Centralized 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: (i) which households to survey for inclusion in the registry and (ii) how to rank surveyed households. We evaluate Indonesia's Unified Database for Social Protection Programs (UDB), among the largest targeting registries in the world, used to provide social assistance to over 25 million households. Linking administrative data with an independent household survey, we find that the UDB system is more progressive than previous, program-specific targeting approaches. However, simulating an alternative targeting system based on enumerating all households, we find a one-third reduction in undercoverage of the poor compared to focusing on households registered in the UDB. Overall, we identify large gains in targeting performance from improving the initial registration stage relative to the ranking stage.
Traditional poverty measures fail to indicate the degree of risk of becoming or remaining poor that households are confronted to. They can therefore be misleading in the context of implementing poverty reduction policies. In this paper I propose a method to estimate an index of ex ante vulnerability to poverty, defined as the probability of being poor in the (near) future given current observable characteristics, using panel data. This method relies on the estimation of the expected mean and variance of future consumption conditional on current consumption and observable characteristics. It generates a vulnerability index, or predicted probability of future poverty, which performs well in predicting future poverty, including out of sample. About 80% of households with a 2000 vulnerability index of 100% are actually poor in 2007. This approach provides information on the population groups that have a high probability of becoming or remaining poor in the future, whether currently poor or not. It is therefore useful to complement traditional poverty measures such as the poverty headcount, in particular for the design and planning of poverty reduction policies.
Proxy-means testing (PMT) is a method used to assess household or individual welfare level based on a set of observable indicators. The accuracy, and therefore usefulness of PMT relies on the selection of indicators that produce accurate predictions of household welfare. In this paper I propose a method to identify indicators that are robustly and strongly correlated with household welfare, measured by per capita consumption. From an initial set of 340 candidate variables drawn from the Indonesian Family Life Survey, I identify the variables that contribute most significantly to model predictive performance and that are therefore desirable to be included in a PMT formula. These variables span the categories of household private asset holdings, access to basic domestic energy, education level, sanitation and housing. A comparison of the predictive performance of PMT formulas including 10, 20 and 30 of the best predictors of welfare shows that leads to recommending formulas with 20 predictors. Such parsimonious models have similar predictive performance as the PMT formulas currently used in Indonesia, although these latter are based on models of 32 variables on average.
Centralized targeting registries are increasingly used to allocate social assistance benefits in developing countries. There are two key design issues that matter for targeting accuracy: (i) which households to survey for inclusion in the registry; and (ii) how to rank surveyed households. We attempt to identify their relative importance by evaluating Indonesia's Unified Database for Social Protection Programs (UDB), among the largest targeting registries in the world, used to provide social assistance to over 25 million households. Linking administrative data with an independent household survey, we find that the UDB system is more progressive than previous, program-specific targeting approaches. However, simulating an alternative targeting system based on enumerating all households, we find a one-third reduction in undercoverage of the poor compared to focusing on households registered in the UDB. Overall, there are large gains in targeting performance from improving the initial registration stage relative to the ranking stage.
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