Impact of Inbreeding on Scientific Productivity: a Case Study of a Japanese University Department

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Please cite as: Morichika, N., Shibayama, S. 2014. Impact of inbreeding on scientific productivity: a case study of a Japanese university department. Research Evaluation (forthcoming).

Impact of Inbreeding on Scientific Productivity: a Case Study of a Japanese University

Department

Noriyuki Morichika a and Sotaro Shibayama

a,b,*

a The University of Tokyo, Department of Technology Management for Innovation

7-3-1 Hongo, Bunky-ku,Tokyo 113-8656, Japan

b The University of Tokyo, Research Center for Advanced Science and Technology

4-6-1 Komaba, Meguro-ku,Tokyo 153-8904, Japan

* Corresponding author

Email: [email protected]

Tel/Fax: +81-3-5452-5371

Abstract

Recent science policies emphasize academic mobility and denounce inbreeding as an

impediment to scientific productivity. This study aims to investigate the impact of inbreeding

on productivity, distinguishing various forms of inbreeding, and to explore the mechanism

behind which inbreeding is translated into productivity, drawing on in-depth longitudinal data

of academics’ careers in a university department in Japan. The results suggest that the effect

of inbreeding on productivity differs with the organizational levels (university, department,

and laboratory) with which inbreeding is defined, as well as with past affiliation to other

institutions (purely inbred vs. silver-corded). A negative effect on productivity is indicated for

inbreeding that occurs at the department level, which seems to be partly explained by

non-merit-based employment criteria. The results also suggest that laboratories consisting of

higher rates of their own graduates yield lower productivity. Finally, inbred academics tend to

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change research subjects less frequently over their career, implying that inbreeding may cause

rise-averseness and deter creativity.

Keywords: Inbreeding; Mobility; Academic labor market; Scientific productivity; Creativity

JEL codes: O31, I23

Funding

This work was supported by Konosuke Matsushita Memorial Foundation, Inamori

Foundation, and Grant-in-Aid for Research Activity Start-up of the Japan Society for the

Promotion of Science [#23810004].

Acknowledgments

We are grateful to two anonymous reviewers for their critical and insightful suggestions.

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1. Introduction

In the modern economy, the academic sector plays a crucial role in innovation system

(Etzkowitz and Leydesdorff, 2000; Stephan, 2012), and the development and training of

academic scientists is one of the central policy issues (OECD, 2002; OECD, 2008). Among

others, recent science policies have emphasized academic mobility as a mechanism for

facilitating network creation and international competitiveness (EC, 2010; MEXT, 2009;

OECD, 2008). Behind this policy trend is a belief among policymakers that mobility allows

knowledge recombination and matching between academics and research institutions whereas

immobility deters productivity (e.g., Horta et al., 2010; Pelz and Andrews, 1966; Stephan,

2012; Velho and Krige, 1984). However, previous empirical findings are rather mixed

(Hunter et al., 2009; Stuen et al., 2012), perhaps due to differences in contexts (e.g., countries,

scientific fields) and in types of mobility (Fernandez-Zubieta et al., 2015). Increasingly

complicated academic career paths might also contribute to the mixed findings (Stephan,

2012).

This study focuses on, among other forms of career patterns, inbreeding as an extreme

form of (im)mobility, in which career development is closed inside an institution and new

employees are hired from among the graduates of the same institution. Consistent to the

mobility-oriented policies, inbreeding has been negatively perceived and, in some countries,

has been prohibited by convention or by law (Navarro and Rivero, 2001). The effect of

inbreeding on scientific production and the mechanism behind possible effects have been

controversial. On the one hand, inbreeding can deter inter-organizational communication

(Horta et al., 2010; Velho and Krige, 1984), compromise creative climates (Pelz and Andrews,

1966), and be associated with particularistic recruitment practices (Horta et al., 2011). On the

other hand, inbreeding can stabilize employment conditions and may help academics

concentrate on creative activities rather than to be distracted by job hunting. Inbreeding may

foster organizational loyalty and decrease transaction costs between faculty members (Horta

et al., 2011). In fact, some studies indicate a positive correlation between inbreeding and

productivity (McGee, 1960), though a negative correlation seems more often suggested

(Hargens and Farr, 1973; Horta, 2013; Inanc and Tuncer, 2011). These competing findings

may be due to differences in study contexts (e.g., countries, fields), but we argue that

variation in inbreeding can confound its impact on productivity. Although inbreeding is often

operationalized as universities employing their own graduates, this definition might be too

broad (Horta, 2013). For example, some academics may keep affiliated with a single

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institution for their entire career, while others may move out to other institutions and later

return to their home institution (so-called silver-corded) (Berelson, 1960). Some academics

may move across departments inside the same university, which is likely to occur in large

institutions and in interdisciplinary fields. Furthermore, in the context of lab-based research,

inbreeding can be defined not only as an attribute of individuals but also as that of lab

membership (i.e., homogeneity of members’ home institutions).

These aspects have often been overlooked in previous literature, perhaps due to

limited access to micro-organizational data, and also in policy debates. Science policies

designed under a rough assumption could be counterproductive in that their definition of

inbreeding may include certain forms of mobility that are justifiable. Thus, this study aims to

examine the impact of inbreeding on scientific productivity, distinguishing different forms of

inbreeding, and to explore the mechanism with which inbreeding is translated into high or

low productivity. To this end, we draw on a case study of a single university department and

collected detailed longitudinal data of lab membership and members’ careers since the 1950s.

Specifically, this study is based on the case of the School of Pharmaceutical Sciences of the

University of Tokyo. The Japanese science system is characterized by relatively low mobility

(Takahashi and Takahashi, 2010), but policymakers have recently adopted pro-mobility and

anti-inbreeding policies (MEXT, 2009), which enables us to see some variation in the

patterns of career development and inbreeding.

