Patterns in Using and Combining Targeting Methods

Choosing among Targeting Methods | 263

methods, provides some observations on the factors that have influenced their development, and discusses elements that may help guide the selection of the method or methods in a given context. It also examines how frequently different methods are used and combined. The attention to delivery systems covered in chapter 4 is also a core topic for all the methods covered in this chapter, as a good delivery system is required for any method to work optimally. The choice of targeting method in a particular setting should also reflect a deep knowledge of the how-tos of the candidate method(s), a topic taken up in chapter 6. Thus, these two chapters might be read iteratively.

Patterns in Using and Combining Targeting Methods

Before turning to considerations for choosing among the different methods, this section briefly examines how commonly each method is used. The data come from a database that is broad but as yet imperfect for the purpose.2 The Atlas of Social Protection: Indicators of Resilience and Equity (ASPIRE) database includes distributional performance indicators derived from household survey–based data on program categories, as described in chapter 2, as well as administrative data at the program level. The administrative data include information on program size (number of beneficiaries and amount of spending) and design parameters including, for social assistance programs, the targeting methods used. Data are available on the targeting methods used for 1,985 of the 2,623 social assistance and general subsidies programs included in the ASPIRE administrative database. The targeting method(s) used for each program was coded by a consultant or World Bank staff member who contributed that particular line of data to the global data set. It was possible to code multiple targeting methods for a program.

About two-thirds of the programs use a single targeting method, with the remaining third using mixed methods. The majority of the latter category combine only two methods (68 percent of those with multiple methods); the remainder (only 10 percent of all programs) use three or more methods. This may be a lower bound on the use of mixed methods. A light scan of the raw data identified several programs that might have coded multiple methods, as apparently sometimes the coders recorded only what they thought of as the most important method. Categorical (as recorded in this data set) and means testing are the main methods that are used by themselves. Geographic, proxy means testing (PMT), and communitybased targeting (CBT) are rarely used alone.

Among the social assistance programs with information on the targeting method, about 40 percent use a household-level targeting method. This section counts programs that use either means testing/HMT),3 PMT, CBT,

264 | Revisiting Targeting in Social Assistance

or a combination. Among the programs that use household targeting, 62 percent are coded as using a means test, 31 percent as using PMT, and 22 percent as using CBT. The percentages sum to more than 100 because programs can use multiple methods; for example, this book discusses several examples of programs that combine PMT and CBT. Of the programs that use household-specific methods, 40 percent use only one method, and most often it is means testing. Where a household-specific method is combined with another method, the overwhelming complementary method is categorical (such as a means-tested child allowance or social pension, or a guaranteed minimum income with different filters for different categories of individuals).

Among the programs that were observed, three-quarters used a categorical method other than geographic targeting, either stand-alone or with another method. Many programs are inherently categorical in design. In looking at the types of programs coded as using categorical targeting, there is a predominance of those related to age, such as family and child allowances, nutrition programs, school feeding, scholarships, provision of school supplies, old-age pensions, and burial grants. These accounted for about three-quarters of all those labeled “categorical targeting” in this data set. The next largest set of categorically targeted programs are disability programs, about 10 percent. Programs for war veterans account for another 5 percent. Categorical targeting is also used in 43 percent of programs that are not inherently categorical, such as poverty alleviation programs, targeted subsidies, or emergency support.

Geographic targeting, which is conceptually another sort of categorical targeting but fortunately coded separately in these data, is used in a quarter of the programs but rarely as the only method. Again, this is interpreted as a lower bound figure due to undercoding of multiple methods. Some programs in Africa that are less than national in scope did not include geographic targeting in their coding, although in the framework of this book, they are called geographically targeted programs. The book also adds “geographic” to the descriptors in more subtle cases where the program is national but rationed and so the caseload is allocated geographically, although the coders did not code them as geographic.

The choice of methods varies by region and income level in unsurprising ways. Low-income and lower-middle-income countries are more likely to use geographic targeting (23 and 17 percent of the programs in these countries, respectively). This method is used relatively infrequently in uppermiddle-income and high-income countries (5 and 4 percent, respectively). Lower income countries are also most likely to use community-based methods (14 percent for low-income countries and 8 percent for lowermiddle-income countries) compared with richer countries, which almost never use CBT and are more likely to use means testing (29 percent of