The Regional Differences Of China's Human Capital Formation To Explore

China has experienced a rapid economic transformation as well as economic growth. Consequently, regional gap becomes an inevitable result, both in income and also in human capital. Human capital has been demonstrated closely related to economic development, and an unequal geographic distribution of human capital will make the unequal distribution of economic development even worse. Furthermore, the difference in human capital will also lead to differences in culture and social development. Thus, in order to reduce the regional difference in human capital to an appropriate level and make sure healthy economic growth, we should first understand the status quo of geographic distribution of human capital, and also the key determinants of it.We collect data of 31 regions from 1997 to 2008 and build an indicator system which includes both human capital stock indicator and human capital structure indicators. Average years of schooling is the human capital stock indicator, and percentage of people with higher education, high school education, middle school education, primary school education (according to the final education level) and illiteracy rate are the 5 human capital structure indicators. We run the cluster analysis and found that northern regions and some central regions are better developed in human capital; western regions are generally poor in human capital. Then we compute the correlated variation of each indicator in the observation period, and found that percentage of people with higher education varies most in 31 regions, then the illiteracy rate, then the percentage of people with high school education. The correlated variations of the rest indicators are relatively small. Meanwhile, the regional difference of illiteracy rate tends to increase during the 12 years, and so does the regional difference of percentage of people with primary school education. Regional difference of the rest declines. Meanwhile, we computed the center of gravity for average years of schooling and percentage of people with higher education, and found that both of the two centers shifted to southwest during the 12 years. Finally, to understand how relevant factors impact the human capital stock, we build a theoretical model and did the corresponding empirical study. In the theoretical model, two processes have been incorporated:migration for education and migration for jobs. Income, education cost, living cost and other factors are added in the model as potential determinants. In the empirical study, we use d the fixed effect model in panel data, and did two regressions:one is against average years of schooling; the other is against percentage of people with higher education. Regressors are selected based on the theoretical model and previous studies. The results show that:income, the number of health personnel per 10,000 inhabitants and the number of street lights per cities are positively related with the two dependent variables; cost has a negative impact on average years of schooling, but do not affect percentage of people with higher education; government's education expenditure per person does increase average years of schooling in a statistical perspective, but government's expenditure on higher education per student dose not affect percentage of people with higher education. Some policy conclusions and suggestions are given at last.