This PDF is a selection from a published volume from the National … · 2011-06-21 · 8.1...
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This PDF is a selection from a published volume from the National Bureau of Economic Research
Volume Title: Science and Engineering Careers in the United States: An Analysis of Markets and Employment
Volume Author/Editor: Richard B. Freeman and Daniel L. Goroff, editors
Volume Publisher: University of Chicago Press
Volume ISBN: 0-226-26189-1
Volume URL: http://www.nber.org/books/free09-1
Conference Date: October 19-20, 2005
Publication Date: June 2009
Chapter Title: Capturing Knowledge: The Location Decision of New Ph.D.s Working in Industry
Chapter Author: Albert J. Sumell, Paula E. Stephan, James D. Adams
Chapter URL: http://www.nber.org/chapters/c11624
Chapter pages in book: (p. 257 - 287)
8.1 Introduction
The placement of newly-minted science and engineering Ph.D.s pro-vides one means by which knowledge is transferred from the university toindustry. The placement of Ph.D.s with industry can be especially impor-tant in facilitating the movement of tacit knowledge. Despite this role, weknow very little about industrial placements. One dimension of ignoranceinvolves the extent to which students stay where trained or leave the area/state after receiving the degree. The policy relevance of this question is ob-vious. Creating a highly-skilled workforce is one of several ways universi-ties contribute to economic growth (Stephan et al. 2004). The mobility ofthe highly educated affects the extent to which knowledge created in uni-
257
8Capturing KnowledgeThe Location Decision of NewPh.D.s Working in Industry
Albert J. Sumell, Paula E. Stephan, and James D. Adams
Albert J. Sumell is an assistant professor of economics at Youngstown State University.Paula E. Stephan is a professor of economics at the Andrew Young School of Policy Studiesat Georgia State University and a research associate of the National Bureau of Economic Re-search. James D. Adams is a professor of economics at the Rensselaer Polytechnic Instituteand a research associate of the National Bureau of Economic Research.
The authors wish to thank Grant Black for comments and the provision of certain MSAdata. Financial support for this project was provided by the Andrew W. Mellon Foundation,the Science and Engineering Workforce Project, National Bureau of Economic Research, andthe National Science Foundation, grant number 0244268. We have benefited from the com-ments of participants at the REER conference, Georgia Institute of Technology, November2003, the NBER meeting on the Economics of Higher Education, fall 2003, and the NBERmeeting of the Science and Engineering Workforce Project, fall 2005. We have also benefitedfrom comments of seminar participants at Université Jean Monnet (St. Etienne, France),Université Pierre Mendes France (Grenoble, France), Katholieke Universiteit Leuven (Leu-ven, Belgium), The European Forum, Robert Shuman Center (San Domenico, Italy), and theUniversitat Pompeu Fabra (Barcelona, Spain). Mary Beth Walker, Rene Belderbos, and BillAmis made helpful comments on an earlier draft. The use of NSF data does not imply NSFendorsement of the research methods or conclusions contained in this chapter.
versities is absorbed by the local economy.1 Having graduates work forneighboring firms strengthens the interface between the university andfirms at the local or state level, and makes it easier for future graduates tofind jobs with employers near the university. Moreover, the availability ofa highly-trained workforce attracts new businesses to the local area.
To the extent that students “fly the coop,” one rationale for investingstate and local resources in universities is weakened. This is especially thecase in today’s environment when universities, in an effort to attract re-sources, herald the role they play in local economic development, mindfulof Stanford’s role in the creation of Silicon Valley, MIT and Harvard’s rolein Route 128, and Duke and the University of North Carolina’s role in theResearch Triangle Park (Link 1995).2
The migration behavior of the highly educated thus not only has long-term implications for the economic health of a region, but also may affectthe amount policymakers are willing to invest in higher education. Thestakes are somewhat different for private institutions than for public insti-tutions. Not beholden to the public sector for funding, it is less essentialthat private institutions demonstrate a local economic impact. Nonethe-less, private institutions receive a number of benefits from the state and lo-cal area, not the least of which is tax-exempt status.
This is not to say that universities are solely focused on keeping theirgraduates close at hand. Placements outside the local area are an indica-tion of success, signaling that the university has the necessary connectionsand reputation to warrant more distant placements.3 Moreover, strong in-dustrial placements, regardless of whether or not they are local, can en-hance future funding opportunities with industry. They can also enrich thealumni base and thus potential donations to the university.
The objective of this chapter is to examine factors that influence theprobability that a highly skilled worker will remain local or stay in the state.Specifically, we measure how various individual, institutional, and geo-graphic attributes affect the probability that new Ph.D.s going to industry
258 Albert J. Sumell, Paula E. Stephan, and James D. Adams
1. Ph.D.s working in industry clearly contribute more than knowledge transfer. Stern(1999) discusses industrial scientists’ interest in “Science,” which to continue Stern’s typology,leads to “Productivity” for the firm. The ability to engage in “Science” provides psychic re-wards for the scientist. The productivity effects experienced by the firm result in part from the“ticket of admission” that the practice of “Science” provides the firm to the wider scientificcommunity (Stern 1999, 11). We focus on the knowledge-transfer role here because of our in-terest in the interface between industry and academe and the geographical dimensions of thisinterface.
2. There is a culture in universities of expecting Ph.D.s going into academe to seek the bestavailable positions, regardless of locale. Attitudes toward industrial placements are less clear-cut. Stephan and Black (1999) find that in the field of bioinformatics, often faculty do not evenknow the name of the firms their students go to work for.
3. Mansfield’s work (1995) suggests that industry, when looking for academic consultants,is likely to use local talent for applied research, but focuses on getting the “best,” regardlessof distance, when basic research is involved.
stay in the metropolitan area or state where they trained. Our study focuseson Ph.D.s who received their degree in one of ten fields in science and en-gineering (S&E) during the period 1997 to 1999. Data come from the Sur-vey of Earned Doctorates, administered by Science Resources StatisticsNational Science Foundation.
The chapter proceeds as follows. Section 8.2 provides a discussion of therole new Ph.D.s play in knowledge transfer. Section 8.3 briefly discusses therole of geographic proximity in promoting knowledge transfer. Section 8.4offers a conceptual model of the individual decision to migrate. Section 8.5discusses the data used for this study and provides some descriptive statis-tics on the migration of industrial Ph.D.s from metropolitan areas andstates, focusing on the ability of metropolitan statistical areas (MSAs) andstates to retain Ph.D.s produced in their region and/or import human cap-ital from other regions. Section 8.6 gives the results from our empiricalanalyses and discusses the policy implications. Section 8.7 concludes bysummarizing and discussing the key findings.
8.2 The Role of New Ph.D.s in Knowledge Transfer
The transmission mechanism by which knowledge flows from universi-ties to firms is varied, involving formal means, such as publications, as wellas less formal mechanisms, such as discussions between faculty and indus-trial scientists at professional meetings. Graduate students are one com-ponent of the formal means by which knowledge is transferred. Much ofgraduate students’ training is of a tacit nature, acquired while working intheir mentor’s lab. These new techniques, which cannot be codified, can betransmitted to industrial R&D labs through the hiring of recently-trainedscientists and engineers. New hires also establish and reinforce existingnetworks between firms and university faculty whereby the firm can ac-quire more ready access to new knowledge being created in the university.4
The Carnegie Mellon Survey of R&D labs in manufacturing located inthe United States asked respondents to rank the importance of ten possiblesources of information concerning public knowledge for a recently com-pleted major R&D project (Cohen, Nelson, and Walsh 2002). A four-pointLikert scale was used. The ten sources included patents, publications/reports, meetings or conferences, informal interaction, recently-hired grad-uates, licenses, cooperative/JVs, contract research, consulting, and personalexchange. The findings show that—across all industries—publications/reports are the dominant means by which R&D facilities obtain knowledgefrom the public sector. Next in importance are informal information ex-change, public meetings or conferences, and consulting. Recently hired
Capturing Knowledge 259
4. Networks have been found to relate to firm performance (Powell et al. 1998; Zucker andDarby 1997).
graduates show up in the second cluster, which, in the overall rankings, islower than the first cluster of sources of public knowledge. In certain indus-tries, however, 30 percent or more of the respondents to the Carnegie Mel-lon Survey indicate that recently hired graduates played at least a “moder-ately important” role in knowledge transfer. These industries are: drugs,mineral products, glass, concrete, cement, lime, computers, semiconductorsand related equipment, and TV/radio. This finding likely relates to the rela-tive importance of tacit knowledge in certain fields and the key role thatgraduate students play in the transmission of tacit knowledge.5
In a related study, Agrawal and Henderson (2002) interviewed sixty-eight engineering faculty at MIT, all of whom had patented and licensed atleast one invention, asking them to “estimate the portion of the influenceyour research has had on industry activities, including research, develop-ment, and production” (53) that was transmitted through a number ofchannels. Consulting headed the list, with a weight of 25.1 percent, fol-lowed by publication at 18.5 percent. Placement of MIT graduates was aclose third at 16.8 percent.
