Some Starting Considerations about Data—Traditional and Big

342 | Revisiting Targeting in Social Assistance

and inference. Thus, the chapter begins with a section on big data writ large and then integrates more details as pertinent in the method-specific sections.

The chapter focuses on the targeting methods that are most intensive in data and/or inference: geographic targeting, means testing, hybrid means testing (HMT), proxy means testing (PMT), machine learning–based extensions of PMT, and community-based targeting (CBT) are discussed, in that order. The chapter gives longer treatment to PMT and its machine learning–based extensions because their heavy reliance on inference requires elaboration on the choices of the algorithms involved. Means testing in contrast, is data intensive, but essentially it relies on addition and subtraction rather than any more complicated math. It may not be possible to do means testing if the available data are inadequate, but it is relatively shorter and simpler to explain the how-tos where data are available. The chapter does not discuss demographic targeting because it requires data—essentially age, maybe civil status, and identification (ID) of some sort—that are not complicated and there is no inference. The sort of basic poverty profiling that is needed to know how age correlates with welfare, if that is desired, is well known and illustrated in chapter 5.

This chapter can be read at multiple levels. It is meant to guide the statisticians, econometricians, or data scientists who may conduct the technical work involved in implementing the detailed design or reform of a targeting method. It is also meant to provide an overview of the choices involved for policy makers whose statistics and mathematics skills are basic or have perhaps grown a bit rusty. These latter readers may not capture every detail in the sections on PMT and machine learning, but they should be able to capture the big ideas.

Some Starting Considerations about Data—Traditional and Big

“Traditional” data for targeting mostly come from household surveys and applicants for programs. The data are gathered expressly to measure welfare. Household surveys such as those used to measure income or consumption and other aspects of the population’s welfare, or to provide weights for the consumer price index, can be detailed. However, they are always confined to the sample, periodicity, and questionnaire, which, even when generous by survey standards, are limited relative to the scope and dynamics that are desirable for eligibility determination. Thus, social protection programs that determine eligibility on an individual- or household-specific basis usually mount their own data collection efforts (whether in office or home visits or via virtual channels) as part of the application process. However, because the application data are collected for this sole purpose, it is usual to try to