Kyoto University,
Graduate School of Economics
Research Project Center Discussion Paper Series

An Empirical Analysis of the Relationship Between Individual
Characteristics and Research Productivity: Relationship
Between Experience as Practical Physician and Productivity

Naomi Fukuzawa

Discussion Paper No. E-13-002

Research Project Center
Graduate School of Economics
Kyoto University
Yoshida-Hommachi, Sakyo-ku
Kyoto City, 606-8501, Japan

July 2013

An Empirical Analysis of the Relationship Between Individual Characteristics andResearch Productivity: Relationship Between Experience as Practical Physician and
Productivity
Naomi FukuzawaAbstract
This paper provides an analysis of the relationship between research performance and individual
characteristics (e.g., career path information) of researchers, based on information provided in the
Curriculum Vitaes (CVs) of 565 excellent researchers within the life sciences and medical sciences
fields in Japan. I specifically analyzed the relationship between the experiences of practicalphysicians and research performance. As a result, I found that the experience as a practical physician
had a statistically positive relationship with the number of research papers, but there was not asignificant relationship with the number of citations. Moreover, the diversity of a researcher’s career
related significantly to the number of citations and patents. An employment experience at a young
age with a company or independent administrative agency had a significant and positive relationshipwith number of coauthors. However, a significant relationship between work experience in a foreigncountry and research performance was not observed.

Keywords
Research productivity・Curriculum Vitae・Career path・Practical physician・Diversity of career・
Research grant

N. Fukuzawa
National Institute of Science and Technology Policy
Ministry of Education, Culture, Sports, Science and Technology
NISTEP Satellite-Office GRIPS 7-22-1 Roppongi, Minato-ku, Tokyo 106-8677, Japan
Tel.: +81-3-5775-2651
E-mail: naomi.fukuzawa27@gmail.com
11. Introduction
A researcher’s characteristics, such as the affiliated research organization, international work
experience, college degrees, and funding from research grants, differ considerably from one
researcher to the next. Therefore, it is important to clarify the relationship between a researcher’s
individual characteristics, including career path and research performance, quantitatively.
This paper offers an empirical analysis regarding the factors that contribute to research
productivity (e.g., number of papers, citations, patents, and coauthors) in the life sciences andmedical sciences fields in Japan. I analyzed the relationship between the experiences of practicalphysicians and research performance, that had not been examined previously. In addition, I haveprovided an explanation of the relationships between research performance and career path (e.g.,
employment experience in an administrative agency, independent agency, company, or university).
Previous studies have analyzed the relationships between career path and research productivity;for example, Dietz et al. (2000) assessed the utility of the Curriculum Vitae (CV) as a data source for
examining the career paths of scientists and engineers. As a result of an ordinary least squares (OLS)
regression using CV data of 190 researchers in biotechnology and microelectronics areas, three
independent variables—number of publications during Ph.D. studies, time in assistant professorshiprank, and patents—related significantly and positively to the number of publications per year after
receipt of the doctoral degree. Dietz and Bozeman (2005) showed that career patterns and somedegree of career diversity are associated with both publication and patent productivity; their findings
were based on CV data of scientists and engineers working at university-based research centers in
the United States.
Jonkers and Tijssen (2008) debated the relationship between scientific mobility and international
collaboration using 76 plant molecular life scientists at the senior level of professor/principal
investigator in China’s research organizations. As a result of their analysis, they showed thatoverseas experience and time since returning to China had significantly positive correlations with thenumber of publications and international co-publications. Time spent abroad also correlated withinternational co-publications. Cañibano et al. (2008) showed that international mobility in the postor pre-doctoral stage had no statistically positive association to publication productivity, based on
CVs of 266 researchers in Spain. Moreover, as a result of categorizing researchers into four groups(i.e., mobile, immobile, excellent, and entrepreneurial) based on data from CVs of 326 researchers
who were grant holders at a Swedish research foundation, Sandström (2009) showed that immobile
scientists are less productive and less entrepreneurial, while entrepreneurial researchers dedicated topatenting and groundbreaking activity have changed their affiliations many times in their careers. Su(2011) showed that post-doctoral training resulted in an increase of 17% for published articles within
three years of earning doctorate degrees, although this positive effect appears to have faded quickly.
Furthermore, findings showed that industry experience within three years after earning a doctoral2

degree resulted in a 28% decrease in publications, based on the CVs of tenured scientists andengineers working at research universities. The CV appears to be useful when investigating a
researcher’s career, as noted in many previous studies (Gaughan and Bozeman 2002; Gaughan and
Ponomariov 2008; Gaughan 2009; Jonkers 2011; Cañibano et al. 2011).
Moreover, a trend within the medical sciences and clinical sciences fields has been observed inrecent years in Japan; Kanda and Kuwahara (2011) showed that the largest ratio of research time
(53.3%) was devoted to basic medicine; further, clinical fields1 researchers were identified ashaving the smallest ratio (22.0%), based on four activities (research activity2, education, social
service3, and others) in the faculty teacher in 2008. In addition, a decrease in time devoted toresearch was noted when compared with 2002 findings, and this decrease was especially large innational and public universities and local national universities. On the other hand, the ratio of time
devoted to social services, including diagnosis and treatment activity, increased. The authors pointed
out,“National universities turn into independent administrative entities, and having changed theorganization and management greatly cause the phenomenon of increasing the social service activity
ratio, like the diagnosis and treatment in the university attachment hospital in the clinical sciences
fields.” 4
Toyoda (2012) showed that the number of papers in clinical medicine fields has displayed a slight
uptrend at reputable national universities and private universities, but a stagnation tendency wasobserved for public universities; furthermore, a decreasing tendency has been noted for local
universities in Japan since 2000.
Therefore, it is suggested that the ratio of time devoted to research decreases in the clinicalmedicine fields, and time devoted to diagnosis and treatment activity becomes greater as social
services increase. Additionally, the number of published papers from reputable national universities
and private universities has experienced a slight uptrend.
Furthermore, this study includes an analysis of the relationship between the experiences of thepractical physician and research productivity. In other words, it verifies whether the experience as apractical physician has a negative relationship to research activities by decreasing research time, or if
it has positive relationship based on the acquisition of knowledge for diagnosis and treatment
activities. In addition, the relationship between a researcher’s career path and research productivity isverified. Until now, there has been no study with an empirical analysis about the relationship among
the researcher’s features, career path, and productivity through econometrics for researchers in
1Clinical fields were classified as internal medicine and surgery.

