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Research Policy 39 (2010) 422434
Contents lists available at ScienceDirect
Research Policy
journa l homepage: www.e lsev ier .com
A taste rieresearc
Michaela Kenan-Flaglerb College of Ma , USA
a r t i c l
Article history:Received 29 JuReceived in re25 December
2Accepted 11 JaAvailable onlin
Keywords:Industrial R&DAcademic scieMotivesTaste for
scienCareer choice
emicHowpotence anesirel chaest tconco PhDrese
1. Introduction
Over the past decade there has been a growing interest in
therole of acadand performtrial scientienhancing a1998; Zucke2009).
Studperformingby offeringscientic coexploit entDing, 2006scholars
haships betwtheir innova2009).
An undedemically te.g., prefereresearch pr
CorresponE-mail add
henry.sauerma1 These auth
tic community, while industrial employers tend to restrict
suchactivities (Kornhauser, 1962; Blume, 1974; Stern, 2004;
Aghionet al., 2008; Lacetera, 2009). Consequently, it has been
argued
0048-7333/$ doi:10.1016/j.emically trained industrial scientists
in rm innovationance. Much of this research has focused on
indus-
sts as conduits for accessing university research and asrms
absorptive capacity (Cockburn and Henderson,r et al., 1998, 2002;
Gittelman and Kogut, 2003; Roach,ies have also shown that rms try
to attract high-graduates by creating academic environments,
e.g.,opportunities to publish and interact with the largermmunity,
and that academic scientists increasinglyrepreneurial opportunities
(Stern, 2004; Stuart and; Bercovitz and Feldman, 2008; Ding, 2009).
Finally,ve begun to examine more systematically the relation-een
industrial scientists motives and incentives andtive activities
(Sauermann andCohen, 2008;Haeussler,
rlying theme in much of this research is that aca-rained
scientists have a strong taste for science,nces for upstream
research, for freedom in choosingojects, publishing, and
interactions with the scien-
ding author. Tel.: +1 404 385 4883.resses: [email protected]
(M. Roach),[email protected] (H. Sauermann).ors contributed equally
to this work.
that relaxing such constraints should increase industrial
scientistsinteractions with the scientic community and also make
industryamoreattractive career option for future cohorts of PhDs.
This focuson rm policies as drivers of scientists research
activities ignorespotential heterogeneity across researchers and,
in particular, self-selection into industrial versus academic
careers. We suggest thatthose PhDswho self-select into industrial
careersmay be less inter-ested in nding their own research
projects, interacting with otherscientists at scientic conferences,
or publishing and keeping upwith the broader literature than those
PhDs who decide to pursuean academic career.While recent empirical
work has begun to con-trast academic and industrial scientists
along a range of dimensions(SauermannandStephan, 2009), PhDs career
choices and their self-selection into industrial R&D as a
potential driver of differencesacross sectors have been virtually
unexplored.
To address this gap, we study PhD students preferences
foremployment in industry versus academia and examine to whatextent
those students aspiring to an industrial career differ
system-atically from those seeking employment in academia.We
surveyedover 400 PhD students in science and engineering elds at
threeTier 1 research universities in the United States. Using this
uniquesurvey data set, we can gain deeper insights into the career
choiceprocess at a very early stage, i.e., prior to the actual
decision,rather than inferring drivers of employment choices ex
post fromobserved employment patterns. Our empirical strategy is to
relate
see front matter 2010 Elsevier B.V. All rights
reserved.respol.2010.01.004for science? PhD scientists academic oh
careers in industry
Roacha,1, Henry Sauermannb,,1
Business School, The University of North Carolina, Chapel Hill,
NC 27599, USAnagement, Georgia Institute of Technology, 800 W.
Peachtree Street, Atlanta, GA 30308
e i n f o
ly 2009vised form009nuary 2010e 10 February 2010
nce
ce
a b s t r a c t
Recent research on industrial and acadtists have a strong taste
for science.in researchers taste for science and toUsing survey
data from over 400 scienPhD students taste for science (e.g., din
basic research) and other individuatry versus academia. Our results
suggweaker taste for science, a greaterest in downstream work
compared timportant implications for innovation/ locate /
respol
ntation and self-selection into
science draws on the notion that academically trained
scien-ever, little attention has been paid to potential
heterogeneitytial selection effects into careers in industry versus
academia.d engineering PhD students, we examine the extent to
whichfor independence, publishing, peer recognition, and
interestracteristics predict preferences for research careers in
indus-hat PhD students who prefer industrial employment show aern
for salary and access to resources, and a stronger inter-students
who prefer an academic career. Our ndings have
arch as well as for managers and policy makers. 2010 Elsevier
B.V. All rights reserved.
-
M. Roach, H. Sauermann / Research Policy 39 (2010) 422434
423
students preferences for employment in industry and academia toa
range of variables including respondents preferences for variousjob
attributes (e.g., how important is freedom to me?),
studentsexpectations regarding the actual availability of those job
attributesin differentperceived atal norms rstudents pu
We ndnicantly pacademia, wjob attributdents withpreferenceto
publish aprefer acadhand, indivas well asand develorms. Indivfer
employmthose concefer a careerdoes not pretions aremostudents exin
industrypreferencesthat PhDs cvery differesciences or
Althougraise the poweaker tasplifying assa taste forconsider
thcomes of inactivities ofby a low deOur ndingtion, scientows,
whiland policy m
2. Backgro
2.1. Science
Upon enknowledgeand techno1994; Cock2006). PhDresearch anpatent
and2008). A pemployed intic commusocieties, asand Hendeso, PhDs
prand are likeity (CohenRoach, 2009
Research on these open science activities in industry typi-cally
focuses on rms policies as primary constraints, implicitlyassuming
that industrial scientists have a strong preference forengaging in
these activities (Henderson and Cockburn, 1994; Stern,
Aghithatudywhotherof pn ofhat sr thpur
ivelyto ential
ctivits, itical
ticulhDsiaw
thoseomnt faam rand i
ior re
onsidies of
acar etan, 2e exas in intipplyon. Ftoratstudembesugggetsscriphis
as at te rolnce
ow d
demo codomy. Wmatly sey rewand rer searing1994types of careers,
students desired type of research,vailability of different types of
positions, departmen-egarding employment in industry and academia,
andblishing and patenting performance.that PhDs preferences for
various job attributes sig-redict a preference for employment in
industry versushile expectations regarding the actual availability
of
es have little effect. More precisely, we nd that stu-a strong
taste for sciencein particular, a strong
for freedom to choose research projects and the abilitys well as
the desire to conduct basic researchstronglyemic careers over
careers in industry. On the otheriduals concerned with salary and
access to resources,the desire to conduct downstream applied
researchpment are more likely to prefer careers in
establishediduals who value responsibility are more likely to
pre-ent in startups over employment in academia, while
rnedwith job security are signicantly less likely topre-in
startups. Although students prior patenting activitydict career
preferences, individualswithmore publica-re likely to prefer
academic employment. Finally,whilepectations regarding the
availability of job attributesand academia have little association
with their career, a descriptive analysis of these expectations
suggestsonsider academic and industrial research careers to bent,
and no less so in the life sciences than in the
physicalengineering.hwedonot observe actual career transitions, our
resultsssibility that PhDs who work in industry may have ate for
science than academic scientists. Hence, the sim-umption that all
academically trained scientists sharescience may be misleading and
future work should
e strength of a taste for science and how it relates to
out-terest. For example, it is conceivable that open sciencerms are
constrained not only by rm policies but alsosire of industrial
scientists to engage in such activities.s contribute to research on
the management of innova-ic labor markets, and university-industry
knowledgee also suggesting concrete implications for
managersakers.
und
and engineering PhDs and rm innovation
tering industrial employment, PhDs bring with themand skills
that often reect the frontiers of science
logy in their particular elds (Rosenberg, 1985; Brooks,burn and
Henderson, 1998; Cohen et al., 2002; Stephan,scientists and
engineers tend to be engaged in upstreamd are responsible for a
disproportionate share of thepublication output in rms (Sauermann
and Cohen,articularly important aspect of the work of PhDsindustry
is thenurturingof tieswith thebroader scien-nity, e.g., via
publishing, memberships in professionalwell as attendance at
professional meetings (Cockburn
rson, 1998; Sauermann and Stephan, 2009). By doingovide rms with
access to critical knowledge channelsly to be key determinants of a
rms absorptive capac-and Levinthal, 1990; Cockburn and Henderson,
1998;).