8.3 The Role of Geographic Proximity in Transmitting Knowledge
Considerable research has focused on the role that geographic proximityplays in transmitting knowledge. Early work by Jaffe (1989), for example,used university research and development expenditures as a proxy for theavailability of local knowledge spillovers as did work by Audretsch andFeldman (1996a, 1996b). More recent work by Feldman and Audretsch(1999), Anselin, Varga, and Acs (1997, 2000), and Black (2001) has fol-lowed suit, shifting the analysis from the state to the consolidated metro-politan statistical area (CMSA). In each study a significant relationship isfound between the dependent variable, which is a measure of innovation,and the proxy measure for local knowledge. Zucker, Darby, and Brewer(1998) take a different path and examine the role that the presence of starscientists in a region play in determining the regional distribution ofbiotech-using firms. They find the number of active stars in the region toplay an important role in determining firm activity. Moreover, the effect isin addition to the role played by general knowledge sources, as measuredby a “top quality university” or number of faculty with federal support.
Two recent studies use patent citations to examine the degree to whichknowledge spillovers are geographically bounded. Thompson (2006) findsthat inventor citations in the United States are 25 percent more likely tomatch the state or metropolitan area of their citing patent than are exam-
260 Albert J. Sumell, Paula E. Stephan, and James D. Adams
5. The second tier-ranking of graduates as a means of knowledge transfer reflects in partthe fact that graduate students contribute indirectly through networking to several pathwaysof knowledge transfer (such as informal information exchange, public meetings or confer-ences, and consulting) that are listed separately on the questionnaire.
iner citations. Almeida and Kogut (1999) explore why patent citations aremore regionally concentrated in certain areas than others, focusing on thesemiconductor industry. They argue that the mobility of engineers plays akey role in explaining citation rates by region. Regions that have high inter-firm mobility of inventors (as measured by inventor address) have higherrates of intraregional citation than regions with low interfirm migration.This suggests that “a driving force for local externalities in semiconductordesign is the mobility of people” (Almeida and Kogut 1999, 906).
These, and countless other studies, go a long way toward establishingthat geographic proximity promotes the transmission of knowledge. They
do not, however, address the extent to which knowledge spillovers are local.
One of the few papers to examine this question was written by Audretschand Stephan (1996) and examines academic scientists affiliated withbiotech companies. Because the authors know the location of both the sci-entist and the firm, they are able to establish the geographic origins ofspillovers embodied in this knowledge-transfer process. Their researchshows that although proximity matters in establishing formal ties betweenuniversity-based scientists and companies, its influence is anything butoverwhelming. Approximately 70 percent of the links between biotechcompanies and university-based scientists in their study were nonlocal.Audretsch and Stephan also estimate the probability that the link is local.
Here we extend the Audretsch–Stephan framework, examining the loca-tion decisions of recent graduates. We are particularly interested in know-ing the degree to which available knowledge spillovers, as measured by theplacement of Ph.D. students, are local and in knowing factors related tothe “stickiness” of Ph.D.-embodied knowledge to the local area.
8.4 Determinants of Migration
There is a vast literature examining factors that influence human migra-tion, much of which owes its origin to the work of Sjaastad (1962), and thatviews migration as an investment decision. An individual will move if sheor he perceives the present value of the stream of benefits resulting from themove, composed primarily of gains in real income, to be greater than thecosts, composed of both pecuniary and psychic costs to moving.
Here we are interested in modeling the decision of a Ph.D. headed to in-dustry to locate outside the city (state) of training versus to stay in the city(state) of training. We assume that the new Ph.D. is interested in maximiz-ing the present value of utility over the life cycle, where the utility functionhas arguments of both income and psychic attributes such as family well-being. The cost of moving involves psychic costs as well as monetary costsof relocation (some of which may be paid by the firm). We assume that theindividual engages in search in an extensive way while in graduate schooland thus does not forego actual income while looking for a job. Moreover,
Capturing Knowledge 261
we assume that capital markets are not perfect and thus individuals withlittle debt are more able to absorb the costs of moving than those with debt.We also assume that individuals with access to a wider network of infor-mation are more likely to move than are those with more limited access.
Our model focuses on whether the Ph.D. leaves where she or he is trained.Three sets of explanatory variables are of interest: variables that reflect at-tributes of the state and local area, variables that reflect individual charac-teristics affecting the present value of the discounted stream of utility frommoving compared to the present value of the discounted stream of utilityfrom staying in the area, and variables that reflect field of training and in-stitutional characteristics. From a policy perspective, we are also interestedin knowing whether individuals trained at a private institution are morelikely to leave than are individuals trained at a public institution. We arealso interested in knowing whether in-state students, as measured by re-ceiving one’s high school, college, and Ph.D. degrees in the same state, aremore likely to stay.
Attributes of the local area include the degree of innovative activity, jobmarket prospects in industry for Ph.D.s, and the desirability of the loca-tion. Innovative activity is measured by such standard measures as patentcounts, R&D expenditures, and so forth; desirability is measured by levelof education and per capita income. Job market prospects for Ph.D.s in in-dustry are measured by an index, explained later, that computes the em-ployment absorptive capacity of the area. Personal characteristics affect-ing the net present value include age, marital status, and the presence ofdependents.
Variables that reflect wider access to networks include the rank of the de-partment as well as whether or not the individual was supported on a fel-lowship during graduate school. We expect individuals who work full orpart time during their last year in graduate school to be more connected tothe local area and therefore more likely to stay. We also expect individualswho return to a job they held before coming to graduate school to be morelikely to remain in the area. The assumption is that proximity plays a rolein selecting the graduate program.
Imperfect capital markets lead us to expect that individuals who leavegraduate school with substantial debt face more constrained searches andthus are more likely to remain local. Preferences are also assumed to affectthe decision to relocate. While difficult to measure, we make inferencesconcerning preferences based on the individual’s past pattern of mobility.
8.5 S&E Ph.D.s in Industry: Where They Come from and Where They Go
Data for this chapter come from the Survey of Earned Doctorates (SED)administered by Science Resources Statistics (SRS) of the National Sci-ence Foundation (NSF). The survey is given to all doctorate recipients in
262 Albert J. Sumell, Paula E. Stephan, and James D. Adams
the United States, and has a response rate of approximately 92 percent.While the SED has always asked graduates whether they have definiteplans to work with a firm, the identity and geographic location of the firmhas only become available to researchers since 1997 and then only in ver-batim form. We have recently used these verbatim files to code the identityof the firm for the period 1997 to 1999.
The analysis is thus restricted to Ph.D.s in science and engineering whomade a definite commitment to an employer in industry between 1997 and1999. This undercounts Ph.D. placements in industry in two notable ways.First, many Ph.D.s who eventually end up working in industry initially takepostdoctoral appointments, particularly Ph.D.s in the life sciences. Sec-ondly, 37.1 percent of Ph.D.s who were immediately planning to work inindustry did not list a specific firm or location because they had not madea definite commitment to an employer at the time the survey was adminis-tered.6 Our results are thus conditional on the acceptance of a positionwith industry at the time the survey was completed and do not apply to allPh.D.s headed to industry.
The fields of training of the 10,121 new Ph.D.s with definite plans towork in industry are given in table 8.1. Not surprisingly, the data is domi-nated by large fields having a tradition of working in industry as well as atradition of not accepting a postdoc position prior to heading to industry.Engineers made up 53 percent of the sample; 12 percent of the sample ismade up of chemists.
For Ph.D.s who had made a definite commitment to an employer in in-dustry and identified the specific name of the firm they plan to work for be-tween 1997 and 1999, 36.7 percent had commitments with an employerthat lay within the same state as their doctoral institution.7
The stay rate is low compared to that for bachelor’s and master’s degreerecipients in science and engineering. The National Science Foundationreports that 62 percent of all recent bachelors in science and engineering inthe United States stay in the state where they received their degree and 60.2percent of all recent masters stay. The stay rate is highest for computer sci-entists (68.4 percent for bachelors and 70.8 percent for masters) and low-est for bachelors in engineering (55.1 percent and masters in the physicalsciences (54.1 percent).8 The Ph.D. stay-rate of 36.7 percent is also lowcompared to recent law school graduates, for whom 57.0 percent with
Capturing Knowledge 263
6. Of the Ph.D.s awarded in the twelve broad S&E fields during this time period, 17,382 ofthe 75,243 had plans to work in industry. Of these, 10,932 (14.5 percent of all Ph.D.s in S&Eduring this time period) had made a definite commitment to an employer in industry andidentified the specific name of the firm they planned to work for. Of these, 10,121 Ph.D.s wereawarded by institutions in the continental United States in one of ten “exact” S&E fields.
7. The percent is based on the 10,932 referred to in footnote 6, which includes Ph.D.strained in psychology and economics, as well as the ten fields listed in table 8.1.
