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Hard budget constraints and artificial intelligence technology

Author

Listed:
  • Zhu, Jun
  • Zhang, Jingting
  • Feng, Yiqing

Abstract

Motivated by the contradiction between a government's hard budget constraints and artificial intelligence, this study constructs a computable general equilibrium model embedded with various fiscal and tax policies to study the impact of artificial intelligence development on the Chinese economy under government budget constraints with different intensities. This paper seeks to find a reasonable policy that takes into account China's employment, income distribution, and budget constraints to achieve common prosperity. It finds that the softer the government's budget constraints, the smaller the negative impact of artificial intelligence on the economy. More specifically, allowing the government to increase its debts and spending is more effective than tax cuts. It is suggested that if the goal is to reconcile the contradiction between hard budget constraints and artificial intelligence, fiscal and tax policy combinations, together with an improvised soft budget constraint, are required to increase the taxation of capital to an appropriate degree. In order to resolve the contradiction between the government's hard budget constraints and the development of artificial intelligence in pursuit of common prosperity, a robot tax should be levied and automation capital taxed.

Suggested Citation

  • Zhu, Jun & Zhang, Jingting & Feng, Yiqing, 2022. "Hard budget constraints and artificial intelligence technology," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    • Handle: RePEc:eee:tefoso:v:183:y:2022:i:c:s0040162522004127
    DOI: 10.1016/j.techfore.2022.121889
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    1. Juan F. Jimeno, 2019. "Fewer babies and more robots: economic growth in a new era of demographic and technological changes," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 10(2), pages 93-114, June.
    2. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    3. Joseph Zeira, 1998. "Workers, Machines, and Economic Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(4), pages 1091-1117.
    4. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.
    5. Daron Acemoglu & Pascual Restrepo, 2018. "Modeling Automation," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 48-53, May.
    6. Philippe Aghion & Céline Antonin & Simon Bunel, 2019. "Artificial Intelligence, Growth and Employment: The Role of Policy," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 510-511-5, pages 149-164.
    7. Philippe Aghion & Benjamin F. Jones & Charles I. Jones, 2018. "Artificial Intelligence and Economic Growth," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 237-282, National Bureau of Economic Research, Inc.
    8. Gries, Thomas & Naude, Wim, 2018. "Artificial intelligence, jobs, inequality and productivity: Does aggregate demand matter?," MERIT Working Papers 2018-047, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    9. Daron Acemoglu & Fabrizio Zilibotti, 2001. "Productivity Differences," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(2), pages 563-606.
    10. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation, and Work," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 197-236, National Bureau of Economic Research, Inc.
    11. 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.
    12. Per Pettersson-Lidbom, 2010. "Dynamic Commitment and the Soft Budget Constraint: An Empirical Test," American Economic Journal: Economic Policy, American Economic Association, vol. 2(3), pages 154-179, August.
    13. Kornai, J, 1979. "Resource-Constrained versus Demand-Constrained Systems," Econometrica, Econometric Society, vol. 47(4), pages 801-819, July.
    14. Gries, Thomas & Naude, Wim, 2020. "Artificial Intelligence, Income Distribution and Economic Growth," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224623, Verein für Socialpolitik / German Economic Association.
    15. Anton Korinek & Joseph E. Stiglitz, 2018. "Artificial Intelligence and Its Implications for Income Distribution and Unemployment," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 349-390, National Bureau of Economic Research, Inc.
    16. repec:nbr:nberch:14019 is not listed on IDEAS
    17. Acemoglu, Daron & Autor, David, 2011. "Skills, Tasks and Technologies: Implications for Employment and Earnings," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 12, pages 1043-1171, Elsevier.
    18. Morgan R. Frank & David Autor & James E. Bessen & Erik Brynjolfsson & Manuel Cebrian & David J. Deming & Maryann Feldman & Matthew Groh & José Lobo & Esteban Moro & Dashun Wang & Hyejin Youn & Iyad Ra, 2019. "Toward understanding the impact of artificial intelligence on labor," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(14), pages 6531-6539, April.
    19. Stiglitz, Joseph E., 2017. "The coming great transformation," Journal of Policy Modeling, Elsevier, vol. 39(4), pages 625-638.
    20. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation and Work," Boston University - Department of Economics - Working Papers Series dp-298, Boston University - Department of Economics.
    21. Guanghua Wan & Chen Wang & Hua Yin & Yan Zhang, 2018. "From Equality of Deprivation to Disparity of Prosperity: The Poverty–Growth–Inequality Triangle in Post†reform China," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 26(2), pages 50-67, March.
    22. Thierer, Adam & Castillo, Andrea & Russell, Raymond, 2017. "Artificial Intelligence and Public Policy," Annals of Computational Economics, George Mason University, Mercatus Center, August.
    23. repec:hal:spmain:info:hdl:2441/7n49nkmngd8448a5ts5gt5ade0 is not listed on IDEAS
    24. Min Xu & Jeanne M. David & Suk Hi Kim, 2018. "The Fourth Industrial Revolution: Opportunities and Challenges," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 9(2), pages 90-95, April.
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    More about this item

    Keywords

    Artificial intelligence; Budget constraint; Common prosperity; General equilibrium model;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy; Modern Monetary Theory
    • H60 - Public Economics - - National Budget, Deficit, and Debt - - - General

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