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Potential of China's grain production: evidence from the household data


  • Huang, Yiping
  • Kalirajan, K. P.


This study investigates whether China has achieved its potential in grain production fully with the existing technology. A stochastic varying coefficients frontier approach is applied on recent household survey data of 1000 grain farmers covering the periods 1993-95. The results indicate that, on average, the actual grain outputs are about 15-35% lower than the potential output. The analysis has identified households' human capital stock, land size and market-oriented reform as important factors contributing positively to grain production efficiency.
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  • Huang, Yiping & Kalirajan, K. P., 1997. "Potential of China's grain production: evidence from the household data," Agricultural Economics, Blackwell, vol. 17(2-3), pages 191-199, December.
  • Handle: RePEc:eee:agecon:v:17:y:1997:i:2-3:p:191-199

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    References listed on IDEAS

    1. Timmer, C P, 1971. "Using a Probabilistic Frontier Production Function to Measure Technical Efficiency," Journal of Political Economy, University of Chicago Press, vol. 79(4), pages 776-794, July-Aug..
    2. 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 435-444, June.
    3. Kalirajan, K P & Obwona, M B, 1994. "Frontier Production Function: The Stochastic Coefficients Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 56(1), pages 87-96, February.
    4. Rosegrant, Mark W. & Agcaoili-Sombilla, Mercedita C. & Perez, Nicostrato D., 1995. "Global food projections to 2020: implications for investment," 2020 vision discussion papers 5, International Food Policy Research Institute (IFPRI).
    5. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
    6. 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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    Cited by:

    1. Boris Bravo-Ureta & Daniel Solís & Víctor Moreira López & José Maripani & Abdourahmane Thiam & Teodoro Rivas, 2007. "Technical efficiency in farming: a meta-regression analysis," Journal of Productivity Analysis, Springer, vol. 27(1), pages 57-72, February.
    2. Kwon, Oh Sang & Lee, Hyunok, 2004. "Productivity improvement in Korean rice farming: parametric and non-parametric analysis," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 48(2), June.
    3. Sizhong Sun, 2006. "Technical Efficiency and Its Determinants in Gansu, West China," Microeconomics Working Papers 21834, East Asian Bureau of Economic Research.
    4. Chang, Hung-Hao & Wen, Fang-I, 2008. "Off-farm Work, Technical Efficiency, and Production Risk: Empirical Evidence from a National Farmer Survey in Taiwan," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6164, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    5. Liu, Yanyan, 2006. "Model Selection in Stochastic Frontier Analysis: Maize Production in Kenya," 2006 Annual meeting, July 23-26, Long Beach, CA 21281, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    6. Wang, Xiaobing & Hockmann, Heinrich, 2012. "Technical Efficiency Under Producer’S Individual Technology: A Metafrontier Analysis," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126755, International Association of Agricultural Economists.
    7. Yanyan Liu & Robert Myers, 2009. "Model selection in stochastic frontier analysis with an application to maize production in Kenya," Journal of Productivity Analysis, Springer, vol. 31(1), pages 33-46, February.
    8. K. Hubacek & L. Sun, 1999. "Land Use Change in China: A Scenario Analysis Based on Input- Output Modeling," Working Papers ir99073, International Institute for Applied Systems Analysis.
    9. Thiam, Abdourahmane & Bravo-Ureta, Boris E. & Rivas, Teodoro E., 2001. "Technical efficiency in developing country agriculture: a meta-analysis," Agricultural Economics of Agricultural Economists, International Association of Agricultural Economists, vol. 25(2-3), September.
    10. Hubacek, Klaus & Sun, Laixiang, 2001. "A scenario analysis of China's land use and land cover change: incorporating biophysical information into input-output modeling," Structural Change and Economic Dynamics, Elsevier, vol. 12(4), pages 367-397, December.
    11. Abdul Wadud, 2013. "Impact of Microcredit on Agricultural Farm Performance and Food Security in Bangladesh," Working Papers 14, Institute of Microfinance (InM).
    12. Anonymous, 1998. "Grain Market Reform in China: Global Implications," Technical Reports 113816, Australian Centre for International Agricultural Research.
    13. Christopher Findlay, 1997. "Grain Sector Reform in China," Chinese Economies Research Centre (CERC) Working Papers 1997-01, University of Adelaide, Chinese Economies Research Centre.
    14. Uaiene, Rafael N. & Arndt, Channing, 2009. "Farm Household Efficiency In Mozambique," 2009 Conference, August 16-22, 2009, Beijing, China 51438, International Association of Agricultural Economists.

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