The migration propensity index: An application to Guatemala [report]

Francisco Ceballos, Manuel A. Hernandez
2020 unpublished
in 1975, provides research-based policy solutions to sustainably reduce poverty and end hunger and malnutrition. IFPRI's strategic research aims to foster a climate-resilient and sustainable food supply; promote healthy diets and nutrition for all; build inclusive and efficient markets, trade systems, and food industries; transform agricultural and rural economies; and strengthen institutions and governance. Gender is integrated in all the Institute's work. Partnerships, communications,
more » ... unications, capacity strengthening, and data and knowledge management are essential components to translate IFPRI's research from action to impact. The Institute's regional and country programs play a critical role in responding to demand for food policy research and in delivering holistic support for country-led development. IFPRI collaborates with partners around the world. Abstract International migration has grown rapidly over the past two decades, at an annual rate of 2.4%, prompting increased interest in identifying the root causes of outmigration and the population groups more likely to emigrate. However, anticipating migration is a complex task, as the decision to migrate is often determined by multiple push and pull factors that are typically interrelated and are not always directly observable. This study proposes the Migration Propensity Index (MPI), a novel approach to indirectly estimate a household's propensity or probability to emigrate. The central idea is to identify and keep track of a reduced set of household-level indicators that are strongly correlated with the (latent) decision of individuals to emigrate. Taken together and converted into an index, the combined indicators reflect the objective likelihood that one or more individuals from a given household will emigrate. The MPI is concise, easy to implement, and statistically rigorous, and avoids asking direct, sensitive questions about migration attempts or intentions, which are prone to refusals and underreporting. We calibrate the index to data for Guatemala, relying on an out-of-sample cross validation procedure using a panel dataset of 2,798 households living in what are considered "vulnerable" municipalities. The data were collected in 2012, 2013, and 2014. We discuss the index design and implementation, including concrete examples of its application. The resulting model includes 12 simple variables (and two location shifters) and correctly identifies 93% of eventual emigrating and non-emigrating households. The MPI can serve policymakers in getting better insights in drivers of migration, monitor present and expected migratory flows, and for targeting of economic and social policies.
doi:10.2499/p15738coll2.133849 fatcat:kofbuwbkhngqjovbx7frfoha5q