Gender Promotion Differences in Economics
Departments in Japan: A Duration Analysis
Shingo Takahashi
and
Ana Maria Takahashi∗
Abstract
We conduct a detailed study of gender promotion differences in Japanese academia
by using a semi-parametric duration model. It is commonly believed by Japanese
academics that there cannot be gender promotion differences in Japanese academia
since promotion is decided mainly based on age, with some adjustments given for
education level. Our results are consistent with this belief. We show that there is
little gender promotion gap. Age alone counts for nearly 60% drop in the survival
probability of not being promoted to full professor for the first 20 years of experience.
A PhD degree from overseas is associated with a 37% lower survival probability at age
40. Experience counts, but the magnitude is small. A heavy emphasis on objective
factors such as age and education qualification may be one reason for the absence of
gender promotion differences. In addition, our semi-parametric analysis reveals that (i)
an incorrect distributional assumption about the unobserved heterogeneity leads to a
significant underestimation of the time dependency in hazard function, and (ii) a nonparametric unobserved heterogeneity specification substantially improves parameter
significance.
I.
Introduction
It has been well documented in many professions that females fare worse than males in
promotions. Our objective in this paper is to investigate whether or not there are gender
differences in promotion among academic economists within Japanese universities.
Previous literature on gender promotion differences in academic labour markets reports
that there are substantial gender differences in promotion within US and UK academia
(Khan, 1993; Ward, 2001; Ginther and Hayes, 2003; Ginther and Khan, 2004). Ginther and
Khan (2004), using a sample of US academic economists, find that the probability of being
promoted to tenure is 13.5% lower for females than males after controlling for various job
∗
Support from the Grant-in-Aid for Scientific Research provided by the Japan Society for the Promotion
of Science is gratefully acknowledged (No.21730207).
Shingo Takahashi: Assistant Professor, International University of Japan, Graduate School of International
Management, 777 Kokusai-cho, Minamiuonuma, Niigata 949-7277 Japan. Phone 81-25-779-1507.
Email [email protected].
Ana Maria Takahashi: University of Utah, Department of Economics, 1645 E Campus Center, Dr. Rm. 308
Salt Lake City, UT, 84112-9300 US. Email [email protected].
1
characteristics and publications. Similar results are obtained by Ginther and Hayes (2003)
by using a sample of US academics in the humanities. Ward (2001), using a sample of
academics from the UK, shows that the probability that a male academic is a full professor
is 10% higher than for a comparable female, after controlling for numerous personal, job,
and human capital characteristics.
In contrast to the abundance of literature in the US and the UK, there have been few
studies about the gender promotion gap within Japanese academia despite a growing public
interest in gender equality in Japan. In 1999, Japanese government enacted the Basic Law
for Gender Equal Society. Consequently, in 2000, the Association of 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 Ministry of Education, Sports,
Science and Technology (MEXT) announced that it would provide 6 million yen in support,
to selected universities, for each female academic hired. Despite such interest in achieving gender equality in academia, evidence regarding the presence or the absence of gender
promotion differences within Japanese academia is not well established. Fujimura (2002)
estimates a rank attainment equation using 648 Japanese academics from the 1992 Carnegie
International Survey on Academic Profession. He finds a positive but statistically insignificant coefficient for the female dummy. However, his model has limited control variables,
lacking important job, institutional and personal characteristics. Therefore, a detailed study
of gender promotion differences is called for.
We conduct one of the first and the most detailed study of gender promotion differences
within Japanese academia by using a data set that we collected via a mail survey administered
in 2008. Our data set contains complete information on the year of each promotion, the
exact timing of job mobility, and the types of universities at which each academic previously
worked. In addition, we have personal information such as the age of each child and the
2
year of marriage. 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
controlling for important time-varying covariates such as the number of young children and
marital status at each point in time during the promotion spell.
We employ the econometric model proposed by Dolton and Von der Klaauw (1995)
that simultaneously allows: a non-parametric estimation of the baseline hazard function
(Han and Hausman, 1990); a non-parametric specification of the unobserved heterogeneity
component (Heckman and Singer, 1984); and the estimation of parameterized coefficients for
the observed explanatory variables. This model enables us to avoid inconsistent estimates of
the hazard function that could arise from a misspecification of the baseline hazard and from
an incorrect distributional assumption about the unobserved heterogeneity component.
The academic labour market in Japan is of interest to researchers for the following
reasons. First, there is a common belief among Japanese academics that promotions are
automatically done based on age, with adjustments given for education and experience.1 If
promotion is decided deterministically in line with this belief, then there is little room for
gender promotion differences. However, there is no empirical study to date that documents
if such belief is in fact true. Therefore, whether or not there are gender promotion differences
within Japanese academia is still an open empirical question.
Second, there are important institutional differences between Japanese and US academia.
As opposed to US academia, in Japanese academia there is no ‘up or out’ tenure-track system2 and most of the employment contracts in Japanese academia are on an unlimited term
basis (that is, life-time employment).3 Consequently, we expect to see a very different picture
1
According to interviews conducted by the authors. We interviewed several academics and representatives
of the Association of Private Universities of Japan (Nihon Shiritsu Daigaku Kyoukai ), and of the Faculty
and Staff Union of Japanese Universities (Zenkoku Daigaku Kosen Kyoshokuin Kumiai ).
2
There are some exceptions. However, it is very rare that universities have tenure-track systems.
3
Since 1997, however, the fixed-term contract has been introduced by the enactment of the Legislation of
the Fixed-Term System for Faculty Members. This type of contract is applied to a rather small number of
academics.
3
of gender promotion differences within Japanese academia as opposed to US academia. Thus,
it is important to conduct a detailed study of the determinants of promotion in Japanese
academia.
The academic labour 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 have used across-industry samples typically did not have
well-defined ranks that are homogeneous across observations. Consequently, these studies
used proxies for job ranks or job advancement. For example, Winter-Ebmer and Zweimuller
(1997) used skill requirements as proxies for job ranks. The use of such proxies always
leaves the question of how comparable the job ranks (as defined by these proxies) are across
observations. Moreover, if the data are cross-sectional, there is the additional problem of
not being able to tell if an individual had been promoted to the rank or an individual has
been initially assigned to that rank. Due to well-defined job ranks in academia we are able
to avoid such problems.
The remainder of the paper is organized as follows: Section II presents theories. Section III discusses relevant empirical literature. Section IV briefly outlines the background
information. Section V describes the empirical methodology. Section VI presents the data,
and Section VII describes the explanatory variables. In Section VIII, we present our main
results that indicate there is little gender promotion gap within Japanese academia. Section
IX discusses why there is little gender promotion gap within Japanese academia. Section X
includes a brief discussion of the selection bias problem, and Section XI concludes.
II.
Theories
The theories of discrimination, which are the most commonly cited theories in the literature
on gender promotion differences, are concerned with the gender wage gap and not the promotion gap; however, with the understanding that promotion is one of the major devices for
4
wage increases, these theories are still relevant for the study of gender promotion differences.
Phelps (1972) developed a statistical discrimination model which assumes that the average
productivity of females is lower than males’. A female may receive lower salary than a male of
the same productive characteristics because employers use the average characteristics of the
female group to predict the female workers’ productivity. Aigner and Cain (1977) modified
Phelps’ model to show that, if the employer is risk averse, females will receive lower wages
even if average ability is the same for both genders. Lundberg and Startz (1983) showed that
an inaccurate evaluation of females’ abilities negatively affects their human capital decisions,
thus causing the females’ wages to be less than the males’.
Becker (1957) developed a taste-based discrimination theory in which discrimination
arises from employers’ distaste against working with a particular group of people. This
model indicates that, in a competitive market, discriminatory firms will disappear in the
long run. Goldberg (1982) modified Becker’s model to incorporate nepotism toward males
and showed that nepotistic firms will survive in the long run. Black (1995) showed that
the existence of discriminatory firms increases the job search cost incurred by females. The
presence of such a search cost gives firms monopsonistic power leading to lower wage offers
for females.
Milgrom and Oster (1987) considered a job assignment problem whereas female workers
are ‘invisible’ in the sense that their ability can be observed only by their current employers
and not by other potential employers. When female workers are invisible, firms can extract
rent from them. However, a promotion increases the visibility of female workers. As a result,
the current employer has incentives not to promote female workers, thus causing gender
promotion differences. Lazear and Rosen (1990) also considered a job assignment problem
where a promotion of a worker incurs a training cost to the firm. Female workers are assumed
to have higher job separation probabilities. Consequently, firms set the threshold ability for
5
promotion higher for females than males in order to compensate for the females’ ex-ante
higher separation probabilities. This causes gender promotion differences.
III.
Previous Empirical Literature
Ginther and Khan (2004), by using a sample of economists from the American Economic
Association (AEA) directory examine gender difference in the probability of attaining tenure.
In a linear probability regression for a sample of 133 academics observed at ten years post
PhD, they find that the probability of being promoted to tenure is 13.5% lower for females
than males, after controlling for the PhD tier, the PhD cohort, the current university tier,
and publications (see Table 2 of their study). Ginther and Hayes (2003), for a sample from
the US Surveys of Doctorate Recipients (SDR) for humanities fields, use a panel data probit
model to estimate gender promotion differences. They show that being female decreases
the probability of achieving tenure by 6.8%, after controlling for personal characteristics,
experience, job and employer’s characteristics, publications, and the field of study.
