7.13 Exclusion and Inclusion Errors

Measuring the Performance of Targeting Methods | 489

would move them above the poverty line, but both of them would receive more money than needed to reach the line. As such, the cost-benefit ratio is 0.70 ($21 was needed and $30 was transferred), and the Gini drops by 18 percent. For scenario 4, there is no impact on the poverty headcount as the transfers were too small, but there is some reduction in the poverty gap (30 percent) as $12 reached the poor of the $30 transferred, representing a cost-benefit ratio of 0.4, and the Gini dropped by 9 percent.

To conclude, many assessments are presented as functions of inclusion and exclusion errors, ignoring the welfare distribution or impacts on poverty and inequality. Exclusion and inclusion errors are rather blunt measures and miss much of the redistributive impacts of social transfers, which are important features of social programs. Both errors are also calculated with different thresholds for different countries/programs, which makes benchmarking difficult. Furthermore, they often do not take fully into account the specificities of the program’s objectives, which may include goals other than poverty reduction. Finally, most such analyses or simulations are based on national household surveys, making little use of data from impact evaluations or process evaluations that may shed light on elements that are important for the success or failure of design or implementation.

5. Comparison of performance across programs of different design must consider multiple performance indicators.

This subsection provides an illustration of a performance assessment using a real household budget survey. Tables 7.13 to 7.16 summarize some of the results for two programs observed in the data, benchmarking against the extreme poverty line. The current poverty rate, FGT(0), in the exercise is 10 percent, FGT(1) is 2.3 percent, and the Gini inequality measure is 0.283.

The initial assessment compares the exclusion and inclusion errors of programs A and B. If the main concern is errors of exclusion, then program A seems more acceptable. If it is errors of inclusion, program B would be preferred. These indicators do not convey the full picture of the programs and their potential impacts, so it is worth digging a bit deeper.

Table 7.13 Exclusion and Inclusion Errors

Program A Program B Exclusion error (%)

55.7 63.7

Source: Original compilation for this publication.

Inclusion error (%)

88.7 49.9