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Global Automation Atlas

Prashant Garg Prashant Garg recently completed his PhD in Economics at Imperial College London. His research covers science, innovation, production, and media using machine learning, causal inference, and network science. He joins Bocconi University as a Postdoctoral Researcher in September 2026. Tommaso Crosta Tommaso Crosta is a PhD candidate in Economics at Bocconi University. His research sits at the intersection of development and labour economics, with additional interests in Bayesian statistics applied to microeconomics and meta-analysis. Jasmin Baier Jasmin Baier is a doctoral candidate in Behavioral Economics and Public Policy at the Blavatnik School of Government, University of Oxford. Her research sits at the intersection of behavioral and development economics, focusing on skills, AI and labor markets, and human-AI interaction. (2026)

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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 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.

Main results

Cross-country exposure, