotion. The KaplanMeier survival curve for this spell (Figure 1B) shows that approximately 50 percent of maleacademics are promoted to full professor within 8 years. Thus, we estimate the probabilityof being promoted to full professor within 8, 9, and 10 years.
Table 10 shows the results. Columns 1 through 3 show the results that do not control
for the gender diﬀerentiated eﬀects of children and marriage. The female coeﬃcient for
promotion within 8 years is positive but not signiﬁcant. The female coeﬃcients for promotion
within 9 years and within 10 years are negative, although they are not statistically signiﬁcant.
However, the marginal eﬀects are large–approximately -0.12 for promotion within 9 and 10
years.
Column 4 shows the model that controls for the gender diﬀerentiated eﬀects of children
and marriage. Since the interactive terms perfectly predicted several outcomes for promotion
within 8 years and 9 years, we present results only for promotion within 10 years. As can beseen, the female coeﬃcients dropped considerably in absolute value with the marginal eﬀect
being -0.04. The interactive term between Female and the number of children is negativeand highly signiﬁcant while the coeﬃcient for the number of children alone is small andinsigniﬁcant. These results conﬁrm our hazard function analysis that the negative eﬀects
of children on promotion disproportionately fall on women. The interactive term between
Female and Married is positive and signiﬁcant, which is inconsistent with our earlier hazard
function analysis.
The probit analyses of the time to second promotion suggest that there is no promotion
diﬀerence among single academics. There is no gender promotion gap for married academics
with one child because the negative eﬀect of children is completely oﬀset by the positiveeﬀects of marriage for females. However, a gender promotion gap emerges among academics
21with two or more children.
Thus, the results of probit promotion analyses are broadly consistent with the major
ﬁndings of the earlier hazard function analysis: (1) there are gender promotion gaps fortime to ﬁrst promotion (for academics with and without children); (2) single women are notpromoted more slowly than men for the second promotion; and (3) there are negative eﬀects
of children both for men and women, but the negative eﬀects are much larger for women.
One inconsistency is that, while childless women are promoted more quickly for the second
promotion according to the hazard function analyses, the probit analyses did not ﬁnd sucha ‘reverse’ gender promotion gap.

7

Discussions and conclusion

By using a unique survey data set of academic economists from Japanese universities, wehave conducted the ﬁrst detailed study of gender diﬀerences in time to promotion within
Japanese academia. Our results suggest that women have a longer ﬁrst promotion spell(from their initial rank to associate professor), and that this gender promotion gap appears
to stem largely from women staying as lecturers longer, the lowest academic rank. The gender
gaps for the second promotion were somewhat complex. Childless women are promoted fromassociate to full professor faster than childless men. However, the negative eﬀects of children
on promotion disproportionately fall on women, so that male and female academics with one
child have almost identical promotion rates, and that women with more than two children
have signiﬁcantly longer promotion spells than comparable men. Nonetheless, we also notethat we did not ﬁnd a ‘reverse’ gender promotion gap among childless academics in oursimpler probit analyses.
What explains the ﬁnding that women must wait longer for promotion to associate professor but are promoted equally or even faster than men to full professor? One possibility isthat some women might have dropped out of the academic labor market before they became22associate professors. Although Japanese universities rarely use the up-or-out tenure track
system, the number of ﬁxed term positions has been on the rise, especially for lecturer andassistant professor positions. For ﬁxed term employees, a promotion is often equivalent toreceiving a permanent position elsewhere after their contracts expire. If female academics
faced tougher criteria for being hired into permanent positions, and if those who could notﬁnd a permanent job dropped out of the academic labor market, only the most productive female academics would remain in the upper academic ranks, possibly making female
promotion appear faster than male promotion.
We can speculate how likely this scenario is. Every three years, MEXT surveys all theentire faculty in all the universities in Japan about their basic job characteristics such as
their monthly salary and the number of courses taught.20 In addition, MEXT also conducts
a survey of every job leaver in every university about their reasons for leaving (questionnaires
ask about the quits that occurred in the year prior to the survey).21 Questionnaires are sent
to the university administrators, not to individual faculty, and the universities are legallyobliged to respond to the survey.
Summary statistics from these surveys from 1989 to date are available online. We used
these statistics to compute the approximate dropout rates in Japanese academia and plotted
them in Figure 5. The deﬁnition of a dropout is a quit that falls into one of the following
categories in the survey: (1) entering a university (as a student), and (2) ‘other reasons’.
In other words, a dropout is a quit with reasons other than moving to another university,
retirement, or death. Therefore, these people have likely left the academic labor market.
As can be seen, men’s dropout rates range between 1.5 percent to 2 percent, while women’s
dropout rates hover between 4 percent and 5 percent–more than twice that of men. Notethat these are average dropout rates, and therefore the dropout rates for younger employees
20
21

