Can machine-learning models predict gendered labor statistics using mobile phone and geospatial data?
High-quality data on rural women’s and men’s labor is imperative for tracking progress on gender equality and women’s empowerment, and for evaluating development interventions aimed at these outcomes. Yet, there remains a general lack of sex-disaggregated data on unpaid care and domestic work, earni...
| Autores principales: | , , , |
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| Formato: | Artículo preliminar |
| Lenguaje: | Inglés |
| Publicado: |
CGIAR GENDER Impact Platform
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/169433 |
| _version_ | 1855534854255411200 |
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| author | Seymour, Greg Follett, Lendie Henderson, Heath Ferguson, Nathaniel |
| author_browse | Ferguson, Nathaniel Follett, Lendie Henderson, Heath Seymour, Greg |
| author_facet | Seymour, Greg Follett, Lendie Henderson, Heath Ferguson, Nathaniel |
| author_sort | Seymour, Greg |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | High-quality data on rural women’s and men’s labor is imperative for tracking progress on gender equality and women’s empowerment, and for evaluating development interventions aimed at these outcomes. Yet, there remains a general lack of sex-disaggregated data on unpaid care and domestic work, earnings, employment and entrepreneurship. Researchers are increasingly looking to digital technologies, such as mobile phones, as an emerging data source with significant potential for closing gender data gaps. In this paper, we attempt to use mobile phone data and machine-learning models to predict gendered labor-market indicators for a large sample of mobile phone users in Ghana. Although our models predict mobile phone subscribers’ sex with reasonable accuracy, they predict women’s and men’s labor-market outcomes only slightly better than random guessing. The models’ mixed results may be partly attributed to noisiness in the data due to disruptions in mobile phone and employment-related behaviors caused by COVID-19. Our results also point to potential methodological limitations in using machine-learning methods and mobile phone data to estimate gendered labor-market indicators, and more generally suggest that we should proceed cautiously when thinking about leveraging digital technologies and machine learning to close data gaps. We conclude the paper with several recommendations for how the methodology might be refined in future work. |
| format | Artículo preliminar |
| id | CGSpace169433 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | CGIAR GENDER Impact Platform |
| publisherStr | CGIAR GENDER Impact Platform |
| record_format | dspace |
| spelling | CGSpace1694332025-02-13T17:36:25Z Can machine-learning models predict gendered labor statistics using mobile phone and geospatial data? Seymour, Greg Follett, Lendie Henderson, Heath Ferguson, Nathaniel gender labour spatial data machine learning High-quality data on rural women’s and men’s labor is imperative for tracking progress on gender equality and women’s empowerment, and for evaluating development interventions aimed at these outcomes. Yet, there remains a general lack of sex-disaggregated data on unpaid care and domestic work, earnings, employment and entrepreneurship. Researchers are increasingly looking to digital technologies, such as mobile phones, as an emerging data source with significant potential for closing gender data gaps. In this paper, we attempt to use mobile phone data and machine-learning models to predict gendered labor-market indicators for a large sample of mobile phone users in Ghana. Although our models predict mobile phone subscribers’ sex with reasonable accuracy, they predict women’s and men’s labor-market outcomes only slightly better than random guessing. The models’ mixed results may be partly attributed to noisiness in the data due to disruptions in mobile phone and employment-related behaviors caused by COVID-19. Our results also point to potential methodological limitations in using machine-learning methods and mobile phone data to estimate gendered labor-market indicators, and more generally suggest that we should proceed cautiously when thinking about leveraging digital technologies and machine learning to close data gaps. We conclude the paper with several recommendations for how the methodology might be refined in future work. 2024-12-30 2025-01-20T05:35:34Z 2025-01-20T05:35:34Z Working Paper https://hdl.handle.net/10568/169433 en Open Access application/pdf CGIAR GENDER Impact Platform Seymour, G., Follett, L., Henderson, H., and Ferguson, N. 2024. Can machine-learning models predict gendered labor statistics using mobile phone and geospatial data? CGIAR GENDER Impact Platform Working Paper 023. Nairobi, Kenya: CGIAR GENDER Impact Platform. |
| spellingShingle | gender labour spatial data machine learning Seymour, Greg Follett, Lendie Henderson, Heath Ferguson, Nathaniel Can machine-learning models predict gendered labor statistics using mobile phone and geospatial data? |
| title | Can machine-learning models predict gendered labor statistics using mobile phone and geospatial data? |
| title_full | Can machine-learning models predict gendered labor statistics using mobile phone and geospatial data? |
| title_fullStr | Can machine-learning models predict gendered labor statistics using mobile phone and geospatial data? |
| title_full_unstemmed | Can machine-learning models predict gendered labor statistics using mobile phone and geospatial data? |
| title_short | Can machine-learning models predict gendered labor statistics using mobile phone and geospatial data? |
| title_sort | can machine learning models predict gendered labor statistics using mobile phone and geospatial data |
| topic | gender labour spatial data machine learning |
| url | https://hdl.handle.net/10568/169433 |
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