Evaluating the Impact of Preferential Trade Agreements on Agricultural and Food Trade: New Insights from Natural Language Processing and Machine Learning

Project Summary

This research will generate new knowledge regarding the formation of preferential trade agreements (PTAs), their impact on global trade, and the consequences for U.S. agricultural and food businesses and employment. To accomplish this goal, we will rely on modern statistical modeling techniques to thoroughly investigate the factors that influence the formation of PTAs. This analysis builds on newly collected PTA data captured with the help of Neural Machine Translation and Natural Language Processing systems. To determine factors that influence the formation of PTAs, we will adopt the Random Forest algorithm. This statistical analysis will provide new insights regarding the role of economic, social, and political factors in forming PTAs with agricultural and food provisions. We will use the newly created dataset to investigate the impact of PTA provisions on agricultural and food trade in the sectoral three-way gravity model context relying on an adaptation of the Prior least absolute shrinkage and selection operator to the Poisson pseudo-maximum likelihood estimator. This innovative machine-learning approach will enable us to incorporate prior information, reduce over-fitting, and facilitate feature selection in a high-dimensional context. We will also assess the impact of PTA provisions on the structure and conduct of the U.S. agricultural and food sector and evaluate employment effects. A better understanding of these trade policy consequences will shed light on a critical driver of structural change. Such knowledge is essential for the functioning of global supply chains. The project will help to inform federal policies that aim to foster the competitiveness of U.S. farmers and ranchers and increase their participation and success in international markets.

Project Objectives

The research will accomplish the following objectives:

Objective 1: Structure and classify agricultural and food provisions in PTA treaties.

Objective 2: Measure the economic, social, and political determinants of PTAs with agricultural and food provisions.

Objective 3: Assess the trade creation and diversion effects of PTA provisions on agricultural and food trade.

Objective 4: Evaluate the impact of PTA provisions on U.S. agricultural and food business activities and labor markets.

Funding Agency

National Institute of Food and Agriculture, Grant No. 2022-67023-36399 [Learn More]

The grant was awarded to Dr. Steinbach.  After his departure to North Dakota State University, Dr. Ding took over as the institutional PI at the University of Connecticut.

Work in Progress

• Preferential Trading in Agriculture: New Insights from a Structural Gravity Analysis and Machine Learning, with Dongin Kim, under review. [Paper]

• What Matters for Agricultural Trade? Assessing the Role of Trade Deal Provisions using Machine Learning, with Stepan Gordeev, Jeremy Jelliffe and Dongin Kim, under review. [Paper]

• Determinants of PTA Design: Insights from Machine Learning, with Stepan Gordeev, under review. [Paper]

Research Team