Gender Disparity in Education and the International Competition for Foreign Direct Investment

Abstract

With few exceptions, the empirical literature on foreign direct investment (FDI) continues to be gender blind. This paper contributes to filling this gap by assessing the importance of gender inequality in education as a determinant of FDI. The authors estimate a standard gravity model on bilateral FDI flows that is augmented by educational variables, including different measures of gender inequality in education. The analysis covers an unprecedented number of both host and source countries of FDI, thereby reducing the risk of distorted results because of a sample selection bias. The results support the view that foreign investors are more likely to favor locations where education-related gender disparities are small. However, the discouraging effects of gender disparity on FDI are restricted to middle-income (rather than low-income) developing host countries and to investors from developed (rather than developing) countries.

Keywords:

• Foreign direct investment
• gender inequality
• education
• JEL Codes: F23, I21, J16

Notes

¹ By contrast, Günseli Berik, Yana van der Meulen Rodgers, and Joseph E. Zveglich (2004) consider openness to trade to be a determinant of gender wage gaps, finding that trade openness is inversely related to women's relative wages in South Korean and Taiwanese industries.

² Avik Chakrabarti (2001) subjects the findings of various studies on FDI determinants to extreme bounds analysis and concludes that few determinants are robust to minor changes in sample selection and the specification of the test equation.

³ Restricting the sample to developing host countries is in line with Bruce A. Blonigen and Miao Grace Wang (2005), who argue strongly against pooling rich and poor countries in empirical FDI studies. Later in this paper, we will further differentiate between low- and medium-income countries within the fairly heterogeneous group of developing countries.

⁴ Overall, the available evidence seems to be in conflict with the hypothesis that exploiting low social standards and repressed worker rights represents an important motivation of FDI. The survey of Drusilla K. Brown (2000) concludes that poor labor practices did not attract FDI; recent studies include Phillipp Harms and Heinrich W. Ursprung (2002), David Kucera (2002), and Matthias Busse (2003, 2004).

⁵ Footloose industries are not tied to any location but tend to move from country to country following government incentives and/or low wages.

⁶ The point made by Shatz (2003) and Matthias Busse, Jens Königer, and Peter Nunnenkamp (2008) about sample selection (see below) suggests a further twist to this debate. While multinational companies may shy away from countries that do not pass a basic threshold in terms of social standards and gender equality, companies may exploit cost advantages once this threshold is passed.

⁷ As noted by Kucera (2002), labor costs tend to decline when some groups of workers are paid less than others for similarly productive work due to discrimination.

⁸ Moreover, Remco H. Oostendorp (2004) stresses the heterogeneous format of available wage data.

⁹ Oostendorp (2004) provides a major exception.

¹⁰ Note that Shatz (2003) argues against panel analyses on education-related determinants of FDI, as he suspects variation over time to be marginal.

¹¹ See Assaf Razin and Efraim Sadka (2007) for an overview of the relevant literature.

¹² As Shatz notes: “national statistical agencies publish bilateral data about the investment activities of their multinationals only for host countries that have sizeable inflows of FDI. This means that nearly all research on foreign direct investment focuses on the winners, countries that have achieved at least some success in attracting FDI. This is a major problem since policy advice is most often sought by the countries that are excluded from analysis” (2003: 118).

¹³ By replicating the regressions for specific sub-groups of countries, we assess the sensitivity of results with respect to sample selection, while the extreme bounds analysis of Chakrabarti (2001) is particularly suited to assess the sensitivity of results with respect to variable selection.

¹⁴ See Eric Neumayer (2002) for a more detailed discussion of alternative approaches.

¹⁵ We divert from the model by Carr, Markusen, and Maskus (2001) in that we use additional control variables. We do not include the interactive terms used by them.

¹⁶ A Hausman test showed that there is no clear preference for the random- or fixed-effects model. Depending on the dependent variable or host country sample, we prefer either a random- or a fixed-effects model.

¹⁷ Note that bilateral FDI flows take negative values if the source country divests in a particular host country (for example, through selling its equity share to local firms and transferring the proceeds back home). We keep negative values with respect to FDI1. However, the results for FDI1 hardly change if we exclude negative values. By contrast, negative bilateral flows are set equal to zero when calculating the share variable FDI2. This helps us include as many observations as possible, while avoiding the somewhat odd notion of negative FDI shares.

¹⁸ In contrast to M&As, greenfield FDI creates new or additional assets.

¹⁹ While a standard Durbin-Watson test showed that we do not necessarily have (first-order serial) correlation in the regressions, we cannot reject the hypothesis of no correlation either. In fact, the evidence is inconclusive.

²⁰ The growth rate of GDP may suffer from endogeneity, as FDI inflows could have an impact on it. In the present context, however, we are not particularly interested in an unbiased estimate of the coefficient on GDP growth. Crucially, any bias in this respect is unlikely to affect the coefficient on our educational indicators – that is, the main interest of the present empirical analysis.

²¹ The data have principally been taken from Robert J. Barro and Jong-Wha Lee (2001). We extended their dataset with more recent figures from United Nations Education, Scientific, and Cultural Organization (UNESCO 2007) to ensure that we can run a panel analysis up to the year 2004. We also performed estimations with average years of schooling for the age group of 15 and above. Unreported results proved very similar to those reported below. The results for the age group of 25 and above may be more reliable, however. This is because average years of schooling for this age group would hardly be affected, even if FDI flows had an impact on the educational attainment of younger cohorts. We owe the point that endogeneity problems may be mitigated in this way to the guest editors of this volume.

²² FDI flows to financial offshore centers can hardly be explained in the context of a gravity model that does not capture tax-related motivations of FDI; including financial offshore centers may thus lead to biased estimation results. We exclude all countries that are on the list of offshore financial centers as reported by Eurostat (2005). For a discussion on tax-induced distortions in international capital flows, see Organisation for Economic Co-operation and Development (OECD 2000).

²³ Since we use the 2005 World Bank definition for the distinction between developing und developed countries, economies like Taiwan and the Republic of Korea fall into the latter category. While this has not been the case for the entire 1978–2004 period, our results do not change much if both countries are treated as developing countries.

²⁴ Obviously, greater openness to trade encourages trade in finished goods, too. In contrast to trade in intermediates, however, the effect of more trade in final goods on FDI flows tends to be ambiguous. This is because the removal of trade barriers for finished goods reduces the incentive to undertake FDI of the “tariff jumping” kind to penetrate protected host-country markets.

²⁵ The results for the remaining variables do not change much if inflation and other insignificant variables are excluded from the analysis. Yet, we include them as they could have an impact on FDI from a theoretical point of view.

²⁶ Note that the mean of 1.06 for FDI1, reported in the Appendix, has to be changed using the reversed transformation equation (3), which results in 1.27. The 2.5 percent increase in the dependent variable then results from the product of -0.128 and -0.25 divided by 1.27.

²⁷ Obviously, it would be desirable to control for the wages of skilled and unskilled labor in our estimations. However, the data situation does not allow us to do so.

²⁸ The results for the control variables are essentially unchanged. Therefore, they are not discussed here in any detail.

²⁹ Another reason for different results is that Shatz (2003) performs a pure cross-country analysis, whereas our findings are based on a panel analysis.

³⁰ Complete results are available from the authors upon request.

³¹ See Gaute Ellingsen, Winfried Likumahuwa, and Peter Nunnenkamp (2006) for a recent analysis of the case of Singapore in this context.

³² Probably, the same applies to resource-seeking FDI undertaken by developing source countries such as China in low-income regions, notably in Africa.

³³ See, for example, the extensive survey of the relevant literature by Robert E. Lipsey (2002).