Dietrich, StephanMeysonnat, AlineRosales, FranciscoCebotari, VictorGassmann, Franziska2022-10-152022-10-152022-09-01Dietrich, 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.0271373https://hdl.handle.net/20.500.12640/3147Globally, 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.application/pdfenginfo:eu-repo/semantics/openAccessAttribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/FloodingHuman familiesChild healthChild marriageInundacionesMachine learningFamilias humanasIndiaSalud infantilMatrimonio infantilBangladeshAprendizaje automáticoIndiaAsiaBangladeshNepalAsiaNepalPakistanPakistáninfo:eu-repo/semantics/articlehttps://doi.org/10.1371/journal.pone.0271373https://purl.org/pe-repo/ocde/ford#3.03.12