Hitotsubashi University
Institute of Innovation ResearchContributions of young scholars in team-based
scientific research

Masatsura Igami
Sadao Nagaoka
John P. Walsh

IIR Working Paper WP#13-02

Jan 2013

Institute of Innovation Research
Hitotsubashi University
Tokyo, Japan
http://www.iir.hit-u.ac.jp

Contributions of young scholars in team-based
scientific research
Masatsura IGAMI1, *, Sadao NAGAOKA2 and John P. WALSH3
1

National Institute of Science and Technology Policy, 3-2-2 Kasumigaseki, Chiyoda-ku, Tokyo, 100-0013(Japan)2Institute of Innovation Research, Hitotsubashi University, 2-1 Naka, Kunitachi, Tokyo, 186-8603 (Japan)

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School of Public Policy, Georgia Institute of Technology, 685 Cherry Street, Atlanta, GA 30332-0345(USA)January 2013
Abstract
How to design and manage a research team has become an increasingly important issue in knowledge creation inscience. This paper aims to understand how young scholars contribute to scientific research in the context of a
research team. We have found that the likelihood of the involvement of postdoctoral fellows is high in researchteams working on a rapidly advancing research theme, facing competitive threat, and in those research teams withforeign-born scholars and when exploiting advanced research equipment. Focusing on the papers where the order of
the authors follows contribution, the probability of postdoctoral fellows becoming the first authors is more likely to
exceed that due to a random assignment in the research teams facing competitive threat, with foreign-born
postdoctoral scholars and when exploiting advanced research equipment. Finally, we have found that the
involvement of postdoctoral fellows is positively associated with research performance in terms of citation counts
and with shorter time to the publication even if the size of project is controlled for. The finding that postdoctoral
fellows accelerate the speed of research is consistent with the fact that they become more likely to be the first authors
in the face of strong scientific competition. These findings illuminate how young scholars contribute to the teambased research.

JEL codes: O30, D23
Keywords
Team; Young scholars; Science; First Author; Competition* Address for correspondence:
National Institute of Science and Technology Policy
3-2-2 Kasumigaseki, Chiyoda-ku, Tokyo 100-0031, Japan
E-mail: igami@nistep.go.jp
Tel: +81-3-6733-4910, Fax: +81-3-3503-3996
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1. Background and purposes
Teams are increasingly the fundamental unit of knowledge creation in science. Recent
studies show that the number of authors in scientific publications has been increasing over time
(Adams et al., 2005; Wuchty, Jones & Uzzi, 2007). These trends indicate that how to design
and manage a research team has become an increasingly important issue in knowledge creation inscience. In this context, it is important for us to deepen our understanding of how young scholars,
especially postdoctoral fellows, contribute to the team-based research. In both the US and Japan,
the number of postdoctoral fellows increased sharply in recent years.1 There are several studies
that shed light on the importance of young scholars in scientific teams. Larivière (2010) foundthat about 30% of Quebec university papers have PhD students as authors.

Black & Stephan(2010) pointed out that PhD students and postdoctoral fellows appear disproportionately more as
first authors in US articles in the journal Science.2 Although this latter result indicates thesignificance of demand side reason for young scholars in science, it does not explain the sources
In this paper we aim at exploring the sources of the contributions of young scholars, inparticular, postdoctoral fellows, to team-based scientific research, based on large-scale original
survey data. We can identify several potential reasons why they are important for enhancing theperformance of research under certain conditions. First, postdoctoral fellows who have recently
completed their PhD training are likely to have the most advanced knowledge and technique to
conduct competitive research. Their participation may become especially important in those areas
where science moves very fast. Second, they can contribute to accelerating the speed of research

1

In the US, the number of postdoctoral fellows, especially in life sciences, increased sharply with the
doubling of the NIH budget. Many of them, however, have had difficulty to find tenure-track positions inhigher education institutions (Stephan, 2012). The situation is similar in Japan. In the first S&T basic
plan (FY1996-2000), the Japanese government introduced a program to increase postdoctoral fellows up
to 10,000 in order to strengthen Japan’s research capability.
2
It is important to note, however, that the order of the authors do not necessarily follow the order ofcontributions. One novelty of our study is that we recognized the first author as the largest contributor
only when the authors are ordered according to their contributions.

2since they can devote their time fully to the research. Thus, they may play an important role when
speed maters in obtaining priority in research (Merton, 1973). Third, they are more mobileinternationally (see section 4 for direct evidence), so that hiring a postdoctoral fellow in a
research team is an efficient way to engage foreign talent (Empirica, 2005; Franzoni, Scellato &Stephan, 2012). The contribution of foreign-born scholars in knowledge creation in science isespecially large in the US (Stephan & Levin, 2001), where high quality research universities
attract students from across the world. Fourth, research facilities/equipment play very importantroles for some scientific research (see Stephan, 2012 for its increasing role). For examples, theinventions and the progress of a particle accelerator, a scanning tunneling microscope, and a
DNA sequencer have been major sources for advancing of research in physics, materials scienceand life sciences. Young scholars may be vital in exploiting these cutting-edge
facilities/equipment.
This paper aims to understand how these potential channels of the contributions of young
scholars affect the involvement of young scholars as well as their (relative) contributions in theteam measured by how frequently they appear as the first authors when the order represents
relative contributions. Furthermore, to the extent that young scholars contribute fundamentally inadvancing research, we expect that their presence is associated with high research performance.
We will verify such prediction.
The results of a Japan-US scientists’ survey on the knowledge creation process in science
are used for the analysis, in addition to bibliographic information. The latter alone provides only
limited information on who are the authors, so that their academic positions, country of origin,and disciplinary diversities as well as whether the order of the authors follow their contributions
are unknown. The survey in Japan was jointly conducted by the Institute of Innovation Research(IIR) of Hitotsubashi University and the National Institute of Science and Technology Policy(NISTEP) from the end of 2009 to the summer 2010 (Nagaoka et al., 2010). The survey in the US
was implemented by the Georgia Institute of Technology, in collaboration with IIR and NISTEP,
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from autumn 2010 to early 2011 (Nagaoka et al., 2011). It collected around 2,100 responses
from scientists in Japan and 2,300 responses from scientists in the US.

The survey collected

information on the research projects that generated the papers that were the target of the surveys.
One advantage of the scientist survey is that it is a comprehensive and standardized micro-data
set, covering the characteristics of research projects, the composition of the research team,research funding source used in the research projects, external knowledge sources that inspired
the research project, serendipities in the research projects, outputs yielded by the research
projects among others. In addition to coverage of both the US and Japan, the scientist survey
also collected data on research projects that yielded top 1% highly cited papers and the research
projects that yielded other papers (normal papers). The rich data regarding research projects
enable us to analyze the relationship between research team characteristics and performance ofthe research teams.
Based upon the dataset obtained by the scientist survey, the ultimate goal of our study is
to address the following questions regarding the research teams.
1. To what extent are young scholars involved in the research team?

