{
 "compiled": "2026-10-06",
 "schema_version": "2026.10.2",
 "tier_scheme": {
  "primary_filing": {
   "tier": 1,
   "label": "Primary filing or official record",
   "source_type": "GOVERNMENT"
  },
  "official": {
   "tier": 1,
   "label": "Original or primary source",
   "source_type": "GOVERNMENT"
  },
  "company_filing": {
   "tier": 1,
   "label": "Company release",
   "source_type": "COMPANY"
  },
  "academic": {
   "tier": 2,
   "label": "Academic research",
   "source_type": "ACADEMIC"
  },
  "industry_body": {
   "tier": 3,
   "label": "Industry source",
   "source_type": "INDUSTRY"
  },
  "trade_reporting": {
   "tier": 4,
   "label": "News reporting",
   "source_type": "NEWS"
  },
  "unclear": {
   "tier": 5,
   "label": "Aggregator or unclear",
   "source_type": "SECONDARY"
  }
 },
 "indicators": [
  {
   "id": "jobs.task_share.zaf",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 51.0,
   "unit": "% of tasks",
   "geography": "South Africa",
   "geo_iso3": "ZAF",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.tun",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 49.3,
   "unit": "% of tasks",
   "geography": "Tunisia",
   "geo_iso3": "TUN",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.egy",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 46.3,
   "unit": "% of tasks",
   "geography": "Egypt",
   "geo_iso3": "EGY",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.mar",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 44.2,
   "unit": "% of tasks",
   "geography": "Morocco",
   "geo_iso3": "MAR",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.dza",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 43.3,
   "unit": "% of tasks",
   "geography": "Algeria",
   "geo_iso3": "DZA",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.ken",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 37.0,
   "unit": "% of tasks",
   "geography": "Kenya",
   "geo_iso3": "KEN",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.nga",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 33.3,
   "unit": "% of tasks",
   "geography": "Nigeria",
   "geo_iso3": "NGA",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.lby",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 33.0,
   "unit": "% of tasks",
   "geography": "Libya",
   "geo_iso3": "LBY",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.sen",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 32.5,
   "unit": "% of tasks",
   "geography": "Senegal",
   "geo_iso3": "SEN",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.zwe",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 31.3,
   "unit": "% of tasks",
   "geography": "Zimbabwe",
   "geo_iso3": "ZWE",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.rwa",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 30.1,
   "unit": "% of tasks",
   "geography": "Rwanda",
   "geo_iso3": "RWA",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.civ",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 29.5,
   "unit": "% of tasks",
   "geography": "Cote d'Ivoire",
   "geo_iso3": "CIV",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.ago",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 29.2,
   "unit": "% of tasks",
   "geography": "Angola",
   "geo_iso3": "AGO",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.gha",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 28.7,
   "unit": "% of tasks",
   "geography": "Ghana",
   "geo_iso3": "GHA",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.zmb",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 26.8,
   "unit": "% of tasks",
   "geography": "Zambia",
   "geo_iso3": "ZMB",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.ben",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 24.6,
   "unit": "% of tasks",
   "geography": "Benin",
   "geo_iso3": "BEN",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.cmr",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 24.5,
   "unit": "% of tasks",
   "geography": "Cameroon",
   "geo_iso3": "CMR",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.tza",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 23.8,
   "unit": "% of tasks",
   "geography": "Tanzania",
   "geo_iso3": "TZA",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.eth",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 22.1,
   "unit": "% of tasks",
   "geography": "Ethiopia",
   "geo_iso3": "ETH",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.tgo",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 21.7,
   "unit": "% of tasks",
   "geography": "Togo",
   "geo_iso3": "TGO",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.uga",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 21.4,
   "unit": "% of tasks",
   "geography": "Uganda",
   "geo_iso3": "UGA",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.moz",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 20.1,
   "unit": "% of tasks",
   "geography": "Mozambique",
   "geo_iso3": "MOZ",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.cog",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 19.7,
   "unit": "% of tasks",
   "geography": "Republic of the Congo",
   "geo_iso3": "COG",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.sdn",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 17.8,
   "unit": "% of tasks",
   "geography": "Sudan",
   "geo_iso3": "SDN",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.mdg",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 17.5,
   "unit": "% of tasks",
   "geography": "Madagascar",
   "geo_iso3": "MDG",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.mwi",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 16.7,
   "unit": "% of tasks",
   "geography": "Malawi",
   "geo_iso3": "MWI",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.bfa",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 14.3,
   "unit": "% of tasks",
   "geography": "Burkina Faso",
   "geo_iso3": "BFA",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.mli",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 14.0,
   "unit": "% of tasks",
   "geography": "Mali",