2004;lishedthis stdegreefor it,insteadquestiosense tto
entesciencea relatdesirea potensuch a
ThuacademOf parating Pacademand ifcally
frdiffereupstrelenge,
2.2. Pr
A cactivittry and(ZuckeFeldmies havcareeron scieand suselectiof
Docber ofthe nusector,also Retant dePhDs, tprocesinto thfor
scietories.
2.3. H
Acaplace tof freesecuritalwaystypicalprimartationa broadand
shDavid,on et al., 2008). Sterns (2004) seminal study has
estab-industrial scientists have a taste for science. However,also
suggests that not all of them do so to the sameile some scientists
value publishing enough to pays are willing to take contracts that
offer more moneyublishing. This interpretation of Sterns study
raises thewhether there is also a systematic self-selection in
thecientists with a weaker taste for science are more likelye
industrial sector while those with a strong taste forsue careers in
academia. If such self-selection leads toweak taste of science in
industry, scientists lack of agage in open science activities
shouldbe consideredasconstraint in addition to any rm policies
discouragingies.seems critical to gain a better understanding of
howly trained PhDs decide to seek employment in industry.ar
interest is a deeper understanding of how gradu-perceive
differences in careers between industry andith respect to
opportunities to engage in open sciencePhDs who prefer an industry
career differ systemati-
those preferring a career in academia with respect tocets of the
taste for science, e.g., their preferences foresearch, publishing,
peer recognition, intellectual chal-ntellectual freedom.
search on S&E PhD employment choices
erable body of research has investigated innovativeestablished
scientists and engineers working in indus-demia, or at the
intersection between the two sectorsal., 2002; Thursby and Thursby,
2004; Bercovitz and008; Sauermann and Cohen, 2008). However, few
stud-mined the initial decisions of junior scientists to
pursuendustry or academia. The existing empirical researchc careers
has emphasized the role of aggregate demandbut tends to overlook
individuals preferences and self-or example, using survey data and
data from the Surveye Recipients, Fox and Stephan (2001) nd that
the num-nts aspiring to become faculty members is larger thanr of
those who will actually nd employment in thatesting imbalances in
the scientic labor market (see
, 1998; Davis, 2005). However, while providing impor-tive data
on career patterns of science and engineeringggregate perspective
does not address the career choicehe level of the individual and
provides limited insightse of individual differences such as in
researchers tasteas potentially important factors affecting career
trajec-
o careers in industry and academia differ?
ia has traditionally been seen as the most desirablenduct
science, offering faculty members a high degree, sufcient resources
to conduct research, as well as jobhile salary and other forms of
pecuniary benets havetered to academics (Stephan and Levin, 1992)
theywereen as less important than in commercial science. Theards in
academic science are said to be related to repu-ecognition in the
community of scholars, embedded int of norms emphasizing priority
in discovery, openness, and academic freedom (Merton, 1973;
Dasgupta and; Stephan, 1996; Sorenson and Fleming, 2004).
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424 M. Roach, H. Sauermann / Research Policy 39 (2010)
422434
The attractiveness of academic positions may have decreasedin
recent years, however. One claim is that it has become moredifcult
to obtain resources and that academics have to spend aconsiderable
amount of time to secure funding from outside agen-cies and
spodependencedominchoofunding age1990; Vallain commercsored
reseapressures thparticular, ftransfer ofdemic
sciencommerciainstitution ocially inmeguaranteedWhile thesea
departurejob attributand Liebeskand Rhoade
Industryto scientistthe key advStephan, 20more attracsidering
the1990; Vallahave historhas been suand that indtain nonpecbenets
ofmunity, mainteract wiincentivesexample, ssiderpublisdecisions
anbemore innHenderson,
Firms mscientists, eof researchdence thatatmosphere2007).
ForAmerican Ssenior reseabout a sciethat researctives of thegood
fundinties to pubDespite thecerns regardto graduateR&D has
bepriate the rmay limit sresults and
While mR&D in larg
ent in startup organizations. Prior studies have shown that
smalland young rms tend to offer lower salaries and lower levels
ofjob security (Oi and Idson, 1999; Carroll and Hannan, 2000;
Brownand Medoff, 2003) while potentially offering more
independence,
ctual(Idsartupers),iallyientian aThusrk enuniti
ncep
ine wceptn estpayof fuA rsploye) arheyle, soothendener prer
thond,ing thing thshedttribrs, cally cticipary. Thualsthe
sationcha
er, erd, Pymenepar006eldsployeptated iditioceivshortandcausn
anns tht lookhe foom oescrites, oers innsors (Hackett, 1990). It
has also been argued that thison funding agencies has constrained
academics free-sing research topicsbecauseof the strong interest
somencies have in the direction of the research (Hackett,s and
Kleinman, 2008). Universities increasing interestial activities,
including patenting, licensing, and spon-rchmay also impose
additional constraints and result inat are not typically associated
with open science. Inaculty may have to spend time dealing with
technologyces and rms, and it is increasingly common for aca-tists
to delay the publication of research results due tol considerations
(Murray and Stern, 2007). Finally, thef tenure is losing some of
its traditional benets, espe-
dical schools, where tenure increasingly comeswithoutsalary and
research funds (Bunton and Mallon, 2007).trends are likely gradual,
they suggest the potential forfrom the norms of science and a
deterioration in somees that academics have traditionally valued
(Argyresind, 1998; Owen-Smith and Powell, 2001; Slaughters, 2004).,
on the other hand, has long offered higher salariess and engineers,
and this salary gap remains one ofantages of employment in industry
(Sauermann and09). Observers also claim that industry has
becometive with respect to research funding, especially
con-deteriorating funding conditions in academia (Hackett,s and
Kleinman, 2008). While industry and academiaically offered very
different research environments, itggested that the sectors are
converging in various waysustry has become more desirable with
respect to cer-uniary job attributes. First, recognizing the
potentialR&D employees involvement with the scientic com-ny rms
now allow their scientists and engineers toth the scientic
community and some even structureand rewards to encourage
professional activities. Forome rms in the biomedical domain
explicitly con-hingandotherprofessional activities in
theirpromotiond there is some evidence that rms that do so tend
toovative (Henderson andCockburn, 1994; Cockburn and1998; Stern,
2004; Ding, 2009).ay also offer signicant levels of freedom to
their PhDspecially to those engaged in more exploratory
kinds(Vallas and Kleinman, 2008). Moreover, there is evi-some rms
actively try to signal such an academicto graduating students
(Henderson, 1994; Copeland,
example, in a recent article in the magazine of theociety for
Biochemistry and Molecular Biology, a GSKarcher explicitly points
out several misconceptionsntic career in the biomedical industry
and suggestshers have, within broad limits set by the general
objec-company, a considerable amount of freedom, veryg to pursue
their research, and plenty of opportuni-
lish and present research ndings (Copeland, 2007).se potential
improvements, however, long-held con-ing industry employmentmay
remain valid and salients. For example, despite the possibility
that industrialcome more open, rms still rely on secrecy to
appro-eturns from their innovations (Cohen et al., 2000).
Thiscientists ability to openly disclose and share researchto
participate in the broader scientic enterprise.uch of the
discussion on industrial science focuses one established rms,
science may look somewhat differ-
intellebilitiesthat stmembpotentthe scGittelm2009).ent
woopport
2.4. Co
In lweconor in asuch asability2005).for emvariablwhat
texampwhileindepestrongto pref
Secregardregardestablithese aadvisoposefuor parindustindividact
inexpecting thehowev
Thiemplotheir dDing, 2Some try emas accinteresket conthe perare
in2005)tive bepositiopositiomarke
In tdata frvide dattribuof carechallenge and more opportunities
to take on responsi-on, 1990; Sauermann and Stephan, 2009). To the
extent
rms have academic roots (e.g., founded by facultythey may also
have a more academic atmosphere,allowing their employees to
interact more freely withc community (Etzkowitz, 1998; Zucker et
al., 2002;nd Kogut, 2003; Owen-Smith and Powell, 2004; Ding,,
within industry, startups may offer somewhat differ-vironments than
established rms, in particular, morees to participate in open
science.
