In Malawi, the Unified Beneficiary Registry (UBR) serves as the primary tool for targeting social protection programmes, but its existing targeting model relies on data that is not dynamically updated and on methodologies restricted to rural areas.

To address this gap and respond to rising urban poverty driven by exogenous shocks, SPRI Global is supporting the National Local Government Finance Committee (NLGFC) to update the current rural Proxy Means Testing (PMT) model and develop a new model specifically for urban households. Using the latest Integrated Household Survey (IHS) data, the team is employing advanced quantitative methods, including exploring Principal Component Analysis (PCA), Ordinary Least Squares (OLS), and Machine Learning techniques, to accurately predict and rank household poverty levels.

Key outputs include a detailed PMT analysis report presenting the new models and a capacity building report following capacity building sessions with national officers. The team works closely with the NLGFC to select the methodology that offers the best balance of accuracy and robustness. The process includes testing the models for reliability and validity, followed by a validation workshop with stakeholders to ensure the proposed models meet the operational needs of the social protection sector

These updated tools will enable the Government of Malawi to effectively identify and support the most vulnerable households across both rural and urban settings.