PhD Students’ Revealed Preferences Over Employment Outcomes and Their
Job Mobility
Annamaria Conti1 and Fabiana Visentin2
Abstract
We conduct a revealed preference analysis that infers PhD students’ preferences
from their career choice under different cohort states. Using data from two major
European universities, we find that the choice set over which students express
their preferences is more elaborate than the simple university-industry dichotomy.
Specifically, PhD students prefer positions in R&D-intensive companies to
employment in low-ranked universities and in non-R&D-intensive firms.
Moreover, positions in R&D-intensive companies and in highly-ranked
universities are equally appreciated. Regarding job mobility, the cohort size of
PhD graduates who initially held postdoctoral positions in low-ranked
universities, negatively affects their likelihood of switching to R&D-intensive
companies or attaining faculty positions.
1. Introduction
The economic literature has expressed concern regarding the increasing imbalance between the
supply of PhD students and the availability of permanent academic positions (Freeman et al.,
2001). The worry is that PhD students may be forced to accept employment for which they are
over-qualified and, hence, be mismatched. For instance, Stephan (2012) remarked that at the end
of the 1960’s, more than 50% of the life science PhD graduates had settled into a tenure-track
faculty position within six years from graduation. However, this percentage had dropped to 15%
by 2006. Despite the concern that PhD graduates may be mismatched, empirical evidence on
PhD students’ preferences over career outcomes is limited. Do PhD students prefer to work in
academia as opposed to working in the industry sector? Are positions in low-ranked universities
1
Conti: Scheller College of Business, Georgia Institute of Technology, Atlanta, Georgia, USA. Email:
[email protected].
2
Visentin: Chair in Economics and Management of Innovation, École Polytechnique Fédérale de Lausanne,
Lausanne, Switzerland. Email: [email protected].
and those in industry equally appreciated? Do they evaluate equally positions in R&D-intensive
companies and those in non-R&D-intensive companies? Inferring information about students’
preferences is a challenging task because they are highly correlated with confounding factors,
such as students’ skills, economic conditions, or supervisors’ connections (Fox and Stephan,
2001).
In this study, we conduct a revealed preference analysis that infers PhD students’
preferences at graduation from their career choice under different cohort states. The premise is
that, ceteris paribus, the choice set available to PhD students who graduate from large cohorts is
more restricted than the one available to students who graduate from smaller cohorts. This is
because large cohorts entail either an increased competition for the students’ most preferred
positions, thereby reducing the chances that the students obtain them, or a decline in the relative
salary offered for these positions, thereby making them less attractive. Accounting for those
factors that are correlated with students’ selection into cohorts and with their career attainments,
the students’ preferred positions are revealed to be the ones that are less frequently attained when
the students’ cohorts are large, and more frequently so, when cohort size is reduced.
Having inferred the PhD students’ preferences at graduation, it becomes interesting to
understand how their cohort size affects their job mobility. Consider, for a moment, those PhD
graduates who have attained an initial position in their least preferred employment categories.
How does their cohort size affect their ability to switch to better positions? A priori, it is not
clear what to expect. The effect may be negative if PhD students who graduated from larger
cohorts are confronted with a large number of competitors each time they attempt to improve
their initial position. Additionally, the effect could be negative because PhD students who
graduate from large cohorts are assigned to lower-value tasks or positions than their colleagues
who graduate from smaller cohorts (Gibbons and Waldman, 2004; Jovanovic, 1979). However, it
could also be that PhD graduates who are forced to a given position by their cohort size have a
higher incentive to invest in human capital than those who graduate from smaller cohorts. This
investment, in turn, increases their chances of improving their initial position. We devote the
second part of our paper to addressing this question.
In our analysis, we examine a sample of 2,345 PhD students who graduated during 19992009 from two major European universities: the Swedish Chalmers University of Technology
2
(Chalmers) and the Swiss Federal Institute of Technology in Lausanne (EPFL). We chose these
universities because they have a number of characteristics in common, which range from them
being leading universities in their own countries, to their specialization in science and
engineering disciplines, and to their active involvement with the industrial sector. For the PhD
graduates in our sample, we collected detailed information on their careers from the moment
they graduated until the beginning of 2013. The richness of our data allows us to go beyond the
much debated distinction between employment in public research institutions and that in the
private sector (Dasgupta and David, 1994). For instance, we rank universities and public research
institutions according to their publications3. We also observe positions in the public
administration, schools, and teaching colleges4. Moreover, within industry we distinguish
between employment in R&D-intensive companies, startups, and non-R&D-intensive
companies. This fine-grained categorization is an important contribution to the existing
literature. Indeed, within industry and also within academia, there is an ample spectrum of
employment categories which differ in a number of significant respects. For instance, jobs in
R&D-intensive firms may be more similar to those in academia than to positions in non-R&Dintensive firms (Stephan, 1996). Likewise, employment in startups could be more similar to the
employment in R&D-intensive firms than to that in non-R&D-intensive firms.
We initially focus on the PhD students’ first position after graduation. We estimate a
series of multinomial logit models in which we relate the PhD graduates’ career outcomes to
their cohort size and other controls. We have an unusually large set of background variables that
allow us to account for factors that may be correlated with the students’ selection into cohorts
and with their employment attainments. For instance, we include measures for employment
demand conditions at the moment a student begins her PhD and at the time she enters the labor
market. Moreover, we control for the student’s research skills and working experience. We also
control for supervisors’ characteristics, such as, their publication and patent output or their
participation in industrial projects. We find that when a PhD graduate’s cohort size is large, she
is less likely to be employed in an R&D-intensive company, as opposed to, working in low3
Unless otherwise specified, we shall include in the “university” category universities as well as public research
centers.
4
Unless otherwise specified, we shall include in the “public administration” category occupations in schools and
teaching colleges.
3
ranked universities, non-R&D-intensive companies, startups, and public administration.
However, she is no less likely to find a position in an R&D-intensive company, as opposed, to a
position in a highly-ranked university. We deduce that employment in R&D-intensive companies
is preferred to employment in all the other positions, except those in highly-ranked universities.
Our preference ranking is consistent with the fundamental transitivity axiom of preference
relations (Samuelson, 1938; Koo, 1963). In fact, we find that positions in highly-ranked
universities are preferred to the same employment categories as those to which positions in
R&D-intensive companies are preferred. Finally, and in line with the transitivity axiom, we find
that employment in low-ranked universities is not preferred to positions in non-R&D-intensive
companies, startups, and public administration. When we analyze PhD graduates in engineering
and in basic sciences, separately, we find that PhD graduates in these categories share similar
preferences. However, the results on the attractiveness of employment in highly-ranked
universities, relative to positions in non-R&D intensive-firms, is strongest for graduates in basic
sciences than for those in engineering. This last finding is most likely due to the fact that, for any
level of the graduates’ cohort size, the proportion of those who are employed in industry is larger
in engineering than in basic sciences.
Our results provide an important contribution to the existing literature on PhD students’
preferences. For instance, Sauermann and Roach (2012) have conducted a survey on PhD
students’ preferences, in which they ask the respondents to rate a number of employment
options. They find that, in general, academic research careers are highly regarded by PhD
students and supervisors play an important role in encouraging these career choices. Our
methodology has three fundamental advantages over their stated preferences’ approach. First, it
is not sensitive to the way survey questions are asked. Second, it does not encounter the problem
that respondents’ reports may be motivated by factors, such as signaling, that have little to do
with disclosing preferences’ information (Beshears et al., 2008). Third, our revealed preference
ordering is not confounded by constraining factors, such as, labor market conditions, PhD
students’ abilities, and supervisors’ and affiliations’ characteristics. Our study also differs from
the one by Sauermann and Roach (2012), in that it examines a more detailed PhD students’
choice set which distinguishes between R&D and non-R&D-intensive companies and between
highly-ranked and low-ranked research institutions. Within the literature stream of students’
preferences, a number of studies have examined the match between ex-ante stated students’
4
preferences and ex-post labor market outcomes (Agarwal and Ohyama, 2012; Sauermann and
Roach, 2013; Pellens et al., 2013). In general, they find that graduates with ex-ante preferences
for research jobs are less interested in pecuniary rewards. We are not interested in this match,
which we note is highly affected by a number of confounding factors. Rather, we use labor
outcomes to infer the students’ preferences.
In addition to being a mechanism that allows PhD graduates to reveal their preferences,
the graduates’ cohort size directly affects their post-degree job mobility. Given the structure of
our data, PhD students are observed for a minimum period of three years and a maximum period
of thirteen years, after graduation. Hence, in the second part of the paper, we examine the impact
of PhD graduates’ cohort size on their medium-term employment attainments. Our analysis
shows that for those PhD graduates who initially held postdoctoral positions in low-ranked
universities, their cohort size negatively affects their likelihood of switching to R&D-intensive
firms or of attaining faculty positions. These effects are consistent with the hypothesis that PhD
holders from large cohorts are assigned to lower-value tasks or positions than their colleagues
who graduate from smaller cohorts. In fact, when we examine their research output after
graduation, we find that, ceteris paribus, PhD graduates’ cohort size adversely impacts their
publication production.
This second set of results contributes to the literature about the impact of economic and
cohort conditions, at graduation, on students’ labor market outcomes (Welch, 1979; Beaudry and
DiNardo, 1991; Bowlus, 1995; Stephan and Ma, 2005; Oyer, 2006 and 2008; Kahn, 2010;
Oreopoulous et al., 2012). As a general finding, recessions or large cohorts negatively affect
students’ wages or career prospects, although the magnitude of the effect depends on the
students’ ability to change jobs within the first years after graduation. Among these studies, the
closest to ours is the one by Stephan and Ma who investigate the effect of PhD graduates’ cohort
size on their propensity to take a postdoctoral position and on their postdoc duration. In contrast
to this work, our focus is on how a PhD graduates’ cohort size affects their likelihood of
improving their initial career attainment. Moreover, we contemplate a finer classification of
employment categories and we also consider a larger set of PhD graduates’ characteristics.
The remainder of the paper is as follows. Section two describes the empirical context.
Section three discusses the empirical method and the results of the revealed preference analysis.
5
Section four examines the impact of PhD graduates’ cohort size on their longer term placements.
Section five concludes.
2. Sample description
We used data from EPFL and Chalmers because these universities have a number of important
aspects in common. First, they are leading institutes of technologies in their own countries.
Second, they hold high positions in a number of European university rankings. For instance,
according to the 2013 Leiden ranking of European universities, both EPFL and Chalmers are
ranked within the top 50 institutions5. Moreover, according to the same ranking, these
universities hold a high score for their collaborations with the industrial sector. As mentioned in
a recent article which appeared in Science, EPFL and Chalmers are also major recipients of
European Commission grants, which are awarded for research projects that often involve
industrial partners6. Their intense collaboration with industry is not surprising given that each of
these universities is surrounded by a dense cluster of companies. To cite a few examples, Volvo,
Ericsson, and SKF have research centers surrounding Chalmers, while Novartis, Nestle, and
Logitech are only a short distance from the EPFL campus. Finally, EPFL and Chalmers host
doctoral programs with similar characteristics. In each case, a PhD program lasts four years and
extensions are very rare. PhD applicants are each selected by a specific supervisor and work with
that supervisor for the duration of their program. The candidates spend most of their time doing
research, given that, they have fulfilled most of their course requirements during their master’s
degree (Conti et al., 2014).
To construct our sample, we obtained from EPFL and Chalmers the lists of PhD students
who had graduated during the 1999-2009 period. This amounts to 1,297 students at Chalmers
and 2,068 students at EPFL. We then collected information on the students’ careers through
extensive searches on their website, their supervisors’ websites, and their LinkedIn profiles.
When the students’ CVs were incomplete, we either contacted them or used sources, such as the
publication database Scopus, to determine their affiliations. In the end, we only retained students
for whom we had complete career information for every year from 1999 to January 2013. These
5
Additional information is available at http://www.leidenranking.com/ranking.
Please refer to http://news.sciencemag.org/people-events/2013/01/graphene-and-brain-modeling-project-winbillion-euro-science-contest.
6
6
students represent 70% of the initial sample; the percentage being 68% for Chalmers and 71%
for EPFL.
Of the total sample, 62.35% had graduated from EPFL, while the rest had graduated from
Chalmers. When classified by discipline, 42.35% had graduated in basic sciences (physics,
chemistry, mathematics, and life sciences), whereas 57.65% were in engineering. The majority
of the PhD students are male (78.93%). During their PhD, the majority the students (83.88%)
had only one supervisor assigned. For those who had more than one supervisor assigned, we
made extensive searches to discern the main supervisor. For this purpose, we used student
dissertations acknowledgements and publication data. From Scopus, we collected students’
publications (including conference proceedings) and found that, on average, they had produced
6.64 articles each during their PhD. The average is 6.54 for Chalmers and 6.71 for EPFL. The
mean publication count is 6.42 for engineering and 6.86 for basic sciences.
