metadata: authors: 0: author: Miguelez,Ernest
count: World
docna: 0: docna: Inventor diasporas and the
            internationalization of technology
docty: Policy Research Working Paper
subtopic: Social Policy,Legal Products,Real & Intellectual Property Law,Judicial System Reform,Human Migrations & Resettlements,Common Property Resource Development,Tertiary Education,International Trade and Trade Rules,Legal Reform,Legislation,Regulatory Regimes,International Migration,Migration and Development,Intellectual Property Rights
teratopic: Health, Nutrition and Population,Law and Development,Information and Communication Technologies,Industry,Private Sector Development
abstracts: cdata!: This paper documents the influence of
            diaspora networks of highly-skilled individuals -- that is,
            inventors -- on international technological collaborations.
            Using gravity models, it studies the determinants of the
            internationalization of inventive activity between a group
            of industrialized countries and a sample of developing and
            emerging economies. The paper examines the influence exerted
            by skilled diasporas in fostering cross-country
            co-inventorship as well as R&D offshoring. The study
            finds a strong and robust relationship between inventor
            diasporas and different forms of international co-patenting.
            However, the effect decreases with the level of formality of
            the interactions. Interestingly, some of the most successful
            diasporas recently documented -- namely, Chinese and Indian
            ones -- do not govern the results.
repnme: repnme: Inventor diasporas and the
            internationalization of technology
display_title: Inventor diasporas and the
            internationalization of technology
listing_relative_url: /research/2016/04/26172212/inventor-diasporas-internationalization-technology
topicv3: Education, Skills Development and Labor
            Market m1326135 1484,Tertiary Education m1326349 482,Patents m1326812 1041,Trade m1326924 698,Immigration m1504162 2837
publishtoextweb_dt: 2016-04-04T00:00:00Z
historic_topic: Health, Nutrition and Population,Law and Development,Information and Communication Technologies,Industry,Private Sector Development
no_of_pages: 71
model_extractions: WPS7619 Policy Research Working Paper 7619 Inventor Diasporas and the Internationalization of Technology _Ernest Miguélez_ Development Economics Vice Presidency Operations and Strategy Team April 2016
Policy Research Working Paper 7619

# **Abstract**

This paper documents the influence of diaspora networks of highly-skilled individuals—that is, inventors—on international technological collaborations. Using gravity models, it studies the determinants of the internationalization of inventive activity between a group of industrialized countries and a sample of developing and emerging economies. The paper examines the influence exerted by skilled diasporas in fostering cross-country co-inventorship as well as R&D offshoring. The study finds a strong and robust relationship between inventor diasporas and different forms of international co-patenting. However, the effect decreases with the level of formality of the interactions. Interestingly, some of the most successful diasporas recently documented— namely, Chinese and Indian ones—do not govern the results.

This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted aternest.miguelez@u-bordeaux.fr.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **INVENTOR DIASPORAS AND THE INTERNATIONALIZATION OF TECHNOLOGY**

_Ernest Miguélez_<sup>_1_</sup> Key words: diaspora networks, international collaborations, inventors, PCT patents, R&D offshoring JEL: C8, J61, O31, O33 1 Ernest Miguélez (corresponding author) is Junior Researcher in the CNRS, affiliated to the GREThA UMR CNRS 5113 research group, Université de Bordeaux. He is also visiting fellow at the AQR-IREA, University of Barcelona, and research affiliate at CReAM, UCL. His e-mail address is <u>ernest.miguelez@u-bordeaux.fr. The research for</u> this article was financed by the Regional Council of Aquitaine (Chaire d’Accueil en Economie de l’Innovation, to Francesco Lissoni) and the Regional Council of Aquitaine (PROXIMO project, to Christophe Carrincazeaux). The author thanks Michel Beine, Gaetan de Rassenfosse, Stuart Graham, William Kerr, Francesco Lissoni, Catalina Martinez, Çaglar Ozgen, Hillel Rapoport, Massimo Riccaboni, Valerio Sterzi, seminar participants at GREThA-Bordeaux University (2013), GATE-LSE Saint Etienne (2014), Temporary Migration Cluster UC Davis (2015), the workshop "The Output of R&D and Innovative Activities: Harnessing the Power of Patent Data” (JRCIPTS Seville, 2013), the 7th MEIDE conference (Santiago de Chile, 2013), the PSDM 2013 conference (Rio de Janeiro, 2013), the 54<sup>th</sup> ERSA conference (Saint Petersburg, Russia, 2014), and the 2<sup>nd</sup> Geography of Innovation Conference (Utrecht, The Netherlands, 2014) for valuable comments; Julio Raffo for helpful discussions on previous versions of this paper; as well as the contribution of three anonymous referees.
International innovation networks are critical stepping stones for accessing frontier knowledge from the most industrialized economies, both in the form of formal information exchanges as well as through knowledge spillovers (Hall 2011).

International spillovers are essential for developing countries to catch up with advanced economies, and this subject matter ranks high in the development policy agenda (World Bank 2010).

However, geographical and cultural barriers seriously hinder the formation of innovation networks across countries, thereby remaining primarily a national phenomenon—contrary to other features such as trade or Foreign Direct Investment (FDI) (Patel and Pavitt 1991). Guellec and van Pottelsberghe de la Potterie (2001) report that only 4.7% of EPO patents and 6.2% of USPTO patents in 1995 have at least one foreign co-inventor. Picci (2010) estimates this figure to be around 8% for European patents in 2005. Cultural and language differences seriously undermine the internationalization of inventive activity. Other features inherent to geographical separation are also relevant, such as difficulties in screening potential partners, managing and administration of common projects across borders, and differences in legal frameworks and the rule of law—especially regarding the issue of intellectual property rights (Montobbio and Sterzi 2013; Foray 1995).

This paper examines how high-skilled migration and the diasporic networks it creates may overcome cross-country barriers and foster the internationalization of inventive activity. Diaspora networks have been largely studied in the context of trade and FDI. More recent evidence has looked at the international diffusion of ideas (Agrawal et al. 2011; Kerr 2008; Oettl and Agrawal 2008) and firms’ internationalization strategy (Foley and Kerr 2013; Saxenian et al. 2002). In parallel, 2
numerous papers have investigated the internationalization of R&D activities (Guellec and van Pottelsberghe de la Potterie 2001; Patel and Vega 1999; Picci 2010). This paper builds on and extends these two streams of literature by looking at the role of migrant inventors in fostering the internationalization of innovation activities.

Specifically, the paper looks at inventor diasporas and transnational inventive activities between developed and developing countries, which is gaining momentum from a development perspective (Clemens et al. 2014; Montobbio and Sterzi 2013). It also aims to see whether differences emerge across the type of technological internationalization—co-inventorship vs. R&D offshoring. Finally, the extent to which countries’ characteristics govern these potential relations is also investigated—that is, whether the least similar countries, for which informal barriers are more acute, have the greatest potential to benefit from diaspora networks.

The study extends the existing literature in a critical way. Contrary to most migration research, which retrieves migration data from decennial censuses, it exploits inventors’ information listed in patent applications. Inventors make up a specific class of workers at the upper end of the skills distribution.<sup>2</sup> Differently from former approaches to inventor migration that use ethnic name recognition algorithms to infer their migratory background (Agrawal et al. 2011; Breschi et al. 2014; Breschi et al 2015; Kerr 2008), direct nationality information of the inventors is used, allowing for the introduction of a richer range of origin and destination countries. Admittedly, the use of nationality data does not come without its limitations, the most important being

> 2. In this respect, the PatVal survey shows that 76.9% of European inventors underwent tertiary education (26% with PhD) (Giuri et al. 2007). Similarly, the RIETI-Georgia Tech inventor survey shows that 94% of US inventors have college degrees (46% with PhD), with 88% of the cases for Japanese inventors (13% with PhD) (Walsh and Nagaoka 2009).

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the loss of all naturalized inventors who have acquired their host country citizenship. Appendix S3 compares nationality data with ethnic name recognition algorithms (see Breschi et al. 2015), which has the advantage of taking into account all the naturalized immigrants who have acquired their host country nationality. Although some differences emerge—large coefficients when name recognition algorithms are used— the main conclusions of this paper remain unaltered.

