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

Social assistance programs targeted at low-income groups cover nearly two billion people in developing countries. Identifying and reaching the intended beneficiaries of these programs can be challenging, especially where a large part of the population works in the informal sector and there are no official income registries. Traditionally, each program has its own method to select recipients depending on its implementing agency, budget and benefit package.

In recent years, an increasing number of low- and middle-income countries are moving from fragmented program-specific targeting mechanisms to a single household targeting registry meant to select recipients of multiple social assistance programs, often with different eligibility rules. For such registries, basic household and individual information is typically collected for a subset of the population that is considered potentially eligible for social assistance. This information is then used to determine eligibility.

In a new journal article published in The World Bank Economic Review, Samuel Bazzi and coauthors identify strategies for improving targeting effectiveness in these new unified registries by evaluating the performance of one of the world’s largest single registries in Indonesia. Established in 2012, the Unified Database for Social Protection Programs (UDB) is intended to cover the poorest 40 percent of the Indonesian population. Over 25 million households have been registered in the UDB using a novel approach based on a pre-listing of households to be surveyed constructed through census-based poverty mapping and suggestions from local communities. The Indonesian government has used the UDB to deliver over $4 billion annually in central government social assistance to date.

Main findings:

• The UDB leads to a substantial reduction in leakage of benefits to non-poor households. For example, the proportion of the richest 40 percent of households receiving health insurance benefits is expected to fall from nearly 40 percent to 25 percent.
• However, under-coverage of eligible households remains relatively high and is due largely to the difficulties of enumerating the right households for inclusion in the UDB.
• A significant decrease in under-coverage is predicted under simulations that consider enumerating a larger set of households before proceeding to the stage of estimating their eligibility for assistance.

The key results highlight the value of increasing the number of households enumerated in the national targeting registry survey. One option is to conduct a census of the full population rather than only select households expected to be poor. If conducting a full census is cost-prohibitive, a related option would be first to identify the poorest areas based on poverty maps and then to survey all households in these geographic areas. Overall, the results suggest that targeting using the UDB is more progressive than with the previous approaches to beneficiary selection used in Indonesia.