Gender Promotion Gap in Japanese Academia in 2004-2013:
Has It Changed Over Time?
Ana Maria Takahashi
Shingo Takahashi
Atsuko Ueda
December ,2019
Discussion Paper No.1914

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANGender Promotion Gap in Japanese Academia in
2004-2013: Has It Changed Over Time?∗
Ana Maria Takahashi†
Kobe University, Graduate School of Economics
Rokkodai-cho 1-1, Nada, Kobe, Hyogo, 657-0013, Japan
E-mail: takahashi a@people.kobe-u.ac.jp
Shingo Takahashi
Hiroshima University, Graduate School for International Development and Cooperation
1-5-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, 739-8529, Japan
E-mail: staka29@hiroshima-u.ac.jp
Atsuko Ueda
Waseda University, School of Political Science and Economics
1-6-1 Nishi-waseda, Shinjuku-ku, Tokyo, 169-8050, Japan
E-mail: aueda@waseda.jp

Abstract
Using a complete survey of the entire faculty covering 2004 to 2013, we examine thegender promotion gap in Japanese academia and assess how it changed over time. The
gap at the full professor rank in national and local public universities stayed constant atslightly above 7 percentage points, while the gap in private universities exhibited a mild
increase from 5.9 to 8.1 percentage points. When we combine all universities, the gap showsa slight increase from 6.9 to 7.8 percentage points. Science, technology, engineering, andmathematics (STEM) fields and social science fields in national and local public universities
have significantly higher gaps than other fields. We do not find consistent evidence thatthe two governmental grants, the ‘Grant Program for Supporting Female Researchers’ andthe ‘Grant Program to Accelerate the Reform of Training Female Researchers’, that aimed
to foster female academics’ careers, have reduced the gender promotion gaps. We also findno evidence that these grants increased the department level share of female faculty.
JEL Classification: J7

1.

Introduction

This paper examines gender promotion differences in Japanese academia using a complete
survey of the entire faculty in Japan covering 2004 through 2013.1 This paper’s goals are
as follows. First, we investigate the extent of gender promotion gaps in Japanese academia
after controlling for observable characteristics, and then assess how these gaps have changed∗

This research was supported by Grant-in-Aid for Scientific Research (B) No.25285094 and Grant-in-Aid
for Young Scientists (B) No.15K17077 provided by the Japan Society for the Promotion of Science.
†
Corresponding author
1
2004, 2007, 2010, 20131

over time. To our knowledge this is the first study to use population census data for such aninvestigation. Second, we examine the gender promotion gap in different fields. In particular, we are interested to see whether the gaps in STEM fields are different from other fields,
given the government’s effort to tackle the under-representation of women in these fields.
Third, we examine if the two governmental grants, ‘Grant Program for Supporting Female
Researchers’ (hereafter Support Grant) and ‘Grant Program to Accelerate the Reform ofTraining Female Researchers’ (hereafter Acceleration Grant) have reduced the gender promotion gap. Our data set covers the period before and after the government began to offer
these two grants. This study is the first attempt to examine the impact of these two grants
on the promotion gap.
Achieving gender equality in academia has been an important policy agenda in Japanfor more than a decade. In 2006, recognizing the low representation of women in academia
and the lack of women in upper ranks, MEXT2 invited universities to apply for the aforementioned Support Grant in which universities were given monetary grants to finance various
work-balance programs.3 In 2009, MEXT invited universities to apply for an even larger
grant, the Acceleration Grant, that specifically aimed to foster the career of young female
researchers in STEM fields by providing funds to the universities to hire and assist young
female academics.4
Despite these initiatives, there is a lack of studies regarding the gender promotion
differences in Japanese academia. Takahashi and Takahashi (2015) show, by using datafrom a mail survey conducted in 2008 for economics departments in Japan, that the genderpromotion gap exists, and is most prominent in the early career, from the initial hiring to2

Ministry of Education, Culture, Sports, Science and Technology
Typical examples of such programs are: the provision of vouchers for the use of baby sitters and theprovision of grants for female researchers with young children to hire research assistants. The grant wasoffered only to universities that have STEM fields due to the particular low representation of women inthese fields. Nevertheless, the grant could be used university-wide.
4
The maximum grant per year was 22 million yen for the Support Grant and 80 million yen for the
Acceleration Grant.
32

associate professor position. Unfortunately, their study is limited to a single field in a single
survey year. This is a significant shortcoming since studies on the US academia consistently
show that gender promotion gap vary across fields, and that the gap declined over time inmany fields, as we briefly summarize below.
Kahn (1993) shows, by using the Survey of Doctorate Recipients (SDR), that mediantime to tenure is 7 years longer for women in economics departments.5 McDowell et al.
(2001) show, by using the data of the members of American Economic Association, thatsuch gender promotion gap might have disappeared by the end of 1980s, while Ginther andKahn (2004) show by using SDR data that gender promotion gaps in economics persisted
well into the 1990s, with women’s probability of attaining tenure lower than men by 13 to
15 percentage points.
While economics exhibits a substantial gender promotion gap, other social science fields
have a better track record. Ginther and Kahn (2009) show that in social science the genderpromotion gap to tenure existed in the 1980s cohorts, but this gap disappeared by thecohort of 1999, although economics was the exception with a sizable gender promotion gap.
As for the natural science, Ginther and Kahn (2009) show, by using SDR data from 1973
to 2001, that there is no gender promotion gap to tenure in aggregate, with the exceptionof life science. As for humanities, Ginther and Hayes (2003) show, by using SDR data, that
there exist gender promotion gaps of similar magnitude in both 1975-1979 and 1980-1989
cohorts, of about 5.9% to 6.5% points.6
The data set we utilize is the School Teachers Survey (Gakkou Kyouin Toukei Chousa)
conducted by MEXT every three years. This is a complete survey of faculty members
in tertiary education. We use the data for the full-time university faculty in four year
5