   "geo_iso3": "MLI",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.som",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 13.7,
   "unit": "% of tasks",
   "geography": "Somalia",
   "geo_iso3": "SOM",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.gin",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 13.6,
   "unit": "% of tasks",
   "geography": "Guinea",
   "geo_iso3": "GIN",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.sle",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 11.9,
   "unit": "% of tasks",
   "geography": "Sierra Leone",
   "geo_iso3": "SLE",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.cod",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 10.8,
   "unit": "% of tasks",
   "geography": "DR Congo",
   "geo_iso3": "COD",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.ner",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 9.9,
   "unit": "% of tasks",
   "geography": "Niger",
   "geo_iso3": "NER",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.tcd",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 9.9,
   "unit": "% of tasks",
   "geography": "Chad",
   "geo_iso3": "TCD",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.bdi",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 9.4,
   "unit": "% of tasks",
   "geography": "Burundi",
   "geo_iso3": "BDI",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.task_share.ssd",
   "pillar": "Jobs",
   "indicator": "Share of tasks economically exposed to AI",
   "value": 3.3,
   "unit": "% of tasks",
   "geography": "South Sudan",
   "geo_iso3": "SSD",
   "date": "2026",
   "source": "Garg, Crosta and Baier, Global Automation Atlas (2026)",
   "source_url": "https://arxiv.org/abs/2605.17086",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Tasks rated 2 or 3 on a 0-3 exposure scale, relabelled under each country's conditions. Not employment-weighted and not a share of jobs.",
   "recompute_from": "data/jobs-atlas-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.mus",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.289,
   "unit": "index (0-1)",
   "geography": "Mauritius",
   "geo_iso3": "MUS",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.som",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.259,
   "unit": "index (0-1)",
   "geography": "Somalia",
   "geo_iso3": "SOM",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.egy",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.254,
   "unit": "index (0-1)",
   "geography": "Egypt",
   "geo_iso3": "EGY",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.swz",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.244,
   "unit": "index (0-1)",
   "geography": "Eswatini",
   "geo_iso3": "SWZ",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.ner",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.241,
   "unit": "index (0-1)",
   "geography": "Niger",
   "geo_iso3": "NER",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.civ",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.24,
   "unit": "index (0-1)",
   "geography": "Côte d'Ivoire",
   "geo_iso3": "CIV",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.tun",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.236,
   "unit": "index (0-1)",
   "geography": "Tunisia",
   "geo_iso3": "TUN",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.tgo",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.235,
   "unit": "index (0-1)",
   "geography": "Togo",
   "geo_iso3": "TGO",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.gmb",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.234,
   "unit": "index (0-1)",
   "geography": "Gambia",
   "geo_iso3": "GMB",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.stp",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.23,
   "unit": "index (0-1)",
   "geography": "Sao Tome and Principe",
   "geo_iso3": "STP",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.bwa",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.228,
   "unit": "index (0-1)",
   "geography": "Botswana",
   "geo_iso3": "BWA",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.gin",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.227,
   "unit": "index (0-1)",
   "geography": "Guinea",
   "geo_iso3": "GIN",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.ben",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.227,
   "unit": "index (0-1)",
   "geography": "Benin",
   "geo_iso3": "BEN",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.mwi",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.223,
   "unit": "index (0-1)",
   "geography": "Malawi",
   "geo_iso3": "MWI",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.gha",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.22,
   "unit": "index (0-1)",
   "geography": "Ghana",
   "geo_iso3": "GHA",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.cpv",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.22,
   "unit": "index (0-1)",
   "geography": "Cabo Verde",
   "geo_iso3": "CPV",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.sdn",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.213,
   "unit": "index (0-1)",
   "geography": "Sudan",
   "geo_iso3": "SDN",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.ago",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.206,
   "unit": "index (0-1)",
   "geography": "Angola",
   "geo_iso3": "AGO",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.sle",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.205,
   "unit": "index (0-1)",
   "geography": "Sierra Leone",
   "geo_iso3": "SLE",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.mli",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.2,
   "unit": "index (0-1)",
   "geography": "Mali",
   "geo_iso3": "MLI",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.ken",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.199,
   "unit": "index (0-1)",
   "geography": "Kenya",
   "geo_iso3": "KEN",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.bfa",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.198,
   "unit": "index (0-1)",
   "geography": "Burkina Faso",