McDowell et al. (2001) use a sample of economists from the AEA directory to estimate
gender promotion differences. After controlling for the type of department, productivity (including measures of the quality of journals), quality of education, fields, experience, personal
characteristics, and self-selection into academia, their panel data ordered probit results show
that females have a lower probability than males to be promoted from rank to rank (females
have 12% lower probability to be promoted to a full professor). However, they find that
there are no unexplained gender promotion differences by the end of the 1980s.
Ward (2001), by using a data set from five Scottish universities, shows in a cross-sectional
ordered probit model that the probability that a male academic is a full professor is 10%
higher than for an otherwise similar female, after controlling for experience, career breaks,
publications, cohort effects, and the PhD tier. Khan (1993) investigates gender promotion
differences among US academics in the fields of economics and management by using a
6
sample from the SDR that lacks information on publications. After controlling for various
characteristics, males’ hazard of promotion to tenure is higher than females’ by a multiplicative factor of 1.56. Broder (1993) estimates a simultaneous equation model in order to show
the determinants of rank attaintment for a sample of 362 male and 30 female US academic
economists. She obtains a lower predicted rank for females, however, the result was not
statistically significant.
Fujimura (2002) estimates a binary logit rank attainment equation for the promotion
to full professor using 648 Japanese academics from the 1992 Carnegie International Survey
on Academic Profession. His control variables are experience, non-academic experience,
gender, research university, the number of articles and the number of books. The coefficient
for the female dummy is positive (0.203); however, it is insignificant. Thus, he does not find
evidence that there are gender promotion differences. However, his model lacks important
control variables such as the number of young children, marital status, type of university,
and career breaks. Therefore, it is difficult to eliminate the possibility that the insignificant
female coefficient is due to the lack of control variables.
IV.
Background
There are three types of universities in Japan: national, public and private. National universities are established and funded by the central government. Public universities are established by local governments, and funded by both the local and the central governments.
Private universities are established by private entities and are financially self-supporting.
Promotion decisions within Japanese universities are made at the department level,
usually by faculty committees or by faculty meetings (kyouju-kai ). In some universities, the
university board and/or the chairman of the board (rijicho) needs to approve the promotion
decisions. However, it is said that the chairman and the university board seldom deny the
7
decision made by the department.4 According to our conversations/interviews with various
Japanese academics, there is a common belief that promotions are automatically done based
on age, with some adjustments given for education and experience. If promotion were a
deterministic function of age, education, and experience, then there would be little room
for gender promotion differences. It is also commonly believed that publications are not
important determinants of promotion since promotion is automatically done based on age.
However, there has been no empirical investigation into whether such beliefs are indeed true.
In April 2004 the academic sector in Japan underwent important changes. The main
change was the ‘corporatization’ of national and public universities. This ‘corporatization’
removed the public employee status of academics from national and public universities,
and allowed these universities greater freedom in various managerial decisions, including
salary and promotion determination. For example, before April 2004, the experience and
age necessary to promote from an assistant professor to associate professor in national and
public universities were regulated by the public servant laws. In addition, according to the
public servants laws, the jobs of public servants were for life. Such regulations were removed
after ‘corporatization’ took effect.5
V.
Empirical Methodology
The duration to promotion is modeled by a proportional hazard function with unrestricted
baseline hazard as follows:
hi (t) = λ(t)exp[Xi (t)0 β]
(1)
4
In our survey, we asked the respondents to indicate who has primary influence on promotion decisions
at their current institution. 66% of the respondents said that a faculty committee has primary influence on
promotion decisions, 10.5% said that the department chair, and 6.4% said that individual faculty members.
Only 4.16% said that the president of the university, and only 1.9% said that the chairman of the board.
5
However, the ‘corporatization’ did not mean a change in the ownership of the universities and national
and public universities are still heavily government sponsored institutions.
8
The duration, t, is the time taken to be promoted from the initial hiring as an academic to
full professor.6 Xi (t) is the vector of characteristics (including gender) of the academic i at
time t, and β is a vector of parameters. λ(t) is the baseline hazard at time t.
For estimation we employ a semi-parametric procedure that non-parametrically estimates the baseline hazard and that parametrically estimates β (Han and Hauseman, 1990).
We describe below the estimation procedure by following Dolton and Von der Klaauw (1995).
The duration for promotion for our data is recorded in complete year intervals. Thus, if the
recorded duration is ti , the individual i was promoted during the interval (ti − 1,ti ]. The
likelihood contribution of a complete duration, ti , is thus written as:
P rob[ti − 1 < T ≤ ti ]
"Z
= P rob
ti −1
0
"Z
= P rob
0
"
= exp −
T
hi (u)du <
hi (u)du ≥
tX
i −1
e
Xi (s)0 β
s=1
= {exp[e
Z T
Xi (ti )0 β
0
Z ti −1
0
Z s
s−1
hi (u)du ≤
Z ti
0
#
#
hi (u)du
"Z
hi (u)du − P rob
#
"
λ(u)du − exp −
"
γ(ti )] − 1}exp −
ti
X
s=1
ti
X
e
Xi (s)0 β
0
e
T
hi (u)du >
Xi (s)0 β
Z s
s−1
#
Z ti
0
#
hi (u)du
(2)
#
(3)
λ(u)du
(4)
γ(s)
s=1
where T is the actual duration and γ(s)=
Rs
s−1
λ(u)du. In computing (3), we assume that
X(t) is constant during each year interval. We also use the fact that the integrated hazard
RT
is a unit exponential variate, that is, prob[
0
hi (u)du > z]=e−z .
The second term of (4) is prob[T ≥ ti ] which is the likelihood contribution for censored
observations. Therefore, the likelihood function for the sample of N observations is written
as:
L1 (ti , di ) =
N
Y
i
Li (ti , di ) =
N
Y
"
{exp[e
Xi (ti )0 β
di
γ(ti )] − 1} exp −
ti
X
s=1
i
We estimate the coefficients, β, and γ(s) (hazard pieces) jointly.
6
Note that, the duration to associate professor is estimated in a similar manner.
9
#
Xi (s)0 β
e
γ(s)
(5)
In a duration model, it is well known that the existence of unobserved heterogeneity
across observations, such as taste for work, could lead to spurious negative time dependency
in hazard estimation, and possibly misleading inferences about the effect of explanatory
variables. Thus, we incorporate unobserved heterogeneity by re-writing the hazard function
as:
hi (t) = λ(t)exp[Xi (t)0 β]eui
(6)
where ui is the unobserved heterogeneity term assumed to be independent of X.7 The most
common distributional assumption is that eui follows gamma distribution with mean 1 and
variance σ 2 . By using the fact that the unconditional probability in the case of gamma
heterogeneity is given by prob[T > ti ]=[1 + σ 2
Pti
s=1
0
2
eXi (s) β γ(s)]−1/σ , the unconditional like-
lihood function in the case of gamma heterogeneity can be written as:
L2 (λ, β, σ) =
N
Y
"
di [1 + σ
2
tX
i −1
Xi (s)0 β
e
−1/σ 2
γ(s)]
] + (1 − 2di )[1 + σ
s=1
i=1
2
ti
X
#
Xi (s)0 β
e
−1/σ 2
γ(s)]
s=1
Heckman and Singer (1984) note that the estimates of the baseline hazard function are very
sensitive to the distributional assumption of the unobserved heterogeneity. Thus, following
Dolton and Von der Klaauw (1995), we apply Heckman-Singer type non-parametric maximum likelihood estimation where the unknown distribution is approximated by a discrete
distribution. The likelihood function is then written as:
L3 (λ, β, p, µ, J) =
N X
J
Y
pj Li (ti , di |µj )
(8)
i=1 j=1
where µj , j=1,..,J are the J points of support, and pj , j=1,..,J are the corresponding probability; that is, pj =prob(ui =µj ). L(ti , di |µj ) is computed in the same way as the Li (ti , di )
in equation (5) except that we replace exp[Xi (t)0 β] with exp[Xi (t)0 β + µj ]. We estimate J-1
mass points (µj ) and J-1 weights (pj ) jointly with hazard pieces, γ(t), and β. µJ is normalized to 0 with pJ = 1 −
PJ−1
j=1
pj . As for the estimation of the number of mass points, J,
7
Similar to most of the empirical hazard function analyses, we are not able to incorporate the possible
correlation between the unobserved heterogeneity and the explanatory variables.
10
(7)
Dolton and Von der Klaauw (1995) suggest to estimate the model for increasing values of J
until the likelihood fails to increase. Meyer (1986) shows that the estimates obtained in this
way are consistent estimates of the model parameters.
VI.
Data
The data utilized in this project were obtained from a survey we administered via a postal
questionnaire. Our survey method follows. First, from the website of the Ministry of Education, Culture, Sports, Science and Technology (MEXT) we obtained an official list of all
four-year universities in Japan. The list contained 747 universities: 87 national, 76 public
and 584 private universities. We accessed each university website provided in the list in order
to collect the names of academics in economics and economics-related departments.8 Due to
the facts that some universities do not list faculty names and some universities do not have
economics departments, we were able to collect only 4353 names from only 132 universities.
Many Japanese economics departments also employ faculty specializing in language education; we eliminated such faculty where possible. In addition, we excluded universities that
accept only female students.
Next, from the 4353 collected names, we selected 1863 academics and mailed them
questionnaires directly. Ideally, the selection method should be random. However, this
could have led to a very small female sample. In order to increase the number of female
observations, we selected all the female-sounding names (287 names), however, the rest of
the selected academics (1576 names) were randomly chosen. Questionnaires were sent from
April to June 2008 and participants could reply either by mail or online. Two reminders were
sent by mail in July and August, and an additional reminder was sent to approximately 600
academics by email. At the end of our survey period, we received 363 responses (252 by mail
8
Often economics departments are combined with business departments to form a larger department. In
this case, names from the business departments were also included.
