The Environmental Bias of Trade Policy

Project Outcomes Statement

Intellectual Merit: This NSF award led to an article on the "Environmental Bias of Trade Policy," published in one of the highest impact journals in economics, the Quarterly Journal of Economics (QJE). This paper quantifies differences in trade policy between clean and dirty industries, investigates political economy explanations for those differences, and uses a quantitative general equilibrium model to study the environmental consequences of changing this asymmetry in trade policy between clean and dirty industries. Building on this work, the PI also published three other papers in high-impact economic journals on trade, pollution, and political economy. Several hundred other papers cite these, Science magazine published a synopsis of this work, and the American Energy Society named the QJE paper the Energy Article of the Year for 2020. This research is on graduate syllabi in trade, environmental, and political economy courses at universities around the US.

Broader Impacts: The PI has discussed research findings from this article with policymakers in many countries. Policy-related organizations which invited discussions of this article from the PI include the US International Trade Commission, the World Bank, Inter-American Development Bank, Federal Reserve Bank, Center for Climate and Energy Solutions (C2ES), Environmental Defense, Flexport, KAS & E3G, the OECD, Sweden's Ministry of Foreign Affairs, the Vienna Institute for International Economic Studies, and the World Trade Organization. Inspired partly by this research, the World Bank is discussing the collection of annual data on differences in trade policy between clean and dirty goods for each. The PI spoke to the EU Secretariat's Committee on International Trade then spent a year as consultant and advisor for the European Commission's Directorate General for Trade discussing ideas building on this paper. A dozen news stories have discussed this research, including a feature in The Economist magazine. This project also led to training of several graduate students in economics who have used these skills in their dissertation research.

The data is publicly available at:
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CTUS2E