GraSPP-DP-E-14-005 and STIG-DP-E-14-005

Moving Out Of Academic Research:
Why Scientists Stop Doing Research?
Aldo Geuna
Sotaro Shibayama
December 2014

GraSPP Discussion Paper E-14-005

STIG Discussion Paper E-14-005

GraSPP-DP-E-14-005 and STIG-DP-E-14-005

Moving Out Of Academic Research:
Why Scientists Stop Doing Research?

Aldo Geuna1 , Sotaro Shibayama 2
Decemberr
2014

Department of Economics and Statistics Cognetti De Martiis,
University of Torino, Lungo Dora Siena 100 A - 10153 Turin, Italy
Phone: +39 011 6703891 / Fax: +39 011 6703895
e-mail: aldo.geuna@unito.it
2
Research Center for Advanced Science and Technology,
The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan
Phone/Fax: +81 354525371
e-mail: shibayama@00.alumni.u-tokyo.ac.jp
1

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Discussion Papers are a series of manuscripts in their draft form. They are not intended forcirculation or distribution except as indicated by the author. For that reason Discussion Papers may
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MOVING OUT OF ACADEMIC RESEARCH:
WHY SCIENTISTS STOP DOING RESEARCH?

Aldo Geuna 1
Department of Economics and Statistics Cognetti De Martiis, The University of Torino
BRICK, Collegio Carlo Alberto
Sotaro Shibayama 2
Research Center for Advanced Science and Technology, The University of Tokyo

Acknowledgments
The authors are grateful to Paula Stephan and Satoshi P. Watanabe for comments andsuggestions. We are grateful for financial support from the Konosuke Matsushita Memorial
Foundation, the Grant-in-Aid for Research Activity Start-up of Japan Society for the
Promotion of Science (#23810004) and the Collegio Carlo Alberto Project ‘Researcher
Mobility and Scientific Performance’. Sotaro Shibayama acknowledges Yasunori Baba’s
generous support for this project.

1

Department of Economics and Statistics Cognetti De Martiis, University of Torino, Lungo
Dora Siena 100 A - 10153 Turin, Italy, Tel: +39 0116703891, Fax: +39 011 6703895; email:aldo.geuna@unito.it
2
Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1
Komaba, Meguro-ku, Tokyo 153-8904, Japan, Tel/Fax: +81 354525371, email:shibayama@00.alumni.u-tokyo.ac.jp

Abstract
This study examines the determinants of exit from academic research which occurs when
academic researchers move into positions in academe which concentrate on non-research
activities such as teaching or administration, or when researchers leave academia and moveinto industry. Drawing on career data for 13,500 Japanese PhD graduates in hard sciences (allscientific fields except social sciences and humanities), we develop a set of econometricmodels to test the determinants of exit from a career in academic research. We find that
academics’ scientific productivity and academic network are negatively correlated withabandoning a university research career, and that female academics, and researchers in lessprestigious universities, tend to exit academic research more easily. Individual andinstitutional network effects play a role mainly for senior researchers. The results indicate
also that the determinants of exit are contingent on scientific field and career stage.

Keywords
Researcher mobility; academic career; academic labor market; exit

2

1. Introduction
Academic research is extremely competitive, and its “up-or-out” nature inevitably results in a
proportion of academics leaving a research career after initial involvement. Although thisselection process is fundamental in explaining the highly skewed scientific production inacademia (Lotka 1926), we know little about what characterizes exit from academic research.
Most of the sociological and economic literature that analyzes science, concentrates on
explaining success, and focuses on the performance of academic stars. However, if theselection mechanisms are imperfect, for example, if selection is driven by other criteria than
merit, then the investment in human capital will be used inefficiently. Policy makers andscientific communities have expressed concern over these issues. Gender and ethnicity
discrimination are examples of biased selection criteria. Wolfinger et al. (2009) indicate thatfemale PhDs are disproportionately more likely to be employed as non-tenured faculty and toexit the paid labor force, even when controlling for academic productivity (Kaminski andGeisler 2012), while Ginther et al. (2011) show that US National Institute of Health (NIH)
grants are less likely to be awarded to certain ethnicities. Such discrimination could exclude
potential talent from continuing an academic research career after completion of the PhD, or
expedite mobility out of an academic research career. There are other factors that also candiscourage promising researchers from pursuing an academic research career after
completion of their doctoral study. Donowitz et al. (2007) suggest that American physicianscientists tend to favor lucrative practitioner careers, and are discouraged by the unstablesystem of funding for junior researchers. In addition, the number of academic positions open
to junior researchers in the US, EU, and Japan has failed to keep pace with the numbers of
new doctoral graduates with the result that even the most capable are opting for nonacademic research jobs (NISTEP 2009a; Stephan 2012).
3

The present study tries to shed more light on the process that induces academics to leave
active research, by examining the determinants of academic research exit based on a sampleof Japanese academics. We define “researcher exit” here as the case of an academic
researcher abandoning research after some period either to take up an academic position that
focuses on non-research activities such as teaching or administration, or to move into
industry. There are several potential determinants of exit from an academic research career;
we focus on individual, institutional, and geographical factors, drawing on the literature on
academic mobility (e.g., Allison and Long 1990; Chan et al. 2002; Crespi et al. 2007, 2006),
search theory models (e.g., Burdett 1978; Jovanovic 1979; Mortensen and Pissarides 1994),
and policy research on academic career design (e.g., Gaughan and Robin 2004; Ginther andKahn 2004; Long et al. 1993).
The prior empirical literature on academic careers is based mostly on the US and Europe.
With the exception of some research on higher education (Arimoto 2011; Teichler et al. 2013;
Yamanoi 2007), the Japanese academic labor market has been understudied. Our main aim in
this chapter is to offer a comprehensive analysis of exit among Japanese academic
researchers. We employ a sample of 13,500 PhD graduates in hard sciences (all scientificfields except social sciences and humanities), who obtained their doctoral degrees in the
period 1985-1989; the data source is the Japanese National Library’s PhD degree database.
We follow the careers of the sample graduates over 20 years (from 1990 to 2010) using theJapanese national research grant program, Grants-in-Aid (GiA) data. 1 Our econometricmodels suggest that the determinants of exit from an academic research career include
scientific productivity and academic network which are negatively correlated with moving
out of academic research, and that female researchers and researchers in less-prestigious
universities have a higher probability of exiting from academic research. The findings suggest

1

GiA is the primary national research funding system in Japan.

4

that the determinants of moving from academia are contingent on scientific field and career
stage, and that the selection process in Japan is, at least partly, based on merit but may also bebased on gender and university prestige, resulting in the unintended exit of potentiallytalented researchers.

2. Career path and exit from academic research career
In Japan and other advanced countries, the professional career of an academic researcher
starts after completion of postgraduate-level education (e.g., PhD degree). Some doctoral
graduates choose to pursue an academic career and continue to do research and teaching inacademia, others focus mainly (or only) on teaching, and some choose research or nonresearch jobs in industry.
We consider the critical points in the academic research career path as those moments where
the probability of leaving an academic research career is higher. Exit can be regarded as a
type of mobility toward non-academic research employment, which may occur at various
points in an academic research career. In some countries such as France and the UK, there are
special PhD programs that allow students financed by companies to pursue more focusedresearch projects that are aligned to the firms’ interests. It is not surprising that most of thesestudents continue their career in industry. If we exclude these cases, the three most important
decision times are: (1) after PhD graduation, (2) at the time of consideration for atenured/permanent position, and (3) after obtaining a permanent position. At these moments,
academics might be tempted to leave the academic labor market as the result of a job offer in
a company, or might decide to focus completely on teaching and/or administration and giveup research activity. However, the probability of moving to a job in industry is small due tothe specificities of the Japanese science market; e.g., in 2004 only 0.1% of academics moved
5

