Summer School "Knowledge Dynamics, Industry Evolution, Economic development", 7-13 July 2013, Maison du Sminaire, Nice. Migration & Innovation Francesco Lissoni GREThA Universit de Bordeaux & CRIOS Universit Bocconi (Milan) Motivation Immigration policies and migration shocks have always affected innovation e.g. early history of patents (David, 1993); scientists run from oppressive regimes (Moser et al., 2011) Steady increase in the global flows of scientists and engineers (S&Es) over the past 20 years, both in absolute terms and as a percentage of total migration flows (Freeman, 2010; Docquier and Rapoport, 2012) Hot policy issues: Destination countries: immigration: selective immigration rules, incl. point-based and other highly-skilled dedicated visas (e.g. H1B in the US) higher education : openness to foreign students, incl. choices on education language science and research : openness to young foreign scientists, esp. in untenured jobs Origin countries:

brain drain threat restrictions to highly-skilled emigration ; higher education policies (migration as outgoing spillovers) brain gain opportunities higher education policies (migration as staple for certain disciplines/institutes) ; pro-returnee policies (incl. adoption of IP legislation, following TRIPs) Key research questions for destination countries 1. Do foreign S&Es increase the destination countrys innovation potential, or do they simply displace the local S&E workforce? 2. Are destination countries increasingly dependent on the immigration of S&Es (including graduate students)? 3. Does such dependence require the implementation of dedicated immigration policies? 4. Entry points of foreign S&Es: education, labour market or foreign subsidiaries? Key research questions for origin countries 1. Net effect of: loss of human capital (brain drain)

(potential) compensating mechanisms: a) Knowledge spillovers from destination countries b) Innovation by returnee S&Es and entrepreneurs 2. Role of intellectual property (IP) in promoting (1) and (2) (e.g.Fink and Maskus, 2005) IP may attract investors knowledge spillovers IP may promote returnee entrepreneurs IP may impede imitation Does IP decrease or increase transaction costs? (markets for technologies vs litigation costs) Today presentations objectives To provide a (selective) overview of main issues and data sources To assess the potential of patent & inventor data to address existing limitations in empirical analysis To provide a more detailed application: research on ethnic spillovers ALL QUESTIONS WELCOME AT ANY POINT AND TIME!!! (dont wait till the end of the presentation... & after lunch I go cycling!)

Data sources, with applications Labour and census data: general and highly skilled migrants Two datasets of paramount importance: Docquier and Marfouk (2006; DM06 most recent release: Docquier et al., 2009) DIOC 2000* & DIOC 2005/6: Database on Immigrants in OECD countries (; Widmaier and Dumont, 2011) * also in extended version (+70 non-OECD countries ; info on scientists and engineers for selected countries) Similar methodologies: stock of foreign born residents in OECD countries in given years (1990 and 2000 for DM06; 2000 and 2005/6 for DIOC), disaggregated by: migrants origin country age class gender 3 levels of educational attainment PLUS figures on the number of residents in origin countries Sources: census data or labour force surveys total emigration from any single origin country: f_stockj=if_stockij

foreign born residents in any destination country i: f_stocki=jf_stockij BrainDrainj = hsf_stockj/(hsf_stockj+hs_residentsj) BrainIntakei = hs_stocki/hs_residentsi Source: Elaboration on DIOC data by Widmaier S. , Dumont J.-C. (2011) Labour and census data limitations 1) Difficulties in defining foreign born individuals (a UK citizen born in Canada by UK parents is counted as foreign-born in census data) PLUS clash with nationality based definition (as in labour surveys) 2) Information is not available on where foreign born individuals received their tertiary education 3) Migrants are assigned to the hs category on the basis of their educational attainments (tertiary education), but it is often the case that they accept jobs for which they are overqualified see evidence by Hunt (2011, 2013) on underemployment of engineering and computer science graduates from LDCs in the US 4) Aggregate data (no way to further sample the individuals and combine with other info or interviews) Ethnic diversity and innovation /1 Alesina, et al. 2013 Reciprocal of HH

