IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i20p13653-d949531.html
   My bibliography  Save this article

How Do Rising Labor Costs Affect Green Total Factor Productivity? Based on the Industrial Intelligence Perspective

Author

Listed:
  • Wei Qian

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

  • Yongsheng Wang

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

Abstract

In the context of the fading demographic dividend, rising labor costs present both opportunities and challenges to China’s green and sustainable development. This paper aims to investigate the impact of rising labor costs on the inter-provincial green total factor productivity (GTFP) of China and to explore the moderating effect of industrial intelligence. Both provincial panel data from 2010 to 2019 and the system GMM model, moderating effect model, and panel threshold model are used to empirically analyze the relationship between the three economic variables. The results show that: Firstly, during the sample period, China’s rising labor costs significant contribute to GTFP, and strengthening green technological progress (GTP) is the main delivery path, though it hinders the improvement of green technological efficiency (GTE). Secondly, industrial intelligence plays an enhanced positive moderating role in the path of labor costs affecting GTFP. Thirdly, grouped regressions show that the role of labor costs only emerges when industrial intelligence reaches a certain high level. Finally, taking industrial intelligence as a threshold dependent variable, labor costs have a non-linear, triple-threshold effect on GTFP. The promotion effect of labor costs increases the most when industrial intelligence exceeds the first threshold. On balance, as the level of industrial intelligence continues to increase, the promotion effect is stronger. The above empirical results are robust under the robustness test of replacement variables and estimation method. The results indicate that the innovation development effect of rising labor costs has to be built on the basis of industrial intelligence development.