   "geo_iso3": "BFA",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.cod",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.197,
   "unit": "index (0-1)",
   "geography": "Congo, Democratic Republic of the",
   "geo_iso3": "COD",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.zwe",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.192,
   "unit": "index (0-1)",
   "geography": "Zimbabwe",
   "geo_iso3": "ZWE",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.zmb",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.187,
   "unit": "index (0-1)",
   "geography": "Zambia",
   "geo_iso3": "ZMB",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.rwa",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.186,
   "unit": "index (0-1)",
   "geography": "Rwanda",
   "geo_iso3": "RWA",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.gnb",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.179,
   "unit": "index (0-1)",
   "geography": "Guinea-Bissau",
   "geo_iso3": "GNB",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.eth",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.177,
   "unit": "index (0-1)",
   "geography": "Ethiopia",
   "geo_iso3": "ETH",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.lso",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.175,
   "unit": "index (0-1)",
   "geography": "Lesotho",
   "geo_iso3": "LSO",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.tza",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.173,
   "unit": "index (0-1)",
   "geography": "Tanzania",
   "geo_iso3": "TZA",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.mdg",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.166,
   "unit": "index (0-1)",
   "geography": "Madagascar",
   "geo_iso3": "MDG",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.moz",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.162,
   "unit": "index (0-1)",
   "geography": "Mozambique",
   "geo_iso3": "MOZ",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.index.bdi",
   "pillar": "Jobs",
   "indicator": "Employment-weighted AI exposure of the occupational structure",
   "value": 0.144,
   "unit": "index (0-1)",
   "geography": "Burundi",
   "geo_iso3": "BDI",
   "date": "2026",
   "source": "Garg, Crosta and Baier, The Jagged Global Economy (2026)",
   "source_url": "https://arxiv.org/abs/2607.05404",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "academic",
   "temporal_status": "modelled",
   "methodology": "Employment-share-weighted average exposure of a country's occupational structure.",
   "recompute_from": "data/jobs-exposure.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "jobs.exposed_share.ssa",
   "pillar": "Jobs",
   "indicator": "Jobs exposed to AI",
   "value": 20,
   "unit": "% of jobs",
   "geography": "Sub-Saharan Africa",
   "geo_iso3": "",
   "date": "2026",
   "source": "IMF, Unlocking the Potential: AI in Sub-Saharan Africa (Departmental Paper, 2026)",
   "source_url": "https://www.elibrary.imf.org/view/journals/087/2026/013/article-A001-en.xml",
   "source_type": "ACADEMIC",
   "tier": 2,
   "tier_label": "Academic research",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "Complement of the source's low-exposure estimate: about four-fifths of jobs in sub-Saharan Africa have limited exposure to AI",
   "recompute_from": "data/reference/home-figures.json",
   "headline": true,
   "note": "Regional only. The source publishes no country split."
  },
  {
   "id": "access.wage_share.mar",
   "pillar": "Access",
   "indicator": "Illustrative AI workload as a share of a monthly minimum wage",
   "value": 55.3,
   "unit": "% of a monthly minimum wage",
   "geography": "Morocco",
   "geo_iso3": "MAR",
   "date": "2026-01-01",
   "source": "Computed from Claude Opus 5 (Anthropic) rate card and the statutory minimum wage",
   "source_url": "",
   "source_type": "DERIVED",
   "tier": 2,
   "tier_label": "Derived from academic and official inputs",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "A month of moderate use = 20 standard workloads at the median frontier rate card ($11.25 per workload), divided by the monthly statutory minimum wage.",
   "recompute_from": "data/affordability.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "access.wage_share.zaf",
   "pillar": "Access",
   "indicator": "Illustrative AI workload as a share of a monthly minimum wage",
   "value": 55.8,
   "unit": "% of a monthly minimum wage",
   "geography": "South Africa",
   "geo_iso3": "ZAF",
   "date": "2026-03-01",
   "source": "Computed from Claude Opus 5 (Anthropic) rate card and the statutory minimum wage",
   "source_url": "",
   "source_type": "DERIVED",
   "tier": 2,
   "tier_label": "Derived from academic and official inputs",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "A month of moderate use = 20 standard workloads at the median frontier rate card ($11.25 per workload), divided by the monthly statutory minimum wage.",
   "recompute_from": "data/affordability.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "access.wage_share.civ",
   "pillar": "Access",
   "indicator": "Illustrative AI workload as a share of a monthly minimum wage",
   "value": 137.8,
   "unit": "% of a monthly minimum wage",
   "geography": "Cote d'Ivoire",
   "geo_iso3": "CIV",
   "date": "2023-01-01",
   "source": "Computed from Claude Opus 5 (Anthropic) rate card and the statutory minimum wage",
   "source_url": "",
   "source_type": "DERIVED",
   "tier": 2,
   "tier_label": "Derived from academic and official inputs",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "A month of moderate use = 20 standard workloads at the median frontier rate card ($11.25 per workload), divided by the monthly statutory minimum wage.",
   "recompute_from": "data/affordability.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "access.wage_share.ken",
   "pillar": "Access",
   "indicator": "Illustrative AI workload as a share of a monthly minimum wage",
   "value": 145.7,
   "unit": "% of a monthly minimum wage",
   "geography": "Kenya",
   "geo_iso3": "KEN",
   "date": "2026-05-01",
   "source": "Computed from Claude Opus 5 (Anthropic) rate card and the statutory minimum wage",
   "source_url": "",
   "source_type": "DERIVED",