To anticipate the results to come, I find a robust effect of highly-skilled diasporas on the internationalization of inventive activity between developed, receiving countries and developing, sending economies: a 10% increase in the inventor diaspora abroad is associated with a 2.0–2.2% increase in international patent collaborations. The evidence found survives the inclusion of a large number of controls, fixed-effects (FE), robustness checks, and identification issues. Moreover, the effect is stronger for inventor-to-inventor collaborations—co-inventorship—than for applicant-to-inventor co-patents—R&D offshoring—suggesting that diaspora effects specifically mediate interpersonal relations between co-workers.<sup>3</sup> The outline of the paper is as follows: section 2 reviews previous theoretical and empirical contributions on the relationship between migration and other international economic interactions. Section 3 presents the novel dataset on inventor migration flows and develops the methodological setting, including all the econometric concerns. Section 4 presents the results and section 5 concludes. The reader may also find the appendix useful, giving more details on the novel data used and robustness results.

> 3. This paper uses the term “applicant” to describe the owner of the patent, unless otherwise stated, which is normally a firm or research institution for which the inventors usually work. I am aware that in some patent jurisdictions the owner is termed “assignee,” although this term is not used here.

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# I. RELATED LITERATURE AND THEORETICAL BACKGROUND

Much of the public debate and academic research on the role of skilled diasporas for development tries to answer a question fraught with political and economic significance: how will growing diasporas—and in particular skilled diasporas— contribute to their home country development?

In the migration literature, diasporas have been defined as “part of a people, dispersed in one or more countries other than its homeland, that maintains a feeling of transnational community among a people and its homeland” (Chander 2001). Potential benefits can be realized exploiting this feeling to the advantage of the home countries, through the individuals’ embedded knowledge as well as through their accessible resources—such as capital or the expatriates’ network of colleagues and acquaintances.

Traditionally linked to diasporas’ role on financial remittances and capital formation, the migration and development literature has also moved to investigating their role in favoring other economic transactions, such as trade (Gould 1994), FDI (Javorcik et al. 2011; Kugler and Rapoport 2007), international diffusion of ideas (Agrawal et al. 2011; Kerr 2008), and firms’ internationalization strategies (Saxenian et al. 2002; Foley and Kerr 2013). Diasporas affect their home countries both directly and indirectly (Kapur and McHale 2005). The direct effect is linked to the diaspora members’ willingness to interact individually with their home countries, in the form of remittances, investments, or sharing ideas and information. This eventually includes the role of returnees’ direct contribution. Highly-skilled migrants may decide to move back or set up entrepreneurial activities in their homelands, while keeping in touch with the destination countries (Wadhwa, Rissing, et al. 2007; Wadhwa, Saxenian, et al. 2007).

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The indirect effects refer to the role of diaspora members in leveraging their home countries’ reputation in international business networks; facilitating searching and matching between partners, customers-suppliers or in the labor market; and in ensuring the contract fulfillments of the two parties involved (Kapur and McHale 2005). Because of their familiarity with local market needs, diasporas provide information about business opportunities in their home countries, and thus are critical in providing access to relevant information otherwise inaccessible because of cultural, language, institutional, administrative, or geographical barriers. Thus, migrant networks lower transaction costs associated with problems of incomplete information. This is particularly the case when informational difficulties are large, when involving countries with very different social and cultural backgrounds.

They also lower the transaction costs associated with the existence of asymmetric information. As Rauch (2003; 2001) posits, social networks operating across national borders build up or substitute for trust when contract enforcement is weak or nonexistent. Indeed, diasporas create trust by establishing a kind of “moral community,” which is used to transmit information about past opportunistic behavior in international business relations. The enforcement mechanism is particularly important in the absence of effective protection of contractual/property rights and should therefore be critical in the relationships of developed-developing countries.

In the trade context, Gould (1994) finds that the stock of migrants in the United States (US) from 47 US trading partners increases US trade with these countries—a pioneering contribution to the topic of migrant networks and trade is due to Greif (1989). This is confirmed by Rauch and Trindade (2002) and Head and Ries (1998), who find that a 10% increase in the number of immigrants increases exports by one 6
percent and imports by three percent (see also Aleksynska and Peri 2014; Felbermayr and Toubal 2010, 2012). Similar conclusions emerge in the case of FDI. Javorcik et al. (2011) investigate the link between the presence of migrants in the United States and US FDI to the migrants’ countries of origin. They find that US FDI to sending countries is positively correlated with the diaspora of that country in the United States— especially migrants with college degree qualifications (see also Kugler and Rapoport 2007).

Less evidence is available on diaspora externalities and international technology cooperation. Saxenian (1999) argues that skilled immigrants in the United States are playing a growing role in linking domestic technology businesses to their countries of origin. Her study on Chinese and Indian immigrant engineers in Silicon Valley shows that these immigrants are uniquely placed to locate foreign partners quickly and manage complex business networks across cultural, institutional, and linguistic boundaries, which is especially relevant in high-tech industries. Systematic evidence for the case of migrant scientists is provided by Scellato et al. (2012) for a group of surveyed scientists from 16 countries. Their study finds that around 40% of foreign-born researchers in these countries maintain research links with their homeland colleagues.

For the specific case of inventors, Agrawal et al. (2011) study knowledge flows between India and the Indian diaspora in the United States, identified through inventors of USPTO patents. Kerr (2008) extends this analysis to nine foreign ethnicities in the United States. By means of citation analysis, he confirms that knowledge diffuses internationally through ethnic networks—especially with regards to the Chinese diaspora, which also has sizeable effects on home country output. Foley and Kerr (2013) find significant effects of US firms’ ethnic inventors in promoting linkages 7
between these firms and their R&D staff home countries, in the form of knowledge flows or R&D alliances. As these authors argue, ethnic inventors in host countries are particularly apposite for helping firms to capitalize on foreign opportunities and overcome barriers to the internationalization of inventive activity. Ethnic inventors usually have the expertise essential for developing products crucial for that particular ethnicity, giving privileged access to foreign markets and business opportunities. Obviously, they possess the language skills and cultural sensitivity necessary to promote international collaborations in their host countries, while at the same time knowing how to conduct business with their homeland colleagues. They also belong to those networks that foster trust and convey information about past opportunistic behavior across national boundaries.

However, this latter evidence might not be conclusive, in the sense that it is limited to the largest receiving country, the United States, and its top providers of foreign scientists and engineers (Breschi et al. 2014). This leads some authors to argue that highly-skilled emigrants may not systematically engage in business networks and knowledge transfers with their homelands but rather that the Indian and Chinese diasporas are so famous in being the exception rather than the rule (Gibson and McKenzie 2012). The present paper sheds some light into this issue too.

II. RESEARCH METHODS

# _Empirical Approach_

The gravity model to be estimated takes the following form: 8
where _COPATijt_ stands for the number of collaborations between i’s developing country (out of 67) and j’s developed country (out of 20) for year t.<sup>4</sup><sup>_β_</sup> _1_<sup>is the parameter of</sup> interest in this work, while _DIASPORAijt_ is the focal variable and is computed as the number of inventor nationals of country i residing in country j for annually repeated 5- year time-windows. _Z ijt_ is a set of bilateral and attribute control variables, and<sup>_τ_</sup> _i_<sup>,</sup>  _j_ , and<sup></sup> _t_<sup>are, respectively, developing, developed, and time FE.</sup>  _ijt_ denotes the error term.

Log-linearizing equation (1) and using OLS techniques would be a straightforward estimation method. However, cross-country co-patents are rare phenomena, which translate into a dependent variable with a very large proportion of zeros, making the logarithmic transformation of these observations impossible.

