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Despite a situation of an active violent conflict, the Yemen Emergency CrisisResponse Project continues to deliver services and cash to the poor and vulnerablenationwide. This paper captures lessons learned from this innovative and pioneering project,which will be particularly relevant for shaping the World Bank's crisis response under IDA 18.

This paper intends to inform the effort of the Sri Lankan government to reform the targeting efficacy of its social protection programs, in particular, Samurdhi, which currently distributes benefits based on self-reported income. We develop a Proxy Means Test (PMT) for Sri Lanka based on the Household Income and Expenditure Survey 2016 and evaluate its performance for targeting benefits of Samurdhi. The paper considers a range of models and policy parameters that could be applied depending on data availability and country preferences. The results indicate that switching to a PMT could considerably improve the targeting performance of Samurdhi and would significantly improve the poverty impact of the program. We find that the performance of the proposed PMT model suffers when the coefficients are estimated from samples smaller than 1,000 households. However, we do not find a similar loss of model performance when the model is estimated from seasonal data, provided the sample size is sufficiently large. The model we propose could be applied to the targeting of a variety of safety net programs after validating and refining our model by conducting a pilot survey.

Asset ownership indices are widely used as inexpensive proxies for consumption. This paper shows that these indices can be augmented using dichotomous indicators for consumption, which are equally easy to obtain. The paper uses multiple rounds of Living Standards Measurement Study surveys from Malawi, Uganda, Rwanda, Tanzania, and Ghana to construct indices with different item subcategories and performs a meta-analysis comparing the indices to per capita consumption. The results show that the standard asset indices, which are derived from durable ownership and housing characteristic indicators, perform well in urban settings. Yet, in rural samples and when identifying the extreme poor, household rankings and poverty classification accuracy can be meaningfully improved by adding indicators of food and semi-durable consumption. The study finds small improvement from using national weights in urban samples but no improvement from using alternative construction methods. With most of Africa’s poor concentrated in rural areas, these are important insights.

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. The authors 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, they 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, they 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.

The 2007/2008 food prices hike has increased the interest in social safety nets programmes to fight food insecurity. Targeting the most in need is central to achieve effectiveness of such interventions. In 2009 in Ouagadougou, Burkina Faso, a food voucher (FV) programme targeted the 25 000 most vulnerable households (8.3% of the population). Targeting used a two-stage process: first geographical selection of poorest districts (similar to 90 000 households); then, in those districts, identification of the most vulnerable households according to a proxy-means test (PMT). Targeted households were entitled to receive FV for 1 year. A first survey was conducted at the beginning of the FV distribution on a representative sample of 2273 households drawn from the poorest districts. One year later a second survey, conducted on a subsample of same households (n = 901), identified those who actually received FV (beneficiary). The performance of the whole process was assessed against household food expenditure, used as the reference measure for vulnerability with a cut-off point of 1513 FCFA (corresponding to the 8.3th percentile of the distribution of expenditure). The 'normalized share of transfers going to vulnerable households' (NSTVH), i.e. proportion of FVs allocated to households below the cut-point, was the main criteria of judgement. Almost twice as many FV were allocated to vulnerable households as compared with a theoretical random distribution all over Ouagadougou (NSTVH = 1.85). When considering the sole targeted districts the NSTVH was only 0.84 (i.e. no more effective than a random distribution), meaning that the geographical stage was effective to select vulnerable districts while the PMT did not perform well to identify the most vulnerable households in those districts. Results could have been improved if only targeted households had received FV (NSTVH = 2.61 and 1.18 for the whole Ouagadougou and targeted districts, respectively). Improved targeting procedures or alternate targeting instruments are needed.

Targeting of national anti-poverty programs in low-income countries commonly relies on statistical procedures involving household-level survey data, while small-scale poverty-alleviation programs often employ so-called community-based targeting, where village communities themselves identify program beneficiaries. Combining data from community-based targeting exercises in north-western Burkina Faso with household-level survey data, we compare the targeting accuracy of community-based targeting with several statistical procedures when the program's purpose is to target consumption-poor households. We find that the community-based assessment targets a similar share of consumption-poor households as the best-performing statistical procedures which are not calibrated with household-level consumption data. Community-based targeting performs relatively better in urban than in rural areas and is not at a disadvantage in larger or more heterogeneous communities. In a cost-benefit analysis we find that in our sub-Saharan African context community-based targeting is far more cost-effective than any statistical procedure for common amounts of welfare program benefits.

Etudes & documents; 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.