The median time for men to be promoted to tenure is 9 years while it is 16 years for women.
Earlier results of the gender promotion gaps in US academia can be found in Katz (1973). Long et al.
(1993) found that women in biochemistry are 10% less likely than men to be promoted to tenure.
63

universities and colleges. This data set contains information of the entire faculty in Japan
regarding the current ranks, total academic experience, as well as other characteristics
such as educational background. The data contain university identifier, university names,
as well as department names allowing us the examination of the gender promotion gaps
across different fields. The data set, however, does not include publication measures; thus,
the estimated gender promotion gap cannot be separated from the gender differences in
productivity. Nevertheless, we consider our study to be an important addition to theknowledge of gender promotion gaps in Japanese academia given the paucity of evidence.
We also note that, several existing similar studies do not control for publications, (e.g.,
McNabb and Wass, 1997).7
To preview our results, the gender promotion gap at the full professor rank in national
and local public universities stayed constant approximately at 7 percentage points, while
the gap in private university exhibits a mildly increasing trend from 5.9 to 8.1 percentage
points. The overall gender gap increased from 6.9 to 7.8 percentage points. We did not findconsistent evidence that the two governmental grants reduced the gender promotion gap,
nor did we find evidence that these grants increased the department level share of female
faculty.
The remainder of this paper is organized as follows. Section 2 outlines the institutional
context, describes the data, and presents sample characteristics. Section 3 analyses thegender promotion gaps over time, and Section 4 examines the effects of the two governmental
grants on the gender promotion gap. Section 5 concludes.
7

Takahashi and Takahashi (2011, 2018) examine the gender salary in Japanese academia while controllingfor academic productivity. They show that even after controlling for academic productivity, there are still
6 to 8 percent gender salary gaps. Takahashi and Takahashi (2010) finds that research productivity is
associated with higher job mobility.

4

2.

Institutional Background and Data

2.1

Institutional Background

Japanese universities are classified into three types: (1) national universities, (2) local public universities (prefectural and city universities), and (3) private universities. National
universities are largely financed by the central government and are under considerable supervision by MEXT. Prefectural and city universities are established by local governments
and are funded by both local and central governments. Private universities are establishedby private entities and they are generally self-supporting, receiving some financial support
from the government. Most of the prestigious research universities are national universities,
such as the University of Tokyo and Kyoto University.
Academic ranks in Japanese universities are: jokyo/joshu (research associate), koushi(assistant professor), jun-kyouju (associate professor), and kyoju (full professor), where thetitles in the brackets are the equivalent ranks in the US academic system. During thesample period, the tenure-track system was still not common and assistant, associate andfull professors were usually tenured positions at the hiring, while research associates were
mostly non-tenured entry positions typically on 2-3 year fixed-term contracts.8 Research
associate positions can entail either teaching and research (jokyo) or research only (joshu).
Since the number of ‘joshu’ is small in our sample, we combine ‘joshu’ and ‘jokyo’ into asingle category called research associate.9

2.2

Data and Variables

This study uses the School Teachers Survey from 2004, 2007, 2010 and 2013. This is acomplete survey of full time teachers in elementary, secondary, and tertiary education in
Japan conducted by MEXT every three years. We restrict the sample to full-time faculty8

Some fixed-term positions allow renewals, although the number of times one can renew is often limited.
Jokyo position was introduced in 2007, and thus, 2004 data do not contain jokyo. After the introductionof jokyo position, the job titles of the majority of joshu were changed to jokyo.
95

in 4-year universities10 , resulting in a sample with 89 national universities, 94 local public
universities, and 607 private universities. We also restrict the sample to faculty between 24
to 62 in order to exclude individuals who might have been rehired after their retirements.
The reason for not including the academics under age 24 is to eliminate misreported data,
as it is unlikely for those who do not reach the master’s degree-finishing age (usually 24) tobe hired as full-time faculty.11
Table 1 reports the summary statistics of the variables used in estimations. Whilemost of the variables are self-explanatory, some variables require explanations. The School
Teachers Survey collects information about the highest degree that each faculty obtained. If
the final degree is from a Japanese institution, the data indicates the type of degree (PhD,
MA, BA or below). Note that those who completed their PhD courses without receiving
the PhD degree are treated as equivalent to having a PhD degree, and therefore their PhD
dummy takes the value one in such a case.12 However, when a respondent obtained hisor her highest degree from a foreign institution, the data only indicate this fact, without
mentioning the type of the degree. Thus, our education dummies are: (1) PhD from Japan,(2) MA from Japan, and (3) highest degree from overseas. The reference category is ‘BA
and below from Japan’. The dummy variable, Belongs to graduate school, captures thepotential effects of having a more research oriented position.
Inbreeding is quite common in Japanese academia (Horta et al. 2011). Inbreeding
is a hiring practice where universities hire their own graduates as faculty right after the10

We exclude two-year colleges and technical colleges.
There are very few observations whose age is below 24, which are likely to be misreporting. In unreported regressions we restricted the sample to age 27-62. Results did not change qualitatively, however.
12
It had been a common practice for Japanese universities not to grant PhD degree immediately after
the completion of the PhD courses. The students who completed the courses but did not received the
PhD degree had to obtain academic jobs without the degree. Then, only after these students accumulated
substantial amount of academic work over the course of their academic careers, did the universities grant
the PhD degrees to these students. It sometimes takes more than a decade for these students to obtain
their PhD degree. The system has changed in the recent years, and most of the universities now grant
PhD degrees immediately after the completion of the PhD courses. For this reason, we consider those whocompleted the PhD courses without the degree to be equivalent to those who have completed the PhD
course with the degree.
11

6

completion of a graduate degree. Morichika and Shibayama (2015) examine the longitudinal
data of academic productivity in the University of Tokyo and find that inbreeding has
negative effects on academic productivity. The School Teachers Survey has information:(i) if a faculty obtained both the BA and the graduate degree from his or her current
institution, or (ii) if the faculty obtained only the graduate degree from his or her current
institution. While data do not tell if these faculty were hired into the current institution
directly after the completion of the graduate degree, they would partly capture the effects
of inbreeding on promotion. Hence, we include two dummy variables: alma mater of bothundergraduate and graduate school, and alma mater of the graduate school only. A sizable
portion of the sample has a degree from the universities where they are currently working:
28% of all faculty obtained both undergraduate and graduate degrees from the current
institution, while 6.7% of them obtained only their graduate degrees from their current
institutions. These statistics confirm the observation in Horta et al. (2011) that academic
inbreeding is common in Japanese academia.
According to Table 1, women are under-represented in full professor positions and areover-represented in assistant professor and research associate positions. On average, women
are younger and less experienced than men. In addition, fewer percentage of women thanmen have PhD degree: 51.5% of men and 37.3% of women received Ph.D. degrees fromJapan. The percentage of women who work in national universities is much lower than thatof men (42.3% for men and 25.5% for women), indicating that women are under-represented
in research oriented positions since national universities tend to be more research-oriented.
This is also confirmed by the statistics that a lower percentage of women belong to graduate
schools than men (22.6% for men and 12.2% for women).
Although the number of national universities is much smaller than that of private
universities, national universities account for 39% of total faculty hired. This is because7national universities typically have many departments hiring many faculty, while private
universities tend to have fewer departments. Private universities account for 53.4% of thetotal observations. The rest is hired by the local public universities. The distribution ofgenders varies substantially across fields. In particular, women are grossly under-represented
in STEM fields.
Figure 1 illustrates the percentage of full professors in each academic experience category using a binned graph, showing that women are less likely to be in the rank of professor
at any given academic experience (except the last experience category). In particular, thegender gap increases considerably after 20 years of experience and at experience 35 the gap
is as much as 0.24. The share of professors for the male sample drops after 35 years of experience. This can be either because male professors who do not have promotion prospects
stay in academia longer, or because some full professors retire early and then rehired atlower ranks. We do not have information to judge which explanation is more likely. We
ultimately decided to keep the observations above experience 35 years, since dropping them
do not much affect the estimation results.13

