{ "id": "AIBD-0094", "title": "Nigeria's Rapid Response Register used machine-learning poverty maps to select 1,163 urban wards for COVID-19 cash transfers", "summary": "ML poverty maps chose 1,163 poorest urban wards for Nigeria's COVID-19 cash register; 49,000 beneficiary households as of 15 October 2021", "date_observed": "2021-01-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Lacking an urban social registry, Nigeria's National Social Safety Nets Coordinating Office (NASSCO) built the Rapid Response Register for a government-funded COVID-19 cash transfer. Ward-level poverty maps came from machine learning (ML) algorithms applied to satellite imagery with household-survey labels, by Berkeley and Columbia researchers with World Bank economists. The maps selected 1,163 of the poorest urban wards, population over 20 million. Residents self-registered by USSD after SMS blasts (first wave June 2021), then in-person verification. Human Rights Watch records the program began in January 2021. The PNAS authors state disbursement began mid-January 2021 (day not given; first day of month used). World Bank monitoring reports 49,000 beneficiary households of the government's COVID-CT as of 15 October 2021. It reports over 1.75 million registrations by November 2021, against a government target of one million households.", "category": "benefit created", "unexpected": false, "purpose": "Geographically target a COVID-19 urban cash transfer to the poorest wards where no social registry existed.", "system": "Ward-level poverty maps from supervised machine learning (deep learning) on satellite imagery, used for geographic targeting in the Rapid Response Register", "model_version": null, "developer": "Joshua Blumenstock (UC Berkeley) and Isabella Smythe (Columbia University), using wealth estimates by Chi et al. (2021), with World Bank economists Jonathan Lain and Tara Vishwanath, for the Government of Nigeria", "deployer": "National Social Safety Nets Coordinating Office (NASSCO), Federal Government of Nigeria", "location": "Urban wards across Nigeria", "domain": "Social protection and cash transfers", "segment": "Government social assistance", "benefit_type": "Increased Scalability", "taxonomy_note": "The good for households is cash, and the type names the register's reach into urban wards it could not otherwise cover.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 2, "reason": "Households in selected wards were beneficiaries of the government's COVID-CT. The PNAS authors say disbursement began mid-January 2021. The World Bank ISR records 49,000 beneficiary households as of 15 October 2021 without saying how many had been paid." }, { "group": "organizational", "depth": 2, "reason": "NASSCO stood up an urban registry within months through USSD self-registration guided by the ML poverty map. Over 1.75 million registrations succeeded (PAD, Nov 2021)." }, { "group": "societal", "depth": 3, "reason": "Material to many: 1,163 wards with over 20 million residents made eligible. PNAS simulation finds the ML maps would cut inclusion errors by 61.7% versus the survey-based benchmark." } ], "ai_role": { "status": "assessed", "statement": "Decisive at the geographic stage: the ML poverty map alone chose which 1,163 wards were eligible. Individual inclusion depended on USSD self-registration and in-person verification, not AI. Payment and registration counts come from World Bank operational documents, not from the map developers. The PNAS paper and World Bank blog are co-authored by the developers." }, "evidence": [ { "kind": "report", "citation": "World Bank (2021). Project Appraisal Document: Nigeria National Social Safety Net Program - Scale Up (P176935).", "title": "Nigeria - National Social Safety Net Program - Scale Up - Project", "url": "https://documents.worldbank.org/curated/en/717321706545772846/pdf/Nigeria-National-Social-Safety-Net-Program-Scale-Up-Project.pdf", "date": "2021-11-23", "written_by": "makers", "quote": "1,163 of the poorest urban wards with a total population of over 20 million people were selected based on the ward-level poverty maps produced by applying cutting-edge machine learning algorithms" }, { "kind": "report", "citation": "World Bank (2025). Implementation Status and Results Report, National Social Safety Net Program-Scale Up (P176935), Sequence 6.", "title": "Disclosable Version of the ISR - National Social