FOREIGN DIRECT INVESTMENT AND COUNTRY-SPECIFIC HUMANCAPITAL

JINYOUNG KIM and JUNGSOO PARK∗

This paper exploits an international bilateral data set over the period 1963–1998to investigate the relationship between foreign direct investment (FDI) and foreign-educated labor in an FDI host country. Workers educated abroad acquire country-specific human capital that is more productive in the host country of study. A foreignsubsidiary sharing a parent firm’s technology will invest more if it has more foreign-educated labor, since it can utilize this labor more productively because of the country-specific human capital. Consistent with our predictions, our empirical findings showthat foreign-educated labor accounted for a sizable portion of growth in FDI flows.(JEL F21, F10)

I. INTRODUCTION

The past three decades have witnessed anunprecedented increase in foreign direct invest-ment (FDI) in the world; annual FDI flowsfrom and to Organisation for Economic Cooper-ation and Development (OECD) countries haveincreased more than 30-fold from 1982 to 2005,while the world trade merely quadrupled dur-ing the same period; the ratio of gross privatecapital flows to gross domestic product (GDP)has risen from 10.3% in 1990 to 32.4% in 2005;the ratio of FDI flows to GDP has climbed upfrom 2.2% in the early 1990s to 4.4% in 2005.1

As FDI gained importance in the internationalmovement of capital, a variety of theoretical andempirical studies have investigated the factorsthat determine FDI.2 In this article, we offer animportant but under-studied determinant of FDI:foreign-educated labor in FDI host countries.

*Jinyoung Kim acknowledges financial support throughthe National Research Foundation of Korea Grant funded bythe Korean Government (NRF-2010-330-B00093). JungsooPark was supported by Sogang University Research Grant.We thank the editor, a referee, and participants in seminarsat several universities for helpful comments. All errors areexclusively the responsibility of the authors.Kim: Professor, Department of Economics, Korea Univer-

sity, Anam-dong, Seongbuk-gu, Seoul 136-701, SouthKorea. Phone 82-2-3290-2202, Fax 82-2-928-4948,E-mail jinykim@korea.ac.kr

Park: Professor, School of Economics, Sogang University,Shinsu-dong, Mapo-gu, Seoul 121-742, South Korea.Phone 82-2-705-8697, Fax 82-2-704-8599, E-mailjspark@sogang.ac.kr

1. World Development Indicators 2007, World Bank.2. See Edwards (1990) and Markusen and Maskus

(2001) for literature surveys on the determinants of FDI.

Specifically, using international bilateral data onFDI and foreign education, we present empiricalevidence on the role of country-specific foreign-educated labor in an FDI host country as a deter-minant of foreign direct investments.

Production involves the process of combin-ing physical capital and the human capital ofemployees. Human capital includes the gen-eral skills of workers, but it also includes theirknowledge of firm-specific technology, man-agerial skills specific to the organization, andefficient communication skills with co-workers.Consequently, the labor that possesses thesefirm-specific skills, which can be readily usedby a firm, can be more productive in that firmthan in other firms. When a firm invests in aforeign country through a subsidiary that sharesthe technology of the parent firm, the labor forcethat has acquired various types of human capitalspecific to the parent firm, and thus to the sub-sidiary, can be more productive in the foreignsubsidiary. For instance, the local managers ofa foreign subsidiary who are able to speak thesame language of the managers of their parentfirm, or who know thoroughly well how theparent firm and its subsidiaries are organized

ABBREVIATIONS

FDI: Foreign Direct InvestmentFDIL: Foreign Direct Investment LevelGDP: Gross Domestic ProductOECD: Organisation for Economic Cooperation and

DevelopmentOLS: Ordinary Least Squares

1

Economic Inquiry(ISSN 0095-2583)

doi:10.1111/j.1465-7295.2012.00478.x© 2012 Western Economic Association International

2 ECONOMIC INQUIRY

and operated, can be more productively utilizedin the subsidiary firm. Since human capital spe-cific to a firm and its foreign subsidiaries canbe acquired through education provided in thecountry of the parent firm, the availability ofworkers in a potential FDI host country whostudied in the parent-firm country can be animportant deciding factor for a firm in investingabroad through its foreign subsidiary.3

Foreign training can provide various typesof country-specific human capital that wouldincrease labor productivity more in the hostcountry of training than in other countries.Country-specific human capital includes lan-guage capital, which has been shown by numer-ous papers such as Chiswick and Miller (1993)and Lazear (1999) to have considerable impor-tance in labor market performances. Knowledgeon firm organization and social system is anotherexample of country-specific human capital thatcan be acquired through foreign training. For-eign education can also enhance the productivityof students in firms of the host country, becauseit provides information about the ability andpotential of students in a form that can be moreeasily processed by firms in the host country.Greater knowledge regarding employees allowsfirms to utilize manpower more efficiently andenhance workers’ productivity, as pointed outby Becker (1993), who discusses low workermobility across countries as evidence of theimportance of country-specific training.

Earlier theoretical models in internationaltrade, such as MacDougall (1960), attributeinternational capital movement to the differencein factor endowment measured by physical cap-ital per worker.4 Empirical findings, however,show that the actual capital flows fall short of thetheoretical prediction that capital moves fromdeveloped economies to developing economiesto take advantage of the higher rates of returns tocapital.5 According to Lucas (1990), assuming

3. Parent firms often train local labor hired in sub-sidiaries at their own expense in order to develop firm-specific skills. For the example of Gillette Corporation, seeLaabs (1993). However, some skill training, such as for-eign language education, is general enough to be acquiredthrough foreign education paid in part by students.

4. With the industrial organization approach to trade,such theoretical models as Helpman and Krugman (1985)predict that a multinational firm will extend its business tocountries that differ significantly in relative endowments,but not to very similar countries. Other trade models, forexample, Kemp (1966) and Jones (1967), consider technol-ogy differences as a major factor in capital movements.

5. To explain the larger flows of FDI between devel-oped economies than between developed and developing

that human capital and physical capital are com-plementary in production, physical capital failsto flow into developing economies owing tothe lack of human capital endowment in thoseeconomies.6

Kim and Park (2011) incorporate country-specific human capital into the standard modelof FDI determination. They argue that theavailability of country-specific foreign-educatedhuman capital, not just human capital in gen-eral, is important in attracting country-specificFDI, because human capital specific to an FDIsource country available in a host country can bemore conducive to the operations of subsidiariesfrom the source country. More specifically, theydevelop a multi-country model of productionwhere domestic and foreign firms utilize capi-tal as well as two types of labor—domesticallyeducated and foreign-educated labor. The formertype of labor has human capital specific to thehome country, and the latter has skills specific tothe foreign country of study. They postulate thatthe two types of labor inputs are imperfect sub-stitutes in production owing to country-specificskills, and that foreign-educated labor has therelative advantage in productivity for foreignsubsidiaries owing to the existence of organi-zational capital (Prescott and Visscher 1980) orfirm-specific technologies learned through for-eign education.