We now present descriptive statistics for the students’ main supervisor. The average
number of papers a supervisor had published in the five years prior to their students’ enrollment
is 21.16. This figure is 25.19 for EPFL and 17.65 for Chalmers. Some supervisors (15.03%) were
granted at least one US patent in the five years prior to a student’s entry into their PhD program.
The percentage is 17.29 for EPFL supervisors and 13.06 for Chalmers supervisors. A
considerable portion of supervisors (27.02%) had worked in the industry sector prior to their
current appointment or had founded technology startups. Finally, 12.42% had participated in
European projects with industrial partners.
Next, we provide information about the positions that our PhD students had attained, just
after graduating. The distribution of PhD students’ first position after graduation, by institution is
illustrated in Figure 1. It clearly reveals that the career patterns of PhD graduates at these
universities are similar. Approximately 54.24% of the graduates had found positions in
academia. The vast majority of them (93.16%) had started as postdocs. Moreover, a considerable
portion (44.02%) had continued to be affiliated with their original institution. Regarding the
other employment categories, 34.03% of the total PhD graduates were initially employed in the
industrial sector (excluding startups), 5.63% in startups, mainly as founders, and 6.10% in public
administration.
7
< Figure 1 about here>
To distinguish between R&D-intensive and non-R&D-intensive companies, we collected
information about the US patents that these companies were granted during the 1999-2009
period, as well as, information about their publications. A company was identified as R&Dintensive if it was in the last percentile of the distribution of companies with similar size, in
terms of patent or publication counts7,8.We used publication data, in addition to patent data,
because there are some companies that publish more than they patent. Based on this
classification, the percentage of PhD graduates that were initially employed in R&D-intensive
companies is 9.55%.
When we distinguish between students in engineering and basic sciences, we observe that
the percentage of PhD graduates who attained a first position in a university is 61.33 in basic
sciences and 49.04 in engineering. Furthermore, the percentage of PhD graduates who attained a
first position in an R&D-intensive company is 10.36 in engineering and 8.46 in basic sciences.
Finally, 28% of the PhD graduates in engineering and 19% of those in basic sciences were
employed in non-R&D-intensive firms, after graduation.
Regarding job mobility, about 38.17% of our sample PhD graduates had changed their
initial employment category. These switches occur, on average, 3.01 years from graduation. Of
the 1,272 PhD graduates who were initially employed in universities, 490 had changed
employment category. The number is 312 for EPFL PhD graduates and 178 for those from
Chalmers. The majority of those who had moved out of academia switched to non-R&Dintensive firms. Of the ones who remained in academia, 264 had become professors. This last
figure is 114 for EPFL graduates and 150 for those from Chalmers.
7
Companies were partitioned into large, medium, and small size categories. According to Eurostat, small size
companies have less than fifty employees; medium size companies have an employee number larger than 49 and
smaller than 250. Finally, large companies have more than 249 employees.
8
As shown in the next sections, we present several robustness checks, which modify the cutoffs initially employed.
8
3. Revealed preference analysis
3.1 Econometric Methodology
To derive PhD students’ preferences over career outcomes, we estimate a series of multinomial
logit models. We examine a student’s first position, following graduation, and consider six
mutually exclusive employment categories. The first category encompasses positions in lowranked universities and public research centers. The second includes positions in highly-ranked
universities and public research centers. The third consists of positions in non-R&D-intensive
companies. The fourth embraces positions in R&D-intensive firms. The fifth consists of
positions in technology startups. Finally, the sixth includes positions in public administration,
schools, and teaching colleges9.
To classify universities into highly-ranked and low-ranked, we used their publication
counts10 distinguishing by research field. We, thus, constructed a dummy that equals one in the
case of universities that are in the last quartile for their number of articles published in the same
field as PhD student i. For PhD students who had pursued their postdoc careers in the same
institution from which they had graduated, we set the indicator variable to zero. While this may
be surprising given the high-rank of EPFL and Chalmers, discussions with these universities’
administrative staff revealed that promising PhD graduates are strongly encouraged to spend at
least some time in another institution11. There may be only two exceptions to this practice. The
first, in the case of EPFL, is when a PhD graduate is employed in a different research group than
the one of her supervisor. The second exception, which regards Chalmers, is when a PhD
graduate is hired as faculty right after graduation12. In both cases, the interested PhD graduates
could be classified in the highly-ranked-institution category. In a complementary analysis, we
modify our initial classification and take these exceptions into account.
9
Because we have yearly data, we do not observe unemployment periods. However, discussions with both
universities’ administrators revealed that these periods typically last less than six months.
10
Publication information was retrieved from Scopus for the period 1996-2006. The fields we consider are physics,
mathematics, chemistry, material science, computer science, and engineering.
11
In the case of EPFL, PhD graduates who continue to be affiliated with this institution during their postdoc are
discouraged from applying for tenure positions.
12
This last practice was more frequent in the early years of our sample than in the most recent ones. Upon
inspection, we did not observe any case in which a Chalmers PhD student was hired as postdoc in a different group
than the one of her supervisor.
9
The equation we estimate is:
Pr(yi=m|xi)=
(
∑
)
(
(1)
)
where Pr(yi=m|xi) is the probability that PhD graduate i attains position m, given xi, and xi is a
vector of covariates which includes, among others, the count of students who graduated in year t,
in the same field as i13. This variable, which we denote as PhD Cohort Sizeit, is our measure for
cohort size. The PhD Cohort Sizeit count includes only those PhD holders who are potential
competitors to graduate i in the job market. In the case of EPFL, this measure encompasses
students who had graduated from EPFL as well as students from the Swiss Federal Institute of
Technology in Zurich (ETHZ). In the case of Chalmers, we include PhD graduates from
Chalmers and from the Swedish Royal Institute of Technology (KTH), which offers very similar
doctoral programs as Chalmers.
Previous studies have shown that a multinomial logit model can be interpreted as a utility
maximization model (McFadden, 1974). The idea is that if there are j choices available to a
rational individual i, the individual opts for choice m, if
>
, for all
utility from a generic choice j can be decomposed into a part labeled
some parameter and a part
that we model as random. If
≠
. Individual i’s
that we observe up to
is independently, identically
distributed extreme value, the probability that the individual opts for m
Pr(
=
) = Pr(
>
=
) = Pr$
−
>
!"## ≠
)
(2)
or
Pr(
−
&
(3)
can be modeled as in expression (1). In our setting, for instance, the probability that a PhD
student is employed in a highly-ranked university after graduation corresponds to the probability
that the utility she derives from that position exceeds that of all other occupations.
Estimating PhD graduates’ choice behavior in relation to cohort size poses two main
challenges. First, selection into a given cohort is not random and the distribution of individuals’
13
We consider the following two fields: basic science and engineering.
10
characteristics changes across cohorts. For instance, employment demand conditions may affect
both students’ decisions to enter a given cohort and their post-graduation career. Second, it could
be that certain students adjust their exit from their PhD program, based on the economic
conditions at the time they are expected to graduate. Namely, if a student expects to graduate in a
recession, she could delay her graduation date until the recession is over, to find a better
placement. While, in our analysis, we definitely have to deal with the first challenge, the second
one does not represent a concern, given that, at both EPFL and Chalmers the PhD duration is
fixed.
To address the first concern, we include in our regressions proxies for individual
characteristics that may be correlated with both selection into a cohort and career outcomes.
Specifically, we control for a student’s gender, age, whether the student is foreign-born, and
whether she worked prior to beginning her doctoral program. Within the foreign student
category, we distinguish between those from EU-15 countries and the remainder. In fact,
employment constraints for the first category of students are less stringent than for the second
category. We also use the PhD student’s publication count as a proxy for her research talent.
Moreover, we built an indicator variable that equals one if an individual was granted at least one
patent, had published articles with industrial partners, or had worked with a company while
attending her PhD program. This last measure is a proxy for the degree to which a student’s
research can be used in industry applications.
We also include measures for a PhD student’s supervisor characteristics. In fact, it could
be that a prospective student enrolls in year t to work with a specific professor and, successively,
that professor helps the student to find a job. Thus, we include measures for the productivity and
status of a supervisor, as well as, proxies for her connections with the industrial sector. In
particular, we control for a supervisor’s pre-sample count of publications and patents, whether
the supervisor had worked in industry, and whether she had been involved in European projects
with industrial partners.
To address the concern that students’ selection into cohorts is affected by expected
employment demand conditions at graduation, we include graduation year fixed effects.
Moreover, similar to Kahn (2010), we include a categorical variable that assumes increasing
11
values the higher the GDP growth of a student’s graduation country14is. We also control for the
availability of postdoctoral positions at a PhD student’s graduation university, using the number
of professors affiliated with EPFL and Chalmers, in the field in which the PhD student is
specialized. We can plausibly expect that the larger this number is the higher is the availability of
postdoc positions. Because PhD graduates may seek postdoc positions abroad, especially in the
US, we built a measure for the availability of these positions. The measure is defined as the
difference between the number of postdocs hired, on a given year, by US research institutions
and the number of US students who, in that year, obtained their PhD in the same field as student
i. Despite the inclusion of this control, we have strong priors that its impact on PhD graduates’
career attainments should be small. In fact, in the vast majority of cases, PhD holders from
Switzerland and Sweden who relocate abroad for their initial postdocs are supported by grants
from their graduation country. Hence, they should be less sensitive to the availability of positions
in the US. To control for the availability of positions in R&D-intensive companies, we used the
number of patent applications filed from Sweden and Switzerland at the European Patent Office
(EPO) in the same year in which i had graduated. We use patent data from the EPO rather than
from the US Patent Office (USPTO) because the EPO started collecting information on patent
applications at an earlier date than the USPTO.
Because prospective PhD students tend to form expectations about employment
conditions at graduation based on employment conditions at enrollment, we add proxies for the
latter. We include the same categorical variable for a country’s GDP growth as the one above,
except that this time the GDP growth is measured in the student’s enrollment year. Moreover,
given the considerable percentage of foreign students in our sample, we include the GDP growth
of a student’s origin country. Finally, we add the count of PhD students who had graduated
during the enrollment year of student i and during the year before.
In all regressions, we control for university-fields fixed effects. The fields that we
consider are engineering and basic sciences. We provide descriptive statistics in Table 1 and
details on the variables’ construction in Table A1 of the Appendix.
< Table 1 about here>
14
Using unemployment data leads to the same results.
12
3.2 Results
Table 2 presents the results from estimating equation (1) for a PhD student’s initial
position, after graduation. Panel A displays the results using as base outcome students’ positions
in low-ranked universities. Results in Panel B are relative to positions in highly-ranked
universities. Panel C displays the results using as base outcome positions in non-R&D-intensive
firms. Panel D presents the results using as a base outcome positions in R&D-intensive firms.
Results in Panel E are relative to employment in startups. Finally, results in Panel F are relative
to positions in public administration. The coefficients we report are relative risk ratios. Ratios
greater than one imply that an increase in the regressor leads to a higher probability that outcome
m is chosen over outcome n, with the opposite for ratios less than one. We cluster standard errors
at the level of the PhD students’ supervisors15.
Table 2 shows that when a PhD graduate’s cohort size is large, the odds of being
employed in an R&D-intensive company decrease relative to working in either a low-ranked
university or a non-R&D-intensive company, or a startup, or public administration. Adding 10
members to the cohort size decreases the graduate’s odds of being employed in an R&Dintensive company by a factor of 0.85, relative to working in a low-ranked university or in a
startup, by a factor of 0.87, relative to working in a non-R&D-intensive company, and by a factor
of 0.86, relative to working in public administration. When we use positions in highly-ranked
universities as a reference category, we do not find a significant impact of cohort size on the
odds of being employed in an R&D-intensive company. Additionally, cohort size does not
significantly affect the odds of attaining positions in low-ranked universities relative to being
employed in either non-R&D-intensive companies or startups, or public administration. From
these findings, we infer that occupations in R&D-intensive companies are preferred to positions
in other employment categories except those in highly-ranked universities. Moreover, positions
in low-ranked universities are not preferred to occupations in either non-R&D-intensive
companies or startups, or public administration.
For our preference ranking to be valid, we need to ensure that it is consistent with the
transitivity axiom of preference relations (Samuelson, 1938; Koo, 1963). In other words, we
15
We obtain very similar results if we cluster standard errors at the level of PhD students who graduated during the
same year and from the same universitydepartment.
13
need to verify that if PhD students at graduation evaluate positions in R&D-intensive companies
and those in highly-ranked universities equally, then they prefer positions in highly-ranked
universities to employment in non-R&D-intensive companies, startups, the public administration.
