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What Is the Focus of Structural Reform in China?—Comparison of the Factor Misallocation Degree within the Manufacturing Industry with a Unified Model

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  • Laiqun Jin

    (School of Business, Ningbo University, Ningbo 315211, China)

  • Changwei Mo

    (School of Economics, Xiamen University, Xiamen 361005, China)

  • Bochao Zhang

    (Institute of Economics, Shanghai Academy of Social Sciences, Shanghai 200235, China)

  • Bing Yu

    (School of Business, Ningbo University, Ningbo 315211, China)

Abstract

The misallocation of production factors, with structural misallocation as an important aspect, is a key instigator of low total factor productivity (TFP) growth rate in China, but one important question is which structural misallocation of what factor is more serious in China. Using China’s manufacturing industrial enterprise data from 1998 to 2013, we calculated and compared the factors misallocation degree among industries, ownerships and regions. The results indicated that, the misallocation among industries was most serious, which led to a TFP loss of 8.12% annually. The misallocation among ownerships ranked second, which led to a TFP loss of 5.49%. The least degree of the misallocation recorded among provinces led to TFP loss of 3.05%. By using the relative severity index, the rank is the same. As to the capital, the misallocation among ownerships was most serious, which led to TFP loss of 4.62%. But as to the labor, the misallocation among industries was most serious, which led to TFP loss of 4.58%. Moreover, the misallocation among ownerships alleviated rapidly from 1998 to 2007, while alleviated slower among industries and regions. However, from 2008 to 2013, all three types of structural misallocation have become worse, especially in labor. These conclusions are important to identify the focus of structural reform in China.

Suggested Citation

  • Laiqun Jin & Changwei Mo & Bochao Zhang & Bing Yu, 2018. "What Is the Focus of Structural Reform in China?—Comparison of the Factor Misallocation Degree within the Manufacturing Industry with a Unified Model," Sustainability, MDPI, vol. 10(11), pages 1-19, November.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:4051-:d:180687
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    2. Yidan Liang, 2023. "Capital and labour distortion in China: a systematic literature review using HistCite," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(2), pages 1759-1784, June.
    3. Tuochen Li & Dongri Han & Shaosong Feng & Lei Liang, 2019. "Can Industrial Co-Agglomeration between Producer Services and Manufacturing Reduce Carbon Intensity in China?," Sustainability, MDPI, vol. 11(15), pages 1-15, July.
    4. Weihua Su & Zhen Wang & Chonghui Zhang & Tomas Balezentis, 2023. "Determinants of the innovation efficiency of strategic emerging enterprises: evidence from the robust frontiers," Economic Change and Restructuring, Springer, vol. 56(3), pages 1433-1465, June.
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    6. Xiaoxiao Chu & Hong Geng & Wen Guo, 2019. "How Does Energy Misallocation Affect Carbon Emission Efficiency in China? An Empirical Study Based on the Spatial Econometric Model," Sustainability, MDPI, vol. 11(7), pages 1-16, April.
    7. Yidan Liang, 2023. "The effect of capital and labour distortion on innovation," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(2), pages 1709-1737, June.
    8. Wang, Bin & Yu, Minxiu & Zhu, Yucheng & Bao, Pinjuan, 2021. "Unveiling the driving factors of carbon emissions from industrial resource allocation in China: A spatial econometric perspective," Energy Policy, Elsevier, vol. 158(C).

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