The model shows that an increase in foreign-educated labor will unambiguously raise thedemand for capital in foreign subsidiaries, andthus for foreign direct investment. According tothis implication, it is likely that having moreU.S.-educated managers in Korea, for example,would make it easier for U.S. firms to directlyinvest in Korea. Furthermore, the direct invest-ment of U.S. firms in Korea will be influenced

economies, “horizontal” models such as Markusen (1984),Horstmann and Markusen (1992), Brainard (1993), andMarkusen and Venables (2000) argue that, given the mod-erate to high trade costs and plant-level as well as firm-level scale economies, multinational firm activity will arisebetween similar countries.

6. Zhang and Markusen (1999) also provide a modelwhere the availability of skilled labor in the host countryinfluences the volume of FDI inflows. Empirical evidencefor the effect of human capital on FDI has been scarcewith mixed results. Root and Ahmed (1979) do not findhuman capital to be a significant factor of FDI inflowsfor 58 developing countries, whereas Noorbakhsh, Paloni,and Youssef (2001) show that human capital has becomea significant determinant of FDI inflows to developingcountries in more recent years. Benhabib and Spiegel (1994)provide indirect empirical support for the role of humancapital in FDI, by showing that human capital inducesgreater accumulation of physical capital stock.

KIM & PARK: FDI AND COUNTRY-SPECIFIC HUMAN CAPITAL 3

TABLE 1Educational Backgrounds of CEOs in Foreign and Domestic Firms in Korea

Foreign Firms Domestic Firms

Number of CEOs Percentage Number of CEOs Percentage

Total 73 218Educated in Korea 51 69.86 175 80.28Foreign educated 22 30.14 43 19.72Countries matched 12 54.55Not matched 10 45.45

Notes: Among the 100 CEOs of the top 100 foreign multinational firms in sales that operated in Korea in 2006–2007,we identified the host countries of tertiary education for 92 CEOs but excluded from our sample 19 CEOs with non-Koreannames. For the sample of CEOs at domestic firms, we first selected the top 500 domestic firms, and then excluded those withsales bigger than the largest foreign firm in our foreign-firm sample. For consistency, we also excluded CEOs of domesticfirms with non-Korean names.

only by the abundance of U.S.-educated Koreanmanagers, and not by the presence of man-agers educated in countries other than the UnitedStates.

We find evidence to support this theory onthe importance of foreign education in attractingFDI when we compare the educational back-grounds of chief executives of Korean domes-tic firms with those of foreign multinationalfirms operating in Korea. We collected data onthe destination countries for tertiary educationof the CEOs at the top 100 foreign multina-tional firms in sales as well as for those at thetop 500 domestic firms for the years 2006 and2007.7 Table 1 reports the proportion of foreign-educated CEOs in foreign and domestic firms.The results in the table indicate that foreignfirms are more likely to hire foreign-educatedexecutives than domestic firms: 30.1% of theCEOs in foreign firms studied in foreign coun-tries, while only 19.7% of CEOs in domesticfirms did so. This difference is statistically sig-nificant by the t-test statistic for difference ingroup proportion at the 5% level.8 Furthermore,

7. Looking up company websites and who’s-who infor-mation on the Internet, we were able to determine the coun-tries of study for 92 CEOs of foreign firms. For the CEOs ofdomestic firms, we used data from the Federation of KoreanIndustries (2008). From the 500 domestic firms, we excludedthose with sales greater than the largest foreign firm in thesample. Furthermore, for both the foreign and domestic firmsamples, we excluded those CEOs with non-Korean names.The sample sizes after the selection are 73 and 218 CEOsfor foreign and domestic firms, respectively, and the meanof sales is 0.4 trillion Korean won for foreign firms and1.05 trillion Korean won for domestic firms.

8. We performed a t-test with the null hypothesis thatthe group proportions of both samples are the same andthe alternative hypothesis that the share of foreign-educatedCEOs is greater for foreign firms. We were able to reject thenull hypothesis at the 5% level (p value = .0322).

we find that among the foreign-educated CEOsof foreign firms, the proportion of those whosehost country of study is the same as the countryof the multinational firm is 54.6%.

To further investigate the validity of ourhypothesis, this paper empirically tests whetherforeign-educated labor attracts FDI from thehost country of education against bilateral paneldata for 63 developed and developing countriesover the period 1963–1998. Our empirical find-ings strongly support our predictions and showthat foreign-educated labor accounts for a siz-able portion of growth in FDI flows during thesample period.

This paper is organized as follows. Section IIdetails the empirical methodology and the dataset used in our empirical analysis. Section IIIshows that the number of foreign-educated stu-dents, which approximates the size of labor poolwith country-specific human capital, has a posi-tive effect on FDI inflow from the foreign coun-try where the students were educated. This effectis robustly present when we control for fac-tors such as transportation cost, market size, andgrowth in FDI host and source countries, tradevolume, and dyad-specific idiosyncratic effects.Section IV concludes the article with policyimplications and a discussion on the importanceof foreign-educated labor in explaining observedtime-series changes in FDI since 1980.

II. EMPIRICAL IMPLEMENTATION

We test our prediction on the relationshipbetween foreign-educated labor and FDI againstbilateral FDI and student flow panel data.We estimate a reduced-form regression modelwith FDI as the dependent variable. A set of

4 ECONOMIC INQUIRY

explanatory variables includes the number ofstudents abroad and other explanatory variablesidentified in the earlier studies on FDI.

A. Description of Variables Used

The data for annual foreign direct investmentinflows and outflows are taken from OECD’sInternational Direct Investment Statistics Year-book (1999), covering 63 countries for theperiod 1980–1998. The countries are listed inAppendix A.

We take the number of students who studiedabroad in country j as proxy for the size oflabor educated in foreign country j . Our dataon students abroad are taken from UNESCO’sStatistical Yearbooks. The data contain annualbilateral flows of students studying abroad atthe tertiary level. The data are available for 63countries over the period 1963–1996, althoughfor some countries the data are not available forall the years.9

One concern for using this variable as a mea-sure for the size of labor educated in foreigncountry j is that some students do not return totheir home countries after completion of studyabroad. Based on the surveys of foreign stu-dents, Glaser (1979) reports that the return ratesamong the students are quite high in most largehost countries, with the exception of the UnitedStates, and non-returning emigrant students typ-ically maintain a close connection with theirhome countries. Moreover, many of these non-returning students show interest in working laterin their home countries as managers for multi-national firms. As a robustness check in theempirical section, we estimate the relationshipbetween students abroad and FDI in the sub-sample that excludes the United States (Table 5).Since a 15-year-lagged value of students abroadis used as an explanatory variable in the baselinemodel of our empirical study (the reason for thetime lag is given in the next section), we use theforeign student data over the period 1965–1983to match with the FDI data for 1980–1998. Thisimplies that the survey results in Glaser (1979)are relevant in characterizing the students in ourdata set.

