Global Automation Atlas

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.

78 pages, 6 figures, 5 Extended Data figures. Substantially revised and expanded. Data and code: this https URL

General Economics (econ.GN); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Applications (stat.AP)

arXiv:2605.17086 [econ.GN]

(or arXiv:2605.17086v2 [econ.GN] for this version)

https://doi.org/10.48550/arXiv.2605.17086 Focus to learn more arXiv-issued DOI via DataCite

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