8. The data are not strictly comparable since the NSF data include U.S. degree recipientswho also received a high school diploma or equivalency certificate in the United States.
known employment status remain in the state of training (National Asso-ciation for Law Placement 1998).
The low stay-within-state rate does not necessarily indicate that the pro-duction of new Ph.D.s is entirely a poor investment from the perspective ofstate policymakers. In the majority of states (twenty-six), one-third ormore of all newly employed Ph.D.s hired by in-state firms graduated froman institution within the state, and in eight states, institutions within thestate supplied the majority of new Ph.D.s to firms within the state.
Table 8.2 displays interstate and interregional migration data.9 Severalnotable patterns become evident. Pacific states are major net importers ofnew Ph.D.s; approximately 40 percent more Ph.D.s have definite plans towork in California, Oregon, and Washington than are produced there. Cal-ifornia dominates in several respects. More Ph.D.s going to industry areproduced in California than in any other state, the state retains a higherpercent of the Ph.D.s it produces than does any other state, and morePh.D.s produced in other states head to California than to any other state.The strong presence of IT firms in Pacific states, especially during the pe-riod of study—as well as the heavy proportion of engineers in the data-base—no doubt contribute to this finding.
New England and Middle Atlantic states train approximately the samenumber of Ph.D.s that they hire. If it were not for New Jersey, however, theMiddle Atlantic region would be a net exporter. New Jersey’s remarkablegain is in large part due to its ability to attract new Ph.D.s from neighbor-ing New York and Pennsylvania. New York provides other states or coun-tries with 591 new industrial Ph.D.s, sending 115 of those to New Jersey
264 Albert J. Sumell, Paula E. Stephan, and James D. Adams
9. Six states (Alaska, Nevada, Hawaii, North Dakota, South Dakota, and Wyoming) eitherproduced or received too few Ph.D.s to report their interstate migration numbers.
Table 8.1 Firm placements of new S&E Ph.D.s by field of training: 1997–1999
% of all Ph.D.s awarded % in field of Ph.D.s Field that identified a firm that identified a firm
All S&E fields 14.5 100 (n � 10,121)All engineering 30.7 53.0 (n � 5,364)Agriculture 9.0 3.0 (n � 308)Astronomy 7.8 0.4 (n � 44)Biology 3.8 6.0 (n � 609)Chemistry 18.7 12.0 (n � 1,216)Computer science 28.4 7.5 (n � 762)Earth science 12.3 2.5 (n � 252)Math 12.5 4.7 (n � 477)Medicine 5.0 4.3 (n � 435)Physics 16.1 6.5 (n � 654)
Tab
le 8
.2In
ters
tate
and
Int
erre
gion
al m
igra
tion
pat
tern
s of
new
indu
stri
al P
h.D
.s 1
997–
1999
Num
ber
of
Num
ber
of
Num
ber
of
Per
cent
of
Per
cent
of
new
Ph.
D.s
new
Ph.
D.s
ne
w P
h.D
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new
Ph.
D.s
ne
w P
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trai
ned
in
wor
king
in
Per
cent
age
prod
uced
that
pr
oduc
ed th
at
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rted
from
St
ate/
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ion
stat
e/re
gion
stat
e/re
gion
gain
or
loss
stay
in s
tate
/reg
ion
stay
in s
tate
/reg
ion
othe
r st
ates
/reg
ions
New
Eng
land
958
885a
�7.
641
543
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.743
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307
34.2
61.7
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23.9
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Tab
le 8
.2(c
onti
nued
)
Num
ber
of
Num
ber
of
Num
ber
of
Per
cent
of
Per
cent
of
new
Ph.
D.s
new
Ph.
D.s
ne
w P
h.D
.s
new
Ph.
D.s
ne
w P
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trai
ned
in
wor
king
in
Per
cent
age
prod
uced
that
pr
oduc
ed th
at
impo
rted
from
St
ate/
Reg
ion
stat
e/re
gion
stat
e/re
gion
gain
or
loss
stay
in s
tate
/reg
ion
stay
in s
tate
/reg
ion
othe
r st
ates
/reg
ions
Lou
isia
na96
78�
18.8
2627
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klah
oma
9649
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28.1
44.9
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as68
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833
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653
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ntai
n55
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14.9
228
40.9
51.9
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zona
197
181
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orad
o19
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1229
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tana
159
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ico
4138
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as
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s
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min
g11
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ific
1,83
1a2,
534
39.7
1,27
069
.450
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lask
as
ss
ss
s
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iforn
ia1,
539
2,12
638
.110
4367
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ss
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s
Was
hing
ton
161
187
16.1
5735
.469
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awai
i15
ss
ss
s
Oth
erP
uert
o R
ico
1718
5.6
1376
.527
.8
Sum
/mea
ns U
.S.
10,9
3210
,303
n.a.
n.a.
n.a.
n.a.
No
tes:
s�
supp
ress
ed. A
t th
e re
ques
t of
Sci
ence
Res
ourc
es S
tati
stic
s, N
atio
nal S
cien
ce F
ound
atio
n (2
005)
, cou
nts
not
repo
rted
if 6
or
less
or
if a
sp
ecifi
cfi
rm c
ontr
ibut
es h
alf o
r m
ore
of th
e co
unt i
n a
cell.
Cou
nts
incl
ude
Ph.
D.s
trai
ned
in e
cono
mic
s an
d ps
ycho
logy
. n.a
. �no
t app
licab
le.
a Sup
pres
sed
cells
not
incl
uded
in s
ums
to p
reve
nt id
enti
fica
tion
of c
ells
.
alone. Pennsylvania is not far behind, losing 518 new industrial Ph.D.s toother areas—seventy-seven to New Jersey.
States in the Midwest (East North Central and West North Central) arenet exporters, hiring approximately one-third fewer Ph.D.s than they train.The brain drain is substantial. As a region, the Midwest retains slightlymore than one-third of those trained, but retention within Midwesternstates (as opposed to within the region) is considerably lower, averagingless than 28 percent. Indiana Ph.D.s are the most likely to find employmentin other states. Of the 376 new industrial Ph.D.s graduating from Indianauniversities in the three-year period, forty-six, a meager 12.2 percent, haddefinite plans to work for a firm in Indiana. Iowa is not far behind.
A state’s ability to retain its highly-trained workers is largely contingentupon the strength of its metropolitan areas. More than 67 percent of newindustrial Ph.D.s who remain in-state work in the same CMSA in whichthey were trained. Table 8.3 takes a closer look at the ability of metropoli-tan areas to retain new industrial Ph.D.s by examining the top twenty-fivedestinations and the top twenty-five producing metropolitan areas.10 Over-all, slightly more than 70 percent of those trained in a CMSA were trainedin a top twenty-five CMSA, while approximately 80 percent of those goingto work in a metropolitan area go to a top twenty-five destination city. It isevident from table 8.3 that areas that produce more industrial Ph.D.s gen-erally hire more Ph.D.s in industry. This is accomplished by both retainingPh.D.s produced in the city and attracting Ph.D.s from other cities. Eigh-teen metropolitan areas are in the top twenty-five in terms of both produc-ing and employing new Ph.D.s going to industry. Furthermore, slightlymore than one out of every three Ph.D.s trained in a top twenty-five met-ropolitan area stays in the area of training, whereas only about one in fiveproduced in all other metropolitan areas stays where trained. This suggeststhat a dynamic is at work: Cities that produce more highly-skilled workersfoster the development of new firms and attract firms wanting access to ahighly-skilled workforce. This in turn attracts more highly skilled workersfrom other areas and encourages retention of those trained in the area.
Particularly interesting is the role of New York/Northern New Jersey,San Francisco/San Jose, Boston, Los Angeles, and the District of Columbia/Baltimore. These five metropolitan areas (although not in the same order)represent the top five metropolitan areas, both in terms of destination and
in terms of the production of Ph.D.s heading to industry. Slightly over onein four of all new S&E Ph.D.s headed to industry was trained in one of
268 Albert J. Sumell, Paula E. Stephan, and James D. Adams
10. Here we focus on Ph.D.s awarded in a CMSA; 1,027 of the new Ph.D.s headed to in-dustry were trained outside a CMSA. Note also that the number of Ph.D.s produced inCMSAs is not equal to the number hired by a CMSA for three reasons: some work outsideCMSAs in the United States, others leave the United States for industrial employmentabroad, and others are trained outside a CMSA but work in a CMSA.
Tab
le 8
.3To
p tw
enty
-five
pro
duci
ng a
nd d
esti
nati
on c
onso
lidat
ed m
etro
polit
an a
reas
: 199
7–19
99
Top
25 p
rodu
cing
con
solid
ated
met
ropo
litan
are
asTo
p 25
des
tina
tion
con
solid
ated
met
ropo
litan
are
as
# th
at
% th
at#
%
C
onso
lidat
ed m
etro
polit
an a
rea
Nst
ayst
ayC
onso
lidat
ed m
etro
polit
an a
rea
Nlo
cal
loca
l
New
Yor
k-N
o. N
ew J
erse
y-L
ong
Isla
nd,
732
423
57.8
San
Fra
ncis
co-O
akla
nd S
an J
ose,
CA
1,36
941
630
.4N
Y-N
J-C
T-P
AN
ew Y
ork-
No.
