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Foreign Direct Investment and Regional Productivity Growth in China

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Openness, Economic Growth and Regional Disparities

Abstract

This chapter presents an analytic framework and empirical evidence expanding our understanding of the roles of foreign direct investment in shaping regional productivity growth in China. The study investigates the impacts of foreign direct investment (FDI) on China’s regional economic performance and growth. In this study, we build two versions of the relevant theoretical model, one excluding and one including human capital. By using a nonlinear least squares regression technique, our empirical analyses based on the two versions of our theoretical model show that foreign direct investment tends to exert both a general growth effect and a convergence effect on productivity across the Chinese provinces over our sample period. Our findings imply, at least in the case of the Chinese regions, that in addition to its direct, static level effect on output as an accumulable production input, foreign direct investment may also exert indirect, dynamic impacts on output through its growth and convergence effects on productivity.

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Notes

  1. 1.

    It should be noted that China’s FDI inflows and foreign trade are closely linked. Foreign Invested Enterprises (FIE’s) generally account for over 50 % of China’s exports and 60 % of China’s imports. See, for example, Whalley and Xin (2010) for a recent discussion of the relationship between China’s FDI inflows and foreign trade.

  2. 2.

    Highly unevenly distributed FDI inflows, substantially different degrees of opening up to foreign trade, and huge income disparities across different regions in China, especially those between coastal and inland provinces, are prominent issues during China’s economic transition (see, for example, Yin 2011). For income disparities, for example, by the year 1999 interregional income inequality in China had exceeded that in any other country (see, for example, Yang 1999), and in the year 2005 per capita income of the richer coastal provinces was 2.5 times higher than that of the inland provinces (see, for example, Zhu et al. 2008).

  3. 3.

    Some of such recent studies on the relationship between China’s growth and opening up include, for example, Zhang (1999), Démurger (2000), DaCosta and Carroll (2001), Yao and Zhang (2001), Démurger et al. (2002), Bao et al. (2002), Hu and Owen (2003), Wang and Gao (2003), Zhang (2006), Madariaga and Poncet (2007), Ouyang (2009), Whalley and Xin (2010) and Jiang (2011), to name but a few.

  4. 4.

    According to Zhang (2006), there are a few exceptions. See, for example, Footnote 1 of Zhang (2006).

  5. 5.

    In addition, there is also some evidence for imports-related spillovers, but it is weaker than for FDI.

  6. 6.

    Kokko (1996) argues that this finding may help explain some of the contradictory findings of earlier empirical spillover studies, most of which have assumed that the spillovers are strictly proportional to foreign presence.

  7. 7.

    See Mankiw, Romer, and Weil (1992), Islam (1995), and Madariaga and Poncet (2007) for details about the specification of the empirical model. The methodology of Mankiw, Romer, and Weil (1992), further strengthened due to the extension of Islam (1995) and others to panel data, can in principle be used to evaluate not only the Solow model but other candidate growth models as well (Bernanke and Gürkaynak 2001).

  8. 8.

    See Mankiw, Romer, and Weil (1992) and Islam (1995) for a justification of the assumed value of (g + δ).

  9. 9.

    Just as in earlier chapters, here for simplicity of exposition we treat the “population” and “the number of workers” as identical.

  10. 10.

    See previous chapters for a justification for the use of the LSDV regression method.

  11. 11.

    Owing to missing data, we can only include 27 out of the 31 Chinese mainland regions in our sample. Also, we can only cover the period 1991–2005 because of missing data prior to 1991.

  12. 12.

    We assume FDI capital depreciates at an annual rate of 3 %. In a sense, this rate is low. However, we have tried other values of the FDI depreciation rate and found the regression results are not sensitive to the change in the assumed FDI depreciation rate.

  13. 13.

    To save space, we have not summarized the regressions results in a table.

  14. 14.

    This nonlinear least squares method is similar in structure to, for example, those used by de la Fuente and Doménech (2001) and Jiang (2011).

