Can call detail records provide insights into women's empowerment? A case study from Uganda
We use CDRs of mobile phone users in Uganda combined with data from a phone survey to train machine-learning models to predict the sex of the mobile phone user and several indicators of economic empowerment such as ownership of a house and land, occupation, and decision-making over household income....
| Autores principales: | , , , , , , |
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| Formato: | Informe técnico |
| Lenguaje: | Inglés |
| Publicado: |
CGIAR Platform for Big Data in Agriculture
2021
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/111023 |
| _version_ | 1855516096780566528 |
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| author | Slavchevska, Vanya Tyszler, Marcelo Burra, Dharani Dhar Seymour, Greg Sementsov, Denys Lierde, Astrid van King, Brian |
| author_browse | Burra, Dharani Dhar King, Brian Lierde, Astrid van Sementsov, Denys Seymour, Greg Slavchevska, Vanya Tyszler, Marcelo |
| author_facet | Slavchevska, Vanya Tyszler, Marcelo Burra, Dharani Dhar Seymour, Greg Sementsov, Denys Lierde, Astrid van King, Brian |
| author_sort | Slavchevska, Vanya |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | We use CDRs of mobile phone users in Uganda combined with data from a phone survey to train machine-learning models to predict the sex of the mobile phone user and several indicators of economic empowerment such as ownership of a house and land, occupation, and decision-making over household income. The most accurate of the models predicts the sex of the mobile phone user with 78% accuracy. The different indicators of economic empowerment are predicted with accuracies ranging from 57% to 61%. We also predict users’ sex and economic empowerment jointly. However, when we predict economic empowerment and then the sex of the user, we achieve high accuracy rates ranging from 81% to 87%. Mobile phone usage data hold potential for gender research although they are not without limitations. |
| format | Informe técnico |
| id | CGSpace111023 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | CGIAR Platform for Big Data in Agriculture |
| publisherStr | CGIAR Platform for Big Data in Agriculture |
| record_format | dspace |
| spelling | CGSpace1110232025-11-06T05:19:07Z Can call detail records provide insights into women's empowerment? A case study from Uganda Slavchevska, Vanya Tyszler, Marcelo Burra, Dharani Dhar Seymour, Greg Sementsov, Denys Lierde, Astrid van King, Brian women's empowerment gender equality access to information mobile equipment potenciación de la mujer igualdad de género acceso a la información empowerment gender women machine learning metadata We use CDRs of mobile phone users in Uganda combined with data from a phone survey to train machine-learning models to predict the sex of the mobile phone user and several indicators of economic empowerment such as ownership of a house and land, occupation, and decision-making over household income. The most accurate of the models predicts the sex of the mobile phone user with 78% accuracy. The different indicators of economic empowerment are predicted with accuracies ranging from 57% to 61%. We also predict users’ sex and economic empowerment jointly. However, when we predict economic empowerment and then the sex of the user, we achieve high accuracy rates ranging from 81% to 87%. Mobile phone usage data hold potential for gender research although they are not without limitations. 2021-01 2021-01-28T09:30:31Z 2021-01-28T09:30:31Z Report https://hdl.handle.net/10568/111023 en Open Access application/pdf CGIAR Platform for Big Data in Agriculture CGIAR Research Program on Policies, Institutions, and Markets Slavchevska, V; Tyszler, M; Burra, D.D.; Seymour, G.; Sementsov, D.; Van Lierde, A.; King, B. (2021) Can call detail records provide insights into women’s empowerment? A case study from Uganda. 34 p. |
| spellingShingle | women's empowerment gender equality access to information mobile equipment potenciación de la mujer igualdad de género acceso a la información empowerment gender women machine learning metadata Slavchevska, Vanya Tyszler, Marcelo Burra, Dharani Dhar Seymour, Greg Sementsov, Denys Lierde, Astrid van King, Brian Can call detail records provide insights into women's empowerment? A case study from Uganda |
| title | Can call detail records provide insights into women's empowerment? A case study from Uganda |
| title_full | Can call detail records provide insights into women's empowerment? A case study from Uganda |
| title_fullStr | Can call detail records provide insights into women's empowerment? A case study from Uganda |
| title_full_unstemmed | Can call detail records provide insights into women's empowerment? A case study from Uganda |
| title_short | Can call detail records provide insights into women's empowerment? A case study from Uganda |
| title_sort | can call detail records provide insights into women s empowerment a case study from uganda |
| topic | women's empowerment gender equality access to information mobile equipment potenciación de la mujer igualdad de género acceso a la información empowerment gender women machine learning metadata |
| url | https://hdl.handle.net/10568/111023 |
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