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: COMPLETE
• Funding Source: AFRI COMPETITIVE GRANT
• Reporting Frequency: Annual
• Accession No.: 1018737
• Grant No.: 2019-67023-29343
• Cumulative Award Amt.: $485,528.00
• Proposal No.: 2018-08514
• Project Start Date: Apr 15, 2019
• Project End Date: Apr 14, 2024
• Grant Year: 2019
• Program Code: [A1641]- Agriculture Economics and Rural Communities: Markets and Trade

Project Director
Khederlarian, A.

Recipient Organization
UNIV OF CONNECTICUT
438 WHITNEY RD EXTENSION UNIT 1133
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

20%

Research Effort Categories

Basic

80%

Applied

20%

Developmental

(N/A)

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
606	5010	3010	100%

Knowledge Area
606 - International Trade and Development;

Subject Of Investigation
5010 - Food;

Field Of Science
3010 - Economics;

Keywords

trade disputes

agricultural exports

counterfactual analysis

machine learning techniques

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 04/14/24

Outputs
Target Audience:The target audience for this research included individuals and organizations with a vested interest in the sustainability and efficiency of the U.S. agricultural sector. This encompassed food producers and importers directly impacted by trade policies and disputes, as well as state and federal public officials responsible for shaping and implementing agricultural and trade policies. Additionally, the findings were relevant to academic researchers and industry analysts focused on agricultural economics and trade dynamics. We reached these groups through various dissemination channels, including publishing an NBER Working Paper, presentations at academic conferences, and industry workshops. By providing data-driven insights into the effects of trade disputes, the project supports informed decision-making and policy development to safeguard the future of U.S. agriculture. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The project has provided several opportunities for training and professional development. Graduate students and the postdoctoral researcher involved in the project received hands-on experience with advanced analytical methods and machine learning techniques, enhancing their skills in data analysis and causal inference, leading to academic and industry placements at top universities and international think tanks. They were also trained in the development and application of novel statistical approaches to construct credible counterfactual trade flows. Additionally, the project facilitated their participation in academic conferences and workshops, where they presented their research findings, received feedback from peers, and networked with experts in the field. These experiences contributed to their professional growth and prepared them for future careers in agricultural economics, trade policy analysis, and related fields. How have the results been disseminated to communities of interest?The results have been disseminated to communities of interest through a comprehensive outreach strategy that included publishing key findings in reputable academic journals, presenting at major conferences and symposiums focused on agricultural economics and international trade, and organizing workshops and seminars aimed at industry stakeholders and policymakers. These sessions translated technical findings into actionable insights for informed decision-making in the agricultural sector. Additionally, we made our data and methodologies publicly accessible through an online platform, allowing other researchers and practitioners to explore our datasets and replicate our analyses. This multifaceted dissemination approach ensured that our research reached a wide audience, fostering further research and supporting policy development in trade disputes and U.S. agriculture. What do you plan to do during the next reporting period to accomplish the goals?Since this is the final reporting period, we focused on publishing the remaining academic papers and continued disseminating the results to the targeted audience. We ensured that the insights gained from this research were shared through reputable academic journals (pending revisions in the Canadian Journal of Economics and Food Policy) and presented at key conferences. Additionally, we engaged with industry stakeholders and policymakers through workshops and seminars to discuss the implications of our findings. By doing so, we aimed to maximize the impact of our work, supporting informed decision-making and policy development in the agricultural sector.