Dropping these zero observations or adding an arbitrary constant to enable logarithmic transformation would be clearly misleading (Burger et al. 2009). In addition, Santos Silva and Tenreyro (2006) show that log-linearizing equation (1) requires  _ijt_ , and therefore _ln εijt_ , to be statistically independent of the regressors, otherwise the condition for consistency of OLS would be violated. As the authors show, there is “overwhelming evidence that the error terms in the usual log linear specification are heteroskedastic” (Op. Cit., 642), making the expected value of the error term depend on one or more explanatory variables, leading to inconsistent OLS estimates. Note importantly that this kind of heteroscedasticity cannot be corrected using standard techniques, such as applying a robust covariance estimator, since it does not only affect the estimation of

> 4. Appendix S1 lists the countries used in this study. Note that some high income countries are included among the list of developing economies (e.g., Luxemburg). Removing them from this group does not alter the results.

9
the standard errors, but also the coefficients. Because of the presence of this heteroscedasticity in the original nonlinear gravity specification, the authors suggest estimating the multiplicative form of the model using nonlinear models, such as the Poisson pseudo-maximum likelihood (PPML), which also provides a natural way of dealing with zero co-patenting and the extreme skewness of the dependent variable, intrinsically heteroscedastic with variance increasing with the mean (Cameron and Trivedi 1998).<sup>5</sup> Thus, equation (1) is estimated by means of PPML using the fact that the conditional expectation of _COPATijt_ in (1) can be written as the following exponential function: (2) _E( COPATijt | X ijt )_ = _exp_ <u>[</u> _β0_ + _β1 ln DIASPORAijt_ + _γn ln Z ijt_ + _τi_ + _τ j_ + _δt_ <u>]+</u> _εijt_ .

# _Data_

# **Inventors’ International Migration**

A large part of the migration literature surveyed above has made use of census-based migration datasets becoming available during the last 15 years, broken down by skills— primary, secondary, and tertiary level of education (for a recent data contribution, see Özden et al. 2011).

In contrast, the present analysis is based on a dataset of inventors with migratory backgrounds applying for PCT patent applications between 1990 and 2010. The use of inventor data for migration analysis comes with two main advantages. First, patent data

> 5. Moreover, Santos Silva and Tenreyro (2010, 2011) use simulation techniques to show that the PPML estimator is generally well-behaved in the presence of a large proportion of zeros. As a robustness check, appendix S8 replicates the main estimations using alternative count data methods for zero-inflated models. No important differences with respect to the main results and conclusions emerge.

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(together with inventor information) are registered and so can be organized on a yearly basis—contrary to census data, which are collected only every 10 years. Second, the level of education attained may still differ markedly among tertiary educated workers. Tertiary education can include nonuniversity tertiary degrees, undergraduate university degrees, and postgraduate and doctorate degrees, which may not be fully comparable across different countries. Inventors on the other hand constitute a specific class of highly-skilled workers that is more homogeneous than the tertiary-educated workers as a whole. They are behind the production of new knowledge and innovation that encourage economic growth and well-being.

The seminal contribution using inventor data for migration research is due to Kerr (2007) and his successive papers, which analyze immigrants’ contribution to US invention. Kerr takes inventors’ names from USPTO applications and assigns them an ethnic affiliation using a commercial repository of names and surnames of US residents classified by likely country of origin<sup>6</sup> —for a more recent contribution along the same lines, see Breschi et al. (2014, 2015). Albeit extremely valuable, Kerr’s contribution comes with some limitations: first, it is focused entirely on US immigration, while migration is a multifaceted phenomenon including numerous receiving countries. Second, it comprises a limited number of countries of origin—though it suffices for his analysis on skilled immigration in the United States. Finally, ethnic methods cannot distinguish between first and second generation immigrants—for example, African immigrants in Europe—or across countries belonging to the same linguistic group—for

> 6. In particular, nine broad ethnic affiliations are identified: English, European, Russian, Hispanic-Filipino, Chinese, Indian, Japanese, Korean, and Vietnamese

11
example, inventors from Australia, Canada, the United Kingdom, and the United States.<sup>7</sup> Recently, the World Intellectual Property Organization (WIPO) released a new dataset on inventors of PCT applications containing not only their current country of residence but also their nationality, representing a promising data source for migration research (Miguelez and Fink 2013). The PCT is an international treaty administered by WIPO offering an advantageous route for seeking for patent protection in more than one jurisdiction (in its 148 contracting states). In general, patent rights only apply in the jurisdiction granting them, be it national (e.g., USPTO) or regional (e.g., the Africa Regional Intellectual Property Organization). To seek for patent protection in multiple countries, applicants need to apply for patents in multiple offices. One simplifying route for doing this is offered by the PCT treaty. In short, by choosing the PCT route, applicants gain additional time—typically 18 months—to decide whether to pursue patent protection internationally and, if so, in which jurisdictions. An international patent right, as such, does not exist, so the applicants still have to apply for patent protection in all countries in which they eventually seek protection. However, the additional time gained by choosing the PCT route is valuable for applicants, as witnessed by the increasing share of international patent protection going through this system (about 54% of multi-jurisdictional applications in 2010).

For the purpose of migration analysis, inventor information retrieved from PCT applications presents several advantages, such as being associated with the most valuable inventions (van Zeebroeck and van Pottelsberghe de la Potterie 2011) or the

> 7. See appendix S3 for a discussion of advantages and disadvantages of name recognition systems to identify the migratory background of inventors, alongside a number of robustness checks carried out using this alternative approach.

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fact that the system applies a set of procedural rules common to all participant countries, hence eliminating the “home bias effect” introduced when working with patent data from one single office. Appendix S2 enumerates the advantages of using PCT data for economic analysis, discusses how countries make use of this system, and details the extent of overlap with other patent data sources, such as the USPTO and the EPO.

More importantly for the sake of the present analysis, PCT patent applications are the only ones recording the nationality of the inventors. The reason for that is as follows: because not all countries are PCT contracting states, only national or resident applicants of a PCT contracting state can file PCT applications. In order to verify that applicants meet at least one of the two eligibility criteria, the PCT application form asks for both nationality and residence. In parallel to this, US laws bind the applicant also to be the inventor; US laws also request the applicant to be an individual, not a firm. Thus, if a given PCT application includes the United States as a country in which the applicant has considered pursuing a patent—a so-called designated state in the application—all inventors are listed as “applicants/inventors,” and their residence and nationality information are, in principle, available.

All in all, between 1990 and 2010, the share of inventors’ records for which we can retrieve nationality and residence information is pretty high, around 80% of the cases.<sup>8</sup> Admittedly, this coverage is unevenly distributed over time—around 60–70% during the 1990s and 70–95% during the 2000s—as well as across countries—the United States (66%), Canada (81%), the Netherlands (74%), Germany (95%), the 8. The use of the word “record” here signifies the unique combination of “inventor name” and “application number.” 13
United Kingdom (92%), France (94%), Switzerland (93%), China (92%), and India (90%), among others.<sup>9</sup> Patent data do not provide unique identifiers for inventors appearing in more than one application. Although this is a drawback, I follow Kerr (2008) and treat each record as if it were a different individual, and compute the migration variables aggregating by country pairs and moving time-windows of five years.<sup>10</sup> Out of all records with complete information, about 5 million, around 9–10% have a migratory background, that is, residence different from their nationality. Figure 1 depicts the evolution of the share of inventors with migratory background (solid line), alongside the same figures broken down by a number of selected receiving countries/areas. As can be observed, the share of worldwide migrant inventors has steadily increased over time.<sup>11</sup> Among the most receiving countries of the world, 9. Compared to other highly patenting countries, the United States, Canada, and the Netherlands present relatively low coverage rates. This is because numerous applications with inventors from these three countries were applied to the USPTO before being extended internationally through the PCT system. In consequence, the United States was not mentioned in the applications as designated state and the inventors were only inventors and not applicants at the same time, which exonerates them from providing nationality information. In principle, there is no reason to believe that this less than complete coverage in these countries may bias the analysis one way or another. However, in order to address this inconsistent coverage of migration information, I repeated the analysis splitting the sample into shorter time windows and excluding some countries. No important differences arise regarding the main conclusions of the study. 10. The priority date of applications is used to allocate individuals in time. By “priority date” I mean the first year the patent was applied worldwide.

