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An empirical analysis of total-factor productivity in 30 sub-sub-sectors of China's nonferrous metal industry

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  • Shao, Liuguo
  • He, Yingying
  • Feng, Chao
  • Zhang, Shijing

Abstract

Based on the panel data of 30 sub-sub-sectors of China's nonferrous metal industry from 2004 to 2013, this paper utilizes a global data envelopment analysis (DEA) to analyze the total-factor productivity (TFP) of China's nonferrous metal industry from both static and dynamic perspectives. We present the fluctuation of TFP in the nonferrous metal industry during 2004–2013 and analyze the key factors responsible for this fluctuation from the perspectives of production techniques, management and scale. The static analysis results show that the overall TFP of China's nonferrous metal industry is relatively low, and production inefficiency in the mining and smelting industries are two primary sources of this overall TFP inefficiency. There are significant differences among the 30 sub-sub-sectors in TFPs. During our sample period, some sub-sub-sectors experienced rapid growth in TFP, while others remained at a low level. The dynamic analysis results show differences in the key factors affecting the TFPs of three sub-sectors. Technical progress was the biggest contributor to the TFP growth in the nonferrous metal smelting sector, while the rapid increase in scale efficiency was the primary source of TFP growth in both the nonferrous metal mining sector and the pressing and processing sector.

Suggested Citation

  • Shao, Liuguo & He, Yingying & Feng, Chao & Zhang, Shijing, 2016. "An empirical analysis of total-factor productivity in 30 sub-sub-sectors of China's nonferrous metal industry," Resources Policy, Elsevier, vol. 50(C), pages 264-269.
  • Handle: RePEc:eee:jrpoli:v:50:y:2016:i:c:p:264-269
    DOI: 10.1016/j.resourpol.2016.10.010
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    1. Dong-hyun Oh, 2010. "A global Malmquist-Luenberger productivity index," Journal of Productivity Analysis, Springer, vol. 34(3), pages 183-197, December.
    2. Henry Tulkens & Philippe Eeckaut, 2006. "Nonparametric Efficiency, Progress and Regress Measures For Panel Data: Methodological Aspects," Springer Books, in: Parkash Chander & Jacques Drèze & C. Knox Lovell & Jack Mintz (ed.), Public goods, environmental externalities and fiscal competition, chapter 0, pages 395-429, Springer.
    3. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    4. Ma, Jinlong & Evans, David G. & Fuller, Robert J. & Stewart, Donald F., 2002. "Technical efficiency and productivity change of China's iron and steel industry," International Journal of Production Economics, Elsevier, vol. 76(3), pages 293-312, April.
    5. Li, Ke & Lin, Boqiang, 2015. "Measuring green productivity growth of Chinese industrial sectors during 1998–2011," China Economic Review, Elsevier, vol. 36(C), pages 279-295.
    6. Wei, Yi-Ming & Liao, Hua & Fan, Ying, 2007. "An empirical analysis of energy efficiency in China's iron and steel sector," Energy, Elsevier, vol. 32(12), pages 2262-2270.
    7. Wang, Zhaohua & Feng, Chao, 2015. "A performance evaluation of the energy, environmental, and economic efficiency and productivity in China: An application of global data envelopment analysis," Applied Energy, Elsevier, vol. 147(C), pages 617-626.
    8. Pastor, Jesus T. & Lovell, C.A. Knox, 2005. "A global Malmquist productivity index," Economics Letters, Elsevier, vol. 88(2), pages 266-271, August.
    9. Wang, Zhaohua & Feng, Chao & Zhang, Bin, 2014. "An empirical analysis of China's energy efficiency from both static and dynamic perspectives," Energy, Elsevier, vol. 74(C), pages 322-330.
    10. Zhenbin Rao, 2016. "Consolidating policies on Chinese rare earth resources," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 29(1), pages 23-28, April.
    11. Renuka Mahadevan, 2003. "To Measure or Not To Measure Total Factor Productivity Growth?," Oxford Development Studies, Taylor & Francis Journals, vol. 31(3), pages 365-378.
    12. Wang, Zhaohua & Feng, Chao, 2015. "Sources of production inefficiency and productivity growth in China: A global data envelopment analysis," Energy Economics, Elsevier, vol. 49(C), pages 380-389.
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    8. Eita, Joel Hinaunye & Pedro, Marcio Jose, 2020. "Modelling total factor productivity in a developing economy: evidence from Angola," MPRA Paper 101304, University Library of Munich, Germany, revised 30 Apr 2020.
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