What determines theirinvolvement?
2. What are the sources of the contributions of young scholars to team-based science researchmeasured by the frequency with which they appear as the first authors when the order of theauthors represents their relative contribution?
3. Is the presence of young scholars associated with higher research performance, controlling for
project size and research budget?
This paper is organized as follows: Section 2 presents an overview of the HitotsubashiNISTEP-Georgia Tech scientist survey; Section 3 shows the descriptive statistics regarding the
involvement of young scholars in research teams; Section 4 discusses hypotheses on thedeterminants of the participation of young scholars in research teams and their contributions.

4Section 5 shows the results of the econometric analysis; Section 6 concludes and summarizes
some remaining research issues.

2. Overview of the surveys
2.1. Identification of focal papers
The population of the survey is articles and letters in the Web of Science database ofThomson Reuters.

Reviews were excluded from the population. The time window of thepapers for the survey is from 2001 to 2006 (in the database year).

Database year refers to theyear when the documents are recorded into the database of Thomson Reuters, which generally
corresponds to the publication year.

The bibliographic information and the number of citations

as of the end of December 2006 were used in the identification of the focal papers. Two sets offocal papers were selected from the population as below:
Highly cited papers (approximately 3,000 in each survey): Top 1% highly cited papers ineach journal field and in each database year; with at least one organization of authors should belocated in Japan for the Japanese survey and in the US for the US survey.

All highly cited

papers in the time window were selected for the Japanese survey and approximately 3,000 highly
cited papers were randomly selected from the highly cited papers in the US survey.
Normal papers (approximately 7,000 in each survey): Randomly selected papers in eachjournal field and in each database year from the population of the survey, excluding the above
highly cited Top 1% papers; with at least one organization of authors should be located in Japanfor the Japanese survey and in the US for the US survey.
We covered all science fields, including the social science, although the coverage of social
science journals by the database is not comprehensive and we have a relatively small number ofthe publications in Japan in this field.

Social sciences were dropped from the rest of the5analysis, given the small sample in Japan and the differences of research activities with naturalsciences.
2.2. Identification of survey targets and research projects for the survey
Corresponding authors or equivalents of approximately 20,000 possible focal papers weresearched and identified as survey targets.

If multiple papers were assigned to a single

corresponding (or equivalent) author, one paper was randomly selected as a focal paper while thepriority was given to the highly cited papers in the selection process. As a result, 7,652 survey
targets were identified for Japanese survey. Of those, there are 1,932 scientists whose focal
paper is the highly cited paper; and there are 5,720 scientists whose focal paper is the normal
paper.

In the US, 8,864 survey targets were identified. Of those, there are 2,882 scientists

whose focal paper is the highly cited paper; and there are 5,982 scientists whose focal paper is thenormal paper.
2.3. Response rate
Out of 7,652 survey targets, we got 2,081 responses in the Japanese survey. The total
response rate was 27.2%.
for the normal papers.

The response rate was 29.3% for the highly cited papers and 26.5%

The total response rate in the US survey was 26.3%.

responses out of 8,864 survey targets.

We got 2,329

The response rate was 27.7% for the highly cited papers

and 25.6% for the normal papers.
2.4. Field classification for the analysis
Results of the survey to be presented in this paper are based on 22 ESI journal fields.
Some results are based on 3 broad fields obtained by further aggregation, i.e. physical sciences,
life sciences, and medicine.
medicine.

Natural sciences aggregate physical sciences, life sciences, andPapers of multidisciplinary fields, those published in the journals like Nature and6

Science, are reclassified into one of 21 fields based on the backward citations of themultidisciplinary papers.
2.5. Data available in the scientist survey
We have constructed a comprehensive and standardized micro-data set from the two surveys,
covering the characteristics of research projects, the composition of the research team, researchfunding used in the research projects, external knowledge sources that inspired the research
project, serendipities in the research projects, outputs yielded by the research projects among
others.
As for the composition of the research team, our surveys asked a respondent to identify theauthors’ academic positions in their organizations, academic field of expertise, the country ofbirth, specialized skill, and the sector of the organization with which the author was affiliated.
This question on the author profile was asked for all authors when the number of authors is 6 orless and for up to 6 authors, the first, last and corresponding authors plus randomly selectedauthors, when the number of authors is 7 or more. In addition to the information obtained by thesurvey, information on the number of authors and affiliations of authors were retrieved from the
Web of Science. This paper focuses on research teams in higher education institutions (HEIs) andin natural sciences.

3. Participation of young scholars in the research teams
A research team can consist of a wide variety of human resources, such as graduate andundergraduate students, postdoctoral fellows, technicians, and professors or other scientists.
There are several studies that show the importance of young scholars in the knowledge creation
process (Larivière, 2010; Black & Stephan, 2010), however these analyses are limited to a
specific journal or country and they do not examine the reasons for the participation of young
scholars.

In this section we first extend their analysis by covering all Web of Science journals
7and by focusing on the publications.

The order of the authors when this is linked to theircontributions is also considered in the analysis of the first authors.
Figures 1 (a) and (b) show the composition of authors, in HEIs, in Japan and the US
respectively.

As for Japan, the share of professors is the largest, followed by associate professor

and assistant professors. Professors account for around 38% of all authors in the highly cited and
35% in the normal papers.

On the other hand, young scholars, who are undergraduates,

graduate students, or postdoctoral fellows, account for 27% and 26% of the authors of highly
cited papers and normal papers, respectively. In the US, professors also account for the largest
share in both the highly cited and normal papers. Young scholars (MA and/or undergraduate
students; PhD students; and postdoctoral fellows) account for 34% of the authors of the highly
cited papers and 33% of the normal papers in the US. The composition of MA and/or
undergraduate student; PhD student; and postdoctoral fellow differs between Japan and the US.
The share of MA and/or undergraduate students is greater in Japan than in the US.
Table 1 summarizes the percentage of young scholars as the first authors of the focal
papers, limiting to those papers in which authors are listed in order of their degree ofcontributions.

In other word, it indicates the types of scientists who made the mostcontributions to the focal papers.
We first look at the contribution of young scholars in the normal papers.

As shown in

Tables 1 (a) and (b), young scholars account for 35% of the first authors in Japan and for 49% of
the first authors in the US. Young scholars appear as the first authors more frequently in the US
than in Japan. The share of young scholars as the first authors is much larger than for all authors
in both countries (compare Table 1 and Figure 1). The contribution of young scholars as the firstauthors is especially large in life sciences in both countries. In life sciences, 45% and 61% of thefirst authors are young scholars in Japan and the US, respectively. In physical sciences, the shareof young scholar first authors is around 30% in Japan and more than 50% in the US.

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The rate of young scholar first authors is even greater for highly cited papers, especially inJapan: increasing from 35% of normal papers to 39% of highly cited papers in natural sciences inJapan and from 49% to 51% in natural sciences in the US. The share of postdoctoral fellows in
the highly cited papers is very large in life sciences both in Japan and the US. As we can see from
Table 1, the composition of postdoctoral fellows and students as the first authors is different by
the type of papers.

The participation of students as first author is more common in the normal

papers compared to the highly cited papers in both countries.