As shown in the table 2, a PhD graduate’s cohort size has a negative and significant impact on
the odds of working in highly-ranked universities, relative to working in non-R&D-intensive
companies, in low-ranked universities, or in public administration. When the reference category
is employment in startups, the coefficient of cohort size is less than one, as expected, but not
statistically significant. The reason, in all probability, is that the startup category contains
relatively few observations (5.6% of the total count). Hence, estimations using this category as
reference are less precise. In the robustness checks that we present in the next section, we
distinguish between R&D-intensive and non-R&D-intensive startups. We combine the R&Dintensive-startups with the R&D-intensive firms to create the new R&D-intensive category. We
do the same with the non-R&D-intensive firms to create the new non-R&D-intensive category.
With this new classification, we obtain as results that, relative to positions in highly-ranked
universities, the effect of cohort size on the odds of being employed in R&D-intensive firms
remains insignificant and the effect on the odds of being employed in non-R&D-intensive firms
continues to be positive and significant16.
The highlighted results are presented in Figure 2 where we plot the predicted
probabilities for each employment category as a function of cohort size. As shown, the likelihood
of employment in highly-ranked universities and in R&D-intensive-firms decreases with cohort
size. Conversely, the likelihood of employment in low-ranked universities, non-R&D-intensive
firms, startups, and the public administration increases with cohort size.
Concerning the controls, we underline some interesting results. For instance, foreign PhD
graduates, especially those from outside of the EU-15, are less likely to be found in public
administration, while women are more likely to be employed in this category. Additionally,
women are more likely to attain positions in universities, regardless of their rank, than positions
in non-R&D-intensive companies. A PhD graduate’s publication count positively affects the
likelihood of employment in universities, regardless of their rank, relative to employment in the
16
We attempted to analyze the two universities, separately. While the results on the coefficients’ signs remain
consistent, the significance is lowered due to the reduced sample size.
14
industrial sector. This positive effect is strongest for highly-ranked universities. Moreover, it is
larger when university employment is compared to employment in non-R&D-intensive firms
rather than to employment in the R&D-intensive ones. Finally, the PhD graduate’s publication
count has a positive effect on the odds of employment in highly-ranked universities relative to
employment in low-ranked universities. As an additional result, individuals with prior experience
in industry or who had pursued more applied research during their PhD are more likely to work
in the industrial sector than being employed in universities. We also find that PhD graduates
whose supervisors were granted patents are more likely to be employed in R&D-intensivecompanies than in universities, regardless of their rank. Concerning our proxies for the
availability of positions at a student’s graduation, we find that the higher a graduation country’s
GDP growth is, the more likely the student is to be employed in R&D-intensive-companies,
relative to low-ranked universities. Similarly, a country’s patent application stock positively
affects the odds of employment in R&D-intensive-companies relative to employment in
universities.
As a last note, we remark that for our multinomial logit model to correctly estimate the
odds associated with the PhD graduates’ occupational attainments, it has to exhibit the
Independence of Irrelevant Alternative property (Luce, 1959). To verify that this property is
satisfied, we performed a Hausman test of the hypothesis that the parameter estimates obtained
on a subset of alternatives do not significantly differ from those obtained with the full set of
alternatives (Hausman and McFadden, 1984). In practice, we compared the coefficients in the
original model to the ones we would have obtained by eliminating one employment category at a
time, and tested that the difference in the coefficients is not significantly different from zero. In
all instances, we could not reject the null hypothesis that the coefficients are the same with pvalues greater than 0.52.
< Figure 2 about here>
< Table 2 about here>
As mentioned above, it may be more appropriate to include in the highly-rankedinstitutions category Chalmers PhD graduates who were hired as faculty in their own university
and EPFL graduates who had stayed at their own affiliation, but had moved to a different
15
research group than their supervisor’s. By doing so, we move 63 students, 49 from Chalmers and
14 from EPFL, to the highly-ranked-university category. The results are reported in Table 3. For
the sake of brevity, we only show the coefficients of interest. As expected, they remain very
similar to those in Table 2. The only exception is that, now, the impact of cohort size on the
likelihood that a PhD graduate is employed in a highly-ranked university rather than in public
administration is not statistically significant, while before it was marginally so. This outcome is
likely to stem from the small size of the public administration category (5% of the total sample
of male PhD graduates) and from the fact that this category includes very heterogeneous
employment profiles.
< Table 3 about here>
3.3 Robustness checks
In this section, we present the results of a series of robustness analyses. We use the same
distinction between employment in highly-ranked and low-ranked universities as in Table 2.
However, analyses available upon request show that, by using the classification adopted in Table
3, the results do not change.
Excluding female PhD graduates from the sample
We begin by excluding female PhD graduates from our sample. One reason for doing so is that
we do not observe whether women had taken maternity leaves during their PhD program.
Maternity leaves may allow women to postpone their entry into the job market and “self-select”
into cohorts of smaller size, which, in turn, could favor the attainment of their most preferred
positions17. An additional reason is that our sample underrepresents women. In fact, the
percentage of female PhD graduates whom ended up being included in our sample is 65 of the
total females at the study’s start, while the percentage of men is 71 of the total males starting18.
As shown in Table 4, the results for the sample of male PhD graduates remain very similar to
those presented in Table 2.
17
In Sweden (but not in Switzerland) men as well as women are allowed to take parental leave. However, statistics
show that, at least for the period we observe, the percentage of parental leave days taken by men is much lower than
the ones taken by women. For more information, please refer to http://rsa.revues.org/456.
18
In analyses available upon request, we estimated a linear model for the probability that a PhD graduate is included
in our sample. Having controlled for graduation year and university-field fixed effects, the coefficient for the female
dummy appeared to be negative and significant.
16
< Table 4 about here>
Reclassifying startups
We then classify startups into R&D and non-R&D-intensive. Accordingly, we collected
information about their publications and the US patents that they were granted as of January
2014. We considered as R&D-intensive those startups that had at least one publication or a
patent. We then included R&D-intensive startups in the category of R&D-intensive firms and the
remaining startups in the category of non-R&D-intensive firms. The results are in Table 5. They
are very similar to the ones presented in Table 2. Positions in highly-ranked universities and in
R&D-intensive firms continue to be preferred to the other employment categories.
< Table 5 about here>
Including all faculty positions in the category of employment in highly-ranked universities
As a second robustness check, we include all PhD students’ faculty positions, after graduation, in
the category of employment in highly-ranked universities. The results are presented in Table 6.
The main results continue to hold. In some cases, the significance of the interest coefficients is
lower than the one for the coefficients in Table 2. This outcome is not unexpected. In fact, the
majority of the PhD students who, after graduation, were employed as professors had joined lowranked universities. These universities were typically located in less developed countries.
< Table 6 about here>
Including more detailed field fixed effects in the regressions
In Table 7 we report the results of a multinomial logit model in which we included more detailed
university-field fixed effects. Specifically, we distinguished between physics, chemistry,
mathematics, life sciences, material sciences, mechanical engineering, electrical engineering,
micro engineering, and civil engineering. The results continue to hold.
< Table 7 about here>
17
Modifying the classification of R&D-intensive and non-R&D-intensive firms using information
about their sector of activities
In Table 8 we modify our initial categorization of R&D-intensive firms and the non-R&Dintensive ones. This time a company c is classified as R&D-intensive if it is in the last percentile
of the distribution of companies with similar size and in the same sector as c, in terms of patent
or publication counts. Using information from the Million Dollar Database and Amadeus, we
assigned our companies to the following sectors: i) computer equipment, electronic, and other
equipment; ii) transportation equipment; iii) measuring, analyzing, and controlling instruments;
iv) metal products; v) food and tobacco products, and chemicals; vi) construction; vii) mining;
viii) retail trade; ix) finance, insurance, and real estate; x) wholesale trade and services19. With
the new classification, the percentage of R&D-intensive companies decreases from 9.9 to 9.4.
Once again, the results are very similar to the ones presented in Table 2.
< Table 8 about here>
Reducing the patent and publication cutoffs for defining R&D-intensive companies
Finally, in Table 9, we classified a company as R&D-intensive if it was in the 75th percentile of
the distribution of companies with similar size, in terms of patent or publication counts. With this
categorization, the percentage of R&D-intensive companies increases from 9.9 to 14.6. The
preference ranking does not change.
< Table 9 about here>
3.4 Distinguishing between engineering and basic sciences
In this section we investigate whether the PhD students’ preferences previously identified vary
across students in basic sciences and engineering. We reproduce the same analyses as in Table 2,
distinguishing between PhD graduates in basic sciences and engineering. The results are reported
in Table 10. Interestingly, we find that PhD students’ preference rankings across the two fields
19
For our categorization we closely followed the Standard Industrial Classification (SIC). Hence, category i)
corresponds to the SIC categories D35-D36. Category ii) corresponds to D37. Category iii) corresponds to D38.
Category iv) corresponds to D32-D34. Category v) corresponds to D20-D31. Category vi) corresponds to C.
Category vii) corresponds to B. Category viii) corresponds to G. Category ix) corresponds to H. Category x)
corresponds to F and I.
18
are similar. Irrespective of the field analyzed, a PhD graduate’s cohort size negatively affects the
odds of employment in highly-ranked universities and R&D-intensive firms relative to lowranked universities and non-R&D-intensive firms. In general, however, the results regarding the
impact of cohort size on the likelihood that a PhD graduate is employed in highly-ranked
universities, relative to being employed in non-R&D-intensive companies, are more robust for
the basic science sample than for the engineering one. This reflects the fact that for any level of a
PhD graduate cohort size, the proportion of those that find employment in industry is higher in
engineering than in basic science.
< Table 10 about here>
The results of this analysis are summarized graphically in Figure 3, which plots the
predicted probability of attaining positions in each of the employment categories analyzed, as a
function of cohort size. Panel A reports the plots for PhD graduates in basic sciences, while
Panel B presents the results for engineering graduates. We obtain very similar results if we
include in the highly-ranked-university category those Chalmers PhD graduates who were hired
as professors in their initial affiliation and those EPFL graduates who continued to be affiliated
with their own university, but had moved to a different research group.
< Figure 3 about here>
4. Cohort size and longer term employment attainments
4.1 Econometric Methodology
In section 3, we showed that positions in highly-ranked universities and in R&D-intensive firms
are the PhD students’ most preferred choices when they graduate. Conversely, positions in lowranked universities, non-R&D-intensive firms, startups, and public administration are the least
preferred. In this section, we examine how a PhD graduate’s cohort size affects her ability to
improve her initial position. A priori, it is not clear what to expect. On the one hand, the effect
could be negative if PhD graduates, who try to improve their initial situation, have to compete
with their cohort members each time they apply for jobs. In fact, if they are mismatched, it is
plausible to assume that their colleagues are also mismatched. Another reason to expect a
negative impact is that PhD students who graduate from large cohorts may be assigned to lower19
value tasks or positions than their colleagues who graduate from smaller cohorts (Gibbons and
Waldman, 2004; Jovanovic, 1979). However, we could also expect a positive relationship if PhD
graduates who are in their least preferred situation, because of their cohort size, are more willing
to invest in human capital than their colleagues who graduate from smaller cohorts.
To address our research question, we restrict the analysis to PhD students who found
postdoctoral positions in low-ranked universities after graduation. We decided to confine our
attention to this graduate category because they represent the largest group among the ones we
are interested in. Moreover, during our sample period, we observed enough mobility from their
initial employment category to another one and from their initial job position to a faculty
position. We estimate Cox proportional hazard models for the hazard that a PhD graduate, who
was initially employed as a postdoc in a low-ranked university, switches to: i) a highly-ranked
university as a postdoc, ii) a faculty position, regardless of the affiliation’s rank, iii) a position in
an R&D-intensive company. Given that we consider three different hazards, we estimate three
similar equations in the following form20:
h(t|xi) =h0(t)exp(xiγm)
(4)
where h(t|xi) is the hazard of switching to another position, h0(t) is the baseline hazard (i.e. the
hazard when all covariates are equal to zero), and xi is a vector of covariates relative to PhD
graduate i. We use the same controls we listed in equation (1). Moreover, we include a dummy
that equals one if the PhD graduate i’s first employment was in her graduation’s country.
Additionally, we include measures for employment conditions and cohort size in the current
year, t. Specifically, we build a variable that is defined as the average between the current year
GDP growth for the country in which PhD graduate i is located and the same year GDP growth,
for the country in which the PhD graduate was previously employed. We also include the current
year’s count of patent applications filed at the EPO from Sweden and Switzerland. We add the
net supply of US postdoc positions in year t, as well as, a count of PhD students who graduated
in t and in t −1, from the same universities that we considered for computing PhD graduate i’s
cohort size. Finally, we include the number of professors employed in i’s graduation university at
20
The results are robust to estimating a probit model and including a variable for a PhD graduate’s experience in the
job market.
20
the year t, for the field in which i is specialized. Each model contains the graduation years as
well as the university-field fixed effects.
4.2 Results
The results are reported in Table 11. We cluster standard errors around individuals.