11
and 111 online). Thus, we achieved a rate of response of 19.5%. This response rate is not too
high but not too low either, when compared to other previous studies that used similar mail
surveys of academics. For example, Moore et al. (2007) achieved a response rate of 13%,
while Ward (2001) obtained a response rate of 30%. Due to incomplete responses the usable
sample is 326 (of which 55 are females). Our female sample, nevertheless, is comparable in
size with samples previously used in the literature – Broder (1993) used a sample with 30
females and 362 males; Ginther and Khan (2004) had 93 males and 95 females.
The percentage of females in our sample is 16.9%. Based on the statistics provided
by the MEXT Statistics of School Education (Gakkou Kihon Chousa), the percentage of
females in economics departments in Japan was 12.6% in 2007. Thus, we over-sampled
females. Over-sampling of females was purposely done in order to increase the precision
of the estimates. In the labour market discrimination literature, over-sampling of minority
groups is not uncommon. For example, McDowell et al. (2001) and Khan (1993) used data
which over-sampled females. Thus, we believe that over-sampling of females does not affect
the relevance of our results.
One may be concerned with respondent biases. For example, if only those who identified
with the purpose of analyzing gender promotion inequalities replied, our estimates would be
biased. However, we believe that such a bias does not exist in our sample, since in our cover
letter, we did not emphasize that the data will be used only to analyze gender inequalities.
Moreover, 90.58% of the respondents (88.10% of the female respondents) replied that they
did not feel discriminated in promotion. Therefore, there is no reason to believe that our
sample is affected by respondent biases.
One may be also concerned with the over-representation of full professors due to nonrandom responses since in typical mail surveys of academics, such a problem is not uncommon
(Blackaby et al., 2005; Moore et al., 2007). In our sample, 63% of respondents are full
12
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 is relatively minor for our sample.9 Finally, we under-sampled private universities
because MEXT does not provide website links for a significant number of private universities.
According to the MEXT Statistics of School Education, 73% of academic economists work
in private universities while only 56% of our sample is in private universities.
VII.
Variables and Descriptive Statistics
Table 1 shows the definitions of our variables. We estimate hazard functions for two different
durations: one equal to the years taken to be promoted from the initial hiring in academia to
full professor; and the other equal to the years taken to be promoted from the initial hiring in
academia to associate professor. We split the data for each year in order to incorporate timevarying covariates. Time-varying covariates are indicated by (†) in Table 1. We have four
categories of control variables: personal, job, institutional, and human capital characteristics.
Personal Characteristics. The female dummy variable captures the gender differences
in the hazard of promotion. We also include various time-varying covariates: age at any given
point in time, and a dummy variable showing if the respondent was married at any given
point in time. Since children could cause interruptions in the academic career and could limit
the time devoted to work (Long et al., 1993), we include variables that show the number of
young children at any given point in time.
Job Characteristics. According to McDowell et al. (2001) and Koplin and Singell
(1996), female economists usually prefer fields like labour economics as opposed to fields like
theory or quantitative methods. Our sample also suggests that females have the highest
representation in the labour economics field. In order to separate the effect of field choice
9
In Blackaby et al. (2005) 28.5% of their sample are full professors, while the representation of full
professors in the population is only 18.8% (UK)(p.3). Similarly, Moore et al. (2007) have 37.3% as full
professors in their sample from the UK, while the representation of full professors in the population is only
18.8% (p.4-5).
13
from the effect of being female, we include a dummy variable for the labour field.10
We control for cohort effects for those who entered the academic labour market in the
1980s, the 1990s, between 2000-2003, and 2004 onward. The cohort that entered academia
from 2004 onward is expected to capture the possible effects of the 2004 national and public
university ‘corporatization’. The 2000-2003 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 is a significant convergence in the percentage of
male and female PhD graduates entering the academia (see Figure 5-B). The 1990s cohort
dummy captures the effects of possible changes in the labour market conditions that caused
such a convergence. The cohort dummy for the 1980s captures the effects of specific labour
market conditions at that time (e.g., the enactment of the 1985 Law of Equal Employment
Opportunity of Men and Women). In addition, we include time dummies to capture possible
systematic differences in the labour market conditions during a specific time period.
Institutional Characteristics. Estimated gender promotion differences could arise if
females are over-represented in universities where promotion hurdle is higher. We asked each
academic to report the exact years of job mobility, as well as the types of all previous universities where they were employed. From this information we constructed dummy variables
indicating whether the respondent was in a private or in a public university at any given
point in time, with national universities being the reference group. Ginther and Hayes (2003)
also controlled for the type of universities academics were previously employed. However,
they did not utilize the information about the exact timing of job mobility. Instead, they
included variables that show the proportion of time each respondent spent in each type of
university. Therefore, our control variables are more precise than those used in the previous
10
In our preliminary estimations, we included controls for 10 other fields (theory, history, economic systems,
growth, quantitative, monetary, fiscal, international, business including accounting & finance, and industrial
organization). This field choice is similar to that of McDowell et al. (2001). The result for the female dummy
coefficient was not altered.
14
literature.
Human Capital Characteristics. We control for the standard human capital measures such as education and non-academic experience. Non-academic experience is the total
number of years worked full-time outside academia. The squared terms of non-academic
experience is included in order to capture the possibility that the rate of return on human
capital continues at a diminishing rate. The variable (PhD) is the dummy variable indicating
that the respondent has a doctorate degree. This variable captures the effect of education
on promotion. In order to capture the differences in PhD programs from which the respondents graduated from, we also include an additional dummy variable for a PhD degree from
overseas. The variable (#Universities worked) shows at how many universities each academic previously worked (including the current university) at any given point in time. This
variables captures possible effects of job mobility on promotion.
To control for additional differences in human capital characteristics, we control for average measures of publications over one’s academic career. Publications are classified according
to their type: single-authored referred articles, co-authored refereed articles, working papers,
single-authored books, co-authored books, books edited, book chapters, and textbooks. In
the prior literature, the quality of research output is controlled for by distinguishing articles
published in top journals. In our survey, however, in order to preserve the anonymity of the
respondents, we did not ask the name of the journal of publication. Therefore, we cannot
directly adjust for the quality of the publication. However, we asked the survey participants
to report the number of publications according to the location of the publisher. Thus, each
type of publication is further divided into subtypes depending on whether it was published
in Japan or in the US/Europe. We expect publications in the US or Europe to be more cited
than those published in Japan since these are published mostly in Japanese. Thus, we can
capture potential differences in the impact of the research output.
15
The average publication measures are constructed by dividing the total number of the
observed publication measures by the total experience as academic as of 2008. By using
average productivity measures, we assume that the academic productivity is roughly constant
over the career. This assumption is likely erroneous. However, omitting to control for
measures of productivity could confound the effect of gender differences in productivity with
gender differences in promotion. Ginther and Hayes (2003) also use average publication
measures to control for productivity differences.
Summary Statistics. We utilize a sample of 326 academics, 271 males and 55 females,
for the analysis of the duration to full professor.11 At the time of the survey (2008), the
average age is 50 for males and 43 for females. 63% of the sample is full professors while 29%
of the sample is associate professors. Only 9% of males and as much as 15% of the females
specialize in the labour field. Females are more likely than males to be found in private
universities (62% of females and 55% of males). More than 50% of females were hired after
the year 2000. 67% of males and 62% of females have PhD degrees, and 12% of males and
7% of females have a PhD degree from overseas.
We split the data for each year in order to incorporate time-varying covariates. Table 2
provides the summary statistics for the sample after the data split. The average age is now
36.6.
VIII.
A.
Estimation Results
Kaplan-Meier Survival Estimates
Before presenting our main results, it is useful to look at the Kaplan-Meier survival estimates.
Figure 1-A plots the Kaplan-Meier survival curves estimates for males and females for the
duration from the initial hiring as academic to full professor. The survival probability is the
11
We eliminated from the analysis those who started their academic career as full professors and those
who begin their academic career in 2008, the year of our survey.
16
probability of not being promoted to full professor at a given time. The survival curve for
females lies slightly above that for males, indicating that females may be promoted slower
than males. At experience equal to 10, the survival probability is 67.6% and 81.8% for males
and females respectively. However, the log-rank test does not reject the null hypothesis that
the survival functions are the same for males and females (p-value=0.176). Thus, without
accounting for differences in the characteristics between genders, the hazard functions do
not differ by gender.
Figure 1-B plots the Kaplan-Meier survival curves estimates for males and females for
the duration from the initial hiring as academic to associate professor. The survival curve
for females lies slightly above that for males and the log-rank test rejects the null hypothesis
that the survival functions are the same for males and females at the 5% significance level
(p-value=0.047). Therefore, without accounting for gender differences in the characteristics,
females take longer to be promoted to associate professor.
B.
Estimates of the Duration of Promotion to Full Professor
Estimated Coefficients for Explanatory Variables. Table 3 shows the coefficients for
the explanatory variables for the duration of promotion to full professor. All the non-dummy
explanatory variables are demeaned so that the estimated baseline hazard shows the baseline
hazard for the average individual.12 The first three columns show the results for our semiparametric specifications, while columns four and five show the results of piecewise constant
hazard models for comparison purposes. Let us first consider the coefficients for our semiparametric model without unobserved heterogeneity. The coefficient for the female dummy
is -0.30, indicating that females may take longer to be promoted to full professor, however,
the coefficient is not statistically significant at any of the conventional significance levels.
The coefficients for age and age squared are highly significant with the coefficient for
12
We demeaned these variables by using the sample averages of the expanded data.