(concentration of residents by country of origin) y : income or productivity per capita kt : vector of geographic characteristics k : vector of fractionalization measures kt : control for institutional development kt : vector of controls for trade openness and trade diversity, and t : time fixed-effect. s : overall, skilled, unskilled t : 1990, 2000 Ethnic diversity and innovation /2 Further positive evidence (on Europe) Ozgen et al. (2011): 170 NUTS2 regions in Europe, observed over two periods knowledge production function & aggregate data, no direct evaluation of migrations impact on innovation Niebuhr (2010) : effects of cultural diversity on the patenting rate of 95 German regions over two years (1995 and 1997) Works by Ottaviano, Peri, Nathan Surveys Global Science Survey (GlobSci)

Franzoni et al., 2012; Scellato et al., 2012 Survey of authors of papers published in high quality scientific journals in 2008, in 16 top-publishing countries (excl China 70% worldwide papers) Key role of foreign authors: Switzerland (57%) US % Sweden (38%) From 33% to 17%: UK, Netherlands, Denmark, Germany, Belgium, and France Low presence (7%-3%): Spain, Japan, and Italy Migration within Europe is mainly intra-continental and driven by proximity and language US as main attractor of Chinese and Indian nationals Limitation: one-off survey / privacy issues (ltd access) / scientists have been historically a globalised community Survey on Careers of Doctorate Holders (CDH) By UNESCO & OECD, 2007 (25 OECD countries; see Auriol, 2007 and 2010) Some interesting info, but doctoral graduates represent only from 1% to 3% of all tertiary graduates Survey on the Mobility of European Researchers (MORE) Report to the European Commission, 2010

Main focus is on academic researchers (data for industrial researchers are based on a non representative sample) No questions directly relevant for the innovation process. CV data (esp. for returnees) Luo et al., 2013: biographical data of Chinese firms executives and CEOs to identify returnees nr SINO patent firm f (returnee dummies, R&D and controls) ceteris paribus, returnee firms patent more Ad hoc data datasets (mainly for natural experiments) Borjas and Doran (2012) End of USSR Migration of Russian mathematicians into the US Affiliation and publication data from intl mathematical societies Displacement effect for US mathematicians in classic Russian fields Ad hoc data datasets (mainly for natural experiments) Moser et al (2014) Racial laws in Nazi Germany Migration of Jewish chemists in the US Historical directories to identify German emigrant chemists

Historical US patents to classify certain technologies as the most affected by migrants upon their arrival Boost to US patents in those technologies (long-lasting effect) Patent & Inventor data al ti n Direct measurement of migrants contribution to innovation in e ot p h Hig destination countries Weight of foreign inventors in terms of patent shares Foreign inventors shares of highly cited patents (Stephan & Levin 2001, Hunt 2011 & 2103 , No & Walsh, 2010 ) Tracking knowledge flows among inventors from the same origin l tia n

e t country, through citation analysis (Kerr 2007 ; Agrawal et hal., o2008 and p g Hi 2011) al ti n e Tracking returnee inventors (Agrawal ; Alnuaimi et al., 2012)pot Low KEY TECHNICAL ISSUE: DISAMBIGUATION inventor data applications to immigration lag behind other applications Key limitation: data apply only to R&D-intensive sectors Migrant inventors contribution: No & Walsh (2010) Survey of over 1,900 US-based inventors on triadic patents Source: No & Walsh (2010)

Self-evaluation: top 10% / in-between/ top 25% / in-between / top 50% / bottom half compared to other inventions in the US in their field during that year The role of self-selection by education: foreign-born individuals are no more likely to invent, once controlling for field and degree (see also Hunt, 2011 and 2013). BUT foreign inventors patent quality is higher than average after controlling for technology class, education level, and firm and project characteristics. Technical issue 1: NAME DISAMBIGUATION Raffo & Luhillery (2009) USPTO data: Lai et al. (forth., Research Policy) EPO data: Pezzoni et al. (forth., Scientometrics) In a nutshell: FULL NAME Address CY Unique IDs?

