Template-type: ReDIF-Paper 1.0
Author-Name: Dietrich, Stephan
Author-Email: dietrich@merit.unu.edu
Author-Workplace-Name: UNU-MERIT, Maastricht University
Author-Name: Meysonnat, Aline
Author-Email: amey@uw.edu
Author-Workplace-Name: University of Washington, Daniel J. Evans School of Public Policy
	 and Governance
Author-Name: Rosales, Francisco
Author-Email: frosales@esan.edu.pe
Author-Workplace-Name: ESAN Graduate School of Business, Lima
Author-Name: Cebotari, Victor
Author-Email: victor.cebotari@uni.lu
Author-Workplace-Name: University of Luxembourg
Author-Name: Gassmann, Franziska
Author-Email: gassmann@merit.unu.edu
Author-Workplace-Name: UNU-MERIT, Maastricht University
Title: Economic development, weather shocks and child marriage in South Asia: A machine 
	 learning approach
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.
Classification-JEL: J1, J12, O15, Q54, R11
Keywords: child marriage, income shocks, machine learning, South Asia
Series: UNU-MERIT Working Papers
Creation-Date: 20210910
Number: 2021-034
File-URL: https://unu-merit.nl/publications/wppdf/2021/wp2021-034.pdf
File-Format: application/pdf
File-Size: 275 Kb
Handle: RePEc:unm:unumer:2021034