BRAIN DRAIN’ OR ‘BRAIN CIRCULATION’ EVIDENCE FROM OECD''S INTERNATIONAL MIGRATION AND R&D SPILLOVERS

Scottish Journal of Political Economy, Vol 55, No 5, November 2008 r 2008 The Author Journal compilation r 2008 Scottish Economic Society Published by Blackwell Publishing Ltd, 9600 Garsington Road, Oxford, OX4 2DQ, UK and 350 Main St, Malden, MA, 02148, USA ‘BRAIN DRAIN’ OR ‘BRAIN CIRCULATION’: EVIDENCE FROM OECD’S INTERNATIONAL MIGRATION AND R&D SPILLOVERS Thanh Len Abstract This paper empirically investigates whether labour mobility can transfer technology across borders based on the panel cointegration method Estimates of specifications on a cross-section of 19 OECD countries during 1980–1990 lend strong support to this thesis Data indicate that international labour movement may help transfer technology across borders in both directions: from donor countries to host countries and vice versa This suggests that migration may more likely create a ‘brain circulation’ rather than a ‘brain drain’ In addition, human capital has a significant impact on the research and development (R&D) diffusion process as it enhances a country’s capacity to learn from a foreign technology base I Introduction In studying the impact of international migration on economic development, many studies (e.g., Haque and Kim, 1995; Wong and Yip, 1999) argue that international migration negatively affects donor countries through the ‘brain drain’ of high skilled workers.1 This brain drain reduces the growth rate of effective human capital that remains in the economy Consequently, the growth rate of per capita income of those countries is retarded However, there is another research line that suggests a ‘brain gain’ associated with that brain drain: a temporary loss of skilled workers may permanently increase the average level of productivity of the source country This is based on the following reasoning: the possibility of migration of qualified educated people to a higher income country raises the return to education and, hence, increases the human capital formation which may be greater than the University of Queensland, St Lucia, QLD 4072, Australia According to Beine et al (2001), ‘brain drain’ not only means the migration of engineers, physicians, scientists or other very highly skilled professionals but can also be broadly defined as the emigration of a fraction of the population that is relatively highly educated as compared with the average n 618 ‘ B R AI N D R A I N’ O R ‘ B R A I N C I R C U L A T I O N ’ 619 negative effect of a brain drain (e.g., Mountford, 1997; Vidal, 1998; Beine et al., 2001).2 Results in the literature are, therefore, mixed So far, much of the debate is based on the impact of migration on the formation of the stock of human capital As human capital is embodied in people and contains knowledge about new technologies and materials, production methods, or organizational skills, it raises the question of whether the international movement of human capital with embodied technology will give rise to technology diffusion across countries With the existence of bilateral worker flows across economies, foreign workers who acquire R&D-induced technological knowledge through on-the-job training and work experience in their home country may contribute to a productivity increase in the host country In addition, people are often tied to their homeland so by maintaining close and frequent contact with people at home (even visiting home occasionally or regularly), those workers can also contribute knowledge they obtained in the host country to productivity improvement in their home country This suggests a pattern of ‘brain circulation’ rather than a draining of skills from one country to another So far, economic research on this brain circulation issue is limited to a small number of sectoral case studies, notably within the software industry.3 These case studies show that when integrating into the business community, migrants transfer technical and institutional know-how between distant countries much faster and more flexibly than most corporations In addition, migrant participation in the labour force of the host country may reveal information about production techniques and productivity in their country of origin.4 This paper will revisit the issue of brain drain and brain gain from the aspect of knowledge spillovers This is achieved by examining the extent to which international labour migration effectively transmits knowledge across countries International R&D spillovers on total factor productivity (TFP) due to worker flows are tested based on the cointegration method against a cross-country data set of 19 OECD countries for the period 1980–1990 The paper also empirically considers the presence of the complementarity between R&D spillovers and investment in human capital: an increase in the level of human capital improves the technological ‘absorptive capacity’ in an open economy context Empirical findings in this study indicate that worker migration can act as a significant channel for R&D spillovers More importantly, the knowledge spillovers may be bidirectional: from a donor country to a host country and vice versa Other possible gains include the return migration of ex ante low-skilled workers who are now equipped with new skills learned abroad (Stark et al., 1997, 1998) and the migrants’ remittances which help alleviate liquidity constraint when financial markets are imperfect (Stark et al., 1997; Beine et al., 2001) See, for example, Saxenian (2002, 2005) The role of migrant networks in promoting bilateral international trade is also recognized due to the work of Rauch and Trinidade (2002), Rauch and Casella (2003) among others For an analysis of the relationship between migration and foreign direct investment, see for example, Kugler and Rapoport (2007) However, these issues are beyond the scope of this paper r 2008 The Author Journal compilation r 2008 Scottish Economic Society 620 THANH LE The results of this study provide novel contributions to the literature on international R&D spillovers and economic growth Recent empirical studies have focused on identifying potential transmission channels of R&D spillovers The main channel is international trade as stated by Coe and Helpman (1995) and many subsequent papers such as Engelbrecht (1997), Lichtenberg and van Pottelsberghe (1998), Keller (1999, 2002), and Frantzen (2000, 2002) These papers find that in the current world of international trade, domestic productivity of one country can benefit from R&D activities occurring in that country’s trading partners Other identified channels include direct foreign technology transfer (Soete and Patel, 1985), foreign direct investment (e.g., van Pottelsberghe and Lichtenberg, 2001), international student flows (e.g., Park, 2004), or pure proximity in a technological space (e.g., Park, 1995; Guellec and van Pottelsberghe, 2001) This paper, therefore, adds a potentially new conduit of technological diffusion to the literature: the international labour movement The remainder of this paper is structured as follows Section II briefly discusses the theoretical and empirical framework based on which econometric estimates of the impact of foreign R&D embodied in imports and the international labour movement on national productivity