‘ B RA IN DRA IN ’ OR ‘ BRA INC I RCULAT I ON ’ : E V I D ENCE FROM

OECD ’ S I N T ERNAT I ONAL M IGRAT I ONAND R&D S P I L LOVER S

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

nUniversity of Queensland, St Lucia, QLD 4072, Australia1 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 definedas the emigration of a fraction of the population that is relatively highly educated as comparedwith the average.

Scottish Journal of Political Economy, Vol. 55, No. 5, November 2008r 2008 The AuthorJournal 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

618

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.

2 Other possible gains include the return migration of ex ante low-skilled workers who arenow 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 (Starket al., 1997; Beine et al., 2001).

3 See, for example, Saxenian (2002, 2005).4 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. Foran analysis of the relationship between migration and foreign direct investment, see forexample, Kugler and Rapoport (2007). However, these issues are beyond the scope of thispaper.

‘BRAIN DRAIN’ OR ‘BRAIN CIRCULATION’ 619

r 2008 The AuthorJournal compilation r 2008 Scottish Economic Society

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 FitKaitL

1�ait ; 8i; 0<a<1;

where i is a country index (i5 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:

logFit ¼ logYit � a logKit � ð1� aÞ logLit:

In addition, the growth accounting method indicates that:

gY ¼ gF þ agK þ ð1� aÞgL;

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

5 The derivation of the estimating equation in this paper is based on the work of Keller(1998). For more details, see Keller (1997, 1998).

THANH LE620

r 2008 The AuthorJournal compilation r 2008 Scottish Economic Society

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:

logFit ¼ bi þ 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, zdit,

and foreign intermediate goods, zfit, 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, zdit is identical to the

cumulative stock of R&D expenditure, SDit, and zfit 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:

SFmit ¼

Xj 6¼i

mijt

yjtSDjt

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 trade-

weighted 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:

SFgit ¼

Xj 6¼i

gijt

njtSDjt

6 In reality, domestically produced intermediate goods and foreign produced intermediategoods can be similar. However, in this paper, for simplicity, they are assumed to be twodisjointed sets so that both of them can be utilized for a country’s production.

7 In Coe and Helpman (1995), the stock of foreign R&D capital is computed as

zfit ¼ SFit ¼

Pj 6¼i

mijt

mit� SDjt, where mit is total imports of country i at time t, and measured as

an index number (19855 1). However, this has been shown by Lichtenberg and vanPottelsberghe (1998) to lead to a misspecified regression equation. In addition, the Coe andHelpman’s method is also challenged by Keller (1998) who claims that regressions usingcounterfactual (randomly created) international trade patterns produce even more positiveR&D spillovers and explain more of the variation in productivity than if actual bilateral tradepatterns are used.

‘BRAIN DRAIN’ OR ‘BRAIN CIRCULATION’ 621

r 2008 The AuthorJournal compilation r 2008 Scottish Economic Society

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:

SFkit ¼

Xj 6¼i

kijt

njtSDjt

where kijt is the stock of country i’s citizens living in country j. Through the on-

the-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 improve-

ment 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:

Fit ¼ SDit;SFmit ;SF

lit;mit

yit;git

nit;kit

nit;Hit

� �;

where SFlit (l5 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.

8 The ‘absorptive capacity’ is defined by Benhabib and Spiegel (1994) and Bils and Klenow(2000).

9 According to Bils and Klenow (2000), if workers need human capital to use advancedtechnology then growth in human capital can help to improve technology.

THANH LE622

r 2008 The AuthorJournal compilation r 2008 Scottish Economic Society

III 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 19855 1 (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 peoplewho are foreign-born while other countries focus on those with foreign citizenship.

‘BRAIN DRAIN’ OR ‘BRAIN CIRCULATION’ 623

r 2008 The AuthorJournal compilation r 2008 Scottish Economic Society

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 1. 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 hetero-

geneity 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 2.