New
Jer
sey-
Lon
g Sa
n F
ranc
isco
-Oak
land
-San
Jos
e, C
A70
641
658
.9Is
land
, NY
-NJ-
CT
-PA
1,29
342
332
.7B
osto
n-W
orce
ster
-Law
renc
e-L
owel
l-B
osto
n-W
orce
ster
-Law
renc
e-L
owel
l-B
rock
ton,
MA
-NH
NE
614
238
38.8
Bro
ckto
n, M
A-N
H N
E58
823
840
.5L
os A
ngel
es-R
iver
side
-Ora
nge
Cou
nty,
CA
525
233
44.4
Los
Ang
eles
-Riv
ersi
de-O
rang
e C
ount
y, C
A48
423
348
.1W
ashi
ngto
n-B
alti
mor
e, D
.C.-
MD
-VA
-WV
327
160
48.9
Was
hing
ton-
Bal
tim
ore,
D.C
.-M
D-V
A-W
V44
316
036
.1C
ham
paig
n-U
rban
a, I
L31
310
3.2
Hou
ston
-Gal
vest
on-B
razo
ria,
TX
340
4814
.1D
etro
it-A
nn A
rbor
-Flin
t, M
I30
410
233
.6C
hica
go-G
ary-
Ken
osha
, IL
-IN
-WI
339
122
36.0
Chi
cago
-Gar
y-K
enos
ha, I
L-I
N-W
I29
012
242
.1Po
rtla
nd-S
eatt
le-T
acom
a, O
R-W
A33
968
20.1
Atl
anta
, GA
282
7325
.9P
hila
delp
hia-
Wilm
ingt
on-A
tlan
tic
Aus
tin-
San
Mar
cos,
TX
282
6723
.8C
ity,
PA
-NJ-
DE
-MD
296
8629
.1L
afay
ette
, IN
279
82.
9D
alla
s-F
ort W
orth
, TX
273
4616
.8M
inne
apol
is-S
t. P
aul,
MN
-WI
266
8632
.3D
etro
it-A
nn A
rbor
-Flin
t, M
I24
110
242
.3P
hila
delp
hia-
Wilm
ingt
on-A
tlan
tic
Cit
y,
Min
neap
olis
-St.
Pau
l, M
N-W
I23
386
36.9
PA-N
J-D
E-M
D26
386
32.7
Aus
tin-
San
Mar
cos,
TX
182
6736
.8P
itts
burg
h, P
A21
742
19.4
San
Die
go, C
A15
955
34.6
Stat
e C
olle
ge, P
A20
97
3.3
Atl
anta
, GA
150
7348
.7M
adis
on, W
I20
816
7.7
Ral
eigh
-Dur
ham
-Cha
pel H
ill, N
C14
451
35.4
Ral
eigh
-Dur
ham
-Cha
pel H
ill, N
C17
851
28.7
Pho
enix
-Mes
a, A
Z12
135
28.9
Port
land
-Sea
ttle
-Tac
oma,
OR
-WA
162
6842
.0D
enve
r-B
ould
er-G
reel
ey, C
O12
054
45.0
Col
umbu
s, O
H15
421
13.6
Cin
cinn
ati-
Ham
ilton
, OH
-KY
-IN
109
2724
.8D
enve
r-B
ould
er-G
reel
ey, C
O14
454
37.5
Alb
any-
Sche
nect
ady-
Tro
y, N
Y10
524
22.9
Gre
ensb
oro-
Win
ston
-Sal
em-H
igh
Poin
t, N
C14
2s
sP
itts
burg
h, P
A10
142
41.6
Alb
any-
Sche
nect
ady-
Tro
y, N
Y13
824
17.4
Cle
vela
nd-A
kron
, OH
9642
43.8
Cle
vela
nd-A
kron
, OH
138
4230
.4In
dian
apol
is, I
N81
00.
0T
ucso
n, A
Z12
724
18.9
St. L
ouis
, MO
-IL
8125
30.9
San
Die
go, C
A12
255
45.1
Roc
hest
er, N
Y M
SA63
1727
.0
Sum
top
25 m
etro
polit
an a
reas
7,12
22,
427a
34.1
Sum
top
25 m
etro
polit
an a
reas
7,75
02,
540
32.8
All
othe
r m
etro
polit
an a
reas
2,78
356
420
.3A
ll ot
her
met
ropo
litan
are
as1,
812
453
25.0
No
tes:
s�
supp
ress
ed. C
ount
s of
6 o
r le
ss n
ot r
epor
ted
at th
e re
ques
t of S
cien
ce R
esou
rces
Sta
tist
ics,
Nat
iona
l Sci
ence
Fou
ndat
ion
(200
5). C
ount
s in
clud
e P
h.D
.s tr
aine
d in
eco
-no
mic
s an
d ps
ycho
logy
.a S
uppr
esse
d co
unt n
ot in
clud
ed in
tota
l to
prev
ent i
dent
ifica
tion
of t
he s
uppr
esse
d co
unt.
these five metropolitan areas, while approximately three out of eight wereheaded to one of these five metropolitan areas.11
Table 8.3 also shows that striking disparity exists in the ability of metro-politan areas to retain new industrial placements. The New York and SanFrancisco areas top the list; each employs about 58 percent of new in-dustrial placements trained in their area. On the other hand, areas likeUrbana-Champaign, Illinois; Lafayette, Indiana; and State College, Penn-sylvania, all of which have a long tradition of training scientists and engi-neers, retain only about 3 percent of their new Ph.D.s headed to industry.This high attrition rate demonstrates that the presence of a large universitydoes not guarantee sufficient job opportunities in the industrial sector toretain S&E Ph.D.s trained locally. Certainly, other factors necessary foreconomic development, such as transportation nodes, nearby amenities,access to venture capital, and so forth, present in cities like San Jose, arelacking in cities like Urbana-Champaign.12
While the universities like Illinois-Urbana/Champaign, Purdue, andPennsylvania State appear to have a low return on their investment interms of the fact that new Ph.D.s leave the city upon graduating, they dosupply new talent to the state and nearby metropolitan areas. The Univer-sity of Illinois-Urbana/Champaign supplies Chicago with about 10 per-cent of its new industrial hires, Purdue University is far and away the topsupplier to Indianapolis, accounting for 21 percent of that city’s industrialhires, and firms in Pennsylvania recruit 8 percent of their new Ph.D. talentfrom Pennsylvania State University.
Table 8.4 shows how migration behavior differs by a Ph.D.’s field of train-ing. While 36 percent of engineers, who constitute about half of all indus-trial S&E hires in our sample, stay in state, 26 percent have plans to stay inthe same metropolitan area; both are close to the mean of all S&E indus-trial hires. Doctorates in agriculture have the lowest stay rates of all S&Efields, with about one in four staying in state, and less than one in ten withplans to work in the same metropolitan area they were trained in. This re-flects in part the fact that Ph.D.s in agriculture on temporary visas are themost likely of any group of S&E Ph.D.s to leave the United States upongraduation (Black and Stephan 2003). By way of contrast, astronomers are
270 Albert J. Sumell, Paula E. Stephan, and James D. Adams
11. The extreme geographic concentration displayed in table 8.3 has been found using sev-eral other measures of innovation. For example, Black (2001) examined the geographic con-centration of innovation using Small Business Innovation Research (SBIR) awards and patentcounts. There is significant overlap with the Ph.D. metropolitan areas: the top five metropol-itan areas in terms of SBIR phase II awards are the same as the top five areas in terms of in-dustrial Ph.D.s produced and hired. Four of the five metropolitan areas are also in the top fivein terms of utility patents issued (Chicago is fourth on the list, while the District of Columbiais eleventh).
12. The lack of a booming industrial sector could prove an asset in the long run. That is,“college towns” may indirectly use their small city size as a tool to attract niche industries aswell as a highly-trained workforce, marketing the lack of disamenities that are present in citieswith large industrial sectors, such as high crime rates, congestion, and air pollution.
the most likely to work in the state and metropolitan area in which theytrained. More than 56 percent of astronomers have employment plans towork in the state of training and about 55 percent have plans to work in themetropolitan area of their doctoral institution.
8.6 Empirical Results
In order to investigate specific factors affecting the decision to stay in thearea of training, we estimate two equations, using two definitions of stay-ing. These equations are shown in table 8.5. In equation (1) we estimate theprobability that a new Ph.D. has made a definite commitment to an indus-trial employer in the same state as their doctoral institution; the dependentvariable in equation (2) is whether or not the new Ph.D. stays in the sameprimary metropolitan area.13 Both equations are estimated using a logitmodel.