MEXT Kyouin Kihon Chosa.
MEXT Koyuin Idou Chousa.

23are likely to be much higher. Thus, given the higher female dropout rates, we speculate thatthe above scenario is a real possibility.
In conclusion, to achieve gender promotion equality, our results suggest that a closemonitoring of gender promotion gaps in the earliest career stages is important. In addition,
further research into the reasons for and the extent of academic labor market dropouts
will provide a more accurate picture of gender promotion gaps in Japanese universities.
Finally, the negative eﬀects of children on promotion that disproportionately fall on female
academics is potentially the largest factor contributing to the gender diﬀerences in promotion
in Japanese academia. While one should be cautious in making causal interpretations of thechildren eﬀects due to the fact that family planning decisions are endogenously determinedbased on unobserved individual traits, the negative association is large enough to warrant
attention, and policies to alleviate the negative career eﬀects of raising children are likely tobe highly beneﬁcial for the advancement of female academics.

Appendix A

Sample sizes by spells and by sample restrictions

Table 3 summarizes sample sizes for diﬀerent regressions. Let us ﬁrst discuss the sample
sizes for the regressions that use the unrestricted sample up to 2008. For the ﬁrst promotion(initial appointment to an associate professor), we can only use respondents that enteredacademia either as lecturers (93) or assistant professors (191). Among them, 11 who are
currently full professors did not report the year of promotion to associate professor. Thus,
the available sample size is 273 observations.
For the second promotion (an associate professor to a full professor), we can only use
observations who had already become associate professor or full professor at the time ofsurvey (301 observations). However, among them, 11 respondents did not report the year
they became associate professor. Another 11 respondents became associate professors in
2008, the survey year, and thus cannot be used in the analysis. This leaves us with 279
24

available observations
When we restrict the sample years up to 2005, we can only use respondents who enteredacademia in 2004 or earlier because the duration of employment for the respondents whoentered academia in 2005 is less than 1, which is treated as zero in the one-year increment
data. This reduces the available observations to 290 for the spell from initial appointment
to full professor. For the ﬁrst promotion, 36 out of 290 respondents started their careers asassociate professors, so they cannot be included in the analysis of this duration. Another 10
respondents who were already associate or full professors at the time of survey did not reporttheir years of promotion to an associate professor. This leaves us with 244 observations forthe spell from initial appointment to associate professor. For the second promotion, 284 (outof 290 available observations) were either associate professors or full professors at the timeof survey. However, 10 of them did not report the year they became associate professor, andanother 36 became associate professors on or after 2005. This leaves us with 238 observations
available for the duration analysis.
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Happyou Youshi Shuuroku (55), 2003, 42-47, 2003-09-20 (In Japanese)26

