Leveraging unsupervised machine learning to examine women's vulnerability to climate change
German Caruso,
Valerie Mueller,
Alexis Villacis
Abstract:We provide an application of machine learning to identify the distributional consequences of climate change in Malawi. We compare climate impact estimates based on drought indicators established objectively from the k‐means algorithm to more traditional measures. Young women affected by drought were 5 percentage points more likely to be married by 18 than those living in nondrought areas. Our approach generates robust results when varying the number of clusters and definition of treatment status. In some cases… Show more
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