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Explaining Production Inefficiency in China’s Agriculture using Data Envelope Analysis and Semi-Parametric Bootstrapping

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  • Monchuk, Daniel C.
  • Zhuo, Chen

Abstract

In this paper we examine more closely the factors associated with production inefficiency in China’s agriculture. The approach we take involves a two-stage process where output efficiency scores are first estimated using data envelope analysis (DEA), and then in the second stage, variation in the resulting efficiency scores are explained using a truncated regression model with inference based on a semi-parametric bootstrap routine. Among the results we find a heavy industrial presence is associated with reduced agricultural production efficiency and may be an indication that externalities from the industrial process, like air and ground water pollution, affect agricultural production. We also find evidence that counties with a large percentage of the rural labor force engaged in agriculture tend to be less efficient, which suggests that policies to facilitate the removal of labor from agriculture, but not necessarily from the rural areas, would bring about enhanced agricultural efficiency and calls into question policies that promote wholesale migration from rural areas. Sensitivity analysis indicates results are robust to influential observations and outliers.

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Paper provided by American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) in its series 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida with number 6456.

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Date of creation: 2008
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Handle: RePEc:ags:aaea08:6456

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Keywords: China's agriculture; DEA; bootstrapping; technical efficiency; Production Economics; C14; Q1; R5;

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  1. Abdulai, Awudu & Huffman, Wallace, 2000. "Structural Adjustment and Economic Efficiency of Rice Farmers in Northern Ghana," Staff General Research Papers 5100, Iowa State University, Department of Economics.
  2. Tulkens, Henry & Vanden Eeckaut, Philippe, 1995. "Non-parametric efficiency, progress and regress measures for panel data: Methodological aspects," European Journal of Operational Research, Elsevier, vol. 80(3), pages 474-499, February.
  3. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
  4. Konstantinos Giannakas & Kien C. Tran & Vangelis Tzouvelekas, 2003. "On the choice of functional form in stochastic frontier modeling," Empirical Economics, Springer, vol. 28(1), pages 75-100, January.
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  6. Fan, Shenggen & Zhang, Xiaobo, 2002. "Production and Productivity Growth in Chinese Agriculture: New National and Regional Measures," Economic Development and Cultural Change, University of Chicago Press, vol. 50(4), pages 819-38, July.
  7. Jean-Paul Chavas & Ragan Petrie & Michael Roth, 2005. "Farm Household Production Efficiency: Evidence from The Gambia," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(1), pages 160-179.
  8. Timo Kuosmanen & Diemuth Pemsl & Justus Wesseler, 2006. "Specification and Estimation of Production Functions Involving Damage Control Inputs: A Two-Stage, Semiparametric Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(2), pages 499-511.
  9. Jirong Wang & Eric J. Wailes & Gail L. Cramer, 1996. "A Shadow-Price Frontier Measurement of Profit Efficiency in Chinese Agriculture," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(1), pages 146-156.
  10. Carter, Colin A. & Chen, Jing & Chu, Baojin, 2003. "Agricultural productivity growth in China: farm level versus aggregate measurement," China Economic Review, Elsevier, vol. 14(1), pages 53-71.
  11. Maria Alberta Oliveira & Carlos Santos, 2005. "Assessing school efficiency in Portugal using FDH and bootstrapping," Applied Economics, Taylor & Francis Journals, vol. 37(8), pages 957-968.
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