11. In order to make these figures comparable, it is worth looking at differences with other migration datasets. While 8.62% of inventors of PCT patents have a migratory background in 2000, data compiled by Beine et al. (2007) show that general migration rates in 2000 for populations aged 25 years and over 14
Canada, Australia, and, notably, the United States, stand out as being the primary receiving countries, when compared to their resident stock of inventors. On the other hand, Japan is, and has been over the years, one of the developed countries with a smaller share of inventor immigrant population.

<u>FIGURE 1. Share of Immigrant Inventors over Time, 1990–2010, by Selected Countries</u> <!-- Start of picture text --> Australia Canada Japan<br>US Europe World<br>1990 1995 2000 2005 2010<br>Year<br>20<br>15<br>10<br>5<br>% Inventors Migratory Background<br>0<br><!-- End of picture text --> Source: Authors’ analysis based on Miguélez and Fink (2013).

Meanwhile, technology-leading European countries, such as Germany or France, lag way behind compared to the United States (figure 2). The exceptional performance of the United States in attracting talent is even more notable when considering only immigrant inventors coming from low- and middle-income economies (see also figure 2). Appendix S4 shows the top 20 most populated inventor migration corridors, where again, the United States stands out as the most typical choice for destination country.

FIGURE 2. Immigration Rates of Iventors, 2001–2010, Receiving Countries were estimated around 1.8%, including 1.1% of immigrants among the unskilled population, 1.8% among populations with secondary education, and 5.4% among populations with tertiary education.

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<!-- Start of picture text --> Immig. rate of inventors Immig. rate from developing countries<br>Switzerland Ireland Belgium USNew ZealandNetherlands Austria UK Australia Canada Denmark Norway Finland Sweden Spain France Germany Italy JapanR. of Korea<br>40<br>30<br>20<br>Immigration rate<br>10<br>0<br><!-- End of picture text --> Source: Authors’ analysis based on Miguélez and Fink (2013).

# **Dependent Variable**

International co-patent data are retrieved from PCT applications (WIPO IPSTATS databases). The first focus is on co-patenting at inventor level—co-inventorship. All the co-inventions between inventors residing in country i and inventors residing in country j are added up by year. To be precise, it includes 67 developing/emerging/transition countries, on the one hand, that co-invent with 20 developed countries, on the other, where diasporas from the former countries reside. If inventors from more than two countries participate in the patent, an international co-inventor for each country-pair is counted, irrespective of the total number of countries involved in that particular invention.<sup>12</sup> 12. Results with alternative ways of computing the dependent variable, where the number of inventors per patent is taken into account, are presented in appendix S5, with no remarkable differences with respect to the results presented in the main text. In contrast, the ranking of inventors listed in patents is not taken on board, due to the difficulties of determining whether the ordering of names bears any similarity with sorting of authors in scientific publications. However, understanding the relationship between immigrant status 16
Next, I measure R&D offshoring using patent applications in which at least one applicant is a resident from country j (developed) and simultaneously at least one inventor is a resident from country i (developing/emerging/transition) —similar R&D offshoring measures in the context of internationalization of inventive activity are used in Guellec and van Pottelsberghe de la Potterie (2001), Harhoff et al. (2013), and Thomson et al. (2013). Again, when inventors come from various countries, a single copatent for each bilateral i-j pair is computed.

It is worth mentioning that previous studies on the determinants of international co-patenting use information from single patent offices only, with few exceptions (Martínez and Rama 2012; Picci 2010). This practice is likely to deliver biased estimates due to the “home bias effect,” which may emerge when using patent data from one single office for cross-country analysis. Since patents at the USPTO, EPO, or JPO, for instance, protect innovation within their respective geographical areas, they are preferred by domestic firms and thus their innovative capability is overestimated with respect to foreign firms. Using data from the PCT mitigates this effect.<sup>13</sup>

# **Control Variables**

Control variables include geographical, linguistic, cultural, and historical barriers to cross-country collaborations. In particular, the great circle distance between the most populated cities of countries (measured in km) is included, as well as a dummy variable indicating whether two countries share a common border, a dummy variable valued 1 if and ranking in a patent is an interesting avenue of research, which goes beyond the scope of the present paper.

> 13. Other biases inherent to the existence of multiple jurisdictions and patent offices are discussed in de

> Rassenfosse et al. (2014)—such as the nonrandom choice of patent office. Again, the use of PCT

applications should mitigate these biases.

17
the same language is spoken in both countries, and a dummy variable valued 1 when the two countries share the same colonial past—these variables come from the CEPII distance database (Mayer and Zignago 2011).

Two additional variables, suggested by Melitz and Toubal (2014), help in controlling for cultural similarities between country pairs: first, an index of language similarity. People whose languages share common roots will likely share similar cultural backgrounds. To compute this index, one single language is assigned to every country, and using information on the classification of languages provided by the Ethnologue Project, a language similarity index based on the distance between branches in this classification is computed.<sup>14</sup> Then the number of branches that coincide between each pair of languages are added together and the result is divided by the sum of branches of each of the two languages (in order to take into account the fact that the granularity of branches may not be the same across languages). As a result, an index between 0 and 1 is obtained, where 0 means complete dissimilarity and 1 means that these two languages are almost the same in linguistic terms.<sup>15</sup> Second, the religious heritage of countries is a critical element of their culture and identity (Guiso et al 2009). Countries with similar religious roots, culturally closer, are likely to interact more (for an application in the trade literature, see, among others, Melitz and Toubal 2014).

14. For example, the linguistic classification of Portuguese, Swedish, and Danish, from the largest, most inclusive grouping to the smallest, is: Indo-European, Italic/Romance, Italo-Western, Western, GalloIberian, Ibero-Romance, West Iberian, Portuguese-Galician (Portuguese); Indo-European, Germanic, North East, Scandinavian, Danish-Swedish, Swedish (Swedish); Indo-European, Germanic, North East, Scandinavian, Danish-Swedish, Danish-Riksmal, Danish (Danish). www.ethnologue.com, accessed May 20, 2014.

15. I arbitrarily set to 0 this variable when the countries share exactly the same language, in order to avoid collinearity with the variable “same language.” 18
Religion similarity is proxied with an index built as follows: for each country, data on the percentage of population adhering to one of eight major religions is retrieved (data from the CIA World Factbook dataset)—these eight religion groups differ slightly from Melitz and Toubal (2014). Then the following formula for each country pair is computed, which results in a variable ranging from 0 (no believers in common) to 1: _Religion_Sim.ij =_ <u>(</u> _%muslimi * %muslim j_ <u>)+ (</u> _%catholici * %catholic j_ <u>)+</u> ( _%orthodoxi * %orthodox j_ )+ ( _%protestanti * %protestant j_ )+ (3) ( _%hinduismi * %hinduism j_ )+ ( _%buddhisti * %buddhist j_ )+ . ( _%easterni * %eastern j_ )+ ( _%judaismi * %judaism j_ ) I also control for the intensity of economic linkages between countries using the share of bilateral trade (exports plus imports, EXP+IMP) between a given pair over their total trade (COMTRADE data).<sup>16</sup> Trade is a conduit of information that may foster technological partnerships too, while it might be linked to the presence of migrants at the same time. I also account for the common technological specialization of countrypairs introducing an index of technological distance measured as where<sup>_f_</sup> _ih_<sup>stands for the share of patents of one technological class h according to the</sup> IPC classification of country i and _f jh_ for the share of patents of one technological class h of country j. Values of the index close to the unity indicate that a given pair of countries are technologically different, and values close to zero indicate that they are technologically similar (Jaffe 1986). Again, PCT patents are used to compute this index.

16. Changing this variable to trade in absolute numbers does not alter the main results and conclusions, though it makes the variable EXP+IMP non-significant—results provided upon request from the author.

19
Finally, two additional attribute variables of individual countries are used. In particular, the number of PCT patents per country, for five-year annually repeated timewindows. This variable controls for the size of country innovation systems, which clearly determines a country’s capacity to collaborate with foreigners, as well as its capacity to attract inventors from abroad or send them to other locations. In addition, it includes GDP per capita, from the World Development Indicators (World Bank), expressed in US$ 2005 at PPP, in order to capture the market potential of countries as well as their capacity to innovate. Table 1 contains summary statistics of the variables included in the models, and appendix S6 the correlation matrix.

