[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118074-en":3,"doc-seo-118074-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118074,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Targeting Social Protection Programs with Machine Learning and Digital Data","Social protection programs are vital for assisting people in poverty, yet many governments and humanitarian agencies lack resources to accurately identify all eligible households. This dissertation examines how novel digital data—such as mobile phone and satellite imagery—can address poor-quality, incomplete, and outdated poverty information in low-income settings. Using machine learning, satellite imagery, and mobile data, the studies in Togo, Afghanistan, and Bangladesh evaluate targeting methods in large real-world interventions, comparing them to administrative eligibility rules and traditional survey-based poverty measures.","UC Berkeley  \nRecent Work  \nTitle  \nTargeting Social Protection Programs with Machine Learning and Digital Data  \nPermalink  \n[https://escholarship.org/uc/item/8wz9q7hv](https://escholarship.org/uc/item/8wz9q7hv)  \nAuthor  \nAiken, Emily  \nPublication Date  \n2024-05-30  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nTargeting Social Protection Programs with Machine Learning and Digital Data  \nby  \nEmily Aiken  \nA dissertation submitted in partial satisfaction of the requirements for the degree of  \nDoctor of Philosophy  \nin  \nInformation Science  \nin the  \nGraduate Division  \nof the  \nUniversity of California, Berkeley  \nCommittee in charge:  \nProfessor Joshua Blumenstock, Chair  \nProfessor Jennifer Chayes  \nProfessor Hany Farid  \nPofessor Solomon Hsiang  \nProfessor Edward Miguel  \nSpring 2024  \nTargeting Social Protection Programs with Machine Learning and Digital Data  \nCopyright 2024  \nby  \nEmily Aiken  \n1  \nAbstract  \nTargeting Social Protection Programs with Machine Learning and Digital Data  \nby  \nEmily Aiken  \nDoctor of Philosophy in Information Science  \nUniversity of California, Berkeley  \nProfessor Joshua Blumenstock, Chair  \nSocial protection programs are essential to assisting the poor, but governments and humanitarian agencies are rarely resourced to provide aid to all those in need, so accurate targeting of benefits is critical. In developed economies, targeting decisions typically rely on administrative income data or broad survey-based social registries. In lowincome countries, however, poverty information is rarely reliable, comprehensive, or up-to-date. Novel sources of digital data — from mobile phones and satellites, in particular—are well suited to fill this gap: they are predictive of wealth in low-income contexts and ubiquitously collected. The research studies in this dissertation design and evaluate new methods for targeting aid in low-resource contexts using machine learning, satellite imagery, and mobile phone data, and evaluate these methods in large, real-world interventions. Across social protection programs in Togo, Afghanistan, and Bangladesh, the studies in this dissertation show that targeting methods based on machine learning and digital data sources identify poor households more accurately than methods based on categorical eligibility criteria like geography or occupation, but typically less accurately than traditional survey-based poverty measurement approaches. These results highlight the potential for digital data and machine learning to improve the targeting of humanitarian aid, particularly when traditional poverty data are unavailable or out-of-date and in settings where conflict, environmental conditions, or health concerns render primary data collection infeasible. These studies also provide empirical evidence on the limitations and risks of digital and algorithmic targeting approaches, including privacy, transparency, fairness, and digital exclusion.  \ni  \nContents  \nContents i  \nAcknowledgements iii  \n1 Introduction 1  \n1.1 Traditional poverty targeting approaches in low-income contexts ...... 2  \n1.2 Evaluating poverty targeting approaches .................... 4  \n1.3 Measuring poverty with machine learning and digital data .......... 6  \n1.4 Uses of digital data for targeting social protection programs ......... 8  \n1.5 Contributions of this dissertation ......................... 8  \n2 Targeting aid with machine learning and digital data in Togo 11  \n2.1 Introduction and context .............................. 12  \n2.2 Methods ....................................... 16  \n2.3 Results ........................................ 32  \n2.4 Discussion ...................................... 47  \n3 Ultra-poverty targeting with machine learning and phone data in Afghanistan 52  \n3.1 Introduction and context .............................. 52  \n3.2 Methods ....................................... 53  \n3.3 Results ......................","cbCaikiu5L5TKGei","https://ap.wps.com/l/cbCaikiu5L5TKGei","pdf",22991126,1,205,"English","en",105,"# Contents\n## 1 Introduction\n## 2 Targeting aid with machine learning and digital data in Togo\n## 3 Ultra-poverty targeting with machine learning and phone data in Afghanistan\n## 4 Comparing community-based and phone-based targeting in Bangladesh\n## 5 Measuring cash transfer impacts with surveys versus digital traces\n## 6 Discussion\n## Bibliography\n## A Supporting materials for Chapter 2\n## B Supporting materials for Chapter 3\n## C Supporting materials for Chapter 4\n## D Supporting materials for Chapter 5","[{\"question\":\"Why is accurate targeting critical for social protection programs?\",\"answer\":\"Social protection programs are essential for assisting people in poverty, but governments and humanitarian agencies often lack enough resources to provide aid to everyone in need, making precise targeting critical.\"},{\"question\":\"How does the dissertation use digital data to improve poverty targeting?\",\"answer\":\"It uses machine learning with digital data sources such as mobile phone data and satellite imagery, which can predict wealth in low-income contexts and are collected more ubiquitously than reliable poverty surveys.\"},{\"question\":\"What do the results show when comparing digital-data targeting to survey-based methods?\",\"answer\":\"Across interventions in Togo, Afghanistan, and Bangladesh, machine learning and digital data identify poor households more accurately than categorical eligibility criteria, but typically less accurately than traditional survey-based poverty measurement approaches.\"}]","Targeting Social Protection Programs with Machine Learning and Digital Data | 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is accurate targeting critical for social protection programs?","Question",{"text":75,"@type":76},"Social protection programs are essential for assisting people in poverty, but governments and humanitarian agencies often lack enough resources to provide aid to everyone in need, making precise targeting critical.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the dissertation use digital data to improve poverty targeting?",{"text":80,"@type":76},"It uses machine learning with digital data sources such as mobile phone data and satellite imagery, which can predict wealth in low-income contexts and are collected more ubiquitously than reliable poverty surveys.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the results show when comparing digital-data targeting to survey-based methods?",{"text":84,"@type":76},"Across interventions in Togo, Afghanistan, and Bangladesh, machine learning and digital data identify poor households more accurately than categorical 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