Using Machine Learning to Capture Heterogeneity in Trade Agreements
| Autor: | Scott L. Baier, Narendra Regmi |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Economics and Econometrics
Transparency (market) business.industry Trade creation ComputingMilieux_LEGALASPECTSOFCOMPUTING Technical barriers to trade Investment (macroeconomics) Machine learning computer.software_genre Competition (economics) Procurement Gravity model of trade Business Artificial intelligence Free trade computer |
| Zdroj: | SSRN Electronic Journal. |
| ISSN: | 1556-5068 |
| DOI: | 10.2139/ssrn.3812599 |
| Popis: | In this paper, we employ machine learning techniques to capture heterogeneity in free trade agreements. The tools of machine learning allow us to quantify several features of trade agreements, including volume, comprehensiveness, and legal enforceability. Combining machine learning results with gravity analysis of trade, we find that more comprehensive agreements result in larger estimates of the impact of trade agreements. In addition, we identify the policy provisions that have the most substantial effect in creating trade flows. In particular, legally binding provisions on antidumping, capital mobility, competition, customs harmonization, dispute settlement mechanism, e-commerce, environment, export and import restrictions, freedom of transit, investment, investor-state dispute settlement, labor, public procurement, sanitary and phytosanitary measures, services, technical barriers to trade, telecommunications, and transparency tend to have the largest trade creation effects. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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