TABLE 1. Summary Statistics

||Observations|Mean|St. Dev|Min.|Max.|
|---|---|---|---|---|---|
|Collab. inv_i- inv_j|26,160|1.26|10.55|0|678|
|Collab. app_i- inv_j|26,160|1.73|14.87|0|708|
|Diaspora size|26,160|22.29|447.18|0|26,661|
|Distance|26,160|6,889.27|4,539.38|59.62|19,629.50|
|Contiguity|26,160|0.01|0.10|0|1|
|Common language|26,160|0.09|0.29|0|1|
|Lang. similarity|26,160|0.16|0.18|0|0.89|
|Colonial links|26,160|0.04|0.18|0|1|
|Religion similarity|26,160|0.13|0.19|0|0.90|
|EXP+IMP|26,160|0.01|0.03|0|0.41|
|Tech.distance|26,160|0.48|0.27|0.02|1|
|# patents_i|26,160|816.86|3,501.12|0|64,990|
|# patents_j|26,160|43,314.25|99,659.75|52|692,364|
|GDP p.c._i|26,160|9,258.23|9,566.77|432.05|74,113.90|
|GDPp.c._j|26,160|29,423.23|6,171.54|11,382.60|48,799.70|

Source: Authors’ analysis based on data described in the text. Notes: “_i” and “_j” stand for, respectively, migrant inventor’s sending country and migrant inventor’s receiving country. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia, and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand, Uzbekistan, and Zimbabwe. Moreover, data for the former Soviet Republics are only available from 1991; data for TFYR of Macedonia, Croatia, and Slovenia only from 1992; and data for the Czech Republic, Slovakia and Eritrea only from 1993. In consequence, the final sample consists of 26,160 observations, instead of 26,800.

Note that all time-variant explanatory variables are lagged one period in order to lessen potential biases caused by system feedbacks. Notwithstanding this common practice, other sources of endogeneity and biased estimates are likely to arise. Hence, I discuss alternative solutions in the results section.

20
III. RESULTS

# _Baseline Estimations_

Table 2 presents the results of baseline PPML estimations, with robust, country-pair clustered standard errors. Column (1) regresses international co-inventorship against the focal variable, plus individual-country and time FE, as well as the list of controls.<sup>17</sup> The effect of the variable is positive and statistically significant. In particular, column (1) shows an elasticity of 0.20. That is to say, a 10% increase in the size of the inventor diaspora abroad is associated with a two-percent increase in international patent collaborations, which is also economically meaningful. This result is of the same order of magnitude as estimates for the case of diasporas and trade (Felbermayr and Toubal 2010; Felbermayr and Toubal 2012; Head and Ries 1998; Rauch and Trindade 2002, although slightly larger than in Aubry et al. 2014) and diasporas and FDI (Aubry et al. 2014).

17. In unreported results it is included bilateral (country-country) fixed-effects—results provided from the authors upon request. In a nutshell, in those estimations all time-variant variables dramatically decrease their coefficients, becoming statistically nonsignificant. However, regressions with only country FE and time FE are preferred because: (1) including bilateral FE removes 35% of the observations of the original sample; and (2) both diaspora networks as well as international co-patenting move slowly over time, which makes it difficult to identify any effect coming from within-pair variation—therefore identification relies on cross-country-pair differences only.

21
TABLE 2. Baseline Specifications without and with Time-Varying Multilateral Resistance

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||PPML|PPML|PPML|PPML|
||Co-|R&D|Co-|R&D|
||inventorship|offshoring|inventorship|offshoring|
|ln(Diaspora)|0.200***|0.0929**|0.243***|0.111***|
||(0.0229)|(0.0407)|(0.0233)|(0.0423)|
|ln(Distance)|-0.267***|-0.105|-0.0468|0.173**|
||(0.0523)|(0.0721)|(0.0552)|(0.0760)|
|Contiguity|0.210*|-0.0996|0.116|-0.271|
||(0.127)|(0.211)|(0.122)|(0.191)|
|Common language|0.625***|0.931***|0.434***|0.703***|
||(0.132)|(0.220)|(0.108)|(0.187)|
|Lang. similarity|0.523**|0.822**|0.405**|0.694**|
||(0.225)|(0.386)|(0.202)|(0.352)|
|Colonial links|0.0654|0.299*|0.0315|0.291**|
||(0.124)|(0.172)|(0.103)|(0.143)|
|Religion similarity|0.706***<br>|0.219<br>|0.612***<br>|0.185<br>|
||(0.256)|(0.446)|(0.235)|(0.419)|
|ln(EXP+IMP)|0.0457**|0.0710***|0.228***|0.329***|
||(0.0197)|(0.0249)|(0.0335)|(0.0495)|
|ln(Tech.distance)|-0.0637|-0.245***|-0.136***|-0.287***|
||(0.0469)|(0.0598)|(0.0506)|(0.0689)|
|ln(# patents_i)|0.334***|0.354***|||
||(0.0530)|(0.0693)|||
|ln(# patents_j)|0.0568|0.333|||
||(0.115)|(0.205)|||
|ln(GDP p.c. _i)|1.098***|1.807***|||
||(0.233)|(0.318)|||
|ln(GDP p.c. _j)|-0.0776|-1.107|||
||(0.552)|(0.823)|||
|Constant|-6.072|-4.780|-0.410|-1.726**|
||(6.006)|(8.651)|(0.652)|(0.791)|
|Observations|26,160|26,160|19,676|20,574|
|Pseudo R2|0.958|0.918|0.978|0.956|
|Sending FE|Yes|Yes|No|No|
|Receiving FE|Yes|Yes|No|No|
|Year FE|Yes|Yes|No|No|
|Sending FE*Time FE|No|No|Yes|Yes|
|Receiving FE*Time FE|No|No|Yes|Yes|
|LogLik|-18492.28|-23746.92|-16226.61|-20463.96|

Source: Authors’ analysis based on data described in the text.

Notes: Country-pair clustered robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. ‘_i’ and ‘_j’ stand for, respectively, migrant inventor’s sending country and migrant inventor’s receiving country. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand, Uzbekistan, and Zimbabwe. Moreover, data for the former Soviet Republics are only available from 1991; data for TFYR of Macedonia, Croatia, and Slovenia only from 1992; and data for the Czech Republic, Slovakia, and Eritrea only from 1993. In consequence, the final sample consists of 26,160 observations, instead of 26,800. The lower number of observations between columns (1), (2) and (3), (4) is due to the inclusion of fixed effects in pseudo-maximum likelihood estimations: the PPML method automatically drops the country-specific fixed-effects (and their corresponding observations) for which the country has zero recorded inventors’ flows to every other country in the sample in order to achieve convergence. Results are comparable to other count data methods without removing these observations (see Santos Silva and Tenreyro 2010 for further details).

22
The results for the remaining explanatory variables are interesting in themselves. As expected, physical distance between the most populated cities exerts a negative influence on the likelihood for cooperation across national boundaries, although sharing a common border barely affects co-inventorship. Common language has a strong positive estimated effect on collaborations between inventors of different countries. Other proxies for cultural similarity, such as language and religion proximity, exert a strong positive effect too. However, historical links between country pairs expressed by their colonial past are not significant. As expected, bilateral trade is positive and significant, while technological distance between countries, that is, how distant countries are in their technological specialization, exerts a negative influence on bilateral co-patents. Finally, both attribute variables—total number of patents and GDP per capita—are significant for the case of origin countries but not for destinations. Thus, it appears that differences across industrialized economies in terms of technological and economic development are relatively minor and are picked up by their country FE.

Column (2) looks at R&D offshoring—co-patents between applicants in developed countries and inventors in developing economies. Comparing the estimates with those of column (1), interesting results emerge. First and foremost, the estimated elasticity of inventor diaspora size is notably reduced in these latter estimations—less than a half. That is, diaspora networks particularly mediate interpersonal relations between co-workers. Meanwhile, they have a more nuanced effect on transnational employer-employee linkages.

