Foreign direct investment and income inequality: Further evidence

World Development, Vol. 23, No. 3, pp. 469-483, 1995

Copyright 0 1995 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0305-750x/95 $9.50 + 0.00

Pergamon 0305-750X(95)00136-7

Foreign Direct Investment and Income Inequality: Further Evidence PAN-LONG TSAI* National Tsing Hua University, Hsin Chu, Taiwan Summary. - This paper examines the relationship between foreign direct investment (FDI) and income inequality. Special attention has been paid to data comparability and model specification. By comparing models with and without geographical dummies, this study shows that: the statistically significant correlation between FDI and income inequality widely obtained in earlier studies might capture more of the geographical difference in inequality than the deleterious influence of FDI, and to the extent that FDI does give rise to more unequal income distribution in the host less-developed countries (LDCs), only East/Southeast Asia, LDCs appear to have been harmed by the inflow of FDI during the 1970s. It should be noted, however, that the second result refers only to the marginal impact, not the total impact. Apart from FDI, our empirical evidence suggests that the level of economic development, the direct role of government and, to a smaller degree, the significance of the agriculture sector in an LDC are crucial determinants of income inequality.

1. INTRODUCTION Foreign direct investment (FDI) has long been a subject of interest in international economics and development fields. The interest has been greatly reinforced in recent years because of the rapid increase in the FDI flowing to less-developed countries (LDCs). It is estimated that total FDI flows to LDCs have reached US$38 billion in 1992, a fourfold increase since the mid-1980s and a 50% increase over the past two years. FDI is currently the dominant form of resources flows to LDCs, accounting for more than 25% of aggregate net flows and exceeding total longterm debt flows. In addition to filling resources gaps, more and more development economists and officials of international institutions believe that FDI could contribute to the growth and development of the host LDCs via channels such as transfer of modem technology and management skills, human capital development as well as exporting market access. While the potential role of FDI in the LDC development process is once again the focus of attention, some fundamental issues remain unresolved. Among these issues the impact of FDI on the host country’s economy might be the most complicated and controversiaL1 Questions related to the impacts and implications of FDI in LDCs have not only stimulated many of theoretical and empirical studies, but have also generated much debate between modernization‘ and dependency theorists. This study focuses on one aspect of FDI, its impact on host developing countries’

income distribution. More specifically, it investigates whether the inflow of foreign capital is associated with a greater income inequality within the host LDCs. The major reason for examining the inequality aspect is that, compared with the growth impact of FDI, the study of this problem is relatively insufficient, though almost all of the existing studies arrive at quite a consistent conclusion that FDI has invariably led to an uneven income distribution in the host countries.2 But with increasing empirical evidence showing that the negative growth impact of FDI proclaimed by dependency theorists might not exist (Hein, 1992), it is by all means interesting and necessary to see whether the distributional effect of FDI reported in previous studies could stand up to a more careful examination. The rest of the paper is organized as follows. Section 2 sketches the arguments of two contending theoretical perspectives on the distributional effect of FDI and reviews a subset of the empirical works on FDI, growth and income inequality. Based on the find-

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* I began this study while I was a Fulbright Scholar at the Economic Growth Center, Yale University. Financial supports from the Fulbright Foundation and the National Science Council, R.O.C., are deeply appreciated. Thanks are due to two anonymous referees for many helpful suggestions. I am particularly indebted to Professor Gustav Ranis for very constructive comments and for sharing with me the inequality data collected by Professor Gary Fields. All remaining errors or oversights are mine exclusively. Final revision accepted: September 6, 1994.

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ings of the pmceding section, section 3 sets up regression models for the empirical study. Characteristics of the sample and the variables used in the empirical study are also discussed. Then the empirical results are repotted. The last section concludes the study.

2. FDI AND INEQUALITY: CONTENDING THEORETICAL PERSPECTIVES Broadly speaking, there are two contending hypotheses about the consequences of FDI: the “developmental/modernization” hypothesis and the “world system/dependency” hypothesis.3 While both emphasize the role of foreign capital in the LDC development process, the predictions each makes about the impacts of FDI on the host economies are quite different. This section briefly sketches the arguments of the two contending perspectives with respect to the distributional impact of FDI in the LDCs and reviews some representative empirical studies.

The modernization perspective is built on the orthodox economic concepts of marginal productivity theory as well as the role of saving and consumption propensities. It stresses that sufficient output must be Firstproduced before it can be redistributed. Inequality is generally perceived as a necessary precondition for eventual improvement of everyone’s income. This line of argument is formalized in the celebrated “Kuznets’ inverted-U curve” hypothesis, according to which income inequality increases at the early stages of development but declines later once a certain stage of development is reached.4 During the early stages of development, a developing economy is typically characterized by: an increase in the share of the population involved in a narrow modem high-income sector of the economy, an increase in the income gap between the high-income and low-income sectors and an increase in inequality within each sector. These characteristics directly result in the increase of overall inequality (Adelman and Robinson, 1989). In the later stages, as more output is produced and enough labor has been transferred from the traditional agriculture sector to the modem industrial sector, the surplus labor in agriculture gradually disappears and the marginal product of the agriculture labor will be raised to the level of the industrial labor. With the increase in real labor income, economic growth and the likely rise of political democracy therefore result in more equal income distribution (Fei and Ranis, 1964, Lenski, 1966). While modernization theorists seldom address the distributional impact of FDI directly and explicitly, their position is clearly implied in their treating for-

eign and domestic capital as homogeneous goods5 According to the modernization theory, it is the presence rather than the origin of the investment that is considered important. Capital, be it foreign or domestic, fosters growth and its benefits eventually spread throughout the whole economy. Therefore, even if FDI initially stimulates growth only in some leading or favored sectors, develops allied local elites or leads to economic dualism, the growth in the leading sectors could in the long run facilitate more even income distribution. For example, FDI in the export-processing zones in East Asia has been very instrumental in increasing employment at low wages, and thus raising the labor sham and improving the size distribution of income. To most modernization theorists, factors such as types of economic system and development strategy are the truly crucial determinants of income distribution. As long as the influences of these factors are properly taken into account, difference in the amount of foreign capital should not cause any significant variance in income inequality. As modernization theorists seldom deal directly with the distributional consequence of FDI, none of the empirical studies by development economists (most of them are classified as modernization proponents) has test hypotheses concerning the relationship between FDI and income inequality. Nevertheless, economists have contributed so much to study the problem of economic development and inequality that it warrants a summary of some representative crosscountry studies. The study by Adehnan and Morris (1973) is widely regarded as the first crosscountry test of the relationship between economic development and inequality. Their results indicate that the Kuznets inverted-U curve does exist. They also find that improvement in human capital in general helps lower income inequality, but direct government activities and the’ short-term growth rate of per capita gross domestic product (GDP) are not significantly related to income distribution. The Adehnan and Morris study has been criticized for several defects, for instance, poor quality of the data and inclusion of strongly dualistic economies such as South Africa as well as highly developed countries such as Japan in the sample. This study, however, has set a pattern for further crosscountry empirical studies, which involves using income shares of selected deciles as dependent variables and adding explanatory variables other than per capita GDP for better fit. The major findings of Adehnan and Morris are confirmed in a more elaborate crosscountry study by Ahluwalia (1976) and a more recent one by Papanek and Kyn (1987). Based on a sample of 60 countries including 40 LDCs, 14 developed countries (DCs) and six socialist countries, Ahluwalia, in addition to establishing the inverted-U curve, find that the structure of production, educational attainments as well as the rate of population growth are the factors appearing signif-