The rest of this paper is organized as follows. Section 2 reviews previous literature on

inbreeding and career development in science. Section 3 illustrates the contextual background

of the Japanese science system. Section 4 describes the data, and Section 5 presents the

results. Finally, Section 6 concludes and discusses some implications.

2. Literature Review

Academic career system differs to some extent by country in the definition of

academic ranks, promotion criteria, and so forth (Soler, 2001). In particular, the practice of

inbreeding is typically observed during the early stages of developing a university system and

is gradually replaced by mobile employment system (Horta et al., 2011). For example, in the

US, the rate of inbreeding is currently generally low (10-20%), but it used to be much higher

at the beginning of the 20th century (Handschin, 1910; Mcneely, 1932). Hargens and Farr

(1973) showed that more than 60% of the faculty members at Harvard University were inbred

in the 1910s. In the modern science system, however, inbreeding is more often considered

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negative than positive for scientific productivity, and in fact, some empirical studies have

shown that inbreeding harms productivity (e.g., Hargens and Farr, 1973; Inanc and Tuncer,

2011; Velho and Krige, 1984). Consequently, some advanced countries have attempted to

prevent inbreeding; for example, university systems in the US, the UK, and Germany tend to

prohibit internal recruitment to facilitate dynamic interaction among academics (Horta et al.,

2011; Navarro and Rivero, 2001),1 whereas other countries, such as Spain, Japan, and Russia,

are relatively tolerant to inbreeding despite rather long history of university systems (Horta et

al., 2010).

Inbreeding has been criticized basically for two reasons. A typical argument against

inbreeding is that inbreeding is a result of non-merit-based selection. Some research

institutions employ and promote academics on the basis less of merit than of other factors

such as social relationships between employers and employees (Horta et al., 2011). Faculty

members in charge of employment may desire to hire candidates who better serve them. For

example, Pezzoni et al. (2012) suggest that career progress in Italy is explained by social ties

with and work commitment to senior members in charge of employment. Furthermore, in

stratified university systems, as in the US, employment decisions are likely to be influenced

by candidates’ previous affiliations, especially by universities from which they earned

degrees (Burris, 2004). This particularistic recruitment can deter outbred productive

academics from joining and compromise institutional productivity (Bedeian and Feild, 1980).

While the above argument contends that low productivity observed in pro-inbreeding

context is a consequence of biased selection in recruitment, literature also suggests that

pro-inbreeding context can lower productivity after employment. Straightforwardly,

inbreeding can limit intellectual interaction with external academics. In fact, Velho and Krige

(1984) found that the communication level of inbred professors is lower than that of outbred

professors, using data of Brazilian academics in agriculture. Horta et al. (2010), based on a

survey of Mexican academics, suggest that inbred professors tend to rely on their internal

network as a knowledge source, which prevents cross-organizational recombination of

knowledge. Literature on creativity has consistently emphasized the indispensable role of

communication, social network, and collaboration (e.g., Amabile and Gryskiewicz, 1987;

Zhou and Shalley, 2012), and these factors are found critical determinants of productivity

particularly in the context of science (Heinze et al., 2009). Furthermore, pro-inbreeding

1 However, this policy may not be sufficient. For example, (Burris, 2004) suggests that narrowly confined PhD

exchange network, especially among elite universities, stratifies the American university system. After research

experience for some years in other universities, academics can come back to their home university.

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environments can bring about introspective organizational climate. Pelz and Andrews (1966)

suggests that inbred professors tend to lack creativity and are less willing to change research

topics. Finally, Masuda (2001) suggests that competition is hindered in research communities

with high inbreeding rates and productivity is compromised.

Although negative sides of inbreeding tend to be emphasized, literature has also

referred to potential advantages of inbreeding such as stronger commitment, efficiency in

routine work, and solidarity among faculty members (Horta et al., 2011). In fact, Simon and

Warner (1992) find that employers tend to avoid employing outbred academics because of

their weak loyalty and uncertainty about their performance. Drawing on a case study of the

Japanese university system, Horta et al. (2011) suggest that inbred professors show stronger

loyalty to their affiliations, which lowers the governance cost and transaction cost between

faculty members, and that they are accustomed to and streamline organizational routines

during their long-term (often life-time) employment. This organizational learning and routine

formation can improve job efficiency in general (Cohen and Bacdayan, 1994) and possibly

allow faculty members to concentrate more on research jobs. Masuda (2001) points out that

such positive aspects are especially relevant when a university system is in preliminary

developmental stages.

Reflecting these conflicting arguments, the overall effect of inbreeding on scientific

productivity has been controversial. The impact of inbreeding is contingent on contexts such

as national science systems and scientific fields. Even in the same country, inconsistent

results have been reported. For example, an early study on American academics found a

positive correlation between inbreeding and scientific productivity (McGee, 1960), but a later

study showed a negative correlation (Hargens and Farr, 1973). No significant effect was

found when Wyer and Conrad (1984) controlled for some confounding factors. Recent

studies seem to suggest more often negative than positive impacts of inbreeding. For example,

Eisenberg and Wells (2000), drawing on a sample of entry-level faculty members in

American Law Schools, suggest that inbred academics are less productive than outbred

academics. Inanc and Tuncer (2011), using a dataset of Turkish Technical Universities,

demonstrate a negative correlation between the department-level ratio of inbred faculty

members and individual-level research productivity.

On the basis of these studies, we aim to explore the impact of inbreeding drawing on

a case study of detailed longitudinal data of lab membership and members’ careers in a

university department in Japan. First, this study examines potentially different effects of

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various types of inbreeding on productivity, distinguishing inbreeding at three organizational

levels (university, department, and laboratory) and comparing inbreeding with and without

external experience (i.e., purely inbred vs. silver-corded). We also investigate how lab

productivity is affected by the inbreeding ratio of lab membership . Second, we examine how

inbreeding is translated into productivity incorporating career measurements. Third, to go

beyond simply evaluating the impact on productivity measured by publication output, we

analyze how inbreeding affects academics’ choice of research subjects. We assume that

inward-looking and conservative climates engendered by inbreeding could hamper academics’

creativity and lead to inflexible subject choice.