TABLE 1: Summary statistics

Male#Obs=270

Female#Obs=56

Summary stat at the time of promotion to full professor or at the censoring
Female01
Age at initial appointment
31.27432.232(4.940)(5.553)
Married
0.830
0.714(0.377)(0.456)
Final degree=Masters
0.326
0.393
(0.470)(0.493)
Final degree=Ph.D from foreign univ
0.119
0.071
(Excluded =Ph.D from Japanese univ)(0.324)(0.260)Private universities
0.526
0.625
(Excluded=natial, city, prefectural univ) (0.500)(0.489)
University type missing
0.026
0.000
(0.159)(0.000)# Kids aged 0-5
0.300
0.232
(0.574)(0.539)#kids aged 6-18
0.852
0.357
(1.020)(0.672)
Annual refereed article rate
0.541
0.740
(0.715)(0.918)Annual book rate
0.117
0.147
(0.174)(0.213)
Cohort 1980s
0.267
0.143
(0.443)(0.353)
Cohort 1990s
0.244
0.339
(0.431)(0.478)
Cohort 2000-2005
0.196
0.375
(0.398)(0.489)
Cohort 2006-2008
0.059
0.107
(Excluded=Cohort before 1979)(0.237)(0.312)Below: Summary stat at the time of survey: Not used for duration analysis
Age at the survey
50.11143.321(11.128)(8.731)
Academic experience
18.83711.089(11.658)(8.651)
Experience at current univ
14.0198.929(10.739)(8.579)Full professor
0.652
0.411
(0.477)(0.496)
Associate professor
0.289
0.429
(0.454)(0.499)Assistant professor
0.052
0.107
(0.222)(0.312)
Lecturer
0.007
0.054
(0.086)(0.227)
Inside brackets are standard deviations.

27

TABLE 2: Academic rank at the initial appointment

Associate professor
Assistant professor
Lecturer (both Joshu & Jokyo)

Male
#Obs
(Percent)

Female#Obs(Percent)

37(13.7%)
153(56.7%)
80(29.6%)

5(8.9%)
38(67.9%)
13(23.2%)TABLE 3: Available samples by spells and by the sample restrictions

Unrestricted sample up to 2008
Restricted sample up to 2005

The ﬁrst promotion

The second promotion

From the initial appointment
to a full professor

273
244

279
238

326
290

Initial promotion: from the initial appointment in academia to an associate professor. Second promotion:from an associate professor to a full professor.

28

FIGURE 1: Kaplan-Meier survival curves

0

10

20
analysis time

0.000.250.500.751.00

Male

30

B: Second promotion

0.000.250.500.751.00

0.000.250.500.751.00

A: First promotion

40

0Female10
20
analysis time
Male

30
Female

C: Initial appointment to full professor

0

10

20
analysis time
Male

30

40

FemaleInitial promotion: from the initial appointment in academia to an associate professor. Second promotion:from an associate professor to a full professor.

29

TABLE 4: Initial Rank Regressions (Restricted sample up to 2005)

Ordered probit

FemaleAge at initial appointment
Married at initial appointment
Private univ at initial appointment
Final Degree=MA
Final Degree=PhD Abroad(Excluded=PhD from Japan)
Cohort 80s
Cohort 90s
Cohort 2000-2005(excluded=Cohort before 1979)
Cut 1
Cut 2

(1)

(2)

(3)

-0.231(0.202)0.135***(0.0198)
0.0249(0.149)0.0666(0.150)-0.0750(0.158)0.346(0.236)0.116(0.215)0.0974(0.224)0.600**(0.244)
3.784***(0.539)5.868***(0.598)0.109(0.341)-0.0863**(0.0368)-0.0720(0.244)-0.750***(0.249)0.0686(0.256)-0.401(0.434)-0.354(0.336)-0.507(0.351)-1.116***(0.408)-0.456(0.498)0.250***(0.0445)-0.192(0.352)-1.309***(0.395)-0.194(0.387)0.439(0.442)-0.263(0.589)-0.850(0.655)-0.0482(0.613)290

2.878***(1.008)290-8.462***(1.300)290

Oprobit results-0.029(0.023)-0.049(0.049)0.078(0.071)

Mprobit results-0.034(0.028)-0.006(0.086)0.040(0.087)

Constant#Obs
Margial eﬀects of Female
Outcome=Assoc prof
Outcome=Assist prof
Outcome=Lecturer

Multinomial Probit
Outcome=FullProf
Outcome=Lecturer

Inside brackets are the robust standard errors clustered at the individual level. *,**,*** signiﬁcant at the
10,5,1 percent levels.