   "tier": 2,
   "tier_label": "Derived from academic and official inputs",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "A month of moderate use = 20 standard workloads at the median frontier rate card ($11.25 per workload), divided by the monthly statutory minimum wage.",
   "recompute_from": "data/affordability.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "access.wage_share.egy",
   "pillar": "Access",
   "indicator": "Illustrative AI workload as a share of a monthly minimum wage",
   "value": 160.5,
   "unit": "% of a monthly minimum wage",
   "geography": "Egypt",
   "geo_iso3": "EGY",
   "date": "2025-03-01",
   "source": "Computed from Claude Opus 5 (Anthropic) rate card and the statutory minimum wage",
   "source_url": "",
   "source_type": "DERIVED",
   "tier": 2,
   "tier_label": "Derived from academic and official inputs",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "A month of moderate use = 20 standard workloads at the median frontier rate card ($11.25 per workload), divided by the monthly statutory minimum wage.",
   "recompute_from": "data/affordability.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "access.wage_share.sen",
   "pillar": "Access",
   "indicator": "Illustrative AI workload as a share of a monthly minimum wage",
   "value": 160.9,
   "unit": "% of a monthly minimum wage",
   "geography": "Senegal",
   "geo_iso3": "SEN",
   "date": "2023-07-01",
   "source": "Computed from Claude Opus 5 (Anthropic) rate card and the statutory minimum wage",
   "source_url": "",
   "source_type": "DERIVED",
   "tier": 2,
   "tier_label": "Derived from academic and official inputs",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "A month of moderate use = 20 standard workloads at the median frontier rate card ($11.25 per workload), divided by the monthly statutory minimum wage.",
   "recompute_from": "data/affordability.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "access.wage_share.nga",
   "pillar": "Access",
   "indicator": "Illustrative AI workload as a share of a monthly minimum wage",
   "value": 343.2,
   "unit": "% of a monthly minimum wage",
   "geography": "Nigeria",
   "geo_iso3": "NGA",
   "date": "2024-05-01",
   "source": "Computed from Claude Opus 5 (Anthropic) rate card and the statutory minimum wage",
   "source_url": "",
   "source_type": "DERIVED",
   "tier": 2,
   "tier_label": "Derived from academic and official inputs",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "A month of moderate use = 20 standard workloads at the median frontier rate card ($11.25 per workload), divided by the monthly statutory minimum wage.",
   "recompute_from": "data/affordability.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "access.wage_share.gha",
   "pillar": "Access",
   "indicator": "Illustrative AI workload as a share of a monthly minimum wage",
   "value": 439.6,
   "unit": "% of a monthly minimum wage",
   "geography": "Ghana",
   "geo_iso3": "GHA",
   "date": "2026-01-01",
   "source": "Computed from Claude Opus 5 (Anthropic) rate card and the statutory minimum wage",
   "source_url": "",
   "source_type": "DERIVED",
   "tier": 2,
   "tier_label": "Derived from academic and official inputs",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "A month of moderate use = 20 standard workloads at the median frontier rate card ($11.25 per workload), divided by the monthly statutory minimum wage.",
   "recompute_from": "data/affordability.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "access.gni_basket_share.ago",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 94.4056,
   "unit": "% of monthly GNI per capita",
   "geography": "Angola",
   "geo_iso3": "AGO",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/AGO/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.bdi",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 1125.0,
   "unit": "% of monthly GNI per capita",
   "geography": "Burundi",
   "geo_iso3": "BDI",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/BDI/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.ben",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 168.75,
   "unit": "% of monthly GNI per capita",
   "geography": "Benin",
   "geo_iso3": "BEN",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/BEN/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.bfa",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 275.5102,
   "unit": "% of monthly GNI per capita",
   "geography": "Burkina Faso",
   "geo_iso3": "BFA",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/BFA/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.bwa",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 36.5359,
   "unit": "% of monthly GNI per capita",
   "geography": "Botswana",
   "geo_iso3": "BWA",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/BWA/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.caf",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 482.1429,
   "unit": "% of monthly GNI per capita",
   "geography": "Central African Republic",
   "geo_iso3": "CAF",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/CAF/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.civ",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 97.1223,
   "unit": "% of monthly GNI per capita",
   "geography": "Cote d'Ivoire",
   "geo_iso3": "CIV",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/CIV/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.cmr",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 145.1613,
   "unit": "% of monthly GNI per capita",
   "geography": "Cameroon",
   "geo_iso3": "CMR",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/CMR/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.cod",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 375.0,
   "unit": "% of monthly GNI per capita",
   "geography": "DR Congo",
   "geo_iso3": "COD",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/COD/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.cog",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 118.4211,
   "unit": "% of monthly GNI per capita",
   "geography": "Republic of the Congo",
