气候变化领域集成服务门户(CCPortal): Leveraging unsupervised machine learning to examine Women's vulnerability to climate change

CCPortal

DOI	10.1002/aepp.13444
	Leveraging unsupervised machine learning to examine Women's vulnerability to climate change
	Caruso, German; Mueller, Valerie; Villacis, Alexis
发表日期	2024
ISSN	2040-5790
EISSN	2040-5804
英文摘要	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, we find the design using k-means to define treatment is more likely to satisfy the assumptions underlying the difference-in-differences strategy than when using arbitrary thresholds. Projections from the estimates indicate future drought risk may lead to larger declines in labor productivity due to women's engagement in early age marriage than other factors affecting their participation rates. Under the extreme representative concentration pathway scenario, drought exposure encourages the exit of 3.3 million women workers by 2100.
英文关键词	drought; labor participation; machine learning; Malawi; marriage
语种	英语
WOS研究方向	Agriculture ; Business & Economics
WOS类目	Agricultural Economics & Policy ; Economics
WOS记录号	WOS:001236549400001
来源期刊	APPLIED ECONOMIC PERSPECTIVES AND POLICY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/287028
作者单位	The World Bank; Arizona State University; Arizona State University-Tempe; CGIAR; International Food Policy Research Institute (IFPRI); University System of Ohio; Ohio State University
推荐引用方式 GB/T 7714	Caruso, German,Mueller, Valerie,Villacis, Alexis. Leveraging unsupervised machine learning to examine Women's vulnerability to climate change[J],2024.
APA	Caruso, German,Mueller, Valerie,&Villacis, Alexis.(2024).Leveraging unsupervised machine learning to examine Women's vulnerability to climate change.APPLIED ECONOMIC PERSPECTIVES AND POLICY.
MLA	Caruso, German,et al."Leveraging unsupervised machine learning to examine Women's vulnerability to climate change".APPLIED ECONOMIC PERSPECTIVES AND POLICY (2024).