| Description |
1 online resource (xxxiv, 539 pages) : color illustrations, maps |
| Series |
Human development perspectives |
|
Human development perspectives.
|
| Contents |
Foreword -- Acknowledgments -- About the Editors and Contributors -- Abbreviations -- Overview. PREMISE: Most countries target some social protection programs to selected people; reviewing the current knowledge on this subject can inform the formulation of policy ; Framing and Terminology: From Objectives to Outcomes ; Synopsis of 10 Key Messages ; Notes ; References -- Chapter 1: Targeting within Universal Social Protection. Essay 1: Where does targeting fit conceptually within Universal Social Protection? ; Essay 2: Where does targeting fit practically within Universal Social Protection? ; Essay 3: What is the rationale for targeting by welfare or other metrics? ; Essay 4: How does thinking about shocks rather than static poverty change the framework? ; Essay 5: Is targeting the poor important for outcomes other than poverty? ; Essay 6: Why is redistribution important? ; Essay 7: What does the distribution of taxes imply about the distribution of transfers? ; Essay 8: Can budgets be raised over time to reduce the need for targeting? ; Essay 9: Does universality increase budgets and thus reduce the need for prioritizing the needy? ; Essay 10: How do human rights frameworks view targeting? ; Conclusion ; Notes ; References -- Chapter 2: Unpacking the empirics of targeting in low- and middle-income countries. Measurement and interpretation ; Recent ASPIRE survey-based evidence of targeting outcomes ; Evidence base for the costs of poverty targeting ; Summary ; Annex 2A: List of ASPIRE household surveys used in analysis ; Annex 2B: Distribution of social assistance beneficiaries, by program type ; Annex 2C: Coverage and distribution of social assistance beneficiaries ; Annex 2D: Costs of operating social registries ; Notes ; References -- Chapter 3: Moving from general abstraction toward implementable concepts in stability and in crisis. Part I: Even in times of stability, welfare measurement for eligibility determination is complex ; Part II: Dynamics add some further challenges ; Notes ; References -- Chapter 4: Improving targeting outcomes through attention to delivery systems. Introduction ; Fortifying weak links in the delivery chain to reduce errors of exclusion and inclusion ; Planning and adapting delivery systems for crisis response ; Client interface: the interaction between people and institutions ; Data systems and their role in supporting eligibility determination and recertification ; Conclusion ; Notes ; References -- Chapter 5: Choosing among targeting methods. Introduction ; Patterns in using and combining targeting methods ; Reflections on patterns of use of targeting methods ; Considerations in choosing among welfare targeting methods ; Methods that rank people according to welfare ; Addition of quantitative information to the decision-making process ; Summary ; Notes ; References -- Chapter 6: How to harness the power of data and inference: technical discussion for selected targeting methods. Some starting considerations about data: traditional and big ; Geographic targeting: big data are revolutionizing poverty mapping ; Key elements for means tests ; Key elements for hybrid means testing ; Key elements for PMT methods: traditional models, processes, and machine learning ; PMT, machine learning, and big data: what do we know? ; Key elements for community-based targeting ; Conclusion ; Notes ; References -- Chapter 7: Measuring the performance of targeting methods. Introduction ; Illustrative case study on how to avoid spurious interpretations ; What to look for when conducting method assessments ; Concluding remarks ; Annex 7A: Formulae for the indicators ; Notes ; References -- Chapter 8: Machine learning and prediction of beneficiary eligibility for social protection programs. Introduction ; Practical considerations for policy makers ; Assessing machine learning for household welfare prediction: potential and provisos ; Applying machine learning processes to traditional PMT models ; Machine learning models ; Assessing machine learning performance on PMT data: a benchmarking experiment ; Overall model choice may not matter: there are few differences between models, especially sizable difference, when they are assessed using standard machine learning metrics ; Policy choice matters: performance on standard machine learning metrics is different from performance on targeting-related metrics ; Model performance differs by which metric is used but is generally irrelevant when magnitude of the difference is considered ; Implementation matters: outcomes using a threshold approach are different from outcomes using a quota approach ; Annex 8A: Data ; Annex 8B: Statistical methodology and supplementary analysis ; Notes ; References |
| Summary |
"Targeting is a commonly used, but much debated, policy tool within global social assistance practice. Revisiting Targeting in Social Assistance: A New Look at Old Dilemmas 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. The book begins by considering why or whether or how narrowly or broadly to target different parts of social assistance and updates the global empirics around the outcomes and costs of targeting. It illustrates the choices that must be made in moving from an abstract vision to implementable definitions and procedures, and in deciding how the choices should be informed by values, empirics, and context. The importance of delivery systems and processes to distributional outcomes are emphasized, and many facets with room for improvement are discussed. The book also explores the choices between targeting methods and how differences in purposes and contexts shape those. The know-how with respect to the data and inference used by the different household-specific targeting methods is summarized and comprehensively updated, including a focus on "big data" and machine learning. A primer on measurement issues is included. Key findings include the following: Targeting selected categories, families, or individuals plays a valuable role within the framework of universal social protection. Measuring the accuracy and cost of targeting can be done in many ways, and judicious choices require a range of metrics. Weighing the relatively low costs of targeting against the potential gains is important. Implementing inclusive delivery systems is critical for reducing errors of exclusion and inclusion. Selecting and customizing the appropriate targeting method depends on purpose and context; there is no method preferred in all circumstances. Leveraging advances in technology -- ICT, big data, artificial intelligence, machine learning -- can improve targeting accuracy, but they are not a panacea; better data matters more than sophistication in inference. Targeting social protection should be a dynamic process."-- Provided by publisher |
| Bibliography |
Includes bibliographical references |
| Notes |
Description based on resource, viewed August 29, 2022 |
| Subject |
Public welfare.
|
|
Human services.
|
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welfare services.
|
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social services.
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Human services
|
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Public welfare
|
| Form |
Electronic book
|
| Author |
Grosh, Margaret E., editor.
|
|
Leite, Phillippe George, editor.
|
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Wai-Poi, Matthew, editor.
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Tesliuc, Emil D. (Emil Daniel), editor.
|
| ISBN |
9781464818158 |
|
1464818150 |
|