3.

Estimation Results

3.1

Basic Results

We apply the ordered probit model to estimate the gender gap in rank attainment:∗′
Ranki,j,t= Xi,j,t
β + aj + ui,j,t(1)

where the subscript i is the individual identifier, which is not unique across the different
waves of the census. Thus, we are not able to keep track of individuals over time. The
subscript, j, is the university identifier, which is unique across different waves of the census.
The subscript, t, is the year identifier. aj is the university fixed effect. Note that this model
13

There are 12,176 observations whose tenure is greater than 35 years, which is 2 percent of the total
observations.

8does not suffer from the incidental parameter problem, as the number of observations canincrease while holding constant the number of academic units. ui,j,t is the standard error
term, which is assumed to follow the standard normal distribution. Xi,j,t is the vector ofobserved characteristics, outlined in the previous section.
∗
Rankijt
is a latent variable for the rank attainment for the observation i, such that

Rankijt =1, 2, 3, 4 indicates that the individual is (1) a research associate, (2) an assistantprofessor, (3) an associate professor, or (4) a full professor, respectively. Gender differences
are estimated by including a female dummy.
Table 2 reports the regression results. The first four rows show the marginal effects ofbeing a female on the probability of being a full professor position for each year, and therest of the rows shows the estimated coefficients. Model 1 pools all the universities, Model
2 restricts the sample to national and local public universities, and Model 3 restricts the
sample to private universities.
Figure 2 plots the computed marginal effects in Table 2. According to Model 1 that
includes all the universities, women are 6.9 percentage points less likely than men to be in
the full professor position in 2004, and this gender gap slightly increased to 7.8 percentage
points in 2013. For national and local public universities, the gender promotion gap isalmost unchanged during the sample period, hovering between 7.4 to 7.8 percentage points.
For private universities, the gender promotion gap of being a full professor is 5.9 percentage
points in 2004 and increases to 8.1 percentage points in 2013. Thus, the gender promotion
gap was smaller for private universities in 2004, but the gap is slightly greater for private
universities at the end of our sample period.
We now examine the gender promotion gaps separately for the following fields: humanities related (humanities, education and art), social sciences, STEM (sciences, technology,

9engineering and math)14 , and medical. These groups cover all the fields, except the homescience field, which is a very small and inter-disciplinary field and thus is dropped fromthe sample. In addition, we separately estimate the gender gaps for (1) national and local
public universities, and (2) private universities. Figure 3 shows the marginal effect of female
on the probability of being a full professor for national and local public universities, andFigure 4 shows the marginal effects for private universities. Table 3 and Table 4 show thecorresponding estimation results.
For national and local public universities (Figure 3), social sciences and STEM fields
have consistently higher gender gaps than the other two fields, humanities and medical
fields. However, the gaps for the ‘high gap group’ (social sciences and STEM) exhibits
a downward trend throughout the sample period. The gap for social sciences was 23.7
percentage points in 2004, but reduced to 16.5 percentage points in 2013.15 The gap in
STEM fields was 15.9 percentage points in 2004 but it narrowed to 11.7 percentage points
in 2013. As for the ‘low gap group’, the gender gap slightly increased over the sample
period. The gap in the humanities related fields was 6.8 percentage points in 2004 and it
increased slightly to 8.5 percentage points in 2013 while the gap for the medical field was
4.3 percentage points in 2004 and it increased to 5.5 percentage points in 2013.
For private universities (Figure 4), STEM fields have a consistently higher gender promotion gap than the rest of the fields. The gap was 8.9 percentage points in 2004 andmildly increased to 10 percentage points in 2013. The rest of the fields (humanities related,social sciences, medical) have smaller gaps than STEM. These gaps mildly increased until
2010, ranging between 5 to 8 percentage points. Then, the gap starts to diverge. Medical
field and humanities continued to exhibit a mildly increasing trend, while social sciences
14

This includes agriculture.
We do not separately estimate the model for economics, since our preliminary estimation results showthat the trend in the estimated gender gap is similar to the rest of the social science fields.
15

10

exhibit a decrease.16
In sum, in national and local public universities, the gender promotion gaps are exceptionally higher in STEM and social science fields. Gender promotion gaps in these fields
narrowed during the 10 years of the sample period, but the gaps at the end of the sample
period were still considerably higher than other fields. In other fields, the gender promotion
gaps were mildly increasing. For private universities, STEM fields have relatively higher
gender promotion gap than other fields, with a mildly increasing trend.
In aggregate, the gender promotion gap in national and local public universities stayed
constant at slightly above 7 percentage points, while the gap in private universities exhibitedan increasing trend from 5.9 to 8.1 percentage points. When we combine public and private
universities, the gender gap exhibited a slightly increasing trend, from 6.9 to 7.8 percentage
points during the sample period. Thus, one takeaway from our results is that the genderpromotion gaps in Japanese academia gender did not improve over time.

4.

