capturing knowledge: the location decision of new phds working in industry
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Capturing Knowledge: The Location Decision of New PhDs Working in Industry. Albert Sumell, Paula Stephan, James Adams SEWP 2005. Acknowledgements. Part of a larger project that was supported by a grant from the Andrew W. Mellon Foundation Support has also come from the SEWP and NSF. - PowerPoint PPT PresentationTRANSCRIPT
Capturing Knowledge: The Location Decision of New PhDs Working in
Industry
Albert Sumell, Paula Stephan, James Adams
SEWP 2005
Acknowledgements
Part of a larger project that was supported by a grant from the Andrew W. Mellon Foundation
Support has also come from the SEWP and NSF
Paper examines degree to which new PhDs going to work in industry remain in the state or MSA where they received their training.
Uses data that has only recently been coded from the Survey of Earned Doctorates, NSF.
Focus on PhDs trained in S&E
Research Question
Framing the Issue
Considerable interest in sources of knowledge inputs in the knowledge production function (Griliches)
Knowledge sources external to the firm have received considerable attention
Proximity to knowledge sources seen to be important in facilitating innovation
Studies of Knowledge Sources/Proximity
Knowledge sources often measured by such indices as university R&D expenditures in the state or MSA
Jaffe and others have shown there to be a significant relationship between proximity to knowledge sources and measures of innovation
Almeida and Kogut as well as Thompson have shown patent citations to be geographically concentrated
Knowledge Sources/ Proximity continued
Audretsch and Stephan flip question and examine extent to which available knowledge is utilized locally by examining the extent to which proximity plays a role in determining formal relationships between university scientists and biotech firms.
A&S find that proximity matters, but not that much; 70% of the ties were non-local.
The location decision of PhDs going to industry provides another lens for studying knowledge sources and the geography/proximity question.
PhDs possess new knowledge, much of which is of a tacit nature—acquired in the university lab.
Firms acquire knowledge by hiring new PhDs. One means by which knowledge is transmitted between universities and firms.
Placement of new PhDs also builds/reinforces networks and provides human capital to the firm.
Surveys indicate that placement of PhDs with industry is one means by which knowledge is transferred from the public sector to industry
PhDs as a Knowledge Input
Creating a highly skilled work force is one way universities contribute to economic growth
More generally, universities use the economic development argument as a lever for state funds.
– Stanford’s role in Silicon Valley, MIT and Harvard’s role in Route 128; Duke and UNC’s role in Research Triangle.
To extent students who go to work in industry leave, one rationale for investing state and local resources in graduate programs is weakened.
Story may be different for private institutions that don’t get funding from state.
Why States and Universities Care
Data Summary of findings by state and MSA Framework for analysis Empirical results Conclusions
Overview of Presentation
SED asks Ph.D. recipients to “name the organization and geographic location where you will work or study.”
Has never been coded for those going to industry; only coded for those going to academe.
We have coded the firm placements for years 1997-1999; currently updating to 2002.
Data misses individuals who take an academic postdoctoral position before going to industry as well as individuals who have not finalized their work plans at time questionnaire is filled out.
Data
17,382 Ph.D.s in S&E during 1997-1999 period planned to work in industry.
10,132 trained in “exact sciences” and engineering had made a definite commitment and identified specific firm
Summary of Data
Field Percent of all graduated identifying a firm
Percent of Firm Placements
All S&E 14.5% 100%
All Engineers 30.7% 53.0%
Agriculture 9.0% 3.0%
Astronomy 7.8% .4%
Biology 3.8% 6.0%
Chemistry 18.7% 12.0%
Computer Sciences 28.4% 7.5%
Earth Sciences 12.3% 2.t%
Math 12.5% 4.7%
Medicine 5.0% 4.3%
Physics 16.1% 6.5%
Firm Placement of New S&E PhDs by Field of Study
37% of all seasoned S&E PhDs were working in industry in 1999
Greater than 50% in chemistry and in engineering
33% in math/computer science 25% in life sciences
Industrial Employment Benchmarks for 1999
Findings with Regard to Retention of Newly Trained PhDs
37% remain in the state where trained 20.5% remain in PMSA where trained Substantial variation in retention rates across
regions, states and MSAs
Percent Staying In State of PhD
Percent Gain or Loss In the State of PhD
Certain States and Regions Stand Out
Pennsylvania retains 23.9% Indiana retains 12.2% Wisconsin retains 18.8% Pacific retains 69.4% Much of this is a Midwest story—major source
of new PhDs; Midwest states retain only 25%
Top 25 Producing MSAs of Industrial PhDs
Top 25 Producing MSAs of Industrial PhDs
Top 25 Destination MSAs of Industrial PhDs
Considerable Overlap
Eighteen metropolitan areas are in the top 25 in producing and employing
Some major producers have low retention rates: – Urbana-Champaign—3.2% retention– Lafayette, Indiana—2.9% retention– State College, Pa—3.3% retention– Madison—7.7% retention
CMSA N # that stay
% that stay
NY 732 423 57.8
SF 706 416 58.9
Boston 614 238 38.8
LA 525 233 44.4
DC 327 160 48.9
Top 5 Producing CMSAs
CMSA N N that stay
% that stay
Champaign
Urbana313 10 3.2
Detroit 304 102 33.6
Chicago 290 122 42.1
Atlanta 282 73 25.9
Austin 282 67 23.8
Lafayette 279 8 2.9
Next 6
Conclusions
Stay rates vary considerably by state Pacific region is a net importer California plays a special role: produces more, retains
more and hires more from out of state Major brain drain from Midwest. Indiana PhDs most likely to find employment in other
states: Stay rate is only 12%
Empirical Analysis
View migration as an investment decision Analysis focuses on whether the PhD leaves
either the state or the city of training Individual is the unit of analysis
Variables
Three sets of variables: – Variables that reflect attributes of state and local
area– Variables that reflect individual characteristics– Variables that reflect field differences, and
institutional characteristics– Logit equations estimated; marginal effects reported
Findings (Tables 6 and 7)
Certain demographic factors affect mobility in expected way: marital status, being a temporary resident.