2Research activity includes collecting data and information associating research, dataprocessing, measurements and experiments, and research meetings.

3Diagnosis and treatment activity is included in the social services classification.
4The independent administrative agency system is a system designed to give an

independent corporate status to the organization. This system has aimed to improve thequality, efficiency, transparency of operations, and autonomous management.
3

Japan.
This paper is organized as follows. Section 2 describes the hypothesis. Section 3 provides details
regarding data. Section 4 explains the method of analysis. Section 5 describes the estimation results,
and Section 6 presents discussions and conclusions.

2. Hypotheses
This section explains the hypotheses regarding the relationship between the researcher’s featuresand research productivity. First, the relationship between experience as a practical physician andresearch productivity has not been analyzed. There are two hypotheses concerning the practicalphysician experience:(1) Experience as a practical physician, except for the residency period, has a negative relationshipwith research performance by decreasing time devoted to research(2) The experience and the knowledge obtained by the practical physician are associatedpositively with research productivity.
Next, concerning career and research productivity, Dietz and Bozeman (2005) have analyzed four
hypotheses by examining the relationship between career patterns and the number of papers andpatents. The authors used scientists and engineers working at university-based research centers in the
United States. They found that: (a) job changes throughout one’s career provide access to new social
networks, resulting in higher productivity; (b) the traditional career path, defined as spending one’s
entire post-Ph.D. career at a university will yield higher productivity; (c) early career experiences
through graduate assistantships and post-doctoral research experiences result in higher productivity;and (d) early productivity based on publishing before obtaining a doctoral degree will yield higher
productivity (precocity). Using the Tobit model, the authors showed that the relationship between
precocity and productivity appears to be weak; furthermore, they observed that postdoctoral research
positions may inhibit productivity. They also showed that more traditional academic career paths that
result in higher publication, and patent rates were associated with careers in industry.
Based on the above, this study provides an analysis of the following hypotheses:(1) As for the variables of job change, employment experience is obtained in an administrative
agency, company, independent administrative agency, foreign country5, or through receipt of a
doctoral degree in a foreign country. Jonkers and Tijssen (2008) observed, “Foreign
experience is expected to have an effect on the researcher’s English proficiency, which
influences his [or her] ability to publish in English language international journals.”(2) As for the traditional career path, the relationship between research activities and career at thesame university from which one graduated or pursued a doctorate, or all positions at the same
5Experience as a researcher in a research institution in a country other than Japan isreferenced.
4

institution, is verified.
(3) As for the early career experiences and precocity, I examined the experiences of theresearchers selected for the Research Fellowship for Young Scientists6 and assistants. It is
possible that the fellowship experience relates positively to research activities because of theenvironment that promotes productive research from a young age. The assistant experience isespecially applicable to the medical sciences fields. While an assistant learns a great deal,
actual time spent in research activities is short. According to the Ministry of Education,
Culture, Sports, Science and Technology (MEXT) (2008), the engagement ratio to an
assistant’s research activities is 36.6% while the average is 39.1%. Moreover, though the ratioof time typically devoted to social service activities, including diagnosis/treatment, and office
work is 23.4% on average, an assistant devotes 31.5% of his/her time to such activities.
Therefore, it can be said that experience both a research fellow and assistant is a proxyvariable associated with independence as researcher.

3. DataIn this analysis, data were obtained from researchers in the life sciences and medical sciences
fields who had been selected for the 21st Century Centers of Excellence (COE) program establishedin 2002 by the Japan Society for the Promotion of Science. The objective of this program is tocultivate a competitive academic environment among Japanese universities. 23 researchers did nothave the paper among 1,255 researchers in this field. 565 responded with career information among
1,232 researchers; ultimately, this group was analyzed for the purposes of this study.
First, I used the Scopus database to document each researcher’s data (i.e., number of papers andcitations) because Scopus assigns author IDs to prominent researchers, facilitating full namesearches and researcher’s institution searches. The author ID function on the Scopus database is notcompletely error-free, thus leading to problems in identifying authors with affiliation changes. To
avoid problems in data accuracy, the collected data were filtered by matching affiliations anddepartments. Since this process could lead to the accidental omission of researchers who hadchanged their affiliations a number of times, I contacted each researcher via e-mail to confirm
his/her list of publications. Moreover, I counted the number of coauthors per paper.
The number of patents, for which I used Espacenet Patent search, offered by the European Patent
Office. Espacenet offers free access to more than 80 million patent documents worldwide, and it
contains information about inventions and technical developments since 1836. Furthermore, it is
6Japan Society for the Promotion of Science (JSPS) provides a special program that

grants fellowships to (1) young Japanese postdoctoral researchers who conduct research
activities at Japanese universities or research institutions on a non-employment basis
and to (2) graduate students who conduct research in Japanese university doctoral
programs. Please refer to http://www.jsps.go.jp/english/e-pd/index.html.
5

possible to search by connecting with Scopus (using SciVerse Hub); searches can be conductedaccording to author’s name, institution, year, and field. For this study, searches were conducted fornumber of patents, author’s full name, and institutions where employed. I refer to publication date inthis paper; it represents the date that a patent application was published. Thus, it represents the datethe document became available to the public. It is necessary to note that the patent used for thisanalysis were limited to the ones for which researchers were identified as inventors.
In addition, I used the KAKEN7 database for the research grant. KAKEN is the database of
Grants-in-Aid for Scientific Research. It was established and provided by the National Institute ofInformatics, with the support of the MEXT and the JSPS. Grants-in-Aid are awarded to promotecreative and pioneering research across a wide spectrum of scientific fields, ranging from thehumanities and social sciences to the natural sciences. Grants are awarded to projects organized byindividual researchers or research groups at Japanese universities or research institutes. Research
results obtained under these grants are published in a number of academic journals. In this study, I
analyzed only those research studies headed by researchers, and I used only direct expenses.
Moreover, information on each researcher’s career was obtained from the CV that was publicly
available on the Web and ReaD&Researchmap database. ReaD&Researchmap8 is a service that theJapan Science and Technology Agency provides on the Internet to support the researchers’ databaseand performance management system constructed by the university and research institutions. This
database can search for 22,000 researchers based on names and affiliated institutions. The template
for a CV was developed for this database; it contains career information, as well as research fields
and papers published.
For the 565 researchers included in this study, I explored their research careers after graduationfrom a university. The cross-section data covers 1996 to 2009.