Table 1

Group mean panel unit root tests (annual data 1980–1990 for 19 countries – Im et al., 2003)

Variable �tN;Ta �pb

Adjusted

meancAdjusted

variancedGroup mean

statistice Decisionf

logF � 2.566 1.421 � 2.046 1.708 � 1.768 I(1)

logSD � 2.165 2.053 � 1.784 1.979 � 1.179 I(1)

logSF � 0.207 2.105 � 1.318 1.457 4.011 I(1)

logSFm � 2.640 2.316 � 1.997 2.153 � 1.980 I(0)

logSF g � 2.301 1.842 � 2.025 1.947 � 0.860 I(1)

logSF k � 1.678 1.000 � 2.075 1.483 1.423 I(1)mylogSFm � 1.528 1.053 � 2.062 1.492 1.903 I(1)

gnlogSFg 0.418 2.368 � 1.355 1.591 6.129 I(1)

knlogSFk � 0.600 1.053 � 1.448 1.292 3.251 I(1)

logH � 2.852 0.895 � 2.102 1.409 � 2.753 I(0)

logH logSFg � 2.011 1.421 � 2.064 1.723 0.176 I(1)

logH logSFk � 1.605 1.474 � 2.037 1.696 1.446 I(1)

Notes: logX is logarithm of X. F is total factor productivity, SD is domestic R&D capital stock; SF isunweighted foreign R&D capital stock; SFm is foreign R&D capital stock embodied in imports; SFg isforeign R&D capital stock embodied in inward foreign population; SFk is foreign R&D capital stockembodied in outward foreign population; m/y is the ratio of imports of goods and services to GDP; g/n is theratio 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.aCross-sectional average of individual Dickey-Fuller �tN;T statistics.bCross-sectional average of individual number of lagged differenced terms in ADF(pi) regression.cCross-sectional average of E½ti;T ðpi ; yiÞ�.dCross-sectional average of Var½ti;T ðpi ; yiÞ�.eThe test statistic W�t which has standard normal distribution.fTest of the null hypothesis of common unit autoregressive root at 5% level (the critical value is � 1.96).

11 It is true that when performed separately on the time series for each country, given thateach country has only 11 annual observations, the power of the tests is really low. The panel

THANH LE624

r 2008 The AuthorJournal compilation r 2008 Scottish Economic Society

Table2

Totalfactorproductivityestimationresults(pooleddata

1980–1990for19countries,209observations–in

level)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

logSD

0.096n

0.067n

0.004

0.102n

0.121n

0.137n

0.107n

0.073n

0.054n

0.073n

0.087n

0.058n

0.063n

0.079n

0.063n

(0.022)

(0.021)

(0.019)

(0.018)

(0.013)

(0.011)

(0.022)

(0.021)

(0.023)

(0.021)

(0.023)

(0.020)

(0.021)

(0.021)

(0.020)

G7�lo

gSD

0.128n

0.079n

0.079n

0.137n

0.141n

0.125n

0.127n

0.064n

0.081n

0.132n

0.135n

0.094n

0.132n

0.131n

0.084n

(0.028)

(0.026)

(0.022)

(0.030)

(0.024)

(0.024)

(0.028)

(0.028)

(0.025)

(0.028)

(0.027)

(0.026)

(0.035)

(0.028)

(0.027)

logSFg(inward)

0.045n

0.024

0.036n

0.160n

(0.016)

(0.015)

(0.013)

(0.068)

logSFk(outw

ard)

0.193n

0.173n

0.147n

0.705n

(0.029)

(0.026)

(0.026)

(0.214)

logSF(unweighted)

0.241n

(0.030)

(m/y)�logSFm(import)

0.305n

0.286n

0.229n

0.294n

0.237n

(0.046)

(0.041)

(0.040)

(0.042)

(0.040)

(g/n)�logSFg

0.302n

(0.135)

(k/n)�logSFk

0.678n

(0.270)

logH�lo

gSFg

0.016n

�0.051

0.013n

(0.007)

(0.027)

(0.006)

logH�lo

gSFk

0.079n

�0.224n

0.059n

(0.013)

(0.096)

(0.012)

