Replication files for GVCs and the Endogenous Geography of RTAs

We compare the observed RTAs to the probability threshold optimizing the joint prediction of signed and unsigned agreements, and derive a counterfactual RTA geography by signing predicted but unsigned agreements. To assess the trade and welfare consequences of signing unsigned agreements in General Equilibrium, we follow Anderson, Larch & Yotov (2018) and use a General Equilibrium Poisson Pseudo Maximum Likelihood (GEPPML) method to solve the system of equations associated with the model. We proceed by estimating a probability model using data over the 1990-2014 period; the fiveyear lag for covariates implies that we will consider agreements that were signed between country pairs over the 1995-2014 period, adding to the usual controls for the indirect involvement of the country pair in the fragmentation of value chains. We evaluate model goodness-of-fit by considering the probability cut-off that provides the best percentage of correctly-predicted events (a countrypair being, or not, members at a given date of an RTA: RTA=1 and RTA=0).: the optimal cut-off probability maximizes the percentages of true positives and true negatives. We focus on the mis-classified RTAs that are classified as FP from the probabilistic model are be used in a structural gravity system to evaluate the welfare changes associated with alternative sets of trade agreements

Identifier
DOI https://doi.org/10.17632/9y8zn8dvyp.1
PID https://nbn-resolving.org/urn:nbn:nl:ui:13-o3-ruaz
Metadata Access https://easy.dans.knaw.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:easy.dans.knaw.nl:easy-dataset:195720
Provenance
Creator Fontagne, L (via Mendeley Data)
Publisher Data Archiving and Networked Services (DANS)
Contributor Lionel Fontagne
Publication Year 2020
Rights info:eu-repo/semantics/openAccess; License: http://creativecommons.org/licenses/by/4.0
OpenAccess true
Representation
Resource Type Dataset
Discipline Various