Impacts
What was accomplished under these goals? Objective 1: Assemble a Complete Dataset of International Trade Disputes Targeting U.S. Agricultural Exports From 1990 to 2018. We compiled a comprehensive dataset of international trade disputes targeting U.S. agricultural exports from 1990 to 2018. This dataset includes detailed information on various trade measures such as anti-dumping and countervailing duties, safeguard measures, sanitary and phytosanitary measures, and other non-tariff barriers. We sourced data from the World Trade Organization, national trade authorities, and other relevant databases, ensuring that our dataset was both exhaustive and accurate. This effort involved mapping trade measures to specific agricultural products using the Harmonized System (HS) codes, which allowed us to track the impact of these disputes on a granular level. By building this dataset, we laid the foundation for a precise and detailed analysis of the trade disputes' impact on U.S. agricultural exports. Objective 2: Prepare a Time-Consistent Monthly Dataset of U.S. Foreign Trade Carefully Disaggregated by Specific Products at the Customs-District Level. We developed a time-consistent monthly dataset of U.S. foreign trade that is disaggregated by specific products at the customs district level. This dataset includes trade flow information down to the 10-digit HS code level, which provides detailed insights into the export and import patterns of individual agricultural commodities. To ensure consistency over time, we addressed issues related to changes in the classification system by creating synthetic product codes that maintain continuity across different periods. This dataset captures the volume and value of trade flows and allows for an analysis of seasonal and temporal variations in trade activities. This high level of disaggregation and time consistency is crucial for accurately assessing the effects of trade disputes on specific agricultural products and regions. 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. We developed and implemented a novel statistical approach using machine learning techniques to create credible counterfactual trade flows. By employing advanced methods such as support vector machines (SVM) and supervised learning models, we were able to predict what trade flows would have looked like in the absence of trade disputes. This approach involved constructing synthetic control groups from similar, unaffected products to serve as benchmarks for comparison. Our machine learning models were trained on pre-dispute trade data and validated to ensure accurate predictions. This innovative methodology allowed us to measure the impact of trade disputes more precisely, accounting for variables such as product characteristics, dispute timing, and market conditions. The results from these models provided robust evidence of the trade destruction, diversion, and deflection effects caused by trade disputes. 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.? We conducted a thorough evaluation of the impact of trade disputes on U.S. agricultural trade by analyzing the data and applying our machine learning models. Our analysis revealed significant differences in the trade impact based on dispute characteristics, such as the type and severity of the trade measures and the specific agricultural products involved. For example, perishable products like fruits and vegetables showed different patterns of trade destruction and diversion compared to non-perishable goods like grains and oilseeds. We also explored the timing of trade measures, noting that the impact varied depending on whether the measures were implemented during peak trading seasons or off-peak periods. By dissecting these nuances, we provided a comprehensive understanding of how trade disputes affect U.S. agricultural exports, helping stakeholders and policymakers to develop more informed strategies to mitigate these impacts in the future.

Publications

• Type: Other Status: Published Year Published: 2024 Citation: Armen Khederlarianz and Sandro Steinbach (2024). "Pre -Trends and Trade Eects of Temporary Trade Barriers," revision requested in the Canadian Journal of Economics
• Type: Other Status: Published Year Published: 2024 Citation: Colin A. Carter and Sandro Steinbach (2024). "The Impact of Retaliatory Tariffs on Agricultural and Food Trade," NBER Working Paper w27147, revision requested in the Journal of the Agricultural & Applied Economics Association.
• Type: Other Status: Published Year Published: 2024 Citation: Colin A. Carter and Sandro Steinbach (2024). "The Impact of the 2018 Trade War on Foreign Trade of Canada, the United States, and Mexico," under review (submitted on January 12, 2024).
• Type: Other Status: Published Year Published: 2024 Citation: Armen Khederlarianz and Sandro Steinbach (2023). "On the Dynamic Trade Effects of Temporary Trade Barriers," FREIT Working Paper 1781
• Type: Other Status: Other Year Published: 2024 Citation: Colin A. Carter, Sijia Chen and Sandro Steinbach (2024). "Revisiting the Eects of Trade Remedies on U.S. Agricultural Import," in preparation for submission
• Type: Other Status: Other Year Published: 2024 Citation: Armen Khederlarianz and Sandro Steinbach (2024). "The Impact of Trade Disputes on U.S. Agriculture: A Data-Driven Approach for Counterfactual Measurement," in preparation for submission.
• Type: Journal Articles Status: Published Year Published: 2023 Citation: Sandro Steinbach and Xiting Zhuangz (2023). "U.S. Agricultural Exports and the 2022 Mississippi River Drought," Agribusiness, first published online
• Type: Journal Articles Status: Published Year Published: 2023 Citation: Colin A. Carter, Sandro Steinbach and Xiting Zhuangz (2023). "Supply Chain Disruptions and Containerized Agricultural Exports from California Ports"
• Type: Journal Articles Status: Published Year Published: 2023 Citation: Sandro Steinbach and Xiting Zhuangz (2023). "The Impact of Maritime Shipping Disruptions on U.S. Tree Nut Exports and Inventories," Agribusiness, 39 (3): 904{911
• Type: Journal Articles Status: Published Year Published: 2022 Citation: Sandro Steinbach (2022). "Port Congestion, Container Shortages, and U.S. Foreign Trade," Economics Letters, 213: 110392.
• Type: Journal Articles Status: Published Year Published: 2021 Citation: Soojung Ahnz and Sandro Steinbach (2021). "COVID-19 Trade Actions in the Agricultural and Food Sector," Journal of Food Distribution Research, 52(2): 51{75.
• Type: Journal Articles Status: Published Year Published: 2022 Citation: Sandro Steinbach and Xiting Zhuangz (2022). "Shipping Container Disruptions Cause Considerable Export Losses for Southern Ports," Southern Ag Today, 2 (17.4).
• Type: Journal Articles Status: Published Year Published: 2021 Citation: Colin A. Carter, Sandro Steinbach and Xiting Zhuangz (2021). "`Containergeddon' and California Agriculture," Agricultural and Resource Economics Update, 25(2): 1{4.
• Type: Journal Articles Status: Published Year Published: 2020 Citation: Colin A. Carter and Sandro Steinbach (2020). "The Impact of Retaliatory Tariffs on Agricultural and Food Trade," CATO Research Briefs in Economic Policy, No. 243.