4. Hypotheses and descriptive statistics
First, we focus on the determinants of the participation of postdoctoral fellows in a
research team and of their contributions. Although we do not have data directly measuring thecontribution of a postdoctoral fellow, we assess his/her contribution by examining whether aparticular factor can account for the probability of a postdoctoral fellow being a member of theresearch team and becoming the first author of the paper respectively. Given that a postdoctoral
fellow is more likely to be engaged in a research project if his/her contribution is high, we expectthat the same set of factors will serve as the determinants for both their participation and theircontribution.
4.1. Sources explaining the participation of postdoctoral fellows in a researchteam and their contributions to research
4.1.1. Novelty and competitive threat
Given that postdoctoral fellows are likely to have the most recent knowledge andtechnique to conduct a research, it is likely that they can contribute most when the novelty of theresearch theme is high. Thus, we propose the following hypothesis regarding the relationship9between the participation and the contribution of postdoctoral fellows and the novelty of theresearch theme.
Hypothesis 1: Postdoctoral fellows are more likely to participate in the research team and theircontributions in the research team tend to be high, when the novelty of research theme is high.
We use the average citation time-lags between the publication year of the focal paper andthe publication years of the references therein as a proxy to measure the novelty of the research
theme.

The focal papers with long citation time-lags are considered the outputs from theresearch on established research theme, while short citation time-lags can be considered toindicate a research area in rapid development. Figure 2 shows box-plots of the citation timelags by four types of teams: only senior scholars; student(s) involved; postdoctoral fellow(s)involved; and both student(s) and postdoctoral fellow(s) involved.

The median citation time-lag(6.77 years) is the longest in research teams consisting of only senior scholars. This mediancitation time-lag is comparable to that in research teams consisting of senior scholars andstudent(s) (6.75 years). The median citation time-lag is shorter by about 1 year in the researchteams in which at least one postdoctoral fellow participated (5.74 years for research teams inwhich only postdoctoral fellow(s) participated, 5.58 years for those where both student(s) andpostdoctoral fellow(s) participated). In the regression analyses, citation time-lag is normalizedby field average in order to eliminate the field dependency of the lag.
The level of competition is also considered. Scientific research is characterized as acompetitive process of gaining reputation by establishing priority (Merton, 1973). Postdoctoral
fellows can contribute to accelerating the speed of research since they can devote their time fully
to the research. Professors spend a significant amount of time for teaching, university
administration, developing project proposals and getting funds, and research management(Decker et al., 2007). Students spend significant time for learning and for their dissertations.
Thus, postdoctoral fellows may play an important role when speed matters in obtaining priority inresearch.
10Hypothesis 2: Postdoctoral fellows are more likely to participate in the research team and theircontributions in the research team tend to be high, when the research team faces a significant
competitive threat of being scooped.
4.1.2. Engaging foreign talent and expertise
Young scholars are more mobile internationally (see the evidence provided below), so thathiring a postdoctoral fellow in a research team is an efficient way to engage a foreign talent. The
contribution of foreign-born scholars in knowledge creation in science is especially large in the
US (Stephan, 2012). We propose the following hypothesis on the relation between
internationalization of the research team and the participation and contribution of young scholars.
Hypothesis 3: Postdoctoral fellows are more likely to participate in the research team and theircontributions in to the research team tend to be high, when the research project is international.
Our scientist survey asked the respondents the country of birth of up to 6 authors. The
combination of the information of international co-authorship and of country of birth of authors
enables us to differentiate the impact of two aspects of internationalization on research activities:
engaging foreign institution and engaging directly a foreign born scholar.

Table 2 summarizes

the results. In Japan, around 85% of the domestic papers involve no foreign-born scientists inthe authors.

In contrast, more than 90% of the internationally co-authored papers involve

foreign-born scientists. These observations indicate that the majority of foreign-born scientists
in the Japanese survey were affiliated with an organization outside of Japan.

In contrast, about

70% of domestic papers involve at least one foreign-born scientist in the US.
Figure 3 shows the distribution of the country of birth of the authors by their academic
positions. Only domestic papers are analyzed in order to exclude the influence of the foreignborn scholars affiliated with foreign institutions outside of Japan or the US.

The percentage ofthe foreign-born scholars varies across the academic position in both countries.

The share is thehighest in the postdoctoral fellows, followed by PhD students.

These results indicate that11

postdoctoral fellows and PhD students are significantly more mobile than those with otheracademic positions both in Japan and the US.
4.1.3. Utilization of cutting-edge facilities/equipment
Research facilities/equipment plays a very important role for scientific research. Youngscholars typically play an important role for operating these machines for research, since theoperation often requires investing time in acquiring new skills. Thus, the adoption of advanced
research equipment would be an important motivation for hiring young scholars.
Hypothesis 4: Postdoctoral fellows are more likely to participate in the research team and theircontributions in to the research team tend to be high, when the state-of-the-art
facilities/equipment is owned by a research team.
In the Japanese survey, we asked respondents whether they used the state-of-the-art
research facilities/equipment such as a supercomputer, a telescope, and a particle accelerator.
We also differentiated between those owned by a research team and the external ones.
Table 3 summarizes the level of the use of facilities/equipment in Japanese survey. The
percentage of the research teams that utilized the internal or external state-of-the-art
facilities/equipment is the smallest in research teams consist of only senior scholars (33% forinternal, 15% for external).

The likelihood of the utilization of the internal state-of-the-art

facilities/equipment increases by around 20% points in research teams with postdoctoral
fellow(s). The probabilities of the utilization of the external state-of-the-art facilities/equipment
also increase; however, the degree of increase is not so significant compared with that in theinternal facilities/equipment, perhaps because there are specialized staffs operating the external
facilities/equipment.

124.2. The participation of postdoctoral fellows in a research and research
performance
If a postdoctoral fellow contributes to research performance significantly as suggested bya high frequency of them becoming the first author, we expect that the performance of theresearch project is high, when it involves a postdoctoral fellow. This will be the case even if wecontrol for the endogeneity that high quality research tends to be large so that it tends to hire a
postdoctoral fellow.
Hypothesis 5: The research project with a postdoctoral fellow performs well, even if we control
for the size of the project.

5. Estimation of the involvement of young scholars and theirimpact on research outputs
This section presents the results of econometric estimations, which test the above
hypotheses. First we analyze how the involvement of postdoctoral fellows and that of the students
can be (separately) accounted for by the characteristics of the research project and the teams(section 5.1).

Second we analyze how the likelihood of the young scholars becoming the firstauthors can be accounted for by those characteristics (section 5.2). In this context, one strong
advantage of our survey is that it enables us to analyze the focal papers in which the first authors
were the most important contributor.

Finally, we assess the impact of young scholars’participation on research outputs using two output indicators (citations and the duration to the
publication, section 5.3). Table 4 summarizes the variables used in the economic models.

Table

5 provides the summary statistics of the variables. In the following estimations we eliminate thesolo-authored papers from our samples, so that our analysis focuses on team- based research.

