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The influence analysis on public expenditure to the technique efficiency of China

Author

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  • CHEN Xun

    (Economy, Industry and Business Management Institute of Chongqing University, Chongqing, 400044, China)

  • YU Jie

    (Economy, Industry and Business Management Institute of Chongqing University, Chongqing, 400044, China)

Abstract

In this paper, we use SFA to estimate the time-variant stochastic frontier model of 31 cities in China. The results tell us that raising the proportion of public expenditure in GDP can lower the technological efficiency, but raising some parts of public expenditure in GDP can promote the technological efficiency. Its realistic meaning is that it is excellent to turn the public expenditure structure to promote the technological efficiency. We computed the technological efficiency of 31 provinces/cities and the results show that the gap between the eastern region and western region is growing much. Finally, we decompose total factor productivity (TFP) and get the following result: from three regions, the biggest influence factor on technological efficiency is the scales economy. Technological progress and allocation efficiency have a smaller influence. From our results, we suggest that technological progress and allocation efficiency from public expenditure and income be raised to influence the TFP rate of change, and have a more efficient public expenditure.

Suggested Citation

  • CHEN Xun & YU Jie, 2006. "The influence analysis on public expenditure to the technique efficiency of China," Frontiers of Economics in China-Selected Publications from Chinese Universities, Higher Education Press, vol. 1(2), pages 296-310, June.
  • Handle: RePEc:fec:journl:v:1:y:2006:i:2:p:296-310
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    File URL: http://journal.hep.com.cn/fec/EN/10.1007/s11459-006-0010-9
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    More about this item

    Keywords

    public expenditure; stochastic frontier; TFP; decomposition;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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