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Revisiting Targeting in Social Assistance

246 | Revisiting Targeting in Social Assistance

social programs. In the not so distant past, information collected by social programs was mostly in paper form and not very extensive. As such, it did not raise substantial issues of data privacy21 nor trigger complex responses to ensure data protection.22 With the generalization of digital information flows and increased use of digital technologies in social programs—ID systems, biometric data, interlinked data for eligibility determination, and digital payment systems—concerns about data privacy and data protection have become important (box 4.6). Data privacy risks can arise from any activity that collects, stores, or processes personal data. Among the information collected by social programs, most concerns are related to management of personal data (any information relating to an identified or identifiable individual), other personally identifiable information (information that permits the identity of an individual to be directly or indirectly inferred, or any information

BOX 4.6

Why Should Social Protection Practitioners Care about Data Protection? Social protection systems process (collect, use, store, and disclose) the personal data of applicants and program beneficiaries. These data need protection. Why? • If personal data are not adequately protected, the data subjects’ right to privacy may be violated and individuals may suffer material, physical, or symbolic harm. • Data protection is essential to create trust among social protection authorities, their staff, and clients. Lack of trust may restrain the access of vulnerable populations to social protection services and benefits, as they may fear that sharing their personal information will lead to harm, discrimination, stigmatization, or surveillance, among other risks. • For social protection practitioners, compliance with organizational or legal data protection and privacy frameworks is important to avoid penalties. • Social protection and privacy are human rights and, therefore, interdependent. This means that one needs the other to be fulfilled. Both are equally important. Source: SPIAC (forthcoming); Enabling Digital & GIZ (2020).

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