metadata: authors: 0: author: Schiff, Maurice
1: author: Wang, Yanling
count: World
docna: 0: docna: North-south trade-related technology
            diffusion, brain drain and productivity growth : are small
            states different ?
docty: Policy Research Working Paper
subtopic: Human Migrations & Resettlements,Educational Sciences,International Trade and Trade Rules,Migration and Development
teratopic: Macroeconomics and Economic Growth,Social Protections and Labor,Private Sector Development,Education
abstracts: cdata!: The economies of small developing states
            tend to be more fragile than those of large ones. This paper
            examines this issue in a dynamic context by focusing on the
            impact of the brain drain on North-South trade-related
            technology diffusion and total factor productivity growth in
            small and large states in the South. There are three main
            findings. First, productivity growth increases with
            North-South trade-related technology diffusion and education
            and the interaction between the two, and decreases with the
            brain drain. Second, the impact of North-South trade-related
            technology diffusion, education, and their interaction on
            productivity growth in small states is more than three times
            that for large countries, with the negative impact of the
            brain drain thus more than three times greater in small than
            in large states. And third, the greater loss in productivity
            growth in small states has two brain drain-related causes: a
            substantially greater sensitivity of productivity growth to
            the brain drain, and brain drain levels that are more than
            five times greater in small than in large states.
repnme: repnme: North-south trade-related technology
            diffusion, brain drain and productivity growth : are small
            states different ?
display_title: North-south trade-related technology
            diffusion, brain drain and productivity growth : are small
            states different
listing_relative_url: /research/2009/01/10212454/north-south-trade-related-technology-diffusion-brain-drain-productivity-growth-small-states-different
topicv3: Innovation, Technology Transfer and
            Diffusion m1326019 1084,Trade m1326924 698,Education m1327048 470,Emigration m1504169 2844,Human Capital Flight m1504170 2845
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Social Protections and Labor,Private Sector Development,Education
no_of_pages: 23
model_extractions: WPS4828 Policy ReseaRch WoRking PaPeR 4828 North-South Trade-related Technology Diffusion, Brain Drain and Productivity Growth Are Small States Different?

_Maurice Schiff Yanling Wang_ The World Bank Development Research Group Trade Team January 2009
Policy ReseaRch WoRking PaPeR 4828

# **Abstract**

The economies of small developing states tend to be more fragile than those of large ones. This paper examines this issue in a dynamic context by focusing on the impact of the brain drain on North-South trade-related technology diffusion and total factor productivity growth in small and large states in the South. There are three main findings. First, productivity growth increases with NorthSouth trade-related technology diffusion and education and the interaction between the two, and decreases with the brain drain. Second, the impact of North-South trade-related technology diffusion, education, and their interaction on productivity growth in small states is more than three times that for large countries, with the negative impact of the brain drain thus more than three times greater in small than in large states. And third, the greater loss in productivity growth in small states has two brain drain-related causes: a substantially greater sensitivity of productivity growth to the brain drain, and brain drain levels that are more than five times greater in small than in large states.

This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the department to understand the migration of skilled labor (brain drain) in small states and how and why it differs from that in larger countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mschiff@worldbank.org.

_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
# **North-South Trade-related Technology Diffusion, Brain Drain and Productivity Growth: Are Small States Different?***

# **Maurice Schiff**<sup>**a**</sup> **and Yanling Wang**<sup>**b**</sup>

JEL: F22, J61 Keywords: brain drain, technology diffusion, trade, productivity growth

* We would like to thank Edgardo Favaro, Alan Winters and seminar participants at the December 2006 World Bank Conference on “Small States: Growth Challenges and Development Solutions” for their helpful comments and suggestions. This paper reflects the authors’ views and not necessarily those of the World Bank, its Board of Executive Directors, or the governments they represent.

a: Trade Unit, DECRG, World Bank, Washington, DC. E-mail: <u>Mschiff@worldbank.org.</u> b: The Norman Paterson School of International Affairs, Carleton University. Ottawa, Canada. E-mail: Yanling_Wang@carleton.ca.
# 1. Introduction

An important literature exists on the effects of countries’ human capital on their productivity growth, with most studies conducted in a closed-economy context. This paper focuses on the differential impact of human capital and South-North brain drain in small and large states. It provides an empirical analysis of the impact on total factor productivity (TFP) growth in the South of i) trade-related technology diffusion, human capital, and country size _S_ , ii) the interaction between pairs of these variables, and iii) the interaction between the three variables.

