Revisiting Targeting in Social Assistance by World Bank Publications

Revisiting Targeting in Social Assistance

428 | Revisiting Targeting in Social Assistance

Using big data for eligibility determination may raise social or ethical concerns that bear consideration. The use of data on personal actions, such as phone calls or social media posts, for eligibility determination takes on a larger cultural dimension of “Big Brother watching,” especially if people do not fully understand that it is not the content of the conversations that is monitored but the somewhat less intimate details of their frequency, length, origin, whether incoming or outgoing, and text or voice (although it does include who else you have been talking to). Will people feel comfortable if how they chat with friends by phone, Facebook, or Twitter; search on Google; or buy on Amazon influences their eligibility for social protection programs? And how will the outcomes be explained? Although this book is not the place, we believe it is important that a deeper look be made into not just what legal protections are required governing data ownership and protection, but more broadly what sociocultural considerations should be explicitly brought to bear on these issues. Big data and machine learning may raise some significant human rights issues (see box 6.10), although work is underway to try to provide privacy guarantees while still allowing public good applications.81 In the case of Togo, the academic team carefully tested for demographic parity and

BOX 6.10

Machine Learning, Big Data, and Human Rights Machine learning, private big data, and biometric technology may generate gains to the delivery system and selection of beneficiaries of social protection programs, but they also pose risks and challenges that must be considered and minimized or mitigated. Among the advantages, biometric identiﬁcation systems help in uniquely identifying people for social protection through systems such as ﬁngerprints, iris, and face recognition, allowing not only such identification but deduplication and interoperability of systems. Machine learning and big data can help in understanding poverty and patterns of need, matching people to programs, and changing the interaction between people and the state (Gelb and Metz 2018). However, there are multiple risks that have implications for human rights. Ohlenburg (2020a) and Sepulveda (2018) are thoughtful sources. Among the risks they cite are (1) inaccuracy of data or exclusions from data or algorithms, (2) identity theft/data protection, and (3) security risks and the misuse of data. 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