FDI AND INCOMEINEQUALITY icant in explaining the variance in income distribution whereas faster short-term growth is not systematically associated with higher inequality at a given level of development. Papanek and Kyn (1987) test both the weaker and the stronger versions of the Kuznets hypothesis.6 They find, among others, that (i)

while there is evidence supporting the stronger version of Kuznets hypothesis, the results in general do not support the existence of the intertemporal Kuznets curve, (ii) neither the role of government in the economy nor the rate of short-term economic growth has statistically significant impact on economic inequality, (iii) the spread of education helps reduce inequality, whereas social-political dualism and a large share of primary exports contribute to greater inequality, and (iv) even after controlling regional-related variables such as education, social-political dualism and structure of exports, income distributions do differ significantly among the geographic regions.

Like Adelman and Morris (1973), there are defects in both Ahluwalia’s as well as Papanek and Kyn’s studies. First, they rely on Jain (1975) for the income distribution data, which are fraught with definition and measurement problems. Different countries use different definitions and differ in the population covered. For instance, the income recipients could be households at the national level, male employees only, national population or households in the urban areas. Second, they pool together developed countries (DCs), LDCs and socialist economies. As pointed out by Saith (1983), the processes linking growth to income distribution in the socialist economies are quite different from those which are assumed to underlie the link for capitalist economies. Moreover, one could argue that the observations from DCs and LDCs are not independent of one another, given the accelerating globalization of world economic and political life today. Finally, the social-political dualism appears to be the single most important factor in determining income distribution according to Papanek and Kyn (1987). There is, however, no commonly accepted criterion to classify a country as dualistic, not to mention measuring the degree of dualism. Even the authors suspect that the inability to appropriately define an economy as dualistic might be the major source for some contradicting results they obtained. (b) Dependency hypothesis In contrast to the implicit argument of the modemization theory, the dependency theory has put forward

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some very specific criticisms about the pernicious distributional effect of FDI. The dependency theory approaches the inequality problem from a world-economy and historical perspective. It maintains that it is the social control and organization of production, rather than economic output and wealth, that affect income inequality. A country’s relative position in the world economy is very critical in determining its income distribution. More precisely, inequality is the counterpart of the relation between the core and the peripheral countries, and the degree of inequality within a peripheral country is determined through the process of dependent development (Girling, 1973; Rubinson, 1976; Bomschier and Chase-Dunn, 1985).’ As industrialization proceeds with the presence of FDI, it is widely observed in the host LDCs that employees in the international sectors tend to form a new social class. These “labor elites” typically earn four to 10 times of the normal wages and other benefits in the comparable domestic sectors (Girling, 1973). To the extent that this dependent development does help pull wages up in the traditional sectors, it is most likely to be accompanied by a more capitalintensive production, which in turn leads to increased unemployment in the traditional sectors. As a consequence, an increase in the relative share of labor income not only fails to bring about greater equality, but contributes directly to rising inequality. In other words, under this type of dependent industrialization, the spread and multiplier effects suggested by the modernization theorists do not actually occur. Furthermore, being integrated into the world economy, the labor elites generally strive for maintaining and stabilizing their privileged status. Thanks to their common interests, the local labor elites from time to time work with foreign investors to suppress or even drive out indigenous entrepreneurs. The attempt of this intercountry interest coalition to maximize its interest has another implication. Within the worldsystem/dependency framework, the state is assumed to have dominant power to affect market and production. Since the labor elites typically consist of powerful actors in the state organization, and since both the labor elites and the state are usually supported by foreign credits, an economic-cum-political “triple alliance” emerges naturally (Evans, 1979). This economic-cum-political alliance then manipulates the exclusive power of the nation state to intervene in the market whenever it does not work for its interest. The formation of the alliance therefore means that there are intrinsic destructive factors in any policy aiming at improving the distribution of income. In fact, it could be one of the most fundamental sources for persistent income inequality in the LDCs. The studies attempting to show empirically the impact of FDI on economic inequality are best surveyed in Bomschier and Chase-Dunn (1985). Among

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the 15 studies reviewed, all but the one by Weede and Tiefenbach (198 1) report positive relationship between FDI and inequality. Although three of the other 14 lend only mild support to the dependency hypothesis, by and large, the detrimental effect of FDI on income distribution seems to be corroborated by the existing literature. In order to identify relevant independent variables for the present analysis, we outline below some of the previous studies. Chase-Dunn’s (1975) first simple examination of the effect of FDI on income inequality is followed by the more sophisticated analysis of Rubinson (1976). Rubinson identifies state strength, direct linancial control and dependence on external markets as the three channels for economic dominance and influence. Using the proportion of government revenue to GDP, the amount of foreign reserves or debits on foreign investment income as well as exports and/or imports as a percentage of GDP respectively as indicators for the three channels, he concludes that the greater the degree of direct foreign control within a country, the greater the degree of income inequality in that country. In particular, in any equation with the Gini coefficient as the dependent variable, the three indicators are always signed as expected and statistically significant. Striking as the findings are, however, there are shortcomings in Rubinson’s study. Among others, the failure to control the quadratic relationship between the level of development and income inequality casts doubt on his results. As a matter of fact, Weede (1980) could not reproduce Rubinson’s results of the egalitarian effect of state strength when he includes the U-shaped relation in Rubinson’s model. Weede and Tiefenbach (1981) first survey crossnational regression analyses of five alternative approaches to explaining income inequality. They then put together the major independent variables from each approach to reanalyze the problem of income inequality. The independent variables include the level of economic development, socialist countries, military participation rate, state strength, democracy and dependency. Using trade structure, export concentration index, partner concentration index and stocks of FDI as the indicators for dependency, their empirical results offer broad support for the strong version of Kuznets hypothesis. Yet none of the indicators of dependency shows any statistically significant correlation with inequality. Like most research by economists, Rubinson (1976) and Weede and Tiefenbach (1981) include DCs and LDCs in their samples. Bomschier (1981) eloquently argues, however, that the failure of Weede and Tiefenbach (1981) to find any effect of FDI on income inequality is caused by pooling both DCs and LDCs in the sample. According to Bomschier, that “dependence tends to increase the amount of inequality within countries” holds only for LDCs. For DCs,

which are the home countries of most FDI, there is a tendency toward an opposite association between FDI and inequality. Consequently, when a sample with both DCs and LDCs in it, the two effects might well cancel out each other and no significant effect could be found. In an analysis with 72 countries including 14 DCs, 52 LDCs and six centrally planning economies (CPEs), Bomschier and Chase-Dunn (1985) introduce a dummy variable for the 14 DCs to demonstrate that FDI does have a significant positive correlation with inequality in the host LDCs and at the same time is negatively correlated to Gini index in the DCs. They have also observed that a higher proportion of public investment to total investment contributes to a meliorating inequality. Yet it is suspected that this is sheerly due to including the six CPEs in the sample, where there is basically no privately owned capital and the Gini coefficient is only about the distribution of labor income which is always more equally distributed than property or total income.