17
the squared term being negative, indicating that the probability of promotion increases at a
diminishing rate with age. We do not find a statistically significant effect of marriage. The
coefficient for the number of young children (between age 0 and 2) is marginally significant,
indicating that each additional child in this age break reduces the hazard of promotion by
the multiplicative factor of exp(-0.68)=0.51.
The coefficient for (Labour) is not statistically significant, indicating that the choice of
labour field does not affect promotion rate. All the time dummies and cohort dummies do not
have statistically significant effects on the promotion rate. Academics who work in private
universities have a higher hazard of promotion by a multiplicative factor of exp(0.42)= 1.52.
Both measures of education, (PhD) and (PhD overseas), do not have statistically significant
effects. The coefficient for the (#Universities worked) is positive (0.27) and statistically
significant, indicating that each job change increases the hazard of promotion to full professor
by a multiplicative factor of exp(0.27)=1.31. This suggests that academics tend to attain
a higher rank upon moving to another university. This may be because universities try to
attract higher quality researchers by providing a higher rank. The coefficient for the private
university dummy has a positive and statistically significant coefficient (0.41), indicating
that academics working at private universities would experience faster promotion to full
professor.
Regarding the publication variables, refereed articles – the most commonly accepted
measures of academic productivity – do not have statistically significant effects on the duration of promotion to full professor. The insignificant effects of refereed articles seem to
confirm the common belief among Japanese academics that publications do not count much
for promotion within Japanese universities, since promotion is automatically done mainly
based on age. Nevertheless, working papers have a statistically significant effect on the
duration of promotion to full professor. During our conversations with several Japanese
18
academics, it was mentioned that promotion is automatically done based on age as long as
academics produce working papers constantly. If this is true, the significant working paper
effect is not implausible.
Let us now discuss below the semi-parametric gamma heterogeneity model. The estimate
of the gamma variance is σ 2 =0.72, and it is not statistically significant at any of the conventional significance levels. Nonetheless, the likelihood ratio test rejects the model without
unobserved heterogeneity in favor of this model (χ2(1) =5.36). The female dummy coefficient
drops considerably in absolute value from -0.30 to -0.18 after controlling for gamma unobserved heterogeneity, and it remains statistically insignificant. Thus, we find little gender
promotion gap to full professor. (#Universities worked) becomes statistically insignificant.
The coefficient for the private university dummy increases from 0.41 to 0.73 and remains
statistically significant. None of the publication variables have statistically significant effects
on promotion.
Finally, we discuss the results of the semi-parametric Heckman-Singer model. We identify three mass points. The first mass point is very small (exp(µ1 )=0.0005) and so is the standard error. The corresponding probability is 0.13, and it is statistically significant at the 1%
significant level. Thus, 13% of the academics have a very low rate of promotion (the hazard of
promotion decreases by a multiplicative factor of 0.0005). The second mass point is still small
(exp(µ2 )=0.017), but it is statistically significant with a corresponding probability equal to
0.31. The third mass point is the normalized mass point (exp(µ3 )=1) with a corresponding
probability equal to 0.66. The log likelihood ratio statistic for H0 :exp(µ1 )=exp(µ2 )=1 has
an asymptotic χ2(2) distribution (see Dolton and Von der Klaauw, 1995:439). The LR test
statistic is 19.74. Thus, we reject the null hypothesis of no unobserved heterogeneity at the
1% significance level in favor of the Heckman-Singer model.
The Heckman-Singer model shows a significantly different picture of promotion as com19
pared to the semi-parametric model without unobserved heterogeneity. The female coefficient
is much smaller in absolute value (-0.15) and it is statistically insignificant, indicating that
there is little gender promotion gap. All the coefficients that are statistically significant for
the model without unobserved heterogeneity remain statistically significant in this model.
In addition, many other coefficients have become statistically significant indicating that the
non-parametric unobserved heterogeneity specification significantly improves the fit of the
model. Both the number of children younger than age 2, and the number of children between age 3 and 6 now have negative and statistically significant effects. An additional child
would decrease the hazard of promotion by the multiplicative factor of exp(-0.91)= 0.40 and
exp(-0.38)=0.63, respectively. All the time dummy variables have negative and statistically
significant coefficients, indicating that promotion decisions in academia are fairly sensitive
to the general labour market conditions, and that promotion has become more difficult after
the 1970s. The cohort for those entering academia in the 1980s has a positive and statistically significant coefficient. However, the coefficients for all the other cohort dummies are
not significant.
The effect of a PhD from overseas increases dramatically from 0.41 to 2.27 after controlling for non-parametric unobserved heterogeneity, and it becomes statistically significant.
Thus, having a PhD from overseas increases the hazard of promotion by a multiplicative
factor of exp(2.27)=9.78. However, having a PhD degree does not have a statistically significant effect on promotion. In Japan, until recently, a PhD was granted as a life-time work
achievement rather than at the completion of a doctoral dissertation. Therefore, not having
a PhD does not indicate lower human capital. (#Universities worked) becomes statistically
insignificant. Among the publication variables, only working papers have a statistically significant effect on promotion. Refereed articles do not have a statistically significant effect
on the duration of promotion to full professor. In section IX we discuss the effect of working
20
papers on the survival probability of promotion.
The last two columns of Table 3 show the estimation results of the piecewise constant hazard function. The coefficients for the piecewise hazard model without heterogeneity are fairly
similar to the coefficients for our semi-parametric model without unobserved heterogeneity.
The coefficients for the piecewise constant gamma heterogeneity model are fairly similar
to those of the semi-parametric gamma heterogeneity model. As in the semi-parametric
gamma heterogeneity model, the log likelihood ratio test rejects the absence of unobserved
heterogeneity.
Estimates of Hazard Pieces for the Duration to Full Professor. Figure 2-A plots
the estimated baseline hazard function, γ(s), for our semi-parametric model without unobserved heterogeneity and semi-parametric gamma model, along with the estimated hazard
pieces for the piecewise constant hazard model without unobserved heterogeneity. The estimated hazard pieces are almost identical for the semi-parametric model without unobserved
heterogeneity and the piecewise constant model. In both models, the baseline hazard functions are almost zero at the beginning of the academic career, and they increase to reach the
first peak (0.017) at experience equal to 12. The second peak (0.05) is reached at experience
equal to 32. The hazard function for the semi-parametric gamma model lies slightly below
the baseline hazard function for semi-parametric model without unobserved heterogeneity.
For both models, the estimated hazard pieces are small, ranging between 0 and 0.06.
Figure 2-B plots the estimated baseline hazard function for our Heckman-Singer model
and for our semi-parametric gamma model. Similar to the semi-parametric gamma model,
the estimated hazard pieces for the Heckman-Singer model are small until the experience
equals 25 years. However, the Heckman-Singer hazard increases sharply to reach the peak of
8.3 at experience equal to 32. Thus, the Heckman-Singer hazard pieces show strong positive
time dependency. A comparison of the two models indicates that an incorrect distributional
21
assumption about the unobserved heterogeneity leads to a significant underestimation of the
time dependency of the hazard function.
C.
Estimates of the Duration of Promotion to Associate Professor
Table 4 shows the coefficients of the explanatory variables for the duration to associate
professor. Note that the sample for the duration to associate professor is smaller (271)
than the sample for the full professor (326). This is because some academics began their
academic career directly as an associate professor, and thus we eliminated them from the
analysis.13 The first three columns show the results for our semi-parametric specifications,
while columns four and five show the results of the piecewise constant hazard function
estimation for comparison purposes.
First, let us compare the statistical fit of each semi-parametric model. For the gamma
heterogeneity model, the estimated variance is σ 2 = 0.49, and it is marginally significant
at the 10% significance level. However, the incorporation of gamma heterogeneity does not
improve the fit of the model; the log likelihood ratio test fails to reject the model without
unobserved heterogeneity at the 5% significance level (χ2(1) = 2.81). The third column
in Table 4 shows the Heckman-Singer model with two mass points. The first estimated
mass point is large (26.12) and it is marginally significant, with a corresponding weight
equal to 0.86. However, the incorporation of non-parametric unobserved heterogeneity does
not improve the statistical fit of the model. In fact, the log-likelihood decreases from 500.8 to -515.2 after the incorporation of non-parametric unobserved heterogeneity. This
could be due to the possibility that we are trapped in a local maximum. However, several
trials with different starting values did not improve the log-likelihood statistic. In addition,
the piecewise constant model also fails to reject the absence of gamma heterogeneity (see
13
In a preliminary estimation, we included in the duration model to full professor a dummy variable for
academics who began their academic career as associate professors. The inclusion of this dummy variable
reduces the female coefficient in absolute value in all the model specifications. In our main results we did
not report this estimation due to the possible discriminatory initial rank assignment.
22
column 4 and 5). Combining all the results, we consider that the semi-parametric model
without unobserved heterogeneity is the most relevant model for the duration of promotion
to associate professor. Thus, we only discuss the coefficients for the semi-parametric model
without unobserved heterogeneity.
The coefficient for the female dummy is -0.15, indicating that females have longer time in
rank than males; however, this coefficient is not statistically significant at any conventional
significance levels. Both age and age squared do not have statistically significant coefficients.
We do not find a statistically significant effect of young children. All the coefficients for the
cohort dummies and time dummies are statistically insignificant.
