Scaling up Social Assistance Where Data is Scarce

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dc.creatorOkamura, Yuko
dc.creatorOhlenburg, Tim
dc.creatorTesliuc, Emil
dc.date2024-05-15T19:01:03Z
dc.date2024-05-15T19:01:03Z
dc.date2024-05-15
dc.date.accessioned2026-07-01T00:34:42Z
dc.descriptionDuring the recent Covid-19 shock (2020/21), most countries used cash transfers to protect the livelihoods of those affected by the pandemic or by restrictions on mobility or economic activities, including the poor and vulnerable. While a large majority of countries mobilized existing programs and/or administrative databases to expand support to new beneficiaries, countries without such programs or databases were severely limited in their capacity to respond. Leveraging the Covid-19 shock as an opportunity to leapfrog and innovate, various low-income countries used new sources of data and computational methods to rapidly develop -level welfare-targeted programs. This paper reviews both crisis-time programs and regular social protection operations to distill lessons that could be applicable for both contexts. It examines three programs from the Democratic Republic of Congo, Togo, and Nigeria that used geospatial and mobile phone usage data and/or artificial intelligence (AI), particularly machine learning methods to estimate the welfare of applicants for individual-level welfare targeting and deliver emergency cash transfers in response to the pandemic. Additionally, it reviews two post-pandemic programs, in Lomé, Togo and in rural Lilongwe, Malawi, that incorporated those innovations into the more traditional delivery infrastructure and expanded their monitoring and evaluation framework. The rationale, key achievements, and main challenges of the various approaches are considered, and cases from other countries, as well as innovations beyond targeting, are taken into account. The paper concludes with policy recommendations and promising research topics to inform the discourse on leveraging novel data sources and estimation methods for improved social assistance in and beyond emergency settings.
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dc.formattext/plain
dc.identifierhttp://documents.worldbank.org/curated/en/099050724145524418/P17191311f545a0971b3db17f9d6820d240
dc.identifierhttps://hdl.handle.net/10986/41548
dc.identifier10.1596/41548
dc.identifier.urihttp://hdl.handle.net/123456789/406530
dc.languageEnglish
dc.languageen_US
dc.publisherWashington, DC: World Bank
dc.relationSocial Protection and Jobs Discussion Papers; 2402
dc.rightsCC BY-NC 3.0 IGO
dc.rightshttps://creativecommons.org/licenses/by-nc/3.0/igo
dc.rightsWorld Bank
dc.subjectSOCIAL PROTECTION AND LABOR
dc.subjectPOVERTY
dc.subjectSOCIAL ASSISTANCE
dc.subjectCASH TRANSFERS
dc.subjectACCESS TO SOCIAL PROGRAMS
dc.subjectADAPTIVE SOCIAL PROTECTION
dc.subjectSOCK RESPONSE
dc.subjectTECHNOLOGY
dc.subjectINNOVATIONS
dc.subjectG2P (GOVERNMENT TO PERSON) PAYMENT
dc.subjectNOVEL DATA SOURCE
dc.subjectCALL DETAIL RECORDS (CDR)
dc.subjectSATELLITE IMAGERY
dc.subjectMACHINE LEARNING
dc.subjectARTIFICIAL INTELLIGENCE
dc.subjectTARGETING
dc.subjectGEOSPATIAL TARGETING
dc.subjectEMERGENCY RESPONSES
dc.subjectCOVID-19 RESPONSES
dc.subjectNO POVERTY
dc.subjectSDG 1
dc.subjectGOOD HEALTH AND WELL-BEING
dc.subjectSDG 3
dc.subjectDECENT WORK AND ECONOMIC GROWTH
dc.subjectSDG 8
dc.subjectINDUSTRY, INNOVATION AND INFRASTRUCTURE
dc.subjectSDG 9
dc.subjectPEACE, JUSTICE AND STRONG INSTITUTIONS
dc.subjectSDG 16
dc.titleScaling up Social Assistance Where Data is Scarce
dc.titleOpportunities and Limits of Novel Data and AI
dc.typeWorking Paper (Numbered Series)

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