3. Context of Japanese Science System

The career system in the Japanese academia is characterized by two features. The

first is hierarchical structure of universities and rigid employment practice. As of 2010, Japan

had 86 national universities, 95 public universities, and 597 private universities (Statistics

Bureau Japan, 2012: 714-715). In Japan, the national universities are the main player of

academic research. Among others, the top seven universities are designated as pre-imperial

colleges with disproportionate prestige and resource allocation. Further among these seven,

the University of Tokyo enjoys an exceptional status. Its history dates back to the foundation

of Tokyo Imperial University in the late 19th

century, the predecessor of the present

University of Tokyo, (Yamanoi, 2007). When it was founded as the first national university in

in Japan, faculty members were recruited from Western advanced countries. As Japanese

graduates were produced in Tokyo Imperial University, they gradually replaced the foreign

professors, which is the beginning of inbreeding. Further, the graduates from Tokyo Imperial

University began to fill faculty positions in other universities subsequently founded, which

created a hierarchical relationship among universities, so called gakubatsu (literally: school

tie). In the postwar period, higher education expanded and numerous universities were built,

but the hierarchy has firmly remained, and inbreeding rates, particularly at prestigious

universities, have stayed high. As of 2003, the inbreeding rate of the University of Tokyo was

78% though it is decreasing (e.g., 98% in 1954) (Yamanoi, 2007).

The second feature of Japanese universities is its chair system. In this system, a full

professor organizes a chair, or a laboratory, as the principal investigator (PI), and under his or

her supervision an associate professor, one or a few assistant professors, other staff, and

students work as a team. This structure has created a feudalistic mentoring relationship

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between a PI and members. Decisions on employment and promotion are basically made at

the department level, and the opinion of the full professor in each chair is highly respected.

As a result, candidates for promotion are often selected within the chair, and thus, inbreeding

is common, even when a call for a new position is publicly advertised (Yamanoi, 2007).

Promotion to full professor or opening of new full-professor positions are usually made when

the head of a chair retires, and a previous associate professor in the same chair is often

promoted. Overall, the chair system facilitates internal promotion based more on seniority

than on merit (Kudo, 2007; Shimbori, 1981).

Although the practice of inbreeding has been more or less accepted until quite

recently in Japan, policymakers have begun to consider the rigid organizational structure and

low mobility as impediments to scientific productivity. The Japanese ministry of science and

education (MEXT) noticed that the inbreeding rate in Japanese universities, 62% at the

postgraduate level as of 1998, was significantly higher than in many of Western developed

countries (MEXT, 2003) and has urged to reduce inbreeding to maintain international

competitiveness. Recent policies have placed the training of young academics as the top

priority, contending that facilitating academic mobility and decreasing inbreeding is vital for

attracting talented young academics. As such, MEXT has implemented several pro-mobility

policies, including a tenure-track system introduced in 2006 after the American system.

Though the system is still in a preliminary stage, it intends to recruit young academics from

outside institutions with fair and transparent evaluation and provide them with research

environment independent of the existing chair system.

4. Sample and Data

4.1. Sample

In order to obtain detailed micro-organizational and longitudinal data of academics’

careers, this study focuses on a single university department. Specifically, we chose the

University of Tokyo for its prestigious status domestically and internationally. The University

of Tokyo has 7,700 faculty members, 14,000 undergraduate students, and 14,000 graduate

students as of 2013.2 It has 15 graduate schools, among which we selected the School of

Pharmaceutical Sciences3 for a few reasons. First, it is the smallest department in the

university, which allows us to collect comprehensive career data. Second, the field of

2 Source: http://www.u-tokyo.ac.jp/stu04/e08_02_j.html

3 This department does not participate in the tenure track program, and it basically adopts the chair system.

http://www.f.u-tokyo.ac.jp/en/

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pharmaceutical sciences is highly interdisciplinary, so graduates from the department can be

employed by different departments, and vice versa. Thus, we can observe not only

intra-departmental inbreeding but also inter-departmental inbreeding. Third, life sciences in

Japan is highly ranked (Adams et al., 2010), and the focal department leads pharmaceutical

science research in Japan.

We first collected the history data of all laboratories and all faculty members in each

laboratory since its foundation drawing on the 50th

anniversary book (UTP, 2008). The

department started as one course in the Medical School of the University of Tokyo, but it

became an independent department in the 1950s. Since then, 32 laboratories were opened,

many of which have continued to date. To these laboratories, 441 faculty members (62 full

professors, 92 associate professors, 287 assistant professors4) have been affiliated. Their

career information was collected from the anniversary book (UTP, 2008). We also collected

the data of career outside the department from a public career database5 and academics’

individual websites. Furthermore, we obtained the data of PhD students in each laboratory

from the PhD database of the University of Tokyo6 and the database of National Diet

Library.7 The department has produced approximately 1,500 PhDs since its foundation.

Finally, we downloaded the data of all publications authored by the faculty members since

1972 from the Thomson Reuters ISI Web of Science (WoS).

The following analyses focus on 20 laboratories among the 32, dropping 12

laboratories that was terminated or have recently opened. As of 2008, the department had 80

faculty members: 20 full professors, who play the role of the PI, 12 associate professors, and

48 assistant professors. The department currently produces approximately 50 PhDs every year.

We created two sets of panel data. The first is an unbalanced panel at the individual PI level,

covering 46 PIs’ careers of 4 - 32 years.8 The second is an unbalanced panel at the lab level,

covering the history of the 20 laboratories, where each laboratory had one to four PIs during

on average 30 years of period. Descriptive statistics for each panel are shown in Appendix 1.

