From VoxDev
When should big data and algorithms be used to determine programme eligibility?
By Josh Blumenstock and Emily Aiken
Although machine learning models using mobile phone data can make poverty targeting faster and more cost-effective, traditional survey-based methods remain more accurate. The optimal approach therefore depends on striking the right balance between cost, accuracy, and programme scale.
In recent decades, hundreds of billions of dollars have been spent on social protection programmes, with around 52.4% of the global population covered by at least one such programme (ILO 2024). While countries devote a substantial portion of GDP to such programmes, there is evidence that in many cases, resources often do not reach the households with the greatest need. For instance, Coady et al. (2004) find that nearly a quarter of poverty-targeted programmes in low-income countries are regressive, providing more benefits to rich households than poor. These targeting errors occur because in many low-income settings, governments and programme administrators typically rely on in-person surveys or decentralised community-based nominations, which are costly and difficult to keep up to date.
Big data and algorithms could make poverty targeting more effective
Novel data sources and advances in artificial intelligence have created new opportunities for deploying algorithms to identify beneficiaries remotely, which could potentially overcome the shortcomings of traditional methods (Blumenstock 2020).
The dramatic expansion of mobile phone adoption across the developing world offers a potentially rich source of information about its users at a granular level. As of 2024, 84% of adults in low- and middle-income countries own personal mobile phones. Amongst adults who do not, 30-50% use someone else’s (World Bank 2025). Researchers have begun exploiting the relationship between people’s mobile phone usage behaviour and their socioeconomic status to develop machine learning models that can help identify the poor (Blumenstock 2015, 2018, Aiken et al. 2022, 2023)...
Joshua Blumenstock is a Chancellor’s Associate Professor at the School of Information at UC Berkeley. Emily Aiken is a PhD alum at the UC Berkeley School of Information and was advised by Blumenstock.