Safety Net Program-Scale Up - P176935 - Sequence No: 6", "url": "https://documents.worldbank.org/curated/en/099012925120031079/pdf/P17693518b066e0ff1b62c194b8b78c78dd.pdf", "date": "2025-01-29", "written_by": "makers", "quote": "Baseline numbers based on the 49,000 beneficiary HHs of the COVID-CT implemented by the government as of October 15, 2021." }, { "kind": "peer-reviewed study", "citation": "Smythe, I. S., Blumenstock, J. E. (2022). Geographic microtargeting of social assistance with high-resolution poverty maps. PNAS 119(32).", "title": "Geographic microtargeting of social assistance with high-resolution poverty maps", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9371736/", "date": "2022-08-01", "written_by": "makers", "quote": "the government elected to use our high-resolution poverty maps to target the COVID-19 Rapid Response Registration (RRR) Cash Transfer Project" }, { "kind": "report", "citation": "Human Rights Watch (2021). Between Hunger and the Virus: The Impact of the Covid-19 Pandemic on People Living in Poverty in Lagos, Nigeria.", "title": "Between Hunger and the Virus", "url": "https://www.hrw.org/report/2021/07/28/between-hunger-and-the-virus/the-impact-of-the-covid-19-pandemic-on-people-living", "date": "2021-07-28", "written_by": "independent", "quote": "a new 'rapid response' cash transfer program, which began in January 2021 and is intended to reach 1 million people nationwide, primarily in urban poor communities" }, { "kind": "press release", "citation": "Blumenstock, J., Lain, J., Smythe, I., Vishwanath, T. (2021). Using Big Data and machine learning to locate the poor in Nigeria. World Bank Data Blog.", "title": "Using Big Data and machine learning to locate the poor in Nigeria", "url": "https://blogs.worldbank.org/en/opendata/using-big-data-and-machine-learning-locate-poor-nigeria", "date": "2021-02-22", "written_by": "makers", "quote": "which is being guided by the mapping exercise described below" } ], "replication": { "produced_by": [ "Deep-learning poverty map trained on geo-located household surveys", "Government decision to adopt the ward ranking for eligibility", "SMS blasts and USSD self-registration", "In-person verification, de-duplication and payment onboarding" ], "must_hold": [ "Ground-truth survey data for training labels", "Ward boundaries compatible with map resolution", "Mobile-phone access in target wards", "Government funding for transfers and identity verification capacity" ], "echoes": [ { "date": "2020-11-02", "setting": "Togo Novissi rural expansion using satellite poverty maps and phone-data targeting", "model_version": null, "outcome": "recurred", "source": "Aiken et al. (2022) Nature, https://pmc.ncbi.nlm.nih.gov/articles/PMC8967719/ (expansion 'beginning in November 2020'); the 2 November 2020 date is from World Bank and Government of Togo (2023), Novissi Togo case study, https://documents.worldbank.org/curated/en/099751009222330502/pdf/IDU0e83f857301ff1047bf082710a8d21ddf42c3.pdf" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "546", "title": "Algorithm to Distribute Social Welfare Reported for Oversimplifying Economic Vulnerability", "url": "https://incidentdatabase.ai/cite/546", "severity": null, "group": "personal", "basis": "World Bank-developed poverty-targeting algorithm for cash transfers (Takaful) oversimplified vulnerability and excluded poor Jordanians; Nigeria's register used World Bank poverty maps." }, "headline": "ML poverty maps select 1,163 wards for Nigeria's COVID-19 cash", "metric": "49,000 beneficiary households of the government's COVID-CT as of 15 October 2021", "comparator": { "against": "survey-based poverty map benchmark (DHS/NLSS data)", "before": "0.79 precision at the ward level (survey benchmark)", "after": "0.92 precision at the ward level (ML-based maps)", "basis": "measured", "quote": "In targeting the poor, the ML-based approach increases precision relative to the benchmark from 0.82 to 0.86 at the LGA level and from 0.79 to 0.92 at the ward level. In targeting the extreme poor, the increase is from 0.30 to 0.39 at the LGA level and from 0.31 to 0.41 at the ward level.