Publication:
Why Do People Move?: A Data-Driven Approach to Identifying and Predicting Gender-Specific Aspirations to Migrate

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dc.contributor.authorHalim, Daniel
dc.contributor.authorSeetahul, Suneha
dc.date.accessioned2023-04-10T19:18:09Z
dc.date.available2023-04-10T19:18:09Z
dc.date.issued2023-04-10
dc.description.abstractWork-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.en
dc.identifierhttp://documents.worldbank.org/curated/en/099554304062330779/IDU05ba8d1e90ec2a046e50bdf10fbe727c45e00
dc.identifier.doi10.1596/1813-9450-10396
dc.identifier.urihttps://openknowledge.worldbank.org/handle/10986/39657
dc.languageEnglish
dc.language.isoen
dc.publisherWorld Bank, Washington, DC
dc.relation.ispartofseriesPolicy Research Working Papers; 10396
dc.rightsCC BY 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/igo/
dc.subjectMIGRATION AND GENDER
dc.subjectMACHINE LEARNING
dc.subjectWORK RELATED MIGRATION
dc.subjectINTERNATIONAL LABOR MIGRATION
dc.subjectMIGRANT HOUSEHOLD SURVEY DATA
dc.subjectMIGRATION DATA BY GENDER
dc.titleWhy Do People Move?en
dc.title.subtitleA Data-Driven Approach to Identifying and Predicting Gender-Specific Aspirations to Migrateen
dc.typeWorking Paper
dspace.entity.typePublication
okr.crossref.titleWhy Do People Move?: A Data-Driven Approach to Identifying and Predicting Gender-Specific Aspirations to Migrate
okr.date.disclosure2023-04-06
okr.date.lastmodified2023-04-06T00:00:00Zen
okr.doctypePolicy Research Working Paper
okr.doctypePublications & Research
okr.docurlhttp://documents.worldbank.org/curated/en/099554304062330779/IDU05ba8d1e90ec2a046e50bdf10fbe727c45e00
okr.guid099554304062330779
okr.identifier.docmidIDU-5ba8d1e9-ec2a-46e5-bdf1-fbe727c45e00
okr.identifier.doi10.1596/1813-9450-10396
okr.identifier.externaldocumentum34036942
okr.identifier.internaldocumentum34036942
okr.identifier.reportWPS10396
okr.import.id385
okr.importedtrueen
okr.language.supporteden
okr.pdfurlhttp://documents.worldbank.org/curated/en/099554304062330779/pdf/IDU05ba8d1e90ec2a046e50bdf10fbe727c45e00.pdfen
okr.region.administrativeEast Asia and Pacific
okr.region.countryIndonesia
okr.sectorOther Non-bank Financial Institutions,Social Protection,Other Industry, Trade and Services,Other Transportation,Other Agriculture, Fishing and Forestry
okr.themeGender
okr.themeSocial dev/gender/inclusion
okr.topicCommunities and Human Settlements::Human Migrations & Resettlements
okr.topicPoverty Reduction::Migration and Development
okr.topicInternational Economics and Trade::International Migration
okr.topicGender::Gender and Social Policy
okr.topicSocial Development::Social Inclusion & Institutions
okr.unitEAP Chief Economist Unit (EAPCE)
okr.unitGender Director (HGNDR)
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