4. Methods of analysis
Regarding methodology used for this study, the numbers of papers and citations reflect research
performance, and the numbers of patents and coauthors used for explained variables. The decline incitations of recent papers is a major problem. To address it, some studies have used normalization,
which is used with forward citation of patents (Hall et al. 2001). The normalization approach wasused in this study by dividing each citation by the corresponding year’s citation mean. Additionally,each explained variable was divided by the researcher’s career length to resolve problems associatedwith differences in length of researchers’ careers. Because researchers’ birth dates were not available,
this study is based on the assumption that a researcher was employed initially at age 28. Furthermore,
the retirement age was established as age 65 in 2009. As an example, if the first year of employments
7http://kaken.nii.ac.jp/
8http://researchmap.jp/

6was 1999, the career length was ten years (i.e., 2009  1999  10 ). Career length was defined
based on the method noted above; it assumes career length as 37 years if it spans 38 years or more.
Because each explained variable is nonnegative, use of the Tobit model, Poisson model, andnegative binomial model were appropriate for this study (Dietz and Bozeman, 2005). The
explanatory variables were: experience of those selected for the Research Fellowship for Young
Scientists, first employment according to three career divisions (i.e., administrative agency 9 ,company, or independent administrative agency10), experience accrued in the above three career
divisions, and diversity of career. Diversity is associated with the number of divisions through whicha researcher belongs (i.e., 0 to 3). I examined diversity of employment excluding academia as it
related to research activities. In addition, assistant experience, research careers in foreign countries,
experience as a practical physician (residency period is excluded), gender (male = 1), amount ofGrants-in-Aid for Scientific Research, career with the university from which one graduated, career at
the same university where doctorate was received, all positions at the same institution, doctoral
degree from foreign countries, type of doctoral degree (e.g., medical science, science, medical
dentistry, engineering, agriculture, pharmacology), and cross-term of fellowship experience andassistant were considered. Table 1 shows the variables used.

<Insert Table 1>

5. Estimated results
This section shows the estimation results of selection probability for disclosing career
information, and estimation results of the relationship between researcher’s features andperformance, according to the Tobit model, Poisson model, and negative binomial model. When thenull hypothesis that data follows the Poisson distribution (i.e., assumes the average to be equal tovariance) is rejected, findings of this study support the results of the negative binomial model.

5.1 Selection probability of career information disclosureThis study includes an analysis of the relationship between research performance and disclosed
9It includes research institute under the direct control of the administrative agency inthis analysis.

10This variable refers to independent administrative agencies as of October 2012, otherthan the incorporated national university. It includes foundations and theInter-University Research Institute Corporation (IURIC). IURIC is classified as anindependent administrative agency in this study to distinguish it from a university,
though it is based on an incorporated national university. IURIC has made animportant contribution to the development of Japan’s academic research by conducting
effective joint research and providing researchers with benefits that are difficult forindividual universities to maintain (e.g., large-scale facilities, considerable amounts ofdata, and academic materials).
7career information, based on data from 1,232 researchers. I estimated the odds ratio using a logit
model; explained variables included verification of complete researcher information (1 = Yes, 0 =No), and the number of papers and citations for the explanatory variables.
The odds ratio is the ratio of the odds of an event occurring in one group to the odds of it
occurring in another group. Odds are the ratio of the probability of a variable becoming one or zero.
Table 2 shows the findings from the logit model and the odds ratio.

<Insert Table 2>

According to the odds ratio, the ratio of the probability of disclosing career information to theprobability of not disclosing, according to an increase in the number of published papers, was 1.340.
In other words, this means that the probability of disclosing career information to the public
increased relatively by 34%. The number of citations was not related necessarily to the selection
probability of disclosing, because a significant result was not obtained by the citation count.
Therefore, because the probability of disclosure is high for a highly published researcher, it isnecessary to note that productivity of researchers targeted by this analysis was higher than that ofresearchers excluded from analysis because of non-disclosure.

5.2 Estimated results for number of papers
The following estimation is based on the analysis of 565 researchers who disclosed career
information. Model 1 uses the diversity of career variable; Model 2 uses the first employment
variable; and Model 3 uses the employment experience variable. The diversity of career variable waszero if a researcher belonged only to academia. The logarithm was taken after 1 was added to the
grant.
Table 3 shows the estimated results for number of papers. The following variables were
statistically positive for the number of papers in all models: research fellowship, first job with anindependent administrative agency, employment with the same university from which the researcher
graduated, experience as a practical physician, male gender, and grant amount. A significant
relationship was not seen for diversity of career; moreover, first job or experience in an
administrative agency showed a significantly negative relationship with the number of papers in thenegative binomial model.

<Insert Table 3>

5.3 Estimated results for number of citations
Table 4 shows the estimated results for number of citations. Diversity of career, first employment,
8

experience in an independent administrative agency, and grant amounts had statistically positive
relationships with citations. Furthermore, the experience as a practical physician was not significant
but the sign was negative. These results differ from those regarding number of papers because theresearch fellowship experience did not relate significantly, nor did the graduation university.

<Insert Table 4>

5.4 Estimated results for number of patents
Table 5 shows the estimated results regarding number of patents. Assistant experience had asignificantly negative relationship, but experience with an independent administrative agency andcareer diversity had significantly positive relationships to patent activity for all models. Moreover,though the research fellowship experience showed a statistically negative relationship, cross-term of
the fellowship and assistant experiences showed positive relationships with patents. Though grantamount related to the number of papers and citations, it did not relate to patents. Because
Grants-in-Aid for Scientific Research primarily support academic research, they do not to relatestatistically to number of patents. From the study sample, 359 researchers did not have patents(63.5%); further, 75.4% of researchers had zero or one patent numbers. Therefore, it is shown thatpatent activity was not the main focus of researchers, and the characteristics of researchers who had
several patents differed from those of general researchers.