Another related issue is that foreign-educatedlabor in our story may include foreign labormigrating from the FDI source country. Onemay argue that the effect of foreign-educated

9. Kim (1998) reports the descriptive analysis of thedata in earlier years, and the effect of foreign education oneconomic growth using the data.

labor on FDI cannot be tested with our dataon foreign students if the students abroad areinversely correlated with foreign labor so thatchanges in the number of students abroad cannotcapture the changes in total foreign-educatedlabor. However, the OECD country sample from1986 to 1995 demonstrates a positive correlationbetween a change in the foreign labor force andthe flow of students abroad with a 15-year lag:the correlation coefficient is 0.4330. In addition,we estimate the effect of students abroad on FDIin the sample of countries with high shares offoreign labor in the total labor force to checkwhether our estimation results are still robust inthose countries that are relatively open to foreignlabor inflows (Table 5).

The third concern is that our measure isnot a stock variable. In the long-run steady-state equilibrium, however, the flow variableof students abroad should have a one-to-onepositive relationship with the stock variable. Ourempirical specifications attempt to estimate therelation of the stock variable of students abroadand FDI in the steady-state equilibrium. Sincea long-run relationship is better estimated withcross-country variations in the data, we reportin the section for sensitivity analysis a panelregression result with between effects.

To control for the transaction cost factorsin FDI, our explanatory variables include thedistance between a source country and a hostcountry, and three binary variables for whetherthe same language is used in two countries,whether they practice the same religion, andwhether one country was once a colony of theother. We also include as explanatory variableswhat are identified as determinants of FDI in theliterature: the ratio of per capita GDP in an FDIsource country to that in a host country, the totalGDP levels and the per-capita GDP growth ratesin both countries, the GDP shares of domesticinvestment and government spending in an FDIhost country, and the real exchange rate of anFDI host country. The summary statistics ofall variables used in this study are providedin Table 2, and the data sources of all controlvariables are described in Appendix A.

B. Econometric Model Specification

Our baseline specification is a log-linearmodel:

ln(FDIij t ) = di + dj + dt + di ·T + dj ·T(1)

+ β0 ln(STDTij t−15) + β′Xijt+ εij t ,

KIM & PARK: FDI AND COUNTRY-SPECIFIC HUMAN CAPITAL 5

TABLE 2Descriptions and Summary Statistics of Variables

Variables Descriptions M SD

STDTNij Number of students from country j who were enrolled at institutions ofhigher education in country i

669.3 1,898

STDTij STDTNij as a share of the population in country j 0.0439 0.1646FDILij Real FDI from country i to country j (1991 U.S. $ billions) 4.817 17.72FDIij FDILij as a share of country i’s GDP 0.0267 0.0975DISTANCE Distance between the capitals of FDI source and host countries (100 km) 58.45 48.02LANGUAGE = 1 if the most popular languages in both countries are the same 0.2054 0.4040RELIGION = 1 if the most popular religions in both countries are the same 0.3283 0.4696COLONY = 1 if the FDI host country used to be a colony of the source country 0.0225 0.1482GDP1 (GDP2) Real GDP of an FDI source (host) country (1991 U.S. $) 1.31e+9

(8.10e+8)1.75e+9

(1.39e+9)POP2 Population size (1,000) 84,935 188,486RELPCGDP Ratio of per capita real GDP of an FDI host country to that of a source

country0.9711 1.269

GROW1 (GROW2) Real GDP growth rate of an FDI source (host) country 0.0395(0.0578)

0.1267(0.3334)

I Domestic investment rate of an FDI host country 21.71 6.764G GDP share of government spending in an FDI host country 17.35 5.354EXCHANGE Real exchange rate of an FDI host country 82.37 36.58TRADEij Real exports of country i to country j (1991 U.S. $ millions) as share of

country i’s GDP3.927 8.656

TARIFF Average tariff rate of an FDI host country 13.24 12.38TOURIST Total annual number of tourists in an FDI host country 1.18e+7 1.36e+7TER1 (TER2) Tertiary school enrollment rate in an FDI source (host) country 35.10

(28.62)14.68

(17.19)SEC1 (SEC2) Secondary school enrollment rate in an FDI source (host) country 93.48

(80.10)15.74

(23.76)STDTNREST Total number of students from country j who studied in foreign

countries other than country i5,973 6,239

FDILREST FDI inflows from countries other than country i 67.80 156.2TOTFDIL Total FDI inflow from all source countries in a given year 7,012 14,924FDISTOCK Stock of total FDI (according to Lane and Milesi-Ferretti, 2001) 58,173 112,094

where FDIij t is the real FDI from country i tocountry j in year t as a share of country i’sGDP, and STDTij t−15 represents the studentsabroad from country j who studied in countryi in year t − 15 as a share of country j ’s pop-ulation. FDI and STDT are paired in a reversedirection as is required in our theory: FDI fromcountry i to country j , and students from coun-try j to country i. Note also that the numberof students abroad 15 years ago is paired withFDI of the current year to account for the timeneeded to acquire education and return home.This time lag will also help avoid the reversecausality and endogeneity problems.10 X ij t isa vector of regressors, including the logarithmsof all the explanatory variables discussed in the

10. Instead of the 15-year gap, we also used gaps ofdifferent years (ranging from 5 to 25 years) as alternatives,which yielded qualitatively the same result regarding theeffect of STDT on FDI (see Section IV).

previous section; di and dj are country-specificdummy variables for an FDI source country anda host country, respectively; and dt is a dummyvariable for calendar years. In this specification,we also include a time trend variable (T ) and itsinteraction terms with dummy variables di anddj to allow for country-specific time trends. Theerror term εij t is assumed to be distributed i.i.d.with zero mean.

As part of sensitivity analysis, we conductedregressions under various types of specifications,including models with the level of FDI as adependent variable and dyad-specific random,fixed, and between effects.

