Introduction

7

Measuring the Performance of Targeting Methods

Phillippe Leite and Priyanka Kanth

Introduction

Too often, a narrow set of measures, limited data, and incautious inferences are used to drive policy discussions, resulting in misleading or incomplete conclusions on the performance of the targeting method used for a program. The literature on the topic is vast1 and this chapter builds on it. A common error is focusing too much on simplistic errors of inclusion and exclusion, especially without considering program size. It is preferable to use measures that consider the full distribution and multiple dimensions of the program. Judgments about the findings must consider any limitations to what can be observed in the evaluation data being used, often from household surveys, with respect to definitions of the unit of observation, sample, measures of well-being, timing of the observations, and so forth. It is also important to understand the program context, rules, and implementation procedures to draw appropriately nuanced conclusions.

The objective of this chapter is to help policy makers, program administrators, and their advisers understand which measurements are most suited to answer which policy questions and to be able to critically read analyses and pick up on weaknesses in the choice of indicators, data, or conclusions drawn. The chapter starts with an illustration of analysis of a real program and some of the nuances involved in developing a good understanding of its strengths and weaknesses with respect to the method used. The chapter