tual model of career preferences
ith prior work on decision making and career choice,ualize each
career option (e.g., employment in academiaablished rm) as
characterized by a vector of attributes, intellectual freedom,
opportunities to publish, or avail-nding (Rosen, 1986; Payne et
al., 1993; Sauermann,t set of factors thatmay inuence students
preferencesment in industry versus academia (our key dependente
students preferences for particular job attributes, i.e.,care about
and what they are looking for in a job. Forme graduates might care
strongly about high salary,rs may nd it more important to be able
to maket decisions about their research agenda. Students with
eferences for a particular attribute should bemore likelye
option that offers relatively more of that attribute.career choices
may depend on students expectationse particular characteristics of
different career options, e.g.,e levels of pay and freedom
available in academia or inrms. PhD students may form expectations
regardingutes as a byproduct of other activities (e.g., by
observingsual conversations with friends, etc.) but may also
pur-ollect such information, e.g., by attending career fairsting in
internships designed to provide exposure toeoretically,
expectations regarding job attributes and
preferences regarding those attributes should inter-ense that
stronger preferences increase the effects ofs on career
preferences. Note that expectations regard-racteristics of
employment options may be inaccurate;ven inaccurate expectations
may affect choices.hD candidates preferences for industry and
academict may also be shaped by social inuences and norms intments
and larger social environment (cf. Stuart and; Azoulay et al.,
2007; Bercovitz and Feldman, 2008).and some institutions have a
longer history of indus-
ment, which may affect what PhD students perceiveble or
desirable jobs. Finally, while we are primarilyn students career
preferences regardless of labor mar-ns, it is likely that students
preferences are shaped by
ed availability of positions. For example, faculty
positionssupply in many elds (Fox and Stephan, 2001; Davis,
students might rate an academic career as less attrac-e of the
anticipated struggles to obtain a tenure-trackd, ultimately,
tenure. Again, it is not the actual supply ofat matters, but
students perceptions of what the labors like.llowing empirical
section of this paper, we use surveyver 400 science and engineering
PhD students to pro-ptive data on students preferences for a range
of jobn students expectations regarding the actual
attributesindustry and academia, and about their preferences
for
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M. Roach, H. Sauermann / Research Policy 39 (2010) 422434
425
research careers in established rms, startup rms, and
academia.We then examine to what extent career preferences are
associatedwith the other key variables.
3. Data and measures
3.1. Sample and survey methodology
We surving elds aNorth CaroWe chose temployed Pthe actual
edirect insighave to relyon ex post e
We app(Dillman, 20Engineeringby the caremany non-ate studentat
one of tto distributgraduate stuthe survey padministratistrators
toincluded athe surveysmode. For thby email anthe
surveyadministratafter our in
Overall,currently eresponse rawe approacable to calcby
adminisestablish reand how manalysis bavey (Rogelbcloser to gring
that moto respondwe excludemation (e.gnumber of v
For thesusing multigeneral pur1987; KingCummings,are predictthe
compledistributionorder to geuncertainty
2 Approximhigher year of
then estimated from all imputations and estimates are
averagedwith appropriate adjustments to standard errors. While
otherimputation methods such as mean substitution or hotdeck
impu-tation articially reduce the standard errors around
estimates,multiple imputation avoids this bias by virtue of using
multiplepredictions for each missing value.
easures
survey instrument included closed-ended as well as
open-queskeyres.correis sec
Attraprim
ss ofstabposi
youpraduareerive).
Prefeasketionden
1=nohosephaic s
dinguipmge,
ther ih, anen threlatfy theetabprooratgainic sc
we ates t,wed astud
Additsurvve saskele ince (p
also cvailabs usinyses btableeyed students pursuing a PhD in
science or engineer-t three major research universities in the U.S.
state oflina, including one private and two public institutions.o
survey current PhD students rather than currentlyhDs to obtain
responses on career preferences prior tomployment decision. Thus,
our data enable us to gainhts into PhDs career decision processes
and we do noton retrospective reports or on indirect inferences
basedmployment patterns.roached respondents using a mixed-mode
strategy07). First, we attended the North Carolina Science
andCareer Fair,which is anannual event jointly organized
er centers of the three universities and which attractsacademic
employers. Second, we contacted the gradu-administrators at science
and engineering departmentshe three institutions and asked them for
permissione printed questionnaires with return envelopes to
thedentsmail boxesor labs. All administrators agreedandacketswere
distributed by either the researchers or theors. After
approximately 3 weeks, we asked the admin-forward a reminder email
to students; the email alsolink to the online version of the
survey. We conductedat the other two institutions exclusively in
the onlineat purpose,we contacted departmental administratorsd
asked them to forward an email with a description ofand the
appropriate link to their graduate students. Allors were asked to
forward a reminder email 12 daysitial request.we obtained 472
responses from students who werenrolled in a science or engineering
PhD program. Thete at the career fair was very high; almost all
studentshed completed the survey while at the fair. We are notulate
the response rate for questionnaires distributedtrators at the
campuses, however, because we cannotliably which administrators
forwarded our requestsany students received it. We conducted a
non-responsesed on the number of missing items in the online
sur-erg and Stanton, 2007); these tests show that studentsaduation
tend to have fewer missing items, suggest-re advanced students may
also have been more likelyto the survey.2 For the analyses reported
in this paper,d 46 cases because they were either missing key
infor-., eld of study) or because they were missing a
largeariables, leaving us with 426 useable cases.e remaining 426
cases, we imputed missing dataple imputation, which is currently
the most advancedpose method to account for item non-response
(Rubin,et al., 2001; Schafer and Graham, 2002; Fichman and2003). In
multiple imputation, missing data elds
ed based on regression equations estimated usingte cases and
including a random draw from an error. This process is repeated
multiple (m=8) times innerate variation around the prediction,
reecting theassociated with missing data. Regression models are
ately 70% of the students in our nal sample were in the third or
atheir PhD program.
3.2. M
Ourendedof ourmeasushowsanalys
3.2.1.Our
tivenein an efacultywouldafter geach cattract
3.2.2.We
graduaResponscale (were cand Steacademof
fungies/eqchallenwithoresearc
Givtuallysimpliinterprchoosecollabnity
toacademThus,attribuSeconding anreect
3.2.3.Our
may halar, weavailabforman
3 Weing the ameasuretor analinterpretions. We will rst provide a
more detailed discussionmeasures and will then provide an overview
of otherDescriptive statistics are provided in Table 1; Table
2lations. The open-ended questions are discussed in thetion.
ctiveness of career optionsary dependent variables are measures
of the attrac-
three distinct research career paths: an R&D positionlished
rm, an R&D position in a startup rm, and ation at a university.
We asked students How attractiveersonally nd eachof the following
employment optionsation, assuming you have the choice? Students
ratedoption on a 5-point scale (1 =not attractive, 5 = very
rences for job attributesd students When thinking about
employment after
, how important to you are the following job attributes?ts rated
the importance of 10 job attributes on a 5-pointt important, 5 =
very important). The 10 job attributesn based on prior work (e.g.,
Stern, 2004; Sauermannn, 2009) and on our own interviews with
industrial andcientists and include: salary and benets,
availabilityand resources, availability of cutting-edge
technolo-ent, job security, responsibility on the job,
intellectual
ability to gain peer recognition, ability to
collaboratenstitutions/organizations, ability topresent andpublishd
freedom to choose projects.at a number of our preference attributes
are concep-ed, we created two index measures (cf. Stern, 2004)
toregression analysis and tomake the resultsmore easilyle. First,
we suggest that the job attributes freedom tojects, opportunities
to publish and present research,e with others outside the
organization and opportu-peer recognition all are traditionally
associated withience (Merton, 1973; Stern, 2004; Wuchty et al.,
2007).veraged students ratings of the importance of theseo create
an index measure of their taste for science.averaged students
preferences for availability of fund-ccess to cutting-edge
technologies and equipment toents desire for access to
resources.3
ional featured variables and controlsey also included questions
regarding other factors thatignicant effects on students career
choices. In particu-d students about their perceptions of the job
attributesthe different kinds of careers, about their research
per-atent and publication counts) and how interested they
reated equivalent index measures of students expectations
regard-ility of these job attributes (see below). We chose to
create all indexg a simple average rather than weighted averages
derived from fac-ecause simple averages ensure that the indices are
comparable andacross career options.
-
426 M. Roach, H. Sauermann / Research Policy 39 (2010)
422434
Table 1Descriptive statistics.
Variable name Type Mean SD Min Max
Attractiveness of careers Established rm 5-Point 3.51 1.37 1
5
Most attract
Preferences
Work desire
Availability
Norms
Performance
Major eld
Controls
are in workopment). Wregarding cis for graducareer optioof
labormajobs in acadeld.