growth are performed A brief data description is given in Section III The main empirical findings and their economic interpretation are reported in Section IV Section V concludes the paper with some closing comments and suggestions for further research II Theoretical and Empirical Framework Empirical regressions in this paper are based on some recent theoretical models of R&D-based growth such as those of Romer (1990), Grossman and Helpman (1991), and Aghion and Howitt (1992, 1998) The production function for a final consumption good Y using labour L and capital K as production inputs is assumed to take the following form5: Yit ¼ Fit Kita L1Àa it ; 8i; < a < 1; where i is a country index (i 1,2, ), t is the time index, and F represents the technical efficiency or TFP The specified production function exhibits constant returns to scale to both production factors but diminishing returns to each production factor employed This implies that an index of TFP is defined in the following way: log Fit ¼ log Yit À a log Kit À ð1 À aÞ log Lit : In addition, the growth accounting method indicates that: gY ẳ gF ỵ agK ỵ aịgL ; where gF, gY, gK, and gL are the rate of growth of TFP, final output, capital stock, and labour force, respectively This implies a causal relationship between TFP growth and output growth: TFP growth can be translated into output The derivation of the estimating equation in this paper is based on the work of Keller (1998) For more details, see Keller (1997, 1998) r 2008 The Author Journal compilation r 2008 Scottish Economic Society ‘ B R AI N D R A I N’ O R ‘ B R A I N C I R C U L A T I O N ’ 621 growth This result is important for calculating international output elasticities of domestic R&D capital stocks later in the study TFP is positively related to the number of differentiated intermediate goods used: log Fit ẳ bi ỵ Z log zit ; 8i; where bi is country i’s specific efficiency factor, and zit is the range of intermediate goods used in country i’s production Intermediate goods can be interpreted largely to include not only physical production inputs but also ideas, know-how, and production knowledge With international flows of goods, services, and labour, both domestic, z dit , and foreign intermediate goods, z fit , can be employed for country i’s production.6 As R&D investment leads to the expansion of product varieties, thus by an appropriate choice of unit normalization, z dit is identical to the cumulative stock of R&D expenditure, SDit, and z fit is captured by the foreign knowledge stock variable, SFit This means that TFP in country i may grow either as a result of domestic innovation or international technological spillovers from foreign countries This study employs the Lichtenberg and van Pottelsberghe’s (1998) and van Pottelsberghe and Lichtenberg’s (2001) methods to construct three different R&D capital stocks (measured in level rather than in index) The first one, the imported-embodied foreign R&D capital stock, is constructed as: X mijt SDjt SFitm ¼ yjt j6¼i where mijt is the value of imported goods and services of country i from country j, and yjt is country j ’s GDP at time t This variable is equivalent to the tradeweighted foreign R&D capital stock computed by Coe and Helpman (1995).7 The focus of this study is to investigate the hypothesis that the international labour movement can serve as a channel for international technology diffusion To this end, this paper proposes two new measures of foreign R&D capital stock They are based on the assumption that flows of foreign workers can effectively transfer knowledge across borders The first new R&D capital stock, the foreign R&D capital stock embodied in the inward labour movement, is calculated by the following: X gijt SDjt SFitg ¼ n j6¼i jt In reality, domestically produced intermediate goods and foreign produced intermediate goods can be similar However, in this paper, for simplicity, they are assumed to be two disjointed sets so that both of them can be utilized for a country’s production In Coe and Helpman (1995), the stock of foreign R&D capital is computed as P m zitf ¼ SFit ¼ j6¼i mijtit Á SDjt , where mit is total imports of country i at time t, and measured as an index number (1985 1) However, this has been shown by Lichtenberg and van Pottelsberghe (1998) to lead to a misspecified regression equation In addition, the Coe and Helpman’s method is also challenged by Keller (1998) who claims that regressions using counterfactual (randomly created) international trade patterns produce even more positive R&D spillovers and explain more of the variation in productivity than if actual bilateral trade patterns are used r 2008 The Author Journal compilation r 2008 Scottish Economic Society 622 THANH LE where gijt is the stock of country j’s citizens living in country i and njt is country j’s population at time t The reason why the stock of people is used rather than flows is that the stock is less volatile than flows In addition, people with embodied knowledge continue their learning process by maintaining their communication with people back home As a result, they continue to convey their knowledge to the country of destination for as long as they stay there The second new foreign R&D capital stock is created to test the hypothesis that people living overseas can also be a channel for transferring knowledge back to their home country It is called the outward labour movement foreign R&D capital stock and is computed as follows: SFitk ¼ X kijt j6¼i njt SDjt where kijt is the stock of country i’s citizens living in country j Through the onthe-job learning process in a host country, foreign workers will learn and contribute to the development of knowledge and technology of that country In addition, people tend to be tied to their homeland so if a number of them return home or maintain close and frequent contact with people at home, their obtained knowledge will, to some extent, contribute to productivity improvement in their home country In order to examine the degree of international R&D spillovers on TFP where labour movement is considered as a significant conduit, this paper extends the original Coe and Helpman’s equation to the following:   m l mit git kit Fit ¼ SDit ; SFit ; SFit ; ; ; ; Hit ; yit nit nit where SFitl (l g, k) denotes alternative foreign R&D capital stocks based on stocks of foreigners by country of origin, mit/yit is the ratio of imports of goods and services to GDP, git/nit is the ratio of total foreigners to domestic population; kit/nit is the fraction of population living and working overseas, and Hit is the average number of years of schooling used as a proxy for the country’s stock of human capital The reason for adding human capital to this specification is to investigate the effect of foreign R&D capital stock on productivity when the domestic labour force becomes more educated (the higher ‘absorptive capacity’)8 and the effect of education itself on productivity.9 To this end, the foreign R&D capital stocks are interacted with marginal propensity to import, inward/outward migration intensity, and stock of human capital This is important for checking how the regression results reported in this paper are robust to the inclusion of other variables The ‘absorptive capacity’ is defined by Benhabib and Spiegel (1994) and Bils and Klenow (2000) According to Bils and Klenow (2000), if workers need human capital to use advanced technology then growth in human capital can help to improve technology r 2008 The Author Journal compilation r 2008 Scottish Economic Society ‘ B R AI N D R A I N’ O R ‘ B R A I N C I R C U L A T I O N ’ III 623 Data Description The annual data set on business sector activity for 19 OECD countries during 1980–1990 is taken from Coe and Helpman (1995) This data set includes TFP indices with 1985 (for every country) and domestic R&D capital stocks (see Coe and Helpman (1995) for a detailed description of the data sources) Average years of education of the labour force, by de la Fuente and Domenech (2001) for the period 1960–1990, are linearly interpolated from 5-yearly data and used as a proxy for human capital While GDP and population for each country come from the OECD National Account Database, bilateral import flows are from the OECD Trade Database National stocks of foreign population by country of origin are sourced from a number of databases including the OECD International Migration Database, the International Labour Organization’s International Labour Migration Database, the Global Data Centre’s Database, the Council of Europe’s Database, as well as from national statistics offices’ databases of the 19 countries There are no complete time series of stocks of foreign population by country of origin for every country during the period 1980–1990 so data for this study are combined from different sources.10 Missing values are estimated using a linear interpolation method Matrices of inward and outward migration shares are computed for every country for each year over the period 1980–1990 These weighting matrices are then used to calculate alternative foreign R&D capital stocks as described above in the text IV Empirical Findings The goal of this study is to estimate the long-run relationship between TFP and the domestic and foreign R&D capital stocks when foreign labour movement is considered as a channel for technological transmission The main econometric technique employed in this paper is a pooled cointegrating method in which the relationship between dependent variable and explanatory variables is estimated in log level terms This method has an attractive econometric property It allows us to test for international R&D spillovers in a panel of countries where every single country has a relatively small number of time-series observations As discussed in Coe and Helpman (1995) and applied in many other TFP research studies, when estimating clearly trended variables in level, the estimated equations should reflect cointegration This means that there exists a long-run relationship between trended variables in these equations A stationary error term is a criterion for judging if an equation is cointegrating If the error term is not stationary, the regression may be spurious A common way to avoid the spurious regression problem is to estimate change specifications, rather than level specifications, by differencing data before running any regressions However, differencing has a disadvantage of removing all relevant information 10 There are some discrepancies in the way foreign population is counted in OECD countries Countries like Australia, Canada, and the USA calculate foreign population based on people who are foreign-born while other countries focus on those with foreign citizenship r 2008 The Author Journal compilation r 2008 Scottish Economic Society 624 THANH LE Table Group mean panel unit root tests (annual data 1980–1990 for 19 countries – Im et al., 2003) Variable tN;T a pb log F log SD log SF log SF m log SF g log SF k m m y log SF g g log SF n À 2.566 À 2.165 À 0.207 À 2.640 À 2.301 À 1.678 À 1.528 0.418 1.421 2.053 2.105 2.316 1.842 1.000 1.053 2.368 k k n log SF log H log H log SF g log H log SF k À 0.600 À 2.852 À 2.011 À 1.605 1.053 0.895 1.421 1.474 Adjusted meanc Adjusted varianced Group mean statistice Decisionf À 2.046 À 1.784 À 1.318 À 1.997 À 2.025 À 2.075 À 2.062 À 1.355 1.708 1.979 1.457 2.153 1.947 1.483 1.492 1.591 À 1.768 À 1.179 4.011 À 1.980 À 0.860 1.423 1.903 6.129 I(1) I(1) I(1) I(0) I(1) I(1) I(1) I(1) 1.292 1.409 1.723 1.696 3.251 À 2.753 0.176 1.446 I(1) I(0) I(1) I(1) À 1.448 À 2.102 À 2.064 À 2.037 Notes: log X is logarithm of X F is total factor productivity, SD is domestic R&D capital stock; SF is unweighted foreign R&D capital stock; SFm is foreign R&D capital stock embodied in imports; SFg is foreign R&D capital stock embodied in inward foreign population; SFk is foreign R&D capital stock embodied in outward foreign population; m/y is the ratio of imports of goods and services to GDP; g/n is the ratio of total foreigners to domestic population; k/n is the fraction of population working and living overseas; and H is the average number of years of education a Cross-sectional average of individual Dickey-Fuller tN;T statistics b Cross-sectional average of individual number of lagged differenced terms in ADF(pi) regression c Cross-sectional average of Eẵti;T pi ; yi ị d Cross-sectional average of Varẵti;T pi ; yi ị e The test statistic Wt which has standard normal distribution f Test of the null hypothesis of common unit autoregressive root at 5% level (the critical value is À 1.96) about common trends shared by level variables and only rendering information on the short-run relationship This paper will first test whether the data series are nonstationary by performing unit root tests The test results based on Im et al (2003) for log levels of TFP, domestic R&D capital stocks, different specifications of foreign R&D capital stocks, and their interaction terms with import ratio, inward/outward migration intensity, and human capital are given in Table Im et al.’s group mean panel unit root test allows each member of the cross-section to have a different autoregressive root and different autocorrelation structures under the alternative hypothesis Its test statistic possesses an asymptotic normal distribution in small-sized panels A brief discussion of the test is provided in Appendix A The study is then extended to different tests for cointegration based on those of Pedroni (1999) Pedroni’s panel ADF test allows for considerable heterogeneity in the panel The test statistics have standard normal distribution where significantly negative statistics indicate rejection of the null hypothesis of no cointegration (see Appendix B for a short discussion about the mechanics of this test).11 The regression results are represented in Table 11 It is true that when performed separately on the time series for each country, given that each country has only 11 annual observations, the power of the tests is really low The panel r 2008 The Author Journal compilation r 2008 Scottish Economic Society (1) log SD G7Álog SD log SFg (inward) 0.096n (0.022) 0.128n (0.028) 0.045n (0.016) log SFk (outward) (2) 0.067n (0.021) 0.079n (0.026) (3) 0.004 (0.019) 0.079n (0.022) (4) 0.102n (0.018) 0.137n (0.030) (5) 0.121n (0.013) 0.141n (0.024) (6) 0.137n (0.011) 0.125n (0.024) (7) 0.107n (0.022) 0.127n (0.028) 0.073n (0.021) 0.064n (0.028) 0.193n (0.029) log SF (unweighted) (9) 0.054n (0.023) 0.081n (0.025) 0.024 (0.015) 0.173n (0.026) (10) 0.073n (0.021) 0.132n (0.028) 0.036n (0.013) (11) 