Targeting in Ultra-Poor Settings : Evidence from Six Countries in Rural Sahel (English)

The main insights of this note are as follows: first, to significantly reduce poverty higher budgets for safety net interventions are needed, and expanding coverage is far more important than fine-tuning targeting methods. After geographical targeting, most PMT and CBT methods perform close to a random allocation of benefits when trying to identify food insecure households. While PMT consistently outperforms CBT in identifying households with the lowest consumption, differences are small when distances to the poverty line are considered. While non-beneficiaries experience significant indirect economic benefits from the program, there is mixed and limited evidence on social cohesion and fairness perceptions of targeting methods. Finally, costs are relatively minor as a share of total resources transferred. The policy note concludes with policy and research implications for contexts with high poverty rates, low inequality levels, and insufficient budgets.