3. EMPIRICAL STUDY As there is no consensus of any theoretical framework to guide empirical studies on the relationship between FDI and inequality, before proceeding further we would like to point out some general considerations in performing the empirical analyses. First, since the primary concern of this study is about the developing world, the sample will be restricted to LDCs. This restriction could also avoid complications caused by DCs and CPEs, which are emphasized so much by Bomschier (1981) and Saith (1983). Second, while there is a host of national attributes proposed when explaining income inequality, only variables which are commonly deemed important and could be defined and measured, at least, relatively objectively will be used. Thus, variables such as dualistic society and degree of democracy that are not well quantified do not appear in the models, though they are by no means unimportant (Papanek and Kyn, 1987; Bollen and Jackman, 1985; Muller, 1988, Simpson; 1990). Third, special attention will be paid to data comparability across countries.

(a) The model Based on the theoretical arguments sketched in section 2 and the specifications of past studies, we postulate the following basic model? GINI = b,, + b,Lh’PCGP + bmPCGP2 + b, FDIS + b,GOV + bdGRiL +b,TRADE + b,GPCGP + b,HCAP + u (1)

FDI AND INCOME INEQUALITY

The meanings of the notations are as follows: GINI = Gini coefficient X 100 LNPCGP = logarithm of real per capita GDP (PCGP) LNPCGP2 = squared LNPCGP FDIS = (stocks of FDI/GDP) X 100 GOV = (share of government services in real GDP) X 100 AGRIL = (agricultural labor force/total labor force) X 100 TRADE = [(exports + imports)/GDP] X 100 GPCGP = average annual growth rate of real per capita GDP HCAP = human capital u = normally distributed disturbance term. The model belongs to the stronger version of the Kuznets hypothesis since there are no time variable and country-specific dummy variables in it. We choose this specification because of the lack of support for the weaker version of Kuznets hypothesis on the one hand, and, on the other hand, it allows us to use all the data in the regression analysis, including multiple observations for countries where data for the same type of coverage are available at different time points. At a later stage, dummy variables for geographical regions will be added to this basic model. The Gini coefficient is used as the dependent variable because it is the most commonly presented inequality measure, not because it is necessarily the best one. More important, it is the only measure in the data set used in this study. According to the invertedU hypothesis, the signs of the coefficients b, and b, should be positive and negative, respectively. The expected sign of b, is positive if the dependency arguments hold; on the contrary, it is expected to be negative (or, at least, nonpositive) by the modernization proponents. Although both dependency and modemization perspectives maintain that the coefficient of GOV (bJ should be negative, they claim so for very different reasons. For the modernization theorists, government could play a major, direct role in providing social and physical overhead capital which is expected to be favorable to income distribution. In fact, one of the major justifications for government economic activities is to improve distribution inequality. Dependency theorists such as Rubinson, however, regard the degree of direct government intervention as an indicator of state strength. Not only can a strong state protect economic agents from the risks and uncertainties of the world economy, but it can also secure privileged access to resources and markets and gain some control over the world markets. The strong state can therefore contribute to reducing inequality. The role of government in an economy measures basically the domestic strength of the state. Rubinson (1976) supplements this proposition with an argument concerning dependency on external markets. The higher the value of TRADE the weaker the state, and

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thus a positive sign of b, is expected in accordance with the discussion in section 2. International economic and development economists, however, traditionally treat TRADE as an indicator of the openness of an economy. It is the very basic theme in intemational trade theory that openness to international markets helps a country reap it comparative advantage which would not be possible in a small domestic market. This, in turn, implies a more efficient use of resources and faster economic growth. Moreover, it is widely recognized that openness to international competition could discipline domestic monopolistic or oligopolistic activities. The sign of b, is therefore expected by the modernization theorists to be negative. There is widespread agreement that the structure of an economy and accumulation of human capital are of crucial importance in determining income distribution. Since the mechanisms through which these two variables affect economic inequality are so well documented in the development literature that we need not reiterate them (Adelman and Morris, 1973; Ahluwalia, 1976; Fields, 1980; Ram, 1984; Papanek and Kyn, 1987). In general, a high proportion of agriculture is regarded as a disequalizer (b, > 0) whereas an improvement in human capital is an equalizer (h, < 0). The variables discussed so far capture essentially long-term structural mechanisms. There has been a hot debate however, on the relationship between short-term economic growth and income distribution. It is argued that a high growth rate in a short period of time requires greater rewards to such well-to-do groups as savers, investors, and entrepreneurs; a higher short-ten-n growth rate thus tends to exacerbate inequality. Yet, most studies could not find any evidence for this alleged pernicious distributional effect of rapid short-term growth (Ahluwalia, 1976; Ram. 1984; Papanek and Kyn, 1987). Accordingly, there is no a priori expectation about the sign of b, which is meant to assess the distributional effect of short-term economic growth.

(b) The data and measurement There are two major difficulties in a crosscountry study of FDI and economic inequality. The comparability of the data constitutes the first one while a reasonable sample size is the other.9 Most of the existing studies make efforts to enlarge the sample size (e.g., Rub&on, 1976; Weede and Tiefenbach, 1981; Bomschier and Chase-Dunn, 1985). But the increase in the observations, if not carefully examined, will inevitably worsen the comparability problem. Therefore this study will try to take care of the comparability problem to the extent that there are still enough observations to allow meaningful regression analyses.

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(i) Inequality The inequality indicator used in this study is the Gini coefficient collected recently by Fields ( 1989).i” This new data set is based on all the familiar sources of inequality such as Paukert (1973), lain (1975) as well as publications of World Bank, United Nations and International Labor Office, which are supplemented by country-specific sources. In addition to data availability, Fields carefully selects the countries according to the following criteria: -

The data base must be an actual household survey or census and national in coverage.11 For comparisons across time, the income concept (income or expenditure) and recipient (household or individual) must be consistent.

