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Foreign Students and the International Diffusion of Scientific and Technological Knowledge. Megan MacGarvie Boston University and NBER ExTra Workshop, EPFL Lausanne September 30, 2006. Labor mobility and international knowledge diffusion.
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Foreign Students and the International Diffusion of Scientific and Technological Knowledge Megan MacGarvie Boston University and NBER ExTra Workshop, EPFL Lausanne September 30, 2006
Labor mobility and international knowledge diffusion • Diffusion of scientific and technical knowledge geographically bounded (JTH, etc.) • Trade and FDI explain diffusion of tech. knowledge (Branstetter, MacGarvie, Veugelers & Cassiman) • Networks and labor mobility are related to trade and FDI patterns(Rauch, Combes et al) • Networks and labor mobility are channels for knowledge diffusion (Breschi & Lissoni, Singh) • What role does international labor mobility play in the diffusion of scientific and technical knowledge?(Agrawal et al, Kim et al, Trajtenberg et al, Kerr) • Ultimate goal: use exogenous variation in the number of students studying in the U.S. and returning to home countries to identify effect of labor mobility on diffusion
Source: Bound, Turner and Walsh (2006), based on Survey of Earned Doctorates microdata
Internationalization of U.S. Doctoral Education in Science and Engineering • Has the increase in foreign doctoral recipients in Science & Engineering led to an increase in the diffusion of knowledge: • From U.S. universities to foreign countries? • From foreign countries to U.S. universities? • Primarily through students who return to their home countries? • Or from those who remain in the U.S. as well? • Contribution to the “brain drain” debate • Asks how U.S. is affected • By the increase in the foreign share of doctoral students (see also Stephan et al, Stuen et al) • as more foreign-born doctorates return to home countries
Brain Drain vs. “Brain circulation” • Saxenian (2002) • Half of Silicon Valley immigrant entrepreneurs surveyed had set up subsidiaries, joint ventures or other business ventures in home countries • More than 80 percent said they share information about technology with people back home. • Agrawal, Cockburn and McHale (2006) • Mobile inventors cited disproportionately in prior locations • Kerr (2006) • Foreign inventors 50% more likely to cite U.S.-based inventors of the same ethnicity
Quantifying the extent of “brain circulation” • This paper uses patent citations and counts of students by country and field to quantify knowledge diffusion to and from U.S. universities • Preliminary evidence suggests: • a robust positive relationship between the number of students moving abroad and foreign cites to U.S. university patents • A positive but more limited effect on U.S. cites to foreign countries from inflows of foreign students • Not much impact on knowledge flows to foreign countries when a larger share of expatriates remain in the U.S. • Not much effect on U.S. cites to foreign countries when a larger share of foreign students move abroad
Data: NSF’s Survey of Earned Doctorates (SED) • Annual data from 1958-2004 • Almost the universe of U.S. doctoral recipients; comprehensive data on demographic and educational characteristics • Key information on students: • University • Field of study • citizenship; location of birth, high school and college • location of post-doctoral employment
Key foreign student variables: • Studmigijkt: number of students obtaining doctorates at university i in field k and year t with plans to move to country j after graduation • Forstudijkt: number of students obtaining doctorates at university i in field k and year t who were either born and attended high school, or attended both high school and college in country j • Include ten years of lags • Also control for the # of doctorates in S&E at university, the # of docs in the field at the university, and the # of docs from the country in the field.
Data: NBER patent database • All US patents and citations (updated to 2002) • Key data items: • Location of inventor • Technology class • Citations • University patents identified via search of assignee names • Omits university-invented patents assigned to third parties • Mostly assigned at the university level for multi-campus systems (i.e. state univs)…so counts of doctorates are rolled up
Key patent variables • Dependent variables: • Bijkt: “Backward” citations by university i’s patents to country j’s patents in field k and year t • Fijkt: “Forward” citations to university i’s patents by country j’s patents in field k and year t • Control variables: • Country's patents • University's Patents • Total citations to country's patents • Total citations to university’s patents • Technological proximity Proxijt = Sc (Pict Pjct)/ √(ScPict2)(ScPjct2)
Descriptive statisticsUnit of observation is a university (i), country (j), field (k) and year(t) combination
F.E. Poisson regression specifications E[Bijkt |Xijkt]= exp(β’Xijkt) ; E[Fijkt |Xijkt] = exp(β’Xijkt) b’X=Stbststudmigijkt-t+Stbftforstudijkt-t+gi+gk+gjgt+dZijkt • = 1 to 10 OR b’X=bsln(Ststudmigijkt-t)+bf ln(Stforstudijkt-t)+gi+gk+gjgt+dZijkt Z includes: Ln(Country’s patents), ln(university’s patents), Prox, ln(country’s fwd cites), ln(university’s fwd cites), ln(# of doctorates in S&E at university), ln(# of docs in the field at the university), and ln(# of docs from the country in the field).
Knowledge Diffusion to U.S. Universities from foreign countries,University-country-year level analysis, 1987-2002Poisson regression with university and country x year fixed effects included. Control variables: country’s patents, university’s patents, country’s fwd cites, university’s fwd cites, prox, total students at univ, total students at univ in field, total students in field from country.
Knowledge Diffusion from U.S. Universities to foreign countries,University-country-year level analysis, 1987-2002Poisson regression with university and country x year fixed effects included. Control variables: country’s patents, university’s patents, country’s fwd cites, university’s fwd cites, prox, total students at univ, total students at univ in field, total students in field from country.
St b_studmigijkt-t b_studmigijkt-t
b_studmigijkt-t St b_studmigijkt-t
Knowledge Diffusion to U.S. Universities from Foreign Countries, by type of institution and development level of the countryFixed-effects Poisson Regression, dependent variable is the number of citations by university i in year t to patents in country j
Knowledge Diffusion from U.S. Universities to Foreign Countries, by type of institution and development level of the countryFixed-effects Poisson Regression, dependent variable is the number of citations by patents in country j to university i in year t
Interpreting the results • Foreign countries cite more of a U.S. university’s patents when more Ph.D.s from the university move to those countries (controlling for the total number of students from that country receiving doctorates) … e = 0.08% in the OECD • Who in the foreign country is doing the citing? • Reverse causality & matching • Timing of diffusion • Foreign countries do not cite more U.S. patents when they send more doctoral students to the U.S. (controlling for inflows of docs from the U.S.)
Interpreting the results • Increases in the number of students from OECD countries receiving doctorates at a U.S. university are associated with increases in citations by U.S. universities to foreign patents • Again, is this picking up the “match” between universities? • U.S. universities do not increase their citations to foreign patents when more doctorates move abroad
Next steps: I.V.s • Identify effect of mobility using exogenous variation arising from: • Macroeconomic and political shocks • Japanese recession; exchange rates • East Germany & USSR • Immigration reform act of 1990 • J-1 visas: foreign residency requirement • Demographics??