Scaling up Social Assistance Where Data is Scarce
During 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.
| Main Authors: | , , |
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| Format: | Working Paper (Numbered Series) biblioteca |
| Language: | English en_US |
| Published: |
Washington, DC: World Bank
2024-05-15
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| Subjects: | SOCIAL PROTECTION AND LABOR, POVERTY, SOCIAL ASSISTANCE, CASH TRANSFERS, ACCESS TO SOCIAL PROGRAMS, ADAPTIVE SOCIAL PROTECTION, SOCK RESPONSE, TECHNOLOGY, INNOVATIONS, G2P (GOVERNMENT TO PERSON) PAYMENT, NOVEL DATA SOURCE, CALL DETAIL RECORDS (CDR), SATELLITE IMAGERY, MACHINE LEARNING, ARTIFICIAL INTELLIGENCE, TARGETING, GEOSPATIAL TARGETING, EMERGENCY RESPONSES, COVID-19 RESPONSES, NO POVERTY, SDG 1, GOOD HEALTH AND WELL-BEING, SDG 3, DECENT WORK AND ECONOMIC GROWTH, SDG 8, INDUSTRY, INNOVATION AND INFRASTRUCTURE, SDG 9, PEACE, JUSTICE AND STRONG INSTITUTIONS, SDG 16, |
| Online Access: | http://documents.worldbank.org/curated/en/099050724145524418/P17191311f545a0971b3db17f9d6820d240 https://hdl.handle.net/10986/41548 |
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| Summary: | During 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. |
|---|