As opposed to the promotion to full professor, academics who work in private universities have a lower hazard of promotion to associate professor. This indicates that private
universities tend to have a longer duration from an assistant professor to an associate professor, but have a shorter duration from an associate professor to a full professor as compared
to national universities. Due to the fact that there is little prior research about Japanese
academia, we are not able to provide an explanation for why there is such a difference in
the pattern of promotion between private and national universities. However, we note that,
due to the absence of a tenure track system in Japan, the difference between an assistant
professor and an associate professor is often blurred. For example, many academics are hired
directly as associate professors. Moreover, Takahashi and Takahashi (2009) report that the
salary premium for associate professors is small and statistically insignificant.
In the case of the duration to associate professor, having a doctorate increases the hazard
of promotion by a multiplicative factor of exp(0.46)=1.58. We do not find a statistically
significant effect of having a PhD from overseas. The coefficient for (#Universities worked)
is positive (0.3) and marginally significant, indicating that each job change increases the
hazard of promotion to associate professor by a multiplicative factor of exp(0.3)=1.35. None
23
of the publication variables have statistically significant effects on the promotion to associate
professor at the 5% significance level.
Figure 3-A plots the baseline hazard function for the semi-parametric model without
unobserved heterogeneity along with the hazard function for the piecewise constant model.
The hazard function for the semi-parametric model lies slightly above that of the piecewise constant model. The baseline hazard function of the semi-parametric model increases
sharply for the first three years to reach 0.22, hovers around 0.25 until year 11, then begins
to decrease. Thus, the estimated hazard function for the duration of promotion to associate
professor is much larger than the hazard function for the duration to full professor. However,
age and age squared no longer have statistically significant coefficients for the duration to associate professor. Thus, experience is a more important factor than age in the determination
of promotion to associate professor.
Figure 3-B compares the estimated hazard functions for all of the semi-parametric models. The semi-parametric gamma model has the highest hazard function while the HeckmanSinger model has the lowest hazard function.
IX.
Why Is There Little Gender Promotion Gap Within Japanese
Academia?
Our estimations suggest that there is little gender promotion gap within economics departments in Japan after controlling for personal, job, institutional, human capital characteristics, and non-parametric unobserved heterogeneity. The absence of gender promotion gap is
consistent with the findings by Fujimura (2002). Takahashi and Takahashi (2009) also find
no gender promotion differences within Japanese academia. However, our results contrast
sharply with major findings in the previous literature that report considerable gender promotion differences within US and UK academia. This raises the question of why promotion
is fairer within Japanese academia than within the US and UK.
24
One possibility is that promotion is indeed automatically done based on age, education
and experience, thus leaving little room for gender promotion differences. In order to check
this possibility, we conduct a sensitivity analysis to investigate the effect of experience, age,
and education on the survival probability of promotion to full professor.14
First, in order to determine the effect of experience on the survival probability of promotion to full professor, we compute the survival probability for the average academic by
holding constant at averages all the non-dummy explanatory variables (including age) and
by setting all the dummy variables equal to zero. Since all the non-dummy explanatory
variables are demeaned, the (age-fixed) unconditional survival function is written as:
Baseline age f ixed survivalf unction : S1 (t) =
J
X
exp[−
j=1
t
X
γ(s)eµj ]
(9)
s=1
Figure 4-A plots the above survival function with Table 5 showing the actual numbers.
The survival probability decreases with experience, but the rate of decrease is rather slow. At
experience equal to 15 years, the survival probability is 94.1%. Even at experience equal to
20, the survival probability is still as large as 73%. This indicates that experience alone does
not significantly count toward promotion to full professor for the first 20 years. However, at
experience equal to 30, the survival probability drops to 41%.
Second, in order to check the effect of age on the survival probability of promotion to
full professor, we allow the age variable to increase by one each year. As an example, we
have computed the survival probability of an academic who entered the academic labour
market at the age of 30, holding constant at averages all other non-dummy variables, except
age, and holding constant all dummies at zero. More specifically, we have computed the
following survival probability:
Age ef f ect : S2 (t) =
J
X
j=1
"
exp −
t
X
#
γ(s)exp[β1 (aget − age) +
β2 (age2t
µj
− agesq)]e
(10)
s=1
14
Some academics in our data started their academic career as associate professors. Since this could be
due to discriminatory initial rank assignment, we focus on the duration to full professor.
25
where aget =30 at t=1 and increases by one with t. Since all the non-dummy variables have
been demeaned, we need to subtract the sample averages, age and agesq, respectively.
Figure 4-B plots the above survival function together with the baseline age-fixed survival
function for comparison. The survival probability is 91% at age 40, and then it drops sharply
to 37% at age 45. This is a drop of as much as 53% within 5 years. Thus, age has a very
strong effect on the survival probability. As suggested by the figure, the survival probability
at a given age deviates considerably from the baseline age-fixed survival probability. At age
48 (20 years after the initial hiring), the survival probability is 15% while the baseline agefixed survival probability is 74% at experience equal to 20; a difference of 59%. Therefore,
age alone counts for nearly 60% drop in the survival probability during the first 20 years
of experience. From this, we can observe how dominant the effect of age is on the survival
probability. Nonetheless, one must be hard pressed to believe that the promotion within
Japanese academia is automatically done based on age. As it is suggested by Figure 4-B,
the probability of not being promoted is still 14% at age 50 and it is still 10% at age 55.
Let us now turn our attention to the effect of education on the survival probability. Since
(PhD overseas) is the only education variable that has a statistically significant effect, we
examine the effect of having a PhD degree from overseas on the survival probability. Figure
4-C plots the survival function for academics with and without a PhD from overseas. The
survival probability is again calculated for the average academic with age at hiring equal to
30. The survival probability for an academic with a PhD from overseas deviates considerably
from that of an academic who does not have such a degree. At age 40, the survival probability
for an academic with a PhD from overseas is lower than that of an academic without such
a degree by as much as 37%. Thus, a PhD from overseas significantly increases the speed of
promotion.
Our estimation results also show that the survival probability differs depending on the
26
type of university. Figure 4-D plots the survival probability for an academic who works in
a private university and for an academic from a national university. As opposed to national
universities, private universities have lower survival probabilities at any given age, although
this effect is modest. At age 45, the survival probability in private universities is lower than
that for national universities only by 8%.
Finally, we discuss the effect of publication productivity on the survival probability of
promotion to full professor. Our empirical results show that refereed articles do not have
statistical significant effects on the promotion probability. However, working papers have
a positive and statistically significant effect on the hazard of promotion to full professor.
In order to examine the actual effect of this type of publication on the promotion rate,
we compare the survival probability of the average academic (WorkPapers=0.62) with the
survival probability of the academic whose working paper publishing rate is at the 75th
percentile (WorkPapers=0.83). Figure 4-E shows the result. There is 7.5 percentage point
difference in the survival probability between the two academics at the age of 41. However,
this difference quickly vanishes at the age of 43 due to the dominant effect of age. Therefore,
the reward for publishing working papers is very small. This is not a surprising result
since working papers do not usually go through a referee process, and thus, they may be
less acknowledged. Combining this result with the fact that all the other publications do
not have statistically significant effects on the duration of promotion, we can conclude that
the reward for publication productivity in terms of promotion is minimal within Japanese
academia.
To sum, our results suggest that age and a PhD from overseas are important determinants of promotion to full professor; with age being the most dominant determinant.
Experience counts, but the effect is not large, at least not for the first 20 years. We also
found that the reward for publication productivity is small. Our results are consistent with
27
the commonly held belief among Japanese academics that promotion is mainly decided based
on age and education level. A heavy emphasis on objective factors, such as age and educational qualifications (PhD from overseas), could be one reason for why there is little gender
promotion gap within Japanese academia.
Finally, we admit that our results could have been influenced by the relatively small
sample size, though the size of our sample is comparable with many of the previous studies.
We hope, however, that our study will stimulate further investigation into the academic
labour market in Japan, a market that has seldom been analysed.
X.
Selection Bias
Self-selection into the academic labour market might be a potential source of bias in the
female coefficient. Since we only observe a sample of those working in academia, we cannot
directly control for selection bias. Therefore, we attempt to discuss potential directions of
the biases by utilizing statistics of PhD graduates in Japan for the period 1969-2007. MEXT
Statistics of School Education provide basic statistics of PhD graduates in social sciences.15
Figure 5-A summarizes the number of PhD graduates in social sciences during the period
1969-2007. There was a very small number of females who graduated from PhD programs in
social sciences until 1990 (the average numbers of males and females were 184.36 and 8.77
per year, respectively). Figure 5-B summarizes the percentage of PhD graduates in social
sciences who entered academia over the period 1969-2007.16 The percentage is much higher
for males than females until 1990 (the average percentages for males and females were 76.8
and 34.0 per year, respectively). However, the percentages appear to converge after 1990.
The average percentages for males and females after 1990 are 64.76 and 63.55, respectively.
The lower percentage of females joining academia before 1990 potentially causes sample
15
16
We do not have separate data for those with a degree in economics.
Numbers include those who joined four year universities as well as two year college.
28
selection bias in our estimation. If females who potentially faced lower promotion prospects
in the academic labour market decided not to join academia, then we may be underestimating
the gender promotion gap. Therefore, our results should be interpreted with caution.
XI.
Conclusion
By using a unique data set of academic economists from Japanese universities, we have conducted one of the first and the most detailed study of gender differences in the duration of
promotion within Japanese academia. We employed a duration model that simultaneously
allows: a non-parametric estimation of the baseline hazard function; a non-parametric specification of the unobserved heterogeneity component; and the estimation of parameterized
coefficients for the observed explanatory variables. It is commonly believed by Japanese academics that there cannot be gender promotion differences within Japanese academia since
promotion is decided mainly based on age, with some adjustments given for education level.