from a job in a university to an industry job (METI 2006).2 In what follows we discuss thethree main moments when the risk of leaving an academic research career is higher, in thecontext of Japan.
In most OECD countries, students have completed their doctoral research by the time theyreach their early 30s; the median age of graduation is 33 in the US, 31 in Switzerland, and 32
in Japan (Auriol 2010). Most PhD graduates who intend to pursue a career in academic
research spend their first period after graduation in a temporary position such as a postdoc,
before achieving their first assistant professor (or equivalent) position (Auriol et al. 2013).
For example, in European countries such as Germany, the Netherlands, and Spain, thepercentage of doctoral graduates on temporary contracts within the five years after graduationis around 40% (Auriol et al. 2013). In Japan, since 2005 doctoral graduates are awarded an
assistant professor position after five or six years of postdoc employment on average, with
only 15% of PhDs achieving this position immediately after graduation (Yamanoi 2007: Ch.
12). The postdoc period has been extending in most countries (Stephan 2012). For example,
in the US in 2006, only 15% of biology PhDs were in tenure-track positions six years after
graduation, compared to 55% in 1973 (Stephan 2012). This discouraging and risky careerprospect can dissuade even excellent academics from pursuing an academic research career.
Among a sample of about 4,000 PhD students in US Tier 1 research universities, Sauermann
and Roach (2012) show that an academic research career is considered attractive by only
about a third of respondents in life sciences and physics. Other careers such as academic
teaching, civil service, employment in an established firm or a start-up are perceived as
extremely attractive by a large share of PhDs students; e.g., 53% of chemistry PhDs
considered a job in an established company as the most attractive career path.
A few studies have examined the determinants of career choice at this early stage. Gaughan2

See section 4 for information on the Japanese academic context.

6and Robin (2004) use US and French data and suggest that the prestige of the undergraduate
institution is associated with the likelihood of obtaining the first tenure-track position.
Similarly, Debackere and Rappa (1995) show that the prestige of the graduate school for
American neural network scientists is significantly correlated with the prestige of the first
employer.
For those PhDs who survive the postdoc period and manage to secure an assistant professor
position, the second critical point in the academic research career is the time at which thefaculty member is considered for a tenured (permanent) position. In the US, tenure is usuallyawarded seven to nine years after initial hire (Stephan 2012). In other countries, the system isless structured and rolling or temporary contracts over periods of four to eight years arecommon. Failure to obtain tenure can often result in job mobility to a lower ranked university
with more or only teaching duties, a move to a job in business, or exit from the market. A few
studies have analyzed the probability of being awarded tenure. First, in a sample ofbiochemistry graduates from US universities, productivity measured by publication count isfound to be positively associated with promotion from assistant to associate professor (withtenure), and to full professor (Long et al. 1993). Second, there is an important genderdifference; several studies indicate that females are less likely to be given tenure, and have to
wait longer for the offer of a permanent position (e.g., Ginther and Kahn 2004; Long et al.
1993). Wolfinger et al. (2009) examine the contract types of first employment and suggestthat female PhDs in the US are less likely to obtain tenured positions and more likely to exit
the labor force. Third, academic mobility across institutions has an influence. Althoughmobility can contribute to researchers’ social capital and productivity, Long et al. (1993) andCruz-Castro and Sanz-Menendez (2010) suggest that mobility delays promotion possibly
because the ‘tenure clock’ is continually being reset.
Finally, academic researchers that have obtained a tenured position (associate professorship)7

may still choose to leave a research career to move to a job in a business organization or torefocus their academic profile towards a teaching and administrative position.

3. Determinants of Exit
This study examines exit from academic research drawing on search theory in labor
economics (Burdett 1978; Jovanovic 1979; Mortensen and Pissarides 1994). Exiting from anacademic research career depends on the probability of receiving an offer (and accepting it)
to pursue a career in academic research compared to academic teaching, administration orindustry. We identify individual, institutional, and geographical factors correlated topersistence in an academic research job.

3.1. Individual Factor
3.1.1. Scientific productivity
The productivity of academics should affect their value in the labor market. Researchorganizations try to retain productive employees, and to dismiss less productive employees(Becker 1962). Highly productive academics have greater chances of both employment in aprestigious university (Allison and Long 1987), and of promotion (Long et al. 1993). Thesestudies suggest consistently that the opportunity for a job in academic research should behigher for more productive academics who consequently have a lower probability of leaving
an academic career (Brewer 1996). In parallel with their research responsibilities, academics
usually engage in non-research activities such as teaching and administration. Someacademics choose (or are forced) to concentrate on non-research jobs in academia and to give8

up their researcher careers. The expertise required for teaching, administration, and research
may coincide to a degree, so productive researchers could become productive teachers or
good administrators. This raises the question of whether higher research productivity leads toa higher chance of a teaching or administration job offer. In relation to social status, salary,etc., academics seem to gain higher utility from a research job than from pure teaching or
administration responsibilities.3 Thus, we hypothesize that productive researchers are unlikely
to become teachers or administrators, and have a low probability of exit. However, we
recognize that there are also a few cases in which extremely productive researchers become
excellent administrators.4
In some scientific fields where science and technology overlap such as transfer and Pasteurquadrant sciences (e.g. biomedical, software engineering), academic research expertise can berelevant for industry research. Thus, productive academics may attract job offers frombusiness (Lazear 1984; Murnane et al. 1991). Stern (2004) and Sauermann and Roach (2014)
suggest that scientists may choose industry jobs if the accompanying higher salaries
compensate sufficiently for loss of freedom to do the research they like and to publish.
Zucker et al. (2002) show that productive (measured by citations to their publications)academics are more likely to move to an industry job in the US biotech field. Thus, if therequirements for the industry job are academic research expertise, the effect of scientificproductivity on exit could be positive.

3

Top executive administrative positions (e.g., provost, dean) may be both prestigious and lucrative. Researchers
who leave research for such senior positions are regarded as examples of voluntary exit from academic research.
However, the number of these job opportunities is limited. In Japan, executive administrators are often
appointed from among professors within the university with some rotation pattern.
4See, for example, the case of David Baltimore Nobel laureate and president of the California Institute ofTechnology (Caltech) from 1997 to 2006.

93.1.2. Funding inputs
Academics need research funding in order to undertake research. In many countries, noncompetitive block grants have been replaced by competitive funds. Since academics’ capacity
to raise competitive funding is correlated with their productivity (Dasgupta and David 1994),
we would expect the availability of competitive funds to be negatively correlated with theprobability of exit since this type of funding is a research input. Even controlling forscientific productivity, stable funding could have a mitigating effect on exit. Academic
research in the natural sciences in particular, is heavily dependent on large research funding
support for laboratory costs and the salaries of PhDs and postdocs. Thus, securing funding isa major concern for laboratory heads (Shibayama et al. 2014; Stephan 2012). Secure funding
should ensure a continuing research career (Donowitz et al. 2007; Zerhouni 2006) andincrease its expected utility, thereby reducing potential exit. Thus, we expect that competitive
funding inputs are negatively associated with the likelihood of exit.

3.1.3. Gender
It is well-known that women are underrepresented in academia. For example, Auriol et al.
(2013) show that less than 40% of PhD graduates in most OECD countries are female. In
Japan, the gender imbalance is particularly pronounced5 with women accounting for only
26% of all PhD graduates in 2006 (NISTEP 2009a). Female researchers are more likely tohave child rearing and domestic responsibilities which are likely to cause earlier exit. Female
researchers are less likely to obtain tenure, and to take longer to achieve it (Ginther and Kahn
2004; Long et al. 1993; Wolfinger et al. 2009). For example, although the situation has

5Japan, ranked 21st in the UN’s gender inequality index, lags behind most OECD countries for improving thegender gap, (http://hdr.undp.org/en/statistics/gii).