The effects of the two governmental programs on
the gender promotion gap

As noted in the introduction, the Japanese government provided two grants, the Support
Grant and the Acceleration Grant to reduce gender inequality in terms of representationand promotion. To estimate the effects of these grants, ideally we separately include in aregression the following two dummy variables: After Support Grant and After Acceleration
Grant, which take the value one in the year in which a university receives the respective
grants. We have to use a modified approach, however, since in our case all the universities
that received the Acceleration Grant had previously received the Support Grant. Therefore,
we cannot estimate the effects of the Acceleration Grant separately from that of the Support
16

In an unreported regression, we estimated this model separately for economics. The trend in the genderpromotion gap was not much different from the rest of the social sciences.

11

Grant. Our actual estimations use the following two dummy variables:(Support Grant Only)jt: This dummy variable takes the value 1 on or after the university j receives the Support Grant. In addition, we impute the value 1 even after the
grant expires, to capture potentially long lasting effects of the grant. This dummy variable,
however, takes the value 0 after the university receives the Acceleration Grant in the casethe university receives this grant.
(Support & Acceleration Grant)jt : This dummy variable takes the value one on or after
the period university j received the Acceleration Grant. The name of the variable contains‘Support &’ because, as noted above, all the universities that received the Acceleration
Grant had previously received the Support Grant, and thus, we cannot estimate the effectsof each grant separately.17
Because of the construction of these variables, the treatment group is the universities
that never received either grants during our sample period. The treatment groups are,thus, (1) universities that received only Support Grant during the sample period, and (2)universities that received both Support and Acceleration grant during our sample period.
To estimate the effects of these grants on gender promotion gap, we also need to take
into account the possibility that these treatment groups might have had pre-existing differences in gender promotion gaps. Thus, we additionally create two time invariant dummy
variables, Ever Received Support Grant Only for the treatment group (1), Ever Received
Supp. & Accel. Grant for these treatment groups, (1) and (2).
We estimate the effects of these grants on gender promotion gap by using the following
linear probability model with university fixed effects:(Above Assistant P rof )ijt = α1 (F emale)i (Y ear04)t + α2 (F emale)i (Y ear07)t (2)+ α3 (F emale)i (Y ear10)t + α4 (F emale)i (Y ear13)t
17

The duration of these grants are usually 4 years, but the effects of these grants are likely to last after
the expiration. That is why this dummy should take a value 1 even after the expiration of the grant.

12

+ β1 (Ever Received Support Grant Only)j (F emale)i+ β2 (Ever Received Supp. & Accel. Grant)j (F emale)i+ γ1 (Support Grant Only)jt(F emale)i+ γ2 (Support & Acceleration Grants)jt (F emale)i′+ Xijtβ + aj + θj Dj t + uit

The dependent variable is the dummy variable that takes the value zero if the academic’s
rank is research associate, the lowest academic rank, and one if the academic’s rank is
assistant professor or above. The reason we focus on the early promotion is because these
two grants focused on helping the career of entry level female academics, and thus, theeffects of these variables are expected to be different for the early promotion than for thelater promotions (i.e., from an assistant professor to an associate professor, and from anassociate professor to a full professor).
α1 to α4 capture the gender promotion gap for the control group. β1 and β2 capture
the pre-existing differences in the gender promotion gap for the two treatment groups. The
coefficients of our interest are γ1 and γ2 . γ1 captures the effect of receiving the Support
Grant only on the gender promotion gap. γ2 captures the effects of receiving both grants
on the gender promotion gap.
Xijt is a vector of other control variables. We control for the university fixed effects,
aj , to capture the differences in the speed of promotion among different universities. Dj is
the university dummy, and θj is the coefficient for the university specific linear time trend.
The inclusion of these trends imposes a significant computational burden, and this is thereason why we use linear probability models instead of probit models.
Table 5 shows the result. Before showing the main estimations that use Above Assistant Prof it as the dependant variable, Model 1 uses an ordered dependent variable to be
13

consistent with our earlier estimations that use ordered dependent variables. This model
uses the probit-adapted OLS estimation proposed by van Praag and Ferrer-i-Carbonell(2004), because estimating this model with ordered probit is infeasible due to the inclusion of university specific time trends.18 Probit-adapted OLS first transforms Rank into astandard-normally distributed, cardinally scaled variable, Y, as follows:
Ys = E(Z|Zs−1 ≤ Z ≤ Zs ) = [φ(Zs−1 ) − φ(Zs )]/[Φ(Zs ) − Φ(Zs )](3)

where, Z is the standard normal distribution, and Zs is the cutoff point such that P (Z ≤Zs )=the cumulative frequency at Rank=s.19 We then estimate an OLS regression using
this variable as the dependent variable.
Model 1 includes all the universities in the sample. As can be seen, the effect ofreceiving only the Support Grant has no effect, as the estimated γ1 is small and statisticallyinsignificant. The estimated γ2 is positive and statistically significant, which indicates thatreceiving both grants might have reduced the gender promotion gap. Model 2 estimates
the linear probability model with Above Assistant Prof as the dependent variable. The
estimated γ1 is again small and statistically insignificant. The estimated γ2 is positive andstatistically significant, again indicating that receiving both Support and Acceleration grant
might have reduced the gender promotion gap.
Model 3 estimates the same model for national and local public university sample.
Estimated γ1 and γ2 are small and statistically insignificant. Model 4 estimates the samemodel for private university. This model does not estimate γ2 because no private universities
have ever received Acceleration Grant. The estimated γ1 is again small and insignificant.
Note however that the primary targets of these two grants were the STEM fields. The
support grants were given only to universities with STEM fields, although the university
18

The application of this method can be found in Stevenson and Wolfers (2008), Luechinger (2009), andLuechinger et al. (2010).
19
Z1 =-∞ and Z4 =∞.