Second, geography per se does not play a significant role in explaining R&D offshoring. The diaspora and geography results put together seem to suggest that personal face-to-face relations and trust building are critical in explaining co23
inventorship—where contracts are usually more tacit and contract enforcement is difficult—but less important in explaining more formal and hierarchical relationships, such as those represented by offshoring relationships, where probably explicit, written contracts are the rule.

Other remarkable differences are worth reporting. For instance, the coefficient associated with a colonial past increases its point estimate and now becomes significant. That is, historical ties between the former metropolis and its formal colonies seem to have left an enduring effect over time that, still today, influences innovation networks across national borders. Finally, the common specialization of countries seems to play a greater role too when looking at applicant-to-inventor co-patents, as compared to inventor-to-inventor collaborations.

FIGURE 3. Yearly Estimated Coefficients of Diaspora on Co-inventorship, 1991–2010 <!-- Start of picture text --> Year<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>.8<br>.6<br>.4<br>ln (Diaspora) * time dummies .2<br>0<br><!-- End of picture text --> Source: Authors’ analysis based on data described in the text.

24
FIGURE 4. Yearly Estimated Coefficients of Diaspora on R&D Offshoring, 1991–2010 <!-- Start of picture text --> Year<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>.8<br>.6<br>.4<br>ln (Diaspora) * time dummies .2<br>0<br><!-- End of picture text --> Source: Authors’ analysis based on data described in the text.

Furthermore, these specifications (columns [1] and [2]) are used to include the focal variable interacted with time dummies, in order to explore the effects of skilled diasporas over time. Figures 3 and 4 present the estimated coefficients of the interaction variables, and show quite interesting, although expected, results. As can be seen, diaspora effects over time on co-inventorship and R&D offshoring present a marked decreasing trend. Quite likely, the use of information and communication technologies (ICT) has made the utilization of international ethnic ties less relevant now than 20 years ago. Similarly, the economic and institutional development of emerging economies has also contributed to mitigate the role of skilled diasporas in reducing the costs of asymmetric information—for example, think of the role of intellectual property in these emerging countries in the aftermath of their subscription to TRIPs, the Trade Related Intellectual Property Agreements that comes with membership of the World Trade Organization (WTO).

Columns (3) and (4) of table 2 mimic estimations (1) and (2) but control for time-variant multilateral resistance. While country FE controls for average multilateral resistance to collaborating over time (Feenstra 2004), some elements of this multilateral 25
resistance are likely to be time-variant and may not be picked up by the attribute variables included (Adam and Cobham 2007).<sup>18</sup> Consequently, these columns include country-specific time dummies, and repeat the main estimations, focusing attention only on bilateral variables. Some nameable differences with respect to the baseline regressions emerge, like a reduced role of distance in explaining inventor-to-inventor collaborations. However, the focal variable remains positive and strongly significant, and it presents coefficients that are slightly larger than previously.

# _Identification: Cultural Proximity and Instrumental Variables_

Table 3 adds interaction terms between the inventor diaspora variable and different dimensions of cultural proximity between countries—common language, language similarity, common colonial past, and religion similarity. Given that transnational migrant networks mitigate the costs of incomplete information beyond country boundaries, one would expect their impact to be stronger for country pairs exhibiting larger informational frictions. Hence, negative and significant interaction terms will provide evidence on the least similar countries relying more on diaspora externalities than pairs of countries that are culturally closer. Results (table 3) partially confirm this extreme: the interaction with colonial ties is negative and significant—also the interaction with language similarity in the last specification.<sup>19</sup> As in Kugler et al. (2013), I interpret these negative coefficients as evidence of a causal link between inventor diasporas and international co-inventive activity. If unobserved confounding factors drive both migration and co-patenting at the same time, 18. For an application to the migration literature, see Bertoli and Fernández-Huertas Moraga 2013.

19. The same estimation procedure using R&D offshoring as the dependent variable delivers not significant coefficients. This is further evidence of the critical role of diasporas for worker-to-worker collaborations and their more nuanced effects for the case of more hierarchical, R&D offshoring relations.

26
they should work in such a way that they are capable of explaining not only the main results—the diaspora-co-patenting relation—but also the differentiated effect of diaspora networks across different cultural dimensions, which is unlikely.

TABLE 3. Inventor Diaspora Effect between Culturally Closer/More Distant Countries

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||PPML|PPML|PPML|PPML|PPML|
|||C<br>|o-inventorshi<br>|p<br>||
|ln(Diaspora)|0.204***<br>(0.0247)|0.199***<br>(0.0229)|0.198***<br>(0.0230)|0.201***<br>(0.0230)|0.206***<br>(0.0249)|
|ln(Distance)|-0.263***|-0.270***|-0.267***|-0.275***|-0.262***|
||(0.0535)|(0.0517)|(0.0520)|(0.0560)|(0.0557)|
|Contiguity|0.213*|0.217*|0.233*|0.216*|0.250**|
||(0.127)|(0.128)|(0.127)|(0.127)|(0.127)|
|Common language|0.690***<br>(0.193)|0.613***<br>(0.132)|0.630***<br>(0.131)|0.628***<br>(0.131)|0.776***<br>(0.198)|
|Lang. similarity|0.508**|0.781***|0.528**|0.532**|0.858***|
||(0.227)|(0.273)|(0.224)|(0.224)|(0.285)|
|Colonial links|0.0502|0.0774|0.473*|0.0724|0.468*|
||(0.131)|(0.124)|(0.248)|(0.126)|(0.253)|
|Religion similarity|0.702***|0.676***|0.692***|0.782***|0.639**|
||(0.254)|(0.262)|(0.256)|(0.267)|(0.287)|
|ln(Dia.)*Com. language|-0.0122<br>(0.0223)||||-0.0302<br>(0.0241)|
|ln(Dia.)*Language sim.||-0.0829<br>(0.0522)|||-0.117*<br>(0.0606)|
|ln(Diaspora)*Colonial|||-0.106**<br>(0.0498)||-0.110**<br>(0.0494)|
|ln(Diaspora)*Religion||||-0.0526<br>(0.0708)|0.00141<br>(0.0758)|
|Controls|Yes|Yes|Yes|Yes|Yes|
|Constant|-6.102|-5.847|-6.941|-5.701|-6.745|
||(6.019)|(6.000)|(5.985)|(6.080)|(6.044)|
|Observations|26,160|26,160|26,160|26,160|26,160|
|Pseudo R2|0.958|0.958|0.957|0.957|0.958|
|Sending FE|Yes|Yes|Yes|Yes|Yes|
|Receiving FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|
|LogLik|-18490.83|-18482.05|-18478.32|-18490.28|-18459.73|

Source: Authors’ analysis based on data described in the text.

Notes: Country-pair clustered robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. ‘_i’ and ‘_j’ stand for, respectively, migrant inventor’s sending country and migrant inventor’s receiving country. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia, and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand, Uzbekistan, and Zimbabwe. Moreover, data for the former Soviet Republics are only available from 1991; data for TFYR of Macedonia, Croatia, and Slovenia only from 1992; and data for the Czech Republic, Slovakia, and Eritrea only from 1993. In consequence, the final sample consists of 26,160 observations, instead of 26,800.

27
More importantly, instrumental variable estimates are also provided. Potential and available candidates for such a role are (i) the size of the bilateral diaspora between countries i and j in 1960 (data from Özden et al. 2011) and its square, and (ii) the size of the unskilled diaspora (migrants with only primary education) originally from country i residing in country j in 1990 (data from Docquier et al. 2009) and its square. First, the stocks of migrants by country of origin in the 1960 censuses (therefore immigrants arrived between the end of the Second World War and 1960) are likely to affect the current stocks of highly-skilled migrants through network effects favoring further migration flows over the long run. Note that these figures include foreign-born people counts in dates closer to the age of mass migration than to the technological revolution of the 1990s and the 2000s. Quite probably, they are uncorrelated with current levels of cross-country collaborations, apart from influence through current skilled diasporas. Similarly, the current stocks of migrants with primary or lower levels of education correlate with current stocks of highly-skilled diasporas. The relation between existing diasporas and existing migration flows not only operates at a labor market level, but also among ethnic communities operating across different skills groups. Large stocks of unskilled immigrants in a given country will mean the existence of attractive factors— for example, amenities—which are also attractive to highly-skilled immigrants (Hunt and Gauthier-Loiselle 2008). On the other hand, uneducated migrants should play a non-existent role in boosting co-inventorship or R&D offshoring with their homelands—justifying their exclusion from the main equations, apart from their effects through inventor diasporas. Moreover, unskilled diaspora data come from the 1990 census—which accounts for the unskilled migrant flows of the 1980s—so as to be more confident that they are unaffected by unobserved factors influencing co-patenting patterns between 1990 and 2010.