III. EMPIRICAL FINDINGS

A. Results from Regression Analysis of FDI

This section reports the results obtained fromthe regression model in subsection “Econometric

6 ECONOMIC INQUIRY

TABLE 3FDI Regressions

(1) (2) (3) (4)

BaselineTrade Volume

AddedExtra Regressors

AddedDependent

Variable = FDI Level

Coef. SE Coef. SE Coef. SE Coef. SE

ln(STDT) 0.240∗∗∗ (0.018) 0.167∗∗∗ (0.018) 0.248∗∗∗ (0.024)ln(STDTN) 0.240∗∗∗ (0.018)ln(DISTANCE) −0.553∗∗∗ (0.031) −0.158∗∗∗ (0.041) −0.597∗∗∗ (0.042) −0.559∗∗∗ (0.031)LANGUAGE 0.140∗∗ (0.056) 0.081 (0.054) 0.151∗∗ (0.076) 0.142∗∗ (0.056)RELIGION 0.321∗∗∗ (0.047) 0.249∗∗∗ (0.046) 0.310∗∗∗ (0.064) 0.326∗∗∗ (0.047)COLONY 0.263∗ (0.148) 0.143 (0.145) 0.174 (0.197) 0.264∗ (0.148)ln(RELPCGDP) 0.887∗ (0.537) 0.378 (0.524) 1.308 (1.250) 0.461 (1.091)ln(GDP2) −0.118 (0.789) −0.394 (0.785) −1.651 (1.802) 0.323 (1.263)ln(GROW1) 0.056∗ (0.033) 0.060∗ (0.032) −0.012 (0.052) 0.070∗∗ (0.034)ln(GROW2) 0.015 (0.029) 0.013 (0.029) 0.018 (0.044) 0.015 (0.029)ln(I) 0.138 (0.290) 0.140 (0.297) 0.316 (0.529) 0.114 (0.292)ln(G) −0.606∗∗ (0.298) −0.416 (0.293) −0.201 (0.432) −0.569∗ (0.301)ln(EXCHANGE) 0.703∗∗∗ (0.171) 1.027∗∗∗ (0.202) 0.694∗∗ (0.347) 0.676∗∗∗ (0.170)ln(TRADE) 0.537∗∗∗ (0.037)ln(TARIFF) −0.042 (0.234)ln(TOURIST) 0.562∗∗ (0.268)ln(TER1) 1.466∗∗∗ (0.811)ln(SEC1) 0.424 (0.589)ln(TER2) −0.806 (0.542)ln(SEC2) 0.417 (0.574)ln(GDP1) 1.152 (1.100)ln(POP2) −0.593 (1.873)Observations 4,761 4,674 2,765 4,765Adjusted R2 0.7137 0.7272 0.7168 0.7334

Notes: The rows show the estimated coefficient and robust standard error (in parentheses) for each independent variable.∗∗∗Significant at 1%; ∗∗significant at 5%; ∗significant at 10%.The dependent variable in all models except Model 4 is the log of the GDP share of FDI. Model 4 uses the logs of

FDI level (FDIL) as the dependent variable and student number (STDTN) as a regressor, which allows the logs of GDP1and POP2 to be included as additional regressors. All models include as regressors source-country-specific constants andtime trends, host-country-specific constants and time trends, and calendar-year dummies. Domestic human capital variables(TER1, TER2, SEC1, and SEC2) included in Model 3 are lagged 5 years instead of 15 years owing to the limitation in dataavailability.

Model Specification” in Section II. In Table 3,the dependent variable is the ratio of FDI toGDP in an FDI source country. All variablesare entered in logs, except binary variables.Model 1 presents our baseline ordinary leastsquares (OLS) estimates. In Models 2 and 3,we introduce additional regressors to the base-line model to further isolate the autonomouseffect of STDT on FDI. In Model 4, we run thesame baseline specification with the FDI level(FDIL) as the dependent variable instead of theGDP ratio of FDI, and the number of students(STDTN) as a regressor in place of the popula-tion share of students, all in logs.

Table 3 indicates that the effect of STDTon FDI is statistically significant and consistentwith our theory: more foreign direct investmentflows into a country with a larger pool of

workers who studied in an FDI source country.STDT has a statistically significant effect in allregressions, regardless of the alternative sets ofexplanatory variables introduced.

Factors such as shorter distance, using thesame language, and having the same reli-gion may reduce the transaction costs in FDIand increase foreign direct investment flows.This prediction is confirmed by all the mod-els in Table 3. The estimated effects of stu-dents abroad on FDI, thus, cannot be ascribedto geographical and cultural proximity of coun-tries, which will generate a bias toward a pos-itive association between students abroad andFDI. The results in Table 3 suggest that moreFDI flows into those countries that used to becolonies of an FDI source country, although theeffect is only marginally significant.

KIM & PARK: FDI AND COUNTRY-SPECIFIC HUMAN CAPITAL 7

Various macroeconomic variables are in-cluded as explanatory variables to incorporatethe earlier findings that show FDI decisionsare affected by changes in macroeconomic con-ditions such as market size, market growth,exchange rates, and investment environment.11

The per capita GDP ratio (RELPCGDP) isincluded as a regressor to proxy the (inverse ofthe) relative returns to capital. We expect that themore FDI invested, the higher (lower) the rate ofreturns to capital in the FDI host (source) coun-try will be. Furthermore, the per capita incomein the source country may proxy the demandfor a clean environment, which can be a pushfactor for FDI to relocate polluting manufactur-ing facilities to other countries. In both effects,the per capita GDP ratio is expected to have anadverse effect on FDI. The results in Table 3support this prediction, albeit the effect is onlymarginally significant.

The real GDP level of an FDI host coun-try (GDP2) is expected to have the effect ofscale economies and a positive association withFDI. This variable, however, shows an insignif-icant effect on FDI. A faster-growing economymay attract more FDI because of the potentialmarket growth in the future.12 The real GDPgrowth rates of an FDI source and host coun-try (GROW1 and GROW2) generally show theexpected effects on FDI, albeit the effects arenot strong.

Our regressions include the GDP share ofdomestic investment in an FDI host country(I) as an explanatory variable. In Kim andPark (2011), both FDI and domestic investmentare endogenously determined and negativelycorrelated, since domestic and foreign firmscompete to produce the same product. On theother hand, earlier studies have suggested that agreater share of domestic investment in a hostcountry may reflect an atmosphere favorabletoward private enterprise, implying a positivecorrelation between domestic investment andFDI. Table 3 shows that the effect of domesticinvestment is positive but insignificant, possiblybecause of these two competing effects. TheGDP share of government spending in an FDIhost country (G) is entered as a regressor tocontrol for the involvement of government in the

11. Refer to Wheeler and Mody (1992) and Barrell andPain (1996) for a discussion of these variables.

12. Williamson (1975), Dunning (1981), and Edwards(1990) show that there is a tendency for multinational firmsto choose host countries with larger market size or greatergrowth potentials.

economy, which may shape the environment forprivate business and foreign direct investment.13

In Table 3, this variable is generally shown tohave a negative effect on FDI, implying thatweak government intervention attracts foreigndirect investment.

