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Foreign direct investment and human capital in developing countries: a panel data approach

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Abstract

Theoretical studies have shown that there is a direct relationship between human capital and foreign direct investment (FDI). However, only a few available empirical studies have attempted to investigate this relationship simultaneously. Using country level panel data from 55 developing countries over the 1980–2011 period, this paper examines the interrelationship between FDI and human capital. Statistical analysis, based on simultaneous equations fixed effect estimation, reveals significant bi-directional causality between human capital and FDI, which suggests that FDI and human capital development policies need to be coordinated. FDI-led economic growth models may not be entirely suitable for all developing countries aiming to replicate the economic success of countries such as Brazil and China unless attention is also paid to human capital development through increased spending on education and training.

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Notes

  1. In a very interesting and comprehensive study, Iwasaki and Suganuma (2015) show that FDI has contributed to significant economic growth in regions of Russia. Prior to this, Anwar and Nguyen (2014) reported that FDI has contributed to significant productivity spillovers in regions of Vietnam. Sun (2009) found that FDI has resulted in positive export spillovers to Chinese firms. Using province level panel data from Vietnam, Anwar and Nguyen (2010) showed that a two-way causation exists between FDI and economic growth.

  2. Zhang et al. (2010) found a positive relationship between the diversity of FDI country origins and productivity of domestic firms. This positive relationship is stronger only when the technology gap between foreign-invested and domestic firms is intermediate. In a related study, Imbriani et al. (2014) reported that the level of technology gap matters considerably for spillovers from FDI. They argue that human capital plays a vital role in absorbing the benefits of FDI-related spillover effects.

  3. It is well-known that theoretical models tend to rely on certain assumptions that are never “descriptively realistic” (Friedman, 1953, p. 153), which can explain the inconsistency between the theoretical and empirical results.

  4. See Anwar and Nguyen (2011) for a detailed discussion.

  5. At the national level, FDI can also indirectly increase the supply of skilled labour through a number of channels. First, as the demand for skilled labour increases, due to inward FDI, skilled wages also increase. In the long term, increase in skilled wages motivates workers in host economies to upgrade their skills through education and/or training. Second, FDI tends to increase the government tax revenue because of the increased economic activity and output, which allows the government to increase its investment in education. Third, inward FDI is a relatively less volatile source of capital for host economies compared to other sources, such as the official development aid, equity or debt. Thus, inward FDI can make a significant contribution towards the development of sound and sustainable macroeconomic policies in host economies. Finally, inward FDI can also help the host developing economies to deal with the issue of “brain drain” through job creation and increased opportunities for professional development.

  6. We used one year lagged GDP growth rate to account for possible reverse causality from FDI to GDP growth rate. A number of studies, including Duttaray et al. (2008) and Li and Liu (2005), have reported that inward FDI affects economic growth. Furthermore, in order to examine the persistency, we also included up to 4 year lagged values. However, these lagged levels (2, 3, and 4) were found to be insignificant. Please see Table 11 in the Appendix for details.

  7. This measure is a flow rather than stock because “data on capital stock are expressed in book values that measure the value of an investment at the time it was made with no adjustments for subsequent inflation and/or exchange-rate variations. Thus the use of capital stock data covering different periods in different countries would introduce a book-value bias.” (Root and Ahmed 1979). In addition, if one deflates the book values of capital stock, it is also likely to introduce further bias. Kim and Park (2013), Kottaridi and Stengos (2010) and Noorbakhsh et al. (2001), among others, also use the flow of inward FDI in their statistical analysis.

  8. In order to check whether the impact of openness to trade on FDI exhibits persistency, we also included various lagged levels (1, 2, 3, and 4) of the trade variable in Eq. 5. The estimated results, as presented in Table 11 (see the Appendix) show that, except for the four period lagged values of tertiary enrolment rate, all the estimated coefficients are statistically insignificant.

  9. To the best of our knowledge, none of the existing studies suggest that “life expectancy” is the best proxy of overall health condition. However, the Australian Institute of Health and Welfare (2015) states that “Life expectancy is the most commonly used measure to describe population health and reflects the overall mortality level of a population.” In fact, Gittens (2006) uses life expectancy as a proxy for health.

  10. Blonigen (2005) argues that estimating the magnitude of the effect of institutions on FDI is not easy. Not only, it is hard to accurately measure the effect of institutions, institutional approaches tend to exhibit significant persistency. Accordingly, within a country, there is likely to be little informative variation in the effect of institutions over time. We believe that our fixed effect estimation can deal with this persistency. We also estimated the model after including the GDP per capita. As can be seen in Table 10 (in the Appendix), some of the explanatory variables, particularly human capital and FDI, have unexpected signs and/or the estimated coefficients are statistically insignificant. Therefore, we decide to follow Noorbakhsh et al. (2001)’s model and Schultz (1961)’s conceptual framework of human capital development by not GDP per capita in our empirical model.

