{
  "abstract": "Automation can displace or complement labour, but this need not be constant across economies. Existing exposure measures typically assign fixed scores to tasks or occupations and capture cross-country variation through employment structure. We posit that exposure to automation depends jointly on task content and country-level conditions. We use a large language model to classify 18,797 work tasks in 124 economies by exposure, labour margin, technology channel and artificial-intelligence materiality. Country conditioning changes the classification of the same task and the occupation exposure rankings, especially in lower-income economies. Country-conditioned scores improve out-of-country predictions of firms' production-technology use and AI adoption; physical-execution exposure tracks industrial-robot use and density, while Atlas scores align with observed work-related ChatGPT and Claude use across activities and countries. The share of tasks exposed to automation ranges from 3.3% to 61.6% across countries. It rises with income but varies considerably within income groups. Lower-income economies are more concentrated in rule-based and labour-substituting forms of automation, whereas physical execution, planning and inference channels, together with labour-augmenting uses of artificial intelligence, become more prominent with development. Combined with employment data, we find that women are disproportionately employed in occupations with substitution-facing exposure. Across countries, higher informality is associated with a smaller exposed-task share but a larger substitution-only share among exposed tasks. Machine-learning hypothesis generation methods point to both economic and institutional drivers: automation is more feasible where firms have machinery, digital records and connected systems, but less so where tasks depend on local knowledge and trusted relationships.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.17086",
  "author_total": 3,
  "authors": [
    {
      "name": "Prashant Garg"
    },
    {
      "name": "Tommaso Crosta"
    },
    {
      "name": "Jasmin Baier"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fjj3ha17m7rgp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.17086",
  "fulltext_url": null,
  "id": "fjj3ha17m7rgp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.17086",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.CY"
    ],
    [
      "arxiv",
      "econ.GN"
    ],
    [
      "arxiv",
      "q-fin.EC"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "openalex",
      "2002|Labor market dynamics and wage inequality"
    ]
  ],
  "openalex": "W7161729999",
  "pdf_url": "https://arxiv.org/pdf/2605.17086",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fjj3ha17m7rgp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:26Z",
      "native_id": "2605.17086",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.17086"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Global Automation Atlas",
  "title_text": "Global Automation Atlas",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:26Z",
  "url": "https://research.draconitic.com/paper/fjj3ha17m7rgp",
  "venue": "arXiv",
  "versions": []
}