Why Do People Move?
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World Bank, Washington, DC
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Work-related migration has many
potential drivers. While current literature has outlined a
theoretical framework of various “push-pull” factors
affecting the likelihood of international migration,
empirical papers are often constrained by the scarcity of
detailed data on migration, especially in developing
countries, and are forced to look at few of these factors in
isolation. When detailed data is available, researchers may
face arbitrary choices of which variables to include and how
to sequence their inclusion. As male and female migrants
tend to face occupational segregation, the determinants of
migration likely differ by gender, which compounds these
data challenges. To overcome these three issues, this paper
uses a rich primary household survey among migrant
communities in Indonesia and employs two supervised
machine-learning methods to identify the top predictors of
migration by gender: random forests and least absolute
shrinkage and selection operator stability selection. The
paper confirms some determinants established by earlier
studies and reveals several additional ones, as well as
identifies differences in predictors by gender.
Palabras clave
MIGRATION AND GENDER, MACHINE LEARNING, WORK RELATED MIGRATION, INTERNATIONAL LABOR MIGRATION, MIGRANT HOUSEHOLD SURVEY DATA, MIGRATION DATA BY GENDER