Enhancing poverty classification in developing countries through machine learning: a case study of household consumption prediction in Rwanda
To address the challenges associated with measuring and classifying household consumption (poverty) in developing countries, such as cost, time gaps, and inaccurate socio-economic data, this study suggests leveraging machine learning (ML) algorithms. We assessed the performance of various ML algorit...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Cogent Economics & Finance |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23322039.2024.2444374 |
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| author | Fabrice Nkurunziza Richard Kabanda Patrick McSharry |
| author_facet | Fabrice Nkurunziza Richard Kabanda Patrick McSharry |
| author_sort | Fabrice Nkurunziza |
| collection | DOAJ |
| description | To address the challenges associated with measuring and classifying household consumption (poverty) in developing countries, such as cost, time gaps, and inaccurate socio-economic data, this study suggests leveraging machine learning (ML) algorithms. We assessed the performance of various ML algorithms using data from 14,580 sample households from the Integrated Household Living Condition Survey (EICV5), considering 87 features. Among the 12 classifiers evaluated, multiple kernel support vector machines, eXtreme gradient boosting, and multinomial logit demonstrated the highest predictive accuracy, ranging between 86.6% and 88.5%. Notably, household food expenditure, the total number of children (<14 years) in the household, and household own food expenditures emerged as the most predictive features for consumption classification. Interestingly, including shock-coping strategies did not significantly improve prediction accuracy. The multiple kernel support vector machine consistently outperformed eXtreme gradient boosting and multinomial logit. These findings suggest that survey questions used to assess poverty in Rwanda could be streamlined, prioritizing important features, particularly those related to household food characteristics. This approach has the potential to address challenges associated with measuring and classifying household consumption in developing countries more effectively. |
| format | Article |
| id | doaj-art-465844e09f4d4a97bfedb4fa28a2da73 |
| institution | DOAJ |
| issn | 2332-2039 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Economics & Finance |
| spelling | doaj-art-465844e09f4d4a97bfedb4fa28a2da732025-08-20T02:52:46ZengTaylor & Francis GroupCogent Economics & Finance2332-20392025-12-0113110.1080/23322039.2024.2444374Enhancing poverty classification in developing countries through machine learning: a case study of household consumption prediction in RwandaFabrice Nkurunziza0Richard Kabanda1Patrick McSharry2The African Centre of Excellence in Data Science, University of Rwanda (UR), Kigali, RwandaThe African Centre of Excellence in Data Science, University of Rwanda (UR), Kigali, RwandaDepartment of Electrical and Computer Engineering, Carnegie Mellon University, Kigali, RwandaTo address the challenges associated with measuring and classifying household consumption (poverty) in developing countries, such as cost, time gaps, and inaccurate socio-economic data, this study suggests leveraging machine learning (ML) algorithms. We assessed the performance of various ML algorithms using data from 14,580 sample households from the Integrated Household Living Condition Survey (EICV5), considering 87 features. Among the 12 classifiers evaluated, multiple kernel support vector machines, eXtreme gradient boosting, and multinomial logit demonstrated the highest predictive accuracy, ranging between 86.6% and 88.5%. Notably, household food expenditure, the total number of children (<14 years) in the household, and household own food expenditures emerged as the most predictive features for consumption classification. Interestingly, including shock-coping strategies did not significantly improve prediction accuracy. The multiple kernel support vector machine consistently outperformed eXtreme gradient boosting and multinomial logit. These findings suggest that survey questions used to assess poverty in Rwanda could be streamlined, prioritizing important features, particularly those related to household food characteristics. This approach has the potential to address challenges associated with measuring and classifying household consumption in developing countries more effectively.https://www.tandfonline.com/doi/10.1080/23322039.2024.2444374Household consumptionSupport Vector machineeXtreme Gradient Boostingmultinomial logitRwandaC52 |
| spellingShingle | Fabrice Nkurunziza Richard Kabanda Patrick McSharry Enhancing poverty classification in developing countries through machine learning: a case study of household consumption prediction in Rwanda Cogent Economics & Finance Household consumption Support Vector machine eXtreme Gradient Boosting multinomial logit Rwanda C52 |
| title | Enhancing poverty classification in developing countries through machine learning: a case study of household consumption prediction in Rwanda |
| title_full | Enhancing poverty classification in developing countries through machine learning: a case study of household consumption prediction in Rwanda |
| title_fullStr | Enhancing poverty classification in developing countries through machine learning: a case study of household consumption prediction in Rwanda |
| title_full_unstemmed | Enhancing poverty classification in developing countries through machine learning: a case study of household consumption prediction in Rwanda |
| title_short | Enhancing poverty classification in developing countries through machine learning: a case study of household consumption prediction in Rwanda |
| title_sort | enhancing poverty classification in developing countries through machine learning a case study of household consumption prediction in rwanda |
| topic | Household consumption Support Vector machine eXtreme Gradient Boosting multinomial logit Rwanda C52 |
| url | https://www.tandfonline.com/doi/10.1080/23322039.2024.2444374 |
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