Human Capital, Physical Capital And TFP In China

China is attracting more and more attention from the world. Scholars are, on the one hand, surprised about how amazing it has been for the development of China over the last three decades, on the other hand, they have challenged and cast doubt on the sustainability of the development of China, especially after 2008’s global financial crisis when the growth rate of GDP in China declined from 14.16% in 2007 to 7.4% in 2014. It is now evoked again that whether the mode of economic development in China is extensive or not.Following the extant literatures, this dissertation summarizes that it has been a prevailing paradigm by most of researchers making judgment according to the estimates of total factor productivity growth rate (TFP). However, results are frustrating, not just because of the divergence of results sometimes, but also because in most cases, they are problematic. It could be resulted from selection of cross-sectional units, adjustment of time series, application of methodologies and data processing, but in some papers, when the first three conditions are the same or at least almost the same, difference among results are still statistically significant. Hence, this dissertation puts faith in the importance of data processing and will be organized as follows:Chapter 1 is the introduction. Chapter 2 presents reviews of literatures on human capital, physical capital and TFP. Chapter 3 and Chapter 4 estimate two factor inputs in various contexts respectively. Chapter 5, based on estimates of two heterogeneous factor inputs and method of growth accounting, presents some associated results of TFP, and further, makes its own judgment about the debates that whether the mode of economic development in China is extensive or not. Chapter 6, using Malmquist-DEA approach, estimates TFP and TFP’s factors to precede a sensitivity analysis on the results in different contexts, and provide an empirical analysis on the impacts of determinants of TFP and TFP’s factors.Questions about to be answered in this dissertation are as follows:Is it for sure that the mode of economic development in China is extensive? What is the estimate of TFP, to what extent has TFP contributed to economic growth in China over the last decades? How to estimate TFP to the best our ability? What are the essential facts of factor inputs? What has been the main driving force of TFP in China, would it be different at different stages of economic development? Do estimates of TFP and TFP’s factors depend on various kinds of factor inputs, are there any big difference among estimates? What kinds of determinants would have impacts on TFP and TFP’s factors and in what direction? Conclusions will not only help economists have a better understanding of China who has illumined the eye of the world over the last decades, but also be instructive to the transformation of the mode of economic development in China today.Conclusions draw from this dissertation include:First, there have been a dramatic increase in returns to education not only in the total sample, but also in the subsamples divided by classified education level. In the total sample, returns to education are increasing from 0.0204 in 1988 to 0.0516 in 2010, while in the subsamples, returns to education are decreasing successively from higher education, secondary education to primary education, and the mean of them across the whole periods are respectively 0.0594,0.0451 and 0.0158. Difference of returns to education between primary school and junior middle school are not statistically significant because of the compulsory education policy implemented earlier in China. These findings are contrary to the prevailing idea that the law of diminishing marginal returns works well in the field of education, which in further means that it might be incorrect to apply time invariant data to solve the heterogeneity problem in estimating human capital. Due to two aspects, human capital per person and numbers of employment, results show that human capital in some regions are not as bad as expected, while in some other regions, human capital are not that good as people thought. In addition, convergence analysis shows that there’re difference between human capital per person and human capital both on a national scale and on a regional scale.Second, logic development about how capital would be evolved, selection between age-efficiency profiles and age-price profiles, heterogeneity problem that are all reflected in the methodology choice between traditional perpetual inventory method (PIM) and extensive PIM do matter in estimates of physical capital. Moreover, it is still a controversial issue about applying which one of the three items, wealth capital stock, productive capital stock and capital services, in the production and productivity analysis. So this dissertation estimates three kinds of physical capital, and they turn out to have big difference between wealth capital stock and productive capital stock, wealth capital stock in homogeneous case and wealth capital stock in heterogeneous case. Among three estimates, physical capital decreases successively from productive capital stock in heterogeneous case, wealth capital stock in heterogeneous case to wealth capital stock in homogeneous case. Regional disparities are statistically significant in which estimates decrease from eastern regions, central regions to western regions while growth rate of physical capital are in a reverse order. Compared to wealth capital stock, productive capital stock indicates an obvious structural break in late 1970s, which means that productive capital stock is less than wealth capital stock before the reform and opening-up, while it is opposite ever since then. However, according to the stylized facts of economic restructuring and institutional changes in China, it is more reasonable to choose productive capital stock. Volume indices of capital services are more complicated, because additional constructions of indices and data processing are needed from the system of national accounts. In despite of that, conclusions like trend of rising, periodic fluctuation and regional disparities would still hold up.Third, based on estimates of two heterogeneous factor inputs and method of growth accounting, results show that average TFP per year in the period from 1978 to 2012 is 4.89% which has contributed to 49.33% of economic growth. Both regional TFP per year and its contribution decline in the order of western regions, central regions and eastern regions. There are significant δ-convergence and β-convergence of TFP both at the country level and at the regional level. Taking experience and facts happened in the developed countries into consideration, judgment made fair enough would be that the mode of economic development in China over the last decades is not