10

improved over recent decades, in 2010 in the US, females accounted for 44%, 37% and 22%
respectively of assistant, associate and full professor positions, (National Science Board
2014: Ch.5). The statistics for Japan show that, in the natural sciences, only 15.7% ofassistant professors are female and a mere 3.8% achieve the position of full professor(NISTEP 2009b: 2-16). Based on these findings, we expect that females are more likely thanmales to exit an academic research career.

3.1.4. Academic career
Search theory shows that length of employment in the same organization stabilizes relations
between employee and employer, and reduces job quit (Farber 1994; Jovanovic 1979). In theacademic context, Crespi et al. (2007) show that the longer the academic remains in one
university, the less likely he/she will move from academia to industry. This would seem tosupport the negative relationship between tenure and exit. However, it is plausible also that
academic institutions appoint researchers with long tenure but diminished research
excellence, to non-research positions. Thus, tenure could be correlated either positively or
negatively with exit.
Job rank (assistant, associate, full professor, etc.) should also affect exit. Promotion is usuallyassociated with research performance; greater seniority equates with greater propensity to doresearch. In addition, seniority brings greater job security and a higher salary, resulting in
greater expected utility from an academic research career. Overall, we expect that job rank is
negatively associated with the likelihood of exit.

3.1.5. Academic network
11

When academics are well embedded in a scientific community and have good connections
with other academics, they are kept apprised of job vacancies. Zucker et al. (2002) show thatthe academic’s external network, measured by the proportion of co-authors from different
institutions, leads to mobility from academia to industry. Also, Crespi et al. (2007) indicatethat network, measured by collaboration with external organizations, facilitates mobility fromuniversities to public research organizations (PROs). Applying this evidence to the academic
network, one can expect that researchers with good individual academic network connections
will also be better informed about academic research job opportunities which consequently
will reduce the likelihood of exit. This may be particularly true in the Japanese context where
human relationships (connections) play an important role in the recruitment of academics(Yamanoi 2007).

3.2. Institutional Factor
3.2.1. Research organization
An institutional factor that is known to influence academic career is organizational prestige.
Undergraduate or graduate study at a prestigious organization is found to be a good predictor
of future academic employment (Gaughan and Robin 2004) and promotion (Long et al.
1993). In addition, it has been suggested that prior experience in an excellent organization
leads to future employment in a prestigious organization (Allison and Long 1987; Debackere
and Rappa 1995). These studies imply that organizational prestige should increase job offers
for academic researchers and lead to higher expected utility, lowering the likelihood of exit.
Prestige is a complex concept that encompasses several factors. First, prestige is associatedwith the availability of resources for research. In general, prestigious organizations have more

12and larger sources of revenue, can invest more generously in research, and can maintainbetter facilities for individual researchers to conduct state-of-the-art research. This should
increase the expected utility of an academic research career. A second factor is theinstitutional academic network. Prestigious organizations can employ excellent researchers
and attract excellent external collaborators. This provides individual academics withadvantages in the form of opportunities for intellectual interactions with peers, and access totheir social capital. Thus, we expect that institutional capital, measured as funding input andinstitutional academic network (peer effects and social capital), will be negatively correlatedwith exit.

3.2.2. Scientific fields
In the context of academic research, individual academics have an affiliation to their
particular university, and become deeply embedded in their respective scientific fields. Thus,
their career paths should be affected by the characteristics of their field. These include
academia-industry linkages which can increase job offers from industry, and are particularly
important in Pasteur-quadrant sciences (compared to pure basic science) (Stokes 1997),
where mobility between academia and business involves lower transaction costs and is morecommon, resulting in higher levels of exit. Field growth is another factor; in expanding fields,
employment is more likely to increase and the enhanced job prospects should improve theexpected utility of an academic research career. Therefore, the probability of exit will behigher in Pasteur-quadrant scientific fields, and field growth will be negatively associatedwith exit.

133.3 Geographical/labor market factors
In general, a career change involves a different work place. A geographical change incurs
search and moving costs, so job vacancies in the same geographical vicinity should be
associated with a higher likelihood of a job change. Disentangling types of employers,
Zucker et al. (2002) show that mobility from academia to industry is positively associatedwith the number of biotech firms, and negatively associated with the number of top-rated
universities in the area. The effect of concentration of universities is unclear because
universities offer both research and teaching jobs. In an attempt to disentangle these two
types of employment opportunities, we expect that the local concentration of researchintensive universities will be negatively correlated with exit, while local concentration ofteaching-oriented universities will be positively correlated with exit. We control also for thesize of the local labor market and supply of qualified researchers.

4. Context of Japanese Academia
Japan has three types of universities - national, regional (i.e., city and prefectural), andprivate universities - which offer four-year degree courses and postgraduate education. In
1985 (our sample is composed of academics who obtained a PhD degree in 1985-1989),
Japan had 95 national, 34 regional, and 331 private universities,6 with national universities
focused on academic research, and most private universities focused on teaching. In 1985,
73% of undergraduate students were enrolled in private universities, and 24% in national
universities, while 38% of graduate students were enrolled in private or regional universities,
and 62% in national universities. In terms of research funding, in 1985, national universities

6

School Survey conducted by the Ministry of Education, Culture, Sports, Science and Technology (MEXT;
http://www.e-stat.go.jp/).

14

received 77% (decreased to 67% in 2010) of the total GiA budget, the primary national
research funding system in Japan. Among national universities, the seven pre-imperial
universities (Tokyo, Kyoto, Osaka, Tohoku, Hokkaido, Kyushu, and Nagoya) are considered
exceptionally prestigious for both research and education. Academic research is also
conducted in PROs (e.g., RIKEN). 7 In 2004, PROs employed approximately 10% of
researchers, and universities 90% (METI 2006: 264).
Japanese universities have a three-level promotion system from the entry position of assistantprofessor or lecturer, to associate professor, and finally full professor. 8 Currently, before
being appointed to an entry position, academics - especially in natural sciences – must spend
a few years as a postdoc. In 2005, the average postdoc period was five or six years (Yamanoi
2007: Ch.12). In the 1980s, postdocs were less common, and young academics were often
appointed as assistants or lecturers directly after graduation. According to a national survey ofnatural scientists (NISTEP 2009b), among respondents aged 26-36 years in 1990 23.5% had
held a postdoc position, 70.5% had not. Among the former, approximately 80% spent three
years or less as a postdoc.
Japan’s academic system has a few relevant features. First, it used to be characterized by
lifetime employment (Shimbori 1981; Takahashi and Takahashi 2009). Until a series ofreforms in the 2000s allowed temporary employment, entry positions were mostly permanent(Watanabe 2011). Second, many Japanese universities operate a hierarchical “chair” system(Yamanoi 2007). The system consisted, and sometimes still is, of a full professor (the“chair”), responsible for a small team of junior researchers in entry positions and perhaps an
associate professor.. Thus, while junior researchers in entry positions were cleared ofunemployment risk, they had to (and have to) compete to be promoted and win a position of7

http://www.riken.jp/en/
The position of assistant professor was officially introduced in Japanese universities in 2007. Previous to this,the position was designated “assistant” (Watanabe 2011).

815

an independent researcher. Third, Japan’s employment practice is characterized by highrigidity and very low cross-sectoral mobility. According to a government statistic (METI
2006: 264) in 2004, only 1.1% of researchers (8,800 out of 790,900) moved across the threesectors of industry, government, and academia. In the same year, 97.4% of 291,100 university
researchers were not mobile, 2.3% moved between two universities, 0.11% moved toindustry, and 0.15% moved to government (i.e., PROs). Thus, industry is a less frequentdestination for academics who leave university employment and, in Japan, exit is more likely
due to academics giving up research and remaining in academia in a teaching or
administrative position.