   "geo_iso3": "COG",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/COG/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.com",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 138.4615,
   "unit": "% of monthly GNI per capita",
   "geography": "Comoros",
   "geo_iso3": "COM",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/COM/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.cpv",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 48.3005,
   "unit": "% of monthly GNI per capita",
   "geography": "Cabo Verde",
   "geo_iso3": "CPV",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/CPV/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.dji",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 68.1818,
   "unit": "% of monthly GNI per capita",
   "geography": "Djibouti",
   "geo_iso3": "DJI",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/DJI/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.dza",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 46.1538,
   "unit": "% of monthly GNI per capita",
   "geography": "Algeria",
   "geo_iso3": "DZA",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/DZA/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.egy",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 82.8221,
   "unit": "% of monthly GNI per capita",
   "geography": "Egypt",
   "geo_iso3": "EGY",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/EGY/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.eth",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 243.2432,
   "unit": "% of monthly GNI per capita",
   "geography": "Ethiopia",
   "geo_iso3": "ETH",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/ETH/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.gab",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 33.3745,
   "unit": "% of monthly GNI per capita",
   "geography": "Gabon",
   "geo_iso3": "GAB",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/GAB/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.gha",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 102.6616,
   "unit": "% of monthly GNI per capita",
   "geography": "Ghana",
   "geo_iso3": "GHA",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/GHA/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.gin",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 156.0694,
   "unit": "% of monthly GNI per capita",
   "geography": "Guinea",
   "geo_iso3": "GIN",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/GIN/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.gmb",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 290.3226,
   "unit": "% of monthly GNI per capita",
   "geography": "Gambia",
   "geo_iso3": "GMB",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/GMB/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.gnb",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 247.7064,
   "unit": "% of monthly GNI per capita",
   "geography": "Guinea-Bissau",
   "geo_iso3": "GNB",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/GNB/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.gnq",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 45.8404,
   "unit": "% of monthly GNI per capita",
   "geography": "Equatorial Guinea",
   "geo_iso3": "GNQ",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/GNQ/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.ken",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 122.7273,
   "unit": "% of monthly GNI per capita",
   "geography": "Kenya",
   "geo_iso3": "KEN",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/KEN/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.lbr",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 325.3012,
   "unit": "% of monthly GNI per capita",
   "geography": "Liberia",
   "geo_iso3": "LBR",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/LBR/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.lby",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 37.2414,
   "unit": "% of monthly GNI per capita",
   "geography": "Libya",
   "geo_iso3": "LBY",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/LBY/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.lso",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 210.9375,
   "unit": "% of monthly GNI per capita",
   "geography": "Lesotho",
   "geo_iso3": "LSO",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/LSO/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.mar",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 61.9266,
   "unit": "% of monthly GNI per capita",
   "geography": "Morocco",
   "geo_iso3": "MAR",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/MAR/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.mdg",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 482.1429,
   "unit": "% of monthly GNI per capita",
   "geography": "Madagascar",
   "geo_iso3": "MDG",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/MDG/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.mli",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 241.0714,
   "unit": "% of monthly GNI per capita",
   "geography": "Mali",
   "geo_iso3": "MLI",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/MLI/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.moz",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 473.6842,
   "unit": "% of monthly GNI per capita",
   "geography": "Mozambique",
   "geo_iso3": "MOZ",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/MOZ/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.mrt",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 122.1719,
   "unit": "% of monthly GNI per capita",
   "geography": "Mauritania",
   "geo_iso3": "MRT",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/MRT/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.mus",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 19.2308,
   "unit": "% of monthly GNI per capita",
   "geography": "Mauritius",
   "geo_iso3": "MUS",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/MUS/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.mwi",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 450.0,
   "unit": "% of monthly GNI per capita",
   "geography": "Malawi",
