Performing Department
Agriculture & Resource Econ
Non Technical 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.
Animal Health Component
0%
Research Effort Categories
Basic
80%
Applied
20%
Developmental
(N/A)
Goals / Objectives
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.
Project Methods
We will achieve the research objectivesby deploying modern statistical modeling techniques to thoroughly investigate the factors that influence the formation of PTAs with agricultural and food provisions. The proposed analysis builds on newly collected PTA data, which we will analyze with the help of innovative NMT and NLP systems. This analysis provides the foundation to thoroughly assess the factors that determine the formation of PTAs with agricultural and food provisions. We will adopt modern ML techniques that behave well in high-dimensional settings allowing us to relax critical assumptions on data required by conventional regression methods. The data-driven RF regression 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 an innovative adaptation of Prior Lasso to the Poisson PML estimator, which will allow us to incorporate prior information, reduce over-fitting, and facilitate feature selection. We will instrumentalize county-level PTA impact estimates to assess their effect on the structure and conduct of the agricultural and food industry, relying on establishment-level data on U.S. business activities. Our research project will enhance the understanding of a highly relevant foreign trade issue that has been largely neglected in the empirical literature while being of vital importance for the future of U.S. agriculture. Because trade policies can affect foreign trade significantly, they play a critical role in the functioning of agricultural and food supply chains. The research will shed light on a crucial driver of structural change in the agricultural and food sector. Such knowledge is essential because it 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. The research will provide essential knowledge on the functioning of markets in light of trade policy changes and enhance market efficiency and performance.We will disseminate the research findings to the public and academic community. This research is highly relevant to the public because it will generate new knowledge regarding the formation of PTAs with agricultural and food provisions, their impact on trade flows, and the domestic consequences of trade liberalization. Furthermore, this research will provide highly relevant insights for the academic community by developing modern ML techniques paired with solid economic modeling and high-frequency trade data for policy impact evaluation. The project will have a dedicated webpage that presents the research findings and conclusions relevant to policymakers. We will work closely with the USDA to publish relevant reports for policymakers and share our research results widely in the executive and legislative branches. It is essential to improve our understanding of the relationship between trade policies and global trade flows. Such insights 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. Because this question is of national scope, we will explore ways to disseminate our research findings through the media actively.We also plan on interacting actively with the academic community. In addition to presentations at academic conferences and industry workshops, we plan on publishing our research in well-recognized academic journals.We will make all non-proprietary data and research methods available to the academic community by providing web access to our datasets and codes via the dedicated project webpage. The dataset on PTAs with agricultural and food provisions, the methods for processing and analyzing this data, and the newly developed ML algorithms, have the potential to be particularly useful for future research activities.