4. Results
4.1. Key des
4.1.1. ExpecWe aske
3=high) thwere availarespectivelytion aboutDont
knowfrequently(average 15sistent withabout the chA
comparisattributes shlevels and lallow scienStartup 5-PointFaculty
5-Point
ive career Established rm DummyStartup DummyFaculty Dummy
for job attributes Intellectual challenge 5-PointFunding and
resources 5-PointJob security 5-PointSalary and benets
5-PointResponsibility 5-PointFreedom to choose 5-PointCutting-edge
tech/equip 5-PointAbility to collaborate 5-PointPublishing
5-PointPeer recognition 5-Point
d Basic 5-PointApplied 5-PointDevelopment 5-PointManagement
5-Point
of jobs Established rm 5-PointStartup 5-PointFaculty 5-Point
Established rm 5-PointStartup 5-PointFaculty 5-Point
Patents yes/no DummyPublications Count
Life sciences DummyPhysical and applied sciences
DummyEngineering DummyYears in program CountMale DummyMarried
DummyNationality USA Dummy
ing on different types of R&D (e.g., basic, applied, devel-e
obtained information about departmental norms
areer choices by asking respondents how common itates in their
department to pursue each of the differentns. Finally, we asked
students about their perceptions
rket conditions by asking them to rate the availability ofemia,
startups, and established rms in their particular
criptive results
tations regarding job attributesd our respondents to rate on a
3-point scale (1 = low,e extent to which they thought the 10 job
attributesble in an established rm, startup, and university,. In
order to assess respondents level of informa-the three employment
options, we also included a box. Table 3 shows that PhDs checked
this box quite
for established rms (average 10%) and for startups%), while only
rarely for universities (1%). This is con-our expectation that PhDs
feel much better informedaracteristics of employment in academia
than industry.on of the dont know response frequency across jobows
thatPhDs felt generallybest informedabout salary
east informed about the degree to which organizationstists to
collaborate with outsiders.
Table 3larly strongsalary anding. The attprojects, thto
collaboraoffering quiity and intesignicantlestablished
Expectaferent. Thepublish reselectual chalreadily avai
The lasttations for eadvantagesconditionaltations forperceived
awhile univeoration, fre
An interbetween emsmaller inresearch eKleinman,2.90 1.28 1 53.49
1.42 1 5
0.57 0.50 0 10.29 0.45 0 10.55 0.50 0 1
4.37 0.71 2 54.25 0.93 1 54.11 0.89 1 54.04 0.86 1 53.94 0.78 1
53.77 1.07 1 53.71 1.03 1 53.70 1.08 1 53.54 1.26 1 53.27 1.04 1
5
3.49 1.26 1 53.98 1.17 1 53.24 1.27 1 52.55 1.33 1 5
3.12 0.97 1 52.83 0.95 1 52.51 0.91 1 5
3.20 1.14 1 52.46 1.08 1 53.07 1.29 1 5
0.07 0.25 0 12.23 2.53 0 18
0.56 0.50 0 10.30 0.46 0 10.14 0.35 0 13.55 1.70 1 80.46 0.50 0
10.42 0.49 0 10.78 0.42 0 1
also shows that established rms are seen as particu-with respect
to job attributes that require resources:
benets, access to cutting-edge technology, and fund-ributes
judged as least available are freedom to choosee ability to present
and publish research, and the abilityte with outsiders. Students
seem to think of startups aste low levels of almost all attributes
except responsibil-llectual challenge. While startups are judged as
offeringy higher levels of freedom and ability to publish thanrms,
the perceived advantage is relatively small.
tions regarding university employment look quite dif-highest
ranked items are the ability to present andarch, the ability to
collaboratewithoutsiders, and intel-lenge; salary and funding and
resources are judged leastlable.column in Table 3 shows the
difference between expec-stablished rms and for universities; i.e.,
the perceivedand disadvantages of these two careers. We see
that,upon respondents having sufciently dened expec-
both established rms and university, rms tend to bes clearly
superior with respect to salary and resources,rsities have a strong
advantage with respect to collab-edom to choose projects, and the
ability to publish.esting question is whether the perceived
differencesployment in established rms and in academia are
the life sciences, reecting a convergence of thenvironments in
industry and academia (Vallas and2008). In Fig. 1, we show the
difference in expecta-
-
M. Roach, H. Sauermann / Research Policy 39 (2010) 422434
427
Table
2Cor
rela
tion
s.
12
34
56
78
910
1112
1314
1516
1.Attra
ctiv
enes
sof
esta
blished
rm
2.Attra
ctiv
enes
sof
star
tup
0.65
32*
3.Attra
ctiv
enes
sof
facu
lty
0.3
332*
0.2
537*
4.Bas
icre
sear
ch0
.137
8*0
.129
2*0.
3100
*
5.Applied
rese
arch
0.38
40*
0.25
96*
0.0
963*
0.11
22*
6.Dev
elop
men
t0.
5311
*0.
4169
*0
.251
4*0
.140
7*0.
4250
*
7.M
anag
emen
t0.
2315
*0.
2020
*0
.108
9*0
.313
9*0.
1383
*0.
2861
*
8.Pr
ef:In
tellec
tual
chal
lenge
0.0
881
0.02
130.
1527
*0.
2347
*0.
0984
*0.
0100
0.0
350
9.Pr
ef:Abi
lity
toco
llab
orat
e0
.127
3*0
.049
80.
2879
*0.
3827
*0.
1934
*0
.007
00.
0092
0.30
86*
10.P
ref:
Cuttin
g-ed
gete
ch/e
quip
0.24
30*
0.21
24*
0.00
290.
2379
*0.
2774
*0.
2541
*0.
0440
0.36
55*
0.41
64*
11.P
ref:
Free
dom
0.2
517*
0.1
474*
0.37
33*
0.38
86*
0.08
140
.053
70
.055
00.
4738
*0.
4985
*0.
2524
*
12.P
ref:
Fundin
g0.
0760
0.0
065
0.18
15*
0.38
86*
0.29
55*
0.08
230
.056
90.
2464
*0.
4528
*0.
4655
*0.
3986
*
13.P
ref:
Publ
ishin
g0
.202
3*0
.168
2*0.
4031
*0.
5202
*0.
1700
*0
.123
2*0
.136
9*0.
4096
*0.
6117
*0.
3839
*0.
6382
*0.
5331
*
14.P
ref:
Peer
reco
gnitio
n0
.036
10
.073
10.
1530
*0.
2245
*0.
0574
0.05
400.
1282
*0.
3060
*0.
4256
*0.
3588
*0.
3964
*0.
2996
*0.
4931
*
15.P
ref:
Res
pon
sibi
lity
0.18
22*
0.13
61*
0.04
430
.039
30.
1956
*0.
1954
*0.
2673
*0.
3393
*0.
1499
*0.
2373
*0.
1892
*0.
1683
*0.
1717
*0.
3328
*
16.P
ref:
Sala
ry0.
3064
*0.
1538
*0
.070
10
.080
30.
1625
*0.
2054
*0.
1773
*0
.028
20
.055
50.
1798
*0
.063
60.
1313
*0
.068
50.
0585
0.29
89*
17.P
ref:
Job
secu
rity
0.18
04*
0.0
121
0.04
560.
1129
*0.
2320
*0.
1819
*0
.010
80.
0287
0.12
16*
0.14
23*
0.11
10*
0.19
42*
0.11
38*
0.16
25*
0.31
22*
0.42
83*
*Si
gni
cantat
5%.
Fig. 1. D
tions (estabattributes aphysical sciferences betend to be sand
the perand freedomneering. Thto think ofthe student
Wealsoversus acadasked respoin an establarly intereinclude:
Establish
Inabilitypany else
Just anot Restricti The inab
command Not bein
Startup
High prenvironmnesses!)
Question Low job May hav
sibilities Not havi
Universi
The cons Pressure Lack of s
the public Professo Too mu
instead o
We codeset of commifference in expectations (established
rmacademia) by eld.
lished rmacademia) by broadly dened eld for keynd nd only small
differences across the life sciences,ences, and engineering.
Surprisingly, the perceived dif-tween research in established rms
and in academiaomewhat larger in the life sciences than in other
elds,ceived industryacademia gap with respect to fundingis
signicantly larger in the life sciences than in engi-
us, the life sciences students in our sample do not seemindustry
and academia as being any less different thans in other
elds.elicited expectations regarding employment in industryemia
using an open-ended question. In particular, wendents What would
you dislike most about a careerlished rm, startup, and a
university? Some particu-sting (though not necessarily
representative) responses
ed rm
topursue an interestingproject ifmoney leads the com-where.her
nameless face, routine, boredom.on of projects and/or limited
chance to share/publish.ility to work with everyone and following
the chains of.g awarded respect for my time and personal life.
rm
obability of tension and frustration due to unstableent (many
scientists are NOT good at starting up busi-.s about long-term
viability of the rm.security, potentially low salary.e to wear many
hats rather than have specic respon-.ng prestige of established
rm.ty
tant struggle and competition to get funding.to publish;
colleagues overly concernedwith prestige.upport for components of
career other than research,ation or perish problem.rs are AWFUL
managers and dont try to improve.ch management involved: you are
the team leader,f the researcher.
d the answers to these questions to reect a smalleron issues.
