Background literature

Innovation can be viewed as an outcome of cumulative efforts in R&D in the domestic country or region. Early empirical evidence on international technological diffusion have shown a country‘s R&D capital stock – a measurement for the stock of knowledge – could positively impact on its own total factor productivity (Coe & Moghadam, 1993; Griliches, 1988), but also on the one of its trade partners (Coe & Helpman, 1995). However, Keller (1997) casts some doubts on Coe & Helpman's (1995) findings showing that similar results were obtained even when using random numbers for trade patterns. In a further analysis, other scholars (Eaton & Kortum, 1996; Keller, 2001) suggest technological diffusion could as well

41 be a consequence of other regional effects not linked to trade or could come from the simultaneous effect of several other channels like geographical distance, Foreign Direct Investment (FDI) and language similarity. Yet, Coe & Helpman (1995) study provides an early empirical evidence on the determinants of the international flows of knowledge or technological diffusion. In general in the innovation literature, variables such as trade, R&D and FDI have been used to proxy the extent of knowledge exchange between countries.

However, finding a good measurement for knowledge flows could be cumbersome to the extent these flows are not tangible and as such it becomes a hard task to find a comprehensive instrument for capturing them. The use of patent citations emerged as a way of overcoming this limitation. This technique was pioneered by Jaffe et al. (1993). Indeed, using patent citations from the United States Patent and Trademark Office (USPTO), Jaffe et al. (1993) investigate patterns of technology diffusion in the US. From geo-localising patents on the basis of their inventors‘ addresses, they find a higher likelihood for US patents to be cited by other patents coming from the same state than by patents from other states, thus pointing out to the existence of localized of knowledge flows. Since then, this technique has been widely applied to various other studies – as no better measurement of knowledge flows or technology diffusion has been found up to date – which have attempted to investigate channels of knowledge flows and what drives such flows at a cross-border level.

One of these studies is conducted by MacGarvie (2005) in which she applies a cross-country gravity model¹³ using citation data from the USPTO in order to test for the determinants of knowledge flows. The author takes the number of patent citations between country pairs as the dependent variable and a proxy to knowledge flows, among ten large countries (for the period 1980–1995). The author then includes some dyadic control variables such as the technological and cultural proximity – a dummy for common language, geographical distance between pair of countries, citing countries‘ imports from cited countries, as well as country‘s specific variables like citing countries‘ employment of R&D workers, FDI and the yearly number of telephone calls. The author finds FDI, geographical, technological and cultural proximity favors knowledge flows between two countries. Meanwhile, trade is associated with knowledge flows only in inventions within the same fields of technology.

13 The gravity model of migration is a model in urban geography derived from Newton's law of gravity, and used to predict the degree of interaction between two places (Rodrigue et al., 2009). In other words, it is a prediction model borrowed from physical gravity by social sciences, and economics in particular, which basically seeks to explain the interaction between two elements with the characteristics of each of these two elements and the distance between them.

42 The international mobility of human capital is gaining attention in the related literature as a channel of international knowledge diffusion. In general, studies on highly-skilled migration and innovation has been for a long time confined to the area of migration and development (De Haas, 2010; Docquier & Rapoport, 2012).¹⁴However, a group of scholars have worked on linking it to innovation studies mainly with the help of patent data (Agrawal et al., 2011;

Breschi et al., 2017; Kerr, 2008; Miguelez, 2016).

There is indeed a well-established theoretical literature on the brain drain phenomenon (Beine et al., 2001; Berry & Soligo, 1969; Bhagwati, 1975; Grubel & Scott, 1966; Johnson, 1967;

Lucas, 1990; Mountford, 1997). At a first glance, the conventional migration literature seems to depict brain drain mainly as the concern for developing nations or for south-north migration. This could be explained by developing nations‘ relatively low endowment in human capital as compared with other nations‘ groups. It thus makes them more likely to be vulnerable to any human capital loss, particularly a loss of their highly qualified one. This explains why high income countries are often cited among the winners in the brain drain debate, in so far that their high endowments in human capital seems to make the impact of the massive highly-skilled emigration they experience negligible in relative terms.

However, if we consider the basic definition of brain drain to be the permanent emigration of qualified workers¹⁵ (Straubhaar, 2000), it should be acknowledged that brain drain is not only restricted to developing countries as high income nations such as Germany, France and the United Kingdom have to face a considerably high rate of emigration of their highly-skilled workers. Indeed, from a data on emigration rates by country group in 1990 and 2000, Docquier et al. (2007) report the proportion of highly-skilled workers among migrants to be much higher than the proportion of highly-skilled workers among residents, this for every

14There has not been yet any general and commonly agreed framework for the definition of a migrant. There are three dimensions that have been used in the literature to define a migrant: nationality, country of birth and duration of stay. In empirical studies, migrant definition seems to be mainly driven by information provided in the used data. Each of these dimensions has its own limitations (for a detailed discussion see Champion (1994)). However the duration of stay dimension seems to be one important element for distinguishing between simple mobility and migration. That is probably one of the reasons justifying the recent emergence of the use of ‗diasporas‘ as a new terminology for qualifying people of same origin, living in one or many places abroad. This new terminology seems to be more flexible in its conceptual ground and in the different dimensions embedded in the definition of a migrant. Diaspora is thus 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).

