This PDF is a selection from a published volume from the National … · 2011-06-21 · 8.1 Introduction The placement of newly-minted science and engineering Ph.D.s pro-vides one

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-


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,


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


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


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


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)

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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


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

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renc

e-L

owel

l-B

osto

n-W

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ster

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renc

e-L

owel

l-B

rock

ton,

MA

-NH

NE

614

238

38.8

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ckto

n, M

A-N

H N

E58

823

840

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ngel

es-R

iver

side

-Ora

nge

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nty,

CA

525

233

44.4

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Ang

eles

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ersi

de-O

rang

e C

ount

y, C

A48

423

348

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ashi

ngto

n-B

alti

mor

e, D

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MD

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-WV

327

160

48.9

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hing

ton-

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tim

ore,

D.C

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D-V

A-W

V44

316

036

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ham

paig

n-U

rban

a, I

L31

310

3.2

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ston

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vest

on-B

razo

ria,

TX

340

4814

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etro

it-A

nn A

rbor

-Flin

t, M

I30

410

233

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hica

go-G

ary-

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osha

, IL

-IN

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339

122

36.0

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cago

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y-K

enos

ha, I

L-I

N-W

I29

012

242

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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

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etro

it-A

nn A

rbor

-Flin

t, M

I24

110

242

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delp

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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

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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

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anta

, GA

150

7348

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adis

on, W

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816

7.7

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eigh

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ham

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pel H

ill, N

C14

451

35.4

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eigh

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ham

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pel H

ill, N

C17

851

28.7

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a, A

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135

28.9

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land

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ttle

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oma,

OR

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162

6842

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r-B

ould

er-G

reel

ey, C

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s, O

H15

421

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cinn

ati-

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ilton

, OH

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109

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burg

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any-

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nect

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kron

, OH

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n, A

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go, C

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polit

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02,

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othe

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polit

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reas

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356

420

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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

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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


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


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

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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


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


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-


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.


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.


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


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.


App

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x

Tab

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A.1

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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 )

References

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Audretsch, D., and M. Feldman. 1996a. Innovation clusters and the industry lifecycle. Review of Industrial Organization 11 (2): 253–73.

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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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This PDF is a selection from a published volume from the National … · 2011-06-21 · 8.1 Introduction The placement of newly-minted science and engineering Ph.D.s pro-vides one