Column I displays the results for the hazard of switching to a highly-ranked university. In our
sample, this switch occurs 2.0% of the time. Column II reports the results for the hazard of
moving into a faculty position, which occurs 2.2% of the time. Column III presents the results
for the hazard of transitioning to an R&D-intensive firm. This transition takes place 1.21% of the
time. Under each column, we first report the results for the entire sample of PhD graduates and
then for male PhD graduates only. Estimates are presented in terms of their effect on the odds of
switching job positions. Thus, a coefficient smaller (larger) than one reflects a negative (positive)
effect.
As shown, the PhD graduates’ cohort size does not have a significant impact on the
probability that they transition to a postdoctoral position in a highly-ranked university from an
initial postdoctoral position in a low-ranked university. Regarding the other outcomes, when we
restrict the sample to male PhD graduates, we find that their cohort size significantly and
negatively impacts the probability that they switch, at some point in time, to a faculty position or
to an R&D-intensive firm. The most likely reason why we observe that a PhD graduate’s cohort
size does not significantly affect her hazard of switching to a postdoc in a highly-ranked
university is that 67% of these positions are outside of an individual’s graduation country. This
percentage compares to 55% of the total professorship positions and to 29% of the positions in
R&D-intensive firms. Indeed, if a PhD graduate takes a second postdoc abroad, then the relevant
cohort members with whom she has to compete with are from outside her graduation country.
One reason why the results are stronger when excluding female PhD graduates is that they are
more likely to temporarily move out of the labor market by taking maternity leaves.
Regarding the results on the controls, we briefly mention that PhD graduates from outside
the EU-15 are more likely to switch to highly-ranked universities, at some point in time.
Moreover, the number of professors in the graduation university has a negative impact on the
probability that she transitions to a highly-ranked university. Regarding the hazard of switching
21
to a faculty position from an initial postdoctoral position in a low-ranked university, the PhD
graduate’s publications have a positive and significant impact. Finally, having being employed in
industry prior to beginning a PhD has a positive effect on the hazard of switching to a position in
an R&D-intensive firm.
< Table 11 about here>
In Table 12, we present the results redefining the category of PhD students who found
postdoctoral positions in low-ranked universities, after graduation. We now eliminate those
EPFL PhD graduates who were still affiliated with their graduation institution, but were
employed as postdocs in a different research group than the one of their supervisor. We only
present the results for the coefficients of interest. The results are very similar to the ones
provided in Table 11.
< Table 12 about here>
4.3 Exploring the drivers’ of the results
To explore the drivers’ of the results reported earlier in section 4.2, we investigate
whether the negative impact of PhD graduates’ cohort size on their hazard of switching to a
faculty position is due to the fact that these PhD graduates are assigned to low-value tasks or
positions when they graduate from large cohorts. To this end, we measure their research output
in the first two years after graduation by counting their publications. We account for a two-year
lag between the conclusion of a research project and the publication of its results21. We then
construct a dummy that takes the value of one if the PhD graduates had a publication count
higher than their field’s average. Hence, we estimate the following logit model:
Pr(yi=1|xi)=()
( ')
(5)
( ')
where the vector xi includes all the regressors we had in equation (1). The results are presented in
Table 13. In Panel A, we examine all PhD students who found postdoctoral positions in lowranked universities, after graduation. In Panel B, we exclude those EPFL PhD graduates who
were still affiliated with their graduation institution, but were employed as postdocs in a different
21
For this reason we exclude PhD students who had graduated in 2008 and 2009.
22
research group than the one of their supervisor. In each Panel, column I displays the results for
the entire sample, while column II presents the results for male PhD graduates only. As shown,
the PhD graduates’ cohort size has a negative impact on their research output. Overall, these
findings are consistent with the hypothesis that, ceteris paribus, when the PhD students’ cohort
size is large, they are likely to be assigned to low-value tasks or positions after graduation. These
assignments, in turn, may hinder their capacity to move to a better outcome, afterwards.
< Table 13 about here>
5. Concluding remarks
Researchers continue to argue strongly that because of the imbalance between the
availability of academic positions and the supply of PhD graduates, the latter are forced to accept
positions for which they are overqualified and, hence, end up being mismatched. However, to
fully understand the extent of PhD graduates’ labor market mismatches, it is first important to
appreciate their career preferences. We develop evidence relevant to this issue by implementing
a novel analysis that infers PhD students’ preferences, from their career outcomes under different
states of their cohort. Having devoted careful attention to factors that affect PhD students’
selection into cohorts, we find that the choice set over which students express their preferences is
more complex than the simple university-industry dichotomy. Specifically, PhD graduates are
less likely to be employed in R&D-intensive firms and in highly-ranked universities, when they
graduate from a large cohort. We,thus, infer that these positions are preferred to positions in lowranked universities, non-R&D-intensive firms, startups, and public administration. When we
consider, separately, PhD graduates in each field, we find that although they have a similar
preference ordering the relative attractiveness of employment in highly-ranked universities is
stronger for graduates in basic sciences than for those in engineering.
Given that large cohorts negatively affect their members’ ability to achieve their most
preferred positions, we devote the second part of the paper to examining whether these affects
are persistent. Specifically, we investigate the impact of cohort size on the likelihood that PhD
graduates, who were initially employed in their least preferred positions, attain better positions,
afterwards. We focus on those PhD graduates who held non-faculty positions in low-ranked
universities, after graduation. Controlling for individuals’ abilities, we find a negative
23
relationship between cohort size and the hazard that PhD graduates become professors or move
to R&D-intensive firms. Moreover, we observe that their research productivity, in the two years
after graduation, is negatively affected by their cohort size. Taken together, these results are
consistent with the hypothesis that large cohorts force their members to lower-value tasks or
positions and, thus, induce them to accumulate less human capital than PhD graduates from
smaller cohorts.
Four comments are in order, here. First, understanding PhD students’ preferences is
especially important in light of the role that PhD students play in the dissemination of academic
knowledge. Because this knowledge is in large part tacit and, hence, difficult to transmit, a
solution is to wrap it up within the PhD graduates and allow them spread that knowledge outside
of their affiliation (Stephan, 2007). We should plausibly expect that the knowledge transmission
efficiency is improved if PhD graduates are employed in positions that match their preferences
and their skills.
Second, prior knowledge of students’ preferences allows supervisors (and their
affiliations) to take measures that reduce job mismatches. As pointed out by Stephan and Levin
(2002), the relationship between PhD students and their supervisors is regulated by means of an
implicit pact which asserts that students provide long working hours in exchange for funding and
the assurance of interesting careers, afterwards. Violating this pact reduces the attractiveness of a
supervisor’s research group to future PhD applicants, with a resulting negative impact on the
research group’s productivity.
Regarding job mobility, given that cohort size conditions, at graduation, have lasting
effects on PhD graduates’ careers, one possibility is to increase the flexibility of the PhD
programs’ duration. In the universities we examined, like in many other universities, the duration
of a PhD program is fixed. This university policy may be relaxed, to a certain extent, in adverse
economic and cohort size conditions.
The last comment regards the methodology we used. As suggested earlier, a revealed
preference analysis does not suffer from the drawbacks that are inherent to a stated preference
analysis. For instance, it does not depend on the way questions are asked and it does not
encounter the problem that the respondents’ reports are often motivated by factors that have little
24
to do with disclosing preferences’ information. We used this methodology to infer PhD students’
preferences at two major European universities. While these universities possess several traits in
common with other institutes of technology, in future research it would be interesting to extend
our analysis to universities that pursue less applied research. Moreover, it would be interesting to
analyze how the PhD graduates’ preference rankings would change in the case of universities
that are not surrounded by clusters of R&D-intensive companies, such as ours. Future work
could profitably use larger samples and observe whether there is preference heterogeneity within
the basic science and the engineering disciplines.
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Appendix A: Variables’ description
Table A1 presents a description of the independent variables we use in our regressions.
< Table A1 about here>
27
Figure 1: Distribution of PhD students’ first position after graduation, by institution
EPFL
Chalmers
Predicted probability
Figure 2 – Predicted probabilities for each employment attainment
PhD cohort size (divided by 10)
Low-ranked universities or public research centers
Highly-ranked universities or public research centers
Non-R&D-intensive firms
R&D-intensive firms
Technology startups
Public administration, schools, teaching colleges
Predicted probability
Figure 3: Predicted probabilities for each employment attainment, by field
PANEL B: ENGINEERING
PANEL A: BASIC SCIENCES
PhD cohort size (divided by 10)
Low-ranked universities or public research centers
Highly-ranked universities or public research centers
Non-R&D-intensive firms
R&D-intensive firms
Technology startups
Public administration, schools, teaching colleges
PhD cohort size (divided by 10)
Table 1: Descriptive statistics
Variable
Obs.
Mean
Std.Dev.
Min
Max
PhD cohort size
2,345
232.28
70.74
63.00
338.00
Employment conditions at enrollment
GDP growth, at entry (Very low, Low, Medium, High)
GDP growth in master's country
# PhD graduations in student i 's enrollement year and in the prior year
2,345
2,345
2,345
1.95
2.70