28
Table 4 presents GMM estimations of the PPML—see Windmeijer and Silva (1997). Column (1) presents the first-stage results. Note that the value of the F-test statistic of the first stage, 344.58, is well above 10, which is usually considered a good threshold, and so the instruments cannot be judged as weak. Moreover, Hansen J statistics for mutual consistency of available instruments are provided at the bottom of columns (2) and (3), and they do not reject the null hypothesis that the excluded instruments are valid and uncorrelated with the error term, so there are no overidentification problems. Column (2) shows GMM estimates using co-inventorship as the dependent variable. It shows a positive and statistically significant relationship between inventor diasporas and international co-inventorship. The GMM results are slightly stronger in terms of magnitude of estimated coefficient relative to former PPML. Thus, analysis suggests that ignoring the endogeneity issue tends to underestimate the effect of migration on international co-inventorship. Note, however, that the difference is small and therefore the coefficients are comparable. On the other hand, results for the case of R&D offshoring remain positive but not significant (column [3]). I interpret these differentiated results as further evidence of the critical role of highly-skilled diasporas for worker-to-worker co-inventorship and their less important effect for applicant-to-inventor relations. Personal face-to-face relations and trust building are critical to explain co-inventorship, where contracts are usually more tacit and contract enforcement is difficult. However, in more formal and explicit relationships where written contracts are the rule, such as R&D offshoring, highly-skilled, technical immigrant workers do not play any role. Of course, one cannot rule out the possibility of measurement error of the diaspora variable due to the naturalization issue discussed above—in fact, results in appendix S3 using ethnic name recognition algorithms point to a larger coefficient on the estimated diaspora-R&D offshoring relation.

29
TABLE 4. GMM Estimates with Instrumented Diaspora

||(1)<br>|(2)<br>|(3)<br>|
|---|---|---|---|
||1<sup>st</sup>stage results|GMM|GMM|
|||Co-inventorship|R&D offshoring|
|ln(Diaspora)||0.223***<br>(0.0774)|0.120<br>(0.170)|
|ln(Distance)|-0.268***|-0.247***|-0.132|
||(0.0315)|(0.0649)|(0.0985)|
|ln(diaspora 1960s)|0.0128<br>(0.0102)|||
|ln(diaspora 1960s)^2|0.00534***<br>(0.00163)|||
|ln(low-skilled diaspora)|0.0549***<br>(0.00971)|||
|ln(low-skilled diaspora)^2|0.0137***<br>(0.00195)|||
|Controls|Yes|Yes|Yes|
|Constant|14.86***|-6.866|-0.0369|
||(1.967)|(5.998)|(8.833)|
|Observations|26,160|26,160|26,160|
|Controls|Yes|Yes|Yes|
|Sending FE|Yes|Yes|Yes|
|Receiving FE|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|
|F-test|344.58|||
|p-value|0.0000|||
|Hansen's J chi2||3.00468|6.13152|
|p-value||0.3909|0.1054|

Source: Authors’ analysis based on data described in the text.

Notes: Country-pair clustered robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. ‘_i’ and ‘_j’ stand for, respectively, migrant inventor’s sending country and migrant inventor’s receiving country. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia, and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand, Uzbekistan, and Zimbabwe. Moreover, data for the former Soviet Republics are only available from 1991; data for TFYR of Macedonia, Croatia, and Slovenia only from 1992; and data for the Czech Republic, Slovakia, and Eritrea only from 1993. In consequence, the final sample consists of 26,160 observations, instead of 26,800. Instruments are centered around their mean, and they are as follows: (i) the size of the bilateral diaspora between countries i and j in the 1960s—and its square, and (ii) the size of the unskilled diaspora original from country i residing in country j in 1990 (migrants with only primary or lower levels of education)—and its square.

One way to see whether my interpretation of the different effects of inventor diaspora is plausible is to include tertiary educated stocks of migrants as extracted from census data. The data come from Docquier et al. (2009) and were originally built from two census rounds, 1990 and 2000, in which the regressions were used, respectively, for the years 1991–2000 and the years 2001–2010 (recall that censuses record data only every 10 years). As can be seen in table 5, column (2), the coefficient for the inventor 30
diaspora variable on international co-inventorship remains strongly significant and largely unaltered when tertiary educated stocks of migrants are included among the regressors—which is not significant (column [1] reproduces the baseline result for comparison purposes). On the contrary, inventor diaspora is no longer significant for the case of R&D offshoring—column (4) as opposed to column (3), while tertiary educated stocks of migrants becomes positive and strongly significant. Although not conclusive, I interpret this result as evidence that other types of skilled migrants may play an important role in boosting R&D offshoring networks, beyond what inventors (most of them scientists and engineers) do. Managers, executives, CEOs, trade agents, venture capitalists, and in general, migrant entrepreneurs, armed with their knowledge of technology markets and their global social capital, might be better placed to favor international R&D businesses and R&D offshoring back home. Unfortunately, these migrant entrepreneurs do not necessarily apply for patents and therefore may remain hidden to our proxy for highly-skilled diasporas. However, confirming this extreme would require further empirical examination that goes beyond the scope of this paper.

TABLE 5. Baseline Regressions with Bilateral Highly-Skilled (HS) Migration Stocks

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Co-inve<br>|ntorship<br>|R&D of<br>|fshoring<br>|
|ln(Inventor diaspora)|0.200***|0.189***|0.0929**|0.0639|
||(0.0229)|(0.0236)|(0.0407)|(0.0408)|
|ln(HS migration)||0.0409<br>(0.0252)||0.103***<br>(0.0362)|
|Controls|Yes|Yes|Yes|Yes|
|Observations|26,160|26,160|26,160|26,160|
|Sending FE|Yes|Yes|Yes|Yes|
|Receiving FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|

Source: Authors’ analysis based on data described in the text. Notes: Country-pair clustered robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

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# _Are China and India Different after All?_

Next, I look at the robustness of the results once the main players are removed from the analysis. This is motivated by the observation that the majority of studies look at the case of the largest receiving country, that is, the United States, and its main providers of skilled talent—that is, China, India, and other Asian economies (Kerr 2008; Agrawal et al. 2011; Breschi et al. 2015; Saxenian 1999, 2006). In the light of this, some scholars argue that lessons from case studies on China and India cannot be extrapolated to other migrant communities—that is, it is difficult to say whether highly-skilled emigrants systematically engage in business networks and knowledge transfers with their homelands or rather that the Indian and Chinese diasporas are so famous for being an exception rather than the rule (Gibson and McKenzie 2012).

In order to explore this issue, table 6 repeats co-inventorship estimations—with and without country-specific time dummies—but removing from the sample either the BRICS countries (Brazil, Russia, India, China, and South Africa) or the United States or both. Contrary to the arguments posited by Gibson and McKenzie (2012), among others, the coefficient accompanying the diaspora variable remains strongly significant and economically meaningful in all models and barely lower when compared to previous estimates.

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TABLE 6. Are China and India the Exception Rather than the Rule?