30

TABLE 5: First promotion: From the initial appointment to an associate professor (Hazard
ratios)Restricted Sample up to 2005

FemaleAge at initial appointment

Model 1Model 2Model 3Model 4Model 5Model 60.494**(0.158)1.060**(0.0244)0.486***(0.129)1.040*(0.0236)0.881(0.178)0.533(0.227)1.042*(0.0245)0.927(0.201)0.680(0.399)1.172(0.119)1.487(0.394)1.017(0.155)1.285(0.698)0.673**(0.116)
0.636**(0.117)0.910(0.224)0.680*(0.150)2.355*(1.209)1.571**(0.333)2.181***(0.555)2.553***(0.797)0.498**(0.158)1.054***(0.0209)

0.501***(0.124)1.035*(0.0207)
0.877(0.163)0.483*(0.191)1.038*(0.0217)
0.892(0.175)0.832(0.444)
1.196*(0.111)1.389(0.343)1.014(0.158)1.361(0.550)0.785(0.122)0.597***(0.103)0.905(0.199)0.829(0.144)2.190*(1.012)
1.639**(0.361)2.207***(0.548)2.940***(0.886)0.936(0.264)1.454(0.527)6.731***(3.891)0.0151***(0.00873)
1.758***(0.0817)
1,212
273
248

MarriedMarried×Female#Kids aged 0-5

1.213**(0.116)#Kids aged 0-5×Female#Kids aged 6-18

1.048
(0.149)#Kids aged 6-18×Female
Private univ

0.686**(0.117)
0.636**(0.116)0.927(0.225)0.692*(0.154)2.083(1.091)
1.572**(0.332)2.199***(0.546)2.591***(0.805)

Final Degree=MA
Final Degree=PhD Abroad(Excluded=PhD Japan)
Annual refereed article rate
Annual book rate
Cohort 80s
Cohort 90s
Cohort 2000-2006
Cohort after 2006
(Excluded cohort: Before 1979)
After 2006
After 2006 × Female
Constantα#Subject-year observations#Subjects#failsUnrestricted Sample up to 2008

0.0143***(0.00986)
1.534***(0.0885)
1,101
244
216

0.0173***(0.0108)
1.744***(0.0814)
1,101
244
216

0.0158***(0.0103)1.753***(0.0811)
1,101
244
216

1.233**(0.107)1.071(0.141)1.010(0.381)1.649***(0.317)0.0172***(0.0101)
1.528***(0.0826)
1,212
273
248

0.795
(0.124)0.603***(0.102)0.931(0.200)0.836(0.143)2.084(0.958)1.645**(0.362)2.245***(0.545)3.042***(0.912)0.928(0.262)1.425(0.512)7.004***(4.108)0.0163***(0.00907)
1.745***(0.0812)
1,212
273
248

Inside brackets are the robust standard errors clustered at the individual level. *,**,*** signiﬁcant at the
10,5,1 percent levels. Regressions use the Weibull baseline hazard. Regressions also include the dummy foruniversity type missing.

31

FIGURE 2: Survival curve estimates for the ﬁrst promotion
B: Effects of age at initial appointment

0

.2

.2

.4

Survival

Survival
.4

.6

.6

.8

.8

1

A: Gender effects

0

0
0

10

20
analysis time
Male

30

40

10

20
analysis time

30

Initial age 31 (Male)
FemaleInitial age 39 (Male)

32

40

TABLE 6: Discarding the duration of a lecturer from the ﬁrst promotion (Hazard ratios)

Restricted Sample up to 2005

FemaleAge at initial appointment

Model 1Model 2Model 3Model 4Model 5Model 60.881
(0.205)0.963(0.0250)0.897(0.183)0.962(0.0246)
0.915(0.183)0.952(0.301)
0.963(0.0244)
0.973(0.232)0.656(0.319)0.981(0.108)1.277(0.327)1.225(0.156)2.403***(0.769)0.510***(0.0831)0.779(0.128)0.948(0.243)0.974(0.152)0.910(0.451)1.265(0.283)1.505(0.424)
1.734*(0.530)0.849(0.215)0.972(0.0206)

0.861(0.187)0.976(0.0203)
0.895(0.162)0.788(0.237)0.976(0.0206)
0.886(0.191)0.924(0.383)1.003(0.105)1.286(0.343)1.180(0.151)
1.731*(0.557)0.578***(0.0833)
0.729**(0.114)0.909(0.210)1.126(0.135)0.750(0.351)
1.190(0.269)1.392(0.383)1.632(0.493)
3.573**(2.163)1.081(0.295)0.992(0.360)0.135***(0.0809)
2.109***(0.109)
867
248
231

