Economic development, weather shocks and child marriage in South Asia: a machine learning approach

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dc.contributor.authorDietrich, Stephan
dc.contributor.authorMeysonnat, Aline
dc.contributor.authorRosales, Francisco
dc.contributor.authorCebotari, Victor
dc.contributor.authorGassmann, Franziska
dc.date.accessioned2022-10-15T12:50:14Z
dc.date.available2022-10-15T12:50:14Z
dc.date.issued2022-09-01
dc.description.abstractGlobally, 21 percent of young women are married before their 18th birthday. Despite some progress in addressing child marriage, it remains a widespread practice, in particular in South Asia. While household predictors of child marriage have been studied extensively in the literature, the evidence base on macro-economic factors contributing to child marriage and models that predict where child marriage cases are most likely to occur remains limited. In this paper we aim to fill this gap and explore region-level indicators to predict the persistence of child marriage in four countries in South Asia, namely Bangladesh, India, Nepal and Pakistan. We apply machine learning techniques to child marriage data and develop a prediction model that relies largely on regional and local inputs such as droughts, floods, population growth and nightlight data to model the incidence of child marriages. We find that our gradient boosting model is able to identify a large proportion of the true child marriage cases and correctly classifies 77% of the true marriage cases, with a higher accuracy in Bangladesh (92% of the cases) and a lower accuracy in Nepal (70% of cases). In addition, all countries contain in their top 10 variables for classification nighttime light growth, a shock index of drought over the previous and the last two years and the regional level of education, suggesting that income shocks, regional economic activity and regional education levels play a significant role in predicting child marriage. Given the accuracy of the model to predict child marriage, our model is a valuable tool to support policy design in countries where household-level data remains limited.en_EN
dc.formatapplication/pdf
dc.identifier.citationDietrich, S., Meysonnat, A., Rosales, F., Cebotari, V., & Gassmann, F. (2022). Economic development, weather shocks and child marriage in South Asia: a machine learning approach. PLoS ONE 17(9), e0271373. https://doi.org/10.1371/journal.pone.0271373
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0271373
dc.identifier.urihttps://hdl.handle.net/20.500.12640/3147
dc.language.isoeng
dc.publisherPublic Library of Science (PLoS)
dc.publisher.countryUS
dc.relation.ispartofurn:issn:1932-6203
dc.relation.urihttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0271373
dc.rightsAttribution 4.0 International*
dc.rightsinfo:eu-repo/semantics/openAccess*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectFloodingen_EN
dc.subjectHuman familiesen_EN
dc.subjectChild healthen_EN
dc.subjectChild marriageen_EN
dc.subjectInundacioneses_ES
dc.subjectMachine learningen_EN
dc.subjectFamilias humanases_ES
dc.subjectIndiaen_EN
dc.subjectSalud infantiles_ES
dc.subjectMatrimonio infantiles_ES
dc.subjectBangladeshen_EN
dc.subjectAprendizaje automáticoes_ES
dc.subjectIndiaes_ES
dc.subjectAsiaen_EN
dc.subjectBangladeshes_ES
dc.subjectNepalen_EN
dc.subjectAsiaes_ES
dc.subjectNepales_ES
dc.subjectPakistanen_EN
dc.subjectPakistánes_ES
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#3.03.12
dc.titleEconomic development, weather shocks and child marriage in South Asia: a machine learning approachen_EN
dc.typeinfo:eu-repo/semantics/article
dc.type.otherArtículo
dc.type.versioninfo:eu-repo/semantics/publishedVersion
local.author.orcidhttps://orcid.org/0000-0003-2347-632X
oaire.citation.issue9
oaire.citation.startPagee0271373
oaire.citation.titlePLoS ONE
oaire.citation.volume17
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