UNU-MERIT and Maastricht University, Maastricht, Netherlands.
University of Washington, Daniel J. Evans School of Public Policy and Governance, Seattle, Washington, United States of America.
PLoS One. 2022 Sep 1;17(9):e0271373. doi: 10.1371/journal.pone.0271373. eCollection 2022.
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 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.
全球范围内，有 21%的年轻女性在 18 岁生日前结婚。尽管在解决童婚问题方面取得了一些进展，但童婚仍然是一种普遍存在的做法，特别是在南亚地区。虽然有关家庭因素预测童婚的文献已经有很多，但关于宏观经济因素导致童婚的证据基础以及预测童婚案例最可能发生在哪里的模型仍然有限。本文旨在填补这一空白，并探讨区域指标，以预测南亚四个国家（孟加拉国、印度、尼泊尔和巴基斯坦）童婚的持续情况。我们应用机器学习技术对童婚数据进行分析，并开发了一个预测模型，该模型主要依赖于区域和本地输入，如干旱、洪水、人口增长和夜光数据，以模拟童婚的发生率。我们发现，我们的梯度提升模型能够识别出很大一部分真实的童婚案例，并正确分类了 77%的真实婚姻案例，在孟加拉国（92%的案例）的准确率较高，在尼泊尔（70%的案例）的准确率较低。此外，所有国家的前 10 个分类变量都包含夜间灯光增长、过去和前两年干旱冲击指数以及区域教育水平，这表明收入冲击、区域经济活动和区域教育水平在预测童婚方面发挥了重要作用。鉴于该模型预测童婚的准确性，我们的模型是支持数据有限的国家制定政策的有用工具。