David John Knight 3 PeachTree Rd, Atlanta GA US 1 1 David John Knight 12 Oxford Rd, Manchester UK 2 1 David J. Knight Georgia Tech Campus

US 1 1 Knight David John 3 PeachTree Rd, Atlanta GA US 1 1 Trade-offs between precision and recall where: Precision and Recall vary by ethnic group (linguistic rules, naming conventions, frequency of names and surnames)

E.g.: East-Asians low precision/high recall Russians high precision/low recall For the low precision/high recall ethnic groups, risk of Over-estimating avg/max inventors productivity Over-estimating the number of returnee inventors Under-estimating the rate of ethnic citations The oppostive holds for high precision/low recall ethnic groups Technical issue 2: ASSIGNING COUNTRY OF ORIGIN Non-disambiguated: i. WIPO-PCT dataset: Nationality of inventors ii. Kerrs USPTO dataset : Linguistic analys of surnames (Melissa commercial DB) ethnicity Disambiguated: iii.Ethnic-Inv pilot dataset (Breschi et al., 2013; Breschi & Lissoni, 2014) Disambiguated inventor data (public) EP-INV database (EPO patents) Harvard-IQSS USPTO inventor Linguistic analysis of names surnames country of association iii.Swedish inventors (Zheng and Ejermo, 2013) Disambiguated inventor (undisclosed data) Big brother Sweden Statistics information on residents

Country of origin as nationality: the WIPO-PCT database Non disambiguated inventor data (by now) Accidental information on nationality PCT (Patent Cooperation Treaty) and the applicants nationality requirement Pre-AIA (American Invents Act, 2012) inventor-is-always-applicant rule at the USPTO PCT filings to be extend at the USPTO carry information on the inventors nationality from 1978 to 2012: >2m PCT filings > 6m relevant records (unique combinations of patent numbers and inventor names) of which 81% have info on the inventors nationality Source: Migulez and Fink (2013) Basic evidence from WIPO-PCT General remarks Globalization of inventors over the past 20 years US as most important, and fastest growing destination evidence even stronger for immigration from non-OECD countries In Europe: key attractor is UK Heavy weight of foreign inventors over resident inventors in small,

R&D-intensive countries (Switzerland, Belgium, Netherlands) Gross vs net emigration in Europe, largest emigration is from UK and Germany, but largest net emigration is from Italy Significant brain-drain from low- and middle-income countries, esp. in Africa NB: this evidence is quite in accordance with evidence from Highly Skilled migration data, but even more extreme for the US Source: Migulez and Fink (2013) Source: Migulez and Fink (2013) Source: Migulez and Fink (2013) Limitations of WIPO-PCT Nationality vs country of birth (vs country of origin) Immigrant inventors can get nationality correlation with nr of patents signed (f. of length of residency, productivity) Not a problem for aggregate studies, but a serious problem for applications to citation or network analysis No more data after 2012: AIA steps in, US become a normal country, end of the party No disambiguation (yet)

Country of origin as name & surname ethnicity Kerr (2007) and following papers: USPTO (non-disambiguated) inventor data Melissa surname database for ethnic marketing (*) (*) US-centric vision of ethnicity (see figures) Ethnic-Inv Pilot Database (Breschi et al., 2013): EPO (soon USPTO) disambiguated inventor data IBM GNR for countries of association Ad hoc studies by origin country, esp. India, based on ad hoc collection of names (Agrawal et al., 2008 and 2011; Almeida et al., 2010; Alnuaimi et al., 2012) Untapped names & surnames dataset, from different disciplines: Geography: ONOMAP (Cheshire et al., 2011; Mateos et al., 2011) Genetics: Piazza et al. (1987) Public health: Razum et al. (2001) Security and anti-terrorism: Interpol (2006)