R2

0.696

0.742

0.755

0.739

0.679

0.682

0.688

0.734

0.748

0.755

0.707

0.750

0.750

0.751

0.771

Adjusted

R2

0.662

0.713

0.727

0.710

0.643

0.646

0.653

0.704

0.718

0.726

0.672

0.672

0.721

0.722

0.744

Cointegrationtests

Panel

ADFstatistica

�4.663�4.925

�0.413

�6.059

�0.136

�0.094

�4.318�4.788�3.066�4.191�3.414�3.802�2.755�4.049�3.995

Decisionb

CI

CI

Retain

null

CI

Retain

null

Retain

null

CI

CI

CI

CI

CI

CI

CI

CI

CI

Notes:Thedependentvariable

islogF(logoftotalfactorproductivity,indexed

as1985

51).Allequationsincludeunreported

country-specificconstants.Tim

edummiesare

omitted

dueto

implausibleresultsobtained.Whiteheteroskedasticity-consistentstandard

errors

are

given

inparentheses.SD

isdomesticR&D

capitalstock;SFmisforeignR&D

capitalstock

embodiedin

imports;SFgisforeignR&D

capitalstock

embodiedin

inward

foreignpopulation;SFkisforeignR&D

capitalstock

embodiedin

outw

ard

foreignpopulation;m/y

isthe

ratioofim

portsofgoodsandservices

toGDP;g/n

istheratiooftotalforeignersto

domesticpopulation;k/n

isthefractionofpopulationlivingoverseas;H

istheaveragenumber

of

years

ofeducation;G7isdummyvariable

equalto

1fortheseven

majorcountriesandequalto

0fortheother

twelvecountries.

nindicatesthatparametersare

statisticallysignificantatthe5%

probabilitylevel.

aPedroni(1999)’sPanel

ADF

statistic

allowsdynamicsandcointegratingvectorto

vary

across

individuals.

bTestofthenullhypothesisofnocointegrationat5%

significantlevel

(thecriticalvalueis�1.96).

‘BRAIN DRAIN’ OR ‘BRAIN CIRCULATION’ 625

r 2008 The AuthorJournal compilation r 2008 Scottish Economic Society

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 logSFm and logH. According to Edmond (2001),

regressions using those variables do not fulfil a necessary condition for

cointegration and should be treated with some doubt.12

Table 2 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 1 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) incorpo-

rated 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 thesetests increases dramatically as the cross-sectional dimension rises. For example, in Pedroni’spanel ADF tests, as long as the time dimension is greater than five, the test statistic is shown tobe distributed as standard normal and the small sample performance of the test is reasonablysatisfactory.

12 There may not be any shared trends among variables when I(0) variables are included inequations with existing I(1) variables. In addition, Pedroni (1999) does not specify cointegrationtests for this type of regression equation. For these reasons, this paper opts not to considerestimation 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 casewhere variables are stationary when the number of observations increases and their distributiondoes not necessarily follow standard t-distribution. Because the specific distribution associatedwith those estimates is unknown, this study follows a large number of research works in theexisting literature (e.g., van Pottelsberghe and Lichtenberg, 2001; Park, 2004) using thestandard 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 ofdomestic R&D capital stock, are negative (incompatible with reality). Therefore, the empiricalestimation is carried out without any time trend.

THANH LE626

r 2008 The AuthorJournal compilation r 2008 Scottish Economic Society

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.16 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 meanreverting (the highest value is 0.578 by Belgium in 1984).

17 I would like to thank the same referee for this interesting comment.

‘BRAIN DRAIN’ OR ‘BRAIN CIRCULATION’ 627

r 2008 The AuthorJournal compilation r 2008 Scottish Economic Society

are modified versions of equations (1) and (2) and account for the inter-

action 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-statistic5 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

THANH LE628

r 2008 The AuthorJournal compilation r 2008 Scottish Economic Society

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 do 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:

eflij ¼

@ log yi@ logSDj

¼ @ log yi

@ logSFli

@ logSFli

@ logSDj¼ bfl

@ logSFli

@ logSDj¼ bfl

@SFli

@SDj

SDj

SFli

;

where l5 g, k. By construction, SFli ¼

Pj 6¼i

lijnjSDj so

@SFli

@SDj¼ lij

nj: Inserting the

results into the above equation gives:

eflij ¼ bfl

lij

nj

SDj

SFli

:

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 3 and 4.

The figures in Tables 3 and 4 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

‘BRAIN DRAIN’ OR ‘BRAIN CIRCULATION’ 629

r 2008 The AuthorJournal compilation r 2008 Scottish Economic Society

Table3

InternationaloutputelasticitiesofdomesticR&D

capitalstocks(inward

migration),1980–1990

AUS

AUT

BEL

CAN

DNK

FIN

FRA

DEU

GRC

ITA

JPN

NLD

NOR

PRT

ESP

SWE

CHE

GBR

USA

Average

AUS

0.000070.000010.000080.000110.000190.000050.000060.001010.000100.000440.000120.000080.00009

0.000050.000080.000040.001150.00016

0.00021

AUT

0.00022

0.000060.000250.000180.000170.000140.003710.000290.000310.000050.000310.000190.00013