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9371736/" }, "later": { "status": "scaled", "date": "2025-01-29", "note": "The World Bank's 2021 scale-up project adopted the same machine-learning poverty maps to choose expansion wards for the RRR, and its January 2025 status report counts over 5 million households supported by the direct benefit transfer program after delays and a switch to biometric-verified payments, without breaking out the RRR-targeted share.", "quote": "The direct benefit transfer program to date has supported over 5 million households. Following the CBN circular of December 2023, mandating the use of NIN or BVN to operate an account after March 2024, and the recommendation from the presidential panel, all transfers since March 2024 are made to beneficiaries who’s NIN or BVN have been verified.", "url": "https://documents.worldbank.org/curated/en/099012925120031079/pdf/P17693518b066e0ff1b62c194b8b78c78dd.pdf" }, "value": null, "developer_country": null, "deployer_country": "Nigeria", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 546 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/546", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Placed Isabella Smythe at Columbia University and credited Chi et al.“s wealth estimates and the two named World Bank economists as the PNAS paper and World Bank blog state them, removed the unsupported word 'paid” from the 49,000-household figure because the ISR says only “beneficiary HHs” as of 15 October 2021, and added the World Bank Novissi case study as the source of the 2 November 2020 echo date that the Nature paper gives only as November 2020.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9371736/; https://blogs.worldbank.org/en/opendata/using-big-data-and-machine-learning-locate-poor-nigeria; https://documents.worldbank.org/curated/en/099012925120031079/pdf/P17693518b066e0ff1b62c194b8b78c78dd.pdf", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since from the cited sources.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9371736/; https://documents.worldbank.org/curated/en/099012925120031079/pdf/P17693518b066e0ff1b62c194b8b78c78dd.pdf", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Scalability under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the deployer added from the organization's own site or a registry.", "source": "https://search.worldbank.org/api/v2/wds?qterm=NASSCO%20National%20Social%20Safety%20Nets%20Coordinating%20Office&format=json&rows=5", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent report, the makers' peer-reviewed study, two reports from the makers and the makers' press release. The cited evidence includes no finding by a regulator or court.", "profile": { "case_id": "AIBD-0094", "catalog_version": "1.0.0", "source_fingerprint": "2c38cee688ad632630951f3abfc422b1428ba2fa70d62d9811d8d9d82e0d75d9", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0094-source-0001", "AIBD-0094-source-0002", "AIBD-0094-source-0003", "AIBD-0094-source-0004", "AIBD-0094-source-0005" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Joshua Blumenstock (UC Berkeley) and Isabella Smythe (Columbia University), using wealth estimates by Chi et al. (2021), with World Bank economists Jonathan Lain and Tara Vishwanath, for the Government of Nigeria", "deployer": "National Social Safety Nets Coordinating Office (NASSCO), Federal Government of Nigeria" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "Decisive at the geographic stage: the ML poverty map alone chose which 1,163 wards were eligible. Individual inclusion depended on USSD self-registration and in-person verification, not AI. Payment and registration counts come from World Bank operational documents, not from the map developers. The PNAS paper and World Bank blog are co-authored by the developers." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "Decisive at the geographic stage: the ML poverty map alone chose which 1,163 wards were eligible. Individual inclusion depended on USSD self-registration and in-person verification, not AI. Payment and registration counts come from World Bank operational documents, not from the map developers. 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PNAS simulation finds the ML maps would cut inclusion errors by 61.7% versus the survey-based benchmark." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "high", "evidence_word": "studied", "claim_status": "Independently studied", "basis": "Sources: one independent report, the makers' peer-reviewed study, two reports from the makers and the makers' press release. 