0.087n (0.023) 0.135n (0.027) 0.160n (0.068) (12) (13) (14) (15) 0.058n (0.020) 0.094n (0.026) 0.063n (0.021) 0.132n (0.035) 0.079n (0.021) 0.131n (0.028) 0.063n (0.020) 0.084n (0.027) 0.147n (0.026) 0.705n (0.214) 0.294n (0.042) 0.237n (0.040) 0.241n (0.030) (m/y)Álog SFm (import) 0.305n (0.046) (g/n)Álog SFg 0.286n (0.041) 0.229n (0.040) 0.302n (0.135) (k/n)Álog SFk 0.678n (0.270) log HÁlog SFg À 0.051 (0.027) 0.016n (0.007) log HÁlog SFk R2 Adjusted R2 Cointegration tests Panel ADF statistica Decisionb (8) 0.696 0.662 À 4.663 CI 0.742 0.713 0.755 0.727 0.739 0.710 0.679 0.643 0.682 0.646 0.688 0.653 À 4.925 À 0.413 À 6.059 À 0.136 À 0.094 À 4.318 CI Retain null CI Retain null Retain null CI 0.079n (0.013) 0.734 0.704 À 4.788 CI 0.013n (0.006) 0.748 0.718 0.755 0.726 0.707 0.672 0.750 0.672 À 3.066 CI À 4.191 CI À 3.414 CI À 3.802 CI À 0.224n (0.096) 0.750 0.721 À 2.755 CI 0.751 0.722 À 4.049 CI 0.059n (0.012) 0.771 0.744 À 3.995 CI 625 Notes: The dependent variable is log F (log of total factor productivity, indexed as 1985 1) All equations include unreported country-specific constants Time dummies are omitted due to implausible results obtained White heteroskedasticity-consistent standard errors are given in parentheses SD is domestic R&D capital stock; SF m is foreign R&D capital stock embodied in imports; SF g is foreign R&D capital stock embodied in inward foreign population; SF k is foreign R&D capital stock embodied in outward foreign population; m/y is the ratio of imports of goods and services to GDP; g/n is the ratio of total foreigners to domestic population; k/n is the fraction of population living overseas; H is the average number of years of education; G7 is dummy variable equal to for the seven major countries and equal to for the other twelve countries n indicates that parameters are statistically significant at the 5% probability level a Pedroni (1999)’s Panel ADF statistic allows dynamics and cointegrating vector to vary across individuals b Test of the null hypothesis of no cointegration at 5% significant level (the critical value is À 1.96) ‘ B R AI N D R A I N’ O R ‘ B R A I N C I R C U L A T I O N ’ r 2008 The Author Journal compilation r 2008 Scottish Economic Society Table Total factor productivity estimation results (pooled data 1980–1990 for 19 countries, 209 observations – in level) 626 THANH LE As shown in Table 1, a unit root test on the pooled data indicates that most of the variables are nonstationary The null hypothesis that the panel has a unit root can only be rejected for log SFm and log H According to Edmond (2001), regressions using those variables not fulfil a necessary condition for cointegration and should be treated with some doubt.12 Table describes all pooled least squares regressions following by standardized test statistics of Pedroni’s panel cointegration tests at the bottom This paper concentrates first on equations that relate the log of TFP to the logs of domestic and alternative foreign R&D capital stocks and then extends the equations to consider the log of human capital All of the equations include unreported country-specific constants to allow for missing country-specific fixed factors such as the influence of institutional variables In every equation, the impact of domestic R&D is allowed to differ between the seven largest countries and the other 12 countries by including an interaction term between domestic R&D capital stock and a dummy variable, G7, which takes the value for the seven largest economies Except for equations (3), (5), and (6), all the other 12 models are confirmed to be cointegrating by cointegration tests In each cointegrating regression, the estimated elasticity of the domestic R&D capital stock is positive and significant.13 The test results reveal that there is a significantly different effect of domestic R&D capital stocks for the G7.14 As shown in Table 2, regressions (1)–(8) show the estimated productivity elasticities of domestic R&D and each of the foreign R&D capital stock (or its corresponding interaction term with import intensity, fraction of population working overseas, fraction of foreign population, or human capital) incorporated into one of the three different channels of technological diffusion With regard to the impact of outside R&D embodied in the movement of workers across borders, equations (1) and (2) show that there may be significant international R&D spillovers and the migration of workers may induce unit root test provided by Im et al (2003) and the panel cointegration test provided by Pedroni (1999) help overcome this disadvantage by deriving the limiting distribution The power of these tests increases dramatically as the cross-sectional dimension rises For example, in Pedroni’s panel ADF tests, as long as the time dimension is greater than five, the test statistic is shown to be distributed as standard normal and the small sample performance of the test is reasonably satisfactory 12 There may not be any shared trends among variables when I(0) variables are included in equations with existing I(1) variables In addition, Pedroni (1999) does not specify cointegration tests for this type of regression equation For these reasons, this paper opts not to consider estimation equations including those I(0) variables 13 According to Coe and Helpman (1995), OLS estimates of a cointegrating equation are ‘super consistent’ because they converge to true parameter values much faster than the case where variables are stationary when the number of observations increases and their distribution does not necessarily follow standard t-distribution Because the specific distribution associated with those estimates is unknown, this study follows a large number of research works in the existing literature (e.g., van Pottelsberghe and Lichtenberg, 2001; Park, 2004) using the standard t-distribution to draw inference about their significance as a limiting case 14 In fact, this paper also tries to include a time trend in every regression equation However, the results are not supportive because the coefficients of most variables, including that of domestic R&D capital stock, are negative (incompatible with reality) Therefore, the empirical estimation is carried out without any time trend r 2008 The Author Journal compilation r 2008 Scottish Economic Society ‘ B R AI N D R A I N’ O R ‘ B R A I N C I R C U L A T I O N ’ 627 substantial technology transfers In equation (1), the elasticity of the foreign R&D capital stock embodied in inward labour movement is positive and highly significant That is, the hypothesis of knowledge gain from attracting highly skilled workers can be confirmed by the estimates By allowing foreign workers to immigrate, host countries seem to be able to enhance their stock of knowledge, thereby increasing their productivity In equation (2), a positive and significant estimate for the elasticity of foreign R&D capital stock embodied in outward labour movement is obtained This is evidence that people working overseas can make contributions to the enhancement of productivity back home through knowledge transfer Contrary to the conventional assumption of ‘brain drain’ associated with emigration, this result is consistent with the emerging theory of ‘brain gain’ within that ‘brain drain’ literature such as that reported