Work

Rapid poverty estimation using ready-to-use mobile phone data: An application to Côte d’Ivoire

Year: 2024

Type: conference-paper

Abstract: Targeting the poor is an integral part of social program design in low-income countries. Geographical targeting gives priority to areas with high concentrations of poverty. However, traditional data sources, such as household surveys often lack the spatial resolution to estimate poverty at a highly disaggregated level and are costly to collect on a regular basis. We leverage the proliferation of big data obtained from mobile devices and satellites to generate poverty measures at a highly disaggregated spatial level in a new country context. Previous applications rely on computationally intensive methods to extract information from raw cellphone transaction data. We show how similar levels of prediction accuracy can be achieved by using key performance indicators (KPIs) that Mobile Network Operators produce regularly as part of their operations, lowering the financial and data access barriers to estimate and update prediction models. This can help to facilitate the use of these data for policy in low- and middle-income contexts.

Source: ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)

DOI: 10.1145/3674829.3675061

Authors: Sveta Milusheva, Oscar Barriga-Cabanillas, Oumaima Makhlouk, Ruiwen Zhang

Institutions: World Bank, Intel (United States), World Bank Group

Language: English

Topic: Human Mobility and Location-Based Analysis

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDGs: No poverty, Reduced inequalities, Industry, innovation and infrastructure

Open Access status: bronze

Funder: Government of the United Kingdom