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

5. 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: a)From U.S. universities to foreign countries? b)From foreign countries to U.S. universities? c)Primarily through students who return to their home countries? d)Or from those who remain in the U.S. as well? Contribution to the “brain drain” debate Asks how U.S. is affected a)By the increase in the foreign share of doctoral students (see also Stephan et al, Stuen et al) b)as more foreign-born doctorates return to home countries

6. 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

7. 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

8. 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

9. Key foreign student variables: Studmig ijkt : number of students obtaining doctorates at university i in field k and year t with plans to move to country j after graduation Forstud ijkt : 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.

11. 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

12. Key patent variables Dependent variables: –B ijkt : “Backward” citations by university i’s patents to country j’s patents in field k and year t –F ijkt : “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 Prox ijt =  c (P ict P jct )/ √(  c P ict 2 )(  c P jct 2 )

13. Field of Study IPC Class / HJT Category Agricultural/Environ mental Sciences IPC A01 (agriculture; forestry; animal husbandry; hunting; trapping; fishing) Biomedical sciences IPC A61 (medical or veterinary science;hygiene), C12M-C12S (biochemistry; microbiology; enzymology; mutation or genetic engineering); C07 (organic chemistry) / HJT 3 – Drugs & Medical Mechanical engineering IPC F01-F17 (mechanical engineering) / HJT 5 -- Mechanical Chemistry and Chemical Eng. IPC C — chemistry; metallurgy / HJT 1 -- Chemical Electrical Engineering IPC H — electricity; G06 -- computing; calculating; counting / HJT 4 – Electrical & Electronic Computer Science IPC G06 -- computing; calculating; counting / HJT 2 – Computers & Communications Physics IPC G — physics Fields of study mapped to patent classes

14. Forward citations Backward citations

15. Variable MeanStd. Dev.Min.Max. studmig jkt 0.03250.334030   studmig jkt-  0.2701.9520216 forstud jkt 0.09230.5665036   forstud jkt-  0.6933.560256 Country's patents 308.95980.317111805 University's Patents 0.4652.8130183 Total citations to country's patents 1397.3965151.57062272 Total number of doctorates in field k at university i 9.17021.1080560 Total number of doctorates at university i 149.14255.602806 Total number of doctorates in field k from country j 19.76350.4210745 Descriptive statistics Unit of observation is a university (i), country (j), field (k) and year(t) combination

16. F.E. Poisson regression specifications E[B ijkt | X ijkt ]= exp(β’X ijkt ) ; E[F ijkt | X ijkt ] = exp(β’X ijkt )  ’X    s  studmig ijkt -  +    f  forstud ijkt -  +  i +  k +  j  t +  Z ijkt  = 1 to 10 OR  ’X  s ln(   studmig ijkt -  )+  f ln(   forstud ijkt -  )+  i +  k +  j  t +  Z ijkt 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).

17. All CountriesOECD Only Regression w student variables measured in levels:    _ studmig t-  -0.015***-0.028** (0.005)(0.014)    _ forstud t-  0.0050.007** (0.004)(0.003) Regression w student variables measured in logs: Ln(   studmig t-  ) -0.094***-0.013 (0.006)(0.011) Ln(   forstud t-  ) -0.021***0.037*** (0.006)(0.012) Knowledge Diffusion to U.S. Universities from foreign countries, University-country-year level analysis, 1987-2002 Poisson 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.

18. All countriesOECD Only Regression w student variables measured in levels:    _ studmig t-  0.032***0.041*** (0.003)(0.010)    _ forstud t-  -0.012***-0.009 (0.002)(0.009) Regression w student variables measured in logs: Ln(   studmig t-  ) 0.044***0.079*** (0.013)(0.009) Ln(   forstud t-  ) -0.033***0.006 (0.011) Knowledge Diffusion from U.S. Universities to foreign countries, University-country-year level analysis, 1987-2002 Poisson 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.

19.    _studmig ijkt-   _studmig ijkt- 

20.    _studmig ijkt- 

21. Knowledge Diffusion to U.S. Universities from Foreign Countries, by type of institution and development level of the country Fixed-effects Poisson Regression, dependent variable is the number of citations by university i in year t to patents in country j Doctoral/Research Universities Master's & Baccalaureate Universities OECD countries Non-OECD countriesOECD Non-OECD countries    _ forstud t-  0.007*0.003-0.0220.006 (0.004) (0.046)(0.024)

22. Knowledge Diffusion from U.S. Universities to Foreign Countries, by type of institution and development level of the country Fixed-effects Poisson Regression, dependent variable is the number of citations by patents in country j to university i in year t Doctoral/Research Universities Master's & Baccalaureate Universities OECD countries Non-OECD countries OECD countries Non-OECD countries    _ studmig t-  0.040***-0.021-0.0120.117 *** (0.003)(0.013)(0.023)(0.053)

23. Auxiliary evidence from individual-level data Using ProQuest, I have identified the undergraduate institutions for all engineering graduates of UC Berkeley, U Ill, and Penn State from 1997-2000 and matched this to patent data Results show that foreign doctorates remaining in the U.S. are disproportionately more likely to be cited in their (OECD) home countries Individual-level analysis of forward citations, by country (1) (2)(3)(4) Full Sample OECD Countrie s OECD Countries, excl USA China, India, Korea, Singapore & Taiwan Dummy=1 if country is student’s home country 0.6461.1730.9660.252 (0.083)* ** (0.148)* ** (0.479)**(0.552) Student’s patent count1.5951.5591.5861.587 (0.048)* ** (0.062)* ** (0.178)** * (0.072)*** Country’s patent count1.0260.9541.3190.981 (0.035)* ** (0.043)* ** (0.130)** * (0.067)*** Constant-18.462-17.778-22.454-17.564 (0.496)* ** (0.576)* ** (2.077)** * (0.950)*** Observations6704437560132619929 Robust standard errors in parentheses, clustered by inventor. * significant at 10%; ** significant at 5%; *** significant at 1%

24. 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) …  = 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.)

25. 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

26. 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??