5. Data Description and Variable Measurement
5.1. Sample and Data
Our sample is composed of a cohort of 13,492 PhD graduates who were awarded a PhD
degree in hard science (all scientific fields except social sciences and humanities) in the
period 1985-1989. 9 We focus on the 1985-1989 cohort because a reform to the funding
system allowed significantly more junior researchers to obtain grants since the mid 1980s(the number of junior grantees in 1980-1984 is about half the number in 1985-1989). This
allows us to trace academic careers more precisely for up to 20 years. The data on PhD
graduates were obtained from the Japanese National Library database10 which provides full9

We exclude social sciences and the humanities because our theoretical framework to explain exit fromacademic research does not apply to these fields. PhD graduates from these fields account for 3.2%. We also
exclude foreign-born graduates because name matching (explained later) for foreigners is difficult for nonunique notations of foreign names in Japanese characters, and because non-native Japanese graduates areunlikely to pursue an academic career in Japan (Franzoni et al. 2012). Foreign-students account for 7.3% of all
PhD graduates. Finally, we do not include so-called paper-based PhDs who are awarded the degree on the basisof their research output (often based on corporate research experience) with no course work requirement; paperbased degrees are usually awarded to senior researchers and their inclusion would bias the analysis.
10
Universities are obliged to archive all PhD dissertations in the National Library. The library creates electronic
data upon receiving dissertations. There is open access to the database (http://opac.ndl.go.jp/).

16

names, degree field, year of degree award, etc.
To trace careers and identify exit from an academic research career, we exploit the GiA
program database. The GiA program is the largest source of funding for academic research inJapan and covers all scientific fields and all ranks of researchers.11 A survey of GiA grantees
in 2006 indicates that only 3% of academic researchers depended for the majority of theirresearch budget on funding sources other than GiA, and that this rate differs between fields
with a maximum of 13% in engineering (Iida 2007: Ch.6).12 Thus, we can reasonably assume
that researchers who have never received GiA funding, or have not continued to receive it,most likely stopped doing academic research.13 For each grant award, the database provides
full names of grantees, grant size, affiliations, collaborators, associated publications after
completion of the grant, fields of research, etc.
We created two datasets; the first includes cross-sectional information for the whole 13,492
PhD graduates, and the second includes information on 5,613 GiA grantees for the period
1990-2010. We linked the two databases on the basis of full names of PhD graduates and GiA
grantees and found 5,613 matches among the 13,492 PhD graduates. 14 We consider that
unmatched PhD graduates who never received GiA funding, exited before embarking on aprofessional academic research career (pre-employment exit).
We built an unbalanced panel that consists of the matched 5,613 PhD graduates who have
11

Generalinformation
on
GiA
can
be
accessed
from
the
MEXT
website(http://www.mext.go.jp/a_menu/shinkou/hojyo/main5_a5.htm). Kneller (2010) and Asonuma (2002) provide
overviews of the GiA and the general budgetary structure in Japanese universities. The GiA database provides
information on all grants awarded under the system since 1965, covering 210,000 university researchers.
12
Among the top 7 national universities, about 84% of full and associate professors had received GiA funding atleast once in the period 2001-2005 (Shibayama 2011). Our exit measure might be less reliable for researchers inprivate universities, where dependence on GiA is lower.
13
GiA grants are also awarded to researchers in PROs. Since PRO researchers are less dependent on competitive
funding, our data might miss very active researchers in PROs and overstate exit. However, we believe that thiseffect is limited because of the higher mobility from PROs to universities (10%) and much lower mobility fromuniversities to PROs (0.1%) (METI 2006: 264).
14
Name ambiguity is not a serious problem since full names in Chinese characters are available in both
databases. For some common names, we differentiate by year of graduation, and funding and scientific field.

17

received GiA funding at least once, and trace their career until exit or up to 2010. We
consider that an academic who appeared in the GiA database but then disappeared has exitedfrom an academic research career after a spell of academic employment (post-employmentexit). Although the two original databases do not provide information on age, we can assume
that PhD degrees were awarded at around 26-31 years of age (NISTEP 2009a). Since theretirement age in most universities in Japan was 60 (although this has now been extended), it
is unlikely that researchers that exited our sample after having started an academic research
career would be retired since they should be around 57 years old in 2010.

5.2. Dependent Variables
For pre-employment exit, we prepared a dummy variable that takes the value zero if a PhD
graduate is matched with the funding database and 1 otherwise. The matching rate is 42%,and thus, the rate of pre-employment exit is 58%.
For post-employment exit, we prepared a dummy variable that is coded 1 for the last year theacademic received a grant from GiA, and zero otherwise. We used the year 2010 version of
the GiA database, and regard academics whose latest record in the database occurred in 2006or later, as survivors, on the assumption that research-active academics are funded at least
once in five years.15

5.3. Independent Variables
We prepared several independent variables for the individual, institutional, and geographicalfactors discussed above, drawing on the funding database and other public data sources. To
15

Most funding is for 3-5 year periods.

18analyze pre-employment exit, we took year of PhD graduation as the measurement year.
For individual level measures we include eight variables. The funding database provides thenumber of publications resulting from each funded project. By dividing this publication countby the number of project members and project duration, we can compute a yearly publication
count for each academic. Since publication count can differ by field and year, we
standardized this measure by field/year mean and standard deviation. We then compute theaccumulated count of publications prior to each year (pub stock). We divide funding amountfor each project by the number of project members and project duration, and summed themfor each year, for each academic, to compute a yearly funding input. We standardize this by
the field/year mean and standard deviation to compute stock value (fund stock). We code adummy variable (female) 1 for female and zero for male. For academic career, we computethe number of years of employment each academic had in a university (job tenure). For
academic rank, we constructed two dummy variables: the variable full prof takes the value 1
for full professor, and the variable associate prof takes the value 1 for associate professor. For
academic network, we count the cumulative number of co-grantees related to a researcher’s
GiA funding (#cograntee) and we also control for the number of universities to which a
researcher was affiliated (mobility).
We constructed four variables for institutional factors. The top seven national universities inJapan are regarded as prestigious research-intensive universities. To measure organizationalprestige, we include a dummy variable that scores 1 for the top seven universities, and zerootherwise (top7). University-level funding input is computed as follows. We first compute the
GiA funds for each intersection of university, field, and year, and then total funds for eachfield/year. We divide the former by the latter to calculate the proportion of funding distributed
to each university (%univ fund). To measure the importance of the institutional academic
network of the grantee university (as a proxy for peer effects and social capital), we count the19number of researchers awarded GiA funding in each year in the same university and the same
field (#researcher). To estimate field growth, we count the number of grantees in each year
and each field, and calculate the annual growth rate (field growth).
Finally, for geographic factors, we constructed four variables at the level of the 47
prefectures. We collected the number of jobs in national universities located in eachprefecture as a proxy for academic research jobs (#national university employment), and thenumber of jobs in private universities as a proxy for teaching-oriented job opportunities(#private university employment).16 This is based on the assumption that national universities
tend to be research-intensive and private universities tend to be teaching-oriented. We alsocollected employment numbers for each prefecture as a measure of employment opportunities
in the private sector (#industrial employment).17 Finally, we take number of PhD graduates inuniversities in the same prefecture to measure the labor supply (#PhD graduate).

6. Econometric Model and Results
The structure of our data allows us to analyze exit at a few distinct moments: (1) immediately
after PhD graduation (pre-employment exit), (2) after the academic embarks on aprofessional researcher career (post-employment exit), divided into (2a) before achieving atenured position, i.e., assistant professor (pre-tenure exit), and (2b) after achieving tenure,i.e., associate or full professor (post-tenure exit).18 Section 6.1 presents the estimations forexit at moment (1) drawing on cross-sectional data for 13,492 PhD graduates, and estimates
16

Source:SchoolSurveyconducted
by
the
MEXT
(http://www.mext.go.jp/b_menu/toukei/chousa01/kihon/1267995.htm).
17
Labor Survey conducted by the Ministry of Internal Affairs and Communications(http://www.stat.go.jp/data/roudou/index.htm). Since data are available from 1997, data for the years 1985-1996
are imputed from year 1997. Employment numbers have been stable since the late 1990s.
18
As discussed above, assistant professor positions were mostly permanent in the past. Thus, in our empiricalsetting, tenured position has the meaning of being granted the promotion to associate professor, academic rankthat is usually associated to a tenured position in the US.