extensive, per se. Five reasons including feeling, common sense and theory, concepts, estimates and contributions of TFP, theory of economic growth and stylized facts in China, structural change and economic growth in China, no direct connection between intensive economic development and all being well are displayed.Fourth, based on estimates of two heterogeneous factor inputs and method of Data Envelopment Analysis (DEA), results show that average TFP per year in the period from 1978 to 2012 is 1.2%. Technical efficiency has been the main driving force of TFP whose average growth rate in the whole period is 0.7% while it is 0.5% for technical progress. Interestingly, the main driving force of TFP varies at different stages. It is a sequential order of technical efficiency, technical progress, technical efficiency and technical progress that corresponds to four periods divided by structural breaks of 1990,1995 and 2001. There’re big difference of TFP and TFP’s factors among provinces, but the main driving force of TFP is still technical efficiency for most provinces. Regional TFP per year declines in the order of western regions, eastern regions and central regions. There are significant δ-convergence and β-convergence of TFP and TFP’s factors both at the country level and at the regional level.Fifth, sensitivity analysis on TFP and TFP’s factors show that there’re big difference among results both in different contexts and stages of economic development. Specifically, difference of both TFP and technical progress are statistically significant in the whole sample, difference of technical efficiency, pure technical efficiency and scale efficiency are not statistically significant when numbers of employment or human capital together with wealth capital stock are taken into consideration, difference of technical efficiency and scale efficiency are statistically significant when wealth capital stock or productive capital stock are taken into consideration. Further analysis on subsamples show that results of three sets of sensitivity analysis indicate obvious structural breaks of 1992,2001 and 2001 respectively.Finally, in the equation where TFP is a dependent variable, coefficients of all variables except for medical supplies are statistically significant. In the equation where technical progress is a dependent variable, coefficients of all variables except for internal demand and international trade are statistically significant. In the equation where technical efficiency is a dependent variable, coefficients of all variables except for interaction term between foreign direct investment (FDI) and human resource, structural change, are statistically significant. In the equation where scale efficiency is a dependent variable, coefficients of all variables except for interaction term between international trade and human resource, degree of non-marketization, are statistically significant.Contributions made in this dissertation are listed below:First, effective combination of two field, time variant features of returns to education and estimates of provincial human capital together. Most of literatures have an unclear understanding of human capital. Usually, they treat average year of schooling as an effective index of human capital which is not included as one of the inputs in the estimates of TFP. Few literatures that try to combine the two field together face several problems like time invariant features of returns to education, diminishing marginal returns to education and incomplete information. This dissertation, applying the China Health and Nutrition Survey (CHNS) data from 1989 to 2011, using Mincer wage equation, estimates returns to education first, then introduces returns to education into the equation from Hall and Jones(1999) to solve the heterogeneity problem, and finally estimates the provincial human capital. It will not only overcome disadvantages listed above, but also have important implications on the current reforms both in the field of education and regional inequality in China.Second, elaborate distinction between homogeneous assets and heterogeneous assets, wealth capital stock and productive capital stock. Most of literatures estimate physical capital on the basis of wealth capital stock in homogeneous case. However, there’re big difference between homogeneous assets and heterogeneous assets, and between wealth capital stock and productive capital stock. It is productive capital stock that has been accepted widely in theory as one of the inputs in the production and productivity analysis, which means estimates, analyses and policies implications in most researches would be misleading. This dissertation distinguishes heterogeneous assets from homogeneous assets, productive capital stock from wealth capital stock, and estimates three kinds of physical capital. It will not only clarify controversial debates on concepts of capital, but also have important implications on understanding the mode of economic growth in China over the last decades and analyzing the direction of economic restructuring in the future.Third, sensitivity analysis on TFP and TFP’s factors and empirical test on determinants of TFP and TFP’s factors. Most literatures focus only on a portfolio of specific inputs (such as employment and wealth capital stock) while neglecting various impacts of others on the estimats of TFP and TFP’s factors. This paper proceeds three sets of sensitivity analysis to show how big difference there are among various results. To present modifications in the empirical test on determinants of TFP and TFP’s factors, this paper introduces additional variables of medicine and interaction term between intervention tendency of government and degree of non-marketization to explore the impact of both health condition and relationshiop between government and state-owned enterprises on TFP and TFP’s factors respectivly. Sensitivity analysis on TFP and TFP’s factors and empirical test on determinants of TFP and TFP’s factors will not only provide more information about the economic development of China over the last decades, but aslo cast light on innovation-driven development strategy and reforms in the field of medicine and state-owned enterprises being carried out in China.Fourth, substantial extension in terms of both cross sections and time series. In most of literatures, there’re 28 cross sections, and most of the time series data end in 2007. Even worse, on the basis of the first two contributions, data quality in most of literatures would be poor. This dissertation tries to fix it cautiously with adding additional 3 cross sections and extending time series data till 2012. This is, of course, not to say that there’s no problem in data processing in this dissertation, however, compared to previous researches, it is safe to say that data in this dissertation have a better quality.