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AI Adoption, Regional Productivity, and Inflation Evidence from Korea and Implications for Monetary Policy

Author

Listed:
  • Cyn-Young Park

    (The South East Asian Central Banks (SEACEN) Research and Training Centre)

  • Kwanho Shin

    (Korea University)

Abstract

This paper examines whether the early diffusion of artificial intelligence (AI) is visible in productivity and price outcomes relevant to monetary policy. We combine firm-level information on AI adoption from Korea’s Survey of Business Activities with annual industry- and region-level data for 2017–2023. We construct value-added- and employment-weighted measures of AI intensity and use their 2019 values as predetermined measures of initial AI intensity. Both measures strongly predict the cross-sectional distribution of AI intensity in 2023. We then estimate reduced-form panel regressions that compare 2023 outcomes across industries and regions with different initial levels of AI intensity, controlling for unit and year fixed effects. We find no systematic evidence that more AI-intensive industries or regions experienced stronger output or labour-productivity growth in 2023. Industry-level price effects are also statistically insignificant and vary across price measures. At the regional level, however, employment-weighted AI intensity is positively associated with overall consumer price inflation, while restaurant price inflation is higher under both measures of AI intensity. These findings suggest that the supply-side benefits of AI had not yet become visible in aggregate productivity by 2023, whereas inflationary pressures may have emerged in some locally determined consumer services. This pattern is consistent with demand responding before productivity gains are fully realised, although our empirical design does not identify the underlying mechanism. The findings have important implications for monetary policy: during the early stages of AI diffusion, central banks should not assume that anticipated productivity gains will immediately expand effective supply or alleviate inflationary pressures. We discuss the implications of this transitional asymmetry for central banks in Asian economies, where rapid AI adoption may coincide with persistent supply constraints and sector-specific price pressures.

Suggested Citation

  • Cyn-Young Park & Kwanho Shin, "undated". "AI Adoption, Regional Productivity, and Inflation Evidence from Korea and Implications for Monetary Policy," Working Papers wp63, South East Asian Central Banks (SEACEN) Research and Training Centre, revised Sep 2026.
    • Handle: RePEc:sea:wpaper:wp63
    as

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    References listed on IDEAS

    as
    1. Aldasoro, I. & Gambacorta, L. & Korinek, A. & Shreeti, V. & Stein, M., 2025. "Intelligent financial system: How AI is transforming finance," Journal of Financial Stability, Elsevier, vol. 81(C).
    2. Simone Lenzu, 2026. "Artificial Intelligence and Monetary Policy: A Framework and Perspective on Cyclical Transmission, Structural Transition, and Financial Stability," Staff Reports 1192, Federal Reserve Bank of New York.
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    Keywords

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    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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