Table 8A.1 presents the definitions, means, and standard deviations forall variables included in the regressions. Table 8.5 provides the coefficientsand z-statistics for the two equations. We restrict the analysis to Ph.D.strained in the continental United States, excluding those trained in Alaska,Hawaii, and Puerto Rico. Table 8.5 also reports the marginal effects of achange in an independent variable, evaluated at the mean. For a dummyvariable these marginal effects show by how much the probability willchange with a change in status; in the case of a continuous variable, theyshow how much the probability will change with a one-unit change in thevalue of the variable. All Ph.D.s who did not report their postdoctoral state
Capturing Knowledge 271
13. The difference between CMSA and PMSA is one of size. Thus, while San Jose is aPMSA, the larger CMSA includes San Francisco and Oakland as well as San Jose. Becauseof issues related to confidentiality, we are not able to display the data at the PMSA level; how-ever, we are able to analyze the data at this level.
Table 8.4 Percent of firm placements staying in state and consolidated metropolitanareas by field of training: 1997–1999
Field % staying in state % staying in CMSA
All engineering 36.3 26.2Agriculture 26.0 9.7Astronomy 56.8 54.5Biology 45.0 34.6Chemistry 28.6 19.7Computer science 36.4 30.6Earth science 28.6 17.9Math 35.0 29.4Medicine 46.0 35.2Physics 45.0 35.0All fields 36.4 26.6
Tab
le 8
.5E
mpi
rica
l res
ults
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ple
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ted
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atio
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quat
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of location or age are excluded from equation (1); Ph.D.s whose doctoralinstitution does not lie in a U.S. primary metropolitan statistical area(PMSA), as well as those who did not report a readable city name or ageare excluded from equation (2).
Table 8.5 shows that, other things being equal, the market for Ph.D.strained in certain fields is significantly less local than for other fields.Specifically, relative to the benchmark of biology, we find individualstrained in agriculture, engineering, chemistry, computer science, and earthscience to be significantly more likely to leave the state of training. Theeffects, in many instances, are substantial, as can be seen by examining themarginal effects. With the exception of earth science, there are no signifi-cant differences at the PMSA level.
Few of the demographic variables play a significant role in determiningwhether the new Ph.D.s stay in close geographic proximity to their institu-tion of training. We do, however, find that Asians, as well as individualswho are underrepresented minorities in science and engineering (non-white, nonasian) are less likely to stay in the state or PMSA of training. Thelatter result may reflect the scarcity and hence wider market for underrep-resented minorities receiving Ph.D.s in science and engineering. Being atemporary resident is also a key factor in determining mobility. Comparedto citizens, temporary residents are considerably more likely to leave thestate as well as to leave the local area. The effect is fairly sizable. Otherthings being equal, temporary residents are about 6 percent more likely toleave either the state or local area than are citizens. Married Ph.D.s are nomore likely to remain in their location of training than are nonmarriedPh.D.s; neither does the presence of children affect mobility, nor is mobil-ity related to being a single parent. However, other things being equal, wefind that married women are more likely to stay in state than are unmarriedwomen. There is no indication, holding marital status constant, thatwomen have differential mobility patterns than do men. We also find nosupport for the hypothesis that mobility decisions are responsive to thepresent value of moving; in neither instance do we find the coefficients oneither age or age-squared to be significant.
Preferences as revealed through past mobility patterns play a significantrole in determining the location decision. We find that doctorates whoearned their Ph.D. in the same state as their college degree are much morelikely to remain in the Ph.D.-granting state than are those who changedstates between college and graduate school. They are also more likely tostay in the same PMSA. The marginal effects are not inconsequential.Other things being equal, “stayers” are about 11 percent more likely to takean industrial position in state and 5 percent more likely to take a positionin the city of training. At the state level we find that individuals who receivetheir Ph.D. and college degree in the state from which they graduated highschool are even more likely to remain in state than are those who moved to
274 Albert J. Sumell, Paula E. Stephan, and James D. Adams
the state to get a college degree and stayed on to receive their Ph.D. At thePMSA level, those who received their degree in the state in which they wereborn are significantly more likely to remain to take a position in industry.The policy implication is clear: accepting Ph.D. students from in state sig-nificantly raises the probably of retention of the highly-skilled work force.At the margin, the cumulative effect of training Ph.D.s who went to bothhigh school and college in the state of doctoral training is 17 percent. Forpublic institutions, this suggests that states capture part of their educa-tional investment.
Variables that reflect wider access to networks are generally significantand with the expected sign. Individuals whose primary source of supportwas a fellowship or dissertation grant are significantly more likely to leavethe state of training than the benchmark.14 Individuals trained at top-ratedprograms15 also are more likely to move, although the effect is field-dependent as well as dependent on the measure of mobility. In five of theten fields studied (engineering, biology, chemistry, math, and medicine), in-dividuals trained at a top program are significantly more likely to leavetheir state than are individuals not trained at a top program in their field.And the marginal effects can be quite strong. Turning to equation (2), wefind that four of the top program variables are negative and significant aswell, suggesting that in smaller geographical areas graduates from top pro-grams leave as well.16
Individuals who worked full or part time during their last year of grad-uate school are assumed to have more information, other things beingequal, concerning jobs in close proximity to their graduate institution. Ourresults support this hypothesis. We find that those working full or part timeare more likely to stay in state and in the primary metropolitan area. Theeffects are large. For example, those who worked part time their last year in
Capturing Knowledge 275
14. The benchmark is those whose primary source of support during graduate school wasother than a fellowship, a dissertation grant, a teaching assistantship, a research assistant-ship, or employer reimbursement.
15. Top fields are based on the 1995 National Research Council (NRC) rankings for allfields except medicine and agriculture. The rankings for the majority of fields are based on the“scholarly quality” scores in the NRC rankings for each relevant program at the institution.For field definitions that were broader than the program definitions in the NRC rankings(such as biology), we calculated the mean for each rated program applicable to our broaderfield for each institution. For the fields of medicine and agriculture, we used the 1998 NSFWeb CASPAR data to rank institutions, due to the absence of data for these fields in the NRCrankings. Institutions in these fields were ranked by total federal R&D expenditures at eachinstitution. In the case of biology and medicine, which have a very large number of Ph.D. pro-grams, seventy-five institutions were included among the top programs. For smaller fields,such as astronomy, the top category includes the top twenty-five programs. In most otherfields, the top category includes the top fifty programs.
16. The engineering, chemistry, and math results persist when we restrict the definition ofa top program to one that ranks in the top ten. In addition, using this more restrictive defini-tion of quality, we find that individuals are more likely to leave the state of training if they ma-triculate from a top computer science or earth science program.
graduate school are 20 percent more likely to remain in state than are thosewho did not work part time, and 14 percent more likely to remain in thesame PMSA.
We also know from the SED whether a doctorate with definite plans is“returning to or continuing in pre-doctoral employment.” Not surpris-ingly, Ph.D.s who indicated they were returning to a previous employer areconsiderably more likely to remain where they were trained. The marginaleffect is particularly strong at the state level (10 percent).17
Student debt level affects mobility, but not in the way hypothesized. In-stead, we find that the probability of remaining in one’s location of train-ing depends negatively upon the amount of debt accumulated in graduateschool. This counterintuitive result may indicate that students who as-sumed debt engage in more search activity than do those with no debt, mo-tivated by the need to find a highly remunerative position.
Finally, we are interested in knowing the degree to which the attributesof the local area affect the decision to leave the state or metropolitan area.Here we examine two dimensions of this relationship: the presence of in-novative activity and the desirability of the state or local area, as proxiedby per capita income and educational attainment.
At the state level, innovative activity is measured by the count of utilitypatents granted, as well as by industrial R&D expenditures and academicR&D expenditures.18 In the PMSA equations we use the Milken index andpatent counts as measures of innovative activity. In all instances, we con-trol for population and land area. Generally speaking, we find that indi-viduals coming from innovative areas are more likely to accept industrialemployment locally. For example, the probability that an individual staysin the city of training is positively related to the number of utility patentsgranted in the city and the Milken Index.19 At the state level, we find thatindividuals are more likely to stay if the state has a high level of industrialR&D activity. Somewhat surprisingly, patent counts are not significant atthe state level.
As a measure of employment opportunities for Ph.D.s in the state (city)of training relative to elsewhere, we construct an index of the relative localabsorptive capacity for Ph.D.’s (ABPhDi), measured as the ratio of the flowof new Ph.D.s produced locally to the stock of Ph.D.s working in local in-
276 Albert J. Sumell, Paula E. Stephan, and James D. Adams
17. A doctorate need not remain local, or even in state, to return to or continue in previousemployment. In fact, 46 percent of new Ph.D.s who indicate they are returning to or contin-uing in previous employment leave their state of training after graduation.
18. Data on academic and industrial R&D expenditures come from the National ScienceBoard (2002), and are computed in 1996 constant dollars for the years 1997, 1998, and 1999.
19. The Milken Index, measured by the Milken Institute, is a measure of high-tech con-centration in the PMSA. By definition, the Milken Index mean for the United States is equalto 1.0. A metro area with an index higher than 1.0 has a higher high-tech concentration thanthe United States, a metro area with an index that is lower than 1.0 has a lower high-tech con-centration.
dustry relative to the same measure aggregated across the United States. Towit, we define the measure as:
ABPhDi � (NPhDIi /PhDIi)/(ΣNPhDIi/ΣPhDIi),
where NPhDIi is the number of new Ph.D.s (in all fields) in location i (de-fined as either the state or PMSA) with plans to work in industry; PhDIi isthe total number of all Ph.D.s in location i working in industry. We hy-pothesize an inverse relationship. We find the variable to be negative andhighly significant in predicting the probability that the individual will re-main at either the state or local level. Clearly, the ability of the local area toabsorb new Ph.D.s is a prime factor in determining whether the individu-als stay.
Our results also indicate that new Ph.D.s are more likely to stay in theirstate of training the higher the per capita income in the area. Somewhatsurprisingly, we do not find per capita income to be significant in thePMSA equation. In neither instance do we find the educational variablesto be significant.20
If higher education were funded at the federal, rather than the state orlocal level, it would make little difference, from an economic developmentperspective, whether the newly trained Ph.D.s remained local, or insteadleft the area of training. However, and as noted earlier, institutions ofhigher education in the United States are a mixed lot. Public institutionsreceive funding from the state, and indirectly, local area, in which they arelocated; private institutions do not. While we do not find a significantdifference regarding the decision to stay in state between public and privateinstitutions, we do find a significant difference at the PMSA level.
Given the important role that retention plays in leveraging public re-sources, we reestimate the basic equations, focusing exclusively on publicinstitutions. The results, presented in appendix table 8A.2, are reasonablysimilar to those presented in table 8.5. The finding that many of the “best”Ph.D.s leave persists when we focus exclusively on public institutions.Specifically, we find that individuals trained at top-rated biology, chem-istry, computer science, math, and medical Ph.D. programs are less likelyto remain in state than are those coming from non-top-rated programs.Moreover, those who were supported on a fellowship or dissertation grant,an indicator of quality, are more likely to leave. Doctorate recipients frompublic institutions are more likely to remain in state if they received theirundergraduate degree from the same state. Where one went to high schoolno longer matters when the sample is restricted to individuals who at-tended public institutions. The public PMSA results are reasonably similarto those for all institutions.
Capturing Knowledge 277
20. These results may reflect our failure to control for the relative values of these variables.Arguably, it is the relative value that affects the decision to stay or leave, not the level of thevariable.
8.7 Conclusion and Discussion
The movement of the highly educated from universities to firms is onemechanism by which knowledge is transferred. Despite the important rolethat industrial Ph.D.s can play in economic development, to date we knowvery little regarding their location decisions. This knowledge gap is espe-cially striking given the focus in recent years on the role that proximityplays in the transmission of knowledge (Feldman 1994; Audretsch andStephan 1996). To help rectify this deficiency, we measure the degree towhich placements are local and what affects the likelihood that a Ph.D. go-ing to work in industry will remain in the same state or metropolitan area.
We find that states and local areas capture knowledge embodied innewly-minted Ph.D.s headed to industry, but not at an overwhelming rate.Only about one in three of those going to industry take a job in the statewhere trained; approximately one in five in the same PMSA. The averages,however, mask wide variations. California retained two out of three of themore than 1,500 Ph.D.s it trained for industry during the period. Indianaretained only one in eight of the 376 it trained. Wide variation exists at themetropolitan level as well: the San Francisco-Oakland-San Jose area re-tained almost 60 percent of those trained in the metropolitan area whotake a position in industry as did the wider New York metropolitan area.By way of contrast, State College, Pennsylvania, retained about 3 percent,as did Champaign-Urbana, Illinois and Lafayette, Indiana.
Our research informs the question of whose knowledge is captured. Wefind that local areas are more likely to retain white students and studentshaving little debt who are returning to a previous position. Being “home-grown” predisposes one to remain as well. Those who receive their Ph.D.in the same state as their undergraduate degree and high school degree aremore likely to stay than those who do not. Those who receive their Ph.D.in the same state as their BA degree, as well as in their birth state, are morelikely to stay in the PMSA.
Graduates from certain fields are especially likely to leave the state: mostnotably agriculture, chemistry, engineering, computer science, and earthscience. Quality matters: top-rated Ph.D. programs are often the ones thatare most likely to produce graduates who leave the area. Those supportedon fellowships or dissertation grants are more likely to leave the state oftraining. Graduates from private institutions are also more likely to find in-dustrial employment outside the metropolitan area of training.
Not surprisingly, and consistent with a wide body of research on inno-vation, we find that local areas are more likely to retain new Ph.D.s if thearea is high in measures of innovation such as patent counts and R&D ex-penditures. The relative absorptive capacity of the local community alsoplays a major role. Champaign-Urbana graduates a large number of newPh.D.s who want to work in industry; yet relative to the United States, fewPh.D.s work in industry in the city.
278 Albert J. Sumell, Paula E. Stephan, and James D. Adams
8.7.1 Discussion
Our results are consistent with the findings of Audretsch and Stephan(1996) concerning the degree to which knowledge is captured locally. Towit, they find only 30 percent of the scientist-firm links they examined tobe local; we find that only 25 percent of new Ph.D.s headed to industry stayin the MSA of training. There are at least two distinctions, however, be-tween Audretsch and Stephan’s work and this work. First, university fac-ulty can be on multiple scientific advisory boards; new Ph.D.s can onlywork for one firm at a time. Second, from the viewpoint of the university,it is entirely different to invest in faculty who establish ties with new firmsout of the area while continuing to work at the university than to educatestudents who leave the area to take a position with a firm. While studentswho leave may expand and diversify the university’s knowledge and sup-port network, the economic returns to the state from such migration arelikely to be relatively low, especially in the short run.
Our findings raise the larger question of whether the role of proximity tothe university is overemphasized in the transmission of public knowledgefrom universities to industry. The top source of public knowledge, accord-ing to the Carnegie Mellon survey of firms (Cohen, Nelson, and Walsh2002), is publications and reports. Neither requires proximity to the scien-tist/engineer. The second source (informal information exchange, publicmeetings, or conferences and consulting) is facilitated by proximity butproximity is not essential. The next tier includes recently-hired graduatestudents. Our research shows that, in this respect, proximity does not playa major role.
We infer that if firms know what they are looking for, proximity to theuniversity is not that important in the transmission of knowledge. Firmscan search for the input. Proximity to the university is most importantwhen the firm does not know what it is seeking or does not want to investheavily in search, or when the scientists involved in the transmission oftacit knowledge have a strong preference for remaining local, as Zucker,Darby, and Brewer (1998) argue that star scientists had.21
States often invest in higher education with the conviction that it stimu-lates local economic development. And certainly research supports thisconviction. Our work, however, casts doubt on the benefits states realizefrom one piece of this investment—the education of a doctoral scientificworkforce—and suggests that states capture but a portion of the economic
Capturing Knowledge 279
21. This discussion raises the further question of the degree to which spillovers result fromnonappropriability. We have argued that tacit knowledge comprises an important componentof the knowledge that new Ph.D.s transmit to firms. Yet tacit knowledge, as Zucker, Darby,and Brewer (1998) point out, facilitates excludability. Thus knowledge transmission, to para-phrase the aforementioned authors, can result from the maximizing behavior of scientistswho have the ability to appropriate the returns to this tacit knowledge rather than from non-appropriability.
benefits resulting from a trained Ph.D. workforce. What we do not investi-gate here is why states are able and willing to educate Ph.D.s who leave af-ter graduation. Is the knowledge and technology transfer produced whilestudents are in graduate school sufficient to justify the expenditure? Dograduate students more than compensate for their educational costs, di-rectly through tuition payments and indirectly through their labors in theclassroom and the laboratory? Is the halo generated from having a top-rated program sufficiently beneficial to the state in terms of general eco-nomic development? Do states reap sufficient long-term economic benefitsfrom the networks created by students who migrate? Or, and perhaps whatis more likely, do these factors collectively provide sufficient benefits to out-weigh the state’s expenditures? Is it, of course, also possible that what weobserve is an indication of a disequilibrium that may hasten to adjust asbleak budget prospects lead states to slash budgets for higher education?Can universities such as Illinois and Purdue continue to educate Ph.D.swho overwhelmingly leave the state after graduation? Or are policymakersignorant of the degree to which it is a leaky system?
