| Summary: | 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
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