In recent years, the World Bank’s Poverty and Equity Global Practice (GP) has increased its capacity to provide more timely information on welfare. This typology takes stock of the growing body of work on real-time welfare monitoring, bringing together existing resources and lessons learned in one place. It aims to offer an overarching roadmap to help teams navigate different approaches and identify the best fit for answering a specific question in a given context. The “best fit” approach may differ across settings depending on a country’s data ecosystem and implementation constraints. This typology systematizes the decision-making process by laying out the various advantages, disadvantages, underlying data requirements, and assumptions of different approaches. While primarily drawing the Poverty and Equity GP’s work, the typology aims to contextualize real-time monitoring within a broader body of research and towards recent innovations in the field.
Authors
- Citation
- “ World Bank . 2024 . Measuring Welfare When It Matters Most: A Typology of Approaches for Real-time Monitoring . © Washington, DC: World Bank . http://hdl.handle.net/10986/42120 License: CC BY-NC 3.0 IGO . ”
- Collection(s)
- Other Social Protection Study
- Identifier externaldocumentum
- 34386421
- Identifier internaldocumentum
- 34386421
- Pages
- 106
- Published in
- United States of America
- Report
- 193390
- Rights
- CC BY-NC 3.0 IGO
- Rights Holder
- World Bank
- Rights URI
- https://creativecommons.org/licenses/by-nc/3.0/igo
- UNIT
- Prosperity-Poverty and Equity-GE (EPVGE)
- URI
- https://hdl.handle.net/10986/42120
- date disclosure
- 2024-09-05
- region geographical
- World
Files
Table of Contents
- Contents 3
- Acknowledgments 5
- Introduction 7
- Methods for Nowcasting Welfare 17
- With a Focus on Monetary Poverty 17
- 1.1 Nowcasting Welfare Using Survey and Other Non-survey Covariates 18
- 1.2 Nowcasting Welfare Using GDP Growth 27
- 1.3 Nowcasting Welfare Using Microsimulations and General Equilibrium Models 33
- Harnessing Data for Real-time Welfare Monitoring 39
- 2.1 Rapid Survey Data Collection 40
- 2.2 Geospatial Data 52
- 2.3 Digital Trace Data 62
- 2.4 Administrative Data 67
- Moving Forward 71
- Identifying Areas for Advancement 71
- References 73
- Annex 1. 95
- Summary of Models Used to Update Poverty Estimates 95
- Annex 2. 97
- Commonly Used Machine Learning Models for Estimating Poverty 97
- Annex 3. 100
- Summary of All Data Sources 100
- Annex 4. 103
- Nowcasting Impacts of Shocks 103
- Vulnerability and Damage Functions 103