Kerr (2007): A pioneer study on ethnic inventors The ethnic inventors share of all US-residents inventors grows remarkably from 1970s to 2000s: 17% 29% in the early 2000s NB: latter figure in the same order of magnitude of estimates of the foreign-born share of doctoral holders in 2003 (26%) but much larger estimates of highly skilled from DIOC 2005/06 (16%) Fastest growing Ethnic groups: Chinese and Indians Technical fields: all science-based and high tech Type of applicants: universities (firms catch up later) Important regional effects ethnic inventors cluster in metropolitan areas growing spatial concentration of inventive activity Selected resources (inventor data) USPTO inventor data: classic disambiguation (2009v): (ref.: Lai et al., 2009) Bayesian disambiguation (2013v): (ref. Lai et al., 2013) EPO inventor data (classic disambiguation): (ref.: Den Besten et al., 2012; Pezzoni et al., 2012) WIPO-PCT inventor data (non disambiguated; nationality) (ref.: Migulez and Fink, 2013) FOREIGN INVENTORS IN THE US: TESTING FOR DIASPORA AND BRAIN GAIN EFFECTS Stefano Breschi 1 , Francesco Lissoni 1 2 2,1 CRIOS, Universit Bocconi, Milan GREThA,Universit Montesquieu, Bordeaux IV 3rd CRIOS Conference Strategy, Organization, Innovation and Entrepreneurship Universit Bocconi-Milan, June 11-12 2014

Motivation To investigate the role of diasporas in knowledge diffusion, with reference to the specific case of: Migrant inventors in the US, from Asia and Europe Local vs international knowledge flows Local: relative weight of ethnic ties vs physical proximity (colocation) and social closeness on the network of inventors International: ethnic & social ties vs multinationals and returnees 40 Outline 1. Background 2. Research questions & tests 3. Ethnic inventor data 4. Results 5. Conclusions

------------------------6. Back-up slides: IPC groups / networks of inventors / name disambiguation / ethnic matching 41 1. Background /i 1.Geography of innovation Localized Knowledge Spillovers (LKS) Jaffe & al.s (1993) test on co-localization of patent citations (JTH test Thompson & Fox-Kean, 2005; Alcacer & Gittelman, 2006; Singh & Marx, 2013) Role of social proximity: co-inventorship, inventors mobility and networks of inventors (Almeida & Kogut, 1999; Agrawal & al., 2006; Breschi & Lissoni, 2009) Ethnicity as further instance of social proximity (Agrawal & al., 2008; Almeida & al., 2010) 2.Migration studies Brain gain vs Brain Brain gain channels: MNEs (Fink & Maskus, 2005; Foley & Kerr, 2011); diaspora associations (Meyer, 2001); returnee migration (Alnuaimi & al., 2012; Nanda a& Khanna, 2010); returnee entrepreneurship (Saxenian, 2006; Kenney & al., 2013) Home countrys citations to patents by migrant (ethnic) 42

inventors (Kerr, 2008; Agrawal et al., 2011) 1. Background /ii 1.Geography of innovation Weak evidence of inventor co-ethnicitys correlation to diffusion (probability to observe a citation between two patent) Co-ethnicity as substitute for co-location Exclusive focus on India reminds of classic research question in migration studies: is the Indian diaspora exceptional? 2.Migration studies Evidence of inventors home-country bias in diffusion patterns, albeit stronger for China and India (possibly only in Electronics and IT) US-bias as destination country & China/India bias as CoO 43 2. Research questions & tests /i 1) DIASPORA EFFECT: foreign inventors of the same ethnic group and active in the same country of destination have a higher propensity to cite one anothers patents, as opposed to patents by other inventors, other things being equal and

excluding self-citations at the company level. 2) BRAIN GAIN EFFECT: patents by foreign inventors of the same ethnic group and active in the same country of destination also disproportionately cited by inventors in their countries of origin 3) INTERACTIONS: how do these effects interact with individuals location in space and on the network of inventors? 44 2. Research questions & tests /ii Basic test: Ethnic inventors cited patents y = citation Citing patents =1 Control patents (same year & IPC group) =0

: REGRESSION: 45 2. Research questions & tests /iii DIASPORA TEST: Ethnic inventors cited patents Citing patents from within the US (local sample) Control patents (same year & IPC group) ( =1 )= ( h , , ) EthnicINV algorith m Co-location at BEA level (n1 inventor

per patent) Min geodesic distance btw inventor teams (back-up slides) 46 2. Research questions & tests /iii DIASPORA TEST: Ethnic inventors cited patents Citing patents from outside the US (international sample) Control patents (same year & IPC group) ( =1 )= ( h , , ) Ethnic-INV algorithm EEE-PPAT

harmonization Min geodesic distance btw inventor teams (back-up slides) 47 3. Data /i EP-INV database: 3 million uniquely identified (i.e. disambiguated) inventors from EPO patents (19782011; Patstat 10/2013 edition) + IBM Global Name Recognition (GNR) system: 750k full names + computer-generated variants For each name or surname: 1. (long) list of countries of association (CoAs) + statistical information on cross-country and withincountry distribution 2. elaboration on (1) with our own algorithms ( back-up slides) 48 Ethnic-INV algorithm /i