4.2. Measurement

4 Different job titles may be given to young academics (e.g., lecturer), but this paper collectively calls them

“assistant professor.” 5 Source: http://researchmap.jp/

6 Source: http://gazo.dl.itc.u-tokyo.ac.jp/gakui/index.html

7 Source: http://www.ndl.go.jp/en/

8 Because publication data from WoS is limited before the 1970s, 46 academics who were active as a full

professor in the focal department after 1980 are used in the following analyses, and the panel data covers 32

years of 1980-2011.

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4.2.1. Dependent variables

Scientific productivity. Scientific productivity is predicted at the individual PI’s level

(Ch.5.2.1) and lab level (Ch.5.2.2). For the former, each PI’s productivity is measured by the

number of papers that PI i published in year t as an author (#pubit).9 For the latter, lab

productivity is measured by the number of papers that lab j published in year t with its PI as

an author (#pubjt).10

In regressions, we take the natural logarithm of these measurements to

mitigate skewness.

Transition of research subjects.11

We first collected the data of references cited in

all papers that PI i published in each year. We assume that when academics do not change

research subjects, a set of references should be stable over time, but that when research

subjects are changed, a new set of references should be cited. Thus, we computed the reuse

rate of references by the percentage of references that PI i cited in year t among all the

references that PI i had cited before year t (%ref reuseit).12

4.2.2. Independent variables

Inbreeding. For each PI, we prepared three dummy variables of inbreeding at three

organizational levels. First, following most previous studies, we prepared a dummy variable

assigned one if PI i graduated from the same university, i.e., the University of Tokyo

(univ-level inbredi). The second dummy is assigned one if PI i graduated from the same

department, i.e., the School of Pharmaceutical Sciences of the University of Tokyo

(dept-level inbredi). The third dummy is assigned one if PI i earned his or her degree from the

same laboratory, i.e., if employed and promoted inside a chair (lab-level inbredi).13

9 We also computed the number of total citations given to the papers that PI i published in year t. Since the

result with citation-count measurement is qualitatively similar to that with publication count, we primarily report

the latter. 10

Both measures are basically the same, except that the unit of the latter is laboratory so PIs may change during

the history of a laboratory. In the Japanese chair system, almost all publications from lab members are

co-authored by the PI of the laboratory in life sciences. 11

Literature has proposed similar measurements. For example, Franzoni et al. (2010) draw on change in

research areas identified by keywords. We used citation information instead of keywords because keywords are

missing in the past in the WoS. Other previous measurements aim to evaluate the novelty of research contents

(Uzzi et al., 2013), but our interest is in how flexibly inbred academics can change research subjects than in how

novel their research is. 12

For example, if an academic’s cited references in a particular year are all included in the list of past

references, %ref reuse = 100, suggesting that the academic continues previous research subject. On the other

hand, if none of an academic’s cited references is included in the list of past references, %ref reuse = 0,

implying that research subjects are largely changed. 13

Some academics obtained multiple degrees from different affiliations though rare. In such a case, we regard it

as inbreeding if at least one degree comes from the same university, department, or laboratory. To be clear, if an

academic is inbred at the lab level, all three dummy variables take the value of one.

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We also prepared inbreeding rates among lab members. Similarly, we computed the

proportion of inbred members in lab j in year t (%univ-level inbred memberjt, %dept-level

inbred memberjt, and %lab-level inbred memberjt).

External experience. We prepared a few measurements of external experience. If PI

i was affiliated in year t with a public research organization (PRO), a private firm, and a

foreign organization, respective dummy variables are assigned one (PROit, firmit, and

foreignit). If PI i had changed affiliations in year t, a dummy variable is assigned one

(mobilityit). Using these measures, we also computed cumulative measurements. That is, if PI

i had been at least once affiliated to a PRO, private firm, or foreign organization before year t,

three dummy variables are respectively assigned one (past PROit, past firmit, and past

foreignit). In addition, the number of institutions to which PI i had been affiliated before year t

is computed (#mobilityit).

Control variables. We control for several factors. First, year t is controlled for

because publication pattern (frequency, etc.) changes over time (yeart). For regressions at the

individual-PI level, the number of years since PI i became a full professor till year t is

computed (#years since promotionit). In addition, we prepare a dummy variable assigned one

if PI i had already been promoted to a full professor before year t (promotedit). We also

compute PI’s pre-promotion productivity by the number of papers that PI i authored before

his or her promotion to a full professor (pre-promotion #pubi). At the lab level, we control for

lab size by the number of PhD students in lab j in year t (#PhDjt) and by amount of yearly

budget in millions of JPY (¥Budgetjt). In addition, the number of years from the opening of

lab j till year t is controlled (lab agejt). Finally, research fields are controlled at the lab level

by five dummies: biology, chemistry, pharmacology, physics, and analysis (field dummiesj).

Because all PIs in our sample are male, we do not control for gender.

5. Results

5.1. Transition of Inbreeding Rate

With the above-mentioned definition of inbreeding,14

we computed inbreeding rates

for full, associate, and assistant professors, respectively. Figure 1 illustrates the transition of

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Our definition of inbreeding, i.e., being employed by the same organization from which they graduated,

allows inbred academics to have external experience in the middle of their career and return to their original

organization. In contrast, inbreeding can be defined narrowly as staying in the same organization since PhD

without external experience (Horta, 2013). We primarily use the former definition because we suppose that

external experience in the case of return mobility often occurs within the network of gakubatsu (a school tie)

and is better understood as a form of inbreeding.

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inbreeding rates from the 1980s to the 2000s. As of the 2000s, all three organizational ranks

taken together, we find that 56% of faculty members are from the same laboratory, 10% from

the same department, and 10% from the same university. Thus, by the commonly used

definition of inbreeding, 76% were inbred and only 24% were outbred. In the 1980s, the

inbreeding rate for all three ranks was about 90%. Thus, inbreeding seems to have decreased

but still remains at a high level. This is consistent with a previous finding that inbreeding is

common in prestigious universities in Japan (Yamanoi, 2007). A breakdown of organizational

ranks indicates that inbreeding is more common at junior career stages than at senior stages.

