Global Automation Atlas

Prashant Garg, Tommaso Crosta and Jasmin Baier

Papers from arXiv.org

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. Here we show that feasible 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. Construct-matched components of the measure correlate strongly with established exposure indices, observed work-related ChatGPT use, AI preparedness and firm-reported adoption. The exposed share of tasks ranges from 3.3% to 61.6%, rises with income yet remains heterogeneous 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. Country conditioning changes occupation exposure rankings, especially in lower-income economies. Combined with employment data, we find that women are disproportionately employed in occupations with substitution-facing exposure. Machine-learning hypothesis generation identifies digital records, capital equipment, local judgement, trust-based markets and data integration as conditions associated with exposure differences.

Date: 2026-05, Revised 2026-07
New Economics Papers: this item is included in nep-tid
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://arxiv.org/pdf/2605.17086 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2605.17086

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().