The use of trade-related technology diffusion as a determinant of productivity ( _TFP_ ) growth in the South is based on the assumption that North-South trade provides a vehicle for the diffusion to the South of technology developed in the North. Second, the South’s absorption capacity – as measured by countries’ average level of human capital – is hypothesized to affect _TFP_ growth as well as the impact of trade-related technology diffusion on _TFP_ growth.

The remainder of this section is organized as follows. Sub-section 1.1 deals with the impact of trade on technology diffusion and _TFP_ growth, Sub-section 1.2 provides figures on the brain drain for various categories of countries and regions, and Sub-section 1.3 presents the main findings.

# 1.1. Trade-Related Technology Diffusion and Productivity ( _<u>TFP</u>_ <u>) Growth</u>

Until about two decades ago, while trade theory emphasized the importance of trade liberalization, empirical estimates of the gains from trade were found to be disappointingly small. The development of endogenous growth theory in the 1980s 2
(Romer 1986, Lucas 1988) allowed policy reform to generate large gains by moving the economy to a higher growth path. Grossman and Helpman (1991) expanded the endogenous growth model by applying it to the open economy. Based on the idea that goods embody technological know-how, they showed that countries can acquire foreign knowledge through trade and increase their growth rate through trade liberalization.

Coe and Helpman (1995) provided an empirical implementation of that model. They constructed an index of ‘foreign R&D’, defined as the trade-weighted sum of trading partners’ R&D stocks, and found for OECD countries that both domestic and ‘foreign R&D’ have a large and significant impact on _TFP_ , and that the latter increases with the economy’s openness. Coe et al. (1997) examined the impact of North-South trade-related technology diffusion on _TFP_ in the South and obtained similar results. This led to other studies by, inter alia, Engelbrecht (1997), Falvey et al. (2002), and LumengoNeso et al. (2005), which have tended to confirm Coe and Helpman’s (1995) findings. Other studies have extended the approach to the industry level, including Schiff and Wang (2006) who added South-South trade-related technology diffusion to the analysis and found a positive impact on _TFP_ in the South, though a smaller one than that obtained from North-South trade.

# 1.2. Brain Drain

This paper focuses on the impact of the brain drain and whether it is different for small than for large states. Brain drain figures are presented in Table 1. The figures are based on Docquier and Marfouk (2006). The table presents skilled and overall emigration rates in 2000, as well as the ratio of the former to the latter (the schooling gap), for 46 3
small developing states – defined by the UN as states with population below 1.5 million – and for other categories of interest. Skilled workers are defined as those with university education.

Row 1 of Table 1 shows that small developing states experience an extremely high level of brain drain (43.2%). In other words, 3 out of every 7 individuals with university education live outside their country of origin. This rate is 2.8 times as large as the 15.3% overall migration rate.

The table also shows a brain drain for small (all) high-income states of 23% (3.5%) or a ratio of 6.5 for small versus all states. The same ratio for developing countries is close to 6 (43.2% versus 7.4%). In other words, the impact of country size on the brain drain seems robust across a wide range of incomes. Moreover, the brain drain for all developing countries (7.4%) is over twice that of high-income countries (3.5%) and the schooling gap is close to four times as high (4.9% versus 1.3% or 3.8 times).

The region with the highest small-state brain drain (74.9%) is the Caribbean (in “Latin America and the Caribbean”), and Table 2 shows that several states’ brain drain is well above 80%. The East Asia and Pacific region (mainly the South Pacific islands) follows, with a brain drain of 50.8%, with several countries over 70% (Table 2). SubSaharan Africa is next with 41.7%, with several countries over 60% (Table 2).<sup>1</sup> Thus, as far as small states are concerned, three out of four skilled Caribbean individuals live outside their country of origin, two out of four in East Asia and Pacific, and two out of five in Sub-Saharan Africa. Though Sub-Saharan Africa (SSA) has the lowest brain drain among these three regions, its schooling gap is more than double that

> 1 Table 2 also shows countries in Central America (Belize) and the Mediterranean (Malta) with brain drain above 50% and Cyprus with brain drain above 30%.