Groen and White (2001, 24) note that incentives of universities andstates with regard to the retention of highly-trained workers differ. They ex-plain: “States have an interest in using universities to attract and retainhigh-ability individuals because they pay higher taxes and contribute moreto economic development. Universities have an interest in their graduatesbeing successful, but little interest in where their students come from orwhere they go after graduation.” The distinction may be less clear in thepost Bayh-Dole world, where public universities promote their science andengineering programs as engines of economic development. One wondershow long these institutions can continue to bake educational cake for otherstates and countries. The fact that in some instances the institutions are themajor supplier of new in-state industrial hires may, of course, mitigate thepolitical pressure to reallocate resources.
The implications drawn from this study are somewhat restricted due tothe limited scope of the data. For example, the attractiveness of certain re-gions and cities may have been inflated during the time period of analysis.When we extend the analysis to years following the boom in informationtechnology we may find a somewhat different picture than we do here. Fur-thermore, the data eliminates Ph.D.s who do not specify a firm as well asPh.D.s who eventually work in industry after taking a postdoc position.The percent of seasoned Ph.D.s going to industry is much larger than thepercent of new Ph.D.s choosing industry, particularly in the life sciences.As a result, if the study were done on location decisions five years follow-ing receipt of degree, as opposed to newly-minted PhDs, the conclusionsmight differ substantially.
280 Albert J. Sumell, Paula E. Stephan, and James D. Adams
App
endi
x
Tab
le 8
A.1
Var
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finit
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and
des
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stat
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sser
tati
on g
rant
0.13
3 (0
.340
)X
Xsu
pp_t
each
asst
Dum
my
vari
able
indi
cati
ng w
heth
er o
r no
t ind
ivid
ual’s
pri
mar
y so
urce
of
supp
ort d
urin
g gr
adua
te s
choo
l was
teac
hing
ass
ista
ntsh
ip0.
148
(0.3
55)
XX
supp
_RA
_tra
inee
Dum
my
vari
able
indi
cati
ng w
heth
er o
r no
t ind
ivid
ual’s
pri
mar
y so
urce
of
supp
ort d
urin
g gr
adua
te s
choo
l was
res
earc
h as
sist
ants
hip,
inte
rnsh
ip,
or tr
aine
eshi
p0.
479
(0.5
00)
XX
supp
_em
ploy
erD
umm
y va
riab
le in
dica
ting
whe
ther
or
not i
ndiv
idua
l’s p
rim
ary
sour
ce o
f su
ppor
t dur
ing
grad
uate
sch
ool w
as e
mpl
oyer
rei
mbu
rsem
ent o
r as
sist
ance
0.05
0 (0
.219
)X
Xsu
pp_o
ther
*D
umm
y va
riab
le in
dica
ting
whe
ther
or
not i
ndiv
idua
l’s p
rim
ary
sour
ce o
f su
ppor
t dur
ing
grad
uate
sch
ool w
as a
nyth
ing
othe
r th
an e
mpl
oyer
, re
sear
ch o
r te
achi
ng a
ssis
tant
, tra
inee
, dis
s. g
rant
, or
fello
wsh
ip0.
189
(0.3
92)
XX
astr
Dum
my
vari
able
indi
cati
ng w
heth
er o
r no
t an
indi
vidu
al’s
fiel
d of
trai
ning
w
as a
stro
nom
y0.
004
(0.0
63)
XX
agri
Dum
my
vari
able
indi
cati
ng w
heth
er o
r no
t an
indi
vidu
al’s
fiel
d of
trai
ning
w
as in
agr
icul
ture
0.03
0 (0
.165
)X
Xal
leng
Dum
my
vari
able
indi
cati
ng w
heth
er o
r no
t an
indi
vidu
al’s
fiel
d of
trai
ning
w
as e
ngin
eeri
ng0.
530
(0.5
00)
XX
Tab
le 8
A.1
(con
tinu
ed)
Mea
n Sa
me
stat
e Sa
me
PM
SA
Var
iabl
eD
efini
tion
(Sta
ndar
d de
viat
ion)
(Eq.
1)
(Eq.
2)
biol
*D
umm
y va
riab
le in
dica
ting
whe
ther
or
not a
n in
divi
dual
’s fi
eld
of tr
aini
ng
was
bio
logy
0.06
0 (0
.229
)X
Xch
emD
umm
y va
riab
le in
dica
ting
whe
ther
or
not a
n in
divi
dual
’s fi
eld
of tr
aini
ng
was
che
mis
try
0.12
1 (0
.314
)X
Xco
mp
Dum
my
vari
able
indi
cati
ng w
heth
er o
r no
t an
indi
vidu
al’s
fiel
d of
trai
ning
w
as c
ompu
ter
scie
nce
0.07
5 (0
.255
)X
Xea
rth
Dum
my
vari
able
indi
cati
ng w
heth
er o
r no
t an
indi
vidu
al’s
fiel
d of
trai
ning
w
as e
arth
sci
ence
0.02
5 (0
.150
)X
Xm
ath
Dum
my
vari
able
indi
cati
ng w
heth
er o
r no
t an
indi
vidu
al’s
fiel
d of
trai
ning
w
as m
athe
mat
ics
0.04
7 (0
.204
)X
Xm
edi
Dum
my
vari
able
indi
cati
ng w
heth
er o
r no
t an
indi
vidu
al’s
fiel
d of
trai
ning
w
as m
edic
ine
0.04
3 (0
.195
)X
Xph
ysD
umm
y va
riab
le in
dica
ting
whe
ther
or
not a
n in
divi
dual
’s fi
eld
of tr
aini
ng
was
phy
sics
0.06
5 (0
.237
)X
Xto
psas
trD
umm
y va
riab
le in
dica
ting
whe
ther
or
not a
n in
divi
dual
’s P
h.D
. fiel
d w
as
astr
onom
y an
d th
eir
Ph.
D. i
nsti
tuti
on w
as to
p-ra
nked
in a
stro
nom
y0.
003
(0.0
51)
XX
tops
agri
Dum
my
vari
able
indi
cati
ng w
heth
er o
r no
t an
indi
vidu
al’s
Ph.
D. fi
eld
was
ag
ricu
ltur
e an
d th
eir
Ph.
D. i
nsti
tuti
on w
as to
p-ra
nked
in a
gric
ultu
re0.
023
(0.1
49)
XX
tops
alle
ngD
umm
y va
riab
le in
dica
ting
whe
ther
or
not a
n in
divi
dual
’s P
h.D
. fiel
d w
as
in e
ngin
eeri
ng a
nd th
eir
Ph.
D. i
nsti
tuti
on w
as to
p-ra
nked
in e
ngin
eeri
ng0.
354
(0.4
78)
XX
tops
biol
Dum
my
vari
able
indi
cati
ng w
heth
er o
r no
t an
indi
vidu
al’s
Ph.
D. fi
eld
was
bi
olog
y an
d th
eir
Ph.
D. i
nsti
tuti
on w
as to
p-ra
nked
in b
iolo
gy0.
039
(0.1
93)
XX
tops
chem
Dum
my
vari
able
indi
cati
ng w
heth
er o
r no
t an
indi
vidu
al’s
Ph.
D. fi
eld
was
ch
emis
try
and
thei
r P
h.D
. ins
titu
tion
was
top-
rank
ed in
che
mis
try
0.06
8 (0
.251
)X
Xto
psco
mp
Dum
my
vari
able
indi
cati
ng w
heth
er o
r no
t an
indi
vidu
al’s
Ph.
D. fi
eld
was
com
pute
r sc
ienc
e an
d th
eir
Ph.
D. i
nsti
tuti
on w
as to
p-ra
nked
in
com
pute
r sc
ienc
e0.
046
(0.2
10)
XX
tops
eart
hD
umm
y va
riab
le in
dica
ting
whe
ther
or
not a
n in
divi
dual
’s P
h.D
. fiel
d w
as
eart
h sc
ienc
e an
d th
eir
Ph.
D. i
nsti
tuti
on w
as to
p-ra
nked
in e
arth
sci
ence
0.01
6 (0
.124
)X
Xto
psm
ath
Dum
my
vari
able
indi
cati
ng w
heth
er o
r no
t an
indi
vidu
al’s
Ph.
D. fi
eld
was
m
athe
mat
ics
and
thei
r P
h.D
. ins
titu
tion
was
top-
rank
ed in
mat
hem
atic
s0.
024
(0.1
54)
XX
tops
med
iD
umm
y va
riab
le in
dica
ting
whe
ther
or
not a
n in
divi
dual
’s P
h.D
. fiel
d w
as
med
icin
e an
d th
eir
Ph.
D. i
nsti
tuti
on w
as to
p-ra
nked
in m
edic
ine
0.02
1 (0
.142
)X
Xto
psph
ysD
umm
y va
riab
le in
dica
ting
whe
ther
or
not a
n in
divi
dual
’s P
h.D
. fiel
d w
as
phys
ics
and
thei
r P
h.D
. ins
titu
tion
was
top-
rank
ed in
phy
sics
0.03
7 (0
.189
)X
Xpr
ivat
eD
umm
y va
riab
le in
dica
ting
whe
ther
or
not a
n in
divi
dual
rec
eive
d th
eir
Ph.
