Source: UNIV OF CONNECTICUT submitted to
THE IMPACT OF TRADE DISPUTES ON U.S. AGRICULTURE: DATA-DRIVEN APPROACHES FOR COUNTERFACTUAL MEASUREMENT
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
NEW
Funding Source
Reporting Frequency
Annual
Accession No.
1018737
Grant No.
2019-67023-29343
Project No.
CONS-2018-08514
Proposal No.
2018-08514
Multistate No.
(N/A)
Program Code
A1641
Project Start Date
Apr 15, 2019
Project End Date
Apr 14, 2023
Grant Year
2019
Project Director
Steinbach, S.
Recipient Organization
UNIV OF CONNECTICUT
(N/A)
STORRS,CT 06269
Performing Department
Agric and Resource Economics
Non Technical 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 to not only 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 that has been so far largely neglected in the empirical trade literature while being of vital importance for the future of agriculture in the United States. Such understanding provides the foundation for a public discussion that is 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.
Animal Health Component
0%
Research Effort Categories
Basic
80%
Applied
20%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
60650103010100%
Knowledge Area
606 - International Trade and Development;

Subject Of Investigation
5010 - Food;

Field Of Science
3010 - Economics;
Goals / Objectives
The long-term goal of this project is to precisely measure the impact of international trade disputes on U.S. agricultural exports. We will accomplish this task by developing novel and innovative methods based on machine learning techniques to construct credible counterfactual trade flows for causal inference. These benchmarks allow us to obtain a more precise identification of the trade destruction effect caused by trade disputes involving the United States.The research objectives are as follows:Assemble a complete dataset of international trade disputes targeting U.S. agricultural exports over the period 1990 to 2018.Prepare a time-consistent monthly dataset of U.S. foreign trade carefully disaggregated by specific product at the customs-district level.Develop a novel statistical approach based on machine learning techniques to create credible trade flowcounterfactuals and measure the impact of agricultural trade disputes more precisely.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.
Project Methods
The primary challenge for accuratelymeasuring the impact of trade disputes on U.S. agricultural exports relates to the specification of a plausible benchmark for the statistical evaluation of policy effects. Such a counterfactual can be either based on a prior probability distribution or derived from a data generating process. An assessment based on priorsis prone to error because reliable information on market characteristics and trend similarities are often not available, particularly at the disaggregated time and product levels. Therefore, we will use additional information on trade flows to improve our benchmark. For this purpose, we will exploit temporal variation in foreign trade data at the product level with machine learning techniques. We build on the assumption that alternative products show similar trade patterns as the products targeted in a trade dispute. These products are neither substitutes nor complements of the affected products. By excluding substitutes and complements, we can ensure that spillover effects in product space do not affect the causal inference strategy. We will implement five modules to create valid counterfactuals and infer the causal relationship between trade duties and U.S. agricultural export trade flows.

Progress 04/15/19 to 04/14/20

Outputs
Target Audience:Target audience for this the Program is individuals with a stake in ensuring a safe food supply including Food producers and importers State and federal public officials Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Graduate and postdoc training and education How have the results been disseminated to communities of interest?NBER Working Paper What do you plan to do during the next reporting period to accomplish the goals?(2) Prepare a time-consistent monthly dataset of U.S. foreign trade carefully disaggregated by specific product at the customs-district level.

Impacts
What was accomplished under these goals? (1) Assembled a complete dataset of international trade disputes targeting U.S. agricultural exports over the period 1990 to 2018. (2) Working on preparing a time-consistent monthly dataset of U.S. foreign trade carefully disaggregated by specific product at the customs-district level.

Publications

  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Steinbach, S., & Carter, C. A. (2020). The Impact of Retaliatory Tariffs on Agricultural and Food Trade. National Bureau of Economic Research, 27147. (lead author, highest rank peer-reviewed economics working paper series).