This paper considers the persistent differences in economic performance across Chinese regions. We introduce a new county- and city-level dataset that spans all of mainland China and provides a detailed view of Chinese regional growth over the period 1997-2005. Non-parametric kernel density estimation is employed to establish the cross-sectional GDP per capita distribution, and the distributional dynamics are investigated using the probability matrix technique and associated stochastic kernel estimator. A set of explanatory variables is then introduced, and several regressions are run to test for conditional ƒÒ-convergence and to pinpoint influential factors for economic growth across counties and cities.
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Paper provided by Bank of Finland, Institute for Economies in Transition in its series BOFIT Discussion Papers with number
21/2007.
Length: 41 pages Date of creation: 04 Jan 2008 Date of revision: Handle: RePEc:hhs:bofitp:2007_021
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Find related papers by JEL classification: O11 - Economic Development, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development R11 - Urban, Rural, and Regional Economics - - General Regional Economics - - - Analysis of Growth, Development, and Changes
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References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
Krasker, William S. & Kuh, Edwin & Welsch, Roy E., 1983.
"Estimation for dirty data and flawed models,"
Handbook of Econometrics,
in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 11, pages 651-698
Elsevier.
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