Social Stigma: A Systematic Review of Cognitive Insights from Behavioral Economics (1940-2019).

There are minority and stigmatized groups who face particular challenges to their full participation in society. This study's objective was to conduct a systematic review to determine theoretical and methodological underpinnings in behavioral economics that explain how stigmatization emerges within the relationship processes and social structures of individuals. Data from 1940 to 2019 were sourced from 12 relevant electronic databases such as Scopus and Web of Science. Following PRISMA guidelines for systematic reviews, 26 studies out of 3459 met the inclusion criteria. Most of the studies applied experimental economics and were published between 2002 and 2018. Overall, the articles focus their research on the experiences of discrimination based on stereotypes and test their hypotheses through economic games. The data synthesis seems to reveal weak conceptual clarity, circular reasoning, and a hint of the problem of infinite regress. Thus, these issues open new and exciting avenues for future research to explore via an array of experimental applications.

Existen grupos minoritarios y estigmatizados que enfrentan retos particulares para lograr su completa participación en sociedad. El objetivo de este estudio fue conducir una revisión sistemática para determinar los cimientos teóricos y metodológicos en economía del comportamiento que explican cómo surge la estigmatización dentro de los procesos de relacionamiento y estructuras sociales de los individuos. Los datos abarcan el período de 1940 a 2019 y se obtuvieron de 12 bases de datos electrónicas relevantes, tales como Scopus y Web of Science. Siguiendo la guía de PRISMA para revisiones sistemáticas, 26 estudios de 3459 cumplieron con los criterios de inclusión. La mayoría de los estudios aplicó economía experimental y fueron publicados entre 2002 y 2018. En general, los artículos enfocan su investigación en experiencias de discriminación basadas en estereotipos y prueban sus hipótesis a través de juegos económicos. La síntesis de los datos parece revelar una débil claridad conceptual, razonamiento circular y un atisbo del problema de regresión infinita. Lo anterior abre nuevas y emocionantes vías de investigación futura para explorar a través de una variedad de aplicaciones experimentales.

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

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.