Novissi -- Geospatial Poverty Mapping for Emergency Cash Transfer Area Targeting (Model 2)

Overview

The Government of Togo used a geospatial poverty-mapping model as the area-targeting step of Novissi Model 2, the rural expansion of its COVID-19 emergency cash-transfer programme. The retained sources clearly support that this component ranked Togo's cantons using satellite-derived and other geospatial indicators so that the poorest areas could be prioritised for programme coverage during the pandemic. The deployment is best understood as a government use of a research-developed targeting component inside a broader emergency programme, rather than as a standalone state-built system with extensive direct technical disclosure.

Novissi was launched in April 2020 to support informal workers affected by pandemic restrictions. In Togo, a small country in West Africa where over 50 percent of the population lives in poverty, the programme was described as an exemplary case of social protection in response to the COVID-19 pandemic in Africa. The government built and deployed a completely contactless, digital cash-transfer system within weeks. Beneficiaries registered using their mobile phones via a USSD menu and received mobile money transfers of approximately 15 US dollars per month for three months. When the programme expanded into rural areas later that year, the Government of Togo faced a familiar social-protection problem: it did not have a dynamic social registry, universal unique identifiers, or sufficiently recent canton-level poverty data to support conventional targeting. The last census had been conducted in 2011, and existing nationally representative household surveys could only produce poverty estimates at the national or regional level, not at the granular canton level needed for geographic targeting. The geospatial model was introduced to help identify which cantons should be eligible for the second phase of the programme.

The strongest source base shows that the model relied on multiple non-traditional data inputs, including satellite imagery, nightlights, road density, elevation, precipitation, urban-form indicators, and population density, together with household-survey data used as ground truth. Specifically, the research team used survey-based estimates of poverty from the EHCVM 2018-2019 nationally representative household survey as ground truth to train a machine-learning algorithm to estimate the wealth of very small regions at the 2.4-kilometre tile level based on geographic characteristics. The algorithm learned that certain patterns in the satellite imagery are indicative of wealth, such as places with metal roofs and high-quality roads, while others are indicative of poverty, such as places with certain types of terrain and weather. The tile-level consumption estimates were then overlaid with high-resolution population density estimates to calculate average per capita household consumption for each of Togo's 397 cantons. The resulting estimates were used to rank cantons from poorest to least poor, and the government selected the 100 poorest cantons for programme coverage, with that number chosen based on the estimated distribution of wealth of the population living in those cantons. The coverage was later expanded to 200 cantons. Publicly available documentation also makes clear that academic and development partners, including researchers from the University of California Berkeley's Center for Effective Global Action and Innovations for Poverty Action, played a substantial role in the technical design and implementation, while government officials validated outputs against operational knowledge and used the resulting rankings in programme administration.

This matters because the decision at issue was consequential: being inside or outside the selected cantons affected whether residents could proceed to the next stage of eligibility screening, where approximately 580,000 citizens lived in the initial 100 poorest cantons and approximately 57,000 were to receive benefits within those areas. At the same time, the public record is stronger on the overall Novissi methodology than on the internal government operating details for this specific area-targeting component. For production purposes, the safest framing is therefore that Togo operationally used a machine-learning-assisted geospatial ranking method in Novissi Model 2, with the methodology and limitations documented mainly through academic and World Bank materials.

The sources also document important limitations. Validation was imperfect, the model depended on pre-crisis survey inputs, and exclusion risks existed for groups poorly represented by the data environment or excluded by downstream administrative requirements. The IPA documentation notes that the research team planned in-person surveys and qualitative interviews designed to determine whether the algorithm's predictions were inadvertently biased against particular types of beneficiaries such as women, illiterate individuals, and marginalised subgroups. This remains a robust and important case, but it should stay framed as a partner-heavy targeting component within a government cash-transfer rollout rather than a highly transparent autonomous government AI system.

Classification

Programme Details

Large-scale unconditional emergency cash transfer programme launched in April 2020 to support informal workers affected by COVID-19. Model 2 extended coverage to rural areas using geospatial and phone-based targeting methods. This case covers only the geospatial area-targeting component used to prioritise cantons for inclusion.

Implementation Details

Risk & Oversight

Deployment & Outcomes

Sources

1. SRC-002-TGO-001 Lawson, C., Koudeka, M., Cardenas Martinez, A. L., Alberro Encinas, L. I. and Karippacheril, T. G. (2023) Novissi Togo: Harnessing Artificial Intelligence to Deliver Shock-Responsive Social Protection. Social Protection and Jobs Discussion Paper No. 2306. Washington, DC: World Bank Group and Government of Togo.
   https://documents1.worldbank.org/curated/en/099751009222330502/pdf/IDU0e83f857301ff1047bf082710a8d21ddf42c3.pdf
2. SRC-001-TGO-001 Aiken, E., Bellue, S., Karlan, D., Udry, C. and Blumenstock, J. (2022) 'Machine learning and phone data can improve targeting of humanitarian aid', Nature, 603, pp. 864-870. doi:10.1038/s41586-022-04484-9.
   https://www.nature.com/articles/s41586-022-04484-9
3. SRC-004-TGO-001 Innovations for Poverty Action (2021) 'Using Mobile Phone and Satellite Data to Target Emergency Cash Transfers in Togo', 12 January. Available at: https://poverty-action.org/using-mobile-phone-and-satellite-data-target-emergency-cash-transfers-togo (Accessed: 27 March 2026).
   https://poverty-action.org/using-mobile-phone-and-satellite-data-target-emergency-cash-transfers-togo
4. SRC-003-TGO-001 World Bank (2021) 'Prioritizing the poorest and most vulnerable in West Africa: Togo's Novissi platform for social protection uses machine learning, geospatial analytics, and mobile phone metadata for the pandemic response'. Washington, DC: World Bank. Available at: https://www.worldbank.org/en/results/2021/04/13/prioritizing-the-poorest-and-most-vulnerable-in-west-africa-togo-s-novissi-platform-for-social-protection-uses-machine-l (Accessed: 24 March 2026).
   https://www.worldbank.org/en/results/2021/04/13/prioritizing-the-poorest-and-most-vulnerable-in-west-africa-togo-s-novissi-platform-for-social-protection-uses-machine-l