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PNAS simulation finds the ML maps would cut inclusion errors by 61.7% versus the survey-based benchmark." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "Households in selected wards were beneficiaries of the government's COVID-CT. The PNAS authors say disbursement began mid-January 2021. 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Project Appraisal Document: Nigeria National Social Safety Net Program - Scale Up (P176935).", "title": "Nigeria - National Social Safety Net Program - Scale Up - Project", "url": "https://documents.worldbank.org/curated/en/717321706545772846/pdf/Nigeria-National-Social-Safety-Net-Program-Scale-Up-Project.pdf", "date": "2021-11-23", "written_by": "makers", "quote": "1,163 of the poorest urban wards with a total population of over 20 million people were selected based on the ward-level poverty maps produced by applying cutting-edge machine learning algorithms" }, { "kind": "report", "citation": "World Bank (2025). 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Between Hunger and the Virus: The Impact of the Covid-19 Pandemic on People Living in Poverty in Lagos, Nigeria.", "title": "Between Hunger and the Virus", "url": "https://www.hrw.org/report/2021/07/28/between-hunger-and-the-virus/the-impact-of-the-covid-19-pandemic-on-people-living", "date": "2021-07-28", "written_by": "independent", "quote": "a new 'rapid response' cash transfer program, which began in January 2021 and is intended to reach 1 million people nationwide, primarily in urban poor communities" }, { "kind": "press release", "citation": "Blumenstock, J., Lain, J., Smythe, I., Vishwanath, T. (2021). Using Big Data and machine learning to locate the poor in Nigeria. 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Project Appraisal Document: Nigeria National Social Safety Net Program - Scale Up (P176935).", "title": "Nigeria - National Social Safety Net Program - Scale Up - Project", "url": "https://documents.worldbank.org/curated/en/717321706545772846/pdf/Nigeria-National-Social-Safety-Net-Program-Scale-Up-Project.pdf", "date": "2021-11-23", "written_by": "makers", "quote": "1,163 of the poorest urban wards with a total population of over 20 million people were selected based on the ward-level poverty maps produced by applying cutting-edge machine learning algorithms" }, { "kind": "report", "citation": "World Bank (2025). Implementation Status and Results Report, National Social Safety Net Program-Scale Up (P176935), Sequence 6.", "title": "Disclosable Version of the ISR - National Social Safety Net Program-Scale Up - P176935 - Sequence No: 6", "url": "https://documents.worldbank.org/curated/en/099012925120031079/pdf/P17693518b066e0ff1b62c194b8b78c78dd.pdf", "date": "2025-01-29", "written_by": "makers", "quote": "Baseline numbers based on the 49,000 beneficiary HHs of the COVID-CT implemented by the government as of October 15, 2021." }, { "kind": "peer-reviewed study", "citation": "Smythe, I. S., Blumenstock, J. E. (2022). Geographic microtargeting of social assistance with high-resolution poverty maps. PNAS 119(32).", "title": "Geographic microtargeting of social assistance with high-resolution poverty maps", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9371736/", "date": "2022-08-01", "written_by": "makers", "quote": "the government elected to use our high-resolution poverty maps to target the COVID-19 Rapid Response Registration (RRR) Cash Transfer Project" }, { "kind": "report", "citation": "Human Rights Watch (2021). Between Hunger and the Virus: The Impact of the Covid-19 Pandemic on People Living in Poverty in Lagos, Nigeria.", "title": "Between Hunger and the Virus", "url": "https://www.hrw.org/report/2021/07/28/between-hunger-and-the-virus/the-impact-of-the-covid-19-pandemic-on-people-living", "date": "2021-07-28", "written_by": "independent", "quote": "a new 'rapid response' cash transfer program, which began in January 2021 and is intended to reach 1 million people nationwide, primarily in urban poor communities" }, { "kind": "press release", "citation": "Blumenstock, J., Lain, J., Smythe, I., Vishwanath, T. (2021). Using Big Data and machine learning to locate the poor in Nigeria. World Bank Data Blog.", "title": "Using Big Data and machine learning to locate the poor in Nigeria", "url": "https://blogs.worldbank.org/en/opendata/using-big-data-and-machine-learning-locate-poor-nigeria", "date": "2021-02-22", "written_by": "makers", "quote": "which