by Mountford (1997), Vidal (1998), and Beine et al (2001) In equation (3), the unweighted foreign R&D capital stock is included The main purpose of this regression is to check whether unweighted foreign R&D capital stocks make any difference in explaining the variation in productivity across countries compared with the migration-weighted patterns.15 It can be seen that the inclusion of this variable makes domestic R&D capital stock insignificant, which is economically implausible In addition, the cointegration test cannot reject the null hypothesis of no cointegration which implies a potential spurious regression in the equation Together, these results refute the explanatory power of the unweighted measure of foreign R&D and support the measures which combine both migration and R&D in affecting TFP growth as found in equations (1) and (2) Equation (4) is Coe and Helpman’s preferred model for this study’s data sample where import-weighted foreign R&D capital stocks are expressed in levels and calculated using Lichtenberg and van Pottelsberghe’s (1998) method As discussed in their paper, the equation is suggestive of the role of trade in the international transmission of R&D benefits The estimated coefficient for the interaction between import ratio and import-weighted R&D capital stock is slightly higher than the original one This is probably because this study uses time-varying import ratios while those of Coe and Helpman are static.16 Equations (1) and (2) are modified to become equations (5) and (6) Although each foreign knowledge stock in these equations consists of migration-weighted foreign R&D capital stocks, these weights may not perfectly capture the level of migration, either inward or outward It might be expected that when two countries have the same composition of migration and face the same composition of R&D capital stocks among economic partners, the country that has more inward and outward migration relative to its population may benefit more from foreign R&D.17 For these reasons, equations (5) and (6) 15 The author is grateful to an anonymous referee for this useful comment To achieve sustainable development, it is necessary that the import-GDP ratios are not high An investigation into the data used in this paper reveals that these ratios are actually mean reverting (the highest value is 0.578 by Belgium in 1984) 17 I would like to thank the same referee for this interesting comment 16 r 2008 The Author Journal compilation r 2008 Scottish Economic Society 628 THANH LE are modified versions of equations (1) and (2) and account for the interaction between each type of migration-weighted foreign R&D capital stock and its corresponding intensity, that is, the fraction of foreign population g/n (for the inward case) or the fraction of population working overseas k/n (for the outward case) It follows that the elasticity of TFP with respect to these foreign R&D capital stocks varies across countries in proportion to each type of migration intensity Although the coefficients of these foreign R&D capital stocks are positive and significant, there is no cointegrating relationship found in the regressions Similar to equation (3), the results of regression for equations (5) and (6) should be disregarded This means that there is no concrete evidence suggesting the impact of the level of migration alone on TFP growth This is an interesting finding given that the combination of migration and R&D strongly drives TFP growth as found in equations (1) and (2) Regressions (7) and (8) each incorporate an interaction term between human capital and the migration R&D capital stocks, the inward and outward migration, respectively It is found that both coefficients of those variables are positive and significantly different from zero One possible explanation is the aspect of absorptive capacity: better education leads to a quicker learning process, and, hence, a higher technological base Regarding equations (9) to (15), all regressions exhibit a cointegrating relationship In equation (9), the inclusion of both migration-weighted R&D capital stocks makes the coefficient of inward stock statistically insignificant; meanwhile the F-test (F-statistic 23.9) rejects the null hypothesis of joint insignificance of those two variables This problem can be explained by the strong correlation between inward and outward R&D capital stocks (the average correlation coefficient is 0.943) While equation (10) incorporates equation (1) into equation (4), equation (12) is a combined version of equations (2) and (4) They each include foreign R&D capital stock embodied in inward/ outward labour movement into a regression with foreign R&D capital stock embodied in trade interacted with import ratio, simultaneously This work greatly improves the goodness-of-fit of those models as shown by the increases in their adjusted R2 The coefficients associated with those terms are both positive and significant while their magnitudes are not very much affected, reinforcing the robustness of the obtained results The estimated elasticity of domestic R&D capital stocks in regressions (10) and (12) are smaller than in equations (1), (2), and (4) This implies that the mistake of not including appropriate effective channels for international R&D spillovers in the regression leads to an upward bias for the estimate of domestic R&D capital stocks Equations (11) and (13) add interaction terms between corresponding migration-weighted foreign R&D capital stock and human capital into equations (1) and (2) Their coefficients are found to be negative (not the expected sign) and insignificant (in equation (11)) Results in equations (14) and (15) indicate that the coefficients of trade-weighted foreign R&D capital stock interacted with import ratio and migration-weighted foreign R&D capital stocks interacted with human capital are positive (the expected sign) and statistically significant Moreover, the statistical fits of the r 2008 The Author Journal compilation r 2008 Scottish Economic Society ‘ B R AI N D R A I N’ O R ‘ B R A I N C I R C U L A T I O N ’ 629 regressions are improved The coefficients of domestic R&D variables are reduced somewhat but this does not affect their statistical significance Although not reported here, appropriate Wald’s tests on the intercepts of all regressions not reject the hypothesis of the existence of country-specific factors against the alternative of a common intercept This conforms to this paper’s earlier assumption about the varying impact of other institutional variables in each country Except for equations (3), (5), and (6) which are not cointegrating and therefore their results are disregarded, all regressions have quite substantial fits In terms of comparisons across models of the same dependent variable, adjusted R2 is an appropriate criterion Equation (15) is, hence, the most preferable due to its highest value of adjusted R2 The coefficient estimates of all models indicate that TFP elasticity with respect to a country’s R&D capital stock is between 0.135 and 0.239 for the seven major countries and in the range 0.054–0.107 for the remaining 12 countries These results are comparable to those obtained in the existing literature such as in Coe and Helpman (1995), Engelbrecht (1997),and Edmond (2001) In the case of migration-embodied