There are 35 LDCs meeting these criteria, with 22 countries having usable data for more than one year. As new information on inequality are available from more recent issues of World Bank’s World Development Report, data for Guatemala (1980), Kenya (1976), Mauritius (1980) and Zambia (1976) are added to Fields’s original data setI* On the other hand, Peurto Rico and Bahamas are excluded from the sample. Puerto Rico is too related to the United States to be admitted as an independent LDC and Bahamas, with population less than 200,000 at the time when data are available, is too small to be of practical importance. (ii) Level of development, role of government and short-term economic growth The most popular proxy for the level of economic

development is per capita GDP.” There is, however, a well-known problem for the comparability of per capita GDP across countries. It is due to the use of official exchange rate to convert GDP measured in domestic currency to GDP measured in US dollar. The inability of the official exchange rate to reflect purchasing power parity could introduce serious errors to such a degree that a switch in per capita GDP ranking among LDCs indeed occurs (Ahluwalia, 1976). To overcome the comparability problem, we use the concept of real per capita GDP devised by Summers and Heston (1984). Measured in 1975 international prices, the biggest advantage of this Summers-Heston real per capita GDP over the per capita GDP used so far is that it is directly comparable across countries and time. For similar reason, we use the share of govemment services in real GDP in Summers and Heston (1984) to represent the direct role of government in an economy.14 The short-term economic growth is defined as the average annual growth rate of real per capita GDP.ls (iii) Investment dependence The primary argument of the dependency theory about the distributional impact of FDI is through the

segmentation of the host economy and the formation of intercountry interest coalitions. It refers, albeit implicitly, to a kind of long-term, dynamic phenomenon. To capture this long-term characteristic of foreign capital, the stock of FDI as a proportion of GDP is used as the indicator for the significance of FDI to the host economy.16 Aside from the fact that this is a measure used extensively, this ratio variable can eliminate the potential errors caused by the official exchange rate since both GDP and the book value of the stock of FDI are expressed in terms of US dollar. UNCTC (1983) is the major source for the stock of FDI, which is supplemented with data from Dunning and Cantwell (1987) and various issues of International Monetary Fund’s Balance of Payments Yearbook.

(iv) Trade dependence and economic structure The data needed to quantify these two variables, imports, exports, agricultural labor force and total labor force, are directly from various issues of World Tables published by World Bank. As usual, trade dependence or the degree of openness of an economy is measured by the total value of a country’s exports and imports as a percentage of GDP. Again this can eliminate the possible biases caused by the official exchange rate. While there are different ways to characterize the structure of an economy, the importance of agriculture is chosen as the indicator because agriculture production is by all means the principal economic activity in most LDCs. The variable is measured as the proportion of total labor force engaging in agriculture activities in accordance with Adelman and Morris (1973). (v) Human capital Two measures are used to approximate the level of human capital (HCAP). The first one, LIT, is the literacy rate directly from Compendium of Statistics on illiteracy (1988, 1990) compiled by UNESCO. It is intended to measure the basic educational level of the stock of the population. The literacy rate is chosen over the primary school enrollment ratio because it relates to the stock of human capital rather than to the flow of investment (Ahluwalia, 1976; Barre, 1991).17 The second one, the secondary school enrollment ratio (SEC), tries to capture the degree of human capital improvement beyond the basic level of education (Ahluwaha, 1976). It is adopted from the World Bank’s World Tables (1983).‘* While Rubinson (1976) finds that the coefficient of the time variable never differs significantly from zero, to be logically consistent, we time-match the independent and dependent variables and take the time lag between them into account. That is, when the dependent variable (the Gini coefficient) is referred to year t, all the independent variables except HCAP are the three-year average of their values at time t, r-l and

FDI ANT.) INCOME INEQUALITY r-2. The

purpose for using the average value is to eliminate short-term fluctuations of the variables. Admittedly, the selected lag might not be substantively long enough for the independent variables causally to affect income distribution, the three-year interval is chosen so as not to jeopardize the sample size. Education however, must surely have a longer term effect, with a substantial time lag. We therefore use the values of the relevant indicators for HCAP roughly 10 years before the year of the Gini coefficient. Most studies in this area, such as Chase-Dunn (1975) and Bomschier and Chase-Dunn (1985), have not paid attention to the time-match problem. Others such as Weede and Tiefenbach (198 1) and Bollen and Jackman (1985) have resorted to the stability of the relevant data, and thus do not time-match the dependent and independent variables. While their results might not be unsound, the fact of even measuring the dependent variable prior to the independent variables in some cases is theoretically untenable. The sample size is determined exclusively by the availability of the data. The Gini coefficient and the stock of FDI are the two variables most crucial in delimiting the sample size. After taking into account all the variables but literacy rate, there are 60 observations for 1968-8 1.I9 (c) The empirical results The model is estimated by the ordinary least squares (OLS) technique. To check influential outliers that might seriously affect the parameter estimates, all data points are included in the initial data run. Using the diagnostic procedure proposed by Belsley, Kuh and Welsch (1980), we have identified seven influential outliers that are excluded from the regression analyses reported below.” Furthermore, to minimize the possibility that the results obtained are based on some ad hoc sample, we proceed the regression analyses with four different subsamples. The subsamples are based on the information of partial plots. Sample 1 includes all the observations; sample 2 deletes cases with FDZS > 100 or FDZS c 1; sample 3 further restricts sample 2 to cases with TRADE < 100, sample 4 excludes from sample 3 cases with GOV > 23. Table 1 presents the empirical results for the four samples. Three equations are estimated for each sample, depending on the specification of the human capital variable. The third equation includes both IlT and SEC because Ahluwalia (1976) suggests that the former might represent some kind of general human capital whereas the latter represents more advanced human resources, and each one has its own independent direct impact on income distribution. The results overall are encouraging, with a little bit more than 60% of the variation in the Gini coefficient explained by the right-hand side variables for all but the first

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samples. The F-statistic is significant at 1% level in each equation, and the signs of the coefficients are basically as expected. Since heteroskedasticity could be a concern in a crosscountry analysis, we have also calculated t-statistics using White’s heteroskedastic consistent standard errors. The results do not differ greatly from what obtained by OLS and reported here.21 The Kuznets hypothesis is generally supported by our empirical evidence. Even in sample 1 where only one of the coefficients of LNPCGP and LhJPCGP2 reaches 10% level of significance, the signs are all consistent with an inverted U-shaped curve. The most striking result from Table 1, however, is the unambiguous positive partial correlation between FDIS and the Gini coefficient. The estimated coefficient of FDIS is statistically significant at 1% level for 10 out of the 12 equations, and significant at 5% level for the remaining two. Our results thus confirm previous findings and support the assertion of the dependency proponents. That is, continuing inflow of foreign capital into the LDCs is very likely to be harmful to the income distributions of the host economies. Now let us turn to the other variables. Contrary to what was reported by Weede and Tiefenbach ( 198 1) as well as Papanek and Kyn (1987), our results lend moderate support to the negative relationship between the degree of government direct economic activities and income inequality. The sign of the estimated coefficient of GOV is always negative, though it loses significance as the sample size gets smaller. It is worth emphasizing however, that, even if government interventions are negatively correlated with more unequal income distribution, the regression analysis does not tell us whether this is due to “state strength” as alleged by dependency theorists or due to growth and the ensued equalizing effect claimed by their modemization counterparts. Table 1 also reveals that the relationship between economic structure and economic inequality is quite stable; a higher proportion of labor force in agriculture is always associated with greater inequality. While the coefficient of AGRIL never reaches 10% level of significance, barring sample 2, its estimated value is always around .15 and larger than its standard error. In other words, improvement in income distribution could be expected with the shrinking of the agricultural sector in the course of industrialization. This finding is at variance with what was obtained by Ahluwalia (1976). who finds that the decline in the share of agriculture output in GDP has disequalizing impact on income distribution. Using the value of imports and/or exports divided by GDP as an indicator for trade dependence, Rubinson (1976) has shown that the more a country depends on external markets, the greater the degree of income inequality. The empirical evidence in Table 1, however, fails to replicate Rubinson’s result. The estimated coefficients of TRADE are uniformly negative,