20the likelihood of pre-employment exit by logit regressions.
In sections 6.2 and 6.3 we examine exit at moments (2a) and (2b). We use panel data for thewhole careers of the 5,613 PhD graduates who received GiA funding at least once. We
estimate the likelihood of post-employment exit by survival analysis, drawing on a duration
model that allows us to analyze a point event (referred to as a failure event) which occurs
after a certain period of time (spell length). The average spell length is 14 years. We draw on
a discrete approach based on the complementary log-log (cloglog) model. 19 Based onPrentice and Gloeckler (1978), the discrete hazard time for individual i in time interval t toexit is estimated by the following function:
hit = 1 – exp{– exp(βXi + θ(t))}
where hit is the hazard rate and θ(t) is the baseline hazard function with spell duration(Jenkins 1995). A set of time dummy variables is included to capture the unobserved timevarying effect on the likelihood of exit.

6.1. Pre-employment Exit
We first examine pre-employment exit defined by no award of GiA funding. Table 1 presents
basic information on exit by field, university, and gender. Table 1A provides a breakdown by
PhD degree field: Medicine (48.2%), Engineering (16.1%), Science (15.4%), Dentistry(10.4%), Agriculture (6.2%), and Pharmacy (3.8%). Exit rates differ substantially by field.
PhDs in Science, the most basic field, show the lowest exit rate (42.3%), or the highest rate ofsurvival in an academic research career. Three medical fields (Medicine, Dentistry,
Pharmacy) show high exit rates (> 60%), probably because of substantial demand for their19

A continuous approach can be employed. A Cox (1972) semi-parametric model yields a similar pattern ofresults.

21

labor in practitioner jobs (doctors, dentists, pharmacists, etc.). Engineering and Agriculture
show medium exit rates (~52%) perhaps due to employment in industry.
Table 1B provides a breakdown by PhD awarding university. During 1985-1989, 142
universities awarded at least one PhD degree in hard sciences. The top seven universities
accounted for 37.9% of PhDs, and their exit rates are somewhat lower (< 50%). Lowerranked universities show higher exit rates, implying that more academic research jobs are
given to graduates from top universities.
Table 1C provides a breakdown by gender and field. The proportion of females in all PhD
graduates is 7.8%. In all fields, exit rates are higher for females (71.3%) than males (57.0%).
The gender difference is greatest in Science (1.44 times more for females than males) andleast in Pharmacy (1.09 times). The three fields with the highest exit rates for female
scientists are Medicine (77%), Dentistry (73%), and Pharmacy (72%).
Table 2 shows the regression results. We regress pre-employment exit using a logit regression
model. Appendix 1A provides descriptive statistics and correlations for the variables. Model 1
suggests that females are significantly more likely to exit before employment. This effectremains strong after introducing other factors in Models 2 and 3.
Model 2 includes institutional factors, and suggests two determinants of exit. Affiliation
during PhD training has a significant effect; graduates from top universities are less likely toexit which is consistent with our hypothesis and summary data. Moreover, the number of
researchers who received a GiA grant in the same university and field, proxy for institutional
academic network, decreases the likelihood of exit. This may be because they help PhD
graduates find employment through their large research network or because research active
departments have greater capacity to employ PhD graduates on a temporary basis.
University-level funding shows no additional effect on exit.
22

Field dummies are collectively significant. The field of Science shows the lowest exit
propensity while Pharmacy, Medicine, and Dentistry show the highest propensity. This result
is mostly consistent with our hypothesis. Since career structure might differ with scientificfield, we ran the same set of regressions separately for each field. The statistical significancevaries across fields but the sign of the correlation is consistent with the aggregate results
except in the case of university-level funding which is negatively correlated with theprobability of exit in Dentistry, and surprisingly, is positively correlated with the probabilityof exit in Engineering (see Appendix 2A for summary results).
Model 3 also includes geographical factors. Employment in research-intensive national
universities has a negative effect, suggesting that the availability of academic research jobs inthe vicinity reduces the likelihood of exit, as hypothesized. In contrast, and as expected,
employment in teaching-oriented private universities, increases the likelihood of exit (moveto a teaching oriented job). Employment in industry does not have a significant effect.
Finally, a large number of graduates from the same geographical area facilitates exit perhaps
due to over-supply and competition for research jobs.

6.2. Post-employment Exit
For academics who do not exit immediately after graduation (i.e. awarded funding at leastonce), we compute survivor functions (Figure 1). Figure 1A illustrates the function for thewhole sample, indicating that academic exit was steady, with 40% persisting after 20 years inresearch. Job ranks at the time of exit, or final position of survivors is full professor (32%),
associate professor (23%), and assistants or lecturer (45%).
We compute survivor functions for a few sample subsets. Figure 1B highlights field

23

differences based on funding fields.20 Funding fields consist of Clinical Medicine (30.3%),
Engineering (17.0%), Mathematics and Physics (12.0%), Dentistry (10.1%), Biology (8.6%),
Agriculture (7.0%), Chemistry (4.9%), and Pharmacy (2.1%). The survivor function shows
particularly rapid exit in Clinical Medicine and Dentistry followed by Pharmacy. The otherfields follow a similar trend with about 60% remaining after 20 years. In terms of PhD
affiliation, the top seven universities account for 49% of those PhD graduates who remain in
academic research. Graduates from these universities are significantly less likely to leavethan those from other universities (Figure 1C). Finally, females, who account for 5.3% of
those PhD graduates who remain in academic research, are more likely to exit than males(Figure 1D).
Table 3 shows the regression results for post-employment exit. Appendix 1A provides
descriptive statistics and correlations for the variables. Model 1 includes only individual-levelfactors. Publication stock has a significantly negative coefficient, suggesting that highperformers tend to continue in academic research. This effect is consistent after controllingfor other factors in Models 2 and 3. Funding input has a negative coefficient, implying thatlarger yearly research funding for individual academics facilitates long-term engagement inacademic research. Though Model 1 shows a negative sign for female, when we control for
institutional and geographical factors (Models 2 and 3), in Model 3 the sign turns positive andsignificant, suggesting that females are more likely to leave academic research. Job tenure
has a negative effect on mobility; i.e., academics who stay for longer in the same university
are less likely to exit. However, this effect turns weakly positive when we control for
geographical factors (Model 3), indicating that once we control for labor market
characteristics, staying longer at the same university might be associated more with non-20

For those academics who did not exit immediately after graduation, we distinguish fields in slightly moredetail drawing on the GiA database. Medicine is split into Basic Medicine and Clinical Medicine; Science issplit into Biology, Chemistry, Mathematics, and Physics.

24research jobs. Seniority reduces the likelihood of exit; i.e., assistant professors are more
likely to exit than associate professors, and associate professors are more likely to exit thanfull professors, which is in line with our hypothesis. For the individual network effect, thenumber of co-grantees shows significantly positive coefficients (Models 2 and 3), which iscontrary to our expectation that a larger academic network helps job search. This result is
discussed further in section 6.3. Finally, previous mobility has a negative coefficient,suggesting that mobile academics tend to remain in academic research for longer. However,this effect decreases when we control for institutional factors (Model 2) which is explainedlargely by the field dummies, and becomes significant and positive in Model 3 when we
control also for geographical factors. One interpretation of this result is that mobility might
be forced rather than voluntary, and that lower performers who are less likely to be granted
tenured positions need to move to lower ranked institutions to obtain more secure andteaching-oriented job.
In relation to institutional factors, our results suggest that academics in prestigious
universities (top7) tend to stay longer in academic research which supports our hypothesis.
The coefficient of number of researchers in the same university and same field is significantly
negative, showing a decreasing likelihood of exit and confirming the presence of an
institutional peer and social capital effect.21 As for funding input at the university level, we
find a small positive correlation with exit, contrary to our hypothesis, which largely
disappears in Model 3. After controlling for social capital and peer effects (#researcher) andorganizational prestige (top7), resource input has only a limited impact on the likelihood of
exit.