spillovers, TFP (and output) elasticity of country i with respect to country j’s domestic R&D capital stock can be calculated as follows: @ log yi @ log yi @ log SFil @ log SFil @SFil SDj ¼ bfl ¼ bfl ¼ ; l @ log SDj @SDj SFil @ log SDj @ log SFi @ log SDj P l @SF l l where l g, k By construction, SFil ¼ j6¼i nijj SDj so @SDij ¼ nijj : Inserting the results into the above equation gives: eflij ¼ eflij ¼ bfl lij SDj : nj SFil This implies that country i’s output elasticity with respect to domestic R&D capital stock of country j is increasing in the number of workers circulating between the two countries and in the level of R&D investment of country j Bilateral elasticities for two alternative channels of R&D spillovers – inward and outward migration – for the period 1980–1990 using the value of migrant stock at the end of the period are computed and represented in Tables and The figures in Tables and indicate, for example, that a 1% increase in the United States R&D capital stock raises Australian output by 0.00220% through an inward flow of workers and by 0.09750% through an outward flow of Australian people to the United States On the other hand, a 1% increase in the Australian R&D capital stock raises US output by 0.00016% through an inward flow of workers and by 0.00433% through an outward flow of US citizens to Australia Technology transfers are clearly stronger from the US to Australia than vice versa no matter what source of diffusion is considered In other words, the US economy benefits less from Australia than the Australian economy benefits from the US Interestingly, for most countries, the impact of other countries’ domestic R&D capital stock is greater through outward worker flows than through r 2008 The Author Journal compilation r 2008 Scottish Economic Society 630 AUS AUS AUT BEL CAN DNK FIN FRA DEU GRC ITA JPN NLD NOR PRT ESP SWE CHE GBR USA 0.00022 0.00009 0.00031 0.00011 0.00012 0.00036 0.00284 0.00008 0.00243 0.00063 0.00215 0.00004 0.00001 0.00004 0.00017 0.00052 0.03268 0.00220 AUT BEL 0.00007 0.00001 0.00006 0.00017 0.00021 0.00011 0.00009 0.00016 0.00012 0.00004 0.00093 0.01157 0.02731 0.00381 0.00001 0.00007 0.00152 0.01221 0.00065 0.00040 0.00110 0.00784 0.00009 0.00007 0.00000 0.00007 0.00004 0.00072 0.00075 0.00042 0.00482 0.00069 0.00191 0.00374 0.00518 0.00302 CAN DNK FIN FRA 0.00008 0.00011 0.00019 0.00005 0.00025 0.00018 0.00017 0.00014 0.00038 0.00020 0.00013 0.00430 0.00041 0.00055 0.00038 0.00024 0.00066 0.00019 0.00020 0.00076 0.00009 0.00123 0.00153 0.00086 0.00421 0.00702 0.00461 0.00574 0.00004 0.00001 0.00001 0.00001 0.00303 0.00061 0.00043 0.00998 0.00048 0.00058 0.00067 0.00116 0.00265 0.00149 0.00089 0.00172 0.00013 0.00536 0.00098 0.00014 0.00012 0.00001 0.00001 0.00220 0.00003 0.00007 0.00008 0.00232 0.00022 0.00772 0.02022 0.00060 0.00137 0.00184 0.00169 0.00486 0.01960 0.00998 0.00470 0.00656 0.01076 0.00713 0.00817 0.00459 DEU GRC ITA JPN NLD NOR PRT ESP SWE CHE GBR USA Average 0.00006 0.00371 0.00069 0.00021 0.00033 0.00027 0.00398 0.00101 0.00029 0.00081 0.00160 0.00018 0.00016 0.00204 0.00568 0.00010 0.00031 0.00050 0.00023 0.00011 0.00007 0.00372 0.00707 0.00008 0.00044 0.00005 0.00013 0.00114 0.00010 0.00010 0.00129 0.00164 0.00000 0.00016 0.00012 0.00031 0.00456 0.00033 0.00020 0.00011 0.00218 0.01201 0.00003 0.00170 0.00115 0.00008 0.00019 0.00027 0.00036 0.00534 0.00101 0.00106 0.00284 0.00001 0.00023 0.00034 0.00149 0.00009 0.00013 0.00087 0.00123 0.00024 0.00012 0.00342 0.00570 0.00000 0.00051 0.00042 0.00189 0.000191 0.00005 0.00024 0.00140 0.00015 0.00052 0.00046 0.00425 0.00749 0.00000 0.00096 0.00042 0.00247 0.00052 0.00017 0.00008 0.00032 0.00008 0.00016 0.00396 0.01772 0.00077 0.00385 0.00004 0.00044 0.00042 0.00068 0.00713 0.00001 0.00009 0.00004 0.00139 0.00050 0.00016 0.00014 0.00011 0.00557 0.01034 0.00002 0.01739 0.00025 0.00129 0.00010 0.00034 0.00145 0.00064 0.00115 0.00025 0.00062 0.00188 0.00056 0.00016 0.00370 0.00445 0.00004 0.00301 0.00227 0.00229 0.00055 0.00007 0.00027 0.00111 0.00179 0.00016 0.00055 0.00039 0.00609 0.00026 0.00018 0.00172 0.01141 0.00006 0.00345 0.00442 0.00135 0.00043 0.00011 0.00012 0.00098 0.00130 0.01203 0.00021 0.00046 0.00085 0.00082 0.00070 0.00115 0.00264 0.00674 0.00005 0.00367 0.00085 0.00192 0.00100 0.00018 0.00040 0.00246 0.00205 0.00902 0.00985 0.00037 0.01065 0.00100 0.00511 0.00016 0.00014 0.00071 0.00063 0.00335 0.00523 0.00841 0.00103 0.00024 0.00104 0.00273 0.00000 0.00006 0.00082 0.00122 0.00859 0.01750 0.00067 0.00079 0.00006 0.00001 0.00013 0.00029 0.00700 0.00528 0.01860 0.00030 0.00010 0.00000 0.00004 0.00030 0.00092 0.00544 0.03286 0.00025 0.00007 0.00047 0.00055 0.00106 0.01241 0.00751 0.00001 0.00008 0.00883 0.00102 0.00976 0.01209 0.00088 0.00085 0.00147 0.01171 0.01529 0.00161 0.00283 0.00129 0.01561 0.00351 0.00266 0.00586 0.00445 0.00262 0.02085 Notes: All calculations are based on equation (1) in Table 2: log Fit ẳ 0:096 log SDit ỵ 0:128G7 log SDit ỵ 0:045 log SFitg Estimated elasticity of output is calculated (using end period migrant stock values) for the column country with respect to the R&D capital stock in the row country THANH LE r 2008 The Author Journal compilation r 2008 Scottish Economic Society Table International output elasticities of domestic R&D capital stocks (inward migration), 1980–1990 AUS AUS AUT BEL CAN DNK FIN FRA DEU GRC ITA JPN NLD NOR PRT ESP SWE CHE GBR USA 0.00029 0.00038 0.00943 0.00032 0.00016 0.00200 0.00635 0.00017 0.00113 0.00466 0.00201 0.00037 0.00001 0.00011 0.00160 0.00339 0.06311 0.09750 AUT BEL 0.00232 0.00114 0.00020 0.00033 0.00602 0.01176 0.00011 0.00015 0.00003 0.00003 0.00130 0.05083 0.07910 0.01872 0.00001 0.00004 0.00074 0.00152 0.00013 0.00038 0.00111 0.02075 0.00020 0.00036 0.00000 0.00003 0.00011 0.00083 0.00138 0.00042 0.02569 0.01159 0.00304 0.00939 0.07138 0.06486 CAN DNK FIN FRA 0.00073 0.00269 0.00199 0.00123 0.00004 0.00019 0.00018 0.00028 0.00015 0.00217 0.00038 0.02209 0.01343 0.00800 0.00992 0.00006 0.00077 0.00031 0.00002 0.00029 0.00005 0.00083 0.00396 0.00131 0.00105 0.01595 0.00946 0.02798 0.00001 0.00002 0.00001 0.00002 0.00013 0.00062 0.00026 0.00297 0.00061 0.00051 0.00037 0.00099 0.00027 0.00165 0.00067 0.00258 0.00009 0.01286 0.00174 0.00037 0.00001 0.00002 0.00006 0.00003 0.00002 0.00055 0.00035 0.00065 0.00016 0.03862 0.12309 0.00108 0.00069 0.00582 0.00321 0.03392 0.00519 0.01531 0.00318 0.01464 0.18295 0.07835 0.03804 0.07387 DEU GRC ITA JPN NLD NOR PRT ESP SWE CHE GBR USA Average 0.00277 0.00232 0.00206 0.00967 0.00041 0.00008 0.00503 0.00785 0.00009 0.00357 0.01026 0.00006 0.00002 0.00126 0.07670 0.00373 0.00020 0.01042 0.01096 0.00006 0.00001 0.01376 0.03348 0.00001 0.00190 0.00017 0.00067 0.00338 0.00010 0.00004 0.00312 0.00610 0.00000 0.00046 0.00871 0.00039 0.01765 0.02525 0.00036 0.00006 0.00624 0.04236 0.00001 0.00075 0.00027 0.00076 0.00015 0.00070 0.00559 0.00595 0.00033 0.00225 0.00622 0.00018 0.00024 0.00043 0.00158 0.00059 0.00001 0.00165 0.01129 0.00002 0.00000 0.08183 0.01199 0.00000 0.00012 0.00004 0.00102 0.00005 0.00085 0.00007 0.00884 0.00142 0.00010 0.00003 0.04604 0.03215 0.00000 0.00066 0.00019 0.00359 0.00017 0.00006 0.00170 0.00065 0.00228 0.00510 0.00454 0.00360 0.00532 0.01267 0.00003 0.00066 0.00066 0.00186 0.00876 0.00002 0.00071 0.00269 0.00218 0.00198 0.01673 0.00057 0.00016 0.02268 