14

had the discretion to use this grant for other fields. The usage of the Acceleration Grant
was limited to supporting young female researchers in STEM fields only. In addition, there
is no private university that received the Acceleration Grant during our sample period.
Thus, the estimated γ1 and γ2 might mistakenly capture a possible field specific time trend,or possibly different time trends between national and local public university and private
universities. In order to eliminate these possibilities, we estimate the same model for STEM
fields.
Model 1 in Table 6 restricts the sample to STEM fields, but includes national, local
public, and private universities. The estimated γ1 is still small and statistically insignificant.
The estimated γ2 is 0.045, and is marginally significant, indicating that the combination of
Support and Acceleration Grants might have reduced the gender promotion gap by 4.5 percentage points. Note, however, that the pre-existing gender promotion gap for universities
that received both grants is 12.3 percentage points higher than that of the universities thatnever received either grants. Thus, even if the gender promotion gap reduced by 4.5%, these
universities still have a much higher gender promotion gap than the rest of the universities.
Model 2 in Table 6 further restrict sample to national and local public universities only.
The estimated effect of Support Grant has become negative and statistically significant. If
we take this result at face value, the Support Grant increased the gender promotion gap innational and local public universities. The estimated γ2 is now small and statistically insignificant, indicating that receiving both Support and Acceleration Grant has not changed
the gender promotion gap in these universities.
Model 3 in Table 6 estimates the same model for private universities. Since none of theprivate universities received the Acceleration grant during our sample period, we can only
estimate the effects of receiving the Support Grant only. The estimated γ1 is 0.075 and is
statistically significant at the 5 percent level, indicating that the Support Grant reduced the15gender promotion gap by 7.5 percentage points. However, note that the universities thatreceived the grant have a pre-existing excess gender gap of 8.4 percentage points. Thus,
the Support Grant simply eliminated the pre-existing excess gender promotion gap, so thatthese universities have a gender promotion gap that is roughly the same as the control
group.
Overall, the results are not consistent across different models. The effects of the Support
Grant is estimated to be nearly zero, positive, or negative. The effects of the Support andAcceleration grants are estimated to be near zero, or positive. Given this inconsistency,
we believe that these results are too weak to support the hypothesis that these two grants
helped reduce the gender promotion gaps.
Although these grant might not have reduced the gender promotion gaps, these grants
might have helped female researchers in another way. Universities could hire a young female
researchers in a research associate position using these grants. These positions tend to beof limited term, and thus, may not increase the speed of promotion. However, an increasein the chance of being hired is definitely beneficial.
If this is the case, these grants might have increased the share of female faculty. To
test this hypothesis, we estimate the following model.
(F emale share)jkt = β0 + β1 (Support Grant Only)jt(4)+ β2 (Support & Acceleration Grants)jt′+ Xjktβ + ajk + ujkt

where j is the index for university, k is the index for departments, and t is the index for theyear. Xjkt includes department level control variables. Since some departments may attract
more female researchers due to their specializations, such as the department of literature,
we include the department level fixed effect ajk .
Figure 5 shows the histogram of the department level female share. The histogram is
16

drawn with the width of 0.01. The greatest concentration of distribution is at zero, with
23.8 percent of all departments having no female faculty. The average female share is 0.214.
Figure 6 shows the histogram of female share restricting to STEM fields. Again, the greatest
concentration of female share is at zero where a staggering 53.7 percent of departments has
no female faculty. The average female share in STEM is 0.059.
Table 7 shows the estimation results. Model 1 includes department level fixed effects.
The coefficients for both Support Grant Only or Support & Acceleration are very small andare statistically insignificant. Model 2 includes university specific time trends. Again, theestimated effects of both grants on female share are small and statistically insignificant.
Finally, we run the same regression by restricting the sample to STEM fields in Model
3. Our two focal coefficients are again very small and statistically insignificant. Thus, we
reject the hypothesis that these grants helped female researcher by increasing the chanceof being hired into academia in a statistically measurable way.

5.

Conclusion

Despite large resources being devoted to increasing women’s representation in academia inJapan, there is a lack of literature documenting the gender promotion differences. Using thecomplete survey of the entire faculty in Japan covering 2004-2013, we examined the genderpromotion gap in Japanese academia and assess how it changed over the sample period. Wethen examined how the two governmental grants, the Support Grant and the Acceleration
Grant that aimed to support young female researchers, have affected the gender promotion
gaps.
Results show that the gender promotion gap at the full professor rank in national andlocal public universities stayed constant at slightly above 7 percentage points, while the gap
in private universities exhibited a mildly increasing trend, from 5.9 to 8.1 percentage points,
17

over the sample period. Overall gender gap, combining private, national, and local public
universities, slightly increased from 6.9 to 7.8 percentage points. STEM and social sciences
fields in national and local public universities have significantly higher gaps as comparedto other fields. The gaps in STEM and social sciences did reduce considerably over time,
though they are still higher than the rest of the fields.
We do not find consistent evidence that the two governmental grants reduced gender
gap. One possible explanation is that, such grants might have helped female researchers
by providing more positions for them, but with poor promotion prospects. If this is thecase, we might observe an increase in the share of female faculty. However, we do not findevidence of an increase in the female share.
Previous work in the US academia has shown the gender promotion gaps narrowed overtime. In contrast, over our sample period, the gender promotion gap in Japanese academia
has not reduced, and the two grants that were meant to reduce this gap did not seem tohelp reduce it. One caveat of this study is that we did not pin down the exact causes ofthese gender promotion gaps. Additional work that would illuminate the sources of thegender promotion gap would be useful in reducing these gaps.

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Ginther, Donna K. and Kahn, Shulamit, 2004. “Women in Economics: Moving Up or
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Perspective”, Asia Pacific Education Review, 12(1), pp. 35-44.
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Katz, David, A., 1973, “Faculty Salaries, Promotions, and Productivity at a Large University”, American Economic Review, 63(3), pp. 469-477.
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McDowell, John. M.; Singell Jr., Larry, D., and Jr. Ziliak, James, P., 2001, “Genderand Promotion in the Economics Profession”, Industrial and Labor Relations Review,
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Takahashi, Ana Maria and Takahashi, Shingo 2015, “ Gender Promotion Differences in Economics Departments in Japan: A Duration Analysis”, Journal of Asian Economics,
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19

Table 1: Summary Statistics
All(n=620,849)

Female(n= 122,745)

Male(n=498,104)

Variables

Mean

Std. Dev.

Mean

Std. Dev.

Mean

Std. Dev.

