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Privatization, Soft Budget Constraint, and Social Burdens: A Random-Effects Stochastic Frontier Analysis on Chinese Manufacturing Technical Efficiency

  • Gao, Song
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Traditional panel stochastic frontier studies on privatization of Chinese State-owned firms face a major challenge, namely, the endogeneity problem. The endogeneity problem is present because decision-making process of privatization in China is very likely influenced by some unobserved characteristics of a firm. In particular, better-performing SOEs are more likely to be chosen for privatization because the local governments may have incentives to attract private investors or to retain momentum for future reform. To deal with this challenge, this paper proposes a two-step stochastic frontier model. The first step addresses the endogeneity issue by estimating the probability of privatization with a random effects probit model. The second step estimation investigates the causes of Chinese manufacturing’s inefficiency with a random-effects stochastic frontier model. The estimation results suggest that privatization, hardening budget constraint and reducing firms’ social obligations have significantly contributed to the improvements of firms’ efficiency. However, no evidence is found that more autonomy for managers and lower debt asset ratio may help improve firms’ efficiency.

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File URL: http://mpra.ub.uni-muenchen.de/24765/1/MPRA_paper_24765.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 24765.

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Date of creation: Mar 2010
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Handle: RePEc:pra:mprapa:24765
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  1. Shahid Yusuf & Kaoru Nabeshima & Dwight H. Perkins, 2005. "Under New Ownership : Privatizing China's State-Owned Enterprises," World Bank Publications, The World Bank, number 7399.
  2. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-74, October.
  3. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1989. "Production Frontiers With Cross-Sectinal And Time-Series Variation In Efficiency Levels," Working Papers 89-18, C.V. Starr Center for Applied Economics, New York University.
  4. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-32.
  5. Dong, Xiao-yuan & Putterman, Louis & Unel, Bulent, 2006. "Privatization and firm performance: A comparison between rural and urban enterprises in China," Journal of Comparative Economics, Elsevier, vol. 34(3), pages 608-633, September.
  6. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-44, June.
  7. Pitt, Mark M. & Lee, Lung-Fei, 1981. "The measurement and sources of technical inefficiency in the Indonesian weaving industry," Journal of Development Economics, Elsevier, vol. 9(1), pages 43-64, August.
  8. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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