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in the other two developing regions. The main reasons are the wider income gap with developed countries and the smaller share of skilled individuals in the population.

# 1.3. Main Findings and Contributions

The contribution of this paper to the open-economy endogenous growth literature is twofold. First, it offers an empirical analysis of the relationship between North-South trade-related technology diffusion, country size and productivity growth in the South. Second, it examines how the impact on productivity growth of changes in such variables as the level of education, trade-related technology diffusion, or both, is affected by country size. The main findings are: i) Trade-related technology diffusion has a positive impact on productivity growth that is several times larger for small than for large states. Consequently, an increase in the degree of openness has a greater impact on productivity growth in small than in large states.

ii) Similarly, education has a positive impact on productivity growth that is several times larger for small than for large states. Hence, the brain drain’s negative impact on productivity growth in small states is a multiple of that for other countries.

iii) In terms of interaction effects, the impact of trade-related technology diffusion on productivity growth increases with the level of education, and this increase is also several times larger for small than for large states. Consequently, the brain drain reduces productivity growth both directly as well as through its interaction with trade-related technology diffusion, with a greater reduction for small than for large states.

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iv) The continuous growth of the North’s R&D over time has a positive impact on the South’s _long-term_ productivity growth, an impact that is substantially greater for small than for large states.

The remainder of the paper is organized as follows. Section 2 presents the empirical framework. Section 3 describes the data and Section 4 provides the empirical results. Section 5 concludes.

# 2. Empirical Framework

As discussed earlier, Coe and Helpman (1995) developed an empirical model to estimate the impact on _TFP_ of North-North trade-related technology diffusion. Their estimation equation is: log _TFPct_ = α + λ _c_ + λ _t_ + β _d_ log _RDctd_ + β _f_ log _RDctf_ + ε _ct_ ; β _d_ , β _f_ > ,0 (1) where λ _c_ (<sup>λ</sup> _t_ ) is a country (time) fixed effect, _RDctd_ ( _RDctf_ ) is the domestic (foreign) R&D stock, ε is an error term, and subscript _c_ ( _t_ ) denotes country (year).

Coe et al. (1997) use a similar model to explain North-South trade-related technology diffusion. However, due to lack of data for most developing countries, the equations they estimate do not include domestic R&D. They only use the foreign R&D stock _RD f_ , which is referred to in this paper as ‘North foreign R&D’ and is denoted by ‘ _NRD_ ’ in our study. Abstracting from domestic R&D is unlikely to be a major problem because most of the world’s R&D is performed in developed countries.<sup>2</sup>

> 2 In 1990, 96% of the world’s R&D expenditures took place in industrial countries (Coe et al., 1997). The share was 94.5% in 1995 (calculated from the World Bank database). Moreover, recent empirical work has shown that much of the technical change in individual OECD countries is based on the international diffusion of technology among the various OECD countries. For instance, Eaton and Kortum (1999) estimate that 87% of French growth is based on foreign R&D. Since developing countries invest much fewer resources in R&D

6
Following Coe and Helpman (1995) and Coe et al. (1997), we define the variable ‘North-foreign R&D’ of developing country _c_ , _NRDc_ as: where _c_ indexes developing countries, _k_ indexes OECD countries, _GDPc_ is the value added of country _c_ , _M ck_ is the value of imports of country _c_ from OECD country _k_ , and _RDk_ denotes the R&D stock in OECD country _k_ . The time variable _t_ is omitted for simplicity. Equation (2) says that, for any country _c_ , _NRD_ is the sum, over all OECD countries _k_ , of the R&D stock of country _k_ , weighted by country _c_ ’s imports from OECD country _k_ divided by country _c_ ’s GDP.

We estimate _TFP_ equations as a function of _NRD_ and a human capital variable, namely the average number of years of education for the population aged 25 and above, denoted by _YE_ . We further add a dummy variable for small states, _S_ 3, in order to examine whether their impact on TFP growth differs from that of large ones. The number of countries with a population of 1.5 million or less (on average over the period) in our sample of fifty developing countries is too small to be of much relevance. We use instead a population of 3 million or less as our definition of ‘small state’, with twelve countries or close to one fourth (24%) of the sample fitting the definition.<sup>3</sup> In the empirical estimation, we also introduce several interaction terms. Two of them are interactions between each of the two explanatory variables and _S_ 3, i.e., _NRD*S_ 3 and _YE*S_ 3. The other two are interactions between the two explanatory variables both for

> than OECD countries, foreign R&D must be even more important for developing countries as a source of growth.