15This definition is in contrast with the definition proposed by the United Nations (UN), which depicts brain drain as a one-way movement, that only covers South-North or developing-developed countries migratory flows and only benefit to the latter countries (Gaillard & Gaillard, 1997). The UN definition is somehow misleading and today would sound obsolete in so far that the world competition for innovation has led to an increasing north-north brain drain.

43 income groups. Furthermore, many high income countries have adopted local policies aiming at keeping their talents at home and attracting foreign ones. Therefore, it appears that all countries share a common concern with respect to brain drain. It is likely that the rate of highly-skilled migration will be maintained over the upcoming years (see Artuç et al. (2015) for evidence from the DIOC data, the Database on Immigrants in OECD Countries). Thus instead of focusing on questions such as which countries are the biggest losers or winners of brain drain, much attention should probably be given to finding strategies in order for sending as well as receiving countries to benefit from highly-skilled migration. This is partly what has been at the core of a new trend of studies that has emerged during the past two decades (pioneered by Stark et al. (1997, 1998) and Stark & Wang (2002)) and which advocates the possibility of some brain gain for both sending and receiving countries from highly-skilled migration.

The benefits from highly-skilled migration to receiving countries are supported by evidence on their over-representation in the host country highly-skilled population and their positive impact on knowledge productivity, thus innovation (No & Walsh, 2010; Stephan & Levin, 2001). Additionally, highly-skilled migrants also strongly contribute to entrepreneurship (Wadhwa et al., 2007) and patenting (Chellaraj et al., 2008; Hunt & Gauthier-Loiselle, 2010) in their host countries (see also Breschi et al. (2014)). One of the main channels of highly-skilled migration to the receiving countries – particularly high income ones – is through higher education. Indeed, universities have been playing a major role in attracting foreign students and postdocs mainly in scientific and technology fields. A study by Black & Stephan (2010) reports foreign students to be accounting for around 45% of graduate students enrolled in science and engineering programs and the share of foreign postdocs to be 60% of all postdocs, this from a 2006 data from the US. An important share of these foreign students and postdocs remain in their host countries after completion of their studies, yet some still maintain ties with their home countries.

While all of the above-cited positive impacts might appear as an evident highly-skilled migration outcome to the receiving countries, it seems not to be the case to the sending countries. However, a different approach to highly-skilled migration has led the recent empirical migration literature to uncover several ways through which brain gain could occur to sending countries. One of these mediums is returnee migrants. For instance, Chacko (2007) provides evidence of the important role of Indian professional immigrants who returned

44 home, in the development of the IT industry in the city of Bangalore. Other studies have reported higher intentions to return among various groups of skilled migrants (Bollard et al., 2011; Gibson & McKenzie, 2012; Kangasniemi et al., 2007). Another approach to brain gain for sending countries in the empirical literature has been to show how co-ethnic ties eased knowledge flow among inventors of same origin back to the migrants‘ source country (Agrawal et al., 2008, 2011; Almeida et al., 2010; Kerr, 2008). Using patent data from the USPTO, Kerr (2008) shows ethnic ties to increase knowledge diffusion from the US to the migrants‘ homelands, as well as technology production in their countries. The effect is even stronger for high-tech industries and for the case of China. The author applies an ethnicity identification technique based on inventor‘s names – which has inspired several other empirical work such as the one by Breschi et al. (2017).

Besides innovation studies, highly-skilled migration is also the subject matter of international economics scholars. Indeed, migrant networks and ethnic ties have been positively linked to a set of variables in the literature such as Foreign Direct Investment (FDI) (Javorcik et al., 2011;

Kugler & Rapoport, 2007), R&D off shoring (Miguelez, 2016) and international trade (Gould, 1994). In these studies, highly-skilled diasporas are depicted as a direct – from diaspora members deliberately interacting with their home economies – and an indirect – from diaspora members serving as an intermediate for easing transactions between host and home economies – vector of economic and knowledge development to their home countries (Kapur & McHale, 2005).

Unfortunately, empirical work on migration and innovation is limited to a group of countries, mainly the high income receiving ones – in particular the US as an historical destination of highly-skilled – and few sending countries like India and China. The focus on these two countries is somehow related to the fact that their highly-skilled migrants make an important part of the total highly-skilled migrants in the US. And this is at the expenses of studies on highly-skilled from other countries like low income countries which are highly-skilled migrant net exporters.

All in all, to the best of our knowledge, very few studies have empirically investigated the actual nature of the link between highly-skilled migration and knowledge flows. While most studies seem to agree on the brain gain for host countries in terms of knowledge flows, this literature has not reached any consensus so far when it comes to the impact on home countries. Some studies have found some positive impacts in terms of knowledge flows as

45 well as development, while the results from other studies remain inconclusive. This might be due to the specificities attached to samples used altogether with the model specifications, which call for some cautions when generalizing findings from these studies. In the present analysis, we will borrow from the model specifications applied by Miguelez (2016) who investigates the role of highly-skilled in the internationalization of knowledge and the one by MacGarvie (2005) who looks into the determinants of knowledge diffusion. Both papers apply a gravity model using patent data.

2.3 Methodology and data