The overriding concern about employment
-
428 M. Roach, H. Sauermann / Research Policy 39 (2010)
422434
Table 3Expectations regarding the availability of job attributes
in different careers (3-point scale).
Established rm Startup University Estab-Univ.
Mean Dont know Mean Dont know Mean Dont know
Salary and benets 2.87 7% 1.95 12% 1.84 1% 1.04Cutting-edge
tech/equip 2.71 9% 1.95 14% 2.20 2% 0.51Funding and resources 2.64
10% 1.68 15% 1.90 2% 0.73Responsibility 2.42 8% 2.80 13% 2.64 1%
0.22Job security 2.27 8% 1.22 11% 2.44 1% 0.17Intellectual
challenge 2.22 9% 2.71 13% 2.87 1% 0.65Peer recognition 1.80 13%
1.90 17% 2.78 1% 0.98Ability to collaborate 1.74 14% 1.94 19% 2.88
2% 1.14Publishing 1.60 11% 1.77 17% 2.96 1% 1.36Freedom to choose
1.33 12% 1.82 15% 2.75 1% 1.43Mean 2.16 10% 1.97 15%
in established rms appears to be the perceived lack of freedom
invarious forms, which was mentioned by 32% of the respondents.The
largest concern about employment in startups is the lack
ofstability and job security, cited by 71% of the respondents.
Themainconcerns ablow pay, cit
In interpbe noted thsystematicacannot asseeven inaccudents job
s
4.1.2. PrefeThe mea
shown in Tasalary are gnition and platter ndinleast
somecommonlypreferencegesting signthus the poences in the
4.1.3. AttraTable 1 s
careers in eAcross all and in acadcareers in s
alizes studecareers wasguishesbetphysical sc
tabliia s
ers eral
odel
Attrarst
areed logiveneivenef a red as
STi =
PREF10 joilityratinWORarchtionitatit forion cnshipout university
employment are funding shortages anded by 22 and 16% of the
respondents, respectively.reting the results reported in this
section, it shouldat students expectations may be inaccurate and
evenlly biased, especially regarding industry careers. Wess the
accuracy of students expectations. However,rate expectations may
have signicant impacts on stu-earch activities and career
choices.
rences for job attributesns of respondents preferences for job
attributes areble 1. Intellectual challenge, funding, job security,
andenerally considered most important, while peer recog-ublishing
are among the least important attributes. Theg is particularly
interesting because it suggests that atPhDs do not have strong
preference for these factorsassociated with the scientic
enterprise. Moreover, themeasures showaconsiderable amount of
variation, sug-icant individual differences in these preferences
andtential for self-selection. There are only minor
differ-importance of job attributes across elds.
ctiveness of positionshows themeansof ourmeasures of the
attractiveness ofstablished rms, startups, and academia,
respectively.elds, we nd that research careers in established
rmsemia are judged as similarly attractive, while researchtartups
are rated as much less attractive. Fig. 2 visu-
and esacademengineare gen
4.2. M
4.2.1.Our
three cordereattractattractness ospeci
ATTR E
wherefor theavailabdentsrms,of resepublicathe
limaccounregressrelatioFig. 2. Most attractive career option (ties
possible).
Models 1of establishthe prefere
4 We also coattractivenesscoded as the m
5 We also esof job attributexpectation ofThe interactiotions
tended tattributes fromin the alternat2.53 1% 0.37
nts implicit choices, e.g., how often each of the threerated as
the most attractive option.4 Fig. 2 also distin-
ween the threebroadlydenedeldsof the life sciences,iences, and
engineering. Life scientists nd academiashed rms similarly
attractive; physical scientists ndomewhat more attractive than
established rms, andnd jobs in established rms more attractive.
Startupsly considered least attractive.
specications and regression results
ctiveness of employment optionsset of models uses the
attractiveness ratings for the
r options as dependent variables and is estimated usingit. These
regressions estimate the determinants of thessof aparticular
careerpath, independentof the judgedss of alternative careers. For
example, the attractive-esearch career in an established company
would be
f (0 + 1PREFi + 2AVAIL ESTi + 3NORMS ESTi+ 4WORKi +
5PERFORMANCEi+ 6CONTROLSi + i) (1)
i is a vector ofmeasures of the respondents preferencesb
attributes, AVAIL ESTi is the respondents rating of theof jobs in
established rms, NORMS ESTi is the respon-g of departmental norms
regarding jobs in establishedKi is a vector of preferences
regarding different types, PERFORMANCEi is a vector including prior
patents ands, and CONTROLSi is a vector of control variables.5
Givenons of cross-sectional survey data,we are unable to fullyall
potential sources of endogeneity and thus interpretoefcients as
reecting correlations rather than causals.
and 2 in Table 4 show the results for the attractiveness
ed rms. We observe several signicant coefcients onnce variables;
in particular, the importance of salary
unt ties, i.e., if established rm and startup are both rated a 4
on thescale and academia a 3, then both established rm and startup
areost attractive option.timated models including the measures of
respondents expectationses and the interactions between preferences
and expectations (e.g.,salary in established rm interacted with the
importance of salary).
n terms were generally not signicant and the measures of
expecta-o have only weak effects. We exclude the measures of
expected job
our featured ordered logit models, but we include these
measuresive-specic logit regressions that follow.
-
M. Roach, H. Sauermann / Research Policy 39 (2010) 422434
429
Table 4Attractiveness of careers (ordered logit).
Established rm Startup Faculty
1 2 3 4 5 6
Preference fSalary
0]Job securit 48*
0]Intellectua
5]Responsib
1]Funding
8]Cutting-ed *
3]Freedom 64
1]Publishing 53
7]Ability to
Peer recog
Index: Tas
Index: Acc
Other variabAvailabilit
Norms for
Number o
Number o
Basic rese
Applied re
Developm
Managem
Male
Nationalit
Field dumOther con
ObservationChi-square
Robust standa* Signicant
** Signicant
and access twith greatethe other hpreferencethe one hancompany
oacademicalto choose twho care le
We alsowork or devmore attracviduals witindicator ofmore
likelyor attribute0.370** 0.389** 0.180[0.138] [0.139] [0.13
y 0.079 0.060 0.3[0.139] [0.139] [0.14
l challenge 0.021 0.077 0.177[0.177] [0.171] [0.18
ility 0.125 0.165 0.258[0.164] [0.162] [0.170.186 0.065[0.150]
[0.13
ge tech/equip 0.430** 0.243[0.123] [0.120.492** 0.2[0.141]
[0.140.224 0.1[0.127] [0.12collaborate 0.217* 0.085[0.109]
[0.125]
nition 0.071 0.188[0.123] [0.114]
te for science 1.028**[0.173]
ess to resources 0.684**
[0.165]
lesy of positions 0.108 0.114 0.127
[0.133] [0.131] [0.119]entering career 0.210 0.224* 0.475**
[0.111] [0.112] [0.112]f patents 0.351 0.309 0.323
[0.373] [0.389] [0.375]f publications 0.027 0.026 0.115*
[0.047] [0.047] [0.050]arch 0.094 0.078 0.032
[0.097] [0.098] [0.090]search 0.362** 0.346** 0.200*
[0.116] [0.112] [0.099]ent 0.517** 0.503** 0.390**
[0.103] [0.098] [0.106]ent 0.033 0.052 0.037
[0.088] [0.087] [0.087]0.205 0.246 0.405*[0.202] [0.200]
[0.203]
y 0.531 0.588 0.026[0.311] [0.308] [0.298]
mies Incl. Incl. Incl.trols Incl. Incl. Incl.
s 426 426 426270.653 262.234 186.977
rd errors in brackets.at 5%.at 1%.
o cutting-edge technology are both signicantly relatedr
attractiveness of working for an established rm. Onand, we observe
a negative relationship between thefor intellectual freedomand the
ability to collaborate ond and the attractiveness of working in an
established
n the other. The latter result suggests that not only doly
trained scientists vary in their preference for freedomheir own
projects and to freely collaborate, but thosess about these
attributes may self-select into industry.nd that individualswho
aremore interested in appliedelopment nd a research career in an
established rmtive. Somewhat surprisingly, we do not nd that indi-h
prior patenting activityoften perceived to be anthe commercial
orientation of academic scientistsareto nd industry attractive.