Suggested Citation

  • Wei Qian & Yongsheng Wang, 2022. "How Do Rising Labor Costs Affect Green Total Factor Productivity? Based on the Industrial Intelligence Perspective," Sustainability, MDPI, vol. 14(20), pages 1-19, October.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13653-:d:949531
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/20/13653/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/20/13653/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liu, Zuankuo & Xin, Li, 2019. "Has China's Belt and Road Initiative promoted its green total factor productivity?——Evidence from primary provinces along the route," Energy Policy, Elsevier, vol. 129(C), pages 360-369.
    2. Jiaqi Yuan & Deyuan Zhang, 2021. "Research on the Impact of Environmental Regulations on Industrial Green Total Factor Productivity: Perspectives on the Changes in the Allocation Ratio of Factors among Different Industries," Sustainability, MDPI, vol. 13(23), pages 1-23, November.
    3. Fan, Haichao & Hu, Yichuan & Tang, Lixin, 2021. "Labor costs and the adoption of robots in China," Journal of Economic Behavior & Organization, Elsevier, vol. 186(C), pages 608-631.
    4. Fan, Haichao & Lin, Faqin & Tang, Lixin, 2018. "Minimum Wage and Outward FDI from China," Journal of Development Economics, Elsevier, vol. 135(C), pages 1-19.
    5. Ren, Shenggang & Yuan, Baolong & Ma, Xie & Chen, Xiaohong, 2014. "International trade, FDI (foreign direct investment) and embodied CO2 emissions: A case study of Chinas industrial sectors," China Economic Review, Elsevier, vol. 28(C), pages 123-134.
    6. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    7. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
    8. Dennis Tao Yang & Vivian Weijia Chen & Ryan Monarch, 2010. "Rising Wages: Has China Lost Its Global Labor Advantage?," Pacific Economic Review, Wiley Blackwell, vol. 15(4), pages 482-504, October.
    9. Li, Yaya & Zhang, Yuru & Pan, An & Han, Minchun & Veglianti, Eleonora, 2022. "Carbon emission reduction effects of industrial robot applications: Heterogeneity characteristics and influencing mechanisms," Technology in Society, Elsevier, vol. 70(C).
    10. Hongbin Li & Lei Li & Binzhen Wu & Yanyan Xiong, 2012. "The End of Cheap Chinese Labor," Journal of Economic Perspectives, American Economic Association, vol. 26(4), pages 57-74, Fall.
    11. Daron Acemoglu & Pascual Restrepo, 2019. "Automation and New Tasks: How Technology Displaces and Reinstates Labor," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 3-30, Spring.
    12. Mayneris, Florian & Poncet, Sandra & Zhang, Tao, 2018. "Improving or disappearing: Firm-level adjustments to minimum wages in China," Journal of Development Economics, Elsevier, vol. 135(C), pages 20-42.
    13. Mirko Draca & Stephen Machin & John Van Reenen, 2011. "Minimum Wages and Firm Profitability," American Economic Journal: Applied Economics, American Economic Association, vol. 3(1), pages 129-151, January.
    14. Song, Malin & Peng, Licheng & Shang, Yuping & Zhao, Xin, 2022. "Green technology progress and total factor productivity of resource-based enterprises: A perspective of technical compensation of environmental regulation," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    15. Dong-hyun Oh, 2010. "A global Malmquist-Luenberger productivity index," Journal of Productivity Analysis, Springer, vol. 34(3), pages 183-197, December.
    16. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    17. Douglas Gollin & Remi Jedwab & Dietrich Vollrath, 2016. "Urbanization with and without industrialization," Journal of Economic Growth, Springer, vol. 21(1), pages 35-70, March.
    18. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    19. Jinhuang Mao & Qiong Wu & Meihong Zhu & Chengpeng Lu, 2022. "Effects of Environmental Regulation on Green Total Factor Productivity: An Evidence from the Yellow River Basin, China," Sustainability, MDPI, vol. 14(4), pages 1-21, February.
    20. Zhang, Dongyang, 2021. "Marketization, environmental regulation, and eco-friendly productivity: A Malmquist–Luenberger index for pollution emissions of large Chinese firms," Journal of Asian Economics, Elsevier, vol. 76(C).
    21. Chaofan Chen & Qingxin Lan & Ming Gao & Yawen Sun, 2018. "Green Total Factor Productivity Growth and Its Determinants in China’s Industrial Economy," Sustainability, MDPI, vol. 10(4), pages 1-25, April.
    22. Zhao, Jun & Jiang, Qingzhe & Dong, Xiucheng & Dong, Kangyin & Jiang, Hongdian, 2022. "How does industrial structure adjustment reduce CO2 emissions? Spatial and mediation effects analysis for China," Energy Economics, Elsevier, vol. 105(C).
    23. Lee, Sanghoon & Oh, Dae-Won, 2015. "Economic growth and the environment in China: Empirical evidence using prefecture level data," China Economic Review, Elsevier, vol. 36(C), pages 73-85.
    24. Die Li & Sumin Hu, 2021. "How Does Technological Innovation Mediate the Relationship between Environmental Regulation and High-Quality Economic Development? Empirical Evidence from China," Sustainability, MDPI, vol. 13(4), pages 1-20, February.
    25. Chen, Feng-Wen & Tan, Yulu & Chen, Fengzhang & Wu, Yong-Qiu, 2021. "Enhancing or suppressing: The effect of labor costs on energy intensity in emerging economies," Energy, Elsevier, vol. 214(C).
    26. Victor Motta, 2020. "Lack of access to external finance and SME labor productivity: does project quality matter?," Small Business Economics, Springer, vol. 54(1), pages 119-134, January.
    27. Taoyuan Wei & Qin Zhu & Solveig Glomsrød, 2017. "A General Equilibrium View of Population Ageing Impact on Energy Use via Labor Supply," Sustainability, MDPI, vol. 9(9), pages 1-12, August.
    28. Xie, Mengmeng & Ding, Lin & Xia, Yan & Guo, Jianfeng & Pan, Jiaofeng & Wang, Huijuan, 2021. "Does artificial intelligence affect the pattern of skill demand? Evidence from Chinese manufacturing firms," Economic Modelling, Elsevier, vol. 96(C), pages 295-309.
