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Modelling artificial intelligence in economics

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  • Thomas Gries

    (Paderborn University)

  • Wim Naudé

    (Cork University Business School, University College Cork)

Abstract

We provide a partial equilibrium model wherein AI provides abilities combined with human skills to provide an aggregate intermediate service good. We use the model to find that the extent of automation through AI will be greater if (a) the economy is relatively abundant in sophisticated programs and machine abilities compared to human skills; (b) the economy hosts a relatively large number of AI-providing firms and experts; and (c) the task-specific productivity of AI services is relatively high compared to the task-specific productivity of general labor and labor skills. We also illustrate that the contribution of AI to aggregate productive labor service depends not only on the amount of AI services available but on the endogenous number of automated tasks, the relative productivity of standard and IT-related labor, and the substitutability of tasks. These determinants also affect the income distribution between the two kinds of labor. We derive several empirical implications and identify possible future extensions.

Suggested Citation

  • Thomas Gries & Wim Naudé, 2022. "Modelling artificial intelligence in economics," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 56(1), pages 1-13, December.
    • Handle: RePEc:spr:jlabrs:v:56:y:2022:i:1:d:10.1186_s12651-022-00319-2
    DOI: 10.1186/s12651-022-00319-2
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    Cited by:

    1. Castillo, Victhalia Zapata & Boer, Harmen-Sytze de & Muñoz, Raúl Maícas & Gernaat, David E.H.J. & Benders, René & van Vuuren, Detlef, 2022. "Future global electricity demand load curves," Energy, Elsevier, vol. 258(C).
    2. Umut Erdem & K. Mert Cubukcu, 2022. "The uneven geography of innovation in Turkey: Visualizing the geography and regional relatedness of patent production," Environment and Planning A, , vol. 54(1), pages 7-10, February.
    3. Fossen, Frank M. & McLemore, Trevor & Sorgner, Alina, 2024. "Artificial Intelligence and Entrepreneurship," IZA Discussion Papers 17055, Institute of Labor Economics (IZA).
    4. Sagarika Mishra & Michael T. Ewing & Holly B. Cooper, 2022. "Artificial intelligence focus and firm performance," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1176-1197, November.
    5. Wang, Linhui & Cao, Zhanglu & Dong, Zhiqing, 2023. "Are artificial intelligence dividends evenly distributed between profits and wages? Evidence from the private enterprise survey data in China," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 342-356.
    6. Gries, Thomas & Naudé, Wim, 2021. "The Race of Man and Machine: Implications of Technology When Abilities and Demand Constraints Matter," IZA Discussion Papers 14341, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

    Artificial intelligence; Automation; Labor economics; Mathematical models;
    All these keywords.

    JEL classification:

    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • E25 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Aggregate Factor Income Distribution

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