439.95
0.69
2.15
130.82
0.00
-12.67
147.00
3.00
34.80
655.00
Employment conditions at graduation
GDP growth, at graduation (Very low, Low, Medium, High)
Net supply of postdoc positions in the US
# Professors in graduation's country
# Graduation country's EPO patent applications
2,345
2,345
2,345
2,345
1.90
5,826.62
108.74
3,345.00
1.01
9,283.69
41.97
605.23
0.00
-2,841.00
46.00
2,316.00
3.00
18,222.00
184.00
4,170.00
PhD graduate characteristics
EU-15 nationality
Non-EU-15 nationality
Age
Female
# Publications during PhD
Involved in applied projects during PhD
Worked prior to PhD
2,345
2,345
2,345
2,345
2,345
2,345
2,345
0.27
0.17
30.51
0.21
6.65
0.17
0.13
0.44
0.38
2.88
0.41
6.49
0.38
0.34
0.00
0.00
25.00
0.00
0.00
0.00
0.00
1.00
1.00
51.00
1.00
100.00
1.00
1.00
Supervisor characteristics
Pre-sample publications
Patenting activity
With prior working experience in industry
Involved in EU projects with industrial partners
2,345
2,345
2,345
2,345
32.88
0.37
0.33
0.19
29.05
0.69
0.47
0.39
0.00
0.00
0.00
0.00
209.00
2.00
1.00
1.00
Table 2: Multinomial logit results for PhD students' employment attainments, after graduation
PANEL A
I
II
Low-ranked
universities or public
research centers
Highly-ranked
universities or public
research centers
Main variable
PhD cohort size (rescaled dividing by 10)
III
IV
V
VI
Non-R&D-intensive
R&D-intensive firms Technology startups
firms
Public administration,
schools, teaching
colleges
0.888***
(0.038)
0.978
(0.037)
0.846***
(0.039)
1.000
(0.071)
0.979
(0.047)
1.107
1.010
(0.229)
(0.031)
1.234
0.983
(0.179)
(0.039)
0.803
1.045
(0.142)
(0.039)
0.757
0.891*
(0.195)
(0.061)
0.963
1.030
(0.204)
(0.053)
0.997
(0.003)
0.999
(0.002)
1.003
(0.003)
1.008
(0.005)
0.998
(0.003)
1.160
1.000
1.037**
0.999
(0.231)
(0.000)
(0.015)
(0.001)
1.132
1.000
1.020*
0.999
(0.157)
(0.000)
(0.012)
(0.001)
1.587**
1.000
1.012
1.002*
(0.307)
(0.000)
(0.015)
(0.001)
1.785
1.000
0.983
1.001
(0.648)
(0.000)
(0.024)
(0.001)
1.081
1.000
1.011
1.001
(0.232)
(0.000)
(0.017)
(0.001)
0.815
0.869
0.924**
0.862
1.211*
1.126
0.602*
(0.147)
(0.183)
(0.033)
(0.140)
(0.130)
(0.279)
(0.163)
0.803
0.517***
0.913***
0.611***
0.466***
2.978***
2.235***
(0.109)
(0.109)
(0.020)
(0.093)
(0.036)
(0.492)
(0.374)
0.939
0.653
0.963
0.861
0.611***
3.865***
1.800**
(0.203)
(0.170)
(0.027)
(0.175)
(0.064)
(0.773)
(0.446)
0.810
0.571
0.964
0.372***
0.740**
3.230***
1.450
(0.179)
(0.198)
(0.035)
(0.120)
(0.090)
(0.844)
(0.409)
0.610**
0.342***
1.032
1.432*
0.532***
1.744*
0.835
(0.147)
(0.115)
(0.027)
(0.274)
(0.066)
(0.511)
(0.266)
Supervisor characteristics
Pre-sample publications (in natural log)
Patenting activity
With prior working experience in industry
Involved in EU projects with industrial partners
1.092
0.939
1.035
0.982
(0.118)
(0.103)
(0.198)
(0.202)
1.092
0.942
1.497***
0.732*
(0.078)
(0.091)
(0.230)
(0.127)
1.428***
1.260*
1.526**
0.993
(0.144)
(0.149)
(0.310)
(0.213)
1.124
1.076
1.264
1.132
(0.144)
(0.147)
(0.281)
(0.258)
1.018
0.891
0.809
1.236
(0.119)
(0.135)
(0.181)
(0.268)
Graduation year fixed effects
University-field fixed effects
YES
YES
Employment conditions at enrollment
GDP growth, at entry (Very low, Low, Medium, High)
GDP growth in master's country
# PhD graduations in student i's enrollement year and in the prior
year
GDP growth, at graduation (Very low, Low, Medium, High)
Net supply of postdoc positions in the US
# Professors in graduation's country
# Graduation country's EPO patent applications
PhD graduate characteristics
EU-15 nationality
Non-EU-15 nationality
Age
Female
# Publications during PhD (in natural log)
Involved in applied projects during the PhD
Worked prior to beginning a PhD
BASEOUTCOME
Employment conditions at graduation
YES
YES
YES
YES
YES
YES
YES
YES
Table 2: continued
PANEL B
Main variable
PhD cohort size (rescaled dividing by 10)
I
II
Low-ranked
universities or public
research centers
Highly-ranked
universities or public
research centers
III
V
IV
VI
Non-R&D-intensive
R&D-intensive firms Technology startups
firms
Public administration,
schools, teaching
colleges
(0.048)
1.102**
(0.052)
0.952
(0.051)
1.126
(0.091)
1.103*
(0.064)
0.904
0.990
(0.187)
(0.030)
1.115
0.973
(0.245)
(0.041)
0.726
1.034
(0.172)
(0.042)
0.684
0.882*
(0.214)
(0.062)
0.870
1.020
(0.230)
(0.058)
1.003
(0.003)
1.002
(0.003)
1.006
(0.004)
1.011**
(0.005)
1.001
(0.004)
Employment conditions at graduation
GDP growth, at graduation (Very low, Low, Medium, High)
Net supply of postdoc positions in the US
# Professors in graduation's country
# Graduation country's EPO patent applications
0.862
1.000
0.964**
1.001
(0.171)
(0.000)
(0.014)
(0.001)
0.976
1.000
0.983
1.000
(0.207)
(0.000)
(0.017)
(0.001)
1.367
1.000
0.976
1.003**
(0.344)
(0.000)
(0.018)
(0.001)
1.538
1.001
0.948*
1.002
(0.594)
(0.000)
(0.027)
(0.002)
0.932
1.000
0.975
1.002
(0.252)
(0.000)
(0.020)
(0.001)
PhD graduate characteristics
EU-15 nationality
Non-EU-15 nationality
Age
Female
# Publications during PhD (in natural log)
Involved in applied projects during the PhD
Worked prior to beginning a PhD
1.226
1.150
1.082**
1.160
0.826*
0.888
1.660*
(0.221)
(0.242)
(0.039)
(0.189)
(0.089)
(0.220)
(0.451)
0.985
0.594**
0.988
0.709**
0.384***
2.644***
3.710***
(0.193)
(0.155)
(0.038)
(0.124)
(0.042)
(0.642)
(1.055)
1.151
0.751
1.043
0.999
0.504***
3.432***
2.988***
(0.290)
(0.219)
(0.042)
(0.213)
(0.068)
(0.937)
(0.967)
0.994
0.656
1.043
0.432**
0.611***
2.868***
2.407**
(0.257)
(0.234)
(0.047)
(0.145)
(0.088)
(0.903)
(0.873)
0.748
0.393***
1.117***
1.662**
0.439***
1.548
1.385
(0.210)
(0.139)
(0.047)
(0.383)
(0.060)
(0.527)
(0.570)
Supervisor characteristics
Pre-sample publications (in natural log)
Patenting activity
With prior working experience in industry
Involved in EU projects with industrial partners
0.916
1.065
0.966
1.019
(0.099)
(0.117)
(0.185)
(0.209)
1.000
1.003
1.446
0.746
(0.116)
(0.141)
(0.330)
(0.158)
1.308**
1.342**
1.475
1.012
(0.175)
(0.192)
(0.375)
(0.255)
1.029
1.146
1.221
1.154
(0.162)
(0.190)
(0.339)
(0.345)
0.932
0.949
0.782
1.259
(0.141)
(0.172)
(0.212)
(0.354)
Graduation year fixed effects
University-field fixed effects
YES
YES
Employment conditions at enrollment
GDP growth, at entry (Very low, Low, Medium, High)
GDP growth in master's country
# PhD graduations in student i's enrollement year and in the prior
year
BASEOUTCOME
1.126***
YES
YES
YES
YES
YES
YES
YES
YES
Table 2: continued
PANEL C
Main variable
PhD cohort size (rescaled dividing by 10)
I
II
III
Low-ranked
universities or public
research centers
Highly-ranked
universities or public
research centers
IV
V
VI
Non-R&D-intensive
R&D-intensive firms Technology startups
firms
Public administration,
schools, teaching
colleges
(0.038)
0.908**
(0.043)
0.865***
(0.043)
1.023
(0.078)
1.001
(0.053)
0.811
1.017
(0.117)
(0.040)
0.897
1.028
(0.197)
(0.044)
0.651**
1.063
(0.124)
(0.052)
0.614*
0.906
(0.164)
(0.067)
0.780
1.048
(0.178)
(0.060)
1.001
(0.002)
0.998
(0.003)
1.004
(0.003)
1.009*
(0.005)
0.999
(0.004)
0.883
1.000
0.980*
1.001
(0.122)
(0.000)
(0.012)
(0.001)
1.025
1.000
1.017
1.000
(0.218)
(0.000)
(0.017)
(0.001)
1.401*
1.000
0.992
1.003***
(0.272)
(0.000)
(0.015)
(0.001)
1.577
1.001
0.964
1.002
(0.574)
(0.000)
(0.024)
(0.002)
0.955
1.000
0.991
1.002
(0.206)
(0.000)
(0.017)
(0.001)
PhD graduate characteristics
EU-15 nationality
Non-EU-15 nationality
Age
Female
# Publications during PhD (in natural log)
Involved in applied projects during the PhD
Worked prior to beginning a PhD
1.245
1.935***
1.095***
1.636***
2.147***
0.336***
0.447***
(0.169)
(0.408)
(0.024)
(0.250)
(0.166)
(0.055)
(0.075)
1.015
1.683**
1.012
1.410**
2.601***
0.378***
0.270***
(0.199)
(0.438)
(0.039)
(0.246)
(0.287)
(0.092)
(0.077)
1.169
1.263
1.055*
1.409
1.311***
1.298
0.805
(0.251)
(0.381)
(0.031)
(0.301)
(0.131)
(0.261)
(0.196)
1.009
1.105
1.056
0.608
1.590***
1.085
0.649*
(0.238)
(0.408)
(0.042)
(0.201)
(0.199)
(0.297)
(0.165)
0.759
0.661
1.131***
2.343***
1.142
0.586*
0.373***
(0.180)
(0.251)
(0.034)
(0.522)
(0.139)
(0.177)
(0.114)
Supervisor characteristics
Pre-sample publications (in natural log)
Patenting activity
With prior working experience in industry
Involved in EU projects with industrial partners
0.916
1.062
0.668***
1.366*
(0.065)
(0.102)
(0.103)
(0.236)
1.000
0.997
0.692
1.341
(0.116)
(0.140)
(0.158)
(0.284)
1.308***
1.338**
1.020
1.357
(0.135)
(0.155)
(0.194)
(0.298)
1.029
1.142
0.845
1.547
(0.138)
(0.197)
(0.208)
(0.419)
0.933
0.947
0.541**
1.688**
(0.119)
(0.167)
(0.137)
(0.408)
Employment conditions at enrollment
GDP growth, at entry (Very low, Low, Medium, High)
GDP growth in master's country
# PhD graduations in student i's enrollement year and in the prior
year
Employment conditions at graduation
GDP growth, at graduation (Very low, Low, Medium, High)
Net supply of postdoc positions in the US
# Professors in graduation's country
# Graduation country's EPO patent applications
Graduation year fixed effects
University-field fixed effects
YES
YES
YES
YES
BASEOUTCOME
1.022
YES
YES
YES
YES
YES
YES
Table 2: continued
PANEL D
Main variable
PhD cohort size (rescaled dividing by 10)
I
II
III
Low-ranked
universities or public
research centers
Highly-ranked
universities or public
research centers
IV
V
VI
Non-R&D-intensive
R&D-intensive firms Technology startups
firms
Public administration,
schools, teaching
colleges
(0.055)
1.050
(0.057)
1.157***
(0.057)
1.183**
(0.095)
1.158**
(0.069)
1.246
0.957
(0.220)
(0.036)
1.378
0.967
(0.327)
(0.039)
1.537**
0.940
(0.293)
(0.046)
0.943
0.852**
(0.273)
(0.060)
1.199
0.986
(0.301)
(0.056)
0.997
(0.003)
0.994
(0.004)
0.996
(0.003)
1.005
(0.005)
0.995
(0.004)
Employment conditions at graduation
GDP growth, at graduation (Very low, Low, Medium, High)
Net supply of postdoc positions in the US
# Professors in graduation's country
# Graduation country's EPO patent applications
0.630**
1.000
0.988
0.998*
(0.122)
(0.000)
(0.015)
(0.001)
0.731
1.000
1.025
0.997**
(0.184)
(0.000)
(0.019)
(0.001)
0.714*
1.000
1.008
0.997***
(0.139)
(0.000)
(0.015)
(0.001)
1.125
1.000
0.971
0.999
(0.438)
(0.000)
(0.027)
(0.002)
0.682
1.000
0.998
0.999
(0.175)
(0.000)
(0.020)
(0.001)
PhD graduate characteristics
EU-15 nationality
Non-EU-15 nationality
Age
Female
# Publications during PhD (in natural log)
Involved in applied projects during the PhD
Worked prior to beginning a PhD
1.065
1.532
1.038
1.161
1.637***
0.259***
0.556**
(0.230)
(0.398)
(0.029)
(0.237)
(0.172)
(0.052)
(0.138)
0.869
1.332
0.959
1.001
1.983***
0.291***
0.335***
(0.219)
(0.388)
(0.039)
(0.214)
(0.266)
(0.080)
(0.108)
0.856
0.791
0.948*
0.710
0.763***
0.770
1.242
(0.183)
(0.239)
(0.028)
(0.152)
(0.076)
(0.155)
(0.302)
0.863
0.874
1.000
0.432**
1.212
0.836
0.806
(0.241)
(0.323)
(0.042)
(0.147)
(0.175)
(0.237)
(0.254)
0.650
0.523*
1.071*
1.663**
0.871
0.451**
0.464**
(0.195)
(0.199)
(0.040)
(0.403)
(0.127)
(0.142)
(0.167)
Supervisor characteristics
Pre-sample publications (in natural log)
Patenting activity
With prior working experience in industry
Involved in EU projects with industrial partners
0.700***
0.794*
0.655**
1.007
(0.071)
(0.094)
(0.133)
(0.216)
0.765**
0.745**
0.678
0.988
(0.102)
(0.107)
(0.172)
(0.249)
0.765***
0.747**
0.980
0.737
(0.079)
(0.087)
(0.187)
(0.162)
0.787*
0.854
0.828
1.140
(0.108)
(0.145)
(0.235)
(0.330)
0.713**
0.707**
0.530**
1.244
(0.104)
(0.121)
(0.147)
(0.357)
Employment conditions at enrollment
GDP growth, at entry (Very low, Low, Medium, High)
GDP growth in master's country
# PhD graduations in student i's enrollement year and in the prior
year
Graduation year fixed effects
University-field fixed effects
YES
YES
YES
YES
YES
YES
BASEOUTCOME
1.182***
YES
YES
YES
YES
Table 2: continued
PANEL E
Main variable
PhD cohort size (rescaled dividing by 10)
I
II
III
Low-ranked
universities or public
research centers
Highly-ranked
universities or public
research centers
IV
V
Non-R&D-intensive
R&D-intensive firms Technology startups
firms
VI
Public administration,
schools, teaching
colleges
(0.071)
0.888
(0.072)
0.978
(0.075)
0.845**
(0.068)
0.979
(0.077)
1.320
1.123*
(0.339)
(0.077)
1.461
1.134*
(0.456)
(0.079)
1.629*
1.104
(0.436)
(0.082)
1.060
1.173**
(0.306)
(0.082)
1.271
1.157*
(0.398)
(0.096)
0.992
(0.005)
0.989**
(0.005)
0.991*
(0.005)
0.995
(0.005)
0.990*
(0.005)
0.560
1.000
1.017
0.999
(0.203)
(0.000)
(0.025)
(0.001)
0.650
0.999
1.055*
0.998
(0.251)
(0.000)
(0.030)
(0.002)
0.634
0.999
1.038
0.998
(0.231)
(0.000)
(0.026)
(0.002)
0.889
1.000
1.030
1.001
(0.346)
(0.000)
(0.029)
(0.002)
0.606
1.000
1.028
1.000
(0.238)
(0.000)
(0.029)
(0.002)
1.234
1.752
1.038
2.688***
1.351**
0.310***
0.690
(0.272)
(0.608)
(0.038)
(0.870)
(0.164)
(0.081)
(0.194)
1.006
1.523
0.959
2.317**
1.636***
0.349***
0.415**
(0.260)
(0.544)
(0.043)
(0.776)
(0.236)
(0.110)
(0.151)
0.991
0.905
0.947
1.643
0.629***
0.922
1.541*
(0.234)
(0.335)
(0.037)
(0.542)
(0.079)
(0.252)
(0.392)
1.158
1.144
1.000
2.315**
0.825
1.197
1.241
(0.323)
(0.422)
(0.042)
(0.787)
(0.119)
(0.339)
(0.392)
0.752
0.598
1.071
3.851***
0.718**
0.540*
0.576
(0.235)
(0.280)
(0.045)
(1.343)
(0.115)
(0.197)
(0.217)
Supervisor characteristics
Pre-sample publications (in natural log)
Patenting activity
With prior working experience in industry
Involved in EU projects with industrial partners
0.890
0.930
0.791
0.883
(0.114)
(0.127)
(0.176)
(0.202)
0.972
0.873
0.819
0.867
(0.153)
(0.144)
(0.227)
(0.259)
0.972
0.875
1.184
0.646
(0.131)
(0.151)
(0.292)
(0.175)
1.271*
1.171
1.208
0.877
(0.175)
(0.199)
(0.343)
(0.254)
0.906
0.829
0.640
1.091
(0.142)
(0.160)
(0.188)
(0.315)
Graduation year fixed effects
University-field fixed effects
YES
YES
Employment conditions at enrollment
GDP growth, at entry (Very low, Low, Medium, High)
GDP growth in master's country
# PhD graduations in student i's enrollement year and in the prior
year
Employment conditions at graduation
GDP growth, at graduation (Very low, Low, Medium, High)
Net supply of postdoc positions in the US
# Professors in graduation's country
# Graduation country's EPO patent applications
PhD graduate characteristics
EU-15 nationality
Non-EU-15 nationality
Age
Female
# Publications during PhD (in natural log)
Involved in applied projects during the PhD
Worked prior to beginning a PhD
YES
YES
YES
YES
YES
YES
BASEOUTCOME
1.000
YES
YES
Table 2: continued
Main variable
PhD cohort size (rescaled dividing by 10)
II
III
Low-ranked
universities or public
research centers
Highly-ranked
universities or public
research centers
IV
V
VI
Non-R&D-intensive
R&D-intensive firms Technology startups
firms
1.021
(0.049)
0.907*
(0.053)
0.999
(0.052)
0.864**
(0.052)
1.021
(0.080)
1.039
0.971
(0.220)
(0.050)
1.149
0.981
(0.303)
(0.056)
1.282
0.954
(0.293)
(0.055)
0.834
1.014
(0.209)
(0.058)
0.787
0.865*
(0.246)
(0.072)
1.002
(0.003)
0.999
(0.004)
1.001
(0.004)
1.005
(0.004)
1.010*
(0.005)
0.925
1.000
0.989
0.999
(0.199)
(0.000)
(0.016)
(0.001)
1.073
1.000
1.026
0.998
(0.291)
(0.000)
(0.021)
(0.001)
1.047
1.000
1.009
0.998
(0.226)
(0.000)
(0.018)
(0.001)
1.467
1.000
1.002
1.001
(0.376)
(0.000)
(0.020)
(0.001)
1.651
1.000
0.972
1.000
(0.650)
(0.000)
(0.027)
(0.002)
1.640**
2.927***
0.969
0.698*
1.880***
0.573*
1.198
(0.396)
(0.985)
(0.025)
(0.133)
(0.234)
(0.168)
(0.382)
1.337
2.545***
0.895***
0.602**
2.277***
0.646
0.722
(0.375)
(0.903)
(0.038)
(0.139)
(0.312)
(0.220)
(0.297)
1.317
1.513
0.884***
0.427***
0.876
1.707*
2.678***
(0.313)
(0.575)
(0.027)
(0.095)
(0.107)
(0.517)
(0.818)
1.539
1.911*
0.933*
0.601**
1.148
2.216**
2.157**
(0.461)
(0.728)
(0.035)
(0.146)
(0.168)
(0.700)
(0.777)
1.329
1.671
0.933
0.260***
1.392**
1.852*
1.738
(0.416)
(0.781)
(0.039)
(0.091)
(0.222)
(0.675)
(0.656)
Supervisor characteristics
Pre-sample publications (in natural log)
Patenting activity
With prior working experience in industry
Involved in EU projects with industrial partners
0.982
1.122
1.236
0.809
(0.114)
(0.169)
(0.277)
(0.176)
1.073
1.053
1.279
0.794
(0.163)
(0.191)
(0.348)
(0.224)
1.072
1.056
1.849**
0.592**
(0.137)
(0.186)
(0.469)
(0.143)
1.402**
1.413**
1.886**
0.804
(0.204)
(0.241)
(0.523)
(0.231)
1.104
1.207
1.562
0.917
(0.173)
(0.233)
(0.459)
(0.265)
Graduation year fixed effects
University-field fixed effects
YES
YES
Employment conditions at enrollment
GDP growth, at entry (Very low, Low, Medium, High)
GDP growth in master's country
# PhD graduations in student i's enrollement year and in the prior
year
Employment conditions at graduation
GDP growth, at graduation (Very low, Low, Medium, High)
Net supply of postdoc positions in the US
# Professors in graduation's country
# Graduation country's EPO patent applications
PhD graduate characteristics
EU-15 nationality
Non-EU-15 nationality
Age
Female
# Publications during PhD (in natural log)
Involved in applied projects during the PhD
Worked prior to beginning a PhD
YES
YES
YES
YES
YES
YES
Public administration,
schools, teaching
colleges
BASEOUTCOME
PANEL F
I
YES
YES
Note: Coefficients are relative risk ratios. N=2,345. Robust standard errors (in parentheses) are clustered around supervisors. ***, **,*: Significantly different from zero at the 1%, 5%, 10% confidence levels. In these analyses,
we consider a PhD student's first position, after graduation.
Table 3: Multinomial logit results for PhD students' employment attainments, after graduation. We include in the highly-ranked-institutions category Chalmers PhD graduates who were hired as faculty in their
own university and EPFL graduates who had stayed at their own affiliation, but had moved to a different research group than their supervisor’s.
A - Baseoutcome:Low-ranked universities or public research centers
PhD cohort size
I
Low-ranked
universities or
public research
centers
II
Highly-ranked
universities or
public research
centers
BASEOUTCOME
0.906**
(0.036)
0.979
(0.037)
0.846***
(0.039)
1.001
(0.071)
0.980
(0.048)
YES
YES
YES
YES
YES
BASEOUTCOME
1.080*
(0.049)
0.933
(0.049)
1.105
(0.087)
1.082
(0.060)
YES
YES
YES
YES
BASEOUTCOME
0.864***
(0.043)
1.022
(0.079)
1.001
(0.053)
YES
YES
YES
BASEOUTCOME
1.183**
(0.096)
1.159**
(0.070)
YES
YES
BASEOUTCOME
0.979
(0.077)
Other controls
B - Baseoutcome: Highly-ranked universities or public research centers
PhD cohort size
Other controls
1.104**
(0.044)
YES
III
IV
Non-R&D-intensive
R&D-intensive firms
firms
V
Technology
startups
VI
Public
administration,
schools, teaching
colleges
C - Baseoutcome: Non-R&D-intensive firms
PhD cohort size
Other controls
1.022
(0.038)
0.926*
(0.042)
YES
YES
1.182***
(0.055)
1.071
(0.057)
1.158***
(0.057)
YES
YES
YES
0.999
(0.071)
0.905
(0.072)
0.978
(0.075)
0.845**
(0.068)
YES
YES
YES
YES
1.020
(0.050)
0.924
(0.051)
0.999
(0.052)
0.863**
(0.052)
1.021
(0.080)
YES
YES
YES
YES
YES
D - Baseoutcome: R&D-intensive firms
PhD cohort size
Other controls
E- Baseoutcome: Technology startups
PhD cohort size
Other controls
F - Baseoutcome: Public administration, schools, teaching colleges
PhD cohort size
Other controls
YES
BASEOUTCOME
Note: Coefficients are relative risk ratios. N=2,345. Robust standard errors (in parentheses) are clustered around supervisors. ***, **,*: Significantly different from zero at the 1%, 5%, 10% confidence levels.
In these analyses, we consider a PhD student's first position, after graduation. The econometric specifications reported include graduation year and university-field fixed effects.
Table 4: Multinomial logit results for PhD students' employment attainments, after graduation. We exclude female PhD graduates
A - Baseoutcome:Low-ranked universities or public research centers
PhD cohort size
I
Low-ranked
universities or
public research
centers
II
Highly-ranked
universities or
public research
centers
BASEOUTCOME
0.863***
(0.043)
YES
0.992
(0.041)
YES
0.801***
(0.043)
YES
0.986
(0.074)
YES
0.956
(0.069)
YES
1.159***
(0.058)
YES
BASEOUTCOME
1.150***
(0.062)
YES
0.928
(0.059)
YES
1.143
(0.100)
YES
1.108
(0.092)
YES
1.008
(0.042)
YES
0.870***
(0.047)
YES
BASEOUTCOME
0.808***
(0.045)
YES
0.994
(0.082)
YES
0.963
(0.069)
YES
1.248***
(0.067)
YES
1.077
(0.069)
YES
1.238***
(0.069)
YES
BASEOUTCOME
1.230**
(0.108)
YES
1.193**
(0.099)
YES
1.014
(0.077)
YES
0.875
(0.076)
YES
1.006
(0.083)
YES
0.813**
(0.072)
YES
BASEOUTCOME
0.969
(0.095)
YES
1.046
(0.076)
YES
0.903
(0.075)
YES
1.038
(0.074)
YES
0.838**
(0.069)
YES
1.032
(0.101)
YES
BASEOUTCOME
Other controls
B - Baseoutcome: Highly-ranked universities or public research centers
PhD cohort size
Other controls
III
IV
Non-R&D-intensive
R&D-intensive firms
firms
V
Technology
startups
VI
Public
administration,
schools, teaching
colleges
C - Baseoutcome: Non-R&D-intensive firms
PhD cohort size
Other controls
D - Baseoutcome: R&D-intensive firms
PhD cohort size
Other controls
E- Baseoutcome: Technology startups
PhD cohort size
Other controls
F - Baseoutcome: Public administration, schools, teaching colleges
PhD cohort size
Other controls
Note: Coefficients are relative risk ratios. N=1,851. Robust standard errors (in parentheses) are clustered around supervisors. ***, **,*: Significantly different from zero at the 1%, 5%, 10% confidence levels.
In these analyses, we consider a PhD student's first position, after graduation. The econometric specifications reported include graduation year and university-field fixed effects.
Table 5: Multinomial logit results for PhD students' employment attainments, after graduation. We include R&D-intensive startups in the category of R&D-intensive firms and non-R&D-intensive
startups in the category on non-R&D-intensive firms
I
II
III
IV
V
Highly-ranked
universities or
public research
centers
Non-R&D-intensive
firms and technology
startups neither
patenting, nor
publishing
R&D-intensive firms
and technology
startups active in
patenting and/or
publishing
Public
administration,
schools, teaching
colleges
BASEOUTCOME
0.888***
(0.038)
YES
0.991
(0.035)
YES
0.871***
(0.038)
YES
0.980
(0.047)
YES
1.126***
(0.048)
YES
BASEOUTCOME
1.115**
(0.052)
YES
0.980
(0.052)
YES
1.103*
(0.064)
YES
BASEOUTCOME
0.879***
(0.041)
YES
0.989
(0.051)
YES
BASEOUTCOME
1.126**
(0.064)
Low-ranked
universities or public
research centers
A - Baseoutcome:Low-ranked universities or public research centers
PhD cohort size
Other controls
B - Baseoutcome: Highly-ranked universities or public research centers
PhD cohort size
Other controls
C - Baseoutcome: Non-R&D-intensive firms and technology startups with no patents nor publications
PhD cohort size
Other controls
1.009
(0.036)
YES
0.897**
(0.042)
YES
D - Baseoutcome: R&D-intensive firms and technology startups wth at least one patent or one publication
1.148***
1.020
PhD cohort size
(0.049)
(0.054)
Other controls
1.138***
(0.053)
YES
YES
YES
1.020
(0.049)
YES
0.906*
(0.053)
YES
1.011
(0.052)
YES
YES
E- Baseoutcome: Public administration, schools, teaching colleges
PhD cohort size
Other controls
0.888**
(0.050)
YES
BASEOUTCOME
Note: Coefficients are relative risk ratios. N=2,345. Robust standard errors (in parentheses) are clustered around supervisors. ***, **,*: Significantly different from zero at the 1%, 5%, 10%
confidence levels. In these analyses, we consider a PhD student's first position, after graduation. We classified as R&D-intensive startups the ones with at least one publication or a patent.
Conversely, we classified as non-R&D-intensive those startups with neither a publication nor a patent. The econometric specifications reported include graduation year and university-field fixed
effects.
Table 6: Multinomial logit results for PhD students' employment attainments, after graduation. We include faculty positions in the category of employment in highly-ranked universities
A - Baseoutcome:Low-ranked universities or public research centers
PhD cohort size
I
II
V
VI
Low-ranked
universities or
public research
centers (excluding
faculty positions)
Highly-ranked
universities or
public research
centers (including
faculty positions)
Technology
startups
Public
administration,
schools, teaching
colleges
BASEOUTCOME
0.913**
(0.034)
YES
0.981
(0.037)
YES
0.842***
(0.039)
YES
1.025
(0.075)
YES
0.982
(0.048)
YES
1.095**
(0.041)
YES
BASEOUTCOME
1.074*
(0.046)
YES
0.922
(0.046)
YES
1.122
(0.088)
YES
1.075
(0.058)
YES
1.019
(0.039)
YES
0.931*
(0.040)
YES
BASEOUTCOME
0.858***
(0.042)
YES
1.045
(0.081)
YES
1.001
(0.053)
YES
1.187***
(0.054)
YES
1.084
(0.054)
YES
1.165***
(0.057)
YES
BASEOUTCOME
1.217**
(0.097)
YES
1.166***
(0.069)
YES
0.976
(0.071)
YES
0.891
(0.070)
YES
0.957
(0.074)
YES
0.822**
(0.066)
YES
BASEOUTCOME
0.958
(0.078)
YES
1.018
(0.050)
YES
0.930
(0.050)
YES
0.999
(0.052)
YES
0.857***
(0.050)
YES
1.044
(0.084)
YES
BASEOUTCOME
Other controls
B - Baseoutcome: Highly-ranked universities or public research centers
PhD cohort size
Other controls
III
IV
Non-R&D-intensive
R&D-intensive firms
firms
C - Baseoutcome: Non-R&D-intensive firms
PhD cohort size
Other controls
D - Baseoutcome: R&D-intensive firms
PhD cohort size
Other controls
E- Baseoutcome: Technology startups
PhD cohort size
Other controls
F - Baseoutcome: Public administration, schools, teaching colleges
PhD cohort size
Other controls
Note: Coefficients are relative risk ratios. N=2,345. Robust standard errors (in parentheses) are clustered around supervisors. ***, **,*: Significantly different from zero at the 1%, 5%, 10% confidence levels.
In these analyses, we consider a PhD student's first position, after graduation. The econometric specifications reported include graduation year and university-field fixed effects.
Table 7: Multinomial logit results for PhD students' employment attainments, after graduation. We include more detailed university-field fixed effects
A - Baseoutcome:Low-ranked universities or public research centers
PhD cohort size
I
Low-ranked
universities or
public research
centers
II
Highly-ranked
universities or
public research
centers
BASEOUTCOME
0.885***
(0.038)
0.967
(0.036)
0.843***
(0.041)
1.014
(0.069)
0.979
(0.049)
YES
YES
YES
YES
YES
BASEOUTCOME
1.093*
(0.052)
0.952
(0.053)
1.146*
(0.092)
1.106*
(0.067)
YES
YES
YES
YES
BASEOUTCOME
0.872***
(0.044)
1.049
(0.078)
1.012
(0.056)
YES
YES
YES
BASEOUTCOME
1.203**
(0.096)
1.161**
(0.073)
YES
YES
BASEOUTCOME
0.965
(0.075)
Other controls
B - Baseoutcome: Highly-ranked universities or public research centers
PhD cohort size
Other controls
1.130***
(0.049)
YES
III
IV
Non-R&D-intensive
R&D-intensive firms
firms
V
Technology
startups
VI
Public
administration,
schools, teaching
colleges
C - Baseoutcome: Non-R&D-intensive firms
PhD cohort size
Other controls
1.034
(0.039)
0.915*
(0.044)
YES
YES
1.187***
(0.058)
1.050
(0.058)
1.147***
(0.057)
YES
YES
YES
0.986
(0.067)
0.873*
(0.070)
0.954
(0.071)
0.831**
(0.066)
YES
YES
YES
YES
1.022
(0.051)
0.904*
(0.055)
0.988
(0.055)
0.861**
(0.054)
1.036
(0.080)
YES
YES
YES
YES
YES
D - Baseoutcome: R&D-intensive firms
PhD cohort size
Other controls
E- Baseoutcome: Technology startups
PhD cohort size
Other controls
F - Baseoutcome: Public administration, schools, teaching colleges
PhD cohort size
Other controls
YES
BASEOUTCOME
Note: Coefficients are relative risk ratios. N=2,345. Robust standard errors (in parentheses) are clustered around supervisors. ***, **,*: Significantly different from zero at the 1%, 5%, 10% confidence levels.
In these analyses, we consider a PhD student's first position, after graduation. The econometric specifications reported include graduation year and university-department fixed effects. Regarding universityfield fixed effetcs, we consider the following fields: physics, chemistry, mathematics, life sciences, mechanical engineering, electrical engineering, micro engineering, civil engineering, and material science.
Table 8: Multinomial logit results for PhD students' employment attainments, after graduation. We categorize firms into R&D-intensive and non-R&D-intensive by comparing them to the other firms that
belong to the same sector
I
Low-ranked
universities or
public research
centers
II
Highly-ranked
universities or
public research
centers
III
IV
BASEOUTCOME
0.889***
(0.038)
0.975
(0.036)
0.857***
(0.043)
1.003
(0.071)
0.980
(0.047)
YES
YES
YES
YES
YES
BASEOUTCOME
1.096*
(0.053)
0.964
(0.054)
1.128
(0.091)
1.102*
(0.064)
YES
YES
YES
YES
BASEOUTCOME
0.879**
(0.048)
1.029
(0.078)
1.005
(0.053)
YES
YES
YES
BASEOUTCOME
1.170*
(0.098)
1.143**
(0.073)
YES
YES
BASEOUTCOME
0.977
(0.076)
Non-R&D-intensive
R&D-intensive firms
firms
V
Technology
startups
VI
Public
administration,
schools, teaching
colleges
A - Baseoutcome: Low-ranked universities or public research centers
PhD cohort size
Other controls
B - Baseoutcome: Highly-ranked universities or public research centers
PhD cohort size
1.125***
(0.048)
Other controls
YES
C - Baseoutcome: Non-R&D-intensive firms
PhD cohort size
Other controls
1.026
(0.038)
0.912*
(0.044)
YES
YES
1.167***
(0.059)
1.037
(0.058)
1.137**
(0.062)
YES
YES
YES
0.997
(0.070)
0.887
(0.072)
0.972
(0.074)
0.855*
(0.072)
YES
YES
YES
YES
1.021
(0.049)
0.908*
(0.053)
0.995
(0.052)
0.875**
(0.056)
1.024
(0.080)
YES
YES
YES
YES
YES
D - Baseoutcome: R&D-intensive firms
PhD cohort size
Other controls
E- Baseoutcome: Technology startups
PhD cohort size
Other controls
YES
F - Baseoutcome: Public administration, schools, teaching colleges
PhD cohort size
Other controls
BASEOUTCOME
Note: Coefficients are relative risk ratios. N=2,345. Robust standard errors (in parentheses) are clustered around supervisors. ***, **,*: Significantly different from zero at the 1%, 5%, 10% confidence levels.
The econometric specifications reported include graduation year and university-field fixed effects. In these analyses, we consider a PhD student's first position, after graduation. A company c is classified as
R&D-intensive if its patent count or its publication count are above the 90th percentile of the distribution of companies in a similar sector and with a similar size as c .
Table 9: Multinomial logit results for PhD students' employment attainments, after graduation. We categorize a company as R&D-intensive if it is above the 75th percentile of the distribution of companies
with a similar size, in terms of its patent count or its publication count
I
Low-ranked
universities or
public research
centers
II
Highly-ranked
universities or
public research
centers
III
IV
BASEOUTCOME
0.889***
(0.038)
YES
0.981
(0.038)
YES
0.889***
(0.037)
YES
1.002
(0.070)
YES
0.980
(0.047)
YES
1.125***
(0.048)
YES
BASEOUTCOME
1.103**
(0.054)
YES
1.000
(0.051)
YES
1.128
(0.091)
YES
1.103*
(0.064)
YES
1.020
(0.040)
YES
0.906**
(0.044)
YES
BASEOUTCOME
0.906**
(0.042)
YES
1.022
(0.079)
YES
0.999
(0.053)
YES
1.125***
(0.047)
YES
1.000
(0.051)
YES
1.104**
(0.051)
YES
BASEOUTCOME
1.128
(0.087)
YES
1.103*
(0.063)
YES
0.998
(0.070)
YES
0.887
(0.071)
YES
0.978
(0.076)
YES
0.887
(0.068)
YES
BASEOUTCOME
0.978
(0.076)
YES
1.020
(0.049)
YES
0.907*
(0.053)
YES
1.001
(0.053)
YES
0.907*
(0.052)
YES
1.023
(0.080)
YES
BASEOUTCOME
Non-R&D-intensive
R&D-intensive firms
firms
V
Technology
startups
VI
Public
administration,
schools, teaching
colleges
A - Baseoutcome: Low-ranked universities or public research centers
PhD cohort size
Other controls
B - Baseoutcome: Highly-ranked universities or public research centers
PhD cohort size
Other controls
C - Baseoutcome: Non-R&D-intensive firms
PhD cohort size
Other controls
D - Baseoutcome: R&D-intensive firms
PhD cohort size
Other controls
E- Baseoutcome: Technology startups
PhD cohort size
Other controls
F - Baseoutcome: Public administration, schools, teaching colleges
PhD cohort size
Other controls
Note: Coefficients are relative risk ratios. N=2,345. Robust standard errors (in parentheses) are clustered around supervisors. ***, **,*: Significantly different from zero at the 1%, 5%, 10% confidence levels.
The econometric specifications reported include graduation year and university-field fixed effects. In these analyses, we consider a PhD student's first position, after graduation.
Table 10: Multinomial logit results for PhD students' employment attainments, after graduation. We report results for PhD graduates in basic sciences and engineering
BASIC SCIENCES
I
II
III
IV
V
Low-ranked
universities or
public research
centers
ENGINEERING
VI
VII
VIII
Low-ranked
universities or
public research
centers
Highly-ranked
universities or
public research
centers
XI
XII
R&D-intensive
firms
Technology
startups
Public
administration,
schools, teaching
colleges
Non-R&Dintensive firms
1.030
(0.075)
YES
0.799**
(0.080)
YES
1.116
(0.226)
YES
1.193
(0.163)
YES
BASEOUTCOME
1.271*
(0.164)
YES
0.985
(0.151)
YES
1.378
(0.312)
YES
1.473**
(0.262)
YES
0.971
(0.070)
YES
0.787*
(0.101)
YES
BASEOUTCOME
0.775**
(0.082)
YES
1.084
(0.219)
YES
1.159
(0.152)
YES
1.175
(0.135)
YES
1.252**
(0.125)
YES
1.015
(0.156)
YES
1.290**
(0.137)
YES
BASEOUTCOME
1.398
(0.301)
YES
1.495**
(0.246)
YES
BASEOUTCOME
0.846
(0.162)
YES
0.896
(0.181)
YES
0.726
(0.165)
YES
0.923
(0.187)
YES
0.715
(0.154)
YES
BASEOUTCOME
1.069
(0.256)
YES
1.182
(0.226)
YES
BASEOUTCOME
0.838
(0.115)
YES
0.679**
(0.121)
YES
0.863
(0.113)
YES
0.669**
(0.110)
YES
0.935
(0.224)
YES
BASEOUTCOME
Highly-ranked
universities or
public research
centers
Non-R&Dintensive firms
R&D-intensive firms
Technology
startups
Public
administration,
schools, teaching
colleges
0.851**
(0.055)
YES
0.997
(0.061)
YES
0.761***
(0.072)
YES
1.057
(0.178)
YES
0.894
(0.082)
YES
BASEOUTCOME
0.810*
(0.101)
YES
1.176**
(0.076)
YES
BASEOUTCOME
1.172**
(0.091)
YES
0.895
(0.089)
YES
1.243
(0.221)
YES
1.051
(0.112)
YES
1.234*
(0.155)
YES
1.003
(0.061)
YES
0.853**
(0.066)
YES
BASEOUTCOME
0.763***
(0.075)
YES
1.061
(0.188)
YES
0.897
(0.080)
YES
1.314***
(0.125)
YES
1.118
(0.112)
YES
1.310***
(0.129)
YES
BASEOUTCOME
1.390*
(0.259)
YES
0.946
(0.159)
YES
0.804
(0.143)
YES
0.943
(0.167)
YES
0.720*
(0.134)
YES
1.118
(0.103)
YES
0.951
(0.101)
YES
1.115
(0.100)
YES
0.851
(0.098)
YES
A - Baseoutcome:Low-ranked universities or public research centers
BASEOUTCOME
PhD cohort size
Other controls
IX
X
B - Baseoutcome: Highly-ranked universities or public research centers
PhD cohort size
Other controls
C - Baseoutcome: Non-R&D-intensive firms
PhD cohort size
Other controls
D - Baseoutcome: R&D-intensive firms
PhD cohort size
Other controls
E- Baseoutcome: Technology startups
PhD cohort size
Other controls
F - Baseoutcome: Public administration, schools, teaching colleges
PhD cohort size
Other controls
Note: Coefficients are relative risk ratios. N in basic sciencies: 993. N in engineering: 1,352. Robust standard errors (in parentheses) are clustered around supervisors. ***, **,*: Significantly different from zero at the 1%, 5%, 10% confidence levels. In these analyses, we consider a PhD student's first position, after
graduation.
Table 11: Regression results for the probability that a PhD graduate who held an initial postdoctoral positions in low-ranked university improves her position
Highly-ranked university or public research
centers
I
All sample
Main variable
PhD cohort size (rescaled dividing by 10)
II
Males
Professorship
III
All sample
R&D-intensive-firms
IV
Males
V
All sample
VI
Males
0.971
(0.049)
0.980
(0.062)
0.922
(0.048)
0.884**
(0.054)
0.906
(0.059)
0.882*
(0.065)
2.999***
0.976
1.005
(0.457)
(0.041)
(0.004)
3.248***
0.994
1.006
(0.560)
(0.055)
(0.005)
2.078***
0.992
1.002
(0.356)
(0.037)
(0.004)
2.236***
0.981
1.005
(0.465)
(0.044)
(0.004)
1.674***
0.953
1.001
(0.299)
(0.067)
(0.004)
1.994***
0.960
1.000
(0.436)
(0.066)
(0.005)
0.667
1.000
1.005
0.998
(0.211)
(0.000)
(0.013)
(0.002)
0.693
1.000
1.007
0.996
(0.250)
(0.000)
(0.015)
(0.003)
0.850
1.000
1.012
0.998*
(0.233)
(0.000)
(0.011)
(0.001)
0.732
1.000
1.019
0.997*
(0.233)
(0.000)
(0.012)
(0.002)
1.163
1.000
1.013
1.002**
(0.333)
(0.000)
(0.016)
(0.001)
1.119
1.000
1.011
1.003**
(0.371)
(0.000)
(0.018)
(0.001)
Employment conditions in year t
Average GDP growth rate year t and year t-1
# PhD graduations in year t and in the year t-1
# Graduation country's EPO patent applications
Net supply of postdoc positions in the US
# Professors in graduation's country
1.018
1.005***
0.998*
1.000
0.934***
(0.038)
(0.001)
(0.001)
(0.000)
(0.018)
1.045
1.006***
0.998*
1.000
0.928***
(0.048)
(0.001)
(0.001)
(0.000)
(0.017)
1.103**
1.006***
0.998**
1.000
0.975**
(0.045)
(0.001)
(0.001)
(0.000)
(0.013)
1.054
1.007***
0.998**
1.000
0.982
(0.048)
(0.001)
(0.001)
(0.000)
(0.015)
1.066
1.004***
0.999
1.000
0.955***
(0.059)
(0.001)
(0.001)
(0.000)
(0.015)
1.083
1.005***
0.999
1.000
0.954***
(0.067)
(0.001)
(0.001)
(0.000)
(0.016)
PhD graduate characteristics
EU-15 nationality
Non-EU-15 nationality
Age
Female
# Publications during PhD (in natural log)
Involved in applied projects during the PhD
Worked prior to beginning a PhD
Stayed in the same country of graduation
1.235
2.020***
0.948
0.749
1.270*
0.624
0.928
0.990
(0.296)
(0.545)
(0.037)
(0.172)
(0.172)
(0.244)
(0.310)
(0.201)
1.038
1.692*
0.929
(0.272)
(0.529)
(0.044)
(0.320)
(0.651)
(0.044)
(0.224)
(0.341)
(0.452)
(0.169)
(0.339)
(0.362)
(0.039)
(0.207)
(0.224)
(0.415)
(0.668)
(0.398)
(0.395)
(0.453)
(0.045)
1.405**
0.942
1.232
0.741
1.038
0.904
0.923*
0.640
1.379**
1.193
2.229***
1.305
1.092
1.052
0.909*
(0.171)
(0.280)
(0.358)
(0.223)
(0.292)
(0.524)
(0.043)
(0.189)
(0.196)
(0.294)
(0.360)
(0.164)
1.190
2.255***
0.946
1.154
0.636
0.949
0.959
1.230
1.934**
0.920*
0.859
1.412**
0.939
1.033
0.822
1.350
0.875
2.824***
1.603
(0.264)
(0.374)
(0.923)
(0.558)
0.986
0.991
0.819
1.044
(0.113)
(0.134)
(0.188)
(0.273)
1.053
0.984
0.776
0.931
(0.141)
(0.152)
(0.193)
(0.273)
0.847*
0.798
0.781
1.281
(0.081)
(0.130)
(0.178)
(0.309)
0.809*
0.881
0.834
1.374
(0.094)
(0.161)
(0.217)
(0.395)
1.043
1.107
1.235
0.988
(0.165)
(0.191)
(0.328)
(0.271)
1.140
0.970
1.057
1.022
(0.230)
(0.207)
(0.300)
(0.308)
Employment conditions at enrollment
GDP growth, at entry (Very low, Low, Medium, High)
GDP growth in master's country
# PhD graduations in student i's enrollement year and in the prior year
Employment conditions at graduation
GDP growth, at graduation (Very low, Low, Medium, High)
Net supply of postdoc positions in the US
# Professors in graduation's country
# Graduation country's EPO patent applications
Supervisor characteristics
Pre-sample publications (in natural log)
Patenting activity
With prior working experience in industry
Involved in EU projects with industrial partners
Graduation year fixed effects
University-field fixed effects
N observations
Log-likelihood
YES
YES
6,377
-755.370
YES
YES
4,815
-549.427
YES
YES
6,450
-799.825
YES
YES
4,892
-598.144
YES
YES
6,618
-495.488
YES
YES
4,982
-377.736
Note: Estimates are presented in terms of their effect on the odds of switching position. A coefficient smaller (larger) than one reflects a negative (positive) effect. Robust standard errors (in parentheses) are clustered around
individuals. ***, **, *: Significantly different from zero at the 1%, 5%, 10% confidence levels.
Table 12: Regression results for the probability that a PhD graduate who held an initial postdoctoral positions in low-ranked university improves her position
Highly-ranked university or public research
centers
I
All sample
II
Males
Professorship
III
All sample
R&D-intensive-firms
IV
Males
V
All sample
VI
Males
Main variable
PhD cohort size (rescaled dividing by 10)
0.978
Other controls
YES
YES
YES
YES
YES
YES
N observations
Log-likelihood
6,249
-747.717
4,700
-542.309
6,328
-783.754
4,775
-580.199
6,506
-479.971
4,883
-362.417
(0.049)
0.989
(0.063)
0.928
(0.049)
0.890*
(0.055)
0.899
(0.059)
0.872*
(0.065)
Note: Estimates are presented in terms of their effect on the odds of switching position. A coefficient smaller (larger) than one reflects a negative (positive) effect. Robust standard errors (in parentheses) are clustered around
individuals. ***, **, *: Significantly different from zero at the 1%, 5%, 10% confidence levels. The econometric specifications reported use the same controls as in Table 11.
Table 13: Logit regressions results for the research output of PhD graduates who had accepted an initial postdoctoral position
in a low-ranked university. We consider the publication output produced in the two years after graduation
I
II
Full sample
Males
0.894**
(0.044)
0.869**
(0.050)
Other controls
Graduation year fixed effects
University-field fixed effects
YES
YES
YES
YES
YES
YES
N observations
R-squared
656
0.250
501
0.220
I
II
Full sample
Males
0.904**
(0.045)
0.880**
(0.052)
Other controls
Graduation year fixed effects
University-field fixed effects
YES
YES
YES
YES
YES
YES
N observations
R-squared
643
0.249
489
0.225
Panel A - DV: Dummy =1 for PhD graduates with a publication count
higher than their field’s average
Main variable
PhD cohort size
Panel B - DV: Dummy indicating if the PhD graduates had a publication
count higher than their field’s average
Main variable
PhD cohort size
Note: ***, **,*: Significantly different from zero at the 1%, 5%, 10% confidence levels. In Panel B, we exclude those EPFL PhD graduates
who were still affiliated with their graduation institution, but were employed as postdocs in a different research group than the one of their
supervisor. Robust standard errors (in parentheses) are clustered around PhD graduates' supervisors. Coefficients are odds ratios. A
coefficient smaller (larger) than one reflects a negative (positive) effect.
Table A1: Details on the variables' construction
Variable
Description
Source
PhD cohort size
# of PhD students who graduated in the same year and in the same field as PhD graduate i . These
students were enrolled with either EPFL or ETH, if i is from EPFL, and with Chalmers or KTH, if i is
from Chalmers
Universitis' HR, Countries' Statistical Offices
Employment conditions at enrollment
GDP growth, at entry
For Sweden, the variable is =0 in the years 2008-2009, =1 in 2001, =2 in 2002, 2003, 2005, and 2007,
=3 in 1999, 2000, 2004, and 2006. For Switzerland, the variable is =0 in 2009, =1 in 2002 and 2003, World Bank
=2 in 1999, 2001, 2004, 2005, and 2008, =3 in 2000, 2006, and 2007
GDP growth in master's country
GDP growth for the country in which PhD graduate i had obtained her master's degree
World Bank
# PhD graduations in student i 's enrollement year and in
the prior year
# of PhD students who graduated in the same year as i 's enrollment year, or in the prior year. We
distinguish between basic science and engineering PhD graduates, depending on i 's specialization
World Bank
Employment conditions at graduation
GDP growth, at graduation
For Sweden, the variable is =0 in the years 2008-2009, =1 in 2001, =2 in 2002, 2003, 2005, and 2007,
=3 in 1999, 2000, 2004, and 2006. For Switzerland, the variable is =0 in 2009, =1 in 2002 and 2003, World Bank
=2 in 1999, 2001, 2004, 2005, and 2008, =3 in 2000, 2006, and 2007
Net supply of postdoc positions in the US
Difference between the number of postdocs hired, on a given year, by US research institutions and the NSF (Survey of Graduate Students and
# of US students who, in that year, obtained their PhD in the same field as i
Postdoctorates in Science and Engineering)
# Professors in graduation's country
# Graduation country's EPO patent applications
# of professors affiliated with EPFL or Chalmers, during PhD student i 's graduation year, for the field in
Universitis' HR, Countries' Statistical Offices
which i is specialized
# of patent applications that Sweden and Switzerland had filed at the European Patent Office in the
OECD
same year as i 's graduation year
PhD graduate characteristics
EU-15 nationality
=1 if PhD graduate is from a EU-15 country
Universities' HR
Non-EU-15 nationality
=1 if PhD graduate is not from a EU-15 country
Universities' HR
Age
Female
# Publications during PhD
PhD student age at graduation
=1 if PhD graduate is female
Natural log of a PhD graduate’s publication count (plus one)
Universities' HR
Universities' HR
Scopus
Involved in applied projects during PhD
=1 if a PhD graduate was granted at least one patent, had published articles with industrial partners, or
Thomson Reuters, Scopus, PhD graduates' CVs
had worked with a company during her PhD
Worked prior to PhD
=1 if PhD graduate worked prior to her PhD
PhD graduates' CVs
Natural log of a supervisor's # of articles published in the 5 years prior to PhD student i 's arrival
=0 if a supervisor was not granted any patent in the 5 years prior to i 's arrival, =1 if she was granted a
# of patents > than 0 and < than 3, and =2 if she was granted more than 2 patents
=1 if a supervisor had worked in industry prior to her current appointment
=1 if a supervisor was involved in European projects with industrial partners in the 5 years prior to i 's
arrival
Scopus
Supervisor characteristics
Pre-sample publications
Patenting activity
With prior working experience in industry
Involved in EU projects with industrial partners
Thomson Reuters
Supervisors' CVs
European Commission CORDIS website