MarriedMarried×Female#Kids aged 0-5

1.009(0.103)#Kids aged 0-5×Female#Kids aged 6-18

1.337**(0.178)#Kids aged 6-18×Female
Private univ

0.510***(0.0784)
0.761*(0.120)0.909(0.224)0.998(0.161)0.798(0.419)1.239(0.275)1.496(0.404)
1.718*(0.515)

Final Degree=MA
Final Degree=PhD Abroad(Excluded=PhD Japan)
Annual refereed article rate
Annual book rate
Cohort 80s
Cohort 90s
Cohort 2000-2006
Cohort after 2006
(Excluded cohort: Before 1979)
After 2006
After 2006 × Female
Constantα#Subject-year observations#Subjects#failsUnrestricted Sample up to 2008

0.220*(0.176)
1.842***(0.0882)
778
219
199

0.232**(0.163)2.027***(0.110)
778
219
199

0.214**(0.151)2.049***(0.110)
778
219
199

1.027
(0.0994)1.290**(0.161)

1.919***(0.317)0.976(0.352)0.156***(0.104)
1.887***(0.0875)
867
248
231

0.575***(0.0809)0.724**(0.110)0.924(0.206)1.136(0.131)0.748(0.360)1.177(0.263)1.420(0.373)1.674*(0.490)3.479**(2.107)1.079(0.291)1.070(0.375)0.132***(0.0793)2.089***(0.108)
867
248
231

Inside brackets are the robust standard errors clustered at the individual level. *,**,*** signiﬁcant at the
10,5,1 percent levels. Regressions use the Weibull baseline hazard. Regressions also include the dummy foruniversity type missing.

33

TABLE 7: Second promotion: From an associate professor to a full professor (Hazard ratios)

Restricted Sample up to 2005

FemaleAge at initial appointment

Model 1Model 2Model 3Model 4Model 5Model 61.124
(0.309)1.109***(0.0197)

1.139(0.311)
1.059**(0.0254)1.442(0.368)2.364**(0.911)1.055**(0.0252)
1.733*(0.503)0.769(0.411)
0.796(0.120)0.291(0.233)1.089(0.0954)0.527**(0.161)2.447***(0.542)0.857(0.187)2.017***(0.421)0.886(0.179)7.883***(3.640)1.262(0.295)1.753**(0.440)8.30e-07***(4.18e-07)

1.104(0.295)
1.103***(0.0174)

0.978(0.254)1.053**(0.0222)1.162(0.254)2.210***(0.655)1.049**(0.0226)1.490(0.373)0.446*(0.192)0.809(0.107)0.423(0.229)1.087(0.0891)0.847(0.176)2.750***(0.580)0.873(0.174)1.744***(0.333)1.127(0.187)5.872***(2.335)1.294(0.309)2.030***(0.484)0.722(0.447)1.26e-05***(9.60e-06)
0.786(0.212)1.724(0.702)0.00011***(8.09e-05)
2.700***(0.199)
1,986
279
189

MarriedMarried×Female#Kids aged 0-5

0.740**(0.109)#Kids aged 0-5×Female#Kids aged 6-18

1.062
(0.0908)#Kids aged 6-18×Female
Private univ

2.312***(0.508)0.823(0.181)1.824***(0.368)0.867(0.173)7.765***(3.619)1.227(0.284)
1.618*(0.408)7.45e-07***(3.71e-07)

Final Degree=MA
Final Degree=PhD Abroad(Excluded=PhD Japan)
Annual refereed article rate
Annual book rate
Cohort 80s
Cohort 90s
Cohort 2000-2006
Cohort after 2006
(Excluded cohort: Before 1979)
After 2006
After 2006 × Female
Constantα#Subject-year observations#Subjects#failsUnrestricted Sample up to 2008

0.00012***(8.44e-05)
2.552***(0.186)
1,707
238
165

0.00011***(8.60e-05)
2.733***(0.200)
1,707
238
165

7.8e-05***(6.51e-05)
2.783***(0.206)
1,707
238
165

0.768**(0.0995)