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||PPML|PPML|PPML|PPML|
|||Co-inve|ntorship<br>||
||No BRICS|No US|No BRICS,<br>no US|No BRICS,<br>no US|
|ln(Diaspora)|0.192***|0.204***|0.200***|0.190***|
||(0.0384)|(0.0358)|(0.0419)|(0.0465)|
|ln(Distance)|-0.447***|-0.325***|-0.515***|-0.269***|
||(0.0631)|(0.0582)|(0.0654)|(0.0759)|
|Bilateral controls|Yes|Yes|Yes|Yes|
|Attribute controls|Yes|Yes|Yes|No|
|Constant|-5.626|-7.494|-5.900|1.753***|
||(7.610)|(5.929)|(7.425)|(0.667)|
|Observations|24,180|24,852|22,971|14,752|
|Pseudo R2|0.900|0.828|0.658|0.725|
|Sending FE|Yes|Yes|Yes|No|
|Receiving FE|Yes|Yes|Yes|No|
|Year FE|Yes|Yes|Yes|No|
|Sending FE*Time FE|No|No|No|Yes|
|Receiving FE*Time FE|No|No|No|Yes|
|Controls|Yes|Yes|Yes|Yes|
|LogLik|-14236.62|-15541.05|-11836.79|-10463.32|

Source: Authors’ analysis based on data described in the text. Notes: Country-pair clustered robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand, Uzbekistan, and Zimbabwe. Moreover, data for the former Soviet Republics are only available from 1991; data for TFYR of Macedonia, Croatia and Slovenia only from 1992; and data for the Czech Republic, Slovakia, and Eritrea only from 1993. In consequence, the final sample consists of 26,160 observations, instead of 26,800.

# _Further Robustness Analysis_

To further check the robustness of the results, table 7 runs the baseline specification using different estimation methods and dependent variables—for the case of coinventorship only. In particular, it includes OLS and Negative Binomial models. For both cases the estimated coefficients are slightly larger compared to table 2, but they are fairly comparable. Column (3) takes on board the potential heterogeneity of international co-patents in terms of quality and weights them according to the number of forward citations received within five years after their priority year. As can be seen, results are robust for this different approach to computing the dependent variable— although further research, possibly at the micro level, should shed more light on this 33
issue. Finally, in order to be sure that the choice of PCT applications to build the coinventorship variable does not bias the results, the baseline regression is replicated using alternative patent data sources, such as the USPTO, the EPO, and “Triadic Patent Families” (TPF) (OECD Triadic Patent Families database, January 2014). TPF consist of a set of patents filed at the EPO, the Japan Patent Office (JPO), and granted by the USPTO that share one or more priority applications. If anything (columns [4] through [6]), it seems that using alternative sources of patent applications may overestimate the relationship between high-skilled migration and international co-patenting.

Appendix S7 replicates the baseline regressions but splits the count of co-patents and foreign inventors into five broad technology fields (Schmoch 2008). Appendix S8 uses alternative count data methods intended to zero-inflated dependent variables, with no important differences with respect to the main results.

TABLE 7. Robustness Checks. Co-inventorship

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||OLS||Citation||||
||<br>co-inv.+1|NegBin|<br>weighted|USPTO|EPO|TPF|
||||<br>Co-inve<br>|ntorship<br>|||
|ln(Diaspora)|0.232***|0.221***|0.165***|0.229***|0.174***|0.219***|
||(0.0136)|(0.0267)|(0.0268)|(0.0230)|(0.0234)|(0.0263)|
|ln(Distance)|-0.0136|-0.453***|-0.291***|-0.213***|-0.243***|-0.207***|
||(0.0156)|(0.0486)|(0.0621)|(0.0546)|(0.0626)|(0.0663)|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|1.089|-6.659|-0.0461|-10.27|-2.879|-1.395|
||(0.835)|(5.554)|(7.122)|(6.523)|(5.891)|(6.584)|
|Obs.|26,160|26,160|26,160|26,160|25,760|24,700|
|R2|0.571|0.354|0.903|0.980|0.937|0.883|
|Sending FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Receiv. FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Authors’ analysis based on data described in the text.

Notes: Country-pair clustered robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand, Uzbekistan, and Zimbabwe. Moreover, data for the former Soviet Republics are only available from 1991; data for TFYR of Macedonia, Croatia and Slovenia only from 1992; and data for the Czech Republic, Slovakia, and Eritrea only from 1993. In consequence, the final sample consists of 26,160 observations, instead of 26,800.

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IV. CONCLUSION This paper examines the impact of highly-skilled migrant networks in high income countries on the internationalization of inventive activity between high income and developing economies—measured as cross-country PCT co-patenting (co-inventorship and R&D offshoring). In order to study this relationship, it makes use of a unique dataset on inventors with a migratory background. To my knowledge, there have been few previous attempts to measure the mentioned links, and therefore this constitutes the main contribution of the paper.

The results show a strong and positive association between highly-skilled diasporas and the internationalization of inventive activity between developed and developing countries. The effect is statistically and economically significant: a 10% increase in the inventor diaspora abroad is associated with a 2.0-2.2% increase in international patent collaborations at the level of inventors. The effect found is robust for the large list of controls and robustness checks performed—such as alternative ways of measuring the dependent and the focal explanatory variables. Regressions also include country FE and country-specific time dummies, in order to control for any timeinvariant and time-variant characteristics of the country of origin or the country of destination of the migrants that may affect the relationship between inventor diasporas and international co-patenting. Hence, any source of bias should come from countrypair (omitted) variables, for which an IV approach is used in order to avoid the possibility of the focal regressors picking up any confounding effect that might bias their point estimates. These findings do not suffice to conclude that a “brain gain” exists that makes up for the loss of highly-skilled human capital of sending economies, although they are undeniably necessary elements. Note, however, that boosting 35
international co-inventorship and team formation is only one of the multiple brain _gain_ effects of emigrant inventors, which may eventually include the international diffusion of knowledge (Kerr 2008; Breschi et al. 2015) or the accumulation of human capital in sending economies (Beine et al. 2008)—higher expected returns to the inventor activity.

In developing/emerging countries, accessing foreign technologies via international collaborations is a hot political issue (Sterzi and Montobbio 2013). From a development policy perspective, these findings support the idea that exploiting highlyskilled diaspora networks in technology frontier economies might be an instrumental way of engaging in international innovation networks. This subject matter ranks high among policymakers in these countries, as witnessed by the recent visit of the Indian Prime Minister to Silicon Valley.<sup>20</sup> Interestingly enough, the effect, although relatively diminished, does not depend on the remarkable performance of particular diasporas abroad, such as Chinese or Indian inventors. Equally, results are not particularly driven by the country both attracting the largest number of migrant inventors and concentrating a significant proportion of North-South international collaborations, that is, the United States. It seems therefore that inventor diaspora effects are exhausted at relatively low levels of highly-skilled diasporas (for similar results for the trade-migration relationship, see Egger et al. 2012).

The IV results suggest that highly-skilled diaspora effects weaken dramatically in the case of R&D offshoring—collaborations between applicants in developed countries and inventors in developing ones. These results seem to suggest that personal

> 20. See: http://www.theguardian.com/world/2015/sep/23/narendra-modi-aims-to-bolster-indias-techcredentials-on-us-visit (accessed 28th September 2015).

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relations and trust building are critical to explain co-inventorship—where contracts are usually more tacit, contract enforcement is difficult, and diaspora networks may play a referral role—but are less important in explaining more formal and hierarchical relationships—where probably explicit, written contracts are the rule. Technical highlyskilled workers, such as scientists and engineers, are possibly not the best placed to boost R&D offshoring networks to their countries of origin, but other skilled workers, such as executives, managers, trade agents, or entrepreneurs, may play a more critical role. Although some preliminary evidence seems to give support to this hypothesis, further research, possibly at the firm and inventor levels, will shed more light on these particular issues.

Of course, using patent data for migration research does not come without limitations. One important caveat is that inventors are only observed when they seek patents. However, not all inventions are patented; indeed, the propensity to patent for each dollar invested in research and development differs considerably across industries. In addition, studies have documented a skewed distribution of patent values, with relatively few patents yielding high economic returns. Another limitation of the dataset used in this study is that it misses inventors with a migratory background that have become nationals of their host country. To the extent that it is easier to gain citizenship in some countries than in others, this introduces a bias in the data. A related bias stems from the possibility that migrants of some origins may be more inclined to adopt the host country’s nationality than migrants from other origins. Unfortunately, the data do not allow the severity of these biases to be assessed. Notwithstanding these caveats, we believe that this database meaningfully captures a phenomenon of growing importance.

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