FemaleRanks
Full professor
Associate professor
Assistant professor
Lecturer
Age
Academic experience
BA & below (excluded category)
PhD from Japan
MA from Japan
Final degree abroad
Alma mater BA&graduate level
Alma mater graduate school
Belongs to graduate school
National university
Local public university
Private university
FieldsHumanities
Education
Art
Social siences
Sciences
Engineering
Agriculture
MedicalHome science
Year 2004
Year 2007
Year 2010
Year 2013

0.198(0.398)

-

-

-

-

0.361
0.256
0.126
0.256
46.30912.158
0.223
0.487
0.247
0.043
0.282
0.066
0.205
0.390
0.076
0.534

(0.480)(0.436)(0.332)(0.437)(9.756)(10.307)(0.416)(0.500)(0.431)(0.204)(0.450)(0.249)(0.404)(0.488)(0.265)(0.499)

0.217
0.251
0.175
0.358
43.787
9.132
0.269
0.373
0.295
0.063
0.233
0.073
0.122
0.255
0.100
0.645

(0.412)(0.433)(0.380)(0.479)(9.888)(9.174)(0.4436)(0.483)(0.456)(0.244)(0.423)(0.260)(0.327)(0.436)(0.300)(0.479)

0.397
0.257
0.115
0.231
46.93012.903
0.211
0.515
0.236
0.038
0.294
0.065
0.226
0.423
0.070
0.507

(0.489)(0.437)(0.319)(0.422)(9.622)(10.434)(0.408)(0.500)(0.424)(0.192)(0.455)(0.246)(0.418)(0.494)(0.255)(0.500)

0.145
0.055
0.028
0.129
0.089
0.154
0.043
0.345
0.012
0.238
0.248
0.256
0.258

(0.353)(0.229)(0.164)(0.335)(0.284)(0.361)(0.203)(0.475)(0.109)(0.426)(0.432)(0.436)(0.438)

0.208
0.069
0.037
0.114
0.036
0.036
0.019
0.434
0.047
0.197
0.236
0.268
0.299

(0.406)(0.253)(0.189)(0.318)(0.187)(0.187)(0.136)(0.496)(0.211)(0.398)(0.424)(0.443)(0.458)

0.130
0.052
0.025
0.133
0.101
0.183
0.049
0.323
0.003
0.248
0.251
0.253
0.248

(0.336)(0.222)(0.157)(0.339)(0.302)(0.387)(0.216)(0.468)(0.058)(0.432)(0.434)(0.434)(0.432)

20

0

.2

.4

.6

.8

Figure 1: The share of full professors by experience

0

10

20
30
Academic experience
female

40

50

male

0

−.025

−.05

−.075

−.1

Figure 2: Gender promotion gap at the full professor rank: All fields

2004

2007

2010
year

All universities (Model 1)
National & Local public univ (Model 2)
Private universities (Model 3)This figure plots the marginal effects in Table 2
21

2013

Table 2: Gender promotion gaps (ordered probit)

Variables

Model 1Model 2Model 3Alluniversities

National & local
public universities

Privateuniversities-0.069***(0.004)-0.069***(0.003)-0.075***(0.003)-0.078***(0.003)-0.078***(0.007)-0.074***(0.006)-0.077***(0.005)-0.077***(0.005)-0.059***(0.004)-0.067***(0.003)-0.075***(0.003)-0.081***(0.003)-0.349***(0.018)-0.352***(0.014)-0.380***(0.013)-0.395***(0.013)0.191***(0.009)-0.071***(0.009)0.071***(0.003)-0.195***(0.007)0.553***(0.020)0.366***(0.019)0.299***(0.032)-0.390***(0.043)-0.446***(0.028)0.172***(0.029)-0.001(0.046)0.454***(0.035)-0.009(0.007)-0.094***(0.008)-0.160***(0.010)Yes
Yes
620,849-0.383***(0.031)-0.360***(0.029)-0.376***(0.024)-0.375***(0.024)0.167***(0.010)-0.045***(0.011)
0.068***(0.003)-0.187***(0.009)0.510***(0.028)0.395***(0.028)0.385***(0.057)-0.307***(0.061)-0.407***(0.036)0.138***(0.025)0.070(0.055)-0.319***(0.021)-0.361***(0.016)-0.405***(0.016)-0.438***(0.017)0.224***(0.015)-0.102***(0.015)0.073***(0.004)-0.205***(0.012)0.629***(0.026)0.309***(0.025)0.267***(0.034)-0.476***(0.045)-0.453***(0.046)
0.543***(0.059)-0.039***(0.008)-0.125***(0.009)-0.201***(0.010)Yes
Yes
289,150

0.023**(0.011)-0.062***(0.014)-0.122***(0.018)Yes
Yes
331,699

Marginal effects
Female×Year 2004
Female×Year 2007
Female×Year 2010
Female×Year 2013
Coefficients
Female× Year 2004
Female×Year 2007
Female×Year 2010
Female×Year 2013
Age
Age squared
Academic experience
Academic experience squared
PhD from Japan(Excluded=BA&below from Japan)
MA from Japan
Final degree abroad
Alma mater BA&graduate level
Alma mater graduate level
Belongs to graduate school
Prefectural or City university(Excluded=Nat. university)
Private university
Year=2007
Year=2010
Year=2013
Field dummies
University fixed effects
Observations

Inside brackets are robust standard errors. *,**,***=
22significant at the 10%, 5%, and 1% levels. Field are:Humanities, Social Sciences, Sciences, Engineering, Agriculture, Medical, Home science, Education, Art.

0

−.05

−.1

−.15

−.2

−.25

Figure 3: Gender promotion gap by fields: National and local public Universities

2004

2007

2010
Year

Humanities related
STEM

This figure is based on the marginal effects in Table 3.

23

Social sciences
Medical

2013

0

−.025

−.05

−.075

−.1

Figure 4: Gender promotion gap by fields: Private universities

2004

2007

2010
Year

Humanities related
STEM

This figure plots the marginal effects in Table 4

24Social sciences
Medical2013

Table 3: Gender promotion gaps by fields in national and local public universities (ordered
probit)Variables