Similarly, we nd no signif-
icant relatioof a career iing employthe perceivsignicantitly
asked sinclude thescious effeccareer prefe
In modescience andsistent witis positivelestablishedcient. To
i0.168 0.008 0.007[0.131] [0.132] [0.129]0.348* 0.123 0.098[0.140]
[0.139] [0.137]0.138 0.100 0.045[0.174] [0.180] [0.177]0.228 0.069
0.002[0.170] [0.167] [0.174]
0.066[0.147]0.177[0.116]0.383**
[0.130]0.378**
[0.129]
0.201[0.123]0.195[0.115]
0.539** 0.797**[0.178] [0.192]0.379* 0.109[0.159] [0.177]
0.145 0.100 0.066[0.117] [0.128] [0.122]0.488** 0.118
0.127[0.110] [0.099] [0.099]0.337 0.038 0.057[0.384] [0.420]
[0.416]0.117* 0.068 0.072[0.051] [0.050] [0.049]0.027 0.205*
0.258**
[0.091] [0.101] [0.094]0.215* 0.317** 0.250*[0.100] [0.109]
[0.102]0.381** 0.231* 0.270*[0.103] [0.111] [0.110]0.052 0.015
0.019[0.087] [0.085] [0.083]0.367 0.063 0.165[0.197] [0.203]
[0.196]0.005 0.014 0.075[0.294] [0.252] [0.254]Incl. Incl.
Incl.Incl. Incl. Incl.
426 426 426181.223 162.561 144.37
nship between prior publishing and the attractivenessn an
established company. Departmental norms regard-ment in established
rms have a positive effect, whileed availability of positions in
established rms has noimpact. Note that our attractiveness
questions explic-tudents to ignore the availability of positions
and weavailability measure only to control for any subcon-ts of
perceived labor market conditions on studentsrences.l 2, we use the
index measures for students taste forpreference for access to
resources. The results are con-
h model 1: PhDs preference for access to resourcesy related with
the attractiveness of a career in anrm,while PhDs taste for science
has a negative coef-llustrate the economic size of these effects,
Panel A in
-
430 M. Roach, H. Sauermann / Research Policy 39 (2010)
422434
Fig. 3. Predictregressions. Na respondentattractiveness
Fig. 3 showsin an establsomewhataincreases frtheir mean.ability
thatrm as veryhigh of 80 tversely, Panrating for epreference
In modeattractiveneviduals preresources tea stronger tin the
prefetiveness ofstudents artups are ratcommon in
Inmodels 56,we report the results for regressions of the
attrac-tiveness of a faculty career. Individuals with a stronger
taste forscience rate a faculty career signicantly higher, whereby
freedom
blishing seem to be the primary drivers. Interestingly, a
con-ith resources does not signicantly reduce the
attractivenessulty career, despite growing concerns in the general
discus-at funding shortages may deter students from pursuing anic
career. As expected, the more individuals are interested
c research, the more appealing is academia, while an
inter-applied work and development decrease the
attractivenessfaculty career. The degree to which individuals want
to bed in management is not associated with the attractivenesser of
the three career options. This is consistentwith the ideasearcnt,
beso so
Choicregr
res ohey er unnofativeand pucernwof a facsion thacademin basiest
inof theengageof eiththat reageme(see al
4.2.2.The
measuThus, ttive (oquestiotive reled attractiveness ratings of
established rm, based on ordered logitote: The lines in each panel
represent the predicted probabilities thatnds employment in an
established rm not attractive (1 on thescale), somewhat attractive
(3) or very attractive (5).
the predicted probabilities that a student nds a careerished rm
not attractive (1 on the attractiveness scale),ttractive (3)or
veryattractive (5) asher taste for scienceom low (1) to high (5),
with all other variables held atNote in particular the steep drop
in the predicted prob-an individual rates a research career in an
establishedattractive (5 on 5-point scale), which decreases from
a
o 6% as taste for science increases to its maximum. Con-el B
shows that the probability of a very attractivestablished rms
increases from 4 to 37% as studentsfor access to resources
increases to its maximum.ls 34, we estimate equivalent regressions
for thess of startups and also nd signicant effects of
indi-ferences for job attributes. Students concerned withnd to rate
startups as more attractive, while those withaste for science rate
startups less attractive. An increaserence for job security is
associated with a lower attrac-startups, consistent with our
observation that manye concerned about job security in startups.
Finally, star-ed more attractive if a career in startups has been
morethe respondents department in the past.
the issue osures of relindividualsdent ratesnew dummtive
careerscore. Theusing alterimplementapproach istics of thecareers)
as(e.g., prefer
ASCLOGequation esthe likelihocoefcient o(no mattermore likelyond
set of eon the likelrms versuoptions (j=FACULTY astially
equivomitted catparticular c
Pr(MOSTi =
6 These regrment the attrmay increasein the impliedthe
attractivendifferent choicon the choicewhat extent thB.h in
industry as well as in academia may involve man-it as team leader
in a rmor as lab director in academia
me management related quotes in Section 4.1.1 above).
e between alternative career optionsessions reported in the
previous section utilized thef career attractiveness independently
for each career.xamined which factors make a particular career
attrac-attractive) to an individual. We will now turn to thewhich
factorsmake industry careersmoreor less attrac-to a career in
academia, thusmore explicitly addressingf career choice.6 For that
purpose, we computed mea-ative preferences that capture the choices
implicit in attractiveness ratings, i.e., which option the
respon-as most attractive (see also Fig. 2). We created threey
variables that take on the value of 1 if the respec-had the highest
(or among the highest) attractivenessthree new variables are
ideally suited to be analyzednative-specic conditional logit
(McFadden, 1974) ased in Statas ASCLOGIT command. A key strength of
thisthat it allows us to model the effects of characteris-
alternatives (e.g., levels of salary available in differentwell
as the effects of characteristics of the individualsence for
salary) on career choices.IT simultaneously estimates multiple
equations. Onetimates the effects of attributes of a career option
onod of that option being chosen. For example, a positiven expected
level of salarywould indicate that an optionif the option is
faculty, startup, or established rm) isto be chosen if it is
expected to pay a high salary. A sec-quations estimates the effects
of individuals attributesihood of choosing a particular option,
e.g., establisheds faculty or startup versus faculty. Given three
career1, . . ., 3), two such equations are estimated and we usethe
omitted category. These two equations are essen-
alent to a multinomial logit model with FACULTY as theegory.
Thus, the probability of a respondent i nding aareer j most
attractive is modeled as
j) = f (0 + 1EXPji + 2AVAILji + 3NORMSji+ 4PREFi + 5WORKi +
6PERFORMANCEi+ 7CONTROLSi + ji) (2)
essions more accurately capture the notion of choice and
comple-activeness regressions discussed earlier. For example, a
variable X1the attractiveness of both options A and B but not lead
to a changechoice of A versus B. On the other hand, a variable X2
may increaseess of option A much more than that of option B and
thus lead to ae. The attractiveness regressions alone do not reveal
the effects of Xsbetween A and B, while the choice regressions
alone do not reveal toe Xs operate via the attractiveness of A
versus the attractiveness of
-
M. Roach, H. Sauermann / Research Policy 39 (2010) 422434
431
Table 5Most attractive option (alternative-specic conditional
logit).
