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Optimal Trade and Industrial Policies in the Global Economy: A Deep Learning Framework

Author

Listed:
  • Zi Wang
  • Xingcheng Xu
  • Yanqing Yang
  • Xiaodong Zhu

Abstract

We propose a deep learning framework, DL-opt, designed to efficiently solve for optimal policies in quantifiable general equilibrium trade models. DL-opt integrates (i) a nested fixed point (NFXP) formulation of the optimization problem, (ii) automatic implicit differentiation to enhance gradient descent for solving unilateral optimal policies, and (iii) a best-response dynamics approach for finding Nash equilibria. Utilizing DL-opt, we solve for non-cooperative tariffs and industrial subsidies across 7 economies and 44 sectors, incorporating sectoral external economies of scale. Our quantitative analysis reveals significant sectoral heterogeneity in Nash policies: Nash industrial subsidies increase with scale elasticities, whereas Nash tariffs decrease with trade elasticities. Moreover, we show that global dual competition, involving both tariffs and industrial subsidies, results in lower tariffs and higher welfare outcomes compared to a global tariff war. These findings highlight the importance of considering sectoral heterogeneity and policy combinations in understanding global economic competition.

Suggested Citation

  • Zi Wang & Xingcheng Xu & Yanqing Yang & Xiaodong Zhu, 2024. "Optimal Trade and Industrial Policies in the Global Economy: A Deep Learning Framework," Working Papers tecipa-781, University of Toronto, Department of Economics.
    • Handle: RePEc:tor:tecipa:tecipa-781
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    References listed on IDEAS

    as
    1. Ahmad Lashkaripour & Volodymyr Lugovskyy, 2023. "Profits, Scale Economies, and the Gains from Trade and Industrial Policy," American Economic Review, American Economic Association, vol. 113(10), pages 2759-2808, October.
    2. Torsten Heinrich & Yoojin Jang & Luca Mungo & Marco Pangallo & Alex Scott & Bassel Tarbush & Samuel Wiese, 2023. "Best-response dynamics, playing sequences, and convergence to equilibrium in random games," International Journal of Game Theory, Springer;Game Theory Society, vol. 52(3), pages 703-735, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Deep Learning; Tariff Wars; Industrial Policies; Optimal Policies; Nash Equilibria; Best-response dynamics; Quantitative Trade Models;
    All these keywords.

    JEL classification:

    • F12 - International Economics - - Trade - - - Models of Trade with Imperfect Competition and Scale Economies; Fragmentation
    • F51 - International Economics - - International Relations, National Security, and International Political Economy - - - International Conflicts; Negotiations; Sanctions
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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