Guiding Social Protection Targeting Through Satellite Data in São Tomé and Príncipe
Social safety net programs focus on a subset of the population, usually the poorest and most vulnerable. However, in most developing countries there is no administrative data on relative wealth of the population to support the selection process of the potential beneficiaries of the social safety net programs. Hence, selection into programs is often multi-methodological approached and starts with geographical targeting for the selection of program implementation areas. To facilitate this stage of the targeting process in São Tomé and Príncipe, this working paper develops High Resolution Satellite Imagery (HRSI) poverty maps, providing both estimates of poverty incidence and program eligibility at a highly detailed resolution (110 m x 110 m). Furthermore, the analysis combines poverty incidence and population density to enable the geographical targeting process. This working paper shows that HRSI poverty maps can be used as key operational tools to facilitate the decision-making process of the geographical targeting and efficiently identify entry points for rapidly expanding social safety net programs. Unlike HRSI poverty maps based on census data, poverty maps based on satellite data and machine learning can be updated frequently at low cost supporting more adaptive social protection programs.
| Main Authors: | , , , , |
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| Format: | Working Paper biblioteca |
| Language: | English English |
| Published: |
World Bank, Washington, DC
2022-10
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| Subjects: | SOCIAL PROTECTION, TARGETING, MACHINE LEARNING, SATELLITE IMAGES, SOCIAL SAFETY NET PROGRAMS, TARGETING POOR BENEFICIARIES, GEOGRAPHICAL TARGETING, POVERTY MAPPING, HIGH RESOLUTION SATELLITE IMAGERY (HRSI), ADAPTIVE SOCIAL PROTECTION PROGRAMS, |
| Online Access: | http://documents.worldbank.org/curated/en/099135010252263269/P176471047cadc0240ba5d08ef8a2bc86b3 http://hdl.handle.net/10986/38222 |
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| Summary: | Social safety net programs focus on a
subset of the population, usually the poorest and most
vulnerable. However, in most developing countries there is
no administrative data on relative wealth of the population
to support the selection process of the potential
beneficiaries of the social safety net programs. Hence,
selection into programs is often multi-methodological
approached and starts with geographical targeting for the
selection of program implementation areas. To facilitate
this stage of the targeting process in São Tomé and
Príncipe, this working paper develops High Resolution
Satellite Imagery (HRSI) poverty maps, providing both
estimates of poverty incidence and program eligibility at a
highly detailed resolution (110 m x 110 m). Furthermore, the
analysis combines poverty incidence and population density
to enable the geographical targeting process. This working
paper shows that HRSI poverty maps can be used as key
operational tools to facilitate the decision-making process
of the geographical targeting and efficiently identify entry
points for rapidly expanding social safety net programs.
Unlike HRSI poverty maps based on census data, poverty maps
based on satellite data and machine learning can be updated
frequently at low cost supporting more adaptive social
protection programs. |
|---|