EP-INV (disambiguated inventor data) IBM GNR data Ethnic-INV algorithm Ethnic inventor data set For the analysis next, we chose the combination of parameters with the highest recall rate, conditional on a precision rate greater than 30% 49 Ethnic-INV algorithm /ii LA RO

IA EP-INV (disambiguated inventor data) RA JIV IBM GNR Data Surname Country of Association LAROIA LAROIA INDIA


Frequency Significance 10 10 99 1 Frequency Significance 90 50 50 30 10 10 10 81 10 1 1 1 1 1

50 Ethnic-INV algorithm /iii To identify a unique country of origin, we build 3 measures Surname Country of Association LAROIA LAROIA INDIA FRANCE First name Country of Association RAJIV



50 50 30 10 10 10 Country of Association 81 10 1 1 1 1 1 Max frequency of first name in Significance of Anglo/Hispanic surname countries

(2) (3) 99 1 0 0 0 0 0 0 50 50 50 50 50 50 50 50 51

Ethnic-INV algorithm /iv LAROIA RAJIV LAROIA RAJIV High Recall High Precision Do indicators (1)-(3) pass all thresholds? Country of Origin = INDIA ? Yes Yes No No

Max frequency of JOINT Significance Country of Significance of surname first name in Association Anglo/Hispa (1) (2) nic countries (3) INDIA 8019 99 50 THRESHOLDS (India-specific) High Recall High Precision (1)

(2) (3) 5000 8000 60 80 30 70 52 3. Data /ii 10 Countries of Origin (CoO) Listed by OECD among top 20 CoO of highly skilled migrants to the US Neither English- nor Spanishspeaking We exclude: Vietnam and Egypt (low

figures) Ukraine and Taiwan (may reinclude them, along with Switzerland & Austria) nr % China 97891 16.30 India 63964 10.65 S. Korea 28796 4.79 United Kingdom 28122 4.68 Germany 26829 4.47 Canada 24660 4.11 Taiwan

22155 3.69 Russian Federation 20497 3.41 Iran 14627 2.44 Mexico 11924 1.99 Japan 11616 1.93 Philippines 11576 1.93 France 10752 1.79 Cuba 9852 1.64

Viet Nam 8403 1.40 Italy 8309 1.38 Poland 7776 1.29 Source: Database on Immigrants in OECD Countries Ukraine 7234 1.20 (DIOC), 2005/06. Egypt 6834 1.14 Puerto Rico 6699 1.12 53 1 Share of ethnic inventors of EPO patent applications by US resident

54 55 56 Table 2. Local and international samples: descriptive statistics Obs Mean Std. Dev. 1. Local sample (citations from within the US) Citation 1211154 0.500 0.500 Co-ethnicity 1211154

0.120 0.325 Social distance 1211154 0.013 0.114 0 Social distance 1211154 0.012 0.109 1 Social distance 1211154 0.008 0.089 2 Social distance 1211154 0.009 0.093 3 Social dist. >3

1211154 0.236 0.425 Social distance 1211154 0.722 0.448 + 1211154 0.172 0.377 Co-location Min Max 96k cited patent 216k citing 0 0

1 1 0 1 0 1 0 1 0 1 0 1

0 1 0 1 57 Table 2. Local and international samples: descriptive statistics (cont.) Obs Mean Std. Dev. Min 2. International sample (citations from outside the US) Citation

1084120 0.500 0.500 0 Co-ethnicity 1084120 0.081 0.272 0 Social distance 0 1084120 0.004 0.063 0 Social distance 1 1084120 0.005 0.072 0 Social distance 2 1084120 0.004 0.066 0 Social distance 3 1084120 0.005

