Scaling up Social Assistance Where Data is Scarce:Opportunities and Limits of Novel Data and AI

During the recent Covid-19 shock (2020/21), most countries used cash transfersto protect the livelihoods of those affected by the pandemic or by restrictions on mobilityor economic activities, including the poor and vulnerable. While a large majority ofcountries mobilized existing programs and/or administrative databases to expand supportto new beneficiaries, countries without such programs or databases were severely limitedin their capacity to respond. Leveraging the Covid-19 shock as an opportunity to leapfrogand innovate, various low-income countries used new sources of data and computationalmethods to rapidly develop -level welfare-targeted programs. This paper reviews bothcrisis-time programs and regular social protection operations to distill lessons that couldbe applicable for both contexts. It examines three programs from the Democratic Republicof Congo, Togo, and Nigeria that used geospatial and mobile phone usage data and/orartificial intelligence (AI), particularly machine learning methods to estimate the welfareof applicants for individual-level welfare targeting and deliver emergency cash transfersin response to the pandemic. Additionally, it reviews two post-pandemic programs, inLome, Togo and in rural Lilongwe, Malawi, that incorporated those innovations into themore traditional delivery infrastructure and expanded their monitoring and evaluationframework. The rationale, key achievements, and main challenges of the various approachesare considered, and cases from other countries, as well as innovations beyond targeting,are taken into account. The paper concludes with policy recommendations and promisingresearch topics to inform the discourse on leveraging novel data sources and estimationmethods for improved social assistance in and beyond emergency settings.