<Insert Table 5>

5.5 Estimated results regarding number of coauthors
Though the number of papers and experience as a practical physician showed significantly
positive relationships, joint authorship through research collaboration was shown in previous studies
to have a positive relationship to research productivity (Price and Beaver 1966; Zuckerman 1967;
Pao 1982; Pravdic and Oluic-Vukovic 1986; Lee and Bozeman 2005; Sooryamoorthy, 2013). Beaver(2001) showed that one of the purposes for collaboration is to enhance efficiency and productivity.
Therefore, the possibility exists that the researcher who is experienced as a practical physician andwho collaborates with many coauthors can maintain a high level of research productivity despite
limited time for research. Consequently, I analyzed the relationship between the number of coauthorsand each variable identified in this study.
Table 6 shows the estimated results for number of coauthors. A significantly positive relationshipwas not identified between experience as a practical physician and number of coauthors. Moreover,though experiences in various career divisions related to the number of coauthors, diversity of career
was not a significant factor. There were many coauthors when a researcher was initially employed by9

a company or independent administrative agency. Therefore, experience in a nonacademic career
while young appears to have an influence on the number of coauthors due to an expanding networkof collaborators. However, if a first job was with an administrative agency, a significantly negative
association in number of coauthors was found. In addition, though fellowship or assistant experience
had significantly positive relationships, experiencing both resulted in a significantly negative
relationship to the number of coauthors. Therefore, the negative relationship appears to be linked totime required to become independent as a researcher.

<Insert Table 6>

5.6 Estimations for researchers who are Doctors of Medical Science (DMSc)
Though there were 188 researchers with experience as practical physicians among the sample of565 researchers. Therefore, I analyzed only 296 researchers who had DMSc . Tables 7–9 show theestimated results for number of papers, citations, and coauthors. For researchers who were DMSc,
the first job with an independent administrative agency related significantly to each explained
variable.
Experience as a practical physician and grant amount had a significantly positive relationship withnumber of papers. Regarding number of citations, experience with a company was significantly
positive in the negative binomial model. Cross-term of the fellowship experience and employment as
an assistant were associated positively with citations.
Regarding number of coauthors, first employment with or experience with a company, and first
employment with an independent administrative agency were significantly positive. Thus, theexperience of nonacademic careers at a young age related to the number of coauthors. Experience asa practical physician did not have a significantly positive relationship with number of coauthors.
Moreover, though diversity of career had no relationship with number of papers, it had a positive
relationship for citations.

<Insert Table 7><Insert Table 8><Insert Table 9>

6. Conclusion
As a result of analyzing the relationship between experience as a practical physician and research
performance, experience as a practical physician had a significantly positive relationship with10

number of papers but not with number of citations. Regarding the hypothesis that assesses whether
the experience as a practical physician has a negative or positive relationship to research, this paper
suggests that knowledge obtained by the practical physician has a positive influence on the numberof papers. On the other hand, it is suggested that decreasing research time leads to difficulty inproducing papers with strong impact. However, since this analysis does not use research time andpractical knowledge as variables, I cannot assert that a decrease in research time and knowledge
acquired from experience as a practical physician influences research performance. Further, thisstudy does not encompass all features of the field of clinical medicine, so it cannot explain fully thereason for a relationship between the practical physician and the number of papers.
Moreover, although it is possible that researchers who have many coauthors maintain theirproductivity even if research time is limited, my analysis indicated that the practical physician
experience did not relate significantly to the number of coauthors. On the other hand, experience
with a company or independent administrative agency at a young age had a positive relationshipwith number of coauthors. Though research experience in a foreign country was expected to relate toresearch performance, no significant relationship was noted.
As for the researcher’s experience from a young age, research fellowship experience had asignificantly positive relationship to the number of papers. There is a possibility that researchers
selected as research fellows were either excellent scholars originally or individuals focused entirelyon research from an early stage. Moreover, a career with an independent administrative agency
related positively to the numbers of papers and citations. It also appears that using large-scale
environmental facilities offered no educational function for students in the Inter-University ResearchInstitute Corporation related positively to research.
As for diversity of career and research performance, there was no relation with number of papers.
Though a career with the university from which one graduated and jobs at the same institution had apositive trend for number of papers, there was a positive relationship between citation count andcareer diversity. The findings suggest that a variety of employment experiences are advantageous for
producing an impact of papers.
However, this study cannot explain all trends in the life sciences and medical sciences field
because this analysis was applicable only to researchers who could attract large-scale competitive
research funding (21st COE program) and who had disclosed career information.

11

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13

Table 1: Description of variables
Variable

Description
Cross-section data of the number of papers of each researcher

Number of papers(divided by researcher's career length)
Cross-section data of the number of papers by researcher

Number of citations(divided by number of average citations each year andresearcher's career length)
Cross-section data of number of patents for each researcher

Number of patents(divided by researcher's career length)
Cross- section data of number of coauthors per paper (divided

Number of coauthors

by researcher's career length)
Experience and encouragement received from grant awarded to

Research Fellowship for Young Scientists

young researcher (1 = Yes, 0 = No)
How many career experiences were obtained in the threedivisions: administrative agency, company, and independent

Diversity of careeradministrative agency? (0 to 3, i.e., 0 = They belonged only toacademia)First employment with an administrative agency

1 = Yes, 0 = No

Employment experience in an administrative agency

1 = Yes, 0 = No

First employer with a company

1 = Yes, 0 = No

Employment experience with a company

1 = Yes, 0 = No

First employment with independent administrative agency

1 = Yes, 0 = No

Employment experience in an independent administrative agency

1 = Yes, 0 = No

Assistant experience

1 = Yes, 0 = No

Foreign research experience

1 = Yes, 0 = No

Experience as a practical physician

The residency period is excluded: 1 = Yes, 0 = No

Male gender

1 = Yes, 0 = No
Grants-in-Aid are awarded to promote creative and pioneeringresearch across a wide spectrum of scientific fields. Grants are

Amount of Grants-in-Aid for Scientific Researchawarded to projects organized by individual researchers or
research groups at Japanese universities or research institutes.
The logarithm was taken after 1 was added to the grant.
Is career as faculty member at the same university from which

Career at university from which the researcher graduated

one graduated? 1 = Yes, 0 = No
Is career at same university as doctoral program? 1 = Yes, 0 =Career at the same university as doctoral programNo

All careers at the same institution

1 = Yes, 0 = No

Doctoral degree from a foreign country

1 = Yes, 0 = No

Doctor of Medical Science (DMSc)

1 = Yes, 0 = No

Doctor of Science

1 = Yes, 0 = No

Doctor of Dentistry

1 = Yes, 0 = No

Doctor of Engineering

1 = Yes, 0 = No

Doctor of Agriculture

1 = Yes, 0 = No

Doctor of Pharmacology

1 = Yes, 0 = No

Other doctoral degree

1 = Yes, 0 = No

Experience as research fellow for young scientists and as

Cross-term of experience in research fellowship and assistant (1assistant= Yes, 0 = No)1

Table 2: Selection probability of career information disclosure
Variables
Odds ratio
P>|z|Coef.(Std. Err.)
1.340
0.000
The number of papers
0.293***(0.0477)
1.106
0.439
The number of citations
0.101(0.134)
Constant-0.775***(0.0854)
Log likelihood is -790.2. Standard errors in parentheses, * p<0.1, ** p<0.05, *** p<0.01.