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WORLD DEVELOPMENT Table 1. Regression equations for FDI and income inequality*

(1) CONSTANTJl LNPCGP LNPCGPZ FDIS GOV

TRADE GPCGP SEC

-158.36 (-1.193) 49.79 (1.272) -2.74 (-.955) .091$ (2.446) -.Ol$ (-2.336) .16 (1.634) -.05 (-1.311) -.599 (-1.725) -.13 (-1.190)

LIT N (sample) F-statistic R2 adjusted

53 7.135t .4856

(1) CONSTANT LNPCGP LNPCGP2 FDIS GOV AGRIL TRADE GPCGP SEC

-554.43t (-2.904) 159.33t (2.850) -10.41t (-2.565) 4457 (4.590) -.003 (-.971) .14 (1.384) -.13t (-2.665) -.41 (-1.319) -.14 (-1.332)

LIT N (sample) F-statistic Rz adjusted

43 11.962t .6762

Sample 1 (2) -219.33 (-1.579) 69.319 (1.694) -4.13 (-1.393) .099t (2.666) -.01t (-3.617) .16 (1.584) -.05 (-1.392) -.688 (-1.967)

-03 (-1.482) 53 7.352t .4942

(3) -208.25 (-1.469) 65.17 (1.549) -3.80 (-1.242) .097$ (2.576) -.01t (-2.536) .16 (1.604) -.04 (-1.246) -661 (-1.899) -.07 (-.506) -.07 (-999) 53 6.453t .4855

Sample 3 (2) -610.76t (-3.128) 177.41t (3.101) -11.77.t (-2.845) .430t (4.351) -.Ol (-1.640) .14 (1.342) -.15t (-3.054) -.44 (-1.378)

-.04 (-.684) 43 11.3697 6639

(1)

(3)

453.11t (-2.977) 134.27t (3.014) 475t (-2.709) .291t (3.744) -.Ol! (-1.803) .06 (.612) -.07$ (-2.251) -.38 (-1.199) -.17$ (-1.666)

48 10.023t 6057

(1)

-555.27.f (-2.765) 159.59t (2.697) -10.43$ (-2.432) .445t (4.476) -.003 (-.822) .14 (1.353) -.13t (-2.589) -.41 (-1.273) -.14 (-1.119) -.OOl (-.016) 43 10.320.t 6663

-569.18t (-2.732) 163.26t (2.698) -10.69$ (-2.440) .439t (4.358) -.002 (-603) .15 (1.343) -.13t (-2.361) -.41 (-1.185) -.12 (-1.087)

40 9.711t 6412

Sample 2 (2) -508.14.t (-3.214) 152.59t (3.299) -10.09t (-3.029) .292t (3.735) -0.1t (-3.263) .05 (.568) -.08$ (-2.417) -.47 (-1.425)

-.08$ (-1.533) 48 9.872t 6016 Sample 4 (2) -614.07t (-2.932) 177.63t (2.917) -11.79t (-2.681) .427t (4.145) -.004 (-.838) .15 (1.297) -.15t (-2.553) -.42 (-1.166)

-.02 C-.331) 40 9.258t .6288

(3) A87.21.t (-3.052) 144.83t (3.084) -9.48t (-2.795) .294t (3.752) -.OlO (-1.934) (E2) -.07$ (-2.189) -44 (-1.328) -.12 (-.979) -.05 (-.75 1) 48 8.8727 6012

(3) -559.26t (-2.594) 159.75$ (2.529) -10.44$ (-2.283)

44

(4.265) -.002 (-.317) .15 (1.344) -.14t (-2.288) -40 (-1.134) -.14 (-1.047) (E2) 40 8.377t .6299

* Seven outliers are omitted from the analysis. The numbers in parentheses are t-statistics; all tests are two-tail tests. t Significantly different from zero at 1% level. $ Significantly different from zero at 5% level. 8 Significantly different from zero at 10% level. and they are significant at the 5% or 1% level in eight out of 12 equations. Therefore, our results tend to sup-

Port the modernization openness argument. Namely, openness to the world economy could enhance com-

FDI AND JNCOMJ3 INEQUALITY

petition, improve efficiency in resource allocation, spur growth and lead to better income distribution. All the estimated coefficients of GPCGP are negative, but only the three based on sample 1 reach 10% level of significance. This concurs with the findings of Ahluwalia (1976), Ram (1984) as well as Papanek and Kyn (1987). Thus there is no evidence of a trade-off between short-term growth rate and income equality. In fact, one could be a little bit more optimistic regarding the correlation between short-term growth and inequality; not only is that not seen to be positive, but it might well be negative. As far as human capital is concerned, the results in Table 1 is somewhat surprising. While the inclusion of either SEC or LJT (but not both) helps improve the goodness of fit, only in one equation the estimated coefficient reaches 10% level of significance. In sample 4, the coefficient of UT even has the wrong sign in the last equation, a result clearly contradicts those reported by Adelman and Morris (1973), Ahluwalia (1976) and Fields (1980). Nevertheless, the result is not different from those of Ram (1984), and Papanek and Kyn (1987). In particular, Papanek and Kyn (1987) find that, while generally related to equality when sociopolitical variables such as government interventions are not included in the regression analysis, education attainments are no longer statistically significant once those variables are taken into account. The results in Table 1 lend quite strong support for the previous Iindings of most dependency research that FDI is closely related to greater inequality in the host LDCs. As a matter of fact, the robustness of the results is confirmed by the extreme bounds analysis (EBA) proposed recently by Levine and Renelt (1992) if our basic model is used.** As with any empirical study, however, the results must be interpreted with caution. The above interpretations make sense only if the model is correctly specified. In particular, when some important explanatory variables are omitted, the least squares estimation of the regression coefficients will be biased and inconsistent. As no empirical works in the social science can be immune from potential misspecitications, we do not wish to take a purist position on the issue. Yet, it is well recognized that countries in the same geographical location tend to share a variety of attributes. Although the basic model has already included in it some region-related variables such as education and economic structure, it is still likely that other variables closely related to geographical regions are omitted from the model. For instance, Girling (1973) points out that unequal income in Latin America, at least in the early phase of development, is rooted in the concentration of land ownership. Unfottunately, a separate, direct test of the impacts of these potentially omitted variables is infeasible for lack of appropriate data. As a proxy, we use two dummy variables, LA (Latin America) and AS (East/Southeast Asia), to determine whether the distributional effects of FDI are contingent upon factors

477

associated with the geographical areas.23The classifications of countries for different regions are presented in Appendix A. The models to be tested are: GINI = b0 + b,LNPCGP + bJl’fPCGP2 + b,FDIS + b.,GOV + bdGRIL + bsTRADE + b,GPCGP + b$ICAP +b&A + b,,,AS + b,,FDISLA + b,$DISAS + 4 (2) where LA=

1 if the country is a Latin American LDC 1 0 otherwise,

AS =

1 if the country is an East/Southeast Asian LDC 1 0 otherwise

FDISLA = FDIS x LA, FDISAS = FDIS x AS.

