Leveraging unsupervised machine learning to examine women's vulnerability to climate change
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...
| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Wiley
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/145032 |
| _version_ | 1855515672869601280 |
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| author | Caruso, German Mueller, Valerie Villacis, Alexis |
| author_browse | Caruso, German Mueller, Valerie Villacis, Alexis |
| author_facet | Caruso, German Mueller, Valerie Villacis, Alexis |
| author_sort | Caruso, German |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace145032 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | CGSpace1450322025-04-08T18:36:18Z Leveraging unsupervised machine learning to examine women's vulnerability to climate change Caruso, German Mueller, Valerie Villacis, Alexis machine learning women vulnerability climate change 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. 2024-12 2024-06-06T17:34:59Z 2024-06-06T17:34:59Z Journal Article https://hdl.handle.net/10568/145032 en Limited Access Wiley Caruso, German; Mueller, Valerie; and Villacis, Alexis. 2024. Leveraging unsupervised machine learning to examine women's vulnerability to climate change. Applied Economic Perspectives and Policy 46(4): 1355-1378. https://doi.org/10.1002/aepp.13444 |
| spellingShingle | machine learning women vulnerability climate change Caruso, German Mueller, Valerie Villacis, Alexis Leveraging unsupervised machine learning to examine women's vulnerability to climate change |
| title | Leveraging unsupervised machine learning to examine women's vulnerability to climate change |
| title_full | Leveraging unsupervised machine learning to examine women's vulnerability to climate change |
| title_fullStr | Leveraging unsupervised machine learning to examine women's vulnerability to climate change |
| title_full_unstemmed | Leveraging unsupervised machine learning to examine women's vulnerability to climate change |
| title_short | Leveraging unsupervised machine learning to examine women's vulnerability to climate change |
| title_sort | leveraging unsupervised machine learning to examine women s vulnerability to climate change |
| topic | machine learning women vulnerability climate change |
| url | https://hdl.handle.net/10568/145032 |
| work_keys_str_mv | AT carusogerman leveragingunsupervisedmachinelearningtoexaminewomensvulnerabilitytoclimatechange AT muellervalerie leveragingunsupervisedmachinelearningtoexaminewomensvulnerabilitytoclimatechange AT villacisalexis leveragingunsupervisedmachinelearningtoexaminewomensvulnerabilitytoclimatechange |