    29. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    30. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    31. Sadorsky, Perry, 2013. "Do urbanization and industrialization affect energy intensity in developing countries?," Energy Economics, Elsevier, vol. 37(C), pages 52-59.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lei Jiang & Xingyu Chen & Yang Jiang & Bo Zhang, 2023. "Exploring the Direct and Spillover Effects of Aging on Green Total Factor Productivity in China: A Spatial Econometric Approach," Sustainability, MDPI, vol. 15(8), pages 1-19, April.
    2. Guang Chen & Akira Hibiki, 2022. "Can the Carbon Emission Trading Scheme Influence Industrial Green Production in China?," Sustainability, MDPI, vol. 14(23), pages 1-22, November.
    3. Zemenghong Bao & Zhisen Lin & Tiantian Jin & Kun Lv, 2024. "Regional Breakthrough Innovation Change Strategies, Ecological Location Suitability of High-Tech Industry Innovation Ecosystems, and Green Energy," Energies, MDPI, vol. 17(16), pages 1-34, August.
    4. Long Qian & Yunjie Zhou & Ying Sun, 2023. "Regional Differences, Distribution Dynamics, and Convergence of the Green Total Factor Productivity of China’s Cities under the Dual Carbon Targets," Sustainability, MDPI, vol. 15(17), pages 1-26, August.
    5. Haibo Chen & Jiawei Lu, 2023. "Does Cultural Agglomeration Affect Green Total Factor Productivity? Evidence from 279 Cities in China," Sustainability, MDPI, vol. 15(9), pages 1-23, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Siying & Liu, Fengshuo, 2024. "Impact of industrial intelligence on green total factor productivity: The indispensability of the environmental system," Ecological Economics, Elsevier, vol. 216(C).
    2. Borsato, Andrea & Lorentz, André, 2023. "The Kaldor–Verdoorn law at the age of robots and AI," Research Policy, Elsevier, vol. 52(10).
    3. Liu, Shihua & Padhan, Hemachandra & P., Jithin & Jose, Annmary & Rahut, Dil, 2024. "Do green trade and technology-oriented trade affect economic cycles? Evidence from the Chinese provinces," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    4. Yang, Chih-Hai, 2022. "How Artificial Intelligence Technology Affects Productivity and Employment: Firm-level Evidence from Taiwan," Research Policy, Elsevier, vol. 51(6).
    5. Qingyan Zhu, 2023. "How Will the Relationship between Technological Innovation and Green Total Factor Productivity Change under the Influence of Service-Oriented Upgrading of Industrial Structure?," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    6. Chaofan Chen & Qingxin Lan & Ming Gao & Yawen Sun, 2018. "Green Total Factor Productivity Growth and Its Determinants in China’s Industrial Economy," Sustainability, MDPI, vol. 10(4), pages 1-25, April.
    7. Baharumshah, Ahmad Zubaidi & Slesman, Ly & Wohar, Mark E., 2016. "Inflation, inflation uncertainty, and economic growth in emerging and developing countries: Panel data evidence," Economic Systems, Elsevier, vol. 40(4), pages 638-657.
    8. Samargandi, Nahla & Fidrmuc, Jan & Ghosh, Sugata, 2015. "Is the Relationship Between Financial Development and Economic Growth Monotonic? Evidence from a Sample of Middle-Income Countries," World Development, Elsevier, vol. 68(C), pages 66-81.
    9. Liu, Shasha & Wu, Yuhuan & Kong, Gaowen, 2024. "Politics and Robots," International Review of Financial Analysis, Elsevier, vol. 91(C).
    10. Acheampong, Alex O., 2019. "Modelling for insight: Does financial development improve environmental quality?," Energy Economics, Elsevier, vol. 83(C), pages 156-179.
    11. Carmela D'Avino, 2015. "Net Interoffice Accounts of Global Banks: The Role of Domestic Funding," IJFS, MDPI, vol. 3(3), pages 1-17, June.
    12. Andrés Rodríguez‐Pose & Roberto Ganau & Kristina Maslauskaite & Monica Brezzi, 2021. "Credit constraints, labor productivity, and the role of regional institutions: Evidence from manufacturing firms in Europe," Journal of Regional Science, Wiley Blackwell, vol. 61(2), pages 299-328, March.
    13. Arminen, Heli & Menegaki, Angeliki N., 2019. "Corruption, climate and the energy-environment-growth nexus," Energy Economics, Elsevier, vol. 80(C), pages 621-634.
    14. Junbai Pan & Kun Lv & Shurong Yu & Dian Fu, 2022. "What Mechanisms Do Financial Marketization and China’s Fiscal Decentralization Have on Regional Energy Intensity? Evidence Based on Spatial Spillover and Panel Threshold Effects Perspectives," IJERPH, MDPI, vol. 19(9), pages 1-27, May.
    15. Khan, Muhammad Atif & Gu, Lulu & Khan, Muhammad Asif & Oláh, Judit, 2020. "Natural resources and financial development: The role of institutional quality," Journal of Multinational Financial Management, Elsevier, vol. 56(C).
    16. Weixiang Zhao & Yankun Xu, 2022. "Public Expenditure and Green Total Factor Productivity: Evidence from Chinese Prefecture-Level Cities," IJERPH, MDPI, vol. 19(9), pages 1-27, May.
    17. Vinayagathasan, Thanabalasingam, 2013. "Inflation and economic growth: A dynamic panel threshold analysis for Asian economies," Journal of Asian Economics, Elsevier, vol. 26(C), pages 31-41.
    18. Richard P.C. Brown & Fabrizio Carmignani, 2015. "Revisiting the Effects of Remittances on Bank Credit: A Macro Perspective," Scottish Journal of Political Economy, Scottish Economic Society, vol. 62(5), pages 454-485, November.
    19. Ines TROJETTE, 2016. "The Effect Of Foreign Direct Investment On Economic Growth: The Institutional Threshold," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 43, pages 111-138.
    20. Diallo, Ibrahima Amadou, 2010. "Analyzing the link between real exchange rate and productivity," MPRA Paper 29548, University Library of Munich, Germany.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13653-:d:949531. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.