As of the 2000s, 56% of assistant professors were inbred while 40% of full professors were

inbred at the lab level. Thus, recruitment seems more open to external academics as the career

stage proceeds. The trend of decreasing inbreeding is most apparent for full professors. In the

1980s, the inbreeding rates were 100%, 93%, and 93% at full, associate, and assistant levels,

but they decreased to 60%, 74% and 78% in the 2000s.

5.2. Inbreeding and Productivity

5.2.1. Individual Level

First, we examine how inbreeding affects the scientific productivity of PIs (Table 1).

The panel data is restricted to the period when each academic plays the PI role of a laboratory.

Model 1 in Table 1A examines the effect of inbreeding at the three organizational levels,

controlling for some attributes of laboratories. The result shows that lab size is positively

associated with productivity (#PhD: b = .035, p < .001; ¥Budget: b = .047, p < .01), that

period since promotion is positively associated with productivity (#years since promotion: b

= .009, p < .1), and that productivity differs by field (p < .01). After these factors are

controlled for, Model 1 shows that inbreeding at the university level has a positive effect

(univ-level inbred: b = .493, p < .05). This positive effect is likely to be attributed to the fact

that graduates of the University of Tokyo are highly selected. Since the recruitment decision

in Japan is usually made at the department and lab levels, the inbreeding variables

corresponding to these levels are of greater interest. Model 1 indicates that department-level

inbreeding has a negative effect (dept-level inbred: b = -.369, p < .05) while lab-level

inbreeding does not have a significant effect (lab-level inbred: p > .1). The former negative

effect is consistent with previous literature.

To investigate the mechanism behind the negative effect of inbreeding, Models 2 and

3 add several variables. One of the problems of inbreeding is that inbred academics lack

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external network, which can hamper their creativity (Horta et al., 2010). To test this

possibility, Model 2 adds experience variables. The result shows no significant effect of

external experience and suggests a stronger negative effect of department-level inbreeding

(dept-level inbred: b = -.460, p < .05). Thus, the result does not support the mediating effect

of external experience. A different line of argument is that the negative effect is a result of

non-merit-based selection (Horta et al., 2011). Model 3 tests this hypothesis by controlling

for pre-promotion productivity. The result shows that the negative effect of department-level

inbreeding disappears (dept-level inbred: b = .014, p > .1), suggesting that the negative effect

of inbreeding is mediated by selection, where lower productivity of inbred academics is

already manifested at the time of promotion. Interestingly, the effect of lab-level inbreeding

becomes larger though still insignificant (lab-level inbred: b = -.185, p > .1).

To further investigate the influence of external experience, Table 1B distinguishes

inbreeding with and without external experience. Model 1 tests the effect of university-level

inbreeding, and the result shows significantly positive coefficients for both variables of the

university-level inbreeding (with external experience: b = .503, p < .05; without: b = .610, p

< .05). The difference is marginal, suggesting that the external affiliation does not matter for

this level of inbreeding. Model 2 focuses on department-level inbreeding, and the result

shows that inbreeding with external experience has a more strongly negative effect (with: b =

-.395, p < .05 vs. without: b = -.288, p > .1), which contradicts the hypothesis that a lack of

external experience deters productivity. A possible interpretation is that relatively high

performers continuously stay in the home affiliation while relatively low performers are

forced to move out before obtaining full professorship (Lawson and Shibayama, 2015).

Model 3 makes a similar distinction for lab-level inbreeding but finds no significant effect.

Further, Models 4-6 incorporate the past productivity of PIs, showing that the effects

of inbreeding observed in Models 1 and 2 largely disappear. These results are consistent with

the above argument that the positive effect of university-level inbreeding is attributed to high

performance of top-school graduates, and that the negative effect of department-level

inbreeding with external experience may be explained by forced mobility of low-performers.

Interestingly, Models 4-6 show significantly negative effects of lab-level inbreeding,

especially strongly when a PI had external experience (Model 6: b = -.332, p < .05). This

suggests that lab-level inbreeding could have a long-lasting detrimental effect even after

promotion.15

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This section examines the mediating effects of experience and pre-promotion performance. Though the same

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5.2.2. Lab Level

Next, we examine the effect of inbreeding rates among lab members in Table 2.

Model 1 includes the PI’s inbreeding variables with some control variables, showing a

consistent result with the previous section.16

Model 2 includes measurements for the

inbreeding rates of lab members, showing a significantly negative effect of lab-level inbred

membership (%lab-level inbred: b = -.467, p < .01). After the PI’s inbreeding is controlled for,

Model 3 still shows this negative effect of the lab level and also indicates a university-level

negative effect and a department-level positive effect. Since these three variables are highly

correlated (Appendix 1B), we also ran separate regressions (Models 4-6). The result suggests

that the department-level inbreeding rate has a positive effect on productivity (Model 5: b

= .353, p < .05) while the lab-level inbreeding rate has a rather negative, though insignificant,

effect (Model 6: b = -.214, p = .11). These opposite effects may reflect the conflicting

mechanisms behind inbreeding effects.17

Excessive homogeneity in membership, restricted

to a single lab background, can cause an introspective organizational climate and restrict

knowledge sources, while a modest extent of inbreeding can improve solidarity and lower the

transaction cost among members (Horta et al., 2011). In addition, merit-based selection may

be compromised when new lab members have to be chosen from too narrow a group of

candidates in the same laboratory.

5.3. Inbreeding and Flexibility of Research Subjects

Next, we examine whether inbreeding restricts the choice of research subjects due to

their lack of creativity or narrow knowledge base (Pelz and Andrews, 1966). Drawing on

citation information, we traced the stability of research subjects through academics careers,

including before promotion to a full professor. Table 3 presents the result of regressions.

