Machine Learning in International Trade Research
Modern trade agreements contain a large number of provisions besides tariff reductions, in areas as diverse as services trade, competition policy, trade-related investment measures, or public procurement. Existing research has struggled with overfitting and severe multicollinearity problems when trying to estimate the effects of these provisions on trade flows. This paper builds on recent developments in the machine learning and variable selection literature to propose novel data-driven methods for selecting the most important provisions and quantifying their impact on trade flows. The proposed methods have the advantage of not requiring ad hoc assumptions on how to aggregate individual provisions and offer improved selection accuracy over the standard lasso. The analysis finds that provisions related to technical barriers to trade, antidumping, trade facilitation, subsidies, and competition policy are associated with enhancing the trade-increasing effect of trade agreements.
| Main Authors: | , , , , , |
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| Format: | Working Paper biblioteca |
| Language: | English |
| Published: |
World Bank, Washington, DC
2021-04
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| Subjects: | TRADE POLICY, TRADE AGREEMENTS, PREFERENTIAL TRADE AGREEMENTS, DEEP TRADE AGREEMENT, MACHINE LEARNING, LASSO, |
| Online Access: | http://documents.worldbank.org/curated/en/730781618338899906/Machine-Learning-in-International-Trade-Research-Evaluating-the-Impact-of-Trade-Agreements https://hdl.handle.net/10986/35451 |
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