Model 1Model 2Model 3Model 4

Humanities
relatedSocialsciences

STEM

Medical-0.068***(0.008)-0.077***(0.009)-0.083***(0.007)-0.085***(0.007)-0.237***(0.021)-0.189***(0.021)-0.178***(0.020)-0.165***(0.019)-0.159***(0.011)-0.141***(0.011)-0.115***(0.012)-0.117***(0.010)-0.043***(0.006)-0.040***(0.007)-0.051***(0.006)-0.055***(0.006)-0.310***(0.037)-0.352***(0.038)-0.376***(0.031)-0.385***(0.029)0.111***(0.017)0.021(0.019)0.063***(0.005)-0.118***(0.016)
0.677***(0.081)0.466***(0.081)0.285***(0.099)-0.234***(0.059)-0.353***(0.040)0.169***(0.048)0.230***(0.060)-0.051***(0.018)-0.144***(0.028)-0.199***(0.029)Yes
51,250-1.053***(0.092)-0.838***(0.091)-0.793***(0.090)-0.733***(0.082)0.090***(0.026)0.027(0.032)0.083***(0.007)-0.221***(0.025)0.591***(0.183)0.429**(0.180)0.619***(0.188)-0.044(0.169)-0.456***(0.105)0.150**(0.072)2.974***(0.227)-0.039(0.028)-0.154***(0.030)-0.269***(0.036)
Yes
25,293-0.677***(0.049)-0.602***(0.046)-0.493***(0.052)-0.498***(0.042)
0.347***(0.011)-0.227***(0.012)0.012***(0.004)-0.062***(0.013)0.936***(0.079)0.834***(0.073)0.985***(0.117)-0.151**(0.073)-0.360***(0.049)0.038*(0.023)
5.959***(0.159)-0.035***(0.009)-0.123***(0.013)-0.210***(0.012)
Yes
117,318-0.272***(0.035)-0.253***(0.041)-0.324***(0.040)-0.348***(0.040)0.244***(0.018)-0.121***(0.019)0.107***(0.005)-0.284***(0.013)0.369***(0.020)0.152***(0.035)0.313***(0.111)-0.392***(0.035)-0.397***(0.035)0.306***(0.059)0.163***(0.046)-0.047**(0.019)-0.146***(0.023)-0.219***(0.022)
Yes
93,454

Marginal effects
Female×Year 2004
Female×Year 2007
Female×Year 2010
Female×Year 2013
Coefficients
Female×Year 2004
Female×Year 2007
Female×Year 2010
Female×Year 2013
Age
Age squared
Academic experience
Academic experience squared
PhD from Japan(Excluded=BA& below from Japan)
MA from Japan
Final degree abroad
Alma mater BA&graduate level
Alma mater graduate level
Belongs to graduate school
Prefectural or City univ
(Excluded=Nat univ)
Year=2007
Year=2010
Year=2013
University fixed effects
Observations

Inside brackets are robust standard errors. *,**,***= significant at the 10%, 5%, and 1% levels. Public
universities are: national, prefectural and city universities. Humanities related includes: Humanities,
25
Education and Art. STEM includes: Sciences, Engineering and Agriculture.

Table 4: Gender promotion gaps by field in private universities (ordered probit)

Variables

Model 1Model 2Model 3Model 4

Humanities
relatedSocialscience

STEM

Medical-0.070***(0.006)-0.067***(0.005)-0.072***(0.005)-0.074***(0.004)-0.068***(0.007)-0.066***(0.007)-0.072***(0.006)-0.064***(0.006)-0.089***(0.011)-0.087***(0.009)-0.091***(0.009)-0.100***(0.009)-0.053***(0.007)-0.061***(0.004)-0.067***(0.005)-0.079***(0.005)-0.307***(0.026)-0.292***(0.023)-0.314***(0.021)-0.325***(0.020)0.216***(0.020)-0.103***(0.020)0.097***(0.005)-0.223***(0.014)0.607***(0.034)0.272***(0.030)0.153***(0.049)-0.382***(0.050)-0.466***(0.046)
0.273***(0.091)-0.040***(0.014)-0.135***(0.017)-0.218***(0.021)
Yes
90,518-0.349***(0.037)-0.342***(0.035)-0.373***(0.028)-0.331***(0.031)0.222***(0.023)-0.103***(0.023)0.125***(0.006)-0.319***(0.017)0.182***(0.053)-0.107**(0.050)0.079(0.069)-0.333***(0.075)-0.590***(0.063)0.786***(0.068)-0.055**(0.022)-0.166***(0.024)-0.248***(0.026)
Yes
54,824-0.400***(0.047)-0.393***(0.041)-0.412***(0.039)-0.451***(0.041)
0.326***(0.024)-0.192***(0.025)0.046***(0.005)-0.189***(0.016)
0.934***(0.074)0.704***(0.068)0.878***(0.102)-0.407***(0.062)-0.456***(0.058)-0.004(0.188)-0.014(0.015)-0.068***(0.024)-0.126***(0.030)Yes
60,027-0.422***(0.061)-0.483***(0.035)-0.531***(0.041)-0.620***(0.044)0.298***(0.014)-0.159***(0.014)0.056***(0.008)-0.173***(0.022)0.514***(0.028)0.138***(0.037)0.254*(0.140)-0.390***(0.054)-0.373***(0.065)0.329**(0.141)0.070***(0.022)-0.044(0.030)-0.095***(0.033)
Yes
120,732

Marginal effects
Female×Year 2004
Female×Year 2007
Female×Year 2010
Female×Year 2013
Coefficients
Female×Year 2004
Female×Year 2007
Female×Year 2010
Female×Year 2013
Age
Age squared
Academic experience
Academic experience squared
PhD from Japan(Excluded=BA& below from Japan)
MA from Japan
Final degree abroad
Alma mater BA&graduate level
Alma mater graduate level
Belongs to graduate school
Year=2007
Year=2010
Year=2013
University fixed effects
Observations

Inside brackets are robust standard errors. *,**,***= significant at the 10%, 5%, and 1% levels. Public
universities are: national, prefectural and city universities. Humanities related includes: Humanities,
Education and Art. STEM includes: Sciences, Engineering and Agriculture.

26

0

5

10

Percent
15

20

25

Figure 5: Histogram of the department level shares of female faculty: All fields

0

.2

.4
.6
.8
Share of female faculty (Department level)

27

1

0

20

Percent

40

60

Figure 6: Histogram of the department level shares of female faculty: STEM fields

0

.2

.4
.6
.8
Share of female faculty (Department level)28

1Table 5: The two grants and the promotion Gap (OLS): All fields
All universities

Variables

α1 :Female×Year 2004
α2 :Female×Year 2007
α3 :Female×Year 2010
α4 :Female×Year 2013
β1 :Ever received Supp.Grant only*Female
β2 :Ever received Supp.&Acell.Grant×Female
γ1 :Support Grant only×Female
γ2 :Support&Acceleration Grant× Female
Age
Age squared
Academic experience
Academic experience squared
PhD from Japan(Excluded=BA&below from Japan)
MA JapanFinal degree abroad
Alma mater BA&graduate level
Alma mater graduate level
Belongs to graduate school
Constant
Year dummies
Field dummies
University fixed effects
R-squared
Observations

National & Local
public univ.

Private univ.

