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

Zi Wang, Xingcheng Xu, Yanqing Yang and Xiaodong Zhu

Working Papers from University of Toronto, Department of Economics

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.

Keywords: Deep Learning; Tariff Wars; Industrial Policies; Optimal Policies; Nash Equilibria; Best-response dynamics; Quantitative Trade Models (search for similar items in EconPapers)
JEL-codes: C61 C63 F12 F51 (search for similar items in EconPapers)
Pages: Unknown pages
Date: 2024-07-24
New Economics Papers: this item is included in nep-cmp, nep-gth and nep-int
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Working Paper: Optimal Trade and Industrial Policies in the Global Economy: A Deep Learning Framework (2024)
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