Our results are consistent with this belief. We have shown that there is little gender promotion gap, after controlling for personal, job, institutional, human capital characteristics, and
unobserved heterogeneity. Age alone counts for nearly 60% drop in the survival probability
of not being promoted to full professor for the first 20 years of experience. A PhD degree
from overseas is associated with a 37% lower survival probability at age 40. Experience
counts but the magnitude is small. The reward for publication is small in magnitude. A
heavy emphasis on objective factors, such as age and educational qualifications, could be one
reason for why there is little gender promotion gap within Japanese academia. Our results
contrast sharply with the results of many previous studies which report substantial gender
promotion gaps within US and UK academia. Our semi-parametric analysis reveals that (i)
an incorrect distributional assumption about the unobserved heterogeneity leads to a significant underestimation of the time dependency in hazard function, and (ii) a non-parametric
unobserved heterogeneity specification substantially improves parameter significance.
29
REFERENCES
Aigner, D. J. and Cain, G. G. (1977) Statistical Theories of Discrimination in Labor Markets,
Industrial and Labor Relations Review, 30(2), 175-187.
Becker, G. S. (1957) The Economics of Discrimination 2nd., Chicago: The University of
Chicago Press.
Black, D. A. (1995) Discrimination in an Equilibrium Search Model, Journal of Labor Economics, 13(2), 309-334.
Blackaby, D., Booth, A. L. and Frank, J. (2005) Outside Offers and the Gender Pay
Gap: Empirical Evidence from the UK Academic Labor Market, Economic Journal,
115(February), F81-107.
Broder, I. E. (1993) Professional Achievements and Gender Differences among Academic
Economists, Economic Inquiry, 31, 116-127.
Dolton, P. and Von Der Klaauw, W. (1995) Leaving Teaching in the UK: A Duration Analysis, Economic Journal, 105(429), 431-444.
Fujimura, Masaji (2002) Daigaku Kyouin no Shotoku Kansu no Keisoku to Shoukaku, Hiroshima University Research Institute for Higher Education Daigaku Ronshu, 117-229
(in Japanese).
Ginther, D. K. and Hayes, K. J. (2003) Gender Differences in Salary and Promotion for the
Faculty in the Humanities 1977-95, Journal of Human Resources, 38(1), 34-73.
Ginther, D. K. and Khan, S. (2004) Women in Economics: Moving up or Falling off the
Academic Career Ladder?, Journal of Economic Perspectives, 18(3), 193-214.
Goldberg, M. S. (1982) Discrimination, Nepotism, and Long-Run Wage Differentials, Quarterly Journal of Labor Economics, May, 308-319.
Han, A. and Hausman, J. A. (1990) Flexible Parametric Estimation of Duration and Competing Risk Models, Journal of Applied Econometrics, (5), 1-28.
Heckman, J. and Singer, B. (1984) A Method for Minimizing the Impact of Distribution
Assumptions in Econometric Models for Duration Data, Econometrica, 52(2), 271320.
Khan, S. (1993) Gender Differences in Academic Career Paths in Economists, American
Economic Review, 83(2), 52-56.
Koplin V. W. and Singell Jr., L. D. (1996) The Gender Composition and Scholarly Performance of Economics Departments: A Test for Employment Discrimination, Industrial
and Labor Relations Review, 49(3), 408-423.
Lazear, E. P. and Rosen, S. (1990) Male-Female Wage Differentials in Job Ladders, Journal
of Labor Economics, Part 2: Essays in Honor of Albert Rees, 8(1), S106-S123.
Long, S. J., Allison, P. D. and McGinnis, R. (1993) Rank Advancement in Academic Careers:
Sex Differences and the Effects of Productivity, American Sociological Review, 58(5),
703-722.
30
Lundberg, S. J. and Startz, R. (1983) Private Discrimination and Social Intervention in
Competitive Labor Market, American Economic Review, 73(3), 340-347.
McDowell, J. M., Singell Jr., L. D. and Ziliak, J. P. (2001) Gender and Promotion in the
Economics Profession, Industrial and Labor Relations Review, 54(2), 224-244.
Meyer, B. (1986) Semi-parametric Estimation of Hazard Models, Department of Economics,
MIT.
Milgrom, P. and Oster, S. (1987) Job Discrimination, Market Forces, and the Invisibility
Hypothesis, Quarterly Journal of Economics, 102(3), 453-476.
Moore, W. J., Newman, R. J. and Terrell, D. (2007) Academic Pay in the United Kingdom and the United States: The Differential Return to Productivity and the Lifetime
Earnings Gap, Southern Economic Journal, 73(3), 717-732.
Phelps, E. S. (1972) The Statistical Theory of Racism and Sexism.”, American Economic
Review, 62(4), 659-661.
Takahashi, A. M. and Takahashi, S. (2009) Gender Salary Differences in Economics Departments in Japan, Unpublished manuscript presented at Kansai Labour Seminar at
Osaka University on May 22.
Ward, M. E. (2001) Gender and Promotion in the Academic Profession, Scottish Journal of
Political Economy, 48(3), 283-302.
Winter-Ebmer, R. and Zweimuller, J. (1997) Unequal Assignment and Unequal Promotion
in Job Ladders, Journal of Labor Economics, 15(1), part 1, 43-71.
31
TABLE 1: Definitions of Variables
Name
Personal characteristics
Female
†Aget
†Marriedt
†(Kids age0-2)t
†(Kids age3-6)t
Job characteristics
Labour
Field missing
Cohort 80s
Cohort 90s
Cohort 2000-03
Cohort 2004
†(Time 80s)t
†(Time 90s)t
†(Time 2000-03)t
†(Time 2004-08)t
Institutional
†(Private univ)t
†(Public univ)t
Human capital
Non-academic exp.
PhD
PhD overseas
†(#Universities worked)t
Publication rates(c)
RefereedSgJP
RefereedSgUSEU
RefereedCoJP
RefereedCoUSEU
WorkPapers
BookSgJP
BookSgUSEU
BookCoJP
BookCoUSEU
BookEdJP
BookEdUSEU
BookChJP
BookChUSEU
Textbook
Publication missing(d)
Definition
1 if female, 0 if male
Age of the respondent at experience equal to t
1 if married at experience equal to t, 0 otherwise
Number of children between age 0-2 at experience equal to t
Number of children between age 3-6 at experience equal to t
1
1
1
1
1
1
1
1
1
1
if
if
if
if
if
if
if
if
if
if
specialized in labour economics, 0 otherwise
field of specialization is missing observation
initially hired as academic in the 80s, 0 otherwise
initially hired as academic in the 90s, 0 otherwise
initially hired as academic between 2000-2003, 0 otherwise
initially hired as academic from 2004 onward, 0 otherwise
the year is in the 80s, 0 otherwise
the year is in the 90s, 0 otherwise
the year is between 2000-2003, 0 otherwise
the year is from 2004 onward, 0 otherwise
1 if working in a private university at experience equal to t
1 if working in a public university at experience equal to t
Total number of years worked as non-academic
1 if holds a PhD, DSc. or DEc.(b)
1 if holds a PhD, DSc. or DEc. from overseas
Number of universities previously worked (including current univ)
Refereed single-authored articles published in Japan
Refereed single-authored articles published in US & Europe
Refereed co-authored articles published in Japan
Refereed co-authored articles published in US & Europe
Working papers published in Japan, US & Europe
Books single authored published in Japan
Books single authored published in US & Europe
Books co-authored published in Japan
Books co-authored published in US & Europe
Books edited published in Japan
Books edited published in the US & Europe
Book chapters published in Japan
Book chapters published in the US & Europe
Textbooks published in Japan, US & Europe
1 if the publication record is missing observation
Notes: (a) † indicates that the variable is time-variant. (b) Doctor of Science (DSc.); Doctor of Economics
(DEc.). (c) Publication rates = the total number of publications over career divided by the total experience
as an academic as of 2008. (d) When publication information is missing, sample averages are imputed. (e)
Publications published in US and Europe also include publication published in other countries.
32
TABLE 2: Summary Statistics
Variable name
Personal
Female
Age
Married
Kids age0-2
Kids age3-6
Job
Labour
Field missing
Cohort 80s
Cohort 90s
Cohort 2000-03
Cohort 2004-08
Institutional
Private Univ
Public Univ
Human capital
Non-academic exp.
PhD
PhD overseas
#Universities worked
Publication rates
RefereedSgJP
RefereedSgUSEU
RefereedCoJP
RefereedCoUSEU
WorkPapers
BookSgJP
BookSgUSEU
BookCoJP
BookCoUSEU
BookEdJP
BookEdUSEU
BookChJP
BookChUSEU
Textbook
Publication missing
All
Male
Female
#Subjects(326)
#Obs.(3296)
# Subjects(271)
#Obs.(2815)
# Subjects(55)
#Obs.(481)
Mean
Std.
Mean
Std.
Mean
Std.
