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Convergence in GDP Growth Rates across the Provinces of China

Persson, Annika and Eriksson, Cecilia (2006)
Department of Economics
Abstract
Economic theory predicts that poorer economies will catch up with richer ones through the process of convergence. The aim of this Master’s thesis is to establish if convergence in GDP growth rates exists across the provinces of China. After performing a steady state analysis of the provinces’ level of GDP per capita a regression analysis is carried out in order to establish if conditional convergence exists across the 31 provinces of Mainland China during the period 1985-2004. Based on the growth model with technology transfer we distinguish eleven variables explaining the variation in growth rate of GDP per capita. The variables are: initial GDP per capita, investments as share of GDP, population growth, productivity in the agricultural... (More)
Economic theory predicts that poorer economies will catch up with richer ones through the process of convergence. The aim of this Master’s thesis is to establish if convergence in GDP growth rates exists across the provinces of China. After performing a steady state analysis of the provinces’ level of GDP per capita a regression analysis is carried out in order to establish if conditional convergence exists across the 31 provinces of Mainland China during the period 1985-2004. Based on the growth model with technology transfer we distinguish eleven variables explaining the variation in growth rate of GDP per capita. The variables are: initial GDP per capita, investments as share of GDP, population growth, productivity in the agricultural sector, school enrolment in higher and primary education, foreign direct investments, patents granted, infrastructure related to population and area of the province and a preferential policy index. In our optimal model initial GDP per capita, investments as share of GDP, population growth, patents granted and the preferential policy index are found significant. The parameter for initial GDP is -0.0000106 implying conditional convergence. Even though the catching up process is slow the conclusion is that convergence in GDP growth rates exists across the provinces of China for the period 1985-2004. (Less)
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@misc{1335046,
  abstract     = {Economic theory predicts that poorer economies will catch up with richer ones through the process of convergence. The aim of this Master’s thesis is to establish if convergence in GDP growth rates exists across the provinces of China. After performing a steady state analysis of the provinces’ level of GDP per capita a regression analysis is carried out in order to establish if conditional convergence exists across the 31 provinces of Mainland China during the period 1985-2004. Based on the growth model with technology transfer we distinguish eleven variables explaining the variation in growth rate of GDP per capita. The variables are: initial GDP per capita, investments as share of GDP, population growth, productivity in the agricultural sector, school enrolment in higher and primary education, foreign direct investments, patents granted, infrastructure related to population and area of the province and a preferential policy index. In our optimal model initial GDP per capita, investments as share of GDP, population growth, patents granted and the preferential policy index are found significant. The parameter for initial GDP is -0.0000106 implying conditional convergence. Even though the catching up process is slow the conclusion is that convergence in GDP growth rates exists across the provinces of China for the period 1985-2004.},
  author       = {Persson, Annika and Eriksson, Cecilia},
  keyword      = {growth,China,Technology Transfer,conditional convergence,steady state,Economics, econometrics, economic theory, economic systems, economic policy,Nationalekonomi, ekonometri, ekonomisk teori, ekonomiska system, ekonomisk politik},
  language     = {eng},
  note         = {Student Paper},
  title        = {Convergence in GDP Growth Rates across the Provinces of China},
  year         = {2006},
}