IMF Working Papers

Industrial Policies for Innovation: A Cost-Benefit Framework

By Daniel Garcia-Macia, Alexandre Sollaci

August 16, 2024

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Daniel Garcia-Macia, and Alexandre Sollaci. Industrial Policies for Innovation: A Cost-Benefit Framework, (USA: International Monetary Fund, 2024) accessed November 21, 2024

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Summary

When and how should governments use industrial policy to direct innovation to specific sectors? This paper develops a framework to analyze the costs and benefits of industrial policies for innovation. The framework is based on a model of endogenous innovation with a sectoral network of knowledge spillovers (Liu and Ma 2023), extended to capture implementation frictions and alternative policy goals. Simulations show that implementing sector-specific fiscal support is only preferable to sector-neutral support under restrictive conditions—when externalities are well measured (e.g., greenhouse gas emissions), domestic knowledge spillovers of targeted sectors are high (typically in larger economies), and administrative capacity is strong (including to avoid misallocation to politically connected sectors). If any of these conditions are not fully met, welfare impacts of industrial policy quickly become negative. The optimal allocation of support entails greater subsidies to greener sectors, but other factors such as cross-sector knowledge spillovers matter. For a sample of technologically advanced economies, existing industrial policies seem to be directing innovation to broadly the right sectors, but to an excessive degree in most economies, including China and the United States.

Subject: Artificial intelligence, Financial sector policy and analysis, Spillovers, Technology

Keywords: AI, Artificial intelligence, Climate policy, Economy openness, Global, Implementation friction, Industrial policy, Innovation, Knowledge spillover, Knowledge spillovers, Policy goal, R&D allocation, Spillovers, Welfare impact

Publication Details

  • Pages:

    36

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

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  • Series:

    Working Paper No. 2024/176

  • Stock No:

    WPIEA2024176

  • ISBN:

    9798400288401

  • ISSN:

    1018-5941