> 3 We use the average population size over the period 1976-1997.

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small and large states, i.e., _NRD*YE_ and _NRD*YE*S_ 3. A positive sign for the first two interaction variables would imply that the productivity-growth impact of _NRD_ and _YE_ is larger in small states, and similarly, a positive sign for _NRD*YE*S_ 3 would imply that the impact of _NRD*YE_ is larger in small states.

The estimation equation is specified in terms of five-year changes in the log of _TFP_ (Dlog _TFP_ ), in the log of _NRD_ (Dlog _NRD_ ) and in _YE_ (D _YE_ ), i.e.: _D_ log _TFPct_ = α + β _N D_ log _NRDct_ + β _Y DYEct_ + β _S S_ 3 + β _NS D_ log _NRDct_ * _S_ 3 + β _YS DYE_ * _S_ 3 + β _NY D_ log _NRDct_ * _DYEct_ + β _NYS D_ log _NRDct_ * _DYEct_ * _S_ 3 + ∑γ _c Dc_ + ∑γ _d Dd_ +ε _ct_ , (3) _c_ = 2 _d_ = 2 where _Dc_ (<sup>_D_</sup> _d_ ) indicates country (year) dummies, capturing country- (year-)specific fixed effects. The equations estimated in Section 4 include equation (3) and variants thereof.

# 3. Data Description

The data cover 50 developing (and transition) countries and 15 industrialized OECD trading partners over the period 1976 to 2002. The 50 developing countries – with the 12 small states in italics – are: Bangladesh, Bolivia, Bulgaria, Cameroon, Chile, Colombia, _Cyprus_ , Ecuador, Egypt, El Salvador, Ethiopia, Greece, Guatemala, Hong Kong (China), Hungary, India, Indonesia, Iran, I.R. of, Israel, Jordan, Kenya, Korea, _Kuwait_ , _Latvia_ , _Macao (China)_ , Malawi, Malaysia, _Malta_ , Mexico, Morocco, Myanmar (Burma), Nepal, Nigeria, _Oman_ , Pakistan, _Panama_ , Peru, Philippines, Poland, Romania, Senegal, _Singapore_ , _Slovenia_ , Sri Lanka, Tanzania, _Trinidad & Tobago_ , Tunisia, Turkey, _Uruguay_ and Venezuela.

The log _TFP_ index is calculated as the difference between the logs of value-added and primary factor use, with the inputs weighted by their income shares, i.e., 8
ln _TFP_ = ln _Y_ − α ln _L_ − 1( − α ) ln _K_ , where α is the mean labor share over the available time period. The labor share is derived as the ratio of the wage bill over value added.

Fixed capital formation used to construct capital stocks, value added, labor and wages, is from the World Bank data set described in Nicita and Olarreaga (2006), all reported in current US dollars at the 3-digit ISIC codes (Revision 2). Value-added is deflated by the US GDP deflator (1991=100). Fixed capital formation is also deflated by the US GDP deflator (1991=100), and capital stocks are derived from the deflated fixed capital formation series using the perpetual inventory method with a 5% depreciation rate.<sup>4</sup> The _TFP_ index is constructed using the deflated value added, capital stocks, labor and its average income share with the formula provided.

R&D expenditure for the 15 OECD countries is taken from OECD ANBERD with ISIC Revision 2 (2002) covering data from 1973 to 1998, and ANBERD with ISIC Revision 3 (2006) covering data from 1987 on. Since ANBERD ISIC 2 and ISIC 3 have 12 years of data overlapping, we are able match the different specifications. The R&D stock in each country is constructed from R&D expenditures using the perpetual inventory method with a 10% depreciation rate.

Bilateral trade data of the 50 developing countries with the 15 industrialized OECD countries at the 4-digit ISIC 2 level are taken from Nicita and Olarreaga (2006). We construct bilateral trade shares for each of the 50 developing countries with respect to each of the 15 OECD countries, as defined in equation (2).