0.000240.000320.001390.000250.00055

0.00046

BEL

0.000090.00017

0.000380.000200.000130.004300.000690.000810.000500.000130.004560.000270.00087

0.001400.000080.000500.000620.00039

0.00085

CAN

0.000310.000210.00011

0.000410.000550.000380.000210.001600.000230.001140.000330.000360.00123

0.000150.000160.000160.001880.00609

0.00082

DNK

0.000110.000090.000160.00024

0.000660.000190.000330.000180.000110.000100.000200.005340.00024

0.000520.003960.000140.000560.00026

0.00070

FIN

0.000120.000120.000040.000200.00076

0.000090.000270.000160.000070.000100.000110.001010.00012

0.000460.017720.000110.000160.00018

0.00115

FRA

0.000360.000930.011570.001230.001530.00086

0.003980.002040.003720.001290.002180.001060.00342

0.004250.000770.005570.003700.00172

0.00264

DEU

0.002840.027310.003810.004210.007020.004610.00574

0.005680.007070.001640.012010.002840.00570

0.007490.003850.010340.004450.01141

0.00674

GRC

0.000080.000010.000070.000040.000010.000010.000010.00037

0.000080.000000.000030.000010.00000

0.000000.000040.000020.000040.00006

0.00005

ITA

0.002430.001520.012210.003030.000610.000430.009980.010650.00103

0.000160.001700.000230.00051

0.000960.000440.017390.003010.00345

0.00367

JPN

0.000630.000650.000400.000480.000580.000670.001160.001000.000240.00067

0.001150.000340.00042

0.000420.000420.000250.002270.00442

0.00085

NLD

0.002150.001100.007840.002650.001490.000890.001720.005110.001040.000790.00030

0.001490.00189

0.002470.000680.001290.002290.00135

0.00192

NOR

0.000040.000090.000070.000130.005360.000980.000140.000160.002730.000060.000100.00025

0.0001910.000520.007130.000100.000550.00043

0.00100

PRT

0.000010.000000.000070.000120.000010.000010.002200.000140.000000.000010.000000.000070.00001

0.000170.000010.000340.000070.00011

0.00018

ESP

0.000040.000040.000720.000030.000070.000080.002320.000710.000060.000130.000040.000470.000080.00088

0.000090.001450.000270.00012

0.00040

SWE

0.000170.000750.000420.000220.007720.020220.000600.000630.000820.000290.000300.000550.008830.00085

0.00161

0.000640.001110.00098

0.00246

CHE

0.000520.004820.000690.001370.001840.001690.004860.003350.001220.007000.000920.001060.001020.00147

0.002830.00129

0.001790.00130

0.00205

GBR

0.032680.001910.003740.019600.009980.004700.006560.005230.008590.005280.005440.012410.009760.01171

0.015610.003510.00266

0.01203

0.00902

USA

0.002200.005180.003020.010760.007130.008170.004590.008410.017500.018600.032860.007510.012090.01529

0.005860.004450.002620.02085

0.00985

Notes:Allcalculationsare

basedonequation(1)in

Table

2:logFit¼

0:096logSD

itþ0:128G7�logSD

itþ0:045logSFg it.

Estim

atedelasticityofoutputiscalculated(usingendperiodmigrantstock

values)forthecolumncountrywithrespectto

theR&D

capitalstock

intherow

country.