2

Table 3: Estimated results for number of papers
Tobit model

Negative binomial model

Model 1Model 2Model 3Model 1Model 2Model 3

Research Fellowship

1.641*(0.845)

1.569* (0.835)

1.608* (0.845)

0.603** (0.251)

0.499** (0.246)

0.586** (0.251)

Diversity of career-0.139 (0.274)

Administrative agency (first)-0.0465 (0.0848)-1.558 (1.279)

Administrative agency-1.990** (0.835)-0.715 (0.812)

Company (first)-0.623** (0.293)-0.115 (0.719)

Company

0.00676 (0.245)-0.509 (0.556)

Independent administrative agency (first)-0.188 (0.185)

1.248** (0.577)

Independent administrative agency

0.526*** (0.172)
0.0394 (0.319)-0.281 (0.331)-0.437 (0.330)

0.0466 (0.0968)

Assistant experience-0.398 (0.328)-0.127 (0.0970)-0.0731 (0.0974)-0.139 (0.0974)

Foreign research experience-0.0237 (0.291)-0.0379 (0.290)-0.0397 (0.291)

0.0117 (0.0878)

0.0122 (0.0876)

0.00745 (0.0877)

Experience as practical physician

0.866** (0.340)

0.915*** (0.338)

0.873** (0.339)

0.237** (0.0982)

0.268*** (0.0971)

0.243** (0.0980)

Male gender

1.333* (0.780)

1.348* (0.776)

1.337* (0.779)

0.678** (0.285)

0.702** (0.283)

0.673** (0.284)

Amount of Grants-in-Aid

0.175*** (0.0588)

0.176*** (0.0585)

0.174*** (0.0588)

0.0632*** (0.0175)

0.0662*** (0.0174)

0.0624*** (0.0175)

Career at same university (graduated)

0.757* (0.428)

0.783* (0.428)

0.772* (0.428)

0.324** (0.137)

0.347** (0.136)

0.328** (0.137)

Career at same university (doctoral)-0.0879 (0.451)-0.0838 (0.455)-0.109 (0.450)-0.0320 (0.147)-0.0101 (0.149)-0.0292 (0.147)

All careers at the same institution

0.397 (0.452)

0.477 (0.444)

0.389 (0.452)

0.122 (0.134)

0.156 (0.131)

0.119 (0.133)

Doctoral degree from foreign country-0.389 (1.083)-0.251 (1.083)-0.341 (1.084)-0.171 (0.345)-0.0404 (0.347)-0.129 (0.346)

Doctor of Medical Science (DMSc)

3.299*** (1.064)

3.174*** (1.069)

3.344*** (1.066)

0.697*** (0.265)

0.666** (0.264)

0.881*** (0.280)

Doctor of Science

2.876*** (1.087)

2.661** (1.093)

2.950*** (1.088)

0.503* (0.274)

0.448* (0.271)

0.698** (0.287)

Doctor of Dentistry

2.446** (1.235)

2.329* (1.239)

2.497** (1.235)

0.379 (0.324)

0.351 (0.322)

0.561* (0.335)

Doctor of Engineering

2.253** (1.118)

2.144* (1.118)

2.407** (1.125)

0.0952 (0.275)

0.0865 (0.271)

0.338 (0.292)

Doctor of Agriculture

2.171* (1.177)

2.040* (1.172)

2.324* (1.184)

0.0570 (0.318)

0.0526 (0.315)

0.301 (0.335)

Doctor of Pharmacology

3.942*** (1.136)

3.698*** (1.139)

4.036*** (1.140)

0.837*** (0.274)

0.775*** (0.270)

1.048*** (0.290)

Other doctoral degree

3.209*** (1.121)

2.977*** (1.123)

3.263*** (1.122)

0.662** (0.310)

0.517* (0.307)

0.814** (0.320)

Research fellow and assistant (cross-term)-0.978 (0.978)-0.791 (0.962)-0.967 (0.977)-0.327 (0.290)-0.175 (0.284)-0.326 (0.290)

Constant-3.611*** (1.384)-3.706*** (1.400)-3.624*** (1.388)-1.039** (0.409)-1.179*** (0.416)-1.217*** (0.421)

Standard errors in parentheses, * p<0.1, ** p<0.05, *** p<0.01.

3

Table 4: Estimated results for number of citations
Tobit model

Negative binomial model

Model 1Model 2Model 3Model 1Model 2Model 3

Research Fellowship-0.0399 (0.354)

0.123 (0.349)-0.0472 (0.354)-0.106 (0.401)

0.0991 (0.402)-0.119 (0.403)

Diversity of career

0.211* (0.115)

Administrative agency (first)

0.290** (0.119)-0.357 (0.535)

Administrative agency-2.426 (2.174)-0.0674 (0.343)

Company (first)-0.266 (0.433)

0.318 (0.301)

Company0.509 (0.361)
0.216 (0.233)

Independent administrative agency (first)

0.385 (0.271)

0.722*** (0.241)

Independent administrative agency

0.901*** (0.222)
0.245* (0.134)

0.332** (0.136)

Assistant experience

0.0263 (0.138)

0.0647 (0.139)

0.0221 (0.139)-0.0131 (0.145)

0.0490 (0.147)-0.00661 (0.145)

Foreign research experience

0.114 (0.122)

0.114 (0.122)

0.109 (0.122)

0.171 (0.132)

0.183 (0.133)

0.168 (0.133)

Experience as practical physician-0.0583 (0.142)-0.0566 (0.141)-0.0567 (0.142)-0.0598 (0.142)-0.0541 (0.141)-0.0587 (0.141)

Male gender

0.264 (0.326)

0.284 (0.324)

0.270 (0.326)

0.589 (0.477)

0.638 (0.478)

0.595 (0.478)

Amount of Grants-in-Aid

0.0978*** (0.0246)

0.100*** (0.0245)

0.0981*** (0.0246)

0.159*** (0.0324)

0.166*** (0.0323)

0.160*** (0.0325)

Career at same university (graduated)

0.168 (0.180)

0.177 (0.179)

0.175 (0.180)

0.260 (0.203)

0.280 (0.209)

0.283 (0.206)

