7.4 Targeting Differential

476 | Revisiting Targeting in Social Assistance

Table 7.4 Targeting Differential

PL

P20

Program size EE IE TD EE IE TD Scenario 1 (%) 20 50 0 50 0 0 100 Scenario 2 (%) 20 50 0 50 50 50 0 Scenario 3 (%) 20 50 0 50 100 100 −100 Scenario 4 (%) 100 0 60 40 0 80 20 Source: Original compilation for this publication. Note: EE = exclusion error; IE = inclusion error; PL = poverty line; P20 = relative poverty line (the poorest 20 percent); TD = targeting differential.

real poverty line. Scenario 3, which misses the two poorest individuals, has a TD of −100 percent for the poorest 20 percent. For the largest program, scenario 4, the zero exclusion error does not imply perfect method performance according to the TD. For the real poverty line, scenario 4 would have the worst performance among the four cases, and for the poorest 20 percent, its performance is close to that of scenario 2, which has exclusion errors and is far from the perfect case in scenario 1.

Wiesmann et al. (2009) suggest looking at the errors in a different way to assess performance as the indicators are clearly related since a larger number of individuals correctly classified as eligible means coverage improvements and reduction of inclusion errors, when holding fixed the size of the population to be protected by the social program. This case is illustrated by the improvements that occur when moving from scenario 3 to scenario 2 to scenario 1. Coverage increases from 0 to 50 and then to 100 percent as inclusion error drops from 100 to 50 to 0 percent for the poorest 20 percent. Increasing the number of good matches at the expense of large increases in the number of targeted units does not help, as scenario 4 shows. In scenario 4, the increase in targeted units from 2 to 10 succeeds in reaching all of the poorest 20 percent, but inclusion errors reach 80 percent.

Wiesmann et al. (2009) present other indicators generated from the same 2 x 2 table, specificity and positive predictive value. The specificity suggests good performance by looking at the rate of proper bad matches, that is, the number of noneligible households that are properly classified as noneligible. The positive predictive value measures the good matches, that is, the eligible who are correctly selected as eligible. A well-performing program will have not only high coverage of the intended population, but also high specificity and high positive predictive value when suitable eligibility criteria are chosen to determine participation. The detailed formulae are presented in annex 7A.