D. f
rom
a p
riva
te in
stit
utio
n0.
324
(0.4
68)
XX
(co
nti
nu
ed)
STpa
tsN
umbe
r of
pat
ents
in th
ousa
nds
gran
ted
in th
e st
ate
of th
e in
divi
dual
’s P
h.D
. ins
titu
tion
bet
wee
n 19
97–1
999
6.49
(6.6
6)X
—ST
acad
RD
Aca
dem
ic R
&D
exp
endi
ture
s in
mill
ions
in th
e st
ate
of th
e in
divi
dual
’s P
h.D
. ins
titu
tion
bet
wee
n 19
97–1
999
in th
ousa
nds
of 1
996
dolla
rs36
.539
(28.
465)
X—
STin
dRD
Indu
stri
al R
&D
exp
endi
ture
s in
mill
ions
in th
e st
ate
of th
e in
divi
dual
’s P
h.D
. ins
titu
tion
bet
wee
n 19
97–1
999
in th
ousa
nds
of 1
996
dolla
rs28
.631
(32.
568)
X—
STsi
zeG
eogr
aphi
c si
ze in
thou
sand
s of
squ
are
mile
s of
the
stat
e of
the
indi
vidu
al’s
Ph.
D. i
nsti
tuti
on75
.852
(66.
31)
X—
STpo
pPo
pula
tion
in h
undr
ed th
ousa
nds
in 2
000
in th
e st
ate
of th
e in
divi
dual
’s P
h.D
. ins
titu
tion
129.
696
(99.
816)
X—
STpe
rhe
Per
cent
of t
he p
opul
atio
n ag
e 25
�in
the
stat
e of
the
indi
vidu
al’s
Ph.
D.
inst
itut
ion
wit
h a
bach
elor
’s de
gree
or
high
er in
199
825
.22
(4.0
6)X
—ST
pcin
cP
er C
apit
a in
com
e in
thou
sand
s in
the
stat
e of
the
indi
vidu
al’s
Ph.
D. i
nsti
tuti
on
in 1
994
22.9
53 (2
.570
)X
—A
BP
hDST
Ph.
D. a
bsor
ptio
n ca
paci
ty in
dex
in th
e st
ate
of th
e in
divi
dual
’s P
h.D
. ins
titu
tion
(s
ee te
xt)
1.12
9 (0
.400
)X
—pm
sapa
tsN
umbe
r of
pat
ents
in h
undr
eds
gran
ted
in th
e P
MSA
of t
he in
divi
dual
’s P
h.D
. in
stit
utio
n be
twee
n 19
97–1
999
8.17
(8.6
8)—
Xm
ilken
ind
Milk
en I
ndex
in th
e P
MSA
of t
he in
divi
dual
’s P
h.D
. ins
titu
tion
in 2
002
1.11
0 (0
.711
)—
Xpm
sasi
zeG
eogr
aphi
c si
ze in
thou
sand
s of
squ
are
mile
s of
the
PM
SA o
f the
indi
vidu
al’s
Ph.
D. i
nsti
tuti
on2.
464
(2.1
16)
—X
pmsa
pop
Popu
lati
on in
hun
dred
thou
sand
s in
the
PM
SA o
f the
indi
vidu
al’s
Ph.
D.
inst
itut
ion
in 2
000
25.2
2 (2
6.54
)—
Xpm
sape
rhe
Per
cent
of t
he p
opul
atio
n ag
e 25
�in
the
PM
SA o
f the
indi
vidu
al’s
Ph.
D.
inst
itut
ion
wit
h a
bach
elor
’s de
gree
or
high
er in
200
031
.572
(6.9
2)—
Xpm
sapc
inc
Per
cap
ita
inco
me
in th
ousa
nds
in th
e P
MSA
of t
he in
divi
dual
’s P
h.D
. in
stit
utio
n in
199
931
.62
(5.8
63)
—X
AB
PhD
MSA
Ph.
D. a
bsor
ptio
n ca
paci
ty in
dex
in th
e P
MSA
(see
text
)3.
547
(4.4
1)—
X
No
tes:
Ast
eris
k (*
) in
dica
tes
the
benc
hmar
k or
con
trol
gro
up. “
XX
” m
eans
the
var
iabl
e is
a d
epen
dent
var
iabl
e in
clud
ed in
the
equ
atio
n. “
X”
Mea
ns t
heva
riab
le is
an
expl
anat
ory
vari
able
incl
uded
in th
e eq
uati
on.
Tab
le 8
A.1
(con
tinu
ed)
Mea
n Sa
me
stat
e Sa
me
PM
SA
Var
iabl
eD
efini
tion
(Sta
ndar
d de
viat
ion)
(Eq.
1)
(Eq.
2)
Table 8A.2 Empirical resultsSample � placements trained in the continental United States in apublic institution
Equation (1): Equation (2): Dependent variable Dependent variable
� SameSTATE � SamePMSA
N � 6,832 N � 5,973
Variable Estimate z-stata Estimate z-stata
Intercept �4.0254*** 16.16 �3.2767*** 7.77age 0.0759 2.49 0.0980* 2.77agesq �0.0005 0.69 �0.0007 0.91female �0.0152 0.01 �0.1222 0.55asian �0.1433* 2.83 �0.3627*** 11.41nonwhite_asian �0.1187 0.92 �0.1546 1.00permres 0.0224 0.04 �0.2446* 3.00tempres �0.3443*** 14.26 �0.5355*** 20.95married 0.0742 0.87 0.1158 1.29female_married 0.0971 0.39 0.1595 0.62wchild 0.0633 0.64 0.0795 0.66singlepar �0.3512** 3.95 �0.3785 2.65samece_phd 0.5645*** 16.37 0.2367 2.00samehs_phd 0.1983 1.22 �0.1240 0.34sameb_phd 0.0685 0.20 0.4018** 5.13return 0.5790*** 44.55 0.3927*** 14.19debtlevel �0.0078*** 7.12 �0.0103*** 7.61preftemp 0.4254*** 33.27 0.4280*** 20.29preptemp 0.6526*** 31.95 0.7237*** 29.67supp_fellow �0.4007*** 11.50 �0.1308 0.78supp_teachasst 0.0711 0.46 0.0886 0.44supp_RA_trainee �0.1450* 2.95 �0.0018 0.00support_employer 0.1579 1.21 0.0406 0.07astr 1.0445 1.69 0.0444 0.00agri �0.8062** 4.17 �0.5502 0.61alleng �0.4062* 3.23 �0.1667 0.40chem �0.6081** 5.92 �0.4540 2.24math �0.1447 0.28 0.0567 0.03comp �0.4831* 3.06 �0.2341 0.52earth �1.1193*** 9.58 �1.2857*** 7.64medi �0.1585 0.28 �0.0339 0.01phys �0.0379 0.02 0.1738 0.28topsastr �0.7449 0.50 0.949 0.01topsagri �0.0336 0.01 �0.5447 0.59topsalleng �0.1305 1.97 �0.3363*** 6.87topsbiol �0.4799* 3.45 �0.5912* 3.82topschem �0.4939*** 7.67 �0.5684** 5.45topscomp �0.4295* 3.68 �0.6812** 6.08topsearth 0.1426 0.15 �0.0452 0.01topsmath �0.7983*** 8.90 �0.4819 2.23topsmedi �0.6370** 5.23 �0.8052** 5.56
(continued )
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286 Albert J. Sumell, Paula E. Stephan, and James D. Adams
topsphys 0.0418 0.03 0.1081 0.15STpats 0.00025 0.18 n.a. n.a.STacadRD �0.00010** 4.42 n.a. n.a.STindRD 0.000021** 5.90 n.a. n.a.STsize 0.0062*** 54.98 n.a. n.a.STpop �0.000014 0.48 n.a. n.a.STperhe 0.0046 0.11 n.a. n.a.STpcinc 0.0001** 5.88 n.a. n.a.ABPhDST �0.2538*** 7.26 n.a. n.a.pmsapats n.a. n.a. 0.0010*** 59.81milkenind n.a. n.a. 0.4875*** 33.36pmsapop n.a. n.a. 0.0000038 0.02pmsasize n.a. n.a. 0.02050 1.59pmsapcinc n.a. n.a. �0.03060** 6.56pmsaperhe n.a. n.a. �0.0070 0.66ABPhDMSA n.a. n.a. �0.0789*** 27.56–2 Log-likelihood 8,857.6 5,869.2
Note: n.a. � not applicable.az-stats are based on chi-square distribution.***Significant at the 1 percent level.**Significant at the 5 percent level.*Significant at the 10 percent level.
Table 8A.2 (continued)
Equation (1): Equation (2): Dependent variable Dependent variable
� SameSTATE � SamePMSA
N � 6,832 N � 5,973
Variable Estimate z-stata Estimate z-stata
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