As a proxy for international competitiveness,we introduce the real exchange rate of an FDIhost country (EXCHANGE) as an explanatoryvariable. Theoretically, overvaluation of the realexchange rate may lead to a reduction in FDI ifFDI and trade are substitutes. On the other hand,a fall in the prices of intermediate good importsfor foreign firms may induce more FDI. Theresults in Table 3 strongly support the formertheory.14

A strong relationship in trade between twocountries may facilitate the flow of FDI aswell as the flow of students between the coun-tries, resulting in a spurious correlation betweenFDI and STDT, without the causal influence ofSTDT on FDI as provided in our theory. Tocontrol for the effect of trade, we introduce thevolume of exports from an FDI source coun-try to a host country as a regressor in Model 2,although trade may be an endogenous variable inFDI regression.15 The result in Model 2 showsthat trade has a significantly positive associa-tion with FDI, consistent with our expectation.Surprisingly, however, we observe a strong andsignificant effect of STDT on FDI, even whentrade is introduced into the regression model.The estimated effect of STDT cannot thereforebe ascribed to a higher trade volume betweentwo countries, and ultimately to the various fac-tors that influence their trade relationship.

Higher tariff in an FDI host country mayreduce imports and instead increase FDI to thehost country, as the relative cost of imports riseswith higher tariff. However, an increase in tariffwould discourage FDI, because a higher tariffwould raise the prices for imported intermediategoods used by multinational firms. Moreover, ahigher tariff can be associated with lower FDI,as the tariff rate reflects the degree of economic

13. Government policies, country-specific incentives,and political variables have also been suggested to play arole in attracting FDI in Edwards (1990), Wheeler and Mody(1992), and Hines (1996).

14. See Cushman (1985), Caves (1989), Froot and Stein(1991), Goldberg and Kolstad (1995), and Blonigen (1997)for discussion on the real exchange rate effect on FDI. Thereal exchange rate index in our study is the price level ofGDP, relative to U.S. prices. An increase in the index impliesrising overvaluation in the real exchange rate.

15. This is one reason why we do not include the tradevariable in our baseline specification.

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TABLE 4Sensitivity Analysis: Dyad-Specific Effects

(1) (2) (3) (4)

No Dyad-Specific Effects Random Effects Fixed Effects Between Effects

Coef. SE Coef. SE Coef. SE Coef. SE

ln(STDT) 0.348∗∗∗ (0.017) 0.323∗∗∗ (0.022) −0.013 (0.035) 0.213∗∗∗ (0.038)Observations 4,761 4,761 4,761 4,761Adjusted R2 0.6434 0.5695 0.3407 0.7887

Notes: The rows show the estimated coefficient and robust standard error (in parentheses) for each independent variable.The coefficient estimates associated with other regressors besides STDT in all models are not reported to save space. Thedependent variable is the log of the GDP share of FDI. All models include as regressors source-country-specific and host-country-specific time trends, and calendar-year dummies. Model 1 excludes dyad-specific effects. Models 2, 3, and 4 employdyad-specific random, fixed, and between effects, respectively. These models show estimates based on Model 1 in Table 3.

∗∗∗Significant at 1%.

and political openness of a country. Anothermeasure for the degree of economic and politicalopenness would be the number of tourists in anFDI host country. The tariff rate and the numberof tourists in an FDI host country are intro-duced as additional regressors in Model 3 to fur-ther isolate the independent effect of STDT onFDI. As expected, the tourist variable shown inModel 3 has a significant and positive asso-ciation with FDI, while the tariff rate has anadverse, but insignificant effect on FDI.

Human capital in an FDI host country isconsidered an important factor for attractingFDI, especially the FDI flows to develop-ing economies, as discussed in Section I. Anincrease in secondary school enrollment mayraise the efficiency units of domestic unskilledlabor, raising the input demands as well as pro-duction levels of domestic and foreign firmsin an FDI host country, and hence increas-ing FDI.16 On the other hand, an increase intertiary school enrollment may raise the effi-ciency units of domestically educated skilledlabor, and therefore, as predicted by our the-ory, an FDI host country will receive less FDIwith an increase in domestically educated labor.The results in Model 3 show that more studentsat the secondary level in an FDI host countrywould attract more FDI, which is consistent withour prediction. The effects of the level of tertiaryschool students in FDI host and source countrieson FDI flows are also supportive of our theory.

B. Additional Sensitivity Tests

Dyad-Specific Effects. Although we includedmany variables as regressors in our regressions

16. See Proposition 3 of Kim and Park (2011) for proof.

in Table 3 to control for dyad-specific character-istics in our data, there may potentially be otherimportant dyad-specific factors that are missingin our analysis. In Table 4, we introduce variousforms of dyad-specific effects in our baselinespecification. We report only the estimated coef-ficients associated with the number of studentsin all parts of this table to save space.

Table 4 starts with Model 1, which doesnot include country-specific constants or dyad-specific effects. However, Model 1 as well asthe other three models includes source-country-specific and host-country-specific time trendsand calendar-year dummies, as in our base-line specification. Models 2, 3, and 4 reportthe regression results from the models withdyad-specific random effects, fixed effects, andbetween effects, respectively. In all modelsexcept Model 3, the effects of STDT are stillstatistically significant and consistently positive.We note that the coefficient associated withSTDT in Model 3 is insignificant, which maybe because there is little within-dyad variationsin both the FDI-to-GDP ratio and the numberof students relative to population. This impliesthat most of the variations in our data take placebetween dyads, and our estimation of the STDTeffect on FDI is largely based on the between-dyads variations, which is more desirable for theestimation of the long-term relationship betweenSTDT and FDI. We also note that our estimationis based on variations across pairs of countries,not across countries, and the results in Model 4are not simply due to country-level differences.

To examine whether the between-dyads effectfound in part A is strongly driven by a fewspecific pairs of countries in the sample (theUnited States and China, for example), and also

KIM & PARK: FDI AND COUNTRY-SPECIFIC HUMAN CAPITAL 9

TABLE 5Sensitivity Analysis: Regional Subsample

(1) (2) (3) (4) (5)

North→North North→South South→NorthW/O China,

United StatesHigh ForeignLabor Share

Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE

ln(STDT) 0.353∗∗∗ (0.026) 0.163∗∗∗ (0.029) 0.021 (0.089) 0.223∗∗∗ (0.020) 0.261∗∗∗ (0.047)Observations 2,790 1,622 287 3,982 1,390Adjusted R2 0.7451 0.6961 0.6066 0.6786 0.7289

Notes: The rows show the estimated coefficient and robust standard error (in parentheses) for each independent variable.The coefficient estimates associated with other regressors besides STDT in all models are not reported to save space. Thedependent variable is the log of the GDP share of FDI. All models include as regressors source-country-specific constants andtime trends, host-country-specific constants and time trends, and calendar-year dummies. The countries included in Model 5are Australia, Austria, Belgium-Luxembourg, Canada, France, Germany, Sweden, Switzerland, and the United States. Thesemodels show the estimates based on Model 1 in Table 3.