0.068 0 Social distance >3 1084120 0.200 0.400 0 Social distance + 1084120 0.781 0.413 0 Same country 1084120 0.085 0.279 0 Same company 1084120 0.024 0.152

0 Returnee 1084120 0.0005 0.022 0 Max 106k cited 272k citing 1 1 1 1 1 1 1 1 1 1 1 58

4. Results DIASPORA EFFECT: positive and significant for all CoO in our sample, except France, Italy, and Poland BUT result is not robust to all model specifications, safe for India and China marginal effect of co-ethnicity is secondary to that of social proximity and colocation Co-ethnicity acts as substitute for physical proximity, and kicks in at large social distances BRAIN GAIN EFFECT: Mixed results: positive and significant for all Asian countries (but Iran) and Russia, but negative or null for the other European countries (unless same country replaced by country of origin) Largest marginal effect belongs to company self-citations Co-ethn. as substitute for company self-citations, and kicks in at large social distances 59 DIASPORA EFFECT: Logit regression, by Country of Origin China India

Iran Japan Korea Co-location Co-ethnicity Co-ethn*Co-loc Soc. dist. 1 Soc. dist. 2 Soc. dist. 3 Soc. dist.>3 Soc. dist. + Constant 0.39*** 0.34*** -0.12*** -1.59*** -2.44*** -2.86*** -3.64***

-3.80*** 3.55*** 0.41*** 0.18*** -0.09*** -1.04*** -1.88*** -2.21*** -3.14*** -3.24*** 3.07*** 0.47*** 0.27** 0.15 -1.66*** -2.07*** -2.54*** -3.60*** -3.64*** 3.48***

0.38*** 0.17*** -0.09 -1.36*** -2.29*** -2.98*** -3.70*** -3.79*** 3.65*** 0.34*** 0.19*** -0.10 -0.59** -1.18*** -2.13*** -2.86*** -2.97*** 2.83*** Observations Chi-sq LogL

Pseudo R-sq 291,804 9372 -195260 0.0346 373,126 8478 -252246 0.0247 33,128 827.9 -22308 0.0285 56,234 1012 -38039 0.0241 59,456

1284 -40205 0.0244 The table reports estimated parameters (bs) ; Robust standard errors in parentheses ; *** p<0.01, ** p<0.05, * p<0.1 60 DIASPORA EFFECT: Logit regression, by Country of Origin (cont.) Germany France Italy Poland Russia Co-location Co-ethnicity

Co-ethn*Co-loc Soc. dist. 1 Soc. dist. 2 Soc. dist. 3 Soc. dist.>3 Soc. dist. + Constant 0.44*** 0.04** -0.04 -1.13*** -1.90*** -2.54*** -3.19*** -3.30*** 3.15*** 0.39*** 0.03 0.04 -1.29*** -1.87***

-2.50*** -3.16*** -3.30*** 3.14*** 0.40*** 0.04 -0.17 -0.78** -1.76*** -2.40*** -3.23*** -3.33*** 3.20*** 0.30*** -0.22 -0.14 -0.29 -1.87*** -2.12*** -3.10*** -3.19***

3.05*** 0.47*** 0.29*** 0.09 -1.25*** -1.69*** -2.38*** -3.14*** -3.30*** 3.11*** Observations Chi-sq LogL Pseudo R-sq 205,858 4667 -138992 0.0259 77,038

1705 -52094 0.0244 53,168 1017 -36024 0.0225 19,078 480.6 -12782 0.0334 42,264 1195 -28368 0.0317 The table reports estimated parameters (bs) ; Robust standard errors in parentheses ; *** p<0.01, ** p<0.05, * p<0.1 61

DIASPORA EFFECT: interaction social distance * co-ethnicity China Germany India Co-location Co-ethnicity Co-ethn*Co-loc Soc. distance >3 Soc. distance + Co-ethn*Soc. Distance>3 Co-ethn.*Soc. Distance + Constant 0.41*** -0.29*** -0.10*** -1.91***

-2.02*** 0.45*** 0.06 -0.05 -1.66*** -1.76*** 0.76*** 0.002 0.418*** 0.55*** -0.03 0.37*** 1.78*** 1.61***

