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Cycle 8 (2019 Deadline)

Voluntary geographic redistribution of Venezuelan immigrants in Colombia

PI: Gina Galindo-Pacheco (, Universidad del Norte, Barranquilla, Colombia
U.S. Partner: Jens Hainmueller, Stanford University
Project Dates: November 2019 - February 2022

Project Overview:
 8-214 Immigrant map
 Venezualan immigrant distribution. Source:Proyecto Migración Colombia, Boletín 01, December 2018. Courtesy of Dr. Galindo-Pacheco
The main goal of this project is to develop a tool for identifying a potential resettlement location to be recommended to immigrants coming from Venezuela to Colombia. The recommended location should optimize on the economic integration and well-being outcomes for the immigrants. The proposed methodology is based on a data-driven algorithm developed by the Immigration Policy Lab (IPL) at Stanford University, where U.S. partner Dr. Jens Hainmueller is based. The algorithm looks into the historic success of economic integration (measured as the likelihood of finding a job) of immigrants at each location and relates such success with key characteristics of the immigrants, (e.g., English skills, age). Then, it predicts the probability of economic integration outcome for each immigrant in each location in the host country. Finally, the algorithm matches locations with family cases in order to maximize the total economic integration outcome for the immigrant families, while respecting constraints such as the number of available spots at each location. This is expected to contribute to balance the distribution of immigrants among locations in Colombia. The PI Dr. Galindo and her team will adapt the algorithm to the Colombian-Venezuelan context, taking into account their interests not only in economic integration but also in the well-being of immigrants, which implies considering the chances of fulfilling their basic needs, such as medical and educational services, among others. The ILP will provide support through all of the stages of the project to help the Colombian team verify and improve their approaches based on the knowledge and experience their U.S. partner has gained through the application his methodology in the United States and Switzerland. The project is particularly innovative in its attempt to develop a data-driven matching system for integration of immigrants coming from and arriving in a developing country. This is particularly relevant, since it is estimated that nearly half of all immigrants migrate among developing countries, mostly, to neighboring countries. Additionally, even though the problem of resettlement of immigrants has been addressed by several researchers in the past, research has focused solely on refugees, whereas this project would focus on Venezuelan immigrants in general.

The Organization of American States has estimated that by the end of 2019 the number of Venezuelan migrants will exceed 5 million, with more than 1 million of them arriving in Colombia, their leading destination. This project seeks to create a predictive tool to guide Venezuelan immigrants to the most appropriate locations in Colombia for voluntary resettlement with higher chances of economic integration and well-being. The tool also considers capacities and socioeconomic attributes of localities for better matching of immigrants and localities. The importance of informing settlement choices and guiding the strategic distribution of immigrants has been highlighted by “Proyecto Migración Venezuela” (PMV), which is sponsored by Revista Semana and USAID, and international organizations. This type of initiative has also attracted the interest of the Colombian government through the Border Management Office (BMO), which operates under the office of the Colombian president. This project is aligned with USAID’s mission of helping people to progress beyond assistance by identifying the locations and attributes that will enable better economic and social development of immigrant families. Furthermore, the project focuses on marginalized populations that are vulnerable and require immediate assistance to settle in a new country. The identification of key attributes or drivers that lead to immigrants successful integration should be helpful in guiding authorities in the development of specific training and social assistance programs.

Project Summary Updates:
As of July-September 2021 reporting period, the project team completed their technical activities of the project. In July 2021 the team submitted an article to the journal PLOS One and continued to work on another article to share the results collected from the focus groups that the team developed during the project. In addition, one of the research assistants has been working on his Master's thesis, which is an extension of the project. This thesis will upgrade the algorithm used in the project, which uses concept drift in Machine Learning to identify changes in trends of the data that is used by the algorithm. During the reporting period the team met with staff from Gerencia de Frontera (the Colombian border control agency), the Colombian Ministry of Labor, and the United Nations High Commission for Human Rights. The team also met with private stakeholders who are interested in implementing their algorithm.
8-214_PI Gina Maria Galindo Pacheco with Prof. Daniel Romero and students 8-214_PI Gina Maria Galindo Pacheco site visit with NAS
PI Dr. Gina M. Galindo Pacheco, co-PI Daniel H. Romero Rodriguez,  and students involved in the project
NAS and USAID PEER team from Washington DC visiting Dr. Pacheco's PEER project in March 2020.

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