Revisiting Targeting in Social Assistance by World Bank Publications

Revisiting Targeting in Social Assistance

Improving Targeting Outcomes through Attention to Delivery Systems | 217

BOX 4 .2

How Big Should a Social Registry Be? Social registries or interoperable social information systems range from covering just a few percent of the population to nearly all (figure B4.2.1). A social registry that is built to support just one or a few very narrowly targeted programs may cover just a few percent of the population. But the increasing trend is for social registries to be used to support a wide range of social programs, and some of these may be more broadly targeted, with some even reaching high up the welfare distribution. Cash transfers may be targeted at the poor—covering only 10 percent or so of the population in countries with very low poverty or very low budgets. But child allowances or social pensions may strive to include all members of the age group. Subsidies for health

Figure B4.2.1 Coverage of Social Registries 100 90 80

Coverage (%)

70 60 50 40 30 20

Median = 21%

Argentina Uruguay Pakistan Dominican Republic Peru Saudi Arabia Chile Phillippines Colombia Jordan Turkey Zambia Ecuador Brazil Indonesia Ukraine Honduras Georgia Djibouti Cabo Verde Senegal Tunisia Yemen, Rep. West Bank and Gaza Gabon North Macedonia Mauritania Togo Haiti South Africa Iraq Benin Costa Rica Mauritius Tanzania Azerbaijan Ethiopia Jamaica Lebanon El Salvador Mali Grenada Bolivia Ghana China Sierra Leone Burkina Faso Eswatini Comoros Côte d’Ivoire Congo, Rep. Madagascar São Tomé and Príncipe Chad Zimbabwe Belize

Source: Delivery Systems Global Solutions Group, Social Protection and Jobs Global Practice, World Bank.

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Revisiting Targeting in Social Assistance

Articles inside

7.5 Performance Triangle for Two Programs

7.9 Relative Efficiency of Programs

Concluding Remarks

7.13 Exclusion and Inclusion Errors

the Poverty Line

7.12 Impacts on Poverty and Inequality

7.3 Inclusion and Exclusion Errors in a 10-Person Economy

7.4 Targeting Differential

What to Look for When Conducting Method Assessments

7.1 Social Program Coverage in Brazil

Illustrative Case Study on How to Avoid Spurious Interpretations

Introduction

References

Notes

B6.11.2 Household Consumption, Assets, and Land Access in PSNP Woredas, by PSNP Beneficiary Status, 2008

Conclusion

B6.11.1 Criteria for Selecting Beneficiary Households

Community-Based Targeting

Key Elements for Community-Based Targeting

6.10 Machine Learning, Big Data, and Human Rights

PMT, Machine Learning, and Big Data: What Do We Know?

Poor Households in Togo

Formulae

Its Drivers

B6.6.1 Distribution of Winners and Losers, by Demographic Group

6.8 Machine Learning Models That Are Commonly Used

B6.6.1 Timeline of Key Developments in Georgia’s Targeted Social Assistance Program

Causal Effect One

Updating the PMT Formula: The 2013–15 Reform of the Georgia Targeted Social Assistance Program

6.3 Illustration of PMT Weights for Selected Variables

Key Elements for PMT Methods: Traditional Models, Processes, and Machine Learning

B6.4.1 Summary of the Availability and Quality of Administrative Data in Tunisia, 2019

Hybrid Means Testing Targeting System: The Case of Tunisia

6.2 Romania: Asset-to-Income Conversion Coefficients

and Proxy Means Testing Models in Algeria

6.1 Albania: Imputation of Farm Income

Key Elements for Hybrid Means Testing

6.2 Income Test under the Hybrid Means Testing Approach

6.2 Treatment of Assets in Means-Tested Programs

Key Elements for Means Tests

Geographic Targeting: Big Data Are Revolutionizing Poverty Mapping

6.1 Example of Classification of Developed Area

6.1 Examples of Big Data

Some Starting Considerations about Data—Traditional and Big

Summary

Notes

References

Targeting Methods

Different Eligibility Approaches

Addition of Quantitative Information to the Decision-Making Process

5.8 Illustrative Lessons from the Simulations Literature

Country

5.6 Profiling Job Seekers to Differentiate Support

Methods That Rank People According to Welfare

a Fragile State

from Mongolia, Bolivia, and Nepal

Considerations in Choosing among Welfare Targeting Methods

Category in Determining Eligibility or Benefits

B5.3.2 Woreda Selection Does Not Add Much to the PSNP’s Targeting Performance

5.1 Humanitarian–Social Protection Alignment

Reflections on Patterns of Use of Targeting Methods

Patterns in Using and Combining Targeting Methods

5.1 Common Targeting Methods

Notes

Introduction

Conclusion

References

4.7 Examples of How Countries Are Providing Data Security

Data Protection?

from Chile and Moldova

Planning and Adapting Delivery Systems for Crisis Response

Program Access That Lead to Errors of Exclusion

Data Systems and Their Role in Supporting Eligibility Determination and Recertification

Client Interface: The Interaction between People and Institutions

Jigisemejiri Program

4.2 How Big Should a Social Registry Be?

Support Program

Fortifying Weak Links in the Delivery Chain to Reduce Errors of Exclusion and Inclusion