21

The number of researchers may be confounded by the size factor, since we control for the proportion offunding distributed to the university (%univ fund), we can interpret the number of researchers in the field in theuniversity as a proxy for social capital and peer effect. We also ran the regression using amount rather thanpercentage of university-level funding and obtained qualitatively similar results.

25

Among field-related factors, Model 2 shows that field growth has a significantly negative
coefficient. This suggests that in expanding fields, exit is less frequent, although when we
control for geographic/labor market characteristics (Model 3), the effect becomes negligible.
The field dummies collectively are significant. Among the nine fields, for post-employment
exit we observe a relatively low propensity in Pharmacy, Biology, and Chemistry, and highpropensity in Agriculture, Clinical Medicine, and Dentistry. We examined field difference byrunning the regressions separately for each field (see Appendix 2B for summary results).
Model 3 includes geographical factors. We observe a negative coefficient of number of jobs
in national universities, suggesting that a higher number of researcher positions facilitates anacademic career. We observe a positive coefficient of number of jobs in private universities,
suggesting that the higher the number of teaching positions, the shorter the time taken for
academics to leave academic research and accept a job in a teaching university. Model 3
indicates also that more PhD graduates from the same geographical area increase thelikelihood of exit, suggesting the effect of oversupply. Finally, the number of jobs in industry
has a negative coefficient. This effect is particularly strong for Biology and Basic Medicine(see Appendix 2B), maybe indicating that the presence of a proximate sizable industry sector
increases the chances of industry funding allowing researchers to continue doing research.
Figure 2 depicts the estimated baseline hazard functions according to the predictions inModel 3, Table 3, for the set of PhD graduates who received GiA funding at least once. To
draw the graphs, we average the predictions of the dependent variables for each subgroup.
The cloglog models include dummy variables for each time period (year), which collectively
are strongly significant. Figure 2A indicates that the probability of exit is initially relatively
high, then it decreases, and after about 10 years starts to increase. By the end of the period theprobability of exit is similar to the initial level. The early peak represents researchers who aretrying to develop an academic research career - probably during their postdoctoral or assistant26

professorship period - prior to obtaining a tenured position. The later increase in the hazard
rate would seem to correspond to academics leaving research and moving to non-researchjobs.
Figure 2B compares initial affiliations and shows that the likelihood of exit from academic
research is lower throughout the whole career, for academics who graduated from aprestigious university. Figure 2C compares gender effects, and shows a much higher exit
probability in the early and later career stages for females.

6.3. Career-Stage Differences
Table 4 compares two career stages - before obtaining a tenured position, i.e., assistantprofessor (Model 1), and after achieving a tenured position, i.e. associate and full professor(Model 2). Scientific productivity is found to decrease the probability of exit in both stages.
Funding stock, as expected, has a negative effect in the most senior stage. It also has a weak
(and positive) effect in the junior stage, perhaps because many junior academics aresupported financially by their supervisors, and obtaining their own funding plays a lessimportant role. We find that females are more likely to exit during the junior stage but if theyobtain a tenured position, their likelihood of exit is not significantly different from that ofmales (Takahashi and Takahashi 2010, 2009). Job tenure shows a positive effect in the senior
stage, implying that academics might be assigned non-research jobs after very long
employment. Mobility has a strongly positive effect in the junior stage but no effect in thesenior stage. As discussed above, the observed mobility might be largely forced andassociated with low performance.22 Having obtained a tenured position, past mobility might
not matter. The number of co-grantees is positively correlated to exit in the early career phase
22

Mobility and performance are negatively correlated during the junior stage.

27

and negatively correlated to exit after obtaining tenure, indicating that the individual
academic network exerts a moderating impact on exit only when the researcher has achieved
a senior position and a well developed, consolidated academic network.
Among institutional factors, university prestige is influential only during the junior stage,suggesting that tenure is more often awarded to graduates from top universities who may bebenefiting from institutional prestige. The number of researchers in the same university
decreases the likelihood of exit only at senior levels, once again indicating that positive
network effects are relevant only for senior academics. Geographic/labor market control
factors have a similar effect in both stages although the number of jobs in private universities
is significantly correlated only with exit by assistant professors, indicating that the choice totake up a career in a more teaching oriented university is usually made at an early stage.

7. Conclusions
This chapter has examined the individual level determinants of exit from an academic
research career, controlling for institutional and geographic/labor market influencing factors,
informed by the literature on academic mobility and academic careers (e.g., Allison and Long
1990; Crespi et al. 2006), and search theory in labor economics (e.g., Mortensen and
Pissarides 1994). The up-or-out nature of an academic research career results in someacademic researchers being forced to abandon an academic research career despite hugeinvestments such as fellowships and supervisory support. This career selection process might
be compromised by biased selection criteria, or a badly designed academic system, leading tounintended exit. Although this is a practical concern (e.g., Cyranoski et al. 2011; Donowitz etal. 2007; Ginther et al. 2011), few studies have examined exit from academia. The study
described in this chapter is an attempt to fill this gap, based on a sample of Japanese28

academics in hard sciences.
The results confirm that productive academics are more likely to continue to do research,suggesting that the selection process is based at least partly on merit which is in line with theliterature on academic careers (Allison and Long 1987; Becker 1962; Long et al. 1993). Froma search theory perspective, the opposite effect is also plausible - that productive researchers
attract offers of industry jobs or non-research jobs in academia; however, the results support
this argument only for the field of Dentistry. Thus, scientific productivity increases job
opportunities in academic research significantly more than other jobs. Coupled with the very
low mobility from academia to industry in Japan (METI 2006), this result might suggest thatthe demand for labor from industry is not being adequately addressed by the academic
system.
We also examined the impact of individual level funding and found that funding decreases
the likelihood of exit only after researchers have obtained a tenured position, indicating that
individual fund-raising capacity plays a role only if the researcher becomes a head oflaboratory. With regard to discrimination in the selection process, our results show thatfemales are more likely to exit an academic research career than males, which is consistent
with the findings for other countries (e.g., Ginther and Kahn 2004; Long et al. 1993). We
found that this effect is especially strong immediately after graduation (70% for females vs.
57% for males), less strong during the junior stage (4.8% vs. 3.8%), and disappears during
the senior career stage (2.5% vs. 2.3%).23 However, the share of active female researchers atsenior level (associate and full professor) is very low (4.4%). The Japanese government has
implemented several policies to try to mitigate the gender gap but it seems that further
intervention is needed. We found also that the number of co-grantees decreases the likelihoodof exit only after award of a tenured position, suggesting that the effect of individual social
23

Predictions are based respectively on Model 3 Table 2, Model 1 Table 4, and Model 2 Table 4.