0.03561 0.00002 0.00845 0.00107 0.00190 0.00053 0.00002 0.00065 0.00273 0.02389 0.00012 0.00152 0.03379 0.00044 0.00006 0.00430 0.00784 0.00002 0.00090 0.00089 0.00314 0.00072 0.00002 0.00051 0.00105 0.00345 0.00433 0.00089 0.00330 0.04997 0.00084 0.00029 0.00812 0.03394 0.00012 0.00852 0.01445 0.00511 0.00240 0.00009 0.00052 0.00359 0.00916 0.04735 0.00370 0.00040 0.00420 0.01270 0.00080 0.00030 0.01370 0.02410 0.00000 0.00170 0.00140 0.00290 0.00160 0.00000 0.00040 0.01230 0.01440 0.01260 0.08590 0.00002 0.00160 0.00036 0.00405 0.00028 0.00002 0.00033 0.00154 0.01791 0.00500 0.13957 0.00186 0.00000 0.00103 0.00007 0.00000 0.00001 0.00177 0.00410 0.00421 0.08012 0.00005 0.00090 0.00004 0.00000 0.00007 0.00028 0.04740 0.00533 0.06633 0.00119 0.00010 0.00000 0.00006 0.00051 0.00135 0.00783 0.16602 0.00061 0.00002 0.00045 0.00112 0.00930 0.01067 0.06878 0.00001 0.00043 0.05408 0.00328 0.01168 0.09914 0.00032 0.00024 0.02480 0.00345 0.05555 0.00078 0.05691 0.01125 0.00695 0.01258 0.01072 0.03419 0.12062 0.08431 0.11033 Notes: All calculations are based on equation (2) in Table 2: log Fit ẳ 0:067 log SDit ỵ 0:079G7 log SDit ỵ 0:193 log SFitk Estimated elasticity of output is calculated (using end period migrant stock values) for the column country with respect to the R&D capital stock in the row country ‘ B R AI N D R A I N’ O R ‘ B R A I N C I R C U L A T I O N ’ r 2008 The Author Journal compilation r 2008 Scottish Economic Society Table International output elasticities of domestic R&D capital stocks (outward migration), 1980–1990 631 632 THANH LE inward worker flows Foreign R&D contributes to productive growth more in small countries than in large countries This study also illustrates the mean international impact of each country’s R&D capital stock in the last column of Table and the last row of Table The US R&D capital stock is shown to be the most influential in R&D spillovers as the average foreign output elasticity with respect to the US R&D is 0.00985% through inflows of US workers and 0.08590% through worker outflows to the US Germany, the UK, France, and Switzerland also affect other countries’ output greatly through their investment in R&D but to a smaller degree than the US In contrast, the potential technology embodied in labour movement in Austria, Greece, Portugal, and Spain only has a marginal contribution to other countries’ productivity V Concluding Remarks The new theory of economic growth puts emphasis on the importance of inventive activities for long-run growth This theory also underlines international economic relations such as international trade (flows of goods and services) and international migration (flows of labour) as effective channels of knowledge transmission to characterize the economic interdependence among countries It additionally stresses the complementarity between domestic R&D activities and human capital investment as the latter improves the quality of the labour force and enhance its capacity to absorb new knowledge and work with more advanced foreign technologies This paper examined the significance of domestic R&D investment, international R&D spillovers, and human capital accumulation for TFP based on cross-country analysis of 19 OECD countries over the period 1980–1990 Panel estimates of cointegrating equations in level terms show that both R&D and human capital have a significant impact on productivity as suggested by the theory While the beneficial effect on TFP from domestic R&D capital stocks, trade-weighted foreign R&D capital stocks, and human capital have been established in the earlier empirical literature, the strong evidence that worker migration plays an important role as an effective conduit of technological transmission is new Foreign R&D has a stronger effect when a country is more open to both trade and migration Contrary to frequent conjectures, outward worker flows may contribute to the improvement of the technological base of donor countries This suggests that migration among OECD countries should be considered as ‘brain circulation’ rather than ‘brain drain’ as it is often thought of This study also found that small countries benefit more from foreign R&D than large countries and the mistake of not taking enough international R&D spillovers into account may lead to an upward bias for the estimate of output elasticity of the domestic R&D capital stock The US contributes largely to the development of the world through its extensive investment in research The results obtained in this paper are quite encouraging They indicate explicit evidence of significant interaction between the level of human capital r 2008 The Author Journal compilation r 2008 Scottish Economic Society ‘ B R AI N D R A I N’ O R ‘ B R A I N C I R C U L A T I O N ’ 633 and the level of foreign R&D This strengthens the theory of the significant impact of human capital in the R&D spillover process The findings from this paper give a new look to education and migration policies as better education and a more open migration policy may facilitate technological diffusion The cross-country study in this paper is a simple investigation of international knowledge spillovers across borders as well as the significance of human capital to technological advance It has been suggested that there is possible feedback from TFP and research process to human capital To characterize this, a simultaneous equation approach may be required This suggests a rich research agenda in the future Appendix A Im et al.’s (2003) unit root test in a heterogenous panel with serially correlated errors This is a standardized t-bar test statistic based on the (augmented) Dickey– Fuller statistics averaged across the groups Consider a panel of data of N crosssections observed over T time periods Suppose that variable yit is generated according to a finite-order AR(pi11) process which can be equivalently expressed as the following ADF(pi) regression: Dyit ¼ ri yitÀ1 þ pi X j¼1 yij DyitÀj þ þ eit with t ¼ 1; ; T for each i N: The lag truncation order for each individual, pi, is determined by the data to eliminate autocorrelation from eit The null hypothesis of unit roots is H0: ri 0, 8i against the alternative H1: rIo0 From the regression, we obtain the following statistic: tNT ¼ N 1X tiT pi ; yi ị; N iẳ1 where tiT ðpi ; yi Þ is the individual t-statistic for testing ri 0, 8i As soon as T ! 1, followed by N ! while N T ! k (a finite non-negative constant), the standardized t-bar statistic is   N pffiffiffiffi P  N tNT À N E tiT pi ; 0ịjri ẳ 0ị iẳ1 s Wt ẳ ! N0; 1ị N P VartiT pi ; 0ịjri ẳ 0ị N iẳ1 where E tiT pi ; 0ịjri ẳ 0ị and VartiT pi ; 0ịjri ẳ 0ị are tabulated in their paper r 2008 The Author Journal compilation r 2008 Scottish Economic Society 634 THANH LE Appendix B Pedroni’s (1999) cointegration tests in a heterogenous panel with multiple regressors (1) Estimate the appropriate level regression and collect the residuals eK it : yit ¼ ỵ b1i x1it ỵ b2i x2it ỵ ỵ bMi xMit ỵ eit (2) Difference the original series and estimate the differenced regression: Dyit ¼ b1i Dx1it þ b2i Dx2it þ Á Á Á þ bMi DxMit þ Zit (3) Calculate LK 211i as the long-run variance of ZK it using an appropriate Kernel estimator, such as the Newey-West estimator (4) Using eK it , estimate the appropriate autocorrelation (for parametric statistics): D eK it ¼ gi eK it1 ỵ Ki X kẳ1 gik D eK itk þ uit The null hypothesis of the test is H0: gi 0, 8i against the alternative H1: gio0 From this regression, we compute simple variance of uK it , denoted sK 2i (5) Calculate Panel t-statistic: Zt;NT ¼ S~NT where S~NT ¼N i¼1 LK À2 11i i¼1 t¼1 Â PN N X T X N X T X eK 2itÀ1 !