   "geo_iso3": "MWI",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/MWI/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.nam",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 62.212,
   "unit": "% of monthly GNI per capita",
   "geography": "Namibia",
   "geo_iso3": "NAM",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/NAM/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.ner",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 360.0,
   "unit": "% of monthly GNI per capita",
   "geography": "Niger",
   "geo_iso3": "NER",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/NER/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.nga",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 198.5294,
   "unit": "% of monthly GNI per capita",
   "geography": "Nigeria",
   "geo_iso3": "NGA",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/NGA/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.rwa",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 234.7826,
   "unit": "% of monthly GNI per capita",
   "geography": "Rwanda",
   "geo_iso3": "RWA",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/RWA/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.sdn",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 300.0,
   "unit": "% of monthly GNI per capita",
   "geography": "Sudan",
   "geo_iso3": "SDN",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/SDN/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.sen",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 151.6854,
   "unit": "% of monthly GNI per capita",
   "geography": "Senegal",
   "geo_iso3": "SEN",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/SEN/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.sle",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 325.3012,
   "unit": "% of monthly GNI per capita",
   "geography": "Sierra Leone",
   "geo_iso3": "SLE",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/SLE/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.som",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 421.875,
   "unit": "% of monthly GNI per capita",
   "geography": "Somalia",
   "geo_iso3": "SOM",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/SOM/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.stp",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 71.0526,
   "unit": "% of monthly GNI per capita",
   "geography": "Sao Tome and Principe",
   "geo_iso3": "STP",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/STP/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.swz",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 72.3861,
   "unit": "% of monthly GNI per capita",
   "geography": "Eswatini",
   "geo_iso3": "SWZ",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/SWZ/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.syc",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 14.0625,
   "unit": "% of monthly GNI per capita",
   "geography": "Seychelles",
   "geo_iso3": "SYC",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/SYC/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.tcd",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 278.3505,
   "unit": "% of monthly GNI per capita",
   "geography": "Chad",
   "geo_iso3": "TCD",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/TCD/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.tgo",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 200.0,
   "unit": "% of monthly GNI per capita",
   "geography": "Togo",
   "geo_iso3": "TGO",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/TGO/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.tun",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 62.7907,
   "unit": "% of monthly GNI per capita",
   "geography": "Tunisia",
   "geo_iso3": "TUN",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/TUN/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.tza",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 212.5984,
   "unit": "% of monthly GNI per capita",
   "geography": "Tanzania",
   "geo_iso3": "TZA",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/TZA/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.uga",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 241.0714,
   "unit": "% of monthly GNI per capita",
   "geography": "Uganda",
   "geo_iso3": "UGA",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/UGA/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.zaf",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 43.0622,
   "unit": "% of monthly GNI per capita",
   "geography": "South Africa",
   "geo_iso3": "ZAF",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/ZAF/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.zmb",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 225.0,
   "unit": "% of monthly GNI per capita",
   "geography": "Zambia",
   "geo_iso3": "ZMB",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/ZMB/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.gni_basket_share.zwe",
   "pillar": "Access",
   "indicator": "Illustrative AI-use basket as a share of monthly GNI per capita",
   "value": 101.5038,
   "unit": "% of monthly GNI per capita",
   "geography": "Zimbabwe",
   "geo_iso3": "ZWE",
   "date": "2025",
   "source": "Computed from Claude Opus 5 rate card and World Bank GNI per-capita data",
   "source_url": "https://api.worldbank.org/v2/country/ZWE/indicator/NY.GNP.PCAP.CD?format=json&mrnev=1&per_page=100",
   "source_type": "DERIVED",
   "tier": 4,
   "tier_label": "Derived from reported pricing and official economic data",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "20 standard workloads at the median frontier rate card ($11.25 per workload), divided by monthly GNI per capita on the Atlas method (annual value divided by 12). Published only when the latest non-empty GNI observation is within the five-calendar-year freshness window.",
   "recompute_from": "data/affordability.csv",
   "headline": false,
   "note": "GNI per capita is a national-average economic indicator, not median earnings, household disposable income, or a typical worker's wage. The baseline rate card is trade-reported, so this derived comparison is not headline eligible."
