This project will developnew insights regarding the formation of PTAs, their impact on global trade flows, and theconsequences for U.S. agricultural and food businesses. We rely on modern statistical modeling techniques to thoroughly investigate the factors that influence the formation of PTAs with agricultural and food provisions. This analysis builds on newly collected PTA data, which are structured and analyzed with the help of Neural Machine Translation (NMT) and Natural Language Processing (NLP) systems. To estimate factors that influence the formation of PTAs with agricultural and food provisions, we will adopt the Random Forest (RF) algorithm. This state-of-the-art supervised machine learning technique behaves well in high-dimensional settings allowing us to relax critical assumptions on data required by conventional regression methods. The 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 this 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 the adaptation of the Prior least absolute shrinkage and selection operator (Prior Lasso) to the Poisson pseudo-maximum likelihood (Poisson PML) estimator. This machine learning (ML) approach enables us to incorporate prior information, reduce over-fitting, and facilitate feature selection. We will instrumentalize PTA policy changes to assess their impact on the structure and conduct of the agricultural and food industry relying on newly collected data on U.S. business activities. A better understanding of these trade policy consequences will shed light on a critical driver of structural change in this vital sector of the economy. The research will help to inform federal policies that attempt to foster the competitiveness of U.S. farmers and ranchers and increase their participation and success in global agricultural and food markets. Consequently, this project will provide essential knowledge on the functioning of markets in light of trade policy changes and, thereby, enhance market efficiency and performance.The proposed research will accomplish the following research 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.
EVALUATING THE IMPACT OF PREFERENTIAL TRADE AGREEMENTS ON AGRICULTURAL AND FOOD TRADE: NEW INSIGHTS FROM NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING
University of Connecticut