À1=2 LK À2 11i eK itÀ1 D eK it i¼1 t¼1 sK 2i It is shown that:   pffiffiffiffi Zt;NT À Y2 ½Y1 ỵ Y3 ị1=2 N ! N 0; f03ị C3ị fð3Þ In this notation Yj, j 1,2,3 are elements of the mean vector Y of Brownian motion functions; f0ð3Þ ẳ Y1 ỵ Y3 ịị1=2 ; 3=2 1=2 1=2 1 ; Y2 Y1 ỵ Y3 ÞÀ3=2 Þ; and C(3) is the À Y2 Y1 ỵ Y3 ị upper sub-matrix of the Brownian motion covariance matrix C Compute the panel cointegration test statistic pffiffiffiffi wN;T À m N pffiffiffi ! Nð0; 1Þ v where wN,T is the appropriate standardized form, m and n are mean and variance adjustment terms respectively and are tabulated in Pedroni’s paper r 2008 The Author Journal compilation r 2008 Scottish Economic Society ‘ B R AI N D R A I N’ O R ‘ B R A I N C I R C U L A T I O N ’ 635 Acknowlegements The first draft of this paper was written when I was a PhD student at the Australian National University I am grateful to Steve Dowrick for his guidance and advice I also would like to thank Aki Asano, Heather Anderson, Rod Tyers, Tim Hatton, two anonymous referees, an editor, and all participants at the following seminars: the Australian National University, the Panel Data Conference at Cambridge University, July 2006, and the Australian Conference of Economists, Hobart, September 2007, for their valuable comments and discussion on earlier versions of this paper Any remaining errors are my own responsibility References AGHION, P and HOWITT, P (1992) A model of growth through creative destruction Econometrica, 60, 2, pp 323–51 AGHION, P and HOWITT, P (1998) Endogenous Growth Theory Cambridge, MA: MIT Press BEINE, M., DOCQUIER, F and RAPOPORT, H (2001) Brain drain and economic growth: theory and evidence Journal of Development Economics, 64, pp 275–89 BENHABIB, J and SPIEGEL, M (1994) The role of human capital in economic development: evidence from aggregate cross-country data Journal of Monetary Economics, 34, pp 143–73 BILS, M and KLENOW, P (2000) Does schooling cause growth The American Economic Review, 90, 5, pp 1160–79 COE, D and HELPMAN, E (1995) International R&D spillovers European Economic Review, 39, pp 859–87 DE LA FUENTE, A and DOMENECH, R (2001) Educational attainment in the OECD, 1960– 1995 Mimeograph, pp 1–24 EDMOND, C (2001) Some panel cointegration models of international R&D spillovers Journal of Macroeconomics, 23, 1, pp 241–60 ENGELBRECHT, H (1997) International R&D spillovers, human capital and productivity in OECD economies: an empirical investigation European Economic Review, 41, pp 1479–88 FRANTZEN, D (2000) R&D, human capital and international technology spillovers: a crosscountry analysis Scandinavian Journal of Economics, 102, 1, pp 57–75 FRANTZEN, D (2002) Intersectoral and international R&D knowledge spillovers and total factor productivity Scottish Journal of Political Economy, 49, 3, pp 280–303 GROSSMAN, G and HELPMAN, E (1991) Innovation and Growth in the Global Economy Cambridge, MA: The MIT Press GUELLEC, D and VAN POTTELSBERGHE, B (2001) R&D and productivity growth: panel data analysis of 16 OECD countries OECD Economic Studies, 33, pp 103–26 HAQUE, N and KIM, S (1995) Human capital flight: impact of migration on income and growth International Monetary Fund Staff Papers, 42, 3, pp 577–607 IM, K., PESARAN, M and SHIN, Y (2003) Testing for unit roots in heterogenous panels Journal of Econometrics, 115, pp 53–74 KELLER, W (1997) Trade patterns, technology flows, and productivity growth Mimeo, pp 1–32 KELLER, W (1998) Are international R&D spillovers trade-related? Analyzing spillovers among randomly matched trade partners European Economic Review, 42, pp 1469–81 KELLER, W (1999) How trade patterns and technology flows affect productivity growth, National Bureau of Economic Research Working Paper Series, No 6990 KELLER, W (2002) Trade and the transmission of technology Journal of Economic Growth, 7, pp 5–24 KUGLER, M and RAPOPORT, H (2007) International labour and capital flows: complements or substitutes? Economic Letters, 94, pp 155–62 LICHTENBERG, F and VAN POTTELSBERGHE, B (1998) International R&D spillovers: a comment European Economic Review, 42, pp 1483–91 r 2008 The Author Journal compilation r 2008 Scottish Economic Society 636 THANH LE MOUNTFORD, A (1997) Can a brain drain be good for growth in the source economy? Journal of Development Economics, 53, pp 287–303 PARK, J (2004) International student flows and R&D spillovers Economic Letters, 82, pp 315–20 PARK, W (1995) International R&D spillovers and OECD economic growth Economic Enquiry, 33, 4, pp 571–91 PEDRONI, P (1999) Critical values for cointegration tests in heterogenous panels with multiple regressors Oxford Bulletin of Economics and Statistics, (Special Issue), pp 653–70 RAUCH, J and CASELLA, A (2003) Overcoming informational barriers to international resource allocation: prices and ties Economic Journal, 113, 484, pp 21–42 RAUCH, J and TRINIDADE, V (2002) Ethnic Chinese networks in international trade Review of Economics and Statistics, 84, 1, pp 116–30 ROMER, P (1990) Endogenous technological change The Journal of Political Economy, 98, 5, pp S71–102 SAXENIAN, A (2002) Brain circulation: how high-skill immigration makes everyone better off The Brooking Review, 20, 1, pp 28–31 SAXENIAN, A (2005) From brain drain to brain circulation: transnational communities community upgrading in India and China Studies in Comparative International Economies, 40, 2, pp 35–61 SOETE, L and PATEL, P (1985) Recherche-developpement, importations de technologie et croissance economique: une tentative de comparaison internationale Revue Economique, 36, 5, pp 975–1000 STARK, O., HELMENSTEIN, C and PRSKAWETZ, A (1997) A brain gain with a brain drain Economic Letters, 55, pp 227–34 STARK, O., HELMENSTEIN, C and PRSKAWETZ, A (1998) Human capital depletion, human capital formation, and migration: a blessing or a ‘curse’? Economic Letters, 60, pp 363–7 VAN POTTELSBERGHE, B and LICHTENBERG, F (2001) Does foreign direct investment transfer technology across borders The Review of Economics and Statistics, 83, 3, pp 490–7 VIDAL, J (1998) The effect of emigration on human capital formation Journal of Population Economics, 11, pp 589–600 WONG, K and YIP, C (1999) Education, economic growth, and brain drain Journal of Economic Dynamics and Control, 23, pp 699–726 Date of receipt of final manuscript: 11 April 2008 r 2008 The Author Journal compilation r 2008 Scottish Economic Society ... assumption of ? ?brain drain’ associated with emigration, this result is consistent with the emerging theory of ? ?brain gain’ within that ? ?brain drain’ literature such as that reported by Mountford (1997),... domestic R&D and each of the foreign R&D capital stock (or its corresponding interaction term with import intensity, fraction of population working overseas, fraction of foreign population, or human... is total factor productivity, SD is domestic R&D capital stock; SF is unweighted foreign R&D capital stock; SFm is foreign R&D capital stock embodied in imports; SFg is foreign R&D capital stock