Most attractiveoption
Estab. rm versusfaculty
Startup versusfaculty
Most attractiveoption
Estab. rm versusfaculty
Startup versusfaculty
Career attrib(Cols. 1a, 2Salary
Job securit
Intellectua
Responsib
Funding
Cutting-ed
Freedom
Publishing
Ability to
Peer recog
Index: Acc
Index: (Ta
Other variabAvailabilit
Norms for
Number o
Number o
Basic rese
Applied re
Developm
Managem
Male
Nationalit
Detailed Other con
Constant
N
Robust standaNote: Allmodeof that (any) arm over empemployment
i
* Signicant** Signicant
where EXPattributes inability of jodepartmenare as den
Inmodeattributes. Wcareers thachoose projcareers. Wi(1a) (1b)
(1c)
utea: expectations; cols. 1bc, 2bc: preferences)
0.366 0.275 0.148[0.196] [0.284] [0.286]
y 0.259 0.199 0.759**[0.191] [0.275] [0.287]
l challenge 0.183 0.18 0.391[0.235] [0.391] [0.419]
ility 0.187 0.254 0.757*
[0.218] [0.309] [0.309]0.325 0.354 0.45
[0.210] [0.394] [0.354]
ge tech/equip 0.114 0.903** 0.733*
[0.215] [0.307] [0.287]0.613** 0.933** 0.627*[0.174] [0.323]
[0.285]0.104 0.511 0.479[0.237] [0.281] [0.267]
collaborate 0.136 0.204 0.149[0.225] [0.265] [0.259]
nition 0.052 0.072 0.085[0.213] [0.221] [0.238]
ess to resources
ste for) science
lesy of positions 0.092
[0.160]entering career 0.140
[0.126]f patents 0.011 0.543
[0.791] [0.806]f publications 0.252** 0.271**
[0.095] [0.092]arch 0.25 0.134
[0.217] [0.204]search 0.747** 0.503*
[0.217] [0.217]ent 0.777** 0.681**
[0.208] [0.221]ent 0.029 0.048
[0.178] [0.173]0.05 0.125[0.483] [0.474]
y 0.466 0.227[0.602] [0.582]
eld dummies Incl. Incl.trols Incl. Incl.
1.745 4.551[2.803] [2.982]
426
rd errors in brackets.ls are estimatedusing alternative-specic
conditional logit. Columns1a and2a show the eflternative being
chosen. Columns 1b and 2b show the effects of characteristics of
the inloyment in academia. Columns 1c and 2c show the effects of
characteristics of the indivn academia.at 5%.at 1%.
ji is a vector of expectations regarding the 10 joboption j,
AVAILij is the respondents rating of the avail-bs in option j,
NORMSji is the respondents rating of
tal norms regarding jobs in option j, and the other termsed in
(1).l 1 in Table 5,we use the separatemeasures for all 10 job
ith respect to expectations (column 1a), we nd thatt are judged
as offering a higher degree of freedom toects are more likely to be
judged as the most attractiveth respect to students preference for
employment in an
establishedviduals witemploymented categortechnologierm. A
prepreferencegies, whileless likelystartups ar(2a) (2b) (2c)
0.412* 0.259 0.170[0.190] [0.290] [0.290]0.232 0.243
0.784**[0.173] [0.276] [0.282]0.191 0.101 0.374[0.209] [0.389]
[0.401]0.185 0.354 0.691*
[0.200] [0.318] [0.326]0.348 0.929* 0.638[0.211] [0.414]
[0.350]0.532* 1.762** 1.188**[0.254] [0.412] [0.362]
0.115[0.146]0.163[0.114]
0.122 0.379[0.760] [0.770]0.215* 0.243**[0.089] [0.093]0.236
0.084[0.204] [0.197]0.597** 0.392*
[0.199] [0.196]0.740** 0.654**
[0.203] [0.211]0.123 0.006[0.172] [0.174]0.242 0.197[0.452]
[0.433]0.559 0.419[0.545] [0.552]Incl. Incl.Incl. Incl.
3.078 5.106[2.679] [2.956]
426
fects of (perceived) characteristics of the alternativeson the
likelihooddividual on the likelihood of choosing employment in an
establishedidual on the likelihood of choosing employment in
startup rm over
rm versus academia (column 1b), we see that indi-h a strong
concern for freedom are less likely to prefert in an established rm
over an academic career (omit-y), while those concerned with access
to cutting-edges are more likely to prefer a career in an
establishedference for startups (column 1c) is associated with afor
responsibility and access to cutting-edge technolo-students
concerned with job security and freedom areto prefer a startup over
academia. Thus, even thoughe thought to offer somewhat higher
levels of freedom
-
432 M. Roach, H. Sauermann / Research Policy 39 (2010)
422434
than established rms, they still have a considerable
disadvantagein that respect compared to academia, and studentswho
care aboutfreedommay self-select out of established rms aswell as
startups.Students with a high interest in applied work and
developmentare more likely to prefer R&D in an established rm.
Finally, stu-dents with a greater number of publications tend to nd
careersin established companies and startups less attractive
relative to anacademic career. Interpreting publications as a
measure of perfor-mance, better students appear to prefer an
academic career.7
Inmodel 2, we use the indexmeasures for students
preferencesaswell as exilar to the rend that a sthe likelihorm or a
stwith accesshood that trm over a
4.2.3. CompThe resu
models of aconditionaltary insightscores revecareer pathfactors
inuanother. Thof a particucareers in insidering thelearn
moreences.
To illustscience havrms becauless attractone could saway
fromuals who aaccess to cuerence forbecause thethey nd
acindividualslikely to prestartups veparticularly
5. Summa
Academtant rolesregarding hment sectorPhDs tasteimplicationas
well assectors.
7 While wethey may alsoperhapsa tastlications on cataste for
scien
To learn more about the career choices of science and
engineer-ing PhDs, we surveyed over 400 PhD students at three major
U.S.research universities. In the rst part of our empirical
analysis, weprovide descriptive data on students expectations
regarding sev-eral key jobrms, startjobattributreport
veryacademia;wrespect to j
to cot todiffelife svergencehe sesociaademd thair preat
stprefepubliacadindivsire tikelyemier emhoseer a cpreferesus hies
inlly asigniacads sh2004ste isredg, itemiay pmpllishinult o
2008desiraintsy) mrsone expr scifor sctingtion,inanncesionaare
Ptastes dof addpectations regarding job attributes. The results are
sim-sultsusing thedetailedmeasures.Most importantly,wetudents taste
for science has a strong negative effect onod that the student
considers a career in an establishedartup most attractive. In
contrast, a students concernto resources has a small positive
effect on the likeli-
he student prefers a research career in an establishedcareer in
academia.
arison of regression modelslts of our two sets of regressions
(ordered probit
ttractiveness scores separately and alternative-speciclogit of
most attractive options) provide complemen-s. While the ordered
logit regressions of attractivenessal how students form attitudes
vis--vis a particular, the alternative-specic logit regressions
show whichence students relative preferences for one career overus,
the ASCLOGIT regressions reect a compound effectlar independent
variable on both the attractiveness ofdustry and on the
attractiveness of academia. By con-results of the two sets of
regressions jointly, we can
about the underlying drivers of students career prefer-
rate, we see that individuals with a strong taste fore a clear
preference for academia over establishedse they tend to both nd
R&D in established rmsive and research in academia more
attractive. Thus,ay they are both pulled into academia and
pushedindustry. On the other hand, we observe that individ-
re concerned about access to resources, in particular,tting-edge
technologies and equipment, have a pref-careers in established rms
over academia primarilyy nd established rms more attractive, not
becauseademia particularly unattractive. Similarly, we see thatwho
care strongly about job security are much lessfer startups over
academia primarily because they ndry unattractive, not because they
would nd academiaattractive.
ry and discussion
ically trained science and engineering PhDs play impor-in both
industry and academia, yet little is knownow graduating PhDs select
into these different employ-s. Systematic selection effects
alongdimensions such asfor science or prior performance may have
important
s for research on innovation in industry and academia,for
research on knowledge ows between the two
interpret publication counts primarily as a measure of
performance,proxy for the importance and individual assigns to
publishing ande for sciencemoregenerally. In that sense,
theobservedeffect ofpub-reer choice would reinforce our nding that
students with a strongerce prefer the faculty career.
abilitythoughceivedin theof conlife sci
In ttors asand acwe nby thend thstrongity topreferhand,the
demore lin acadto prefwhile tto prefcareer
Ourndingferencgenerahave aing inthat ha(Stern,that
tacompatraininan acadence mFor exaof pubthe reset
al.,lowerconstrstrategHendeprovidbroadetastesuggesinnovadetermprefereeducatextentgain
a ndingtance oattributes associated with employment in
establishedups, and academia, on students preferences for thosees,
andonstudents careerpreferences.Our respondentsdifferent
expectations regarding careers in industry andhile academia is
thought tohaveaclear advantagewith
ob attributes such as freedom to choose projects andllaborate
across organizational boundaries, industry isoffer higher salaries
and more resources. These per-rences between industry and academia
are not smallerciences than in other elds, despite the recent
notionence between academic and industrial research in thes (Vallas
and Kleinman, 2008).cond part of our empirical analysis, we examine
the fac-ted with students preferences for careers in industryia.