Career at same university (doctoral)-0.104 (0.189)-0.0780 (0.191)-0.108 (0.189)-0.138 (0.217)-0.0607 (0.227)-0.148 (0.219)

All careers at the same institution

0.0704 (0.189)

0.0480 (0.186)

0.0676 (0.189)

0.132 (0.213)

0.108 (0.210)

0.130 (0.213)

Doctoral degree from foreign country-0.211 (0.453)-0.0370 (0.453)-0.186 (0.454)-0.405 (0.617)-0.121 (0.612)-0.357 (0.617)

Doctor of Medical Science (DMSc)

0.844* (0.445)

0.845* (0.447)

0.873* (0.446)

0.974*** (0.378)

1.062*** (0.385)

1.164*** (0.406)

Doctor of Science

0.419 (0.455)

0.399 (0.457)

0.446 (0.456)

0.405 (0.391)

0.484 (0.396)

0.578 (0.416)

Doctor of Dentistry0.392 (0.517)

0.391 (0.518)

0.419 (0.517)

0.332 (0.488)

0.444 (0.494)

0.513 (0.509)

Doctor of Engineering

0.169 (0.468)

0.189 (0.468)

0.209 (0.471)-0.136 (0.404)-0.00807 (0.405)

0.0596 (0.438)

Doctor of Agriculture

0.122 (0.493)

0.143 (0.491)

0.168 (0.496)-0.490 (0.531)-0.325 (0.532)-0.276 (0.558)

Doctor of Pharmacology

0.548 (0.475)

0.488 (0.477)

0.590 (0.477)

0.600 (0.394)

0.621 (0.397)

0.804* (0.425)

Other doctoral degree

0.266 (0.469)

0.178 (0.470)

0.291 (0.470)

0.0293 (0.488)-0.0214 (0.489)

0.173 (0.497)

Research fellow and assistant (cross-term)

0.194 (0.409)

0.0803 (0.403)

0.205 (0.409)

0.365 (0.458)

0.214 (0.456)

0.390 (0.461)

Constant-1.199** (0.579)-1.276** (0.586)-1.235** (0.581)-3.321*** (0.657)-3.634*** (0.677)-3.541*** (0.681)

Standard errors in parentheses, * p<0.1, ** p<0.05, *** p<0.01.

4

Table 5: Estimated results for number of patents
Tobit model
Model 1
Research Fellowship-0.744** (0.290)

Diversity of career

0.136** (0.0570)

Administrative agency (first)

Poisson model

Model 2-0.610** (0.280)

Model 3-0.766*** (0.295)

Model 1Model 2Model 3

-3.590 (4.076)-3.139 (4.075)-3.585 (4.076)

0.756** (0.332)-0.0631 (0.276)

Administrative agency-0.253 (1.826)-0.199 (0.191)

Company (first)-0.700 (1.455)

0.255* (0.141)

Company

0.694 (0.758)
0.154 (0.114)

Independent administrative agency (first)

0.457 (0.705)

0.119 (0.120)

Independent administrative agency

0.432 (0.616)
0.167** (0.0653)

Assistant experience-0.172*** (0.0660)-0.172** (0.0677)-0.176*** (0.0663)

Foreign research experience

0.000830 (0.0610)

0.00263 (0.0616)-0.00178 (0.0609)

Experience as practical physician

0.0309 (0.0700)

0.0179 (0.0703)

0.0327 (0.0698)

Male gender-0.0407 (0.162)-0.0292 (0.163)-0.0364 (0.161)

Amount of Grants-in-Aid

0.0120 (0.0122)

0.0135 (0.0123)

Career at same university (graduated)

0.105 (0.0962)

Career at same university (doctoral)-0.0530 (0.100)

All careers at the same institution
Doctoral degree from foreign country

0.946*** (0.360)-0.884** (0.374)-0.950** (0.381)-0.890** (0.375)

0.0812 (0.384)

0.0813 (0.386)

0.0621 (0.385)-0.0878 (0.426)-0.135 (0.425)-0.0885 (0.427)

0.289 (1.204)

0.400 (1.200)

0.286 (1.204)

0.0124 (0.0122)-0.0331 (0.0658)-0.0323 (0.0678)-0.0340 (0.0657)

0.110 (0.0972)

0.115 (0.0967)

0.335 (0.637)

0.488 (0.679)

0.450 (0.666)-0.0429 (0.103)-0.0578 (0.101)-0.389 (0.665)-0.474 (0.712)-0.512 (0.691)

0.0754 (0.0934)

0.0477 (0.0925)

0.0729 (0.0931)

0.331 (0.664)

0.0762 (0.648)

0.325 (0.667)

0.184 (0.227)

0.252 (0.230)

0.210 (0.227)-0.00631 (1.456)

0.201 (1.418)

0.105 (1.428)

Doctor of Medical Science (DMSc)

0.158 (0.211)

0.187 (0.215)

0.234 (0.218)

0.879 (0.989)

0.992 (1.052)

1.335 (1.199)

Doctor of Science

0.0481 (0.218)

0.0813 (0.221)

0.122 (0.224)

0.206 (1.042)

0.416 (1.096)

0.666 (1.239)

Doctor of Dentistry

0.118 (0.248)

0.141 (0.251)

0.193 (0.253)

0.366 (1.454)

0.419 (1.501)

0.821 (1.612)

Doctor of Engineering

0.103 (0.219)

0.125 (0.221)

0.197 (0.228)-0.0666 (1.014)

0.171 (1.081)

0.604 (1.275)

Doctor of Agriculture

0.104 (0.236)

0.144 (0.237)

0.199 (0.245)-0.366 (1.274)

0.0954 (1.296)

0.388 (1.490)

Doctor of Pharmacology

0.326 (0.220)

0.343 (0.223)

0.412* (0.227)

1.280 (0.986)

1.386 (1.058)

1.763 (1.216)

Other doctoral degree-0.0373 (0.235)-0.0436 (0.237)

0.0251 (0.238)-0.170 (1.371)-0.109 (1.365)

0.249 (1.431)

Research fellow and assistant (cross-term)

0.821*** (0.309)

0.697** (0.299)

0.848*** (0.315)

4.817 (4.113)

4.332 (4.107)

4.824 (4.115)

Constant-0.434 (0.281)-0.469 (0.290)-0.519* (0.288)-3.127* (1.603)-3.114* (1.694)-3.587** (1.775)

Standard errors in parentheses, * p<0.1, ** p<0.05, *** p<0.01.

