Revisiting Targeting in Social Assistance by World Bank Publications

Revisiting Targeting in Social Assistance

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accurate PMT prediction might be, as well as identify some variables that would need to be in the model regardless of their significance level. An intriguing way of overcoming some of the limitations of traditional household surveys that are designed to be representative of the general population is to use data that are more keyed to the poorer population. Some surveys oversample to be able to estimate proper indicators among the poor. For example, in Mexico, the Socioeconomic Conditions Module was created as a complement to the National Survey of Household Income and Expenditure (ENIGH), with the purpose of providing the necessary information for the National Council for the Evaluation of Social Development Policy to carry out the measurement of multidimensional poverty at the national and state scales. The ENIGH and Socioeconomic Conditions Module combination was implemented starting in 2008, and to be able to estimate poverty and multidimensional poverty by state, the ENIGH sample, which was 35,146 households, almost doubled to reach 70,106 households. Another option is to use data or samples from the social registry, which by design is concentrated among the poorer (see box 6.7).

BOX 6.7

Using Data from the Social Registry to Update PMT Formulae As more countries are investing in developing integrated social registries to support the delivery of one or many programs, the data gathered can be used to calibrate beneficiary selection formulae in ways that overcome some of the limitations of traditional household surveys. Paes de Barros et al. (2016) show that basing a proxy means testing (PMT) formula on the Brazilian Unified Registry of Social Programs (Cadastro Único, or CadÚnico) would improve eligibility determination for the Renda Melhor guaranteed minimum income–type program created by the state of Rio de Janeiro in 2011, to complement the Bolsa Familia program for extremely poor families. Until 2016, the Renda Melhor program offered each of the Bolsa Familia beneficiaries in the state of Rio de Janeiro an extra benefit that would complement the post–Bolsa Familia beneficiary income by the income gap needed to reach to the extreme poverty line of R$100 (US$29) per capita. Therefore, to be in the program, a family should be eligible for the Bolsa Familia program through its means-test approach continued next page

Revisiting Targeting in Social Assistance

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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