The coefficient of the dummy variable LA (AS) reflects the initial difference in the mean values of the Gini coefficient between Latin American (East/ Southeast Asian) LDCs and the reference group. The distributional impact of FDI on the reference group is represented by the coefficient of FDIS, whereas the coefficient of the interaction term FDISLA (FDISAS) captures the difference in the distributional impact of FDI between Latin American (East/Southeast Asian) countries and the reference group. Table 2 displays the least squares estimates for model (2). Since Table 1 shows that the distributional effects of SEC and LIT are quite similar and including both of them only results in lower R, Table 2 only reports the equation using SEC as the indicator of human capital. The differences between equation (L) of Table 1 and Table 2 are astonishing. The R increases in each and every subsample, ranging from 10 percentage points (sample 3) to 26 percentage points (sample l), a great improvement in the explanatory power over the basic model. More important, not only does the partial correlation between FDIS and the Gini coefficient become insignificant, but the estimated coefficient of FDIS even has a negative sign for every sample. This gives strong indication that, when regional differences are taken into explicit consideration, accumulation of foreign capital in itself bears no relation to inequality in the countries of the reference group. Moreover, for all four samples the estimated coefficients of LA and FDISLA are never significant at any commonly accepted level. Therefore, taking the Latin American LDCs as a whole, there is no evidence tbat its inequality as measured by the Gini coefficient differs in any significant way from that of the reference group. Similarly, no significant difference in the distributional impact could be found between the FIX in Latin America and that in the reference group, though the coefficient of FDISLA is always positive. The picture changes dramatically when we look at the East/Southeast Asian LDCs. Table 2 shows that the estimated coefficient of AS is universally negative

418

WORLD DEVELOPMENT Table 2. Regression equations for FDI and income inequality with regional dummies* Sample 1

CONSTANT LNPCGP LNPCGP2 FDIS GOV AGRIL TRADE

-300.14.t (-2.967) 96.02t (3.156) -6.45t (-2.893) -.585 (-.893) -.01t (-4.587) .25t (3.065) ,002 (.075)

Sample 2 -394.52t

GPCGP

SEC LA AS FDISLA FDISAS N (sample)

E-statistic R adjusted

(Z9) .14 (1.414) .96 (242) -21.93t (-6.070) .63 (953) 1.73t (2.750) 53 14.347t .7549

(-3.154) 122.67t (3.333) -8.27t (-3.116) -.591 (-.844) -.01t (-3.499) .17$ (1.864) -.03 (-.880) -.05 (-,156) .08 (688) -.99 (-.216) -19.54? (4.736) .74 ’ (1.031) 1.72$ (2.548) 48 12.912t .7526

Sample 3

Sample 4

-491.32t (-3.115) 147.57t (3.209) -9.99t (-3.00) -.367 (-.472) -.01t (-2.703) .23P (2.054) -.05 (-1.057) .05 (177) .07 (558) .39 (.072) -17.54t (-3.997) .63 (775) 1.45s (1.994) 43 13.953t

-I48.61$ (-2.555) 137.93t (2.729) -9.36$ (-2.572) -662 (-.572) -.Ol$ (-2.294) - .19 (1.373) -.03 (-.46 1) .14 (-.43 1) .04 (267) -1.86 (-.239) -19.53t (-3.491) .93 (769) 1.12 (1.628) 40 11.438t .7626

* Seven outliers are omitted from the analysis. The numbers iu parentheses are t-statistics; all tests are two-tail tests. t Significantly different from zero at 1% level. $ Significantly different from zero at 5% level. fi Significantly different from zero at 10% level. and significant. At the same time, all the coefficients of FDZSAS are positively signed, though only three of them reach a significant level and the degree of significance decreases as the sample size becomes smaller and smaller. These results suggest that the LDCs in East/Southeast Asia generally have more equal income distribution than their counterparts in the reference group. They also imply that the increase in foreign capital might have more unfavorable distri-

butional impact in East/Southeast Asian LDCs than in the LDCs of the reference group (as well as in Latin America). The information of the two tables suggests that the significant effect of EDI on income distribution in Table 1 might well reflect the facts that, during the period under consideration, the degree of inequality is less severe in East/Southeast Asia than in other areas and FDI plays a more important role in countries of Latin America and the reference group. To the extent that FDI does exercise a deleterious influence on host countries’ income distribution, at least in the 197Os, it is more a phenomenon in Asia than other developing areas. This finding diametrically contra-

dicts the deep-rooted impression among the dependency theorists that FDI did most harm to the Latin American and the African economies. A possible explanation for this seemingly counterintuitive result runs as follows. Notice that a regression coefficient represents only the marginal, instead of the total, impact of the relevant explanatory variable on the dependent variable. FDI has had a much longer history as well as much more pervasive intluence in Latin America and, probably Africa, than in East/Southeast Asia. If there is some socially tolerable ceiling for inequality and the countries in LA and the reference group have reached somewhere around that ceiling before the time period covered by our sample, then the estimated coefficients of FDIS and FDISLA in model (2) are very likely to be insignificant even if

the inequality in those countries is actually caused by the intlow of FDI. In other words, even if the total deleterious distributional impact of IDI does exist and the initial marginal impact is strong, the marginal impact estimated by using the data in the later time period could be negligible. By contrast, the inflow of

FIX AND INCOMEINEQUALITY FDI into East/Southeast Asian areas is a relatively recent phenomenon. Most countries in the AS group have not accepted FDI in a considerable amount until the 1960s and 1970s. This implies that the coefficient of FDISAS should have better chance to capture the initial marginal impact of FDI on the host countries’ income distribution if it exists. As an illustration of what could happen, let us look at a hypothetical example. Figure 1, shows sample points belonging to the LA/reference group, AS (0 and n , respectively), and for possible sample points for the LA/reference group which could have been observed prior to the time period under consideration (0). When model (1) is estimated, the slope of line (a) is the estimated coefficient of the variable FDIS, which is significantly greater than zero. When model (2) is estimated, the slope of line (b) is the estimated coefficient of FDISAS, which is again significantly greater than zero. But the estimated coefficient of FDISLA (or FDIS) represented by the slope of line (c) can hardly be distinguished from zero. But, it is also clear from Figure 1 that the slope of line (c) would have been significantly positive (not shown) had the observations “0” been included in the regression analysis. Therefore, the negligible marginal impact based on the sample points indicating LA/reference group does not correctly reflect the potential inlluence of FDI.” Figure 2 plots the observed data on stocks of FDI and GINZ measures for Latin American and East/Southeast Asian countries. It is consistent with Figure 1 so that the above argument seems to be supported by the observed data. As for the effect of economic development, Table 2 again confirms the Kuznets inverted U-shaped curve hypothesis reported in Table 1. The estimated coefficient of GOV is consistently equal to -.Ol and is statistically significant at 1% level in each sample, implying that the direct role of government economic activities could be a crucial independent determinant of income inequality. Like Table 1, economic struc-

.(b) (c)

-

5: tj

FDIS

Figure 1. Hypothetical data for GINI and FDIS.