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5.1. Participation of young scholars in the research teamThe likelihood of the participation of young scholars (postdoctoral fellows and students
separately) into a research team is estimated based on a logit regression by incorporating thevariables representing the above hypotheses as independent variables. In this estimation, we poolthe sample of “normal papers” and “highly cited papers”, given that there is no obvious
difference between these two types of the papers on the likelihood of the participation of young
scholars, given our set of independent variables (in particular, project size).3 The model for
estimation is given by the following:
Indicator variable   1 (citation_lag )   2 (threat )   3 ( foreign_talent )  4 ( foreign_inst )   5 (equipment_owned )   6 (equipment_ext )   (controls )  (1)

Two indicators, or dependent variables, are the following: One is the dummy variable regarding
the participation of postdoctoral fellows (“POSTDOC_PARTICIPATION”) and another is thedummy variable regarding the participation of students (“STUDENT_PARTICIPATION”).
Each variable stands at 1 for participation and at 0 otherwise.
The main explanatory variables are the following. Citation lag (“LG_CITATION_LAG”)
is the log of the average citation time-lags between the publication year of the focal paper and the
publication year of the references therein, normalized by science field average (that is, the percent
difference from the field average). Threat (two dummies, “Moderately concerned” and“Concerned”) are based on a subjective evaluation of the level of concern of the respondents
about the possibility of competitors would have priority over research results (Hagstrom, 1974).
The variable stands at 2 (“Concerned”) if the level of concerned is high, at 1 (“Moderately
concerned”) if the level of concern is moderate, at 0 if a respondent was not at all concerned
about the priority loss. 4 Foreign talent (“Domestic paper with foreign-born scholars”) is a3

The results using only normal papers are similar to those presented here. They are available from theauthors on request.
4
In the actual estimation, the variable is recoded as two 1/0 dummies.

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dummy representing the case where all authors belong to the domestic institutions but foreignborn scholars exit in the authors. Foreign institution (“International paper”) is a dummy
representing the case where both domestic and foreign institutions exist in the paper as theaffiliations of the authors.
We also introduce the following control variables. The first is the diversity of the field ofexpertise of the research team. The combination of knowledge across the fields of science isgetting crucial in the knowledge creation (Saka, Igami & Kuwahara, 2010). Stephan (2012 andreferences therein) point out that increasing importance of interdisciplinary research andemerging disciplines encourages PIs (principal investigators) to collaborate with scientists with adifferent knowledge and skill set. Thus, there is a possibility that young scholars are hired inorder to diversity the field of expertise of the research team, so that we control for suchpossibility by introducing the index of the diversity of expertise (“DIV_EXPERTISE”). In our
survey, we collected information about the field of expertise of authors.

It was found that thedegree of diversity is different by the type of involvement of young scholars.

The percentage of

research teams that cover two or more fields of expertise is about 42% in research teams
consisting of only senior scholars. The percentage increases by 10 percentage points when
postdoctoral fellows are involved in the research team.

The percentage is the lowest in researchteams consisting of senior scholars and students.
We include the number of authors (“LG_AUTHORS”) as a control variable in the
regression analysis.

The possibility of the involvement of young scholars would increase withan increasing number of authors on the focal paper.
(“LG_EXP_MANMONTH”) is also considered.

Research expenditure per man-month

As for the personnel expenditures, thesurveyed amount includes only those for employing scientists and technicians specifically for the
research projects, which are typically defrayed by extramural funds. For this reason, the survey
mainly measures personnel cost on young scholars employed for the project. The likelihood of
the involvement of young scholars would increase in the research projects that use more research15expenditure per man-month since more financial resources become available for hiring them for agiven time period, and the effect would vary depending on positions of young scholars, since they
have different financial needs.
Dummy variables for the fields of science (“FIELD21”) are included to measure fielddependency of the participation of young scholars. As we have seen in the previous section, thedegree of participation of postdoctoral fellows and PhD students strongly depends on the field of
science. Finally, a country dummy (“COUNTRY”, 1 for the US) is also included.
Tables 6 and 7 show the results of estimation about the involvement of postdoctoral
fellows and students, respectively. The coefficients presented are the marginal effects. We show
the estimation results for the sample pooling the US and Japan and that of only Japan, for bothrobustness check and for the fact that only the survey in Japan had information on the utilizationof facilities/equipment. In all models, the number of authors in the focal paper(“LG_AUTHORS”) has a positive and statistically significant coefficient.

The amount ofresearch expenditure per man-month (“LG_EXP_MANMONTH”) has a positive impact only on
the participation of a postdoctoral fellow, which makes sense since the entire cost of a
postdoctoral fellow often comes from the project research budget.
Controlling for these, citation time-lag (“LG_CIATION_LAG”) has a negative andstatistically significant coefficient in the Models 1-1, 1-3 with p < 0.01 , and Model 1-4 with p <0.05.

Given that citation time-lag is the average differential between the publication year of the

focal paper and of the references, the likelihood of the participation of postdoctoral fellows
increases in research teams working in rapidly advancing research theme in each research field.
The estimated coefficient is smaller but significant in the Japanese sample (Model 1-1 vs. Model
1-4), although it becomes insignificant when type of authorship is introduced (Model 1-7). The
result supports the first part (on participation) of hypothesis 1, especially for the US.

In

contrast, citation time-lag has a positive and statistically significant coefficient in the Models 2-1,
2-3, and 2-4 for students.

This may indicate that students are more involved in a research
16

project based on more established scientific base, since they are in the process of learning, while
postdoctoral students are involved in an emerging science field.
Competitive threat on research priority (“THREAT”) also increases significantly theprobability of the participation of postdoctoral fellows (p < 0.01 for Models 1-1 and 1-3, and p <0.05 for Model 1-4), but has no impact on the participation of students. The likelihood of theparticipation of postdoctoral fellows becomes higher when the level of concern to the priority loss
is high in both the sample pooling the US and Japan and that of the Japanese sample, although thesignificance is lost in Model 1-8 where all independent variables are introduced. The result isalso consistent with the first part (on participation) of hypothesis 2.
As for internationalization of the research team, the involvement of the foreign-bornscholars in domestic paper (that is, the paper involving only domestic universities or research
institutions) has a positive and statistically significant (p < 0.01) coefficient in all models.

The

results strongly support the first part (on participation) of hypothesis 3 for postdoctoral fellows.
When foreign-born scholars participate in domestic research, they are often postdoctoral fellows(but not students) and they tend to have excellent research capability, due in part to the selection
process (e.g., costs of going abroad). International co-authorship also increases the possibility ofthe participation of postdoctoral fellows in all models, while it has negative impact on theparticipation of students. International institutional collaboration facilitates the foreign
postdoctoral fellows to participate in a research project.
The likelihood of the participation of postdoctoral fellows increases significantly if theresearch team owns the state-of-the-art facilities/equipment (Models 1-6 and 1-8, p < 0.01),
consistent with hypothesis 4.

Exploiting state-of-the-art facilities/equipment is an importantreason for the participation of postdoctoral fellows who have specialized skill to operate them.
The number of fields of expertise (“DIV_EXPERTISE”) has no significant correlation
with the involvement of postdoctoral fellows or students.

Diversity of field of expertise is more

likely to be achieved through collaboration among senior scholars having different fields of17

expertise rather than through participation of recruiting young scholars.