  },
  {
   "id": "access.wage_share.africa_mean",
   "pillar": "Access",
   "indicator": "Illustrative AI workload as a share of a monthly minimum wage",
   "value": 187.3,
   "unit": "% of a monthly minimum wage",
   "geography": "Africa (8 countries with a statutory wage)",
   "geo_iso3": "",
   "date": "2026",
   "source": "Computed from Claude Opus 5 rate card and statutory wages",
   "source_url": "",
   "source_type": "DERIVED",
   "tier": 2,
   "tier_label": "Derived from academic and official inputs",
   "evidence_status": "derived",
   "temporal_status": "modelled",
   "methodology": "Mean across countries with a statutory minimum wage.",
   "recompute_from": "data/affordability.csv",
   "headline": true,
   "note": ""
  },
  {
   "id": "infra.capacity.morocco",
   "pillar": "Infrastructure",
   "indicator": "Data centre capacity named in the register",
   "value": 500.0,
   "unit": "MW named (all delivery statuses)",
   "geography": "Morocco",
   "geo_iso3": "MAR",
   "date": "2026",
   "source": "Observatory infrastructure register",
   "source_url": "https://blogs.nvidia.com/blog/egypt-africa-ai-ecosystem/",
   "source_type": "NEWS",
   "tier": 4,
   "tier_label": "News reporting",
   "evidence_status": "trade_reporting",
   "temporal_status": "announced",
   "methodology": "Sum of published capacity in the register. Projects without a published capacity figure are excluded, never counted as zero. This inventory total includes all labelled delivery statuses; it is not operational supply. Use the status breakdown for operating capacity.",
   "recompute_from": "data/infrastructure.json",
   "headline": false,
   "note": "0.0 MW of this is serving load."
  },
  {
   "id": "infra.capacity.nigeria",
   "pillar": "Infrastructure",
   "indicator": "Data centre capacity named in the register",
   "value": 100.0,
   "unit": "MW named (all delivery statuses)",
   "geography": "Nigeria",
   "geo_iso3": "NGA",
   "date": "2026",
   "source": "Observatory infrastructure register",
   "source_url": "https://businessday.ng/technology/article/nsia-bets-on-100mw-ai-data-centre-to-power-nigerias-digital-economy/",
   "source_type": "NEWS",
   "tier": 4,
   "tier_label": "News reporting",
   "evidence_status": "trade_reporting",
   "temporal_status": "announced",
   "methodology": "Sum of published capacity in the register. Projects without a published capacity figure are excluded, never counted as zero. This inventory total includes all labelled delivery statuses; it is not operational supply. Use the status breakdown for operating capacity.",
   "recompute_from": "data/infrastructure.json",
   "headline": false,
   "note": "0.0 MW of this is serving load. 2 of 3 projects in this country carry no published capacity and are excluded rather than counted as zero."
  },
  {
   "id": "infra.capacity.south-africa",
   "pillar": "Infrastructure",
   "indicator": "Data centre capacity named in the register",
   "value": 40.0,
   "unit": "MW named (all delivery statuses)",
   "geography": "South Africa",
   "geo_iso3": "ZAF",
   "date": "2026",
   "source": "Observatory infrastructure register",
   "source_url": "https://furtherafrica.com/2026/08/04/equinix-wins-approval-for-major-cape-town-data-centre-campus/",
   "source_type": "NEWS",
   "tier": 4,
   "tier_label": "News reporting",
   "evidence_status": "trade_reporting",
   "temporal_status": "announced",
   "methodology": "Sum of published capacity in the register. Projects without a published capacity figure are excluded, never counted as zero. This inventory total includes all labelled delivery statuses; it is not operational supply. Use the status breakdown for operating capacity.",
   "recompute_from": "data/infrastructure.json",
   "headline": false,
   "note": "0.0 MW of this is serving load. 3 of 4 projects in this country carry no published capacity and are excluded rather than counted as zero."
  },
  {
   "id": "infra.capacity.ghana",
   "pillar": "Infrastructure",
   "indicator": "Data centre capacity named in the register",
   "value": 10.0,
   "unit": "MW named (all delivery statuses)",
   "geography": "Ghana",
   "geo_iso3": "GHA",
   "date": "2026",
   "source": "Observatory infrastructure register",
   "source_url": "https://www.africadatacentres.com/africa-data-centres-announces-that-it-will-start-construction-on-a-new-facility-in-accra-ghana/",
   "source_type": "NEWS",
   "tier": 4,
   "tier_label": "News reporting",
   "evidence_status": "trade_reporting",
   "temporal_status": "announced",
   "methodology": "Sum of published capacity in the register. Projects without a published capacity figure are excluded, never counted as zero. This inventory total includes all labelled delivery statuses; it is not operational supply. Use the status breakdown for operating capacity.",
   "recompute_from": "data/infrastructure.json",
   "headline": false,
   "note": "0.0 MW of this is serving load."
