In developing countries, identifying the poor for redistribution or social insurance is challenging because the government lacks information about people’s incomes. This paper reports the results of a field experiment conducted in 640 Indonesian villages that investigated two main approaches to solving this problem: proxy-means tests, where a census of hard-to-hide assets is used to predict consumption, and community-based targeting, where villagers rank everyone on a scale from richest to poorest. When poverty is defined using per-capita expenditure and the common PPP$2 per day threshold, we find that community-based targeting performs worse in identifying the poor than proxy-means tests, particularly near the threshold. This worse performance does not appear to be due to elite capture. Instead, communities appear to be using a different concept of poverty: the results of community-based methods are more correlated with how individual community members rank each other and with villagers’ self-assessments of their own status than per-capita expenditure. Consistent with this, the community-based methods result in higher satisfaction with beneficiary lists and the targeting process.
Thats from Targeting the Poor: Evidence from a Field Experiment in Indonesia by Abhijit Banerjee et al. This result is surprising. With a standard indicator there are more poor people than with perception-based poverty measure.