Given that one of the

crucial functions of research projects is training of young scholars, it would be quite likely thatthe field of expertise of young scholars coincides with that of the PIs. From the view point of
postdoctoral fellows, it may be too risky for them to join in the research team led by a PI whose
field expertise is different from that of the postdoctoral fellows.
5.2. Contribution of foreign young scholars as the first authors
The analyses in the last section showed that the participation of the postdoctoral fellows ishigh in the research team with foreign-born scholars, in the research team working on thecompetitive and rapidly advancing research theme, and in the research project exploiting
advanced research equipment. This section analyzes more directly their contributions byanalyzing whether the probability of the postdoctoral fellows becoming the first author is high ifthese characteristics are satisfied, when the order of the authors follows the levels of theircontributions. We estimate the following model by OLS (Ordinal Least Squares Method):
POSTDOC_FIRST_PROB  1 (citation_lag )   2 (threat )   3 ( postdoc _ foreign _ %)  4 (equipment_owned _ eff )   (controls )  (2)

We introduce the dependent variable which shows whether the probability of postdoctoral
fellows becoming the first authors exceed that when the first authorship is randomly assigned (thelatter probability is simply equal to the share of postdoctoral fellows in the entire authors).
Variable POSTDOC_FI RST_PROB  POSTDOC_FI RST  POSTDOC_PE RCENTAGE is thedifference between POSDOC_FIRST, a dummy variable for whether a postdoctoral fellow is thefirst author or not and POSTDOC_PERCENTAGE, which is the percentage of postdoctoral
fellows among the authors of the focal paper. If the postdoctoral fellow becomes the first author
disproportionately, the average of POSTDOC_FIRST_PROB will be positive.
To assess the sources of the contributions of postdoctoral fellows, four independent
variables are considered.

They are log citation lag (“LG_CITATION_LAG”), competitive18

threat (“THREAT”), the percentage of foreign-born among the postdoctoral fellows in theauthors (“POSTDOC_FOREIGN_PERCENTAGE”), and a level of effectiveness of the owned
facilities/equipment

for

the

research(“ADV_EQUIP_OWNED_EFFECTIVE”).

The

POSTDOC_FOREIGN_PERCENTAGE is supposed to measure the importance of foreign young
talent in increasing research productivity, and the ADV_EQUIP_OWNED_EFFECTIVE
represents the effectiveness of the state-of-the-art facilities/equipment for the research.

This

latter variable stands at 2 if the facilities/equipment was used and very effective, at 1 if the
facilities/equipment was used, but not effective, and at 0 if the facilities/equipment was not used.
OLS regression is used to estimate the above equation, using the pooled sample of“normal” and “highly cited papers”.5

In the estimations, we limited our samples to the focal

papers in which the authors were ordered accordingly with their contributions. Furthermore, we
had to limit our samples only to those with 2 – 6 authors and with one or more postdoctoral
fellows, since we have information on the authors’ characteristics only up to 6 authors.

Due to asmall number of the resulting samples (N = 394 for the US and Japan and N = 153 for Japan), we
controlled for the effects of the field of science by FIELD3 instead of FIELD21. Note that we do
not need to control for the project size and the number of the postdoctoral fellows, since weestimate whether the probability of the postdoctoral fellows becoming the first authors exceeds
the probability due to a random assignment of the first author which is equal to the share of the
postdoctoral fellows in the authors.
Results are shown in Table 8. The LG_CITATION_LAG has no significant correlation
with the contribution of postdoctoral fellows.

As shown in the last section, the probability of theparticipation of postdoctoral fellows is higher in a research team working on the rapidly
advancing theme, but the novelty of research theme per se does not make a postdoctoral fellow
the first author of the paper. On the other hand, the probability of a postdoctoral fellow becoming

5

The results using only normal papers are similar to those presented here. They are available from theauthors on request.

19

the first author is significantly high when competitive threat is high (significant in Models 3-1
and 3-4 with p < 0.01 and Model 3-7 with p < 0.1). It becomes insignificant only if all the otherindependent variables are introduced. The result supports the second part of hypothesis 2, but not
the second part of hypothesis 1
The

percentage

offoreign-born

among

the

postdoctoral

fellows(“POSTDOC_FOREIGN_PERCENTAGE”) has positive and statistically significant coefficientsin Models 3-2 and 3-5 with p < 0.01 and in Model 3-3 p < 0.1. The likelihood of a postdoctoral
fellow becoming the first author increases if he/she is foreign-born.

The foreign-born

postdoctoral fellows are not just mobile workforces; they are an important contributor ofknowledge creation in science. The result supports the second part of hypothesis 3
The possibility of the postdoctoral fellows become the first author is higher than average
if the facilities/equipment is effective in research (Model 3-6 with p < 0.01).

This result

supports the second part of hypothesis 4. One of the direct contributions of the postdoctoral
fellows on the research may be their skill in operating the state-of-the-art facilities/equipment
effectively to conduct the research.
5.3. Impact of young scholars on research performance
Finally we will assess the impact of young scholars’ participation on research outputs.
We use ordered citation counts of the focal papers as proxies to measure the quality of the focal
paper and the time to the publication from a research project as a performance of the research
project.

We use only the “normal papers” as our sample for these estimations, since “highly

cited papers” are selected by citation performance. The model for estimations is as follows:
Research _ performance   1 ( postdoctoral _ %)   2 ( student _ %)   3 ( foreign _ talent )  4 ( foreign _ inst )   (controls )  (3)

As for the ordered citation counts (“ORDERED_CITATION_COUNTS”), we firstclassified the focal papers by whether they are within the top 10%, 20%, 30%, and 40% in20forward citations as of the end of 2011. Base on the classification, 5, 4, 3, 2, 1 point were given
to the top 10%, 20%, 30%, 40%, and other papers, respectively. The ordered citation counts
were determined by each ESI’s field and each database year in order to eliminate the difference incitation behavior by the field of science and the effects of the truncation.
The second performance measure is the duration of the research project to the publicationof the focal paper (“LG_DURATION_PUBLICATION”).

As we have seen in the previous

section, the postdoctoral fellows are more likely to be involved in the research project focusing
on the research theme in rapid advancement and facing competitive threat.

Our survey

identified how many years it took from the launch of the research project to the publication of the
focal paper, by asking the scientists the year they actually started their research projects.
Ordered logit regression is used to estimate the impact of the involvement of young
scholars on the ordered citation counts.

Ordinary least square regression is used to estimate theduration from the launch of the project to the publication of the focal paper.

In order to estimate

the contribution of young scholars, the percentage of postdoctoral fellows among the authors(“POSTDOC_PERCENTAGE”)

and

the

percentage

ofstudents

among

the

authors(“STUDENT_PERCENTAGE”) are adopted as independent variables. We limited our samples
only to the normal papers in the analysis. We introduce the same variables as controls as for themodel explaining the participation of young scholars, in addition to the type of authorship. In
particular, project size (the number of authors and the level of funding) controls for the quality of
the research and the endogeneity of the share of the postdoctoral fellows.
Table 9 summarizes the results of the regression analysis.

Even after the project size andthe field of science are controlled for, the percentage of postdoctoral fellows among the authorsof the focal papers (“POSTDOC_PERCENTAGE”) has positive and statistically significant
coefficients in the ordered citation counts (p < 0.1).

