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

TL;DR

This paper introduces DL-opt, a deep learning framework that efficiently solves for optimal trade and industrial policies in complex general equilibrium models, revealing sectoral heterogeneity and welfare implications.

Contribution

The paper develops a novel deep learning approach combining NFXP, implicit differentiation, and best-response dynamics to solve for optimal policies in multi-sector trade models.

Findings

Nash industrial subsidies increase with scale elasticities.

Nash tariffs decrease with trade elasticities.

Global dual policy reduces tariffs and enhances welfare.

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.