0.146
36.575
0.784
0.252
0.319
0.353
6.128
0.411
0.484
0.576
0
36.554
0.803
0.274
0.346
6.022
0.398
0.500
0.595
1
36.701
0.674
0.129
0.166
6.721
0.469
0.354
0.425
0.079
0.032
0.295
0.271
0.085
0.039
0.270
0.175
0.456
0.444
0.279
0.193
0.069
0.031
0.298
0.250
0.079
0.027
0.253
0.174
0.457
0.433
0.270
0.162
0.139
0.033
0.279
0.389
0.123
0.106
0.347
0.180
0.449
0.488
0.328
0.308
0.510
0.091
0.500
0.287
0.491
0.095
0.500
0.294
0.622
0.064
0.485
0.246
1.605
0.610
0.092
1.376
4.045
0.488
0.289
0.619
1.573
0.623
0.097
1.374
4.079
0.485
0.296
0.623
1.792
0.536
0.062
1.389
3.837
0.499
0.242
0.599
0.257
0.059
0.117
0.076
0.613
0.058
0.002
0.091
0.007
0.033
0.003
0.186
0.021
0.039
0.076
0.418
0.152
0.343
0.194
0.576
0.109
0.012
0.161
0.034
0.082
0.017
0.309
0.086
0.086
0.264
0.249
0.057
0.116
0.082
0.622
0.054
0.002
0.092
0.006
0.035
0.003
0.184
0.018
0.038
0.076
0.390
0.141
0.318
0.204
0.579
0.097
0.011
0.158
0.032
0.085
0.010
0.319
0.050
0.077
0.266
0.305
0.068
0.123
0.040
0.559
0.081
0.002
0.085
0.008
0.020
0.004
0.197
0.034
0.049
0.071
0.554
0.206
0.460
0.116
0.557
0.164
0.018
0.182
0.042
0.059
0.036
0.237
0.190
0.128
0.257
This table shows the summary statistics for the expanded data. For a snapshot description
of the data at the survey time, see Section VII.
33
TABLE 3: Duration of Promotion to Full Professor
Semi-parametric
Variables
Female
Piecewise constant
Without
With gamma Heckman
heterogeneity heterogeneity Singer
-0.296
(0.327)
†Age
1.369***
(0.390)
Age2
-0.014***
(0.005)
†Married
0.212
(0.352)
†Kids age0-2
-0.681*
(0.387)
†Kids age3-6
0.018
(0.146)
Labor
0.162
(0.374)
Cohort 80s
0.242
(0.409)
Cohort 90s
0.107
(0.653)
Cohort 2000-03 -0.597
(1.317)
Cohort 2004-08 -2.776
(3.173)
†Time 80s
0.590
(0.803)
†Time 90s
0.395
(0.870)
†Time 2000-03 0.062
(0.967)
†Time 2004-08 0.946
(1.021)
†Private univ
0.415**
(0.204)
†Public univ
-0.021
(0.368)
Non-academic
-0.021
experience (0.060)
(Non-academic 0.003
experience)2 (0.003)
PhD
0.124
(0.280)
PhD overseas
0.414
(0.370)
-0.183
(0.424)
1.459***
(0.453)
-0.014***
(0.005)
0.540
(0.472)
-0.811*
(0.416)
-0.128
(0.175)
-0.018
(0.489)
0.514
(0.507)
0.445
(0.819)
-0.453
(1.629)
-2.903
(3.774)
0.567
(0.800)
-0.057
(0.922)
-0.430
(1.067)
0.470
(1.133)
0.733**
(0.289)
-0.030
(0.492)
0.004
(0.082)
0.0004
(0.005)
0.268
(0.334)
0.878
(0.538)
-0.153
(0.445)
2.553***
(0.478)
-0.025***
(0.005)
2.107***
(0.498)
-0.912**
(0.422)
-0.381**
(0.191)
0.346
(0.617)
2.019***
(0.549)
1.340
(0.832)
-1.487
(2.050)
-6.342
(4.154)
-1.736**
(0.780)
-2.846***
(0.964)
-3.094***
(1.159)
-2.206*
(1.185)
0.992***
(0.313)
-0.894
(0.526)
0.193**
(0.087)
-0.002
(0.004)
0.070
(0.345)
2.272***
(0.647)
34
Without
With gamma
heterogeneity heterogeneity
-0.196
(0.253)
1.313***
(0.249)
-0.014***
(0.003)
0.191
(0.241)
-0.633**
(0.267)
0.020
(0.136)
0.107
(0.215)
0.159
(0.279)
-0.012
(0.432)
-1.038
(0.872)
-2.816**
(1.352)
0.568
(0.473)
0.379
(0.533)
0.119
(0.632)
0.777
(0.649)
0.391**
(0.170)
-0.016
(0.263)
-0.025
(0.044)
0.003
(0.002)
0.125
(0.160)
0.366
(0.294)
0.102
(0.491)
1.699***
(0.381)
-0.017***
(0.004)
0.416
(0.397)
-0.861**
(0.423)
0.017
(0.236)
-0.167
(0.643)
0.823
(0.771)
0.610
(0.861)
-0.842
(1.222)
-3.892
(2.170)
0.797
(1.292)
-0.059
(1.364)
-1.043
(1.543)
0.577
(1.461)
0.844**
(0.383)
-0.248
(0.576)
0.031
(0.118)
0.0001
(0.006)
0.381
(0.372)
1.407**
(0.625)
Table 3 Continued
†#Universities worked
RefereedSgJP
RefereedSgUSEU
RefereedCoJP
RefereedCoUSEU
WorkPapers
BookSgJP
BookSgUSEU
BookCoJP
BookCoUSEU
BookEdJP
BookEdUSEU
BookChJP
BookChUSEU
Textbook
0.271*
(0.139)
0.177
(0.320)
0.171
(1.216)
0.026
(0.523)
0.582
(0.915)
0.376*
(0.195)
1.489
(1.392)
-2.476
(17.287)
0.424
(0.763)
1.220
(5.430)
1.889
(1.472)
-8.082
(12.547)
-0.397
(0.412)
1.571
(2.919)
0.865
(1.614)
σ2
0.354
(0.210)
0.324
(0.483)
-0.175
(1.784)
-0.065
(0.652)
0.586
(1.338)
0.437
(0.270)
2.486
(1.607)
-5.410
(17.293)
0.083
(1.183)
1.499
(9.599)
2.164
(1.869)
-14.909
(14.838)
-0.706
(0.556)
4.285
(3.704)
1.460
(2.079)
0.717
(0.430)
exp(µ1)
exp(µ2)
p1
p2
Log-likelihood
# Obs.
# Subjects
-514.267
3296
326
-511.584
3296
326
0.185
(0.204)
0.559
(0.558)
-0.729
(1.845)
-1.069
(1.191)
0.515
(1.715)
1.843***
(0.352)
3.018
(2.242)
-3.874
(26.988)
1.640
(1.250)
8.732
(5.857)
2.735
(2.344)
-13.593
(14.200)
-1.373
(0.732)
5.927
(3.618)
3.435
(2.229)
0.245**
(0.098)
0.168
(0.197)
0.148
(0.661)
0.001
(0.143)
0.484
(0.456)
0.340***
(0.116)
1.285*
(0.694)
-1.832
(7.738)
0.386
(0.402)
1.225
(1.517)
1.632**
(0.653)
-8.156
(9.140)
-0.378
(0.284)
1.664
(2.048)
0.799
(1.003)
0.278
(0.217)
0.392
(0.329)
-0.443
(1.025)
-0.089
(0.268)
0.513
(0.605)
0.497*
(0.254)
2.615
(1.767)
-2.540
(25.696)
0.057
(1.029)
1.166
(2.668)
2.420**
(1.088)
-22.528
(19.809)
-0.751
(0.706)
5.281***
(2.011)
1.086
(1.779)
0.193***
(0.056)
0.0005
(0.001)
0.017**
(0.008)
0.130***
(0.033)
0.314***
(0.046)
-504.396
3296
326
-66.086
3296
326
-61.094
3296
326
Notes: (a) † indicates a time-variant variable. (b) exp(µ3) is normalized to one with p3=1-p1-p2. (c) Inside
the parentheses are std. errors. For the semi-parametric models, BHHH procedure is used. For the piecewise
models, robust standard errors are reported. ***Significant at the 1%, ** at the 5%, * at the 10% level. (d)
Regressions include (Field missing) and (Publication missing) dummies. (e) Obs. promoted =201.
