Accuracy and Poverty Impacts of Proxy Means-Tested Transfers: An Empirical Assessment for Bolivia

In the absence of reliable and exhaustive income data, Proxy Means Tests (PMTs) are frequently employed as a cost-effective way to identify income-poor beneficiaries of targeted anti-poverty programs. However, their usefulness depends on whether proxies accurately identify the income poor. Based on Receiver Operating Characteristics (ROC)-analysis, we find that PMTs perform poorly in terms of identifying poor households in Bolivian data when transfers are targeted narrowly to the poor but that the true positive rate is highly responsive to increases in the proportion of beneficiaries. Using non-parametric regression-techniques, we show that the resulting leakage can largely be confined to the non-poor close to the poverty line. However, simulating the impact on poverty measures of a uniform transfer to beneficiaries across inclusion rates suggests that the largest poverty impact is attained with very narrow targeting. Hence, the link between targeting accuracy and poverty impact is weak.