The estimation also shows that the time tothe publication declines with increasing the percentage of postdoctoral fellows in the team.
These results support hypothesis 5.
21

The number of authors also has a positive impact on the ordered citation counts, as
expected. International collaboration as well as the involvement of foreign-born scholars indomestic papers also have positive impacts on ordered citation counts.

Positive impact ofinternational co-authorship (Glänzel, 2001; Saka & Kuwahara, 2011) and the number of authors
on citations (Glänzel el al., 2006; Leimu & Koricheva, 2005) is consistent with the results ofprevious literatures.

6. Conclusions and discussions
This paper aimed to analyze how young scholars contribute to team-based scientificresearch, based on a new original large scale survey of scientists in the US and Japan. Our study
has confirmed that young scholars are important contributors for research efforts both in Japanand the US. The percentage of young scholars among the first authors is remarkably high in thosepapers in which the authors are ordered by their contribution. The contribution of young scholars
varies across the fields of science. The likelihood of the young authors becoming the first author
is high in life sciences. In the US, postdoctoral fellows are the first author on half of highly cited
papers in life sciences. The postdoctoral fellows are more often the first authors of the top 1%
highly cited papers than normal papers.
We have found that the likelihood of the involvement of postdoctoral fellows is high inresearch teams working on the rapidly advancing research theme, facing competitive threat inpriority, those with foreign-born scholars, and in research projects exploiting advanced researchequipment. Focusing on the papers where the order of the authors follows contribution, theprobability of postdoctoral fellows becoming the first authors is more likely to exceeds that due
to a random assignment in research teams facing competitive threat, with foreign-born
postdoctoral scholars and when exploiting advanced research equipment. Thus, our analysis
identified four significant sources of the contributions of postdoctoral fellows to the research andthree of them are also significant for making them the first authors of the papers where the order22

matters. Finally, we have found that the involvement of postdoctoral fellows is positively
associated with research performance in terms of citation counts, as well as for shortening thetime to the publication even if the size of project and internationalization of research team iscontrolled for. The finding that postdoctoral fellows accelerate the speed of research is consistent
with the fact that they are more likely to be the first authors when the team faces competitive
threat. These findings underpin importance of young scholars in the research team and illuminate
how they contribute.
On the other hand, the number of fields of expertise has no significant correlation with the
involvement of postdoctoral fellows and students.

Diversity of field of expertise is more likely

to be achieved through collaboration among senior scholars having different fields of expertise
rather than through recruiting young scholars. The mechanism for collaboration across fields is
another issue which we would like to address in a future study.
The recent comprehensive analysis on the mobility of scientist shows that postdoctoral
fellows are the most mobile and the percentage of foreign postdoctoral fellows are high (morethan 40%) in major developed countries (Franzoni, Scellato & Stephan, 2012; Van Noorden,
2012), consistent with our findings. Our analysis showed that foreign born postdoctoral fellows
do not simply provide labor force for operating research equipment, even though this is also an
essential part of the research. They are also important contributors of knowledge creation inscience.

Given increasing mobility of the postdoctoral fellows, how to attract young scholars

will become a very important policy issue for enhancing the national capability in knowledge
creation in science.

Acknowledgements
This research has been being conducted in collaboration between Institute of Innovation Research (IIR) of
Hitotsubashi University and National Institute of Science and Technology Policy (NISTEP) as a part of “Industryuniversity-government joint research on the innovation process” with support of Special Funds for Education and23

Research of The Ministry of Education, Culture, Sports, Science and Technology.

This work has also been

supported by JSPS KAKENHI 21243020． Georgia Institute of Technology implemented the survey in the US in
collaboration with IIR and NISTEP. Paula Stephan significantly contributed to the design of our survey. We havebenefitted significantly from the participants in conference on "The Organisation, Economics and Policy of Scientific
Research" in February 2011 (Torino); EUSPRI Conference on "Path - breaking innovation. Understanding, managing
and providing support for continuous radical change in science and innovation" in June 2012 (Milano); and 17th
International Conference on Science and Technology Indicators in September 2012 (Montreal).

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25

Figure 1. Compositions of authors in academic position (a paper basis, natural sciences)(a) Higher education institutions, Japan (natural sciences)
6.2%

13.3%
M.A. and/or undergraduate student

Highly cited
papers(398)

16.2%

9.1%

37.9%

6.2%

Postdoctoral fellow

11.2%
9.9%

PhD student

Assistant professor level

16.1%

Associate professor level

Normalpapers(1143)

16.2%

10.1%

35.2%

6.0%

Other

6.4%
0%

20%

Professor level

40%

60%

80%

100%

(b) Higher education institutions, US (natural sciences)
2.1%

9.1%
M.A. and/or undergraduate student

Highly cited
papers(489)

14.6%

10.0%

34.0%

12.8%

Postdoctoral fellow

17.5%
4.8%

PhD student

Assistant professor level

10.0%

Associate professor level

Normalpapers(991)

14.4%

12.2%

32.2%

13.1%

Other

13.3%
0%

20%

Professor level

40%

60%

80%

100%

Note: “Other” includes technician, the others and unknown.

Figure 2. Citation time-lags by the type of the involvement of young scholars

Only senior scholars

Students involved

Postdoctoral fellows involved

Both students and postdoctoral fellows involved

0

5

10

15

20

Time-lag (year)26

Figure 3. Country of birth of authors by their academic positions(a) Japan

Professor level(1459)

98%

2%
0%

Associate professor level(707)

97%

1%
0%
1%
0%

Assistant professor level(753)

97%

1%
0%
1%
1%
0%

Postdocoral fellow(225)

4%
3%
12%5%
0%

76%

PhD student(473)

2%
0%
6%
5%
0%

87%

MA and/or undergraduate
student(498)

97%

1%
0%
1%
1%
0%

Other(236)

95%

2%
1%
1%
1%
0%

0%
Japan

USA

China

20%

40%

Other ASIA

60%

80% 100%

EuropeOther(b) US

Professor level(1194) 1%

71%

3%
7%9%9%

Associate professor level(428) 0%

70%

5%8%10%7%

Assistant professor level(419) 1%

59%

9%11%12%9%

Postdocoral fellow(579) 5% 30%
PhD student(598) 1%

49%

MA and/or undergraduate0%
student(176)

Japan

USA

China

18% 15%6%12%
78%

Other(506) 1%
0%

19% 13% 24% 9%

61%
20%

40%

Other ASIA

3%
6%
3%
10%
8%6%7% 18%
60%

80% 100%

EuropeOther

Note: “Other” includes technician, the others and unknown.