Average years of education, tertiary education completion ratio, and secondary school completion ratio for the population aged 25 and above are obtained by annualizing

> 4 Given that the data reported in Nicita and Olarreaga (2006) are in current US dollars, we use the US GDP deflator. In the empirical analysis, country-specific as well as year dummies are used in order to control for some of the distortions that might be present because of the conversion.

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the five-year averages in Barro and Lee (2000). There are several countries included in the sample that are not included in the Barro and Lee dataset. We matched each of these countries with other countries included in Barro and Lee, using real GDP per capita and government expenditure as a share of GDP per capita.

Observations for a typical country consist of five five-year periods. With 50 developing countries and no missing observations, that would give a sample size _n_ = 250. However, we do have some missing observations (with _n_ = 230) for production and trade data, and the sample is unbalanced.

# 4. Empirical Findings

Given that changes in openness, foreign R&D and education are unlikely to have an immediate impact on productivity growth, we specify the estimated equations in terms of five-year changes in the log of _TFP_ , the log of _NRD_ , and in _YE_ , where the letter “D” before the variable indicates a five-year change. In other words, the estimated equations are specified in terms of the growth rate of _TFP_ and _NRD_ , and in terms of the change in _YE_ . We estimate nine equations, all variants of equation (3) above. The results are presented in Table 3.

Table 3 shows that the coefficient β _N_ of Dlog _NRD_ is positive and significant in all nine regressions. Denote the coefficient β _N_ for small states by β _NS_ (equation (3)). The value of β _N_ ranges from .269 to .615, and falls to a range of .269 to .397 when the variable Dlog _NRD*S_ 3 is included in the regression. For instance, in equation (1), β _N_ = .490 (significant at the 1% level). It falls to .269 (significant at the 10% level) in equation (2). On the other hand, β _NS_ = .964 (significant at the 1% level) in the same equation. The 10
impact φ _NS_ of Dlog _NRD_ on Dlog _TFP_ in small states is φ _NS_ ≡ β _N_ + β _NS_ = .269 + .964 = 1.233. Thus, the impact of Dlog _NRD_ in small states is over four times the impact in large countries, i.e., φ _NS_ > 4 β _N_ . The same result obtains in equations (6) and (9), while φ _NS_ > 3 β _N_ in equations (5) and (8).

The coefficient β _Y_ of the education variable D _YE_ ranges from .721 to .807, with significance of 1% or 5% in equations (1), (2), (3) and (5). However, β _Y_ falls to between .194 and .310 and is no longer significant when the variable for small states, D _YE*S_ 3, is included in the regression. For instance, in equation (1), β _Y_ = .766 (significant at the 5% level). Adding D _YE*S_ 3 in equation (4) results in a value β _Y_ = .242 (not significant), with the coefficient for small states β _YS_ = 1.075. The impact of D _YE_ for small states is equal to φ _YS_ ≡ β _Y_ + β _YS_ = 1.317, or over five times the impact in large countries, i.e., φ _YS_ > 5 β _Y_ . Similar results are obtained in equations (6) to (9), with the ratio φ _YS_ /<sup>β</sup> _Y_ > 6 in equation (7), > 5 in equation (8), and > 4 in equations (6) and (9).

The coefficient β _NY_ of the interaction effect Dlog _NRD*_ D _YE_ ranges from 1.618 to 1.701, with significance level of 5% or 10%, in regressions (3), (5), (7) and (8). Once the variable Dlog _NRD*_ D _YE*S_ 3 (with coefficient β _NYS_ ) is added to the regression (equation (9)), β _NY_ falls to .726 and is no longer significant. On the other hand, β _NYS_ = 2.966 (significant at the 10% level), and the impact of Dlog _NRD*_ D _YE_ in small states is φ _NYS_ ≡ β _NY_ + β _NYS_ = 3.792 > 5 β _NY_ .

The results provided in Table 3 imply that the effects of Dlog _NRD_ , D _YE_ and Dlog _NRD*_ D _YE_ on Dlog _TFP_ in small states are systematically greater than in large 11
countries. Equation (9) – which includes all the explanatory variables and is our preferred equation – shows that the impact of Dlog _NRD_ is more than 4 times greater in small states than in large countries, and the impact of D _YE_ (Dlog _NRD*_ D _YE_ ) is more than 5 times greater.