THANH LE630

r 2008 The AuthorJournal compilation r 2008 Scottish Economic Society

Table4

InternationaloutputelasticitiesofdomesticR&D

capitalstocks(outward

migration),1980–1990

AUS

AUT

BEL

CAN

DNK

FIN

FRA

DEU

GRC

ITA

JPN

NLD

NOR

PRT

ESP

SWE

CHE

GBR

USA

Average

AUS

0.002320.00114

0.00073

0.002690.00199

0.00123

0.002770.00785

0.00373

0.001900.00871

0.00076

0.00059

0.000850.00170

0.00269

0.023890.00433

0.00370

AUT

0.00029

0.00020

0.00004

0.000190.00018

0.00028

0.002320.00009

0.00020

0.000170.00039

0.00015

0.00001

0.000070.00065

0.00218

0.000120.00089

0.00040

BEL

0.00038

0.00033

0.00015

0.002170.00038

0.02209

0.002060.00357

0.01042

0.000670.01765

0.00070

0.00165

0.008840.00228

0.00198

0.001520.00330

0.00420

CAN

0.00943

0.006020.01176

0.013430.00800

0.00992

0.009670.01026

0.01096

0.003380.02525

0.00559

0.01129

0.001420.00510

0.01673

0.033790.04997

0.01270

DNK

0.00032

0.000110.00015

0.00006

0.00077

0.00031

0.000410.00006

0.00006

0.000100.00036

0.00595

0.00002

0.000100.00454

0.00057

0.000440.00084

0.00080

FIN

0.00016

0.000030.00003

0.00002

0.00029

0.00005

0.000080.00002

0.00001

0.000040.00006

0.00033

0.00000

0.000030.00360

0.00016

0.000060.00029

0.00030

FRA

0.00200

0.001300.05083

0.00083

0.003960.00131

0.005030.00126

0.01376

0.003120.00624

0.00225

0.08183

0.046040.00532

0.02268

0.004300.00812

0.01370

DEU

0.00635

0.079100.01872

0.00105

0.015950.00946

0.02798

0.07670

0.03348

0.006100.04236

0.00622

0.01199

0.032150.01267

0.03561

0.007840.03394

0.02410

GRC

0.00017

0.000010.00004

0.00001

0.000020.00001

0.00002

0.00002

0.00001

0.000000.00001

0.00018

0.00000

0.000000.00003

0.00002

0.000020.00012

0.00000

ITA

0.00113

0.000740.00152

0.00013

0.000620.00026

0.00297

0.001600.00186

0.000460.00075

0.00024

0.00012

0.000660.00066

0.00845

0.000900.00852

0.00170

JPN

0.00466

0.000130.00038

0.00061

0.000510.00037

0.00099

0.000360.00000

0.00005

0.00027

0.00043

0.00004

0.000190.00066

0.00107

0.000890.01445

0.00140

NLD

0.00201

0.001110.02075

0.00027

0.001650.00067

0.00258

0.004050.00103

0.00090

0.00119

0.00158

0.00102

0.003590.00186

0.00190

0.003140.00511

0.00290

NOR

0.00037

0.000200.00036

0.00009

0.012860.00174

0.00037

0.000280.00007

0.00004

0.000100.00061

0.00005

0.000170.00876

0.00053

0.000720.00240

0.00160

PRT

0.00001

0.000000.00003

0.00001

0.000020.00006

0.00003

0.000020.00000

0.00000

0.000000.00002

0.00001

0.000060.00002

0.00002

0.000020.00009

0.00000

ESP

0.00011

0.000110.00083

0.00002

0.000550.00035

0.00065

0.000330.00001

0.00007

0.000060.00045

0.00043

0.00032

0.00071

0.00065

0.000510.00052

0.00040

SWE

0.00160

0.001380.00042

0.00016

0.038620.12309

0.00108

0.001540.00177

0.00028

0.000510.00112

0.05408

0.00024

0.00078

0.00273

0.001050.00359

0.01230

CHE

0.00339

0.025690.01159

0.00069

0.005820.00321

0.03392

0.017910.00410

0.04740

0.001350.00930

0.00328

0.02480

0.056910.01125

0.003450.00916

0.01440

GBR

0.06311

0.003040.00939

0.00519

0.015310.00318

0.01464

0.005000.00421

0.00533

0.007830.01067

0.01168

0.00345

0.006950.01258

0.01072

0.04735

0.01260

USA

0.09750

0.071380.06486

0.18295

0.078350.03804

0.07387

0.139570.08012

0.06633

0.166020.06878

0.09914

0.05555

0.034190.12062

0.08431

0.11033

0.08590

Notes:Allcalculationsare

basedonequation(2)in

Table

2:logFit¼

0:067logSD

itþ0:079G7�logSD

itþ0:193logSFk it.

Estim

atedelasticityofoutputiscalculated(usingendperiodmigrantstock

values)forthecolumncountrywithrespectto

theR&D

capitalstock

intherow

country.

‘BRAIN DRAIN’ OR ‘BRAIN CIRCULATION’ 631

r 2008 The AuthorJournal compilation r 2008 Scottish Economic Society

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 3 and the last row of Table 4. 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 interna-

tional 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

THANH LE632

r 2008 The AuthorJournal compilation r 2008 Scottish Economic Society

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 cross-

sections 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 riyit�1 þXpij¼1

yijDyit�j þ ai þ eit with t ¼ 1; . . . ;T

for each i 2 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 5 0,

8i against the alternative H1: rIo0.

From the regression, we obtain the following statistic:

�tNT ¼1

N

XNi¼1

tiT ðpi; yiÞ;

where tiT ðpi; yiÞ is the individual t-statistic for testing ri 5 0, 8i. As soon as

T ! 1, followed by N ! 1 while NT! k (a finite non-negative constant), the

standardized t-bar statistic is

W�t ¼

ffiffiffiffiNp

�tNT � 1N

PNi¼1

E tiT ð pi; 0Þjri ¼ 0ð Þ� �ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1N

PNi¼1

Var tiT ð pi; 0Þjri ¼ 0ð Þs ! Nð0; 1Þ

where E tiT ðpi; 0Þjri ¼ 0ð Þ and Var tiT ð pi; 0Þjri ¼ 0ð Þ are tabulated in their

paper.