Where you went to high school and college matters Networks matter: those who worked are more likely to
stay; those who are returning to a job are more likely to stay.
Those with debt more likely to leave
Some of these effects are quite large
Temporary residents 7 percent more likely to leave state or MSA.
Those who earn PhD in same state they went to college in are 12% more likely to stay in state; 4% more likely to stay in city.
Those who got PhD in same state they went to high school and college in are 18% more likely to stay in state
“Best” are more likely to leave
In five of ten fields studied (engineering, biology, chemistry, math and medicine) individuals trained at a top program are significantly more likely to leave state than are those from “non-top” programs
Marginal effects can be quite large—math 10% Four of top program variables negative and significant
in the PMSA equation Individuals supported on fellowships more likely to
leave
Technological Infrastructure Matters
More likely to stay in state higher are state industrial R&D expenditures
More likely to stay in PMSA higher the patent count; larger the Milken index
What we call absorptive capacity matters as well:
ABPhDi= (NPhDIi /PhDIi)/(ΣNPhDIi/ΣPhDIi)
Per Capita Income
Individuals are more likely to stay in states with higher per capita income.
We do not find per capita income to be significant in PMSA equation
Public Results
Limit analysis to public institutions Results are reasonably similar Finding that many of the “best” leave persists
– In terms of quality– In terms of having been supported on a fellowship
Role of local amenities
Sumell is using data to explore the role that local amenities play in attracting PhDs to work in an area.
Estimates a nested logit model of location decision of new PhDs
Has numerous measures of amenities—including natural and publicly provided
We explore degree to which the decision to remain in PMSA is affected by relative desirability of local area with regard to sun light, temperature and humidity
Variables/results
Relative measure of January and July temperatures
Relative measure of January sunlight Relative measure of July humidity Results are counterintuitive: suggest
individuals are more likely to leave sunny winter climates and stay where temperatures are considerably higher in the summer
Conclusion
States and MSAs capture knowledge but not at an overwhelming rate.
Whose knowledge is captured? Certain characteristics predispose individuals to stay– Married, returning to a job, home grown– “Best” are more likely to leave;
Quality of program Fellowship support
Conclusion continued…
S&Es more likely to stay in high tech areas as measured by patent counts, R&D expenditures, etc.
Absorptive capacity matters
Proximity
When knowledge is tacit, proximity matters But proximity to what? Simplifying assumption is that
tacit knowledge sticks to its source: the University. Our research reminds us that tacit knowledge is not as
sticky to the university as some would think. It’s proximity to scientists that matter and scientists—
especially freshly minted scientists—are mobile; tacit knowledge becomes embodied in an input to the firm
Reminiscent of Audretsch and Stephan work that finds that proximity matters but it doesn’t matter that much.
University and state capture but a small piece of this.
Raises the Question: Is Proximity to the University Overemphasized?
Carnegie Mellon survey of firms asks for sources of public knowledge
The top source (publications/reports) does not require proximity to knowledge source.
Second source facilitated by proximity but proximity not essential (informal information exchange, public meetings or conferences and consulting)
Next tier includes recently hired graduate students—we’ve just shown proximity not that important.
Conclude
If firms know what they are looking for, proximity to the university is not that important. Firms can “buy” the input. (Relates to a Mansfield result)
Proximity to the university is most important when firm does not know what it is seeking or does not want to heavily invest in search.
Nonappropriability?
Discussion raises further question of degree to which spillovers result from nonappropriability
Tacit knowledge comprises an important component of the knowledge that new PhDs transmit to firms.
Yet tacit knowledge (Zucker, Darby, and Brewer 1998) facilitates excludability
Means that knowledge transmission can result from maximizing behavior of scientists who have the ability to appropriate the returns to this tacit knowledge rather than from nonappropriability
The state perspective: Why?
States often invest in higher education with conviction that it stimulates economic development.
Universities use such an argument as a lever for resources.
Our work casts doubt on benefits states realize from one piece of the investment: doctoral students.
Why continued…
Are benefits produced while students sufficient? Meet state R&D needs? Purdue supplies 21% of Indianapolis
industrial hires Is halo of having a top rated program beneficial to state in other
ways? Is what we observe an indication of disequilibrium? Can “Purdues” and “Illinoises” continue? Are policy makers ignorant of degree to which it is a leaky
system? Is small firm lobby extremely effective? Do results explain investment in the margin in science parks,
venture capital funds, etc? Case of University of Minnesota
Usual Caveats
Data base misses individuals who do not have definite plans; individuals who go from post doc to industry are not counted in industrial employment.
Econometric issues: modeling spatial effects; selection bias in terms of who answers the questionnaire.
1997-1999 were boom years in the U.S.; may cloud the results
Interested in the data?
Workshop sometime next fall NSF/SEWP sponsored Focus on uses and potential uses of SRS
generated NSF data on scientists and engineers
Let me know if you or a student/young researcher are interested in participating