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Digital Economy, Industry Heterogeneity, and Service Industry Resource Allocation

Author

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  • Wei Qian

    (College of Economics and Management, Northeast Agricultural University, Harbin 150030, China
    These authors contributed equally to this work.)

  • Huan Liu

    (College of Economics and Management, Northeast Agricultural University, Harbin 150030, China
    These authors contributed equally to this work.)

  • Fanghui Pan

    (College of Economics and Management, Northeast Agricultural University, Harbin 150030, China)

Abstract

Based on the traditional framework of resource mismatch theory analysis and existing literature studies, this paper constructs a model of resource mismatch efficiency loss including the digitalization factor of the service industry, measures the resource mismatch of China’s service industry and its sub-sectors, and empirically analyzes the impact of digital economy development on resource mismatch of service industry using panel data of each sub-sector of China’s service industry from 2001 to 2020. The findings reveal that: (1) Resource mismatch exists in China’s service industry, and the degree of mismatch of capital is more serious than that of labor. (2) Traditional service industries with low digitalization have serious efficiency losses, while emerging service industries with high digitalization have almost no efficiency losses. (3) The increase in the development of the digital economy can significantly improve the resource mismatch in the service industry; appropriate government intervention can improve the capital mismatch but not the labor mismatch; the increase in the proportion of state-owned enterprises is conducive to improving the labor mismatch but not the capital mismatch. Meanwhile, the results of the industry heterogeneity test show that the increase in the digital economy can improve the resource mismatch of both emerging and traditional service industries, but the improvement is more obvious for emerging service industries. Therefore, in the context of the development of the digital economy, we make the following suggestions. The government intervenes appropriately in the capital market, develops emerging service industries, and formulates different digital transformation policies for different industries. Relevant enterprises increase their efforts in technology research and development, and actively explore the direction of digital transformation of service industries. The government and enterprises work together to promote the improvement of China’s economic development level.

Suggested Citation

  • Wei Qian & Huan Liu & Fanghui Pan, 2022. "Digital Economy, Industry Heterogeneity, and Service Industry Resource Allocation," Sustainability, MDPI, vol. 14(13), pages 1-18, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:8020-:d:852874
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    References listed on IDEAS

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    1. Chang-Tai Hsieh & Peter J. Klenow, 2009. "Misallocation and Manufacturing TFP in China and India," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 124(4), pages 1403-1448.
    2. Aoki, Shuhei, 2012. "A simple accounting framework for the effect of resource misallocation on aggregate productivity," Journal of the Japanese and International Economies, Elsevier, vol. 26(4), pages 473-494.
    3. Wen, Huwei & Zhong, Qiming & Lee, Chien-Chiang, 2022. "Digitalization, competition strategy and corporate innovation: Evidence from Chinese manufacturing listed companies," International Review of Financial Analysis, Elsevier, vol. 82(C).
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    Cited by:

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