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Has agricultural labor restructuring improved agricultural labor productivity in China? A decomposition approach

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  • Baležentis, Tomas
  • Li, Tianxiang
  • Chen, Xueli

Abstract

This paper presents a framework for analyzing the changes in agricultural labor productivity with regards to the structural, land intensity, and land productivity effects. This approach allows for the residual-free decomposition of data from different levels of aggregation. The logarithmic mean Divisia index was applied for the analysis and a data envelopment analysis model was constructed to identify potential gains in agricultural labor productivity due to the optimization of input use and output production. The proposed approach was applied to the case of China over the period of 1997–2017. Province-level data were used to identify the major driving factors behind agricultural labor productivity change. Land productivity change appeared to be the major source of agricultural labor productivity gains in China. The structural change was rather negligible, suggesting that the reallocation of the agricultural labor force did not add to the agricultural labor productivity growth in China. A frontier analysis indicated that agricultural labor productivity could increase by some 45% on average in case full technical efficiency is achieved.

Suggested Citation

  • Baležentis, Tomas & Li, Tianxiang & Chen, Xueli, 2021. "Has agricultural labor restructuring improved agricultural labor productivity in China? A decomposition approach," Socio-Economic Planning Sciences, Elsevier, vol. 76(C).
    • Handle: RePEc:eee:soceps:v:76:y:2021:i:c:s0038012120308041
    DOI: 10.1016/j.seps.2020.100967
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    as
    1. Färe, Rolf & Grosskopf, Shawna, 2010. "Directional distance functions and slacks-based measures of efficiency: Some clarifications," European Journal of Operational Research, Elsevier, vol. 206(3), pages 702-702, November.
    2. Wang, Xiang & Shao, Shuai & Li, Ling, 2019. "Agricultural inputs, urbanization, and urban-rural income disparity: Evidence from China," China Economic Review, Elsevier, vol. 55(C), pages 67-84.
    3. Ang, B.W., 2015. "LMDI decomposition approach: A guide for implementation," Energy Policy, Elsevier, vol. 86(C), pages 233-238.
    4. Kingsley E. Haynes & Mustafa Dinc, 1997. "Productivity Change in Manufacturing Regions: A Multifactor/Shift‐Share Approach," Growth and Change, Wiley Blackwell, vol. 28(2), pages 201-221, March.
    5. Mugera, Amin W. & Langemeier, Michael R. & Featherstone, Allen M., 2012. "Labor Productivity Growth in the Kansas Farm Sector: A Tripartite Decomposition Using a Non-Parametric Approach," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 41(3), pages 1-15, December.
    6. Zhao, Jingfeng & Tang, Jianmin, 2018. "Understanding agricultural growth in China: An international perspective," Structural Change and Economic Dynamics, Elsevier, vol. 46(C), pages 43-51.
    7. Ang, B. W., 2005. "The LMDI approach to decomposition analysis: a practical guide," Energy Policy, Elsevier, vol. 33(7), pages 867-871, May.
    8. Lopez, Rigoberto A. & He, Xi & De Falcis, Eleonora, 2017. "What Drives China’s New Agricultural Subsidies?," World Development, Elsevier, vol. 93(C), pages 279-292.
    9. Wang, Sun Ling & Huang, Jikun & Wang, Xiaobing & Tuan, Francis, 2019. "Are China’s regional agricultural productivities converging: How and why?," Food Policy, Elsevier, vol. 86(C), pages 1-1.
    10. Zhang, Jun & Liu, Xiaofeng, 2013. "The evolving pattern of the wage–labor productivity nexus in China: Evidence from manufacturing firm-level data," Economic Systems, Elsevier, vol. 37(3), pages 354-368.
    11. Dorward, Andrew, 2013. "Agricultural labour productivity, food prices and sustainable development impacts and indicators," Food Policy, Elsevier, vol. 39(C), pages 40-50.
    12. Almeida, Alexandre N. & Bravo-Ureta, Boris E., 2019. "Agricultural productivity, shadow wages and off-farm labor decisions in Nicaragua," Economic Systems, Elsevier, vol. 43(1), pages 99-110.
    13. Sarah Cook, 1999. "Surplus labour and productivity in Chinese agriculture: Evidence from household survey data," Journal of Development Studies, Taylor & Francis Journals, vol. 35(3), pages 16-44.
    14. Rawski, Thomas G. & Xiao, Wei, 2001. "Roundtable on Chinese Economic Statistics Introduction," China Economic Review, Elsevier, vol. 12(4), pages 298-302.
    15. Xiangfei Xin & Fu Qin, 2011. "Decomposition of agricultural labor productivity growth and its regional disparity in China," China Agricultural Economic Review, Emerald Group Publishing, vol. 3(1), pages 92-100, January.
    16. Giannakis, Elias & Bruggeman, Adriana, 2018. "Exploring the labour productivity of agricultural systems across European regions: A multilevel approach," Land Use Policy, Elsevier, vol. 77(C), pages 94-106.
    17. Xiangfei Xin & Fu Qin, 2011. "Decomposition of agricultural labor productivity growth and its regional disparity in China," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 3(1), pages 92-100, February.
    18. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    19. Kwan, Fung & Wu, Yanrui & Zhuo, Shuaihe, 2018. "Surplus agricultural labour and China's Lewis turning point," China Economic Review, Elsevier, vol. 48(C), pages 244-257.
    20. Chen, Jiandong & Cheng, Shulei & Song, Malin, 2018. "Changes in energy-related carbon dioxide emissions of the agricultural sector in China from 2005 to 2013," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 748-761.
    21. Färe, Rolf & Grosskopf, Shawna, 2010. "Directional distance functions and slacks-based measures of efficiency," European Journal of Operational Research, Elsevier, vol. 200(1), pages 320-322, January.
    22. Shouying Liu & Ruimin Wang & Guang Shi, 2018. "Historical Transformation of China's Agriculture: Productivity Changes and Other Key Features," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 26(1), pages 42-65, January.
    23. Chen, Jiandong & Xu, Chong & Cui, Lianbiao & Huang, Shuo & Song, Malin, 2019. "Driving factors of CO2 emissions and inequality characteristics in China: A combined decomposition approach," Energy Economics, Elsevier, vol. 78(C), pages 589-597.
    24. Fang Cai, 2018. "The Great Exodus," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 10(1), pages 3-15, February.
    25. Bhattacharyya, Arunava & Parker, Elliott, 1999. "Labor productivity and migration in Chinese agriculture A stochastic frontier approach," China Economic Review, Elsevier, vol. 10(1), pages 59-74.
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