Staff Discussion Notes

Preview Citation

Format: Chicago

Fernanda Brollo, Era Dabla-Norris, Ruud de Mooij, Daniel Garcia-Macia, Tibor Hanappi, Li Liu, and Anh D. M. Nguyen Broadening the Gains from Generative AI: The Role of Fiscal Policies, (USA: International Monetary Fund, 2024) accessed October 5, 2024

Disclaimer: This Staff Discussion Note represents the views of the authors and does not necessarily represent IMF views or IMF policy. The views expressed herein should be attributed to the authors and not to the IMF, its Executive Board, or its management. Staff Discussion Notes are published to elicit comments and to further debate.

Summary

Generative artificial intelligence (gen AI) holds immense potential to boost productivity growth and advance public service delivery, but it also raises profound concerns about massive labor disruptions and rising inequality. This note discusses how fiscal policies can be employed to steer the technology and its deployment in ways that serve humanity best while cushioning the negative labor market and distributional effects to broaden the gains. Given the vast uncertainty about the nature, impact, and speed of developments in gen AI, governments should take an agile approach that prepares them for both business as usual and highly disruptive scenarios.

Subject: Artificial intelligence, Automation, Capital income, Corporate income tax, Economic sectors, Financial crises, Labor, Labor markets, National accounts, Taxes, Technology

Keywords: Advance public service delivery, AI, Artificial intelligence, Automation, Capital income, Corporate income tax, Fiscal policy, Gen AI, Global, IMF Staff Discussion Note SDN2024/002, Income support, Job displacement, Labor market, Labor markets, Market power, Replacement ratio, Role of fiscal policies, Social protection systems, Tax systems, Tax treatment, Technological change

Publication Details

  • Pages:

    47

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

    ---

  • Series:

    Staff Discussion Notes No. 2024/002

  • Stock No:

    SDNEA2024002

  • ISBN:

    9798400277177

  • ISSN:

    2617-6750