The welfare effects of trade policy experiments in quantitative trade models

Abstract

This paper compares the solution methods and baseline calibration of three different quantitative trade models (QTMs): computable general equilibrium (CGE) models, structural gravity (SG) models and models employing exact hat algebra (EHA). The different solution methods generate identical results on counterfactual experiments if baseline trade shares or baseline trade costs are identical. SG models, calibrating the baseline to gravity-predicted shares, potentially suffer from bias in the predicted welfare effects as a result of misspecification of the gravity equation, whereas the other methods, calibrating to actual shares, potentially suffer from bias as a result of random variation and measurement error of trade flows. Simulations show that predicted shares calibration can generate large biases in predicted welfare effects if the gravity equation does not contain pairwise fixed effects or is estimated without domestic trade flows. Calibration to actual shares and to fitted shares based on gravity estimation including pairwise fixed effects display similar performance in terms of robustness to the different sources of bias.

Estimating the Effect of AfCFTA on Intra-African Trade using Augmented GE-PPML

“…As predicted by Bekkers (2019), our results are situated within the interval of predictions in past CGE studies, especially those that simulate a reduction in tariff measures. These studies estimate the change in intra-African trade following AfCFTA to be between 4.3% and 32.8%, with a range varying between 24% and 25.3%.…”

“…Simultaneously, it quantifies the effect of policy intervention, an absent feature in ex-post econometric studies. However, as a tool for policy experiments, GEPPML yields similar results to CGEs and Exact-hat Algebra when specified correctly (Bekkers, 2019).…”

“…Foster et al (2011) used average trade flows 3 years prior and after the signing of a preferential free trade agreement. However, this approach can potentially bias the estimates (Bekkers, 2019).…”