Model 1
Dept var=
Rank
modified

Model 2
Dept var=
Above
Assist prof

Model 3
Dept var=
Above
Assist prof

Model 4
Dept var=
Above
Assist prof

-0.147***(0.011)-0.140***(0.009)-0.154***(0.009)-0.162***(0.009)-0.021(0.024)-0.138***(0.033)0.001(0.019)0.045**(0.021)
0.054***(0.009)0.016*(0.009)0.041***(0.001)-0.111***(0.004)0.199***(0.011)0.135***(0.012)0.086***(0.018)-0.186***(0.021)-0.228***(0.014)0.107***(0.015)-2.924***(0.200)
Yes
Yes
Yes
0.586
620,849-0.011**(0.005)-0.014***(0.004)-0.029***(0.004)-0.039***(0.004)-0.036***(0.011)-0.092***(0.015)-0.001(0.008)0.021**(0.010)0.104***(0.002)-0.092***(0.002)0.013***(0.001)-0.032***(0.003)0.107***(0.005)0.076***(0.005)0.107***(0.007)-0.082***(0.009)-0.099***(0.007)0.023**(0.011)-2.038***(0.050)
Yes
Yes
Yes
0.424
620,849-0.013(0.008)-0.003(0.007)-0.014*(0.008)-0.019**(0.008)-0.038**(0.016)-0.086***(0.016)-0.011(0.010)0.005(0.013)0.097***(0.003)-0.082***(0.003)0.021***(0.001)-0.055***(0.004)0.098***(0.006)0.075***(0.007)0.121***(0.014)-0.067***(0.013)-0.096***(0.008)0.021*(0.011)-1.978***(0.071)
Yes
Yes
Yes
0.420
289,150-0.013**(0.006)-0.023***(0.005)-0.038***(0.004)-0.049***(0.004)-0.037**(0.015)0.001(0.012)0.106***(0.003)-0.095***(0.003)0.007***(0.001)-0.016***(0.003)0.122***(0.006)0.070***(0.006)0.106***(0.007)-0.096***(0.011)-0.086***(0.010)0.019**(0.010)-2.018***(0.068)
Yes
Yes
Yes
0.444
331,699

Inside brackets are robust standard errors. *,**,***= significant at the 10%, 5%, and 1% levels. Model 1
uses Probit Adapted OLS and Model 2-4 uses a linear probability model.

29

Table 6: The two grant and the promotion gap (OLS): STEM fields

Variables

α1 :Female×Year 2004
α2 :Female×Year 2007
α3 :Female×Year 2010
α4 :Female×Year 2013
β1 :Ever received Supp. Grant only×Female
β2 :Ever received Supp.&Accel. Grant×Female
γ1 :Support Grant only×Female
γ2 :Support&Acceleration Grant×Female
Age
Age squared
Academic experience
Academic experience squared
PhD from Japan(Excluded=BA&below from Japan)
MA JapanFinal degree abroad
Alma mater BA&graduate level
Alma mater graduate level
Belongs to graduate school
Constant
Year dummies
Field dummies
University fixed effects
R-squared
Observations

All univ.

PublicPrivateModel 1
Dept var=
Above
Assist prof

Model 2
Dept var=
Above
Assist prof

Model 3
Dept var=
Above
Assist prof

-0.053***(0.014)-0.039***(0.011)-0.040***(0.011)-0.044***(0.010)-0.076***(0.021)-0.123***(0.024)0.011(0.018)0.045*(0.027)0.142***(0.002)-0.130***(0.002)0.005***(0.001)-0.012***(0.003)0.165***(0.018)0.135***(0.017)0.157***(0.025)-0.032**(0.014)-0.076***(0.010)0.012(0.010)-3.028***(0.065)
Yes
Yes
Yes
0.384
177,345-0.114***(0.026)-0.076***(0.021)-0.050**(0.020)-0.050***(0.018)-0.034(0.028)-0.036(0.031)-0.041**(0.020)-0.020(0.321)0.147***(0.002)-0.133***(0.003)0.005***(0.001)-0.016***(0.004)0.194***(0.025)0.171***(0.024)0.204***(0.036)-0.024(0.017)-0.073***(0.012)0.011(0.010)-3.277***(0.049)
Yes
Yes
Yes
0.388
117,318-0.056***(0.015)-0.047***(0.013)-0.065***(0.010)-0.066***(0.011)-0.084**(0.036)

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

30

0.075**(0.036)0.125***(0.006)-0.116***(0.006)0.003**(0.001)-0.008**(0.003)0.128***(0.019)0.077***(0.015)
0.093***(0.021)-0.064***(0.014)-0.081***(0.016)-0.024(0.043)-2.427***(0.148)
Yes
Yes
Yes
0.407
60,027

Table 7: The impact of grants on female share: Dept var=Department level female share(OLS)
Model 1Model 2Model 3

Variables

All fields

All fields

STEM only

Agejkt /10

0.135(0.096)-0.020**(0.010)0.004(0.019)-0.003(0.006)-0.030(0.024)-0.006(0.026)0.047(0.038)0.004(0.024)0.058***(0.020)-0.003(0.006)0.006(0.011)
0.015(0.236)
Yes
Yes
No
0.063
15,361

0.152(0.099)-0.021**(0.010)-0.017(0.020)0.002(0.006)-0.036(0.023)-0.008(0.025)0.053(0.039)0.003(0.022)
0.053***(0.020)-0.008(0.009)-0.009(0.016)-0.038(0.243)
Yes
Yes
Yes
0.204
15,361-0.124(0.196)0.006(0.020)0.007(0.070)0.008(0.018)-0.160*(0.090)-0.154*(0.084)0.313(0.284)-0.062(0.048)0.018(0.039)-0.006(0.020)-0.026(0.038)0.593(0.437)
Yes
Yes
Yes
0.213
2,027

(Agejkt /10)2
Academic experiencejkt /10(Academic experiencejkt /10)2
P hDf romJapanjkt(Excluded BA&below from Japan)
M aster f rom Japanjkt
F inal degree abroadjkt
Alma mater BA&graduate leveljkt
Alma mater graduate leveljkt
Have Support Grant only
Have Support & Acceleration Grant
Constant
Field dummies included
Department level fixed effect
University specific time trend
R-squared
Observations

The over-bar indicates that the variable is the department level average. Inside brackets are robust standard
errors. *,**,***= significant at the 10%, 5%, and 1% levels.

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