5

Table 6: Estimated results for number of coauthors
Tobit model
Model 1
Research Fellowship2.615*** (0.585)

Diversity of career

0.0968 (0.190)

Administrative agency (first)

Negative binomial model

Model 22.862*** (0.567)

Model 32.610*** (0.582)

Model 1Model 20.642*** (0.139)

0.694*** (0.134)

Administrative agency-0.837** (0.393)-0.894 (0.560)-0.353* (0.191)

1.392*** (0.489)

Company0.449*** (0.138)
0.618 (0.383)

Independent administrative agency (first)

0.639*** (0.139)

0.0365 (0.0536)-1.443* (0.869)

Company (first)

Model 30.208* (0.110)

1.684*** (0.392)

Independent administrative agency

0.447*** (0.0996)
0.0773 (0.220)

Assistant experience

0.221 (0.227)

0.425* (0.225)

0.242 (0.228)

0.0330 (0.0613)
0.0679 (0.0639)

0.126** (0.0638)

0.0751 (0.0640)

Foreign research experience

0.106 (0.201)

0.125 (0.197)

0.0910 (0.200)

0.0373 (0.0563)

0.0408 (0.0555)

0.0320 (0.0562)

Experience as practical physician

0.293 (0.235)

0.322 (0.229)

0.292 (0.234)

0.0803 (0.0621)

0.0864 (0.0606)

0.0819 (0.0617)

Male gender

0.225 (0.540)

0.286 (0.527)

0.255 (0.537)

0.0716 (0.154)

0.0882 (0.151)

0.0802 (0.153)

Amount of Grants-in-Aid

0.0242 (0.0407)

0.0305 (0.0397)

0.0277 (0.0405)

0.00644 (0.0112)

0.00754 (0.0109)

0.00721 (0.0111)

Career at same university (graduated)

0.196 (0.296)

0.210 (0.291)

0.227 (0.295)

0.0634 (0.0831)

0.0724 (0.0830)

0.0806 (0.0834)

Career at same university (doctoral)-0.187 (0.312)-0.0847 (0.309)-0.185 (0.311)-0.0517 (0.0879)-0.0172 (0.0891)-0.0583 (0.0881)

All careers at the same institution

0.366 (0.313)

0.472 (0.302)

0.357 (0.312)

0.111 (0.0862)

0.137 (0.0836)

0.108 (0.0859)

Doctoral degree from foreign country-0.887 (0.750)-0.512 (0.736)-0.796 (0.748)-0.254 (0.223)-0.132 (0.220)-0.226 (0.223)

Doctor of Medical Science (DMSc)

1.071 (0.736)

1.044 (0.726)

1.192 (0.735)

0.322* (0.192)

0.341* (0.198)

0.389* (0.202)

Doctor of Science-0.0410 (0.753)-0.210 (0.743)

0.0262 (0.751)-0.00536 (0.198)-0.0250 (0.203)

0.0466 (0.207)

Doctor of Dentistry0.210 (0.855)

0.174 (0.842)

0.309 (0.852)

0.0780 (0.229)

0.0952 (0.233)

0.139 (0.237)

Doctor of Engineering-0.465 (0.774)-0.635 (0.760)-0.402 (0.776)-0.181 (0.207)-0.216 (0.212)-0.136 (0.220)

Doctor of Agriculture-0.840 (0.815)-1.014 (0.796)-0.753 (0.817)-0.324 (0.226)-0.341 (0.229)-0.271 (0.236)

Doctor of Pharmacology

0.302 (0.786)

0.0501 (0.774)

0.440 (0.786)

0.114 (0.207)

0.0658 (0.212)

0.190 (0.219)

Other doctoral degree

0.761 (0.776)

0.514 (0.763)

0.846 (0.773)

0.201 (0.199)

0.141 (0.202)

0.253 (0.205)

Research fellow and assistant (cross-term)-1.871*** (0.677)-1.933*** (0.654)-1.814*** (0.674)-0.422** (0.167)-0.422*** (0.160)-0.404** (0.166)

Constant

1.994** (0.958)

1.580* (0.951)

1.793* (0.957)

0.776*** (0.259)

0.631** (0.266)

0.685** (0.268)

Standard errors in parentheses, * p<0.1, ** p<0.05, *** p<0.01.

6

Table 7: Estimated results for number of papers (Only DMSc)
Tobit model

Negative binomial model

Model 1Model 2Model 3Model 1Model 2Model 3

Research Fellowship-0.952 (1.742)-1.376 (1.720)-1.113 (1.786)-0.298 (0.490)-0.356 (0.491)-0.355 (0.503)

Diversity of career-0.341 (0.482)

Administrative agency (first)-0.0762 (0.130)-2.567 (2.393)

Administrative agency-2.565 (1.607)
1.960 (1.493)

Company (first)

0.379 (0.366)

0.0806 (3.375)

Company

0.0832 (0.959)
1.176 (2.058)

Independent administrative agency (first)

0.356 (0.537)

1.529 (1.158)

Independent administrative agency

0.584* (0.305)-0.656 (0.509)

Assistant experience-0.711 (0.469)-0.566 (0.471)-0.705 (0.470)-0.170 (0.141)-0.209* (0.124)-0.156 (0.124)-0.213* (0.124)

Foreign research experience

0.550 (0.461)

0.462 (0.465)

0.609 (0.460)

0.166 (0.125)

0.135 (0.125)

0.168 (0.125)

Experience as practical physician

0.812* (0.416)

0.879** (0.413)

0.821** (0.416)

0.239** (0.111)

0.279** (0.111)

0.237** (0.112)

Male gender

1.533 (1.305)

1.487 (1.301)

1.483 (1.298)

0.729* (0.427)

0.689 (0.425)

0.726* (0.425)

Amount of Grants-in-Aid

0.159** (0.0800)

0.158** (0.0796)

0.162** (0.0803)

0.0398* (0.0204)

0.0433** (0.0204)

0.0407** (0.0207)

Career at same university (graduated)

0.681 (0.721)

0.700 (0.726)

0.640 (0.725)

0.218 (0.200)

0.232 (0.200)

0.223 (0.200)

Career at same university (doctoral)

0.123 (0.818)

0.175 (0.838)

0.173 (0.819)

0.0331 (0.231)

0.102 (0.239)

0.0128 (0.231)

All careers at the same institution

0.974 (0.719)

1.017 (0.713)

1.001 (0.715)

0.271 (0.189)

0.280 (0.188)

0.269 (0.188)

Doctoral degree from foreign country-1.317 (1.950)-1.247 (1.944)-1.231 (1.940)-0.513 (0.600)-0.467 (0.598)-0.475 (0.598)

Research fellow and assistant (cross-term)

0.840 (2.047)

1.366 (2.008)

0.967 (2.084)

0.321 (0.575)

0.414 (0.570)

0.374 (0.586)

Constant-0.479 (1.587)-0.684 (1.584)-0.509 (1.589)-0.175 (0.485)-0.317 (0.488)-0.161 (0.488)

Standard errors in parentheses, * p<0.1, ** p<0.05, *** p<0.01.

