Human capital, economic growth, and regional inequality in China

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Abstract

We show how regional growth patterns in China depend on regional differences in physical, human, and infrastructure capital as well as on differences in foreign direct investment (FDI) flows. We also evaluate the impact of market reforms, especially the reforms that followed Deng Xiaoping's “South Trip” in 1992 those that resulted from serious hardening of budget constraints of state enterprises around 1997. We find that FDI had a much larger effect on TFP growth before 1994 than after, and we attribute this to the encouragement of and increasing success of private and quasi-private enterprises. We find that human capital positively affects output and productivity growth in our cross-provincial study. Moreover, we find both direct and indirect effects of human capital on TFP growth. These impacts of education are more consistent than those found in cross-national studies. The direct effect is hypothesized to come from domestic innovation activities, while the indirect impact is a spillover effect of human capital on TFP growth. We conduct cost-benefit analysis of hypothetical investments in human capital and infrastructure. We find that, while investment in infrastructure generates higher returns in the developed, eastern regions than in the interior, investing in human capital generates slightly higher or comparable returns in the interior regions. We conclude that human capital investment in less-developed areas is justified on efficiency grounds and because it contributes to a reduction in regional inequality.

Introduction

This paper reports research on the effects of human capital, infrastructure capital, and foreign direct investment (FDI) on regional inequality and economic growth in China. China's dramatic economic growth since the beginning of economic reform in 1978 has been very uneven across regions. We investigate these related trends for two reasons: (i) to understand their causes; (ii) to derive implications for policies to harness the causes of growth to reduce inequality in other countries. We model two roles for human capital: (i) educated workers embody human capital that contributes directly to output in the production process itself; (ii) human capital, particularly that represented by higher education, plays an important role in total factor productivity (TFP) growth. Infrastructure capital is hypothesized to affect GDP through TFP growth, as is FDI.

We specify and estimate a provincial aggregate production function in which inputs are specified to include physical capital and two categories of labor: (i) less-educated workers, those who have no junior high school education and (ii) educated workers, those who have some junior high school education or above. The estimated output elasticities of the three inputs are used to calculate factor marginal products and also TFP at existing provincial factor quantities. We then estimate a TFP growth model in which the arguments are human capital operating directly and through regional technology spillovers, infrastructure capital, physical-capital vintage effects, foreign direct investment, and marketization. FDI is treated as an endogenous variable.

We derive three sets of hypothetical policy implications from our empirical results. (1) We use our estimated production function parameters to calculate marginal products of labor and capital and then project how the reallocation of labor to equalize marginal products across regions would affect per capita GDP and the number of workers in each region. (2) We project results of another reallocation scenario—the impact on the time path of regional GDP ratios of a tax-transfer scheme that would increase investment in human capital and/or infrastructure capital. (3) We calculate internal rates of return to policies that would reallocate resources to investment in infrastructure and human capital. We believe the results have important implications for an understanding of economic growth in general, for factors contributing to China's rapidly rising regional inequality, and for the design of policies that would lead to a more equitable distribution of the benefits of growth within the world's most rapidly expanding economy.

The remainder of this paper proceeds as follows. Section 2 provides some background information. In Section 3 we lay out our methodology. Section 4 describes our data. Section 5 reports our empirical results for aggregate production functions and TFP-growth models. In Section 6, we conduct cost–benefit analysis by computing the rates of return to investment in human capital and telephone infrastructure. In addition, we perform a hypothetical experiment by evaluating alternative investment strategies in reducing regional inequality. Section 7 concludes and provides policy recommendations.

Section snippets

Background

By the year 2000, China found itself with not only one of the highest rates of economic growth but also one of the highest degrees of rural–urban income inequality in the world (Yang, 2002). The rural–urban disparity feeds the wide regional economic inequality (Yang, 2002), which is a relatively new phenomenon in China's last half century. From the beginning of the Mao era through 1986, inequality across major regions (as measured by the coefficient of variation of per-capita real gross

Methodology

We estimate provincial aggregate production functions in which inputs are specified to include physical capital and two categories of labor: (i) less-educated workers, those who have no junior high school education and (ii) educated workers, those who have some junior high school education or above. The estimated output elasticities of the three inputs are used to calculate factor marginal products and also TFP at existing quantities of the inputs. This strategy permits us to investigate two

Data

Our data are from various years of the China Statistical Yearbook (State Statistical Bureau, 1996, State Statistical Bureau, 1996, 1998, 1999, 2002 and 2003), Population Census Office, 1983, Population Census Office, 1993, Population Census Office, 2001, Annual Population Change Survey (State Statistical Bureau, 1993, 1996–2000, 2002 and 2003), Hsueh et al. (1993), Fu (2004), and China Data Online (2008). One important feature of this study is that our data are not only deflated over time but

Empirical results

Table 3 reports estimation results for a provincial-level production function with two types of labor categorized according to educational attainment. All standard error estimates are robust to corrections for serial correlation, heteroskedasticity, and cross-sectional correlation based on Driscoll and Kraay (1998).

Column (1) reports the standard 2-way fixed effects (FE) estimate. In this specification, the estimated elasticity of less-educated worker is negative and marginally significant.23

Policy implications

In order to illustrate the economic importance of our estimation results, we calculate the impacts of possible policy interventions through human capital and infrastructure investments. An output-maximizing policy maker would rely on rates of return in designing an optimal investment policy, and knowledge of these returns can be derived from the results of studies such as ours. We estimate the internal rates of return to investment in education and telecommunication infrastructure with

Conclusion and recommendations

China's spectacular economic growth has benefited its provinces and regions quite unequally. China has not only one of the highest rates of economic growth but also one of the highest degrees of regional income inequality in the world. We investigate the determinants of the regional dispersion in rates of economic growth and TFP growth. We hypothesize that they can be understood as a function of several interrelated factors, which include investment in physical capital, human capital, and

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    We are grateful to our two anonymous referees for their exceptionally thoughtful review of earlier versions of the paper and the Editor for suggestions on improving our arguments and presentation. We thank Xian Fu, Renyu Li, Li Liang, Yang Peng, Zhimin Xin, Luping Yang and Xiaobei Zhang for their able and enthusiastic help in compiling data for this research. Carsten Holz was generous in helping us with conceptual issues and data problems. Sylvie Demurger generously provided her data on infrastructure and the population with schooling at the secondary level and higher. We thank Josef Brada, Stephen Cosslett, Isaac Ehrlich, Paul Evans, Joe Kaboski, Cheryl Long, Zhiqiang Liu, Masao Ogaki, Pok-sang Lam, David Romer, Yong Yin, and Shujie Yao for their helpful comments. The paper has benefited from participants in seminars at the University at Buffalo Economics Department, at the Conference on the Chinese Economy, sponsored by CERDI/IDREC, University of the Auvergne, France, and at the ASSA Meetings.

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