29

capital may be moderated by seniority.
In relation to institutional factors, as Gaughan and Robin (2004) and Debackere and Rappa(1995) imply, academics in prestigious universities are less likely to exit at the start of theircareers (45% for the top seven vs. 65% for the rest) and during the assistant professor period(2.7% vs. 4.6%). 24 Since academic inbreeding is common - particularly in high-ranked
universities in Japan (Yamanoi 2007), our result might indicate that opportunities foracademics whose careers start in low-ranked universities are unreasonably hampered. From ascience policy perspective, mobility across institutions should be facilitated so that talented
researchers have more equal access to the institutional capital of prestigious universities, or
so that institutional capital at low-ranked universities is boosted. Our results suggest also thatthe number of researchers in the same workplace reduces exit at either entry or senior(associate and full professor) level. This effect is significant even after controlling for the sizeof university-level funding input, implying that social capital plays a pivotal role.
Importantly, this effect is negligible in the pre-tenure term, suggesting that institutional social
capital can help in the identification of entry positions for students, or may help in continuing
academic research activities after tenure, but does not play a significant role at assistantprofessor level.
Among geographical factors, employment opportunities related to teaching jobs at prefecturelevel facilitates the exit of assistant professors, suggesting academics’ move from research toteaching jobs within academia especially during the early phase of their career.
Overall, these results imply that the selection process in Japan is based at least in part, on
merit but might be compromised by unfair selection or inequality between genders andamong institutions, resulting in unintended exit. We also found some evidence that only

24

Predictions are based respectively on Model 3 Table 2 and Model 1 Table 4.

30

senior academics (associate and full professor) are able to benefit from positive individual
and institutional network effects. In other words, junior academics searching for tenured
positions can be unreasonably forced to exit due to social network effects favoring senioracademics. The network effect also extends to the senior academic’s students in relation totheir first placements, implying that the social capital of senior academics can influence who
joins academia.
These findings should be interpreted with care. Due to the structure of the data, thedestination of academics after leaving research is not completely clear. The study in thischapter assumes, based on Japanese mobility statistics, that most academics leaving research
become university teachers or administrators (rather than industry employees), or exit thelabor force. However, the data might include different types of exit and also might include
false exit (e.g., death, emigration). This study draws on a large sample in a first attempt to
examine the main characteristics of exit from an academic research career. Future researchshould address the limitations outlined above by studying a smaller, more detailed sample(e.g., survey data). The specificity of our sample is also a limitation. Country and time
specificities need to be considered because career progress is heavily dependent on the designof the academic system. Future research should examine exit from an academic research
career in different national and historical contexts.

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36

Table 1
Description of Pre-employment Exit
(A) By Field
PhD Field
ScienceEngineering
Agriculture
Pharmaceutical
Medicine
Dentistry
Total#Graduate
2,073(15.4%)
2,170(16.1%)
841(6.2%)
506(3.8%)
6,503(48.2%)
1,399(10.4%)
13,492(100.0%)#Matched
1,196(21.3%)
1,026(18.3%)
403(7.2%)
167(3.0%)
2,303(41.0%)
518(9.2%)
5,613(100.0%)

%Exit
42.3%
52.7%
52.1%
67.0%
64.6%
63.0%
58.4%

(B) By University
Rank
1
2
3
4
5
6
7

University
U Tokyo
Kyoto U
Osaka U
Tohoku U
Kyushu U
Hokkaido U
Nagoya U

10
20
30
40
50

Top 7
Tsukuba U
Keio UTokyo Jikei Med U
Akita U
Hamamatsu Med U
Total#Graduate
1,343(10.0%)
986(7.3%)
699(5.2%)
605(4.5%)
549(4.1%)
499(3.7%)
437(3.2%)
5,118(37.9%)
303(2.2%)
163(1.2%)
114(0.8%)
91(0.7%)
70(0.5%)
13,492 (100.0%)#Matched742(13.2%)
543(9.7%)
367(6.5%)
312(5.6%)
292(5.2%)
230(4.1%)
259(4.6%)
2,745(48.9%)
134(2.4%)
70(1.2%)
52(0.9%)
28(0.5%)
29(0.5%)
5,613 (100.0%)

%Exit
44.8%
44.9%
47.5%
48.4%
46.8%
53.9%
40.7%
46.4%
55.8%
57.1%
54.4%
69.2%
58.6%
58.4%

Notes: Ranked by the number of PhD graduates.

(C) By Gender

PhD Field
ScienceEngineering
Agriculture
Pharmaceutical
Medicine
Dentistry
Total

Male
1,909
2,054
742
454
5,876
1,278
12,313#Graduate
Female %Female
144
7.0%
68
3.2%
77
9.4%
51
10.1%
590
9.1%
106
7.7%
1,036
7.8%

Male
1,129
991
368
153
2,164
488
5,293

37

#Matched
Female %Female
59
5.0%
28
2.7%
34
8.5%
14
8.4%
134
5.8%
28
5.4%
297

5.3%

%Exit
Male
Female
40.9%
51.8%
50.4%
66.3%
63.2%
61.8%
57.0%

59.0%
58.8%
55.8%
72.5%
77.3%
73.6%
71.3%

%Exit
Female/Male
1.44
1.14
1.11
1.09
1.22
1.19
1.25

Table 2

Prediction of Pre-employment Exit
Model 1

Individual factors
graduation year
femaleInstitutional factors
top7%univ fundln(#researcher)field growthfield dummies
Geographical factors
ln(#national univ employment)ln(#private univ employment)ln(#industrial employment)ln(#PhD graduate)
χ2 testLog likelihood
N

.007
.587

***

Model 2(.012)(.070)

.023
.567
-.237
.003
-.310
.141
YES

74.459-9158.37
13492

***

796.663-8797.26
13492

***
***
***

***

Model 3(.020)(.072).012.566(.061)(.008)(.043)(.144)

-.242
-.005
-.278
.135
YES

***

-.246
.064
-.021
.113

***

832.348-8779.42
13492

***

***

***

**
†

(.020)(.072)(.061)(.008)(.044)(.145)(.075)(.021)(.047)(.062)

Notes: Logit regressions. Unstandardized coefficients (standard errors in parentheses). Two-tailed test. †p<0.1; * p<0.05; ** p<0.01; *** p<0.001.

38

Table 3

Prediction of Post-employment Exit
Model 1

Individual factors
pub stockfund stock
femalejob tenureassociate prof
full prof
mobilityln(#cograntee)
Institutional factors
top7%univ fundln(#researcher)field growthfield dummies
Geographical factors
ln(#national univ employment)ln(#private univ employment)ln(#industrial employment)ln(#PhD graduate)
χ2 testLog likelihood
N

-.031
-.022
-.256
-.157
-.400
-.707
-1.356.022

27624.50-13810.44
81331

***
***
**
***
***
***
***

***

Model 2
-.034
-.007
.021
-.025
-.607
-.869
-.405
.204

***

-.199
.036
-.455
-1.616
YES**(.006)(.005)(.079)(.005)(.061)(.094)(.023)(.023)

28218.26-11957.66
81331

***
***
***
***
***

***
***
***

***

Model 3(.006)(.005)(.080)(.005)(.062)(.093)(.032)(.024)

-.036
-.013
.171
.008
-.499
-.792
.086
.120

***

(.066)(.009)(.020)(.181)

-.297
.018
-.118
-.255
YES

***

-.588
.083
-.327
.399

***

28287.25-11337.45
81331

***

*
*
†
***
***
*
***

†
***

***
***
***

(.006)(.005)(.081)(.005)(.063)(.093)(.036)(.025)(.068)(.010)(.028)(.229)(.044)(.023)(.038)(.042)

Notes: Complementary log-log model. Unstandardized coefficients (standard errors in parentheses). Two-tailed test. †p<0.1; * p<0.05; ** p<0.01; *** p<0.001.