Model 1 shows a positive coefficient for the number of years since promotion (b = .367, p

< .001), so research subjects are becoming more stable through career development. However,

since the dummy variable for having been promoted to a full professor has a negative effect

variables could have moderating effects, we did not find significant effects when incorporating interaction terms

in regressions. 16

We do not control for pre-promotion productivity of individual PIs because we cannot compute it for some

PIs due to the restriction of publication data. Instead, we assume that the selection effect is incorporated in the

individual-level inbreeding variables. 17

To further investigate the conflicting effects of inbreeding and see if there is an optimal level of inbreeding

rates, we run regressions with quadratic terms of the inbreeding rates. However, we did not find curvilinear

relationship.

15

(promoted: b = -4.107, p < .05), academics gain greater autonomy in choosing research

subjects after earning the PI’s position. Among experience factors, a dummy of PRO shows a

negative effect (b = -4.477, p < .1), suggesting that research subjects are more changeable in

PROs than in universities. This may be because research in PROs is mission-oriented and

research subjects are affected by societal needs but not only by academics’ preferences.

Model 2 further includes inbreeding variables, showing that lab-level inbreeding has a

positive effect (b = 2.567, p < .05). This result is consistent with the argument that inbreeding

can narrow academics’ perspectives (Pelz and Andrews, 1966). Based on this regression

model, we estimate the rates of reused references as a function of the number of years

before/after promotion. Comparing the inbred and outbred academics according to the

lab-level inbreeding definition, Figure 2 shows similar trajectories for both groups after

promotion. However, before promotion, while the inbred group shows a rather stable pattern,

the outbred group shows lower stability. That is, outbred academics more often change

research subjects until earning full professorship. In addition, outbred academics experience a

drastic change in research subjects at the year of promotion (t = 0). Promotion for outbred

academics means that they attained independence from their previous PI, whereas inbred

academics, even after attaining a PI’s position, may be influenced by the inertia of the

laboratory’s past research path.

6. Conclusions and Discussion

The training of academic scientists is essential in sustaining innovation system, and

policymakers have been implementing various policies on academic career development

(OECD, 2002; OECD, 2008). Among others, academic mobility has been emphasized as an

instrument to reinforce scientific productivity (EC, 2010; MEXT, 2009) while inbreeding has

been denounced as an impediment to productivity (Horta et al., 2011; Navarro and Rivero,

2001). Despite this policy trend, no clear consensus has been reached concerning the effects

of immobility or inbreeding, possibly due to different research settings and definition of

inbreeding. The current study aims to contribute to the literature by offering a case study of

the Japanese scientific community with in-depth career data. We focus on a single university

department and collected the career data of all their lab members since the 1950s, with which

the effect of inbreeding on scientific productivity and its background mechanisms are

investigated.

The result indicates a negative correlation between inbreeding at the department

16

level and scientific productivity, and this seems to be explained by the selection for

promotion and employment. That is, faculty members hired from outside tend to be high

performers, whereas those promoted internally (i.e., inbred academics) might have been

promoted even if more productive candidates are available outside the institution. This result,

on the one hand, supports the hypothesis that inbreeding is associated with particularistic and

non-merit-based selection (Bedeian and Feild, 1980; Horta et al., 2011). On the other hand,

the fact that high-performers are recruited from outside implies that productivity plays a

certain role at least in the selection of outbred academics.18

Since literature attributes the potential negative effect of inbreeding to a lack of

cross-organizational communication and social network (Horta et al., 2010), this study also

investigates the effect of external experience by comparing purely inbred academics and

those who moved out once and returned to the home institution (Berelson, 1960). However,

our data do not support this hypothesis. To the contrary, the results show that inbred

academics with previous external experience are less productive than those without such

experience. This is possibly because relatively low performers are forced to move out before

returning home (Lawson and Shibayama, 2015).

This study examines inbreeding measured at different organizational levels.

Inbreeding is often defined as universities employing their own graduates, which includes

some variations. For example, some academics may change departments inside one

university while others are employed inside a single department, and yet others are promoted

in the same laboratory. Our results imply that the effect of inbreeding at different

organizational levels have different impacts on productivity. While inbreeding at the

department level is negatively correlated with productivity, inbreeding at the university level

is positively correlated. We further investigate the inbreeding rates of lab members; i.e., the

degree of homogeneity of membership. The results indicate that laboratories consisting of

higher proportion of their own graduates yield lower productivity. This is consistent with the

hypothesis that inbreeding creates a non-creative climate and compromises productivity

(Horta et al., 2010; Pelz and Andrews, 1966; Velho and Krige, 1984).

Finally, this study tests whether inbreeding affects academics’ choice of research

18

We additionally investigated if productivity matters for the selection among inbred candidates by comparing

PIs and their potential internal rivals. As a potential rival for each inbred PI, we identified an academic who

earned a degree from the same lab around the same year and who had been in the same lab approximately until

the year of the PI’s promotion. The result suggests that inbred PIs, who got internally promoted, were

significantly more productive than their rivals, who had to move out. Thus, decisions for promotion and

employment, regardless of inbred or outbred, is based on merit at least partly.

17

subjects. The results show that inbred academics do not change research subjects as often as

outbred academics. Although infrequent subject change may not necessarily mean low

productivity or lack of creativity (Canibano et al., 2008), our result is consistent with the

argument that inbred academics tend to be introspective and unwilling to take risks on novel

subjects (Pelz and Andrews, 1966).

In Japan, inbreeding has been rather common since the foundation of the modern

university system, especially in elite institutions. Recent policies have emphasized mobility

and criticized inbreeding for its potentially negative effects on academics’ creativity and

scientific productivity. Following a global trend, policymakers have implemented several

policies to facilitate mobility and decrease inbreeding. Part of our findings is supportive to

this policy direction. However, the broad definition of inbreeding, used in the policies,

includes some variations, and our results suggest that they have different effects on

productivity. Such variations should be taken into consideration in designing future policies.