1.071(0.0830)

0.611*(0.157)2.431*(1.154)0.00018***(0.000114)
2.453***(0.179)
1,986
279
189

2.571***(0.529)0.859(0.173)
1.548**(0.290)1.103(0.173)
5.952***(2.366)1.263(0.302)
1.874***(0.442)0.665(0.415)1.26e-05***(9.75e-06)
0.783(0.211)
1.532(0.666)0.00015***(0.000106)
2.672***(0.194)
1,986
279
189

Inside brackets are the robust standard errors clustered at the individual level. *,**,*** signiﬁcant at the
10,5,1 percent levels. Regressions use the Weibull baseline hazard. Regressions also include the dummy foruniversity type missing.

34

FIGURE 3: Eﬀects of having one child (Second promotion)

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
㻜

㻡

㻝㻜

Male with a kid

㻝㻡

㻞㻜

㻞㻡

㻟㻜

Female with a kid

㻟㻡㻠㻜

Female no kids

FIGURE 4: Eﬀect of having two children (Second promotion)

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
㻜

㻝㻜

㻞㻜

Married male with 2 kids㻟㻜Married female with 2 kids

35㻠㻜

TABLE 8: Duration from the initial appointment to a full professor (Hazard ratios)

Restricted Sample up to 2005

FemaleAge at initial appointment

Unrestricted Sample up to 2008

Model 1Model 2Model 3Model 4Model 5Model 60.527
(0.221)1.173***(0.0256)

0.483*(0.187)1.136***(0.0268)
0.973(0.275)1.250(0.512)
1.138***(0.0266)
1.283(0.406)
0.236*(0.195)0.890(0.135)1.043(0.697)1.219**(0.110)1.165(0.559)1.384*(0.259)0.649**(0.137)1.741**(0.432)0.933(0.190)4.611**(2.792)
1.517*(0.365)2.205***(0.663)2.63e-06***(1.20e-06)

0.541(0.213)1.160***(0.0251)0.466**(0.160)1.118***(0.0239)0.877(0.226)1.153(0.431)
1.122***(0.0243)
1.177(0.340)0.211**(0.147)0.891(0.118)1.098(0.667)1.224**(0.110)1.359(0.387)1.500**(0.276)
0.669**(0.130)1.465(0.363)1.119(0.198)5.068***(2.582)
1.553*(0.376)2.713***(0.747)1.260(0.795)4.22e-05***(3.38e-05)
0.698(0.187)1.974(0.934)1.50e-06***(1.31e-06)
3.320***(0.194)
3,305
326
199

MarriedMarried×Female#Kids aged 0-5

0.901(0.133)#Kids aged 0-5×Female#Kids aged 6-18

1.244**(0.108)#Kids aged 6-18×Female
Private univ

1.331
(0.247)
0.636**(0.135)1.573*(0.399)0.957(0.189)3.628*(2.494)1.500*(0.357)2.178***(0.639)1.21e-06***(5.37e-07)

Final Degree=MA
Final Degree=PhD Abroad
Annual refereed article rate
Annual book rate
Cohort 80s
Cohort 90s
Cohort 2000-2006

0.911(0.117)1.272***(0.105)

Cohort after 2006
After 2006
After 2006 × Female
Constantα#Subject-year observations#Subjects#fails1.81e-06***(1.77e-06)
3.130***(0.192)
2,911
290
174

1.26e-06***(1.22e-06)
3.390***(0.203)
2,911
290
174

8.29e-07***(7.72e-07)
3.419***(0.203)
2,911
290
174

0.708
(0.180)1.703(0.814)3.87e-06***(3.80e-06)
2.978***(0.190)
3,305
326
199

1.446**(0.261)0.658**(0.127)1.351(0.338)1.149(0.194)
3.786**(2.282)
1.543*(0.369)2.760***(0.742)
1.246(0.781)
1.09e-05***(8.66e-06)
0.689(0.184)1.761(0.807)2.37e-06***(2.09e-06)
3.292***(0.193)
3,305
326
199

Inside brackets are the robust standard errors clustered at the individual level. *,**,*** signiﬁcant at the
10,5,1 percent levels. Regressions use Weibul baseline hazard. Regressions also include the dummy foruniversity type missing.

