Three Essays on Technology, Human Capital, Income, and the Economic Growth of Provinces in China
This research project contains three studies. The first study investigates the effects of the gap between technology in practice and the technological frontier, human capital per worker, and technological change on the economic growth rates of provinces in China. By developing a theoretical model and its corresponding panel-dynamic econometric model, the empirical result implies that both human capital and frontier technology have positive and significant effects on economic growth. Furthermore, it shows that an increase in human capital boosts the impact of technological progress on the economic growth of a province. This study contributes to the literature by exploring the effects of technological heterogeneity and human capital on the economic growth of provinces in China. In the process, it illustrates how distance to technological frontier and technological advancement can be measured. The second study attempts to explain why technological progress increases income inequality not only between unskilled and skilled labour but also within each skill group among wage earners in China. The theoretical model designed in this study followed the design of Galor and Moav (2000), but with less restrictive assumptions. By applying a dataset that combines survey data and provincial-level data, we are able to test the key hypotheses predicted by Galor and Moav (2000). The empirical result implies that China’s technological advancement is ability-biased, and thereby increases income inequality between and within the skilled-labour and unskilled-labour groups. This study contributes to the literature by developing and estimating an econometric model that can be used to test the important hypotheses predicted by Galor and Moav. It also makes a contribution by explicitly demonstrating how technological change and ability level can be measured. In the last study, we will explore the drivers of and draggers on economic growth of each province in China by applying a non-parametric index number method which does not impose restrictions on any aspects of the growth process. Drawing on the research done by Kumar and Russell (2002), Henderson and Russell (2005), Badunenko, Henderson and Russell (2013), and Walheer (2016), but differently, it has been assumed in this study that the technological frontier will obey the various return to scale considering the economic scale for different provinces in China also varies. We then decompose per capita income growth into six components which are technological change, productive efficiency, physical capital deepening, human capital deepening, change in the size of employed population, and change in population. The key driver of economic growth in the 5 East Region is high efficiency, while it is the accumulation of physical capital for the Central Region. The West Region benefits the most from human capital accumulation, and the Northeast Region’s economic growth is most driven by technological advancement. Moreover, the study also shows that the provincial economies in China are converging over time. The finding in this study has important implications that it provides policymakers the information about the factors they should focus on to stimulate the economy and achieve more balanced economic growths across provinces.