{
  "title": "Aggregate Gains from AI and Their Distribution: Global Evidence from Usage Data",
  "publication": "IMF Working Papers, July 10, 2026",
  "sourceUrl": "https://www.imf.org/en/publications/wp/issues/2026/07/10/aggregate-gains-from-ai-and-their-distribution-global-evidence-from-usage-data-577586",
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  "summary": "How large is the labor cost saved by AI, and how is it distributed across occupations? Using five waves of the Anthropic Economic Index (January 2025 to February 2026), we construct two novel measures from observed AI usage across countries over the world.",
  "sections": [
    {
      "heading": "Core findings",
      "content": "- Using five waves of the Anthropic Economic Index (January 2025 to February 2026), the authors construct two novel measures from observed AI usage across countries.\n- The AI concentration index (ACI) indicates:\n  - In developing economies, virtually all AI usage-based value is generated in a small professional enclave (ACI near 1.0).\n  - High-income economies average 0.4 to 0.5, with concentration declining in many countries.\n- The labor cost equivalent (LCE) values the time currently saved by AI at $2.7 trillion annually (3.4% of GDP), presented as an indicative measure of the labor cost of that time."
    },
    {
      "heading": "Measures and data",
      "content": "- Data source: Five waves of the Anthropic Economic Index (January 2025 to February 2026).\n- Novel measures constructed:\n  - AI concentration index (ACI): quantifies how concentrated AI usage-based value is across occupations within countries.\n  - Labor cost equivalent (LCE): values time saved by AI in dollar terms and as a share of GDP."
    },
    {
      "heading": "Distributional patterns and predictors",
      "content": "- Concentration patterns:\n  - Developing economies: ACI near 1.0, indicating concentration of AI usage-based value in a small professional enclave.\n  - High-income economies: ACI averages 0.4 to 0.5, with evidence of declining concentration in many countries.\n- Predictors of concentration:\n  - Income predicts concentration.\n  - Regulatory readiness predicts concentration.\n  - Lacking an official English language slows broadening of AI usage, posing a barrier for developing countries."
    },
    {
      "heading": "Key statistics and publication metadata (from source)",
      "content": "- LCE: $2.7 trillion annually.\n- LCE as share of GDP: 3.4% of GDP.\n- Time span of index waves: January 2025 to February 2026.\n- Pages: 36.\n- Volume: 2026.\n- Issue: 147.\n- Series: Working Paper No. 2026/147.\n- DOI: https://doi.org/10.5089/9798229051279.001.\n- ISBN: 9798229051279.\n- ISSN: 1018-5941.\n- Publication date: July 10, 2026.\n- Authors: Rachel Yuting Fan, Ha Minh Nguyen."
    },
    {
      "heading": "Implications for policy and research",
      "content": "- Broadening AI usage outside a small professional enclave appears limited in developing economies; policy and regulatory readiness matter for diffusion.\n- Language barriers (absence of an official English language) are identified as a tangible obstacle to broader AI adoption in developing countries.\n- The $2.7 trillion LCE (3.4% of GDP) provides an indicative aggregate scale of current time savings from AI, informing labor cost and national accounts considerations.\n\nIMF Working Papers — \"Aggregate Gains from AI and Their Distribution: Global Evidence from Usage Data\" (Rachel Yuting Fan, Ha Minh Nguyen), July 10, 2026.\n\n---\n\n Content in this bundle\n\n- Working Paper\n  - Working Paper (Markdown version){rel=\"alternate\" type=\"text/markdown\"}\n  - Working Paper (PDF){rel=\"external\" type=\"application/pdf\"}\n\n---\n\nSource: https://www.imf.org/en/publications/wp/issues/2026/07/10/aggregate-gains-from-ai-and-their-distribution-global-evidence-from-usage-data-577586"
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    "Authors: Rachel Yuting Fan, Ha Minh Nguyen",
    "Published: July 10, 2026",
    "Series: IMF Working Papers",
    "DOI: https://doi.org/10.5089/9798229051279.001",
    "Using five waves of the Anthropic Economic Index (January 2025 to February 2026), the authors construct two novel measures from observed AI usage across countries.",
    "The AI concentration index (ACI) indicates:",
    "The labor cost equivalent (LCE) values the time currently saved by AI at $2.7 trillion annually (3.4% of GDP), presented as an indicative measure of the labor cost of that time.",
    "Data source: Five waves of the Anthropic Economic Index (January 2025 to February 2026).",
    "Novel measures constructed:",
    "Concentration patterns:",
    "Predictors of concentration:",
    "LCE: $2.7 trillion annually.",
    "LCE as share of GDP: 3.4% of GDP.",
    "Time span of index waves: January 2025 to February 2026.",
    "Pages: 36.",
    "Volume: 2026.",
    "Issue: 147.",
    "Series: Working Paper No. 2026/147.",
    "DOI: https://doi.org/10.5089/9798229051279.001.",
    "ISBN: 9798229051279.",
    "ISSN: 1018-5941.",
    "Publication date: July 10, 2026.",
    "Authors: Rachel Yuting Fan, Ha Minh Nguyen.",
    "Broadening AI usage outside a small professional enclave appears limited in developing economies; policy and regulatory readiness matter for diffusion.",
    "Language barriers (absence of an official English language) are identified as a tangible obstacle to broader AI adoption in developing countries.",
    "The $2.7 trillion LCE (3.4% of GDP) provides an indicative aggregate scale of current time savings from AI, informing labor cost and national accounts considerations.",
    "**Working Paper**"
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