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

Stephan Dietrich (), Aline Meysonnat (), Francisco Rosales (), Victor Cebotari () and Franziska Gassmann ()
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Stephan Dietrich: UNU-MERIT, Maastricht University
Aline Meysonnat: University of Washington, Daniel J. Evans School of Public Policy and Governance
Francisco Rosales: ESAN Graduate School of Business, Lima
Victor Cebotari: University of Luxembourg

No 2021-034, MERIT Working Papers from United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT)

Abstract: Globally, 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 78% of the true marriage cases, with a higher accuracy in Bangladesh (90% of the cases) and a lower accuracy in Nepal (71% 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, the 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.

Keywords: child marriage; income shocks; machine learning; South Asia (search for similar items in EconPapers)
JEL-codes: J1 J12 O15 Q54 R11 (search for similar items in EconPapers)
Date: 2021-09-10
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-dev and nep-his
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