Revisiting Targeting in Social Assistance by World Bank Publications

Revisiting Targeting in Social Assistance

350 | Revisiting Targeting in Social Assistance

been used since the 2000s to help social programs decide where to locate services. For social protection, they are often used to allocate budget or create quotas for the number of people who should be covered by programs in each area. In recent years, advanced technology for geotagging and processing large databases has allowed researchers and policy makers to explore different uses of small area estimation for supporting social protection and targeting, but mostly these sensing data are used in poverty assessments. The following discussion draws on Areias et al. (forthcoming), which summarizes the application of machine learning to big data to facilitate greater geographical targeting; chapter 8 summarizes the application of machine learning to PMT. Satellite imagery has been used to produce local area poverty estimation. For example, Engstrom, Hersh, and Newhouse (2021) use a convolutional neural network, which is a machine learning approach, with high- resolution satellite imagery, in Sri Lanka to classify objects (for example, roof type, paved roads, railroads, and so forth) (photo 6.1). They then impute welfare estimates using the Elbers, Lanjouw, and Lanjouw (2003) approach into the 2011 Census of Population and Housing. That is, for each household in the census, per capita consumption is estimated based on models developed from the Household Income and Expenditure Survey using household indicators that are common to both the census and the

Photo 6.1 Example of Classification of Developed Area (Buildings)

a. Raw image

b. Image with developed area classiﬁer

Source: Engstrom, Hersh, and Newhouse 2021. Note: Panels a and b, respectively, show the raw and classified images for developed area classifier from raw satellite imagery. The areas in green show true positive building classifications. The images in red show false positives, areas erroneously classified as buildings.

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