∗∗∗Significant at 1%.

to address other issues arising from regional dif-ferences, we provide sensitivity analysis involv-ing regional subsamples in Table 5, where weboth exclude and include various regions fromthe analysis. Overall, we still obtain qualitativelysimilar results (see the following subsection on“Regional Subsamples”).

One concern in our model specification isthat there is no theoretical validation for aparticular lag between STDT and FDI, althoughwe have tried various lags for sensitivity. Itis also possible that the effect of STDT onFDI may last for several years to warrant adynamic panel-data model. The between-effectsmodel can offer an estimate for the aggregateeffect of the numbers of students with variouslags on the dyad-specific aggregate of FDI, asthe between-effects regression is performed inaccordance with the dyad-specific means of thedependent variable and the regressors. Note thatin the between-effects model, our dependentvariable is the average value of ln(FDI) over theperiod 1980–1998, while the student variableis averaged over the period 1965–1983, whichhelps avoid the problem of endogeneity in thestudent variable.

Regional Subsamples. To seek economic favorsfrom a particular developing country, such asprovision of incentives for FDI inflows or reduc-tion of regulations on FDI, a developed countrymay selectively admit more students from thedeveloping country, which would later have afavorable policy toward the developed coun-try. A positive relation between STDT and FDImay arise because of this, which is beyond theemployment effect proposed in our model. Since

this type of political consideration is expectedto play a more significant role between a devel-oped country and a developing country, we esti-mate our baseline regression model in Model 1of Table 5 with a subsample that includes onlythe FDI flows between the developed “North”countries. We find that the effect of STDT ismuch more pronounced in this regression thanin a regression with the whole sample. Thissuggests that we cannot attribute the positivelink between STDT and FDI in our empiricalfindings to the political consideration discussedabove.

Table 5 also reports the results with sev-eral alternative sets of subsamples: “North toSouth” FDI flows in Model 2, and “South toNorth” flows in Model 3. A rather surpris-ing finding is that the effect of STDT is morepronounced for FDI flows between two devel-oped countries than for those from developedto developing countries. One possible reason isthat the quality of education acquired by stu-dents from North countries is higher than thatacquired by students from “South” countries, sothat the foreign-educated labor in North coun-tries attracts more FDI.17 We see an insignif-icant effect of STDT on FDI for the “Southto North” subsample, probably because of the

17. In the 2000–2001 academic year, only 29% of allforeign students from Africa who studied in the UnitedStates were enrolled in graduate programs, while 40% ofstudents from Europe were enrolled in graduate programs,as shown in Open Doors, Institute of International Education(2001). According to the UNESCO Statistical Yearbook, theUnited States is the largest host country of foreign students,attracting more than 30% of foreign students worldwide in1996, while France is the second largest, with 12% of foreignstudents.

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TABLE 6Sensitivity Analysis: Country-Specific Foreign Students

(1) (2) (3)

Dependent Variable =Students from the Rest of

the Countries AddedModel 4 of Table 3 Rerunwith the Smaller Sample

FDI from the Rest of theCountries as the Dep. Var.

ln(FDIL) ln(FDIL) ln(FDILREST)

Coef. SE Coef. SE Coef. SE

ln(STDTN) 0.252∗∗∗ (0.021) 0.258∗∗∗ (0.021) −0.043∗∗∗ (0.009)ln(STDTNREST) 0.154∗∗∗ (0.055)Observations 3,692 3,692 3,692Adjusted R2 0.7307 0.7301 0.8625

Notes: The rows show estimated coefficient and robust standard error (in parentheses) for each independent variable.The coefficient estimates associated with other regressors besides STDT in all models are not reported to save space. Thedependent variables are the logs of FDI level in Models 1 and 2 and of FDI from the rest of the countries in Model 3.All models include as regressors source-country-specific constants and time trends, host-country-specific constants and timetrends, and calendar-year dummies. Models 1 and 2 show estimates based on model 4 in Table 3. Model 3 includes onlyln(GDP2), ln(POP2), ln(GROW2), ln(I), ln(G), and ln(EXCHANGE) as additional regressors.

∗∗∗Significant at 1%.

small number of countries included and the shorttime series for each country.

In order to investigate whether our resultshave been mainly influenced by the inclusionof China and the United States, which are thelargest sender of students abroad and the largestsource of foreign direct investment, respectively,we exclude all observations involved with thetwo countries in Model 4. The U.S. observationsare excluded, with the additional concern ofthe high non-returning rate among foreign stu-dents, as discussed in Section II. In Model 5, weaddress the issue of foreign labor in the domesticlabor market discussed in Section II, by per-forming a regression on a subsample with FDIhost countries having high foreign labor forceshares.18 Even in the regressions with these sub-samples, the effect of STDT on FDI is quanti-tatively robust.

Country-Specific Foreign Students. A rise inthe number of foreign-educated students whostudied in one country may have an influenceon FDI inflow from other countries of foreignstudy. In Table 6, we include the total number ofstudents from country j who studied in foreigncountries other than country i (STDTNREST)as a regressor in addition to STDTN, using our

18. We select FDI host countries whose average share offoreign labor in total labor force over the period 1986–1995is more than 5%, according to World Development Indica-tors (2001). These countries are Sweden, France, Austria,Germany, Belgium-Luxembourg, the United States, Switzer-land, Canada, and Australia.

baseline specification in Model 4 of Table 3.For comparison, this baseline specification isrerun with a smaller sample of observationsthat have non-missing values of STDTNREST(see Model 2 in Table 6). Model 1 shows thatSTDTNREST has a negative impact on the FDIinflow from country i to countryj, while theeffect of STDTN is significant and positive.The negative cross-effect is again confirmed inModel 3, where the FDI inflows from countriesother than country i (FDILREST) are negativelyassociated with STDTN; a rise in a country-specific STDTN reduces FDI inflows from therest of the countries.

This negative association may be becausethere is competition amongst the FDI sourcecountries. Although a rise in country-specificforeign-educated labor will make a favorableenvironment for all FDI inflows to the hostcountry, it will be most favorable for FDI fromthe country where the workers were educated.Competition may then crowd out the opportunityfor investment from the rest of the countries.However, Model 1 in Table 7 indicates thatthe net impact of STDTN on the total FDIinflow from all countries (TOTFDIL) is positive,albeit insignificant. To further investigate thenet impact of STDTN, we use the FDI stockobtained from Lane and Milesi-Ferretti (2001)as the dependent variable in Model 2 in Table 7.This result also shows a positive effect.

