Regional Disparity of Productivity in China: A Stochastic Frontier Approach
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This paper analyzes temporal variations in the productivity performance of the Chinese economy at the regional level. Specifically, the focus is on regional disparity and regional temporal movement of productivity. It applies a stochastic frontier model to the Chinese provincial input-output panel data of 30 different regions over the period of 1993-2003. The empirical results confirm those of previous studies that Shanghai is the most productive economy in China and the eastern region is the best performer in productivity. Our analysis indicates that the gap between the eastern region and other regions(central, and western) is quite substantial, but the western region has made relative fast enough improvements of its efficiency to be able to narrow the gap with the eastern. This may have come about as a result of government investment in infrastructure and in social facilities such as education and health care, stimulating the performance of the less developed provinces.
Abstract
This paper analyzes temporal variations in the productivity performance of the Chinese economy at the regional level. Specifically, the focus is on regional disparity and regional temporal movement of productivity. It applies a stochastic frontier model to the Chinese provincial input-output panel data of 30 different regions over the period of 1993-2003. The empirical results confirm those of previous studies that Shanghai is the most productive economy in China and the eastern region is the best performer in productivity. Our analysis indicates that the gap between the eastern region and other regions(central, and western) is quite substantial, but the western region has made relative fast enough improvements of its efficiency to be able to narrow the gap with the eastern. This may have come about as a result of government investment in infrastructure and in social facilities such as education and health care, stimulating the performance of the less developed provinces.
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