Immigrants resettlement in developing countries: A data-driven decision tool applied to the case of Venezuelan immigrants in Colombia.

Abstract

Immigrants' choice of settlement in a new country can play a fundamental role in their socio-economic integration. This is especially relevant if there are important gaps among these locations in terms of significant factors such as job opportunities, quality of health service, among others. This research presents a methodology to perform a recommended geographic redistribution of immigrants to improve their chances of socio-economic integration. The proposed methodology adapts a data-driven algorithm developed by the Immigration Policy Lab at Stanford University to allocate immigrants based on a socio-economic integration outcome across available locations. We extend their approach to study the immigration process between two developing countries. Specifically, we focus on the case of the arrival of immigrants from Venezuela to Colombia. We consider the absorptive capacity of locations in Colombia and include the health and education needs of immigrants in our analysis. From the application in the Venezuelan-Colombian context, we find that the proposed redistribution increases the probability that immigrants access formal employment by more than 50%. Furthermore, we identify variables associated with immigrants' formal employment and discuss specific strategies to improve the probability of success of vulnerable immigrants.