Working Paper: CEPR ID: DP17325
Authors: Holger Breinlich; Valentina Corradi; Nadia Rocha; Michele Ruta; Thomas Zylkin; JMC Santos Silva
Abstract: 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. In this paper, we build 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 impacton 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. We find 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.
Keywords: Lasso; Machine Learning; Preferential Trade Agreements; Deep Trade Agreements
JEL Codes: F14; F15; F17
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| technical barriers to trade (F13) | trade flows (F10) |
| antidumping measures (F18) | trade flows (F10) |
| trade facilitation (F13) | trade flows (F10) |
| subsidies (H20) | trade flows (F10) |
| competition policy (L49) | trade flows (F10) |