36

TABLE 9: Probit analysis of the ﬁrst promotion: Spell from the initial appointment to an
associate professor

Dep var: Promotion within⇒

FemaleAge at initial appointment
Married (at censoring)

3 years(1)

4 years(2)

5 years(3)

3 years(4)

4 years(5)

5 years(6)-0.442*(0.264)0.0567*(0.0291)-0.197(0.205)-0.0100(0.271)0.102***(0.0371)-0.215(0.217)-0.269(0.265)0.0755**(0.0373)-0.244(0.230)0.116(0.121)0.00611(0.118)-0.00166(0.118)-0.579***(0.183)-0.357*(0.190)0.0228(0.312)0.0279(0.191)-0.0520(0.600)-1.308(0.829)229-0.525***(0.190)-0.423**(0.193)-0.182(0.326)-0.179(0.193)0.0519(0.592)-2.111**(1.052)224-0.301(0.191)-0.520***(0.196)-0.147(0.342)-0.196(0.189)0.539(0.614)-1.082(1.071)223-0.574(0.440)0.0554*(0.0290)-0.174(0.221)-0.205(0.646)
0.0538(0.127)0.678*(0.378)-0.580***(0.186)-0.369*(0.191)0.0207(0.313)1.73e-05(0.190)-0.0977(0.610)-1.226(0.835)229-0.0756(0.422)0.101***(0.0369)-0.198(0.233)-0.134(0.601)-0.0284(0.123)0.475(0.422)-0.523***(0.189)-0.425**(0.194)-0.185(0.327)-0.197(0.195)0.0624(0.592)-2.071*(1.059)224-0.510(0.419)0.0745**(0.0371)-0.293(0.255)0.197(0.608)-0.0252(0.124)0.286(0.414)-0.298(0.191)-0.531***(0.197)-0.189(0.339)-0.202(0.191)0.527(0.611)-0.987(1.084)
223

Married×Female
Total#Kids (at censoring)
Total#Kids×Female
Private university (at censoring)
Final degree=MA
Final degree=PhD abroad(Excluded=PhD Japan)
Annual reference article rate
Annual book rate
Constant#Obs

Inside brackets are the robust standard errors. *,**,*** signiﬁcant at the 10,5,1 percent levels.

37

TABLE 10: Probit analysis of the second promotion: Spell from an associate professor to afull professor

Dep var: Promotion within⇒

FemaleAge at initial appointment
Married (at censoring)

8 years(1)

9 years(2)

10 years(3)

10 years(4)

0.156(0.354)0.0796***(0.0259)
0.127(0.340)-0.317(0.376)0.107***(0.0327)-0.261(0.417)-0.385(0.389)0.0615**(0.0313)-0.108(0.427)-0.171(0.107)-0.118(0.110)-0.0883(0.111)1.018***(0.218)-0.133(0.241)
0.502(0.362)-0.0290(0.218)1.922**(0.900)-2.997***(0.839)177

0.899***(0.227)-0.0562(0.265)0.690*(0.406)0.160(0.266)2.401**(1.109)-3.246***(1.050)174

0.711***(0.243)-0.0876(0.274)0.982*(0.551)0.00725(0.264)2.990**(1.299)-1.571(1.009)173-0.164(0.629)0.0609*(0.0316)-0.0809(0.510)5.169***(1.876)-0.0475(0.114)-5.285***(0.913)0.689***(0.247)-0.0519(0.279)0.978*(0.552)0.0417(0.269)2.910**(1.295)-1.657(1.062)
173

Married×Female
Total#Kids (at censoring)
Total#Kids×Female
Private university (at censoring)
Final degree=MA
Final degree=PhD abroad(Excluded: PhD Japan)
Annual reference article rate
Annual book rate
Constant#Obs

Inside brackets are the robust standard errors. *,**,*** signiﬁcant at the 10,5,1 percent levels.

38

FIGURE 5: Academic labor market dropout rates in Japan

0.06
0.05
0.04
0.03
0.02
0.01
0
1989

1992

1995

1998

Male dropout rates

2001

2004

Female dropout rates

39

2007