As shown in Table 1, the share of migrants who are skilled is larger than the share among residents (Docquier and Schiff, 2008), implying that the brain drain reduces the average level of education _YE_ and thus reduces productivity growth. Second, since the interaction effect of education and ‘foreign R&D’ (the diffusion of technology from the North to the South) is positive, it implies that the brain drain reduces the absorption capacity of developing source countries. In other words, the brain drain reduces the impact that the diffusion of technology from the North has on productivity growth, and this reduction is greater for small states than for large ones. In fact, the loss in productivity growth when this interaction effect is taken into account is close to three times as high (193% higher) in small states than in the other countries, rather than 16% higher when the interaction effect is not taken into account.

Third, small states also tend to suffer from significantly higher brain drain rates. Among developing countries, the brain drain in 2000 was 43.2% for small states and 7.4% for all developing countries, with the former close to six times greater than the latter. Thus, the negative impact of the brain drain is larger in small states both because their TFP growth is more sensitive to the brain drain and because the brain drain is substantially greater in these states.

These results are subject to an important caveat. A recent literature has argued that the loss in human capital is smaller than the brain drain because of a brain gain, a concept 12
unrelated to return migration by some of the skilled migrants. Rather, this literature argues that a brain gain obtains because the positive probability of emigration and of earning a higher salary abroad raises the expected return to education and provides an incentive to acquire more of it. The change in the stock of human capital or net brain gain is the difference between the brain gain and the brain drain.

Several studies argue that under certain conditions, the net brain gain might actually be positive, implying that the incentive effect of the brain drain on human capital accumulation is larger than the brain drain itself. For instance, a recent study by Beine et al. (2008) finds that the net brain gain is negative for most developing countries, particularly in the case of small states, though it tends to be positive in the very large countries where the brain drain is small (the _number_ of skilled migrants may be large, though) such as Brazil, China, India, and others.

Thus, the brain drain would be expected to result in a reduction in TFP growth in most developing countries, and particularly in small states. There are two reasons for that. First, as shown in Tables 2 and 3, the brain drain is close to six times larger in small states than in large ones; and ii) the very large states seem to experience a net brain gain rather than a brain drain, which is not the case for small states (Beine et al., 2008).

Moreover, the difference in the impact of the brain drain on TFP growth between small states and the larger states may be even greater than in the absence of a brain gain because the net brain gain remains highly negative for the small states while that for the larger states tends to be positive (see Beine et al., 2008).

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# 5. Conclusion

This paper examined the impact of North-South trade-related technology diffusion on _TFP_ growth in the South. It contributes to the open-economy endogenous growth literature by offering an empirical analysis of the impact of the brain drain on productivity ( _TFP_ ) growth, of the relationship between country size and _TFP_ growth, and between a combination of country size, brain drain and North-South trade-related technology diffusion, on the one hand, and _TFP_ growth on the other. The main findings are: i) _TFP_ growth increases with trade-related technology diffusion, and the increase is substantially larger for small states than for large ones; ii) Education has a positive impact on _TFP_ growth, and the increase is substantially larger for small states than for large ones; iii) The share of migrants who are skilled is larger than the share of residents who are skilled, implying that the brain drain has a negative impact on _TFP_ growth, and that the impact is larger (in absolute value) for small than for large states; iv) The impact of the interaction of trade-related technology diffusion and education on _TFP_ growth is positive, and this impact is greater for small than for large states.

Thus, _TFP_ growth in small states is more sensitive to changes in the brain drain, to changes in North-South trade-related technology diffusion, and to the interaction between the two. Moreover, small states are more open to trade and thus have higher levels of North-South trade-related technology diffusion. This is another reason why _TFP_ 14
growth in small states would react more strongly to changes in trade-related technology diffusion.

Brain drain levels are also substantially larger in small than in large states, causing greater losses in _TFP_ growth in the former than in the latter. Hence, there are two reasons for the greater negative impact of the brain drain in small than in large states: a) the former’s _TFP_ growth is more sensitive to the brain drain and b) its brain drain is substantially larger.

The continuous growth of the North’s R&D over time has a positive impact on the South’s _long-term_ productivity growth, an impact that is substantially greater for small than for large states.

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