Our results need to be interpreted with caution. In particular, the generalizability of

this study is limited due to the peculiar sample. For example, we suppose that the positive

effect of university-level inbreeding is explained by the research excellence of the top

university, which may not be observed in lower-ranked universities. A similar effect may be

found in top universities in countries with a stratified university system. Our focal department

is the smallest in the University of Tokyo, which may strengthen the negative aspect of

inbreeding (e.g., introspective climate, restricted network), so the effect might be less evident

in larger departments. Overall, future research needs to investigate inbreeding in different

contexts in terms of countries, scientific fields, university ranks, and so forth. This study

draws on bibliometric and CV analyses, but more qualitative approach is needed to confirm

mechanism behind which inbreeding affects productivity.

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21

Figure 1 Transition of Inbreeding Rate

Note: The inbreeding rates are computed for 10 years in each time period: 1976-1985 (1980), 1986-1995 (1990), and 1996-2005 (2000).

22

Figure 2 Transition of Research Subjects

Note: %Ref reuse is predicted for inbred and outbred academics based on Model 2 of Table 3.

10

15

20

25

30

%R

ef R

euse

-15 -10 -5 0 5 10 15#Years since promotion to full professor

Inbred Outbred

23

Table 1 Prediction of Scientific Productivity at the Individual PI Level: Dependent Variable = ln[1+#Pubit]

(A) Base model

Model 1 Model 2 Model 3

Control

Yeart -.002

(.005) -.002

(.005) -.014

(.010)

#Years since promotionit .009

† (.005) .009

(.006) .018

† (.010)

#PhDit .035

*** (.007) .035

*** (.008) .037

*** (.008)

ln[¥Budgetit] .047

** (.015) .046

** (.015) .048

** (.017)

Field dummiesi YES

YES

YES

ln[1 + pre-promotion #pubi]

.228 * (.089)

External experience

Past PROit

.116

(.159) .087

(.135)

Past firmit

-.009

(.184) .010

(.164)

Past foreignit

.088

(.201) -.097

(.172)

#Mobilityit

-.056

(.068) .057

(.077)

Inbreeding

Univ-level inbredi .493

* (.206) .484

* (.207) .327

(.211)

Dept-level inbredi -.369

* (.179) -.460

* (.214) .014

(.220)

Lab-level inbredi -.043

(.144) -.055

(.157) -.185

(.142)

χ2 test 66.90

***

67.79

***

81.35

***

Log likelihood -334.84

-334.39

-295.73

#Observation 529

529

467

#PI 45 45

36

Note: Unstandardized coefficients (standard errors in parentheses). Two-tailed test. †p<0.10;

*p<0.05;

**p<0.01;

***p<0.001. Ordinary least squares

regression with random-effects of individual PIs. The panel is restricted to the period during which each academic plays a role of PI (i.e., the period

before the academic was promoted to a full professor is dropped from the analysis). Model 3 drops nine PIs who were promoted before the 1970s and

whose pre-promotion productivity we cannot compute.

24

(B) Inbreeding with vs. without external experience

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Control

Yeart -.002

(.005) -.002

(.005) -.001

(.005) -.009

(.009) -.009

(.009) -.008

(.009)


(.006) .008

(.006) .008

(.006) .013

(.009) .013

(.009) .012

(.009)

#PhDit .036

*** (.007) .036

*** (.007) .035

*** (.007) .036

*** (.008) .036

*** (.008) .037

*** (.008)

ln[¥Budgetit] .047

** (.015) .047

** (.015) .047

** (.015) .050

** (.017) .050

** (.017) .050

** (.017)

Field dummiesi YES

YES

YES

YES

YES

YES

ln[1 + pre-promotion #pubi]

.206 * (.085) .206

* (.085) .201

* (.085)

Inbreeding & external experience

Univ-level inbredi

.503 * (.205) .512

* (.204)

.302

(.200) .310

(.199)

(with external experience) .503

* (.205)

.302

(.200)

(without external experience) .610

* (.275)

.440

† (.259)


* (.183)

-.355

* (.177) -.081

(.195)

-.035

(.191)

(with external experience)

-.395

* (.183)

-.081

(.195)

(without external experience)

-.288

(.218)

.057

(.218)

Lab-level inbredi -.069

(.149) -.069

(.149)

-.267

* (.133) -.267

* (.133)

(with external experience)

-.160

(.178)

-.332

* (.153)

(without external experience)

.037

(.160)

-.151

(.147)

χ2 test 67.30

***

67.30

***

68.08

***

80.42

***

80.42

***

80.80

***


-334.64

-334.25

-296.19

-296.19

-296.00

#Observation 529

529

529

467

467

467

#PI 45 45

45

36

36

36


*p<0.05;

**p<0.01;


regression with random-effects of individual PIs. The distinction of inbreeding with and without external experience is made for each organizational level

separately to avoid collinearity.

25

Table 2 Prediction of Scientific Productivity at Lab Level: Dependent Variable = ln[1+#Pubjt]

Model1 Model2 Model3 Model4 Model5 Model6

Control

Yeart .007

* (.003) .006

† (.003) .006

* (.003) .007

* (.003) .009

** (.003) .005

† (.003)


* (.004) .013

** (.004) .011

* (.004) .010

* (.004) .007

† (.004) .013

** (.004)

Lab agejt .003

(.002) .005

* (.002) .004

† (.002) .003

(.002) .003

(.002) .004

† (.002)

#PhDjt .041

*** (.009) .040

*** (.009) .037

*** (.009) .040

*** (.009) .038

*** (.009) .041

*** (.009)

ln[¥Budgetjt] .092

*** (.017) .089

*** (.018) .095

*** (.017) .091

*** (.017) .088

*** (.017) .096

*** (.018)

Field dummiesj YES

YES

YES

YES

YES

YES

Inbreeding of PI

Univ-level inbredi .718

*** (.123)

.851

*** (.142) .684

*** (.137) .672

*** (.125) .725

*** (.123)


*** (.112)

-.850

*** (.127) -.597

*** (.112) -.675

*** (.117) -.580

*** (.113)

Lab-level inbredi .014

(.084)