1.71*** Observations Chi-sq LogL Pseudo R-sq 291,804 11787 -195749 0.0322 205,858 5730 -139315 0.0237 373,126 10150 -252663 0.0231

0.42*** -0.20*** -0.07*** -1.78*** -1.88*** Same results for other CoO The table reports estimated parameters (bs) ; Robust standard errors in parentheses ; *** p<0.01, ** p<0.05, * p<0.1 62 DIASPORA EFFECT: estimated probability of citation (interaction social distance * co-ethnicity) 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3

0.2 0.1 0 India social distance3 social distance>3 social distance=+ (0,0) (co-located,co-ethnic): (0,1) (1,0) 63 BRAIN GAIN EFFECT: Logit regression, by Country of Origin China German France y

India Co-ethnicity 0.37* 0.83*** 0.87*** 1.05*** Same company 1.22*** 1.06*** 1.25*** 1.16*** Soc. dist.>3 -1.10*** -0.75*** -0.90*** -0.99*** Soc. dist. + -1.26*** -0.74*** -0.97*** -1.10*** Co-ethn*Soc. 0.14 -0.43*** -0.36* -0.55* dist.>3 Co-ethn.*Soc. dist. -0.03 -0.59*** -0.60*** -0.71** + Constant 1.17*** 0.62*** 0.87*** 1.04*** Observations Chi-sq

LogL Pseudo R-sq 265,116 3277 -181671 0.0114 183,419 3192 -125047 0.0164 70,328 1187 -47900 0.0174 327,368 3007 -225036 0.00828

Italy Japan Korea Russia 0.46 0.17 -0.30 1.67 0.94*** 1.36*** 0.99*** 1.23*** -1.17*** -1.34*** -1.33*** -0.77*** -1.31*** -1.37*** -1.50*** -0.98*** -0.38 0.28 0.04 -0.80

-0.36 0.03 0.72* -1.07 1.24*** 1.25*** 1.41*** 0.90*** 47,806 522.7 -32803 0.0101 54,944 1172

-37246 0.022 50,928 613.9 -34928 0.0106 39,433 468 -27070 0.00963 The table reports estimated parameters (bs) ; Robust standard errors in parentheses ; *** p<0.01, ** p<0.05, * p<0.1 64 BRAIN GAIN EFFECT: estimated probability of citation (with company selfcitations) 1 0.9 0.8

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 India social distance3 social distance>3 social distance=+ (same company, co-ethnic) :(0,0) (0,1) (1,0) 65

5. Conclusions & further research Findings on diaspora effects for India (and China) are compatible with Agrawal et al.s (2008) as well as our own research on social distance mixed evidence for other countries may be due to quality of ethnic-inv algorithm Findings on brain gain effects for India (less so for China) are compatible with Kerrs (2008), and we highlight the role of MNEs mixed evidence for other countries may be due to quality of ethnic-inv algorithm and company names harmonization Further research: Data quality issues Additional topics: skill-bias immigration hypothesis 66 Back-up slides 67 IPC groups 68

Network of inventors: co-invention & mobility Two 2-mode (affiliation) networks: 1) Inventors to Patents 2) Patents to Applicants cross-firm inventors 1-mode network of inventors 69 Social distance between patents What is the distance between patent 1 and patent 4? The shortest path connecting inventors in the two teams d(1,4)=1 70


Addresses on patents Technological classes of patents Social networks Citation linkages Disambiguated EPO data 71 Inventor name disambiguation /ii Without careful disambiguation, this pair will count as a co-ethnic citation, whereas it is just a personal self-citation citing patent cited patent 72

Ethnic-INV algorithm /v Nationality of inventors derived from WIPO-PCT dataset (Miguelez, 2013) Nationality country of birth (or country of origin). For example, RAJIV LAROIA born in India in 1962, PhD in US in 1992, nationality on patents US Nationality data available only up to 2012 To benchmark our algorithm, we use nationality to compute precision and recall rates at different thresholds 73 Dots: combination of parameters Blue dots: efficient combinations Joint significance: 1000 Significance surname: 0 Frequency first name: 100 Joint significance: 1000 Significance surname: 0

Frequency first name: 10 74 75 76 77

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