419

0

0

8 AS 0 LA

SF 0

50

loo

I50

FDIS Figure 2. Observed data on GINI and FDIS in AS and LA. ture (AGRIL) tends to be positively significantly correlated with inequality. Contrary to what is presented in Table 1, however, the estimated coefficients of both TRADE and GPCGP in Table 2 become insignificantly different from zero. This, once again, indicates that the results based on model (1) could be due to regional difference in equality, since East/Southeast Asian LDCs, on average, are more open to foreign trade, grow at higher rates and have more equal income distribution. Finally, like Table 1, Table 2 provides no evidence for the hypothesis that improvement in human capital is favorable for more equal income distribution. To sum up, our empirical results suggest that25 -

the statistically significantly positive correlation between FDIS and income inequality obtained in previous studies and our basic model is more likely to reflect the geographical difference in inequality than the perverse distributional impact of FDI. Although regression analyses could not in whatsoever way tell us the total distributional impact of FIX, our results do provide some evidence for a positive correlation between FDI and inequality in East/Southeast Asian countries during the 1970s. Accordingly, our findings are generally consistent with the argument of the dependency theorists, - the empirical results in this paper provide quite robust support for the existence of the (strong version) Kuznets Curve, - the direct role of government or state strength has received some support for their expected impact on inequality, though this has very different implications for different perspectives, - while there is some evidence showing that a higher proportion of agricultural labor force is positively correlated with higher inequality, the results are somewhat fragile for they are sensitive to variations in sample size, and -neither a faster short-term growth appears to bring about greater inequality nor an improve-

480

WORLD DEVELOPMENT

ment in human capital helps achieve greater equality. Finally, openness to (or dependence on) foreign markets seems to have no correlation with inequality.

4. CONCLUDING

REMARKS

Attitudes toward FIX in LDCs have changed considerably over the last two decades. While the policies during the 1970s generally aimed at discouraging foreign capital inflows, today many developing countries are actively seeking FDI by redefining their development strategies, liberalizing their economies, and implementing a range of new policies. There are fundamental issues, however, concerning the impacts of FDI on the host country’s economy remaining unresolved. This study takes up the problem of the relationship between FDI and income inequality. The key features of the present study are: restricting sample to LDCs to avoid complications from centrally planned economies (CPEs) and DCs; using only the relatively more recent and more reliable inequality data of the LDCs collected by Fields (1989); relying mainly on the internationally comparable data for the relevant independent variables compiled by Summers and Heston (1984); including only variables that are commonly regarded as important and are able to be, at least relatively objectively, quantified, and improving the specification by introducing into the model geographical dummies which are typically absent from the existing studies about FDI and income inequality. Generally speaking, the results are encouraging. Our model (2) explains more than 75% of the variation in the Gini coefficient, which is far better than any of the existing crosscountry studies on this topic. As far as the relationship between FDI and inequality is concerned, two salient features emerge from our analyses. First, the partial correlation between stocks of FDI and inequality estimated by using the basic model is extremely sensitive to the inclusion of geographical dummies. This implies that the statistically significant correlation between FDI and income inequality widely obtained in earlier studies might capture more of the geographical difference in inequality than the deleterious influence of FDI. Second, to the extent that FDI does give rise to more

unequal income distribution in the host LDCs, only the East/Southeast Asian LDCs appear to be the ones really harmed by the inflow of FDI during the period under consideration. We have to reiterate, however, that the above statement refers to the marginal impact only. There is no way to tell from a regression coefficient the total impact of any explanatory variable on the dependent variable. In other words, the absence of significant coefficients for FDIS and FDISLA in model (2) does not deny the potential existence of a total deleterious influence of FDI on income distribution in the countries in Latin America and the reference group. But, even in the marginal sense our results tend to be supportive of the arguments of dependency theorists, a result running counter to what was obtained by Hein (1992) about the growth effect of FDI. Apart from the impact of FDI, the present study suggests that level of economic development is generally a crucial determinant of income distribution as implied in the celebrated Kuznets U-shaped curve hypothesis. It also reveals that direct government economic activities are closely associated with less income inequality. While there are indications that a higher proportion of agriculture labor force is positively correlated with higher inequality, the results are somewhat fragile to sample selection. Moreover we could not find any evidence in support of the relationship between income inequality and trade dependence (openness), short-term growth rate or human capital improvement. It is admitted that the use of crosscountry data for studying what is essentially a country-specific economic process over time needs well-known caveats. Yet the lack of time-series data has greatly reduced the feasibility of more ideal country case studies. Therefore, we take Ahluwalia’s position that the results presented above should be viewed as suggestive, not definitive. A number of interesting questions remain to be answered, for instance, questions concerning the distributional effect of different types of FDI, FDI from different home countries, land ownership and political democracy all deserve careful investigation in the future. Subject to these caveats, however, it is believed that this study has helped advance out knowledge about the essential features of the relationship between FDI and income inequality.

NOTES 1. To be sure, FDI is a flowconceptand it should not be confused with stocks of FDI, which is the focus of this study. This will become clear from our model specification and variable description. Following the convention in this area, however, we use the term FDI uncritically in the discussion to mean FDI activities in general, 2. For the literature about the growth impact of FDI, see Bomschier and Chase-Dunn (1985).

3. Admittedly, there are several variants within each perspective, in particular, when examined over time. The fact however, that there is no basic disagreement about the impacts of FDI among the variants in each perspective warrants the present classification (Biersteker, 1982; Bomschier and Chase-Dunn, 1985). For convenience, we will use “modernization” and “dependency” for the two perspectives from now on. Bomschier (1983) provides a succinct description of the two perspectives.

FDI AND INCOME INEQUALITY

4. Besides Kuznets’ works (Kuznets, 1955,1963), excellent surveys of the relationship between income distribution and economic development can be found in Cline (1975) and Bacha (1977). See also Isaac (1981) for a succinct discussion of the modernization perspective. 5. To be precise, this refers only to the increase in either domestic or foreign investment which represents an increase in production resources. Most modernization proponents emphasize that FDI can contribute more to economic growth than domestic investment in the LDCs since it directly fills the foreign exchange gap and brings in more efficient technology as well as advanced marketing and managerial Sk&.