  11. Countries included in dataset 1 are shown in the Appendix Table 5.

  12. When we separate the transition economies from non-transition economies, included in our sample, the two samples yield very different results (see Appendix Tables 8 and 9). These results are also different from those reported in Table 3. In the case of transition economies, irrespective of the measure of human capital used, the estimated coefficient of human capital is positive and statistically significant. However, the estimated coefficient of FDI is positive and significant only when tertiary enrolment rate is used as a proxy for human capital (see Appendix Table 8). In the case of non-transition economies, for all three measures of human capital used, the estimated coefficient of human capital is statistically insignificant. However, the estimated coefficient of FDI is positive and significant when tertiary enrolment rate and the average of secondary and tertiary enrolments are used as a proxy for human capital (see Appendix Table 9). These results indicate that the estimated results are sensitive to the choice of countries included in the sample, which is not surprising. As indicated by Xu (2000), Wang and Wong (2009) and Borensztein et al. (1998), in order to fully benefit from FDI inflows, a country must also have a minimum threshold level of human capital.

  13. In order to focus on the role of different measures of human capital and the countries included in the sample, we re-estimated the empirical model by using a smaller sample that consists of 38 countries. Using this smaller sample, the estimated results for different measures of human capital are reported in Tables 12 and 13 in the Appendix. The estimated coefficient of human capital is statistically insignificant when the tertiary enrollment and average enrollment rates are used as a proxy for human capital. The estimated coefficient of FDI is also statistically insignificant when secondary enrollment rate is used as a proxy for human capital. When the average years of schooling are used as proxies, the coefficients of all variables, except life expectancy, are statistically insignificant. These results show that human capital measures do not matter much but, based on the results presented in Tables 3 and 12, it can be argued that countries included in the sample does matter.

  14. The complete regression results are available upon request.

  15. Noorbakhsh et al. (2001) argue that one should always be cautious with the empirical analyses because proxies used to measure the effect of certain directly unobservable variables (for example human capital) may not always capture the actual variations in the relevant variable. A case in point is the work of Wilhelms and Witter (1998): when they used enrolment rate as a proxy of human capital, the impact of human capital on FDI was found to be statistically insignificant but when they used “urbanization and rural population density” as a proxy for human capital, the impact human capital on FDI was found to be statistically significant. In real life, due to lack of data and potential measurement errors, it is often extremely difficult to estimate the true relationship between explanatory and dependent.

  16. It is however worth pointing out that the evidence concerning the impact of FDI on economic growth via human capital is mixed. For example, Xu (2000), Wang and Wong (2009), and Borensztein et al. (1998) conclude that FDI contributes to economic growth only when a certain threshold level of human capital is available, Carkovic and Levine (2002) and Durham (2004) find no impact of FDI on economic growth through human capital.

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Acknowledgments

This paper has greatly benefitted from very useful comments and suggestions received from two anonymous reviewers. However, the authors are solely responsible for all remaining errors and omissions.

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Authors and Affiliations

  1. College of Business, Law and Governance, James Cook University, Townsville, QLD, 4718, Australia

    Veasna Kheng & Sizhong Sun

  2. School of Business, University of Sunshine Coast, Maroochydore DC, Sunshine Coast, 4558, QLD, Australia

    Sajid Anwar

  3. School of Commerce, University of South Australia, Adelaide, SA, 5001, Australia

    Sajid Anwar

  4. School of Economics and Trade, Shanghai Lixin University of Commerce, Songjiang District, Shanghai, 201620, China

    Sajid Anwar

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  1. Veasna Kheng
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Correspondence to Sajid Anwar.

Appendix

Appendix

See Tables 5, 6, 7, 8, 9, 10, 11, 12, 13 and 14.

Table 5 Countries included in the sample
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Table 6 Regression results when the average years of secondary schooling is used as proxy for human capital
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Table 7 Regression results after excluding China from the sample
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Table 8 Regression results from a sample of transition economies
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Table 9 Regression results using a sample of non-transition developing countries
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Table 10 Regression results when GDP per capita is included in the model
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Table 11 Regression results (from dataset 1) when additional lags of GDP and Openness are included in the model
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Table 12 Regression results from a sample of 38 developing countries (as listed in Table 14)
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Table 13 Regression results from a sample of 38 developing countries (as listed in Table 14)
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Table 14 List of the 38 developing countries (sample period: 1980–2011)
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Kheng, V., Sun, S. & Anwar, S. Foreign direct investment and human capital in developing countries: a panel data approach. Econ Change Restruct 50, 341–365 (2017). https://doi.org/10.1007/s10644-016-9191-0

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