35
TABLE 4: Duration of Promotion to Associate Professor
Semi-parametric
Piecewise constant
Variables
Without
With gamma Heckman
heterogeneity heterogeneity Singer
Without
With gamma
heterogeneity heterogeneity
Female
-0.146
(0.334)
0.431
(0.363)
-0.005
(0.005)
-0.006
(0.242)
-0.037
(0.179)
0.128
(0.180)
0.152
(0.316)
0.434
(0.396)
0.522
(0.576)
-0.280
(0.805)
-0.267
(0.987)
0.230
(0.375)
-0.339
(0.545)
0.585
(0.678)
1.127
(0.875)
-0.572***
(0.214)
-0.273
(0.323)
0.022
(0.133)
-0.007
(0.015)
0.460**
(0.219)
-0.478
(0.416)
-0.140
(0.152)
0.385**
(0.160)
-0.004**
(0.002)
0.007
(0.152)
-0.004
(0.117)
0.095
(0.120)
0.151
(0.198)
0.349
(0.239)
0.385
(0.365)
-0.209
(0.510)
-0.396
(0.569)
0.229
(0.247)
-0.214
(0.365)
0.538
(0.400)
0.896*
(0.495)
-0.471***
(0.132)
-0.242
(0.194)
0.003
(0.041)
-0.005*
(0.003)
0.390***
(0.132)
-0.392**
(0.182)
†Age
†Age2
†Married
†Kids age0-2
†Kids age3-6
Labour
Cohort 80s
Cohort 90s
Cohort 2000-03
Cohort 2004-08
†Time 80s
†Time 90s
†Time 2000-03
†Time 2004-08
†Private univ
†Public univ
Non-academic
experience
(Non-academic
experience)2
PhD
PhD overseas
-0.368
(0.385)
0.487
(0.413)
-0.005
(0.006)
0.094
(0.283)
-0.011
(0.206)
0.109
(0.208)
0.184
(0.376)
0.837
(0.505)
0.914
(0.704)
0.207
(0.934)
0.165
(1.146)
0.128
(0.407)
-0.676
(0.604)
0.526
(0.756)
0.945
(0.961)
-0.855***
(0.274)
-0.371
(0.375)
0.015
(0.158)
-0.011
(0.018)
0.611**
(0.276)
-0.806
(0.496)
36
-0.614**
(0.295)
0.616
(0.381)
-0.007
(0.006)
0.374
(0.258)
-0.018
(0.195)
-0.086
(0.211)
0.120
(0.289)
1.198***
(0.405)
1.120*
(0.611)
1.076
(0.810)
0.844
(1.050)
0.174
(0.356)
-0.623
(0.509)
0.353
(0.652)
0.588
(0.835)
-0.978*
(0.229)
-0.120
(0.356)
-0.017
(0.109)
-0.011
(0.011)
0.648***
(0.232)
-0.907**
(0.405)
-0.140
(0.152)
0.385**
(0.160)
-0.004**
(0.002)
0.007
(0.152)
-0.004
(0.117)
0.095
(0.120)
0.151
(0.198)
0.349
(0.239)
0.385
(0.365)
-0.209
(0.510)
-0.396
(0.569)
0.229
(0.247)
-0.214
(0.365)
0.538
(0.400)
0.896*
(0.495)
-0.471
(0.132)
-0.242
(0.194)
0.003
(0.041)
-0.005*
(0.003)
0.390***
(0.132)
-0.392**
(0.182)
Table 4 Continued
†#Universities worked
RefereedSgJP
RefereedSgUSEU
RefereedCoJP
RefereedCoUSEU
WorkPapers
BookSgJP
BookSgUSEU
BookCoJP
BookCoUSEU
BookEdJP
BookEdUSEU
BookChJP
BookChUSEU
Textbook
0.296*
(0.170)
0.040
(0.281)
0.190
(0.768)
-0.345
(0.363)
0.386
(0.581)
0.146
(0.176)
0.051
(1.519)
7.798
(9.040)
0.667
(0.676)
-3.229
(3.499)
-0.089
(1.667)
-16.736*
(9.686)
-0.545*
(0.319)
1.947
(1.319)
-0.360
(1.320)
σ2
0.564**
(0.244)
0.014
(0.325)
0.167
(0.876)
-0.446
(0.449)
0.379
(0.687)
0.222
(0.215)
-0.030
(1.905)
11.936
(11.528)
0.788
(0.820)
-4.523
(4.095)
0.345
(2.013)
-28.026**
(12.691)
-0.242
(0.445)
2.050
(1.870)
-0.546
(1.615)
0.493*
(0.279)
exp(µ1)
p1
Log-likelihood
#Obs.
# Subjects
-500.823
1181
271
-499.418
1181
271
0.842**
(0.235)
-0.036
(0.287)
-0.208
(0.881)
-0.193
(0.317)
0.052
(0.638)
0.123
(0.181)
1.029
(1.258)
13.886
(8.999)
0.478
(0.727)
-1.042
(7.869)
0.053
(1.861)
-4.440
(13.788)
-0.536
(0.388)
2.769
(1.858)
-0.527
(1.380)
0.201*
(0.105)
0.074
(0.129)
0.159
(0.299)
-0.281*
(0.147)
0.391
(0.321)
0.126
(0.100)
0.024
(0.529)
6.150
(3.760)
0.546*
(0.288)
-2.729*
(1.505)
0.206
(0.794)
-14.692
(7.353)
-0.437
(0.344)
1.752*
(0.958)
-0.414
(0.478)
0.201*
(0.105)
0.074
(0.129)
0.159
(0.299)
-0.281*
(0.147)
0.391
(0.321)
0.126
(0.100)
0.024
(0.529)
6.151
(3.760)
0.546*
(0.288)
-2.729*
(1.505)
0.206
(0.794)
-14.693
(7.353)
-0.437
(0.344)
1.752*
(0.958)
-0.414
(0.478)
10−7 **
(6 × 10−8 )
26.125*
(13.850)
0.865***
(0.029)
-515.198
1181
271
-240.556
1181
271
-240.556
1181
271
Notes: (a) † indicates a time-variant variable. (b) exp(µ2) is normalized to one with p2=1-p1. (c) Inside the
parentheses are std. errors. For the semi-parametric models, BHHH procedure is used. For the piecewise
models, robust standard errors are reported. ***Significant at the 1%, ** at the 5%, * at the 10% level. (d)
Regressions include (Field missing) and (Publication missing) dummies. (e) Obs. promoted =248.
37
TABLE 5: Sensitivity Analysis of Survival Probability of Promotion to Full Professor
Sensitivity analysis
Experience
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
Age
Baseline
survival
Age
effect
PhD
overseas
effect
Private
univ.
effect
Working
papers
effect
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
1.000
1.000
1.000
1.000
1.000
1.000
0.999
0.998
0.995
0.992
0.987
0.978
0.966
0.954
0.941
0.910
0.887
0.841
0.812
0.728
0.702
0.656
0.594
0.594
0.594
0.514
0.514
0.449
0.449
0.414
0.414
0.375
0.375
0.375
0.375
0.375
1.000
1.000
1.000
1.000
1.000
1.000
0.999
0.997
0.985
0.962
0.910
0.754
0.570
0.465
0.422
0.383
0.349
0.278
0.236
0.154
0.140
0.123
0.112
0.112
0.112
0.103
0.103
0.093
0.093
0.084
0.084
0.074
0.074
0.074
0.074
0.074
1.000
1.000
1.000
1.000
1.000
0.999
0.994
0.969
0.874
0.723
0.537
0.417
0.377
0.327
0.265
0.162
0.128
0.106
0.097
0.069
0.060
0.044
0.027
0.027
0.027
0.013
0.013
0.005
0.005
0.002
0.002
0.001
0.001
0.001
0.001
0.001
1.000
1.000
1.000
1.000
1.000
1.000
0.998
0.991
0.962
0.904
0.789
0.554
0.436
0.406
0.379
0.304
0.248
0.166
0.139
0.110
0.105
0.097
0.084
0.084
0.084
0.068
0.068
0.053
0.053
0.040
0.040
0.028
0.028
0.028
0.028
0.028
1.000
1.000
1.000
1.000
1.000
1.000
0.999
0.995
0.979
0.945
0.873
0.678
0.501
0.432
0.407
0.358
0.315
0.232
0.191
0.129
0.121
0.112
0.103
0.103
0.103
0.091
0.091
0.080
0.080
0.068
0.068
0.056
0.056
0.056
0.056
0.056
Column 3: Unconditional baseline survival for the average academic, holding constant at averages all the
non-dummy variables and setting all dummy variables equal to zero; Column 4: Survival probabilities of
an academic who entered into academia at age 30, holding constant at average all variables except for age;
Column 5-7 compute survival probabilities for those who entered into academia at the age of 30; Column 7:
Survival function for those whose working paper publication rates are at the 75th percentile level (0.83).
38
FIGURE 1:
Kaplan−Meier Survival Estimates
0.25
0.50
0.75
1.00
B: Duration to associate professor
0.00
0.00
0.25
0.50
0.75
1.00
A: Duration to full professor
0
10
20
analysis time
Male
30
40
0
10
Female
20
analysis time
Male
30
40
Female
FIGURE 2:
Estimated Hazard Function for the Duration to Full Professor
B: Duration to full−professor
0
2
Baseline Hazard
4
Baseline Hazard
.02
.04
6
8
.06
A: Duration to full−professor
10
20
30
Experience in years
40
0
0
0
Semi−parametric no heterogeneity
10
20
30
Experience in years
40
Semi−parametric gamma
Semi−parametric Heckman−Singer
Piecewise constant no heterogeneity
Semi−parametric gamma
39
FIGURE 3:
Estimated Hazard Function for the Duration to Associate Professor
B: Duration to associate professor
0
.2
.5
Baseline Hazard
.4
.6
Baseline Hazard
1
1.5
.8
2
1
A: Duration to associate professor
0
0
0
10
20
30
Experience in years
40
10
20
30
Experience in years
40
Semi−parametric no heterogeneity
Semi−parametric no heterogeneity
Semi−parametric gamma
Piecewise constant no heterogeneity
Semi−parametric Heckman−Singer
40
FIGURE 4:
Sensitivity Analysis (Actual numbers are in Table 5)
0
Survival probability
.2
.4
.6
.8
1
A: Baseline age−fixed survival
1
6
11 16 21 26 31 36 41
Experience in years
0
0
Survival probability
.2 .4 .6 .8 1
C: PhD from overseas effect
Survival probability
.2 .4 .6 .8 1
B: Age effect
30 35 40 45 50 55 60 65
Age
30 35 40 45 50 55 60 65
Age
Baseline age−fixed
No PhD from overseas
Age effect
PhD from overseas
0
0
Survival probability
.2 .4 .6 .8 1
E: Working paper effect
Survival probability
.2 .4 .6 .8 1
D: Private university effect
30 35 40 45 50 55 60 65
Age
30 35 40 45 50 55 60 65
Age
National universities
Average (0.62)
Private universities
75th percentile (0.83)
41
FIGURE 5:
PhD Graduates Statistics
Number of graduates
0 100 200 300 400
A: Number of PhD graduates in social science
1970
1980
1990
year
% of students hired by universities
.2
.4
.6
.8
1
male
2000
2010
female
B: Percentage of PhD graduates hired by universities
1970
1980
1990
year
% male
2000
2010
% female
Source: MEXT Statistics of School Education.
42