.179

† (.093) .009

(.085) .009

(.084) .067

(.090)

Inbreeding rate of lab

%Univ-level inbred memberjt

.283

(.260) -.841 **

(.309) .106

(.187)

%Dept-level inbred memberjt

.218

(.259) 1.304 ***

(.305)

.353 * (.165)

%Lab-level inbred memberjt

-.467 **

(.146) -.584 ***

(.159)

-.214

(.136)

χ2 test 147.08

***

118.46

***

168.51

***

147.40

***

151.63

***

149.55

***


-614.70

-589.67

-600.23

-598.11

-599.15

#Observation 663

663

663

663

663

663

#Lab 20 20 20 20 20 20


*p<0.05;

**p<0.01;


regression with random-effects of laboratories.

26

Table 3 Prediction of Transition of Research Subjects: Dependent Variable = %Ref reuseit

Model 1 Model 2

Control


*** (.067) .370

*** (.068)

Promotedit -4.107

* (1.599) -3.543

* (1.616)

ln[1 + #pubit] 2.000

** (.750) 1.992

** (.750)

Field dummiesi YES

YES

External experience

PROit -4.477

† (2.558) -4.928

† (2.562)

Firmit -.948

(2.944) -1.206

(2.935)

Foreignit .113

(2.926) -.251

(2.930)

Mobilityit -2.102

(1.720) -2.189

(1.722)

Inbreeding

Univ-level inbredi

3.807

(3.177)

Dept-level inbredi

-1.485

(2.396)

Lab-level inbredi

2.567 * (1.165)

χ2 test 55.24

***

61.80

***


-4132.63

#Observation 1022

1022

#PI 46 46


*p<0.05;

**p<0.01;


regression with random-effects of individual PIs. We do not include the measurements of lab attributes (i.e., ¥budget, #PhD) unlike Tables 1 and 2, since

this regression includes the pre-promotion period when academics do not have their own lab.

27

Appendix 1 Description and Correlation a

(A) Individual-PI level panel

Variable Mean S.D. Min Max 1 2 3 4 5 6 7 8 9 10 11 12

1. ln[1 + #pubit] 2.36 .66 .00 3.93

2. ln[1 + pre-promotion #pubi] 3.76 .91 1.61 5.08 .38

3. Yeart 1996 9.12 1980 2011 -.04 .52

4. #Years since promotionit 9.55 6.94 .00 39.00 -.14 -.22 .11

5. #PhDit 4.66 3.58 .00 18.00 .31 .19 .02 -.03

6. ln[¥Budgetit] 2.67 1.54 .00 6.03 .20 .06 -.10 -.18 .39

7. Past PROit .23 .42 .00 1.00 -.12 -.09 .19 -.21 -.05 .10

8. Past firmit .11 .32 .00 1.00 -.13 -.19 -.10 -.15 -.19 .02 .29

9. Past foreignit .11 .32 .00 1.00 .06 .17 .22 -.08 .13 .01 .05 -.05

10. #Mobilityit 2.32 1.43 1.00 7.00 -.19 .07 .34 -.12 -.16 .00 .50 .24 .32

11. Univ-level inbredi .86 .34 .00 1.00 .07 -.17 -.22 .17 .03 -.03 -.10 .07 -.20 -.51

12. Dept-level inbredi .74 .44 .00 1.00 -.02 -.22 -.23 .10 .14 .00 -.14 -.07 -.12 -.63 .67

13. Lab-level inbredi .49 .50 .00 1.00 .04 -.01 -.07 -.13 .13 -.03 -.17 -.24 -.13 -.53 .39 .58

Note: Bold italic: p < .05. #Observation = 529 (#PI = 45). The panel is restricted to the period at which each academic plays a role of PI (i.e., the period

before the academic was promoted to a full professor is dropped from the analysis).

a Bold italic: p < .05. (A) #Observation = 529 (#PI = 45). The panel is restricted to the period at which each academic plays a role of PI (i.e., the period before

the academic was promoted to a full professor is dropped from the analysis). (B) #Observation = 663 (#Lab = 20).

28

(B) Lab level panel

Variable Mean S.D. Min Max 1 2 3 4 5 6 7 8 9 10 11

1. ln[1 + #pubjt] 2.39 .71 .00 3.93

2. Yeart 1993 10.97 1973 2010 .14

3. #Years since promotionit 9.40 5.89 1.00 28.00 .09 -.03

4. Lab agejt 45.25 31.69 .00 117.00 .16 .21 .02

5. #PhDjt 4.60 3.44 .00 18.00 .26 .07 .06 .19

6. ln[¥Budgetjt] 2.51 1.51 .00 6.03 .22 .15 -.13 .11 .36

7. Univ-level inbredi (PI) .87 .33 .00 1.00 .14 -.22 .13 .14 .01 -.06

8. Dept-level inbredi (PI) .73 .45 .00 1.00 .06 -.15 .00 .20 .12 -.01 .62

9. Lab-level inbredi (PI) .47 .50 .00 1.00 .13 .08 .06 .51 .13 .01 .36 .58

10. %Univ-level inbred memberjt .87 .23 .00 1.00 .18 -.29 .23 .18 .09 .00 .75 .51 .34

11. %Dept-level inbred memberjt .80 .26 .00 1.00 .18 -.30 .21 .24 .17 .02 .62 .73 .45 .84

12. %Lab-level inbred memberjt .61 .33 .00 1.00 .15 -.14 .25 .50 .26 .10 .46 .53 .70 .57 .68

Note: Bold italic: p < .05. #Observation = 663 (#Lab = 20).

Impact of Inbreeding on Scientific Productivity: a Case Study of a Japanese University Department

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