Addition of Quantitative Information to the Decision-Making Process

310 | Revisiting Targeting in Social Assistance

• As noted in chapter 4, privacy and security protections on databases used or built for eligibility determination are necessary, and they are a focus of policy action in countries that are working to secure the right to privacy.

Transparency and social cohesion are other desirable attributes that are harder for household-specific methods than for categorical ones. With means testing or HMT, the notion that the method is supposed to sort the poorer from the richer is usually understood, but the details of what counts in the calculations may be less so. PMT may be understood by policy makers, but it is often not well understood by communities. Accounts of the acceptability of CBT differ. All these methods draw lines within communities in ways that risk “othering” or social tensions. These disadvantages must be set against the method’s power to rank or assess welfare more accurately than the non-household-specific methods.

Addition of Quantitative Information to the Decision-Making Process

Although much of the decision on which targeting method to choose will be based on the qualitative factors just discussed, some empirics can also be helpful. One part of these comes from country-specific analysis, simulations, and modeling, and another part comes from international evidence.

Country-specific simulations can help quantify for a specific country some of the trade-offs involved in different design parameters—definitions of eligibility and targeting methods, the level of benefits, and the cost of the program—via their impacts on poverty or measures of targeting outcomes. A common scenario is to simulate different targeting methods for a fixed budget: a program that gives benefits to all children or to all the elderly versus a geographically targeted program, versus one that ranks households with a means test or PMT, or one that combines some or each of these methods. Often perfect targeting and universal benefits are simulated as well, not so much as policy proposals but to anchor the endpoints of the spectrum of choices. A complementary set of simulations may be done with the same scenarios for targeting methods but with the benefit kept constant and the required budget allowed to vary.

The simulations will be only approximations and must be interpreted as such. Simulations usually assume perfect implementation (100 percent take-up and no errors or fraud). Sometimes analysts try to make some allowance for imperfect execution, for example, by assuming that a certain portion of the target group will fail to apply for the program or be misassessed. Analysts may also reduce the benefits to be distributed by different amounts to approximate administrative costs. More often, the scenarios

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are left simple and the caveats are handled in interpretation. Even with adjustments, the simulations cannot deal so well with intangibles, for example, the social acceptance of some methods over others or the interaction between methods, politics, and budgets.

The metrics used to assess the simulations are important: • This chapter recommends giving great weight to the impact on the poverty gap in the assessment. This is relatively simple to interpret and explain to policy makers and the public. It embodies a social welfare function that gives greater weight to the poorest than the less poor and the only just poor. It will thus register changes when a transfer is received by a poor person, even if the transfer is insufficient to lift that person fully out of poverty.21 • Understanding the impact on the poverty headcount is also useful and easy to explain. However, it gives a lot of weight to transfers to those just around the poverty line rather than to the poorest as the former are easier to bring out of poverty. • Looking at errors of exclusion or inclusion separately may also be useful as there may be special political sensitivities to these, but they are inherently partial measures.

An example of such a simulation is presented in table 5.3 and figures 5.7 and 5.8, comparing various categories with a household-specific method and pure geographic targeting. The simulations are for a large and diverse middle-income country with average levels of inequality and poverty at the $3.20 line of around 30 percent. In the example, a policy maker who wants to reduce poverty has a budget of 0.5 percent of GDP. With a 30 percent poverty rate, this budget implies an average transfer equivalent to 14 percent of the poverty line for each member of the household.

The policy maker then has her analyst simulate different program designs and approaches. She simulates beneficiary eligibility based on: • A household-specific method. In this country, the policy maker may judge, based on rates of formality and the coverage of administrative registers, that PMT is the most pertinent among means testing/HMT/

PMT. Of course, the same sort of comparison can be done simulating the outcomes of means testing or HMT if one of them is deemed more pertinent, as shown in figure 3.3, in chapter 3, for HMT and box 5.8 for means testing. It is harder to simulate CBT. For guidance between PMT and CBT, the next subsection reviews the evidence from field experiments with comparative treatment arms. • Geographic targeting. • Categorical: all households with children younger than six years. • Categorical: all households with elderly members over age 64. • Categorical: all households with a widow younger than age 65.