As an additional test to check the validityof our prediction on the FDI effect of studentsabroad, an alternative specification to Model 1

KIM & PARK: FDI AND COUNTRY-SPECIFIC HUMAN CAPITAL 11

TABLE 7Sensitivity Analysis: Aggregate FDI

(1) (2)

Dependent Variable = ln(TOTFDIL) ln(FDISTOCK)

Coef. SE Coef. SE

ln(STDTN) 0.005 (0.006) 0.002 (0.002)Observations 4,349 4,485Adjusted R2 0.9110 0.9931

Notes: The rows show estimated coefficient and robust standard error (in parentheses) for each independent variable. Thecoefficient estimates associated with other regressors besides STDT in all models are not reported to save space. The dependentvariables in Models 1 and 2 are the logs of aggregate FDI from all countries and of FDI stock, respectively. Both modelsinclude as regressors source-country-specific constants and time trends, host-country-specific constants and time trends, andcalendar-year dummies. Both models include only ln(GDP2), ln(POP2), ln(GROW2), ln(I), ln(G), and ln(EXCHANGE) asadditional regressors.

in Table 7 was estimated with total portfolioinvestments to an FDI host country as the depen-dent variable, instead of total FDI.19 Interest-ingly, the coefficient associated with STDTNin this regression was insignificant with the t-statistic of −0.06. This finding lends potent sup-port to our theory that country-specific humancapital attracts FDI, not necessarily other typesof international capital flows.

Alternative Time Lags between FDI and STDT.In Table 3, we report the results when the timelag between FDI and the number of students is15 years. To test the sensitivity of our resultsto different lags of STDT, we use alternativespecifications, where STDTt-s (s = 5, 6 . . . , or25) is introduced as a regressor in place of ourbaseline-model variable STDTt-15. In each spec-ification, the effect of the number of studentsabroad with the respective alternative lag isfound to be significantly pronounced.20 We alsouse a specification where all the STDT variableslagged from 10 years to 20 years are simulta-neously included. The sum of all the estimatedcoefficients associated with the lagged variablesof STDT is found to be positive and statisticallysignificant. Moreover, the magnitude of the sum

19. The regression result is not reported to save space.Bilateral portfolio investment flows were not available inInternational Financial Statistics (IFS), 2001.

20. The results are not shown to save space. Inter-estingly, the coefficient on STDT rises in general as theyear lag gets greater, peaks in the specification with the22-year lag (coefficient of 0.3042), and falls as the lagincreases beyond 22 years. This may reflect the fact thatthose students abroad who have acquired foreign educationvery recently or long ago do not contribute to the presentpool of labor that is conducive to current foreign directinvestment.

is very close to and statistically not differentfrom the estimated effect of STDT with a 15-year lag in model 1 of Table 3. These resultsindicate the robustness of our estimated effectof STDT on FDI.

IV. CONCLUDING REMARKS

We used bilateral FDI and foreign studentdata for 63 developed and developing countriesover the period 1963–1998 to test our proposi-tion that an increase in country-specific foreign-educated labor will raise FDI inflow from theforeign country where the labor was educated.

Despite the limitations of our data, theempirical evidence in this paper strongly con-firms our proposition under various alternativespecifications, controlling for the determinantsrecognized in the literature. Our results also indi-cate that country-specific foreign-educated laboronly attracts FDI from the host country of for-eign education. In fact, the FDI inflows fromother countries are crowded out. The net effectof foreign-educated labor on total FDI inflow,however, is positive. Consistent with our theory,we have evidence that foreign-educated laborattracts FDI, but not necessarily other types offoreign capital.

The estimated effect of students abroad onFDI, presented in Table 3, is not only statisti-cally but also quantitatively significant. Accord-ing to our simple calibration exercise, the changein ln(STDT) can explain approximately 14.7%of the actual change in ln(FDI) from 1980to 1998 (see Appendix B for the calculationmethod).

Needless to say, we have left a numberof issues unaddressed in this paper. Regarding

12 ECONOMIC INQUIRY

our empirical analysis, a more extensive surveyof data on the non-return rates of studentsabroad and bilateral flows of foreign laborwould provide us with a more accurate measureof the foreign-educated labor in our empiricalimplementation. We leave the study of theseissues to future work.

APPENDIX A

The 63 countries included in this study are Algeria,Argentina, Australia, Austria, Belgium-Luxembourg, Brazil,Bulgaria, Canada, Chile, China, Colombia, Costa Rica,Czech Republic, Czechoslovakia, Denmark, Egypt, Finland,France, Germany, Greece, Hong Kong, Hungary, Iceland,India, Indonesia, Iran, Ireland, Israel, Italy, Japan, Korea,Kuwait, Libya, Malaysia, Mexico, Morocco, The Nether-lands, Netherlands Antilles, New Zealand, Norway, Panama,Philippines, Poland, Portugal, Romania, Russia, Saudi Ara-bia, Singapore, Slovakia, Slovenia, South Africa, Spain,Sweden, Switzerland, Taiwan, Thailand, Turkey, Ukraine,United Arab Emirates, United Kingdom, United States,USSR, and Venezuela.

Data on language, religion, and colony are from Encyclo-pedia Britannica. Data on distance are from the Internet mapsearch engine at www.indo.com/distance. Data on per capitaGDP, total GDP, GDP shares of domestic investment andgovernment spending, and real exchange rates are obtainedfrom Penn World Tables (Mark 6). The bilateral trade flowdata for 63 countries over 1976–1999 are taken from WorldBank Institute’s Trade and Production Database. Data ontariff rates are available in Trends in Average Tariff Ratesfor Developing and Industrial Countries 1980–99 of WorldBank Institute. Data on tourists and school enrollment ratesare from World Development Indicators (2001). Corporateincome tax data are from Devereux, Griffith, and Klemm(2002), and total portfolio investments are from Interna-tional Financial Statistics, published by IMF (2001).

APPENDIX B

In our data, the actual sample mean values of ln(FDI)were −6.001 in 1980 and −4.919 in 1998, while the cor-responding sample mean values of ln(STDT) were −5.964and −5.303, respectively. Using the estimated coefficientsin Model 1 of Table 3, and assuming that all variables otherthan FDI and STDT are constant at the sample mean valuesof the variables in 1980, we calculate the predicted changein the mean value of ln(FDI) from 1980 to 1998 explainedby the change in the sample mean value of ln(STDT) from1965 to 1983. The predicted change in the mean valueof ln(FDI) from 1980 to 1998 is 0.240(−5.303 + 5.964) =0.159, whereas the actual change in the sample mean valueof ln(FDI) during the same period was 1.081. This calcula-tion suggests that the change in ln(STDT) can explain 14.7%(= 0.159/1.081) of the actual change in ln(FDI) during thisperiod.