  },
  {
   "id": "infra.capacity.kenya",
   "pillar": "Infrastructure",
   "indicator": "Data centre capacity named in the register",
   "value": 6.4,
   "unit": "MW named (all delivery statuses)",
   "geography": "Kenya",
   "geo_iso3": "KEN",
   "date": "2026",
   "source": "Observatory infrastructure register",
   "source_url": "https://techafricanews.com/2026/08/07/airtels-150m-kenya-data-centre-now-targets-july-2027-launch/",
   "source_type": "NEWS",
   "tier": 4,
   "tier_label": "News reporting",
   "evidence_status": "trade_reporting",
   "temporal_status": "announced",
   "methodology": "Sum of published capacity in the register. Projects without a published capacity figure are excluded, never counted as zero. This inventory total includes all labelled delivery statuses; it is not operational supply. Use the status breakdown for operating capacity.",
   "recompute_from": "data/infrastructure.json",
   "headline": false,
   "note": "6.4 MW of this is serving load. 2 of 3 projects in this country carry no published capacity and are excluded rather than counted as zero."
  },
  {
   "id": "infra.active-data-centre-capacity-in-africa",
   "pillar": "Infrastructure",
   "indicator": "Active data centre capacity in Africa",
   "value": 360.0,
   "unit": "MW",
   "geography": "Africa",
   "geo_iso3": "",
   "date": "2026",
   "source": "Africa Data Centres Association",
   "source_url": "https://africadca.org/en/data-centres-in-africa-2026-the-economic-report",
   "source_type": "INDUSTRY",
   "tier": 3,
   "tier_label": "Industry source",
   "evidence_status": "industry_body",
   "temporal_status": "measured",
   "methodology": "Continental accounting published by the industry association.",
   "recompute_from": "data/reference/home-figures.json",
   "headline": true,
   "note": "In service, as reported by the industry association."
  },
  {
   "id": "infra.data-centre-capacity-under-construction-",
   "pillar": "Infrastructure",
   "indicator": "Data centre capacity under construction in Africa",
   "value": 238.0,
   "unit": "MW",
   "geography": "Africa",
   "geo_iso3": "",
   "date": "2026",
   "source": "Africa Data Centres Association",
   "source_url": "https://africadca.org/en/data-centres-in-africa-2026-the-economic-report",
   "source_type": "INDUSTRY",
   "tier": 3,
   "tier_label": "Industry source",
   "evidence_status": "industry_body",
   "temporal_status": "announced",
   "methodology": "Continental accounting published by the industry association.",
   "recompute_from": "data/reference/home-figures.json",
   "headline": true,
   "note": "Under construction is not operational capacity."
  },
  {
   "id": "infra.data-centre-capacity-planned-in-africa",
   "pillar": "Infrastructure",
   "indicator": "Data centre capacity planned in Africa",
   "value": 656.0,
   "unit": "MW",
   "geography": "Africa",
   "geo_iso3": "",
   "date": "2026",
   "source": "Africa Data Centres Association",
   "source_url": "https://africadca.org/en/data-centres-in-africa-2026-the-economic-report",
   "source_type": "INDUSTRY",
   "tier": 3,
   "tier_label": "Industry source",
   "evidence_status": "industry_body",
   "temporal_status": "announced",
   "methodology": "Continental accounting published by the industry association.",
   "recompute_from": "data/reference/home-figures.json",
   "headline": true,
   "note": "Planned capacity is a pipeline, not supply."
  },
  {
   "id": "infra.africa's-share-of-global-data-centre-cap",
   "pillar": "Infrastructure",
   "indicator": "Africa's share of global data centre capacity",
   "value": 0.6,
   "unit": "% of global",
   "geography": "Africa",
   "geo_iso3": "",
   "date": "2026",
   "source": "Africa Data Centres Association",
   "source_url": "https://africadca.org/en/data-centres-in-africa-2026-the-economic-report",
   "source_type": "INDUSTRY",
   "tier": 3,
   "tier_label": "Industry source",
   "evidence_status": "industry_body",
   "temporal_status": "measured",
   "methodology": "Continental accounting published by the industry association.",
   "recompute_from": "data/reference/home-figures.json",
   "headline": true,
   "note": "Capacity share, not population or GDP share."
  }
 ]
}