Program targeting with machine learning and mobile phone data
Evidence from an anti-poverty intervention in Afghanistan
Datos Bibliográficos
| ID | 11888395 |
|---|---|
| Autores | Emily Aiken (0000-0003-4374-3536, University of California, Berkeley), Emily L Aiken, Guadalupe Bedoya (World Bank), Joshua E Blumenstock (0000-0002-1813-7414, University of California, Berkeley, autor de correspondencia), Aidan Coville (0000-0002-3437-6239, World Bank) |
| Año | 2022 |
| Volumen | 161 |
| Páginas | 103016-103016 |
| Fecha de publicación | 2022-11-28 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Journal of Development Economics (JOURNAL) |
| Identificadores de la revista | ISSN: 0304-3878 • E-ISSN: 1872-6089 |
| Editorial | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.jdeveco.2022.103016 |
| OpenAlex | W4310255196 |
| Idioma | EN |
| Citas recibidas | 11 |
| Referencias citadas | 28 |
Can mobile phone data improve program targeting? By combining rich survey data from a “big push” anti-poverty program in Afghanistan with detailed mobile phone logs from program beneficiaries, we study the extent to which machine learning methods can accurately differentiate ultra-poor households eligible for program benefits from ineligible households. We show that machine learning methods leveraging mobile phone data can identify ultra-poor households nearly as accurately as survey-based measures of consumption and wealth; and that combining survey-based measures with mobile phone data produces classifications more accurate than those based on a single data source
Consumption (sociology · Data science · Economic growth · Economics · GSM services · Internet privacy · Intervention (counseling · Machine learning · Mobile device · Mobile phone · Mobile technology · Phone · Poverty · Statistics · Survey data collection · Telecommunications · World Wide Web · Computer Science · Human Mobility and Location-Based Analysis · ICT in Developing Communities · Income, Poverty, and Inequality · Mathematics · Psychology
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| Obras citantes distintas | 11 |
|---|---|
| Citas por año | 5,5 |
| Intervalo de citas | 2024 - 2027 (4) |
| Velocidad de citación | recent |
| Altamente citado | No |
| Tipos de cita | Neutras: 10 |