Targeting is a commonly used, but much debated, policy within global social assistance practice. This book examines the well- known dilemmas in light of the growing body of experience, new implementation capacities, and the potential to bring new data and data science to bear.
- Chapter 1 presents a series of essays on the factors that shape choices around why or whether or how narrowly/broadly to target different parts of social assistance.
- Chapter 2 updates the global empirics around the outcomes and costs of focusing benefits on the poor or vulnerable.
- Chapter 3 illustrates the options and choices that must be made in moving from an abstract vision of focusing resources on the poor or vulnerable to more specific concepts and implementable definitions and procedures, and how the many choices should be informed by values, empirics and context.
- Chapter 4 provides a brief treatment of delivery systems and processes showing their importance to distributional outcomes and suggesting the many facets with room for improvement.
- Chapter 5 discusses the choice between targeting methods, how differences in purposes and contexts shape those.
- Chapter 6 summarizes and comprehensively updates the know-how with respect to the data and inference used by the different household-specific targeting methods.
- Chapter 7 contains a primer on measurement issues, going much deeper than usual and explaining how better measurement can lead to clearer understanding of targeting issues.
- Chapter 8 explores machine learning algorithms for household-specific mechanisms for eligibility determination.