6. The weaker version (the intertemporal Kumets curve) “assumes that all the countries lie on a family of parallel Kuznets curves that are constant in time but have distinct levels of income inequality.” Therefore, it “does not try to explain differences in the levels of income inequality across the countries. It just tries to test whether the hypothesis is tenable that all countries evolve in time along a stable U-shaped curve.” Gn the other hand, the stronger version (the intertemporal and cross-country Kuznets curve) assumes that “the crosscountry Kuznets curve is the same as the intertemporal one and that it is the same for all the countries.” (Papanek and Kyn, 1987). 7. Dependent development is not limited to investment dependence discussed here; it also includes industrialization characterized by trade and/or aid dependence. 8. Notice that population growth rate is not included in the basic model. This is because the growth rate of real per capita GDP (GPCGP) is only the difference between the growth rate of GDP and that of population. We have, however, tested the effect of population growth on inequality along with the growth rate of GDP but found no significant result. 9. As pointed out by a referee, there are difficulties other than the two mentioned. For example, the unavoidable sample bias that arises from selecting countries on the basis of the adequacy of data, and the unavoidable deficiencies in connections between available measures and the concepts they are supposed to represent. 10. The income distribution data compiled by van Ginneken and Park (1984) might be better than Fields’s in terms of international comparability. The sample size however, is too small when the sample is restricted to LDCs and the availability of the data on stocks of FDI is taken into account. 11. Excluded are synthetic estimates of income distribution from national accounts, government-stipulated wage rates, average crop yields per hectare, or the like. 12. The data for the indicated years of these four countries are classified by Fields (1989) as “Data Not In Hand, But May Exist Elsewhere.” 13. We choose this measure by following all the empirical studies reviewed in section 2, though a rise in per capita GDP is more suitable for the concept of economic growth than economic development, which requires more.

481

14. Admittedly, this index is flawed since government can effectively intervene the economy through ways not captured by its share in real GDP, for instance, changing tax policies or imposing regulations. This is the only proxy available in Heston-Summers data set, however, and is widely used in empirical studies to represent the role of government (Barro, 1991). 15. That is, GPCGP = [(PCGDP,-PCGDP,_,)‘” - 11X 100, where PCGDP, is the real per capita GDP at year t when a Gini coefficient is observed, and PCGDP,, is the real per capita GDP three years before year t. 16. We have also tried a measure similar to PEN (penetration) defined in Bomschier and Chase-Dunn (1985). The basic results are not different from what was reported in this paper. 17. As Barre (1991) points out, however, the literacy rate might not be that attractive since it appears to be measured in an inconsistent way across countries, and is particularly inaccurate for the LDCs. 18. There are some missing values on the enrollment ratio which are estimated by interpolation. 19. The literacy rate for Fiji (1977) is missing. This does not cause serious problems however, since Fiji turns out to be an influential outlier, being deleted from most of the analyses. 20. The outliers are Guatemala (1980), Jamaica (1980), Kenya (1976), Sierra Leone (1967-69). Zambia (1976), Fiji (1977) and Singapore (1977-78). 2 1. Since a country could have more than one observation at different years, which is a kind of pooled “semi’-longitudinal sample, we have performed Durbm-Watson tests. No first-order autocorrelation is detected in any of the regressions, however. 22. In our EBA, the I variables include the two Kuznets terms; the M variable is FDIS; the 2 variables are GOV, AGRIL. TRADE, GPCGP, SEC and LIT. Parenthetically, a corollary derived from this analysis is that passing EBA is not sufficient for the robustness of a regression estimate unless all the relevant explanatory variables are included as Z variables. 23. Since the regional dummies presumably capture a variety of unspecified historical, social and political attributes which groups of countries have in common, they are not defined exclusively in terms of contiguity. As Appendix A shows, however, the countries in the category AS happen to coincide with those locating in East/Southeast Asia We did try regional dummies based on continental categories; the basic conclusions stay the same as reported below. 24. An important lesson emerges from this hypothetical example. Namely, the coefficient of FDIS estimated from model (1) is very likely to capture more of the regional difference in income inequality than the correlation between inequality and FDIS. This is demonstrated in Appendix B, where the coefficient of FDZS of model (1) (the slope of line (a)) is still positive even though both line (b) and line (c) are essentially horizontal. In other words, even though there is no

482

WORLD DEVELOPMENT

correlation between inequality and FDIS at all, a sufficient difference in income inequality between regions could make the estimated coefficient of FDIS based on models without regional dummies signi6cantIy greater than zero. It is an obvious mistake if the result is intetpreted as implying that FDI does have a deleterious impact on the income disttibution of the host LDCs. 25. To. ensure that the results are not fragile, we have per-

formed some sensitivity analyses. Fit the Levine-Renelt EBA is aDDlied to establish that AS and FDISAS are robust. .. Second, following Randolph and I_ott (1993), we have specified the Kuznets terms in other four alternative forms: (1) b,LHPCGP + b,PCGp; (2) b,PCGP + b,(l/PCGP); (3) b,PCGP + b,(l/LNPCGP); (4) b,PCGP + b,(l/LNPCGP2).

These specifications, however, do not change our conclusions.

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Development: Third Survey (New York: United Nations, 1983). UNESCO, Compendium of Statistics on Iiliteracy (Paris: UNESCO, various issues). Van Ghneken, W. and J. Park, Generating Internationally Comparable Income Distribution Estimates (Geneva: International Labor Office, 1984). Weede, I?., “Beyond misspecification in sociological analyses of income inequality,“American Sociological Review, Vol. 4.5 (June, 1980). pp. 497-501. Weede, E. and H. Tiefenbach, “Some recent explanations of income inequality: an evaluation and critique,” International Studies ~~r~r~y, Vat. 25 (June, 1981), pp. 255-282. World Bank, World Development Report (New York Oxford University Press, various issues). World Bank, World Tables (Baltimore, MD: Johns Hopkins University Press, various issues).

APPENDIX A: COUNTRY LIST AND YEARS OF OBSERVATIONS Asia

71,76,81 70,76,78,80 70,76 70,76,79

Philippines Singapore Taiwan hailed

71 72/73,77/%* 72,80 68/69,75/76,81

Brazil

70,72,76,78,80

Chile Colombia Costa Rica El Salvador Guatemala

71 71 71,77,79 76P7 80*

Honduras Jamaica Mexico Panama Trinidad & Tobago

67/68 73, go* 69,77

Nepal Pakistan Sierra Leone Sri Lanka Tunisia Turkey Zambia

76/77 69f70,70/71,71172,79 67/69* 73,78119 74/z 68,73 76*

Hong Kong

Indonesia Korea Malaysia Latin America

::/72,75/76

Reference Group

Bangladesh Egypt Fiji India Iran Kenya Mauritius

7317476177 74175 77* 75fl6 73174 76* 80181

* Indicates influential outliers. APPENDIX B

(aI /