  15. 15.

    These province-level regions in mainland China include provinces, ethnic minority autonomous regions, and municipalities. For the sake of brevity, however, we call all these province-level regions ‘provinces’.

  16. 16.

    For the sake of self-containedness of this chapter, we repeat the description of Zhang (2008)’s calculation procedure in this footnote as follows, though the same description of the calculation procedure has appeared already in Chap. 5. To calculate the annual capital depreciation rate, Zhang (2008) has assumed a geometrically diminishing relative efficiency of capital goods. Under this assumption, the official rate of residual value to the total value of capital goods, which is 3–5 %, implies that, when capital goods come to the end of their life duration, their relative efficiency is only (about) 4 % of new capital goods. The three broad components of fixed capital formation, namely, “completion of construction and installation”, “purchases of equipment and instruments”, and other investments, have an average life duration of 45 years, 20 years and 25 years, respectively. The rates of depreciation for the three broad components are then calculated to be 6.9 %, 14.9 % and 12.1 %, respectively (for all provinces). To calculate the rate of depreciation for overall fixed capital formation, Zhang (2008) then uses 63 %, 29 % and 8 %, which are national-level percentage shares of the three components in total fixed formation (corresponding provincial-level data are unavailable), averaged over the period 1952–2004, as the relative weights of the three components comprising total fixed capital formation. This method leads to the result of an annual rate of depreciation for fixed capital formation of 9.6 % for all provinces.

  17. 17.

    See, for example, Chow and Li (2002), Chow (2008), Zheng, Hu, and Bigsten (2009) and Brandt and Zhu (2010) for related discussions. Specifically in the case of China or the Chinese province-level regions, according to the national income accounts and the national input–output tables constructed by the National Bureau of Statistics of China (NBS), we are able to find that the factor share of capital (α) is roughly 0.5 in the non-agricultural sector, about 0.3 in the agricultural sector, and about 0.40–0.42 for the entire economy (Brandt and Zhu 2010). However, Brandt and Zhu (2010) argue that the high factor share of labor in agriculture (which is about 0.7) is inconsistent with estimates made on the basis of household data, which, instead, suggest a labor share in the vicinity of 0.5. Therefore, Brandt and Zhu (2010) assume that α is 0.5 for all sectors in their study throughout their sample period. The difference between the value of α in China and those in some other countries may suggest that in China a different technology is being used or production inputs are organized in a different way.

  18. 18.

    The theoretical model as well as the subsequent empirical analysis in this section does not incorporate the possibility of interregional spatial dependence in terms of the effects of FDI. According to theoretical arguments from new economic geography and endogenous growth models, the phenomenon of such spatial dependence has been associated with the existence of spillovers that cross regional borders. Fingleton and Lόpez-Bazo (2006), for example, advocate applying appropriate tools of spatial econometrics to test for the existence of such spillovers and estimate the magnitude of their effects. However, such tests are beyond the scope of our present analysis in this chapter.

  19. 19.

    A similar functional form has been used in a cross-country growth study of Hall and Jones (1999).

  20. 20.

    Better-educated workers have a comparative advantage in implementing new technologies. See, for example, Benhabib and Speigel (1994) and Prescott (1998).

  21. 21.

    The derivative dρ/dE is the return to schooling estimated in a Mincerian wage regression (Mincer 1974).

  22. 22.

    These rates of return are based on Psacharopoulos (1994)’s survey of evidence from many countries on return-to-schooling estimates. For a recent discussion of human capital and economic growth in China, see Fleisher, Li, and Zhao (2010).

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  1. College of International Finance and Commerce, Shanghai International Studies University, Shanghai, China, People’s Republic

    Yanqing Jiang

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Jiang, Y. (2014). Foreign Direct Investment and Regional Productivity Growth in China. In: Openness, Economic Growth and Regional Disparities. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40666-9_6

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  • DOI: https://doi.org/10.1007/978-3-642-40666-9_6

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