7

Table 8: Estimated results for number of citations (Only DMSc)
Tobit model

Negative binomial model

Model 1Model 2Model 3Model 1Model 2Model 3

Research Fellowship-0.552 (0.846)-0.657 (0.823)-0.792 (0.869)-0.620 (0.679)-0.595 (0.695)-0.912 (0.708)

Diversity of career

0.295 (0.234)

Administrative agency (first)

0.322* (0.175)-0.717 (1.145)

Administrative agency-3.413 (4.671)
0.548 (0.727)

Company (first)

0.310 (0.493)-0.0345 (1.615)

Company-0.00423 (1.414)
1.526 (1.002)

Independent administrative agency (first)

1.553** (0.670)

1.956*** (0.554)

Independent administrative agency

1.412*** (0.350)
0.226 (0.248)

0.277 (0.192)

Assistant experience-0.144 (0.228)-0.0663 (0.225)-0.115 (0.229)-0.176 (0.181)-0.126 (0.181)-0.139 (0.183)

Foreign research experience

0.174 (0.224)

0.162 (0.223)

0.195 (0.224)

0.213 (0.183)

0.240 (0.186)

0.229 (0.184)

Experience as practical physician-0.0787 (0.202)-0.0562 (0.198)-0.0566 (0.203)-0.0657 (0.161)-0.0152 (0.160)-0.0310 (0.162)

Male gender

0.532 (0.633)

0.462 (0.622)

0.517 (0.631)

0.768 (0.641)

0.674 (0.638)

0.757 (0.641)

Amount of Grants-in-Aid0.102*** (0.0388)

0.104*** (0.0381)

0.108*** (0.0391)

0.116*** (0.0336)

0.131*** (0.0338)

0.131*** (0.0350)

Career at same university (graduated)

0.273 (0.350)

0.233 (0.347)

0.315 (0.353)

0.362 (0.283)

0.354 (0.289)

0.421 (0.286)

Career at same university (doctoral)-0.141 (0.397)-0.0103 (0.401)-0.173 (0.399)-0.269 (0.312)-0.110 (0.330)-0.331 (0.314)

All careers at the same institution

0.186 (0.349)

0.154 (0.341)

0.201 (0.348)

0.239 (0.286)

0.236 (0.283)

0.254 (0.286)

Doctoral degree from foreign country-0.490 (0.947)-0.346 (0.930)-0.467 (0.944)-0.866 (1.023)-0.633 (1.011)-0.850 (1.022)

Research fellow and assistant (cross-term)

1.340 (0.993)

1.405 (0.961)

1.577 (1.014)

1.393* (0.779)

1.337* (0.785)

1.701** (0.803)

Constant-0.648 (0.771)-0.722 (0.758)-0.740 (0.773)-2.040*** (0.731)-2.287*** (0.732)-2.222*** (0.742)

Standard errors in parentheses, * p<0.1, ** p<0.05, *** p<0.01.

8

Table 9: Estimated results for number of coauthors (Only DMSc)
Tobit model

Poisson model

Model 1Model 2Model 3Model 1Model 2Model 3

Research Fellowship

1.395 (1.097)

1.097 (1.066)

0.726 (1.119)

0.323 (0.240)

0.238 (0.237)

0.160 (0.251)

Diversity of career-0.415 (0.303)

Administrative agency (first)-0.103 (0.0761)-1.819 (1.484)

Administrative agency-0.699 (0.530)-0.844 (0.936)

Company (first)-0.246 (0.258)

6.186*** (2.092)

Company

1.043*** (0.347)
2.654** (1.290)

Independent administrative agency (first)

0.504** (0.255)

1.308* (0.718)

Independent administrative agency

0.292* (0.159)-0.462 (0.319)

Assistant experience-0.181 (0.295)

0.0410 (0.292)-0.0944 (0.295)-0.114 (0.0809)-0.0422 (0.0712)

0.0126 (0.0724)-0.0217 (0.0721)

Foreign research experience

0.0660 (0.290)

0.0834 (0.288)

0.100 (0.288)

0.0160 (0.0705)

0.0205 (0.0717)

0.0239 (0.0707)

Experience as practical physician

0.169 (0.262)

0.260 (0.256)

0.234 (0.261)

0.0427 (0.0632)

0.0642 (0.0633)

0.0591 (0.0636)

Male gender

0.0107 (0.821)-0.0969 (0.807)-0.0102 (0.813)

0.00199 (0.191)-0.0245 (0.191)-0.00116 (0.191)

Amount of Grants-in-Aid-0.0105 (0.0503)-0.0128 (0.0494)

0.00666 (0.0503)-0.00315 (0.0120)-0.00365 (0.0119)

0.00105 (0.0121)

Career at same university (graduated)-0.0324 (0.454)-0.00263 (0.450)

0.120 (0.454)-0.00760 (0.108)-0.000945 (0.110)

0.0339 (0.111)

Career at same university (doctoral)

0.151 (0.515)

0.363 (0.520)

0.0245 (0.513)

0.0402 (0.124)

0.0931 (0.129)

0.00389 (0.125)

All careers at the same institution

0.263 (0.452)

0.417 (0.442)

0.296 (0.448)

0.0632 (0.107)

0.100 (0.107)

0.0711 (0.108)

Doctoral degree from foreign country-0.868 (1.228)-0.712 (1.206)-0.842 (1.215)-0.256 (0.340)-0.219 (0.340)-0.250 (0.340)

Research fellow and assistant (cross-term)

0.262 (1.288)

0.671 (1.245)

0.942 (1.306)

0.0249 (0.278)

0.136 (0.273)

0.192 (0.287)

Constant

3.976*** (0.999)

3.502*** (0.982)

3.702*** (0.996)

1.381*** (0.236)

1.264*** (0.236)

1.314*** (0.238)

Standard errors in parentheses, * p<0.1, ** p<0.05, *** p<0.01.

9