Leveraging Mobile Phone Data with Machine Learning to Target and Fight Poverty

Researchers from UC Berkeley and the World Bank have developed a machine learning model using non-traditional administrative data (such as call detail records) to accurately target ultra-poor households for government anti-poverty programs in low to medium-income countries.

The model was found to have comparable accuracy to other methods, such as asset-based wealth index or a consumption metric, while also reducing the time and marginal costs required to implement a targeted program.

However, there are ethical considerations to be taken into account, such as access to data, privacy issues, and potential data manipulation.

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