‘BRAIN DRAIN’ OR ‘BRAIN CIRCULATION’ 633

r 2008 The AuthorJournal compilation r 2008 Scottish Economic Society

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 ai þ b1ix1it þ b2ix2it þ � � � þ bMixMit þ eit

(2) Difference the original series and estimate the differenced regression:

Dyit ¼ b1iDx1it þ b2iDx2it þ � � � þ bMiDxMit 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 it�1 þXKi

k¼1gikD eK it�k þ uit

The null hypothesis of the test is H0: gi 5 0, 8i against the alternative

H1: gio0. From this regression, we compute simple variance of uK it,

denoted sK2i .

(5) Calculate Panel t-statistic:

Zt;NT ¼ ~S2NT

XNi¼1

XTt¼1

LK �211i eK2it�1

!�1=2

�XNi¼1

XTt¼1

LK �211i eK it�1D eK it

where ~S2NT ¼ 1

N

PNi¼1 sK

2i . It is shown that:

Zt;NT �Y2 Y1ð1þY3Þ½ ��1=2ffiffiffiffiNp! N 0;f0ð3ÞCð3Þfð3Þ

� �In this notation Yj, j5 1,2,3 are elements of the mean vector Yof Brownian motion functions; f0ð3Þ ¼ ððY1ð1þY3ÞÞ�1=2;� 1

2Y2Y

�3=21 ð1þY3Þ�1=2;� 1

2Y2Y

�1=21 ð1þY3Þ�3=2Þ; and C(3) is the

upper sub-matrix of the Brownian motion covariance matrix C.

Compute the panel cointegration test statistic

wN;T � mffiffiffiffiNp

ffiffiffivp ! Nð0; 1Þ

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.

THANH LE634

r 2008 The AuthorJournal compilation r 2008 Scottish Economic Society

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 EconomicReview, 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. Journalof Macroeconomics, 23, 1, pp. 241–60.

ENGELBRECHT, H. (1997). International R&D spillovers, human capital and productivityin OECD economies: an empirical investigation. European Economic Review, 41,pp. 1479–88.

FRANTZEN, D. (2000). R&D, human capital and international technology spillovers: a cross-country analysis. Scandinavian Journal of Economics, 102, 1, pp. 57–75.

FRANTZEN, D. (2002). Intersectoral and international R&D knowledge spillovers and totalfactor 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 dataanalysis 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 andgrowth. 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 spilloversamong 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 orsubstitutes? Economic Letters, 94, pp. 155–62.

LICHTENBERG, F. and VAN POTTELSBERGHE, B. (1998). International R&D spillovers: acomment. European Economic Review, 42, pp. 1483–91.

‘BRAIN DRAIN’ OR ‘BRAIN CIRCULATION’ 635

r 2008 The AuthorJournal compilation r 2008 Scottish Economic Society

MOUNTFORD, A. (1997). Can a brain drain be good for growth in the source economy? Journalof 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. EconomicEnquiry, 33, 4, pp. 571–91.

PEDRONI, P. (1999). Critical values for cointegration tests in heterogenous panels with multipleregressors. Oxford Bulletin of Economics and Statistics, (Special Issue), pp. 653–70.

RAUCH, J. and CASELLA, A. (2003). Overcoming informational barriers to internationalresource allocation: prices and ties. Economic Journal, 113, 484, pp. 21–42.

RAUCH, J. and TRINIDADE, V. (2002). Ethnic Chinese networks in international trade. Reviewof 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 communitiescommunity upgrading in India and China. Studies in Comparative InternationalEconomies, 40, 2, pp. 35–61.

SOETE, L. and PATEL, P. (1985). Recherche-developpement, importations de technologieet croissance economique: une tentative de comparaison internationale. Revue Econo-mique, 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, humancapital 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 transfertechnology 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 PopulationEconomics, 11, pp. 589–600.

WONG, K. and YIP, C. (1999). Education, economic growth, and brain drain. Journal ofEconomic Dynamics and Control, 23, pp. 699–726.

Date of receipt of final manuscript: 11 April 2008

THANH LE636

r 2008 The AuthorJournal compilation r 2008 Scottish Economic Society