Progress 04/15/22 to 04/14/23

Outputs
Target Audience:Presented research results at academic conferences, seminars, and research workshops with significant involvement of policymakers (USDA ERS, NBER, AAEA, ASSA, IATRC). Changes/Problems:PI (Dr. Steinbach) moved institutions (UConn -> NDSU), new PI (Khederlarian) took over. What opportunities for training and professional development has the project provided?One-on-one work to mentor graduate students and postdocs working on this project. Graduate students and postdocs participated in several workshops and conferences. How have the results been disseminated to communities of interest?Results were disseminated through presentations at conferences, workshops, and seminars, and through a dedicated project webpage. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? We achieved the following objectives during the reporting period: Assemble a complete dataset of international trade disputes targeting U.S. agricultural exports from 1990 to 2018. (complete) We prepared a time-consistent monthly dataset of U.S. foreign trade carefully disaggregated by specific products at the customs-district level. (complete) We developed 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. (completed)

Publications

• Type: Journal Articles Status: Published Year Published: 2023 Citation: The Impact of Maritime Shipping Disruptions on U.S. Tree Nut Exports and Inventories, with Xiting Zhuang, Agribusiness, 2023, first published online.
• Type: Journal Articles Status: Published Year Published: 2023 Citation: Agri-Food Trade Resilience among Food-Deficit Countries during the COVID-19 Pandemic, with Soojung Ahn, International Food and Agribusiness Management Review, 2023, first published online.
• Type: Journal Articles Status: Published Year Published: 2023 Citation: The Impact of COVID-19 Trade Measures on Agricultural and Food Trade, with Soojung Ahn, Applied Economic Perspectives and Policy, 2023, 45(2): 911-927.
• Type: Journal Articles Status: Published Year Published: 2023 Citation: Supply Chain Disruptions and Containerized Agricultural Exports from California Ports, with Colin A. Carter and Xiting Zhuang, Applied Economics Perspectives and Policy, 2023, 45(2): 1051-1071.
• Type: Journal Articles Status: Published Year Published: 2023 Citation: A Portrait of Firms that Trade in Meat Products, with Heidi Schweizer and Xiting Zhuang, Western Economics Forum, 2022, 20(2): 618.

Progress 04/15/21 to 04/14/22

Outputs
Target Audience:Presented research results at academic conferences, seminars, and research workshops with significant involvement of policymakers (USDA ERS, NBER, AAEA, ASSA). Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?One-on-one work with mentor for graduate student and postdoc Participation in several workshops and conferences forgraduate student and postdoc How have the results been disseminated to communities of interest?Through presentations at conferences, workshops, and seminars, and through a dedicated project webpage. What do you plan to do during the next reporting period to accomplish the goals?Complete development of ML algorithms and finalized project paper drafts for submission. Make available code and data to research and policy community.

Impacts
What was accomplished under these goals? We achieved the following objectives during the report period: Assemble a complete dataset of international trade disputes targeting U.S. agricultural exports over the period 1990 to 2018. (complete) Prepare a time-consistent monthly dataset of U.S. foreign trade carefully disaggregated by specific product at the customs-district level. (complete) 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. (in progress)

Publications

• Type: Journal Articles Status: Published Year Published: 2022 Citation: Steinbach, S. (2022). Port Congestion, Container Shortages, and U.S. Foreign Trade. Economics Letters, 213: 110392.
• Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: S. Steinbach (2021). Exchange Rate Risk and Global Food Supply Chains, NBER Working Paper, w29164.

Progress 04/15/20 to 04/14/21

Outputs
Target Audience:Presented research results at academic conferences and research workshops involving policymakers (ERS, NBER). Changes/Problems:COVID-19 had a significant impact on this project. We planned to hire a Post Doc for the funding cycle. Due to immigration limitations, we were not able to get the candidate into the U.S. to work on the project. What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest?Conference and academic workshop participations. Cooperation with ERS. What do you plan to do during the next reporting period to accomplish the goals?A Postdoctoral Researcher was hired for the next funding period. We will work on objectives (3) and (4): 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.

Impacts
What was accomplished under these goals? We achieved objectives (1) and (2) 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.

Publications

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).