REFERENCES

Barrell, R., and N. Pain. “An Econometric Analysis of U.S.Foreign Direct Investment.” Review of Economics andStatistics, 78, 1996, 200–07.

Becker, G. S. Human Capital. Chicago: University ofChicago Press, 1993.

Benhabib, J., and M. Spiegel. “The Role of Human Capitalin Economic Development: Evidence from AggregateCross-Country Data.” Journal of Monetary Economics,34, 1994, 143–73.

Blonigen, B. A. “Firm-Specific Assets and the Link betweenExchange Rates and Foreign Direct Investments.”American Economic Review, 87(3), 1997, 447–65.

Brainard, S. L. “A Simple Theory of Multinational Corpo-rations and Trade with a Trade-off between Proximityand Concentration.” NBER Working Paper No. 4269,1993.

Caves, R. E. “Exchange-Rate Movements and ForeignDirect Investment in the United States,” in TheInternationalization of U.S. Markets, edited byD. B. Audretsch and M. P. Claudon. New York: NewYork University Press, 1989, 199–228.

Chiswick, B. R., and P. W. Miller. “Language in the Immi-grant Labor Market,” in Immigration, Language andEthnicity: Canada and the United States, edited byB. R. Chiswick. Washington DC: AEI Press, 1993.

Cushman, D. O. “Real Exchange Rate Risk, Expectations,and the Level of Direct Investment.” Review of Eco-nomics and Statistics, 32, 1985, 297–308.

Devereux, M. P., R. Griffith, and A. Klemm. “CorporateIncome Tax Reforms and International Tax Compe-tition.” Economic Policy, 17(35), 2002, 450–95.

Dunning, J. H. International Production and the Multina-tional Enterprise. London: George Allen & Unwin,1981.

Edwards, S. “Capital Flows, Foreign Direct Investment, andDebt-Equity Swaps in Developing Countries.” NBERWorking Paper No. 3497, 1990.

Federation of Korean Industries. Archives of Korean Busi-ness Figures (Hankook Je-gye In-sa Rok). Seoul: Fed-eration of Korean Industries, 2008.

Froot, K. A., and J. C. Stein. “Exchange Rates and For-eign Direct Investment: An Imperfect Capital MarketsApproach.” Quarterly Journal of Economics, 106(4),1991, 1191–217.

Glaser, W. A. The Brain Drain: Emigration and Return. TheUnited Nations Institute for Training and Research,Report No. 22. Elmsford, NY: Pergamon Press, 1979.

Goldberg, L. S., and C. D. Kolstad. “Foreign Direct Invest-ment, Exchange-Rate Variability and Demand Uncer-tainty.” International Economic Review, 36, 1995,855–73.

Helpman, E., and P. Krugman. Market Structure and Inter-national Trade. Cambridge, MA: MIT Press, 1985.

Hines Jr, J. R. “Altered States: Taxes and the Locationof Foreign Direct Investment in the U.S.” AmericanEconomic Review, 86, 1996, 1076–94.

Horstmann, I., and J. R. Markusen. “Endogenous MarketStructures in International Trade.” Journal of Interna-tional Economics, 20, 1992, 225–47.

Institute of International Education. Open Doors: Reporton International Educational Exchange. New York:IEE, 2001.

International Monetary Fund. International Financial Statis-tics. Washington, DC: IMF, 2001.

Jones, R. W. “International Capital Movements and theTheory of Tariffs and Trade.” Quarterly Journal ofEconomics, 81, 1967, 1–38.

Kemp, M. C. “The Gain from International Trade andInvestment: A Neo-Heckscher-Ohlin Approach.”American Economic Review, 56, 1966, 788–809.

Kim, J. “Economic Analysis of Foreign Education andStudents Abroad.” Journal of Development Economics,56, 1998, 337–65.

KIM & PARK: FDI AND COUNTRY-SPECIFIC HUMAN CAPITAL 13

Kim, J., and J. Park. “Foreign-Educated Labor and ForeignDirect Investment: Theory and Evidence.” WorkingPaper, Discussion Paper Series, Institute of EconomicResearch, South Korea, 2011.

Laabs, J. “How Gillette Grooms Global Talent.” PersonnelJournal, 72, 1993, 64–70.

Lane, P., and G. M. Milesi-Ferretti. “The External Wealthof Nations: Measures of Foreign Assets and Liabilitiesfor Industrial and Developing Countries.” Journal ofInternational Economics, 55(2), 2001, 263–94.

Lazear, E. P. “Culture and Language.” Journal of PoliticalEconomy, 179(6), 1999, S95–126.

Lucas, R. “Why Doesn’t Capital Flow from Rich to PoorCountries?” American Economic Review, 80, 1990,92–96.

MacDougall, G. D. A. “The Benefits and Costs of PrivateInvestment from Abroad: A Theoretical Approach.”Economic Record, 36, 1960, 13–35.

Markusen, J. R. “Multinationals, Multi-Plant Economies,and the Gains from Trade.” Journal of InternationalEconomics, 16, 1984, 205–26.

Markusen, J. R., and K. E. Maskus. “General-EquilibriumApproaches to the Multinational Firm: A Review ofTheory and Evidence.” NBER Working Paper, No.8334, 2001.

Markusen, J. R., and A. J. Venables. “The Theory ofEndowment, Intra-Industry and Multinational Trade.”Journal of International Economics, 52, 2000,209–34.

Noorbakhsh, F., A. Paloni, and A. Youssef. “Human Cap-ital and FDI Inflows to Developing Countries: NewEmpirical Evidence.” World Development, 29(9), 2001,1593–610.

OECD. International Direct Investment Statistics Yearbook1999. Paris: OECD Publishing, 1999.

Prescott, E., and M. Visscher. “Organizational Capital.”Journal of Political Economy, 88(3), 1980, 446–61.

Root, F. R., and A. A. Ahmed. “Empirical Determinants ofManufacturing Direct Foreign Investment in Develop-ing Economies.” Economic Development and CulturalChange, 27, 1979, 751–67.

Wheeler, D., and A. Mody. “International Investment Loca-tion Decisions: The Case of U.S. Firms.” Journal ofInternational Economics, 33, 1992, 57–76.

Williamson, O. Markets and Hierarchies: Analysis and Anti-Trust Implications. New York: Free Press, 1975.

World Bank. World Development Indicators, various years.Zhang, K., and J. R. Markusen. “Vertical Multinationals and

Host-Country Characteristics.” Journal of Develop-ment Economics, 59, 1999, 233–52.