Using two complementary econometric techniques,t students career
preferences are strongly predictedferences for various job
attributes. More precisely, weudents with a strong taste for
science, in particular, arence for freedom to choose research
projects, the abil-sh, and the desire to conduct basic research,
stronglyemic careers over careers in industry. On the otheriduals
concerned with salary, access to resources, ando conduct downstream
research and development areto prefer careers in established rms
over a career
a. Individuals who value responsibility are more likelyployment
in startups over employment in academia,concerned with job security
are signicantly less likelyareer in startups. While patents are not
associated withrences, publications predict a preference for
academia.lts suggest several important implications. First, our
ghlight the importance of considering individual dif-scientists
preferences and professional orientation
nd raise the possibility that industrial scientists
maycantlyweaker taste for science than scientistswork-emia. While
our ndings do not contradict prior workown that industrial
scientists have a taste for science), they suggest that it is
important to consider whetherweak or strong. While it may be
relatively strong whento rm employees that did not go through
graduatemay be weak compared to PhDs who intend to pursuec career.
A consideration of the strength of a taste for sci-rovide new
insights into scientists research activities.e, it raises the
question whether observed lower levelsg and other academic
activities in industry are solelyf constraints imposed by rms
(Stern, 2004; Aghion) or if they are also a function of industrial
scientistse to engage in such activities. In the latter case,
relaxingimposed by rms (e.g., as part of an open innovationay not
necessarily bear fruit. In fact, the research byand Cockburn (1994)
suggests that rms may need tolicit incentives to their employees to
engage with the
entic community and cannot rely on scientists innateience alone.
Our ndings, as well as other recent workthat scientists preferences
play an important role inraise the more general question of the
sources andts of students preferences. To what extent are
theseinherited? To what extent are they shaped by early
l experiences and by socio-economic variables? TowhathD students
socialized during their graduate training orfor sciencebasedonearly
research success?While ournot answer these questions, they
highlight the impor-ressing these questions in future research.
-
M. Roach, H. Sauermann / Research Policy 39 (2010) 422434
433
Second, our nding that students think of academia and indus-try
as very different in terms of job attributes seems at odds withthe
notion of convergence between the two sectors and suggeststwo
interesting avenues for future research. First, empirical work
isneeded to ebetween thresentative(for a recenfutureworkand
identifyjob choicesdents may bcareers in irms as wevide such
inattempts cu
For induour resultskey attractiresources fstrong
concassociatedlish and sharesearch topose a dilewith a
stroHenderson,signicant grms, this sment shoulsome of theto
persist.
While outrial careerwith studenicy makersbeen a
concresearcherssectors, inctransitions,rms are jurelated
factcutting-edgto careers itages of acaa strong
apperformancacademia sdents. Howthe resourcin researcherelative
attr
Our studconsider dyfor job attribof studentsduce compland
careerticular, somthat they prerences forfurther chanable to
cleafor job attrtion betwebecause it
ences of those scientists who may ultimately seek employment
inindustry.
Second, we observe only students career preferences but notwhich
career paths they eventually take. Career preferences and
te emes aut alployly forn acarial sforce inin
o obryachowing a. Sucdecioutcultiilitying,
eorelongperfeationrwore coded mpitets caor faborat wheia. T
ientiely totualhelp
sts pry as
wled
acknDoheox, Dei Zch Poannund
nces
P., Des, andN., Lithe conizat, P., Dvior:nizatz, J.,
Findivid.S., 19y 3, 4valuate and quantify actual similarities and
differencese two sectors along a range of characteristics, using
rep-samples from the life sciences as well as other eldst example,
see Sauermann and Stephan, 2009). Second,should examine the
accuracy of students expectationsany potential systematic biases
that may lead to poor
. Good decisions require accurate information, and stu-enet from
more actively collecting information aboutndustry as well as other
alternative careers. Whilell as professional associations
increasingly try to pro-formation to students, it is not clear how
effective suchrrently are.strial employers seeking to hire
high-potential PhDs,suggest that resource related factors are
currently theon, and this includes not only salaries, but
especiallyor research. At the same time, students seem to haveerns
about low levels of attributes that are typicallywith academic
science including the ability to pub-re research and freedom
regarding the choice of onespics. While these concerns may be
overdrawn, theymma for rms that actually want to attract studentsng
taste for science (Henderson, 1994; Cockburn and1998). Combined
with our nding that students feelaps in their informationabout
theworkenvironment inuggests that rms that offer a more academic
environ-d send stronger signals to PhD students,
counteractingstereotypes about employment in industry that seem
r focus has been on students choice to enter an indus-, our
results also provide insights for those concernedts decisions to
pursue academic careers, including pol-and academic administrators.
In particular, there hasern that funding shortages and other
challenges juniorface in academia may drive out students into
other
luding industry. While we do not observe actual careerour data
lend some support to this notion; we nd thatdged as much more
favorable with respect to resourceors, and students who value
access to resources such ase technology, but also funding and
salary, are attractedn industry. On the other hand, the traditional
advan-demia,most notably intellectual freedom, seem to havepeal to
students. Moreover, students with higher paste are more likely to
prefer an academic career. Thus,eems to remain an attractive career
path to many stu-ever, our results also suggest that further
increases ineadvantagesof industrialrmsandpotential reductionsrs
freedom in academiamay signicantly decrease theactiveness of
academic careers.y is not without limitations. First, our study
does notnamic effects, such as the extent to which preferencesutes
andcareer aspirationsmaychangeover the course
graduate education. Such dynamic effects could intro-ex
interactions between preferences for job attributesaspirations,
making causal statements difcult. In par-e students may determine
early in their PhD programefer one career over the other and those
students pref-jobattributes suchas independenceandpublishingmayge
to reect those career goals.While itwouldbedesir-rly identify
causal relationships between preferencesibutes and career
aspirations, even the mere correla-en these variables may have
important implicationsprovides insights into the characteristics
and prefer-
ultimaoutcomdates bthe emto apptions iindustSuch
sciencesciencequest tindusterally,matchsciencedentscareeralong
mavailabmatchand ignin thetial imimplicthat oufor motwo-si
Desstudenences fto collapredicacademthat scare
likconcepshouldscientiindust
Ackno
WeJasonMary Fand WResearSauermman Fo
Refere
Aghion,focu
Argyres,andOrga
AzoulaybehaOrga
Bercovitthe
Blume, SPolicployment patterns may differ, however, because
nalre determined not just by self-selection of job candi-so by
employers choice of particular candidates. Whileer side should
matter less if students decide not evencertain types of positions,
a general shortage of posi-demia may ultimately force some students
into theector even if theyhave a strongpreference for academia.d
entry into industry may raise the average taste forindustry, but it
should also raise the average taste foracademia (only hardcore
academics persist in theirtain a faculty position), with ambiguous
effects on theademia difference in the taste for science. More
gen-ever, future work is needed on employeremployeelong multiple
dimensions in the particular context ofh work should examine the
relative importance of stu-sions versus those of employers in
determining nalomes.Moreover, conceptualizingmatching as
occurringple dimensions (e.g., pay, publishing opportunities, andof
resources) also highlights the potential for imperfect.g., if
students over-emphasize certain job attributesothers that are less
salient but turn out to be importantterm (Sauermann, 2005). Future
research on poten-ctions in the matching process may suggest
importants for students as well as potential employers. We hoperk
on the employee side provides a useful starting pointmprehensive
studies of science careers as outcomes ofatching processes.
its limitations, our study provides novel insights intoreer
decisions and suggests that PhD students prefer-ctors such as
independence, publishing, and the abilitytewithothers, aswell as
for access to resources stronglyther students prefer a career in
industry or a career inhis, in turn, highlights the importance of
recognizingsts taste for science and preferences more
generallydiffer across individuals as well as between sectors.
A
ization of scientists preferences as a matter of degreefuture
work seeking to examine more explicitly how
references relate to research activities and outcomes inwell as
in academia.
gements
owledgevaluable comments from JonathonCummings,rty, Erika
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Paula Stephan, Long Vo
hang. We also thank two anonymous reviewers andlicy editor Lee
Fleming for helpful comments. Henryacknowledges support from the
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A taste for science? PhD scientists academic orientation and
self-selection into research careers in
industryIntroductionBackgroundScience and engineering PhDs and firm
innovationPrior research on S&E PhD employment choicesHow do
careers in industry and academia differ?Conceptual model of career
preferences
Data and measuresSample and survey
methodologyMeasuresAttractiveness of career optionsPreferences for
job attributesAdditional featured variables and controls
ResultsKey descriptive resultsExpectations regarding job
attributesPreferences for job attributesAttractiveness of
positions
Model specifications and regression resultsAttractiveness of
employment optionsChoice between alternative career
optionsComparison of regression models
Summary and discussionAcknowledgementsReferences