is being guided by the mapping exercise described below" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "ML poverty maps chose 1,163 poorest urban wards for Nigeria's COVID-19 cash register; 49,000 beneficiary households as of 15 October 2021", "account": "Lacking an urban social registry, Nigeria's National Social Safety Nets Coordinating Office (NASSCO) built the Rapid Response Register for a government-funded COVID-19 cash transfer. Ward-level poverty maps came from machine learning (ML) algorithms applied to satellite imagery with household-survey labels, by Berkeley and Columbia researchers with World Bank economists. The maps selected 1,163 of the poorest urban wards, population over 20 million. Residents self-registered by USSD after SMS blasts (first wave June 2021), then in-person verification. Human Rights Watch records the program began in January 2021. The PNAS authors state disbursement began mid-January 2021 (day not given; first day of month used). World Bank monitoring reports 49,000 beneficiary households of the government's COVID-CT as of 15 October 2021. It reports over 1.75 million registrations by November 2021, against a government target of one million households." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Ward-level poverty maps from supervised machine learning (deep learning) on satellite imagery, used for geographic targeting in the Rapid Response Register" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Joshua Blumenstock (UC Berkeley) and Isabella Smythe (Columbia University), using wealth estimates by Chi et al. (2021), with World Bank economists Jonathan Lain and Tara Vishwanath, for the Government of Nigeria" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Nigeria's Rapid Response Register used machine-learning poverty maps to select 1,163 urban wards for COVID-19 cash transfers", "headline": "ML poverty maps select 1,163 wards for Nigeria's COVID-19 cash" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "2c38cee688ad632630951f3abfc422b1428ba2fa70d62d9811d8d9d82e0d75d9" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Lacking an urban social registry, Nigeria's National Social Safety Nets Coordinating Office (NASSCO) built the Rapid Response Register for a government-funded COVID-19 cash transfer. Ward-level poverty maps came from machine learning (ML) algorithms applied to satellite imagery with household-survey labels, by Berkeley and Columbia researchers with World Bank economists. The maps selected 1,163 of the poorest urban wards, population over 20 million. Residents self-registered by USSD after SMS blasts (first wave June 2021), then in-person verification. Human Rights Watch records the program began in January 2021. The PNAS authors state disbursement began mid-January 2021 (day not given; first day of month used). World Bank monitoring reports 49,000 beneficiary households of the government's COVID-CT as of 15 October 2021. It reports over 1.75 million registrations by November 2021, against a government target of one million households.", "replication": { "produced_by": [ "Deep-learning poverty map trained on geo-located household surveys", "Government decision to adopt the ward ranking for eligibility", "SMS blasts and USSD self-registration", "In-person verification, de-duplication and payment onboarding" ], "must_hold": [ "Ground-truth survey data for training labels", "Ward boundaries compatible with map resolution", "Mobile-phone access in target wards", "Government funding for transfers and identity verification capacity" ], "echoes": [ { "date": "2020-11-02", "setting": "Togo Novissi rural expansion using satellite poverty maps and phone-data targeting", "outcome": "recurred", "source": "Aiken et al. (2022) Nature, https://pmc.ncbi.nlm.nih.gov/articles/PMC8967719/ (expansion 'beginning in November 2020'); the 2 November 2020 date is from World Bank and Government of Togo (2023), Novissi Togo case study, https://documents.worldbank.org/curated/en/099751009222330502/pdf/IDU0e83f857301ff1047bf082710a8d21ddf42c3.pdf" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 31, "reviewed": 0, "record_mapped": 31, "not_assessed": 60, "complete": false, "by_state": { "value_present": 31, "not_reported": 0, "unresolved": 0, "not_assessed": 60, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } }