Finding the Poor vs. Measuring Their Poverty: Exploring the Drivers of Targeting Effectiveness in Indonesia

Centralised targeting registries are increasingly used to allocate social assistance benefits in developing countries. This paper provides the first attempt to identify the relative importance of two key design issues for targeting accuracy: (1) which households to survey for inclusion in the targeting registry and (2) how to rank surveyed households. We evaluate the performance of Indonesia’s Unified Database for Social Protection Programmes (UDB), the largest targeting registry in the world, which is used to provide social assistance to more than 25 million households. Linking administrative data with an independent household survey, we find that the UDB system is more progressive than previous targeting approaches used in Indonesia, leading to a decrease in benefit leakage to non-poor households. However, if poor households are not surveyed in the first place, even a perfect ranking method cannot prevent their exclusion. Under a simulation that considers enumerating and estimating proxy-means testing (PMT) scores for all households (as in a census), we estimate a one-third decrease in undercoverage compared to focusing on households that have been registered in the UDB. Investigating household- and community-level correlates of misenumeration and misclassification, we find evidence that local communities use different definitions of poverty and have better information on the welfare status of their members.