Estimating gender inequalities in labor-market outcomes using mobile phone data
Mobile phone data holds promise for contributing to slow-filling gaps about women and men’s labor. We generated gender-specific predictions of three labor market indicators (employment, unemployment and underemployment) using machine learning models that analyzed digital trace data and geospatial da...
| Autores principales: | , , |
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| Formato: | Blog Post |
| Lenguaje: | Inglés |
| Publicado: |
CGIAR
2023
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| Acceso en línea: | https://hdl.handle.net/10568/137808 |
| _version_ | 1855540404297924608 |
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| author | Seymour, Greg Follett, Lendie Henderson, Heath |
| author_browse | Follett, Lendie Henderson, Heath Seymour, Greg |
| author_facet | Seymour, Greg Follett, Lendie Henderson, Heath |
| author_sort | Seymour, Greg |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Mobile phone data holds promise for contributing to slow-filling gaps about women and men’s labor. We generated gender-specific predictions of three labor market indicators (employment, unemployment and underemployment) using machine learning models that analyzed digital trace data and geospatial data. While the models correctly predict mobile phone users’ gender in most cases, they predict users’ labor market status much less accurately. With further refinement, we believe the methodology still shows prospects for filling gender data gaps in individual-level labor market statistics. |
| format | Blog Post |
| id | CGSpace137808 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | CGIAR |
| publisherStr | CGIAR |
| record_format | dspace |
| spelling | CGSpace1378082025-06-30T08:32:20Z Estimating gender inequalities in labor-market outcomes using mobile phone data Seymour, Greg Follett, Lendie Henderson, Heath data surveys gender labour market machine learning spatial data Mobile phone data holds promise for contributing to slow-filling gaps about women and men’s labor. We generated gender-specific predictions of three labor market indicators (employment, unemployment and underemployment) using machine learning models that analyzed digital trace data and geospatial data. While the models correctly predict mobile phone users’ gender in most cases, they predict users’ labor market status much less accurately. With further refinement, we believe the methodology still shows prospects for filling gender data gaps in individual-level labor market statistics. 2023-11-29 2024-01-16T18:42:24Z 2024-01-16T18:42:24Z Blog Post https://hdl.handle.net/10568/137808 en Open Access CGIAR Seymour, Greg; Follett, Lendie; and Henderson, Heath. 2023. Estimating gender inequalities in labor-market outcomes using mobile phone data. CGIAR Blog. |
| spellingShingle | data surveys gender labour market machine learning spatial data Seymour, Greg Follett, Lendie Henderson, Heath Estimating gender inequalities in labor-market outcomes using mobile phone data |
| title | Estimating gender inequalities in labor-market outcomes using mobile phone data |
| title_full | Estimating gender inequalities in labor-market outcomes using mobile phone data |
| title_fullStr | Estimating gender inequalities in labor-market outcomes using mobile phone data |
| title_full_unstemmed | Estimating gender inequalities in labor-market outcomes using mobile phone data |
| title_short | Estimating gender inequalities in labor-market outcomes using mobile phone data |
| title_sort | estimating gender inequalities in labor market outcomes using mobile phone data |
| topic | data surveys gender labour market machine learning spatial data |
| url | https://hdl.handle.net/10568/137808 |
| work_keys_str_mv | AT seymourgreg estimatinggenderinequalitiesinlabormarketoutcomesusingmobilephonedata AT follettlendie estimatinggenderinequalitiesinlabormarketoutcomesusingmobilephonedata AT hendersonheath estimatinggenderinequalitiesinlabormarketoutcomesusingmobilephonedata |