39

Table 4

Prediction of Post-employment Exit by Career-Stage
Model 1(pre-tenure)

Individual factors
pub stockfund stock
femalejob tenure
full prof
mobilityln(#cograntee)
Institutional factors
top7%univ fundln(#researcher)field growthfield dummies
Geographical factors
ln(#national univ employment)ln(#private univ employment)ln(#industrial employment)ln(#PhD graduate)
χ2 testLog likelihood
N

-.029
.003
.187
.009

***

.142
.164

**

-.342
.007
.025
-.289
YES

***

-.709
.077
-.298
.456

***

19652.04-8298.86
52584

***

*

***

**
***
***

Model 2
(post-tenure)(.008)(.007)(.088)(.006)

-.040
-.033
-.028
.021
-.137
.022
-.098

(.048)(.029)

***
***
**

*

(.075)(.011)(.037)(.259)

-.107
-.007
-.298
-.113
YES

(.051)(.026)(.043)(.049)

-.383
.070
-.397
.372

***

8066.32-2933.41
28747

***

***

***
***

(.009)(.010)(.213)(.008)(.094)(.059)(.048)(.187)(.029)(.045)(.488)(.081)(.053)(.076)(.078)

Notes: Complementary log-log model. Unstandardized coefficients (standard errors in parentheses). Two-tailed test. †p<0.1; * p<0.05; ** p<0.01; *** p<0.001.

40

Figure 1

Survivor functions (N = 5,613)(A) Whole Sample

0.00

0.20

0.40

0.60

0.80

1.00

Kaplan-Meier survival estimate

0

5

10
analysis time1520(B) Scientific fields
0.00 0.20 0.40 0.60 0.80 1.00

Kaplan-Meier survival estimates

0

5

10
analysis time
Agriculture
Biology
Clinical Medicine
Engineering
Pharmaceutical

15
Basic Medicine
Chemistry
Dentistry
Math & Physics

41

20

(C) PhD Universities (Top 7 vs. Others)

0.00

0.20

0.40

0.60

0.80

1.00

Kaplan-Meier survival estimates

0

5

10
analysis time
Others

15

20

Top 7

(D) Gender (female vs. male)

0.00

0.20

0.40

0.60

0.80

1.00

Kaplan-Meier survival estimates

0

5

10
analysis time
Male

15

20

Female42

Figure 2

Baseline hazard function

0.02

baseline hazard
.04

.06

.08

(A) Whole Sample

0

5

10
#years since graduation

1520(B) PhD Universities (Top 7 vs. Others)
Others.04.020baseline hazard

.06

.08

Top 7

0

5

10

15

20

0

5

10

#years since graduation

43

15

20

(C) Gender (Male vs. Female)
Female.04.020baseline hazard

.06

.08

Male

0

5

10

15

20

0

5

10

#years since graduation
Graphs by female

44

15

20

Appendix 1

Descriptive Statistics and Correlation(A) Pre-employment Exit
Variable

Mean S.D.
Min
Max
1
2
1. pre-employment dropout
.58
.49
.00
1.00
2. graduation year
1987
1.41 1985 1989.00
3. female
.08
.27
.00
1.00
.08
.02
top7
4.
.38
.49
.00
1.00 -.19 -.02
%univ
fund
5.
4.24
4.48
.00 26.48 -.18 -.02
6. ln(#researcher)
3.72
1.03
.69
5.93 -.19.04
7. field growth
.13
.19
-.10
.53 -.01 -.76
8. ln(#national univ employment)
7.81
.91
5.06
8.92 -.08 -.02
9. ln(#private univ employment)
7.78
2.21
.00 10.21 -.03 -.01
10. ln(#industrial employment)
7.85
.83
5.81
8.76 -.02 -.02
11. ln(#PhD graduate)
5.30
1.33
.69
7.04 -.06.06
Notes: N = 13,492. For time-variant variables, we use the value of each variable at graduation year.

45

3

4

5

6

7

8

9

10

-.02
-.01
-.03
-.01
.00
.00
.01
.00

.70
.71
.04
.31
.20
.12
.29

.79
.04
.48
.38
.31
.46

-.02
.40
.29
.23
.37

.03
.01
.02
-.03

.81
.78
.95

.87
.88

.83

(B) Post-employment Exit
Variable

Mean

S.D.

Min

Max

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

1.

post-employment dropoutit

.03

.18

.00

1.00

2.

pub stock

.27

7.16

-12.88100.94-.04

3.

fund stock

.50

7.76

-13.42

186.16

-.04

.69

4.

female

.05

.21

.00

1.00

.01

-.02

-.04

5.

job tenure

7.88

6.47

1.00

77.00

.01

.02

.03

.01

6.

associate prof

.65

.48

.00

1.00

.04

-.20

-.18

.05

-.22

7.

full prof

.24

.43

.00

1.00

-.03

.05

.05

-.05

.14

-.76

8.

mobility

1.26

.79

.00

8.00

-.01

.05

.03

-.03

-.27

-.41

.24

9.

ln(#cograntee)

1.38

1.16

.00

5.01

-.02

.31

.33

-.03

.33

-.52

.31

.32

10.

top7

.35

.48

.00

1.00

-.05

.11

.15

-.01

.03

.07

-.04

-.14

.06

11.

%univ fund

3.69

4.25

.00

26.48

-.04

.11

.17

.01

.03

.09

-.06

-.15

.05

.72

12.

ln(#researcher)

3.79

1.24

.69

6.06

-.02

.12

.17

-.02

.11

.08

-.05

-.18

.12

.64

.73

13.

field growth

.04

.11

-.20

.73

-.01

-.01

-.01

.00

-.11

.17

-.11

-.17

-.18

.02

.03

.02

14.

ln(#national univ employment)

7.81

.91

5.06

9.05

-.02

.05

.10

.01

.07

.06

-.06

-.08

.04

.33

.45

.32

.00

15.

ln(#private univ employment)

7.82

2.08

.00

10.49

-.01

.04

.08

.00

.08

.02

-.03

-.04

.06

.24

.37

.24

-.02

.83

16.

ln(#industrial employment)

7.75

.86

5.75

8.79

-.01

.02

.05

.01

.02

.08

-.07

-.07

-.02

.18

.31

.18

.01

.79

.88

17.

ln(#PhD graduate)
Notes: N = 81,331.

5.74

1.44

.69

8.17

-.02

.06

.10

.01

.20

-.08

.02

.01

.18

.29

.40

.30

-.08

.92

.88

46

16

.81

Appendix 2

Summary of Predictions by Career-Stage by Field(A) Pre-employment Exit
All fields

Individual factors
graduation year
femaleInstitutional factors
top7%univ fundln(#researcher)field growthGeographical factors
ln(#national univ employment)ln(#private univ employment)ln(#industrial employment)ln(#PhD graduate)

ScienceEngineering

Agriculture

Pharmaceutical

Medicine

Dentistry
+

+++

+++

+++

---

--

--

--

-

+++

---

---

+

-----

---

-

++

+++

++

-

-

+

Notes: +/- p<0.05; ++/-- p<0.01; +++/--- p<0.001.

47

(B) Post-employment Exit

Individual factors
pub stockfund stock
femalejob tenure
full prof
mobilityln(#cograntee)
Institutional factors
top7%univ fundln(#researcher)field growthGeographical factors
ln(#national univ employment)ln(#private univ employment)ln(#industrial employment)ln(#PhD graduate)

All fields

Math&Physics

Biology

ChemistryEngineering

Agriculture

Pharmaceuticals

Basic
Medicine

ClinicalMedicine

Dentistry

1

2

1

2

1

2

1

1

1

1

1

1

1

2

--

---

---

-

-

-

+

---

2

2

+

-

2

2

++

+++

-

-

+
++

+++

n.a.

n.a.

++

+++
-

---

+

n.a.

n.a.

+
-

n.a.

n.a.

+++

+

n.a.

n.a.

+
--+

---

-

---

-

-

--

-

-

-

-

-

--++

---

+++

+++

---

-

-

++
---

n.a.

++

++

+
--

---

+++

-

---

n.a.

--

---

---

2

-

+

+++

2

-

-

+

+

Notes: +/- p<0.05; ++/-- p<0.01; +++/--- p<0.001. Column 1 corresponds to pre-tenure exit and column 2 to post-tenure exit.

48

+

+

-

--