27

Table 1 Percentage of young scholars among first authors of the focal papers in (a) Japan and (b) the US when theauthors are listed in order of the contributions(a) Japan
JPN
N
Natural sciences

849

Normal papers
Young
Postdoctoral
Students
scholars
fellows
35.0%
25.5%
9.5%

N
274

Highly cited papers
Young
Postdoctoral
Students
scholars
fellows
39.1%
19.0%
20.1%

Physical sciences

448

31.0%

22.3%

8.7%

158

32.9%

18.4%

14.6%

Life sciences

270

45.2%

34.1%

11.1%

66

51.5%

19.7%

31.8%

Medicine

131

27.5%

18.3%

9.2%

50

42.0%

20.0%

22.0%

(b) USUSAN
Natural sciences
Physical sciences

606

Normal papers
Young
Postdoctoral
Students
scholars
fellows
49.3%
30.7%
18.6%

298

53.4%

37.9%

15.4%

N
261
129

Highly cited papers
Young
Postdoctoral
Students
scholars
fellows
50.6%
22.6%
28.0%
56.6%

38.0%

18.6%

Life sciences

177

60.5%

33.3%

27.1%

59

64.4%

13.6%

50.8%

Medicine

131

25.2%

10.7%

14.5%

73

28.8%

2.7%

26.0%

Table 2 Type of authorship (affiliated institutions) vs. Composition of the countries of birth of the authors

Country of birth

JPN
N
Domestic authorship
International authorship

Only Japan

1,099
441

86.3%
7.0%

0.1%
1.8%

Japan +
other country
13.6%
91.2%

Country of birth

USA
N
Domestic authorship
International authorship

Only other country

Only USA

1,074
393

33.1%
3.3%

Only other country
16.9%
28.0%

USA +
other country
49.9%
68.7%

Table 3 Percentage of the research teams that used state-of-the-art research facilities/equipment owned by the teams(results of Japanese survey)

Only senior scholar

Internal
External
facilities/equipment facilities/equipment
437
33.4%
14.6%

Student involved

652

40.3%

15.6%

Postdoctoral fellow involved

215

50.7%

21.4%

Both student and postdoctoral fellow involved

155

51.6%

25.2%

N

28

Table 4 Summary of variables used in the estimation models
Variable
ADV_EQUIP_EXT
ADV_EQUIP_OWNED
ADV_EQUIP_OWNED_EFFECTIVE
COUNTRY
DIV_EXPERTISE
FIELD3
FIELD21
LG_AUTHORS
LG_CITATION_LAG
LG_DURATION_PUBLICATION
LG_EXP_MANMONTH
ORDERED_CITATION_COUNTSPOSTDOC_FIRST_PROB
POSTDOC_FOREIGN_PERCENTAGE
POSTDOC_PARTICIPATIONPOSTDOC_PERCENTAGE
STUDENT_PARTICIPATIONSTUDENT_PERCENTAGE
THREAT

TYPE_AUTHORSHIP

Description
1: Team used the state-of-the-art external research facilities/equipment
0: Otherwise
1: Team owned the state-of-the-art research facilities/equipment
0: Otherwise
2: Facilities/equipment was used and very effective
1: Facilities/equipment was used, but not effective
0: Facilities/equipment was not used
County dummy (0: JPN, 1: USA)The number of the field of expertise covered by a team
Field dummies (3 large fields)
Field dummies (21 fields in the Essential Science Indicators)
The number of authors (in logarithm)
Normalized average citation time-lags between the publication year of the focal
paper and the publication year of the references therein (in logarithm)
How many years it took from the launch of research projects to the publication of
the focal paper (in logarithm)
Research expenditure per man-month (in logarithm)
Ordered citation counts of the focal paper
The probability of a postdoctoral fellow becoming the first author
The percentage of foreign-born in postdoctoral fellows
1: One or more postdoctoral fellows are in the authors of the focal paper
0: Otherwise
Percentage of postdoctoral fellows in the authors of the focal paper
1: One or more students are in the authors of the focal paper
0: Otherwise
Percentage of students in authors of the focal paper
2: Level of concerned is high
1: Level of concern is moderate
0: Not at all concerned about the priority loss.
2: International paper
1: Domestic paper with foreign-born scholars
0: Domestic paper without foreign-born scholars

Table 5 Summary statistics of the variables used in the estimation models

Variable

Observations

Mean

Std. Dev.

Min

Max

ADV_EQUIP_EXT

1,342

0.417

0.493

0

1

ADV_EQUIP_OWNED

1,342

0.173

0.378

0

1

ADV_EQUIP_OWNED_EFFECTIVE

1,342

0.794

0.958

0

2

DIV_EXPERTISE

2,568

1.630

0.830

1

6

LG_AUTHORS

2,568

1.497

0.601

0.693

5.521

LG_CITATION_LAG

2,568-0.0790.422-2.550

1.108

LG_DURATION_PUBLICATION

2,417

1.392

0.547

0.000

3.611

LG_EXP_MANMONTH

2,568

7.577

1.802

-0.92416.300ORDERED_CITATION_COUNTS

2,561

3.098

1.772

1

5

POSTDOC_FIRST_PROB

560

0.215

0.461

-0.667

0.857

POSTDOC_FOREIGN_PERCENTAG

887

0.631

0.455

0

1

POSTDOC_PARTICIPATION

2,568

0.346

0.476

0

1

POSTDOC_PERCENTAGE

2,568

0.111

0.180

0

1

STUDENT_PARTICIPATION

2,568

0.544

0.498

0

1

STUDENT_PERCENTAGE

2,568

0.196

0.214

0

1

THREAT

2,568

1.005

0.739

0

2

29

Table 6 Prediction of the involvement of postdoctoral fellows (Marginal effects)
Model 1-1
JPN + USA

LG_CITATION_LAG
THREAT(Base: not concerned)

Moderately concerned
Concerned

TYPE_AUTHORSHIP Domestic paper with foreignborn scholars(Base: Domestic paper
without foreign-bornscholars)International paper

ADV_EQUIP_OWNED
ADV_EQUIP_EXT
DIV_EXPERTISE
LG_AUTHORS
LG_EXP_MANMONTH
COUNTRY
FIELD21
Chi-squared
Log-likelihood
Psuedo-R2
Observations

Logit
Marg eff.
-0.116***[0.024]0.089***[0.025]0.112***[0.031]-Model 1-2
Model 1-3
JPN + USA
JPN + USA
POSTDOC_PARTICIPATION
Logit
Logit
Marg eff.
Marg eff.
-0.105***[0.025]0.080***[0.025]0.107***[0.031]0.283***
0.273***[0.029][0.029]0.248***
0.245***[0.028][0.028]-Model 1-4
JPN

Model 1-5
JPN

Logit
Marg eff.
-0.065**[0.030]0.079**[0.038]0.081**[0.041]-

Logit
Marg eff.
-

Model 1-6
JPN
POSTDOC_PARTICIPATION
Logit
Marg eff.
-

-

-

-

-

0.295***[0.049]0.226***[0.033]
-

-

-

-

-

-

-

0.021*[0.013]0.135***[0.020]0.032***[0.007]0.187***[0.023]0.015[0.013]0.113***[0.020]0.035***[0.006]
0.059**[0.024]0.017[0.013]0.103***[0.020]0.029***[0.006]
0.091***[0.026]0.003[0.016]0.118***[0.024]0.029***[0.008]--0.005[0.017]0.084***[0.024]0.029***[0.007]-0.078***[0.026]0.026[0.033]-0.002[0.017]0.112***[0.024]0.030***[0.008]-Model 1-7
JPN

Model 1-8
JPN

Logit
Marg eff.
-0.046[0.031]
0.062[0.038]0.081**[0.041]0.290***[0.049]0.223***[0.033]-

Logit
Marg eff.
-0.044[0.031]
0.055[0.038]0.067[0.041]0.284***[0.049]0.224***[0.033]0.071***[0.026]0.014[0.032]-0.01[0.017]0.069***[0.024]0.023***[0.007]--0.006[0.016]0.079***[0.024]0.025***[0.007]
-

YES

YES

YES

YES

YES

YES

YES

YES

309.097***-1454.021
0.122
2568

374.166***-1415.507
0.145
2568

397.956***-1396.445
0.157
2568

114.125***-692.391
0.096
1342

181.505***-658.696
0.140
1342

114.824***-692.086
0.096
1342

189.621***-654.858
0.145
1342

195.060***-650.503
0.151
1342

Robust standard errors in parentheses. Significant at *, **, ***: 90%, 95%, 99%.

30

Table 7 Prediction of the involvement of students (Marginal effects)
Model 2-1
JPN + USA

LG_CITATION_LAG
THREAT(Base: not concerned)

Moderately concerned
Concerned

TYPE_AUTHORSHIP Domestic paper with foreignborn scholars(Base: Domestic paper
without foreign-bornscholars)International paper

ADV_EQUIP_OWNED
ADV_EQUIP_EXT
DIV_EXPERTISE
LG_AUTHORS
LG_EXP_MANMONTH
COUNTRY
FIELD21
Chi-squared
Log-likelihood
Psuedo-R2
Observations

Logit
Marg eff.
0.058**[0.025]0.010[0.026]0.010[0.030]-Model 2-2
Model 2-3
JPN + USA
JPN + USASTUDENT_PARTICIPATION
Logit
Logit
Marg eff.
Marg eff.
0.053**[0.026]0.013[0.026]0.010[0.031]-0.093***-0.090***[0.029][0.029]-0.218***-0.217***[0.027][0.027]-

Model 2-4
JPN

Model 2-5
JPN

Logit
Marg eff.
0.068*[0.035]-0.034[0.041]-0.064[0.043]-

Logit
Marg eff.
-

Model 2-6
JPN
STUDENT_PARTICIPATION
Logit
Marg eff.
-

-

-

-

-

-

-0.063[0.050]-0.288***[0.035]
-

-

-

-

-

-

-

-0.001[0.014]0.094***[0.021]-0.014**[0.007]
0.053**[0.025]0.000[0.014]0.135***[0.022]-0.013**[0.007]
0.092***[0.026]0.000[0.014]0.139***[0.023]-0.011*[0.007]
0.092***[0.027]0.005[0.020]0.116***[0.030]0.000[0.009]-0.014[0.021]0.173***[0.032]0.000[0.009]-0.010[0.031]-0.025[0.041]
0.005[0.020]0.110***[0.030]-0.006[0.009]-Model 2-7
JPN

Model 2-8
JPN

Logit
Marg eff.
0.046[0.037]-0.024[0.043]-0.072[0.045]-0.061[0.050]-0.288***[0.035]-

Logit
Marg eff.
0.047[0.037]-0.024[0.043]-0.073[0.045]-0.061[0.050]-0.288***[0.035]0.014[0.032]-0.016[0.043]0.014[0.022]0.179***[0.033]0.004[0.009]-0.014[0.021]0.179***[0.033]0.005[0.009]
-

YES

YES

YES

YES

YES

YES

YES

YES

226.338***-1642.014
0.072
2568

270.026***-1613.074
0.089
2568

274.097***-1610.891
0.090
2568

121.877***-844.325
0.084
1342

164.365***-814.197
0.117
1342

117.720***-847.388
0.081
1342

171.048***-811.408
0.120
1342

171.334***-811.268
0.120
1342

Robust standard errors in parentheses. Significant at *, **, ***: 90%, 95%, 99%.

31

Table 8 Prediction of the probability of postdoctoral fellows becoming the first authors
Model 3-1
JPN + USA

LG_CITATION_LAG
THREAT(Base: not concerned)

Moderately concerned
Concerned

POSTDOC_FOREIGN_PERCENTAGE
ADV_EQUIP_OWNED_EFFECTIVE
COUNTRY
FIELD3F
R-squared
Adj-R-squared
Observations

Model 3-3
JPN + USA

Model 3-4
JPN

Model 3-5
JPN

OLS
Coeff
-0.020[0.089]0.176***[0.065]0.199***[0.069]
-

OLS
Coeff
-

-

-

-

-

0.213***[0.064]---0.145***[0.047]
-

OLS
Coeff
0.006
[0.054]0.07[0.047]0.103*[0.054]0.099*[0.053]-Model 3-6
JPN
POSTDOC_FIRST_PROB
OLS
Coeff
-

0.105**[0.042]0.047[0.050]0.049[0.051]

-

-

OLS
Coeff
0.003
[0.055]0.099**[0.044]0.139***[0.051]-Model 3-2
JPN + USA
POSTDOC_FIRST_PROB
OLS
Coeff
-

0.112***[0.032]-Model 3-7
JPN

Model 3-8
JPN

OLS
Coeff
-0.005[0.087]0.125*[0.073]0.151*[0.077]0.117[0.079]
-

OLS
Coeff
0.001
[0.091]0.078[0.079]0.104[0.081]0.106[0.080]0.064[0.039]
-

-

YES

YES

YES

YES

YES

YES

YES

YES

11.788***
0.155
0.142
394

18.174***
0.153
0.145
394

10.850***
0.163
0.148
394

4.211***
0.121
0.091
153

6.286***
0.105
0.087
153

6.638***
0.111
0.093
153

3.872***
0.133
0.098
153

3.909***
0.149
0.108
153

Robust standard errors in parentheses. Significant at *, **, ***: 90%, 95%, 99%.

32

Table 9 Prediction of the impact of the involvement of young scholars
Model 4-1

POSTDOC_PERCENTAGE
STUDENT_PERCENTAGE
TYPE_AUTHORSHIP(Base: Domestic paper
without foreign-bornscholars)

Domestic paper with foreignborn scholars
International paper

DIV_EXPERTISE
LG_AUTHORS
LG_EXP_MANMONTH
COUNTRY
FIELD21
Constant
Chi-squared/F
Log-likelihood/R-squared
Psuedo-R2/Adj-R-squared
Observations

Model 4-2
JPN + USAORDERED_
LG_DURATION_
CITATION_COUNTS
PUBLICATION
Ordered Logit
OLS
Marg eff.
Coeff
0.059*-0.198**[0.034][0.087]0.022-0.016[0.024][0.069]0.039***-0.034[0.015][0.037]0.041***-0.051[0.015][0.036]-0.0050.002[0.006][0.018]0.057***
0.117***[0.011][0.031]
0.021***
0.007[0.003][0.009]0.042***-0.194***[0.013][0.035]
YES

YES

-

1.336***[0.084]7.824***
0.093
0.079
1720

228.197***-2357.618
0.051
1824

Robust standard errors in parentheses. Significant at *, **, ***: 90%, 95%, 99%. The estimated marginal effectshows that the likelihood of the focal paper becoming top 10% both for the model4-1.

33