Foreign Students and the International Diffusion of Scientific and Technological Knowledge

1. Foreign Students and the International Diffusion of Scientific and Technological Knowledge Megan MacGarvie Boston University and NBER ExTra Workshop, EPFL Lausanne September 30, 2006

2. 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

3. Source: Bound, Turner and Walsh (2006), based on Survey of Earned Doctorates microdata

4. 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

5. 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

6. 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

7. 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

8. 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.

10. 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

11. 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)

12. Fields of study mapped to patent classes

13. Descriptive statisticsUnit of observation is a university (i), country (j), field (k) and year(t) combination

14. 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).

15. 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.

16. 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.

17. St b_studmigijkt-t b_studmigijkt-t

18. b_studmigijkt-t St b_studmigijkt-t

19. 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

20. 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

21. 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.)

22. 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

23. 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??