The Impact of Trade Disputes on U.S. Agriculture: Data-Driven Approaches for Counterfactual Measurement

Project Summary

This research project aims at measuring the impact of trade disputes on agriculture in the United States. Because U.S. farmers and food processors sell a significant share of their production abroad, the growing number of trade disputes is a primary concern for the future viability of agriculture. However, so far, we know little about the implications of these policies because reliable counterfactuals for causal inference are generally unavailable. Therefore, this project aims at providing the necessary tools to precisely measure the impact of international trade disputes on U.S. agriculture. We accomplish this task by developing novel and innovative analytical methods based on machine learning techniques. These methods allow us to construct credible counterfactual trade flows, obtain a more precise identification of trade effects and evaluate the impact according to dispute characteristics, product specificities, and timing of trade measures. The analysis will enable us not only to assess the importance of trade disputes for agriculture in the United States but also to provide the necessary means to determine the impact of future trade disputes. Therefore, our project will enhance the understanding of a highly relevant foreign trade issue largely neglected in the empirical trade literature while being vital for the future of agriculture in the United States. Such understanding provides the foundation for a public discussion based on facts and allows for informed decisions. Consequently, our research will enhance market efficiency and performance by providing essential knowledge on the functioning of markets in light of trade disputes.

Project Objectives

The research will accomplish the following objectives:

Objective 1: Assemble a complete dataset of international trade disputes targeting U.S. agricultural exports from 1990 to 2018.

Objective 2: Prepare a time-consistent monthly dataset of U.S. foreign trade carefully disaggregated by specific products at the customs-district level.

Objective 3: Develop a novel statistical approach based on machine learning techniques to create credible trade flow counterfactuals and measure the impact of agricultural trade disputes more precisely.

Objective 4: Evaluate the impact of trade disputes on U.S. agricultural trade and explore differences in the trade impact according to dispute characteristics, product specificities, and timing of trade measures.

Funding Agency

National Institute of Food and Agriculture, Grant No. 2019-67023-29343 [Link]


Work in Progress

Research Team

Colin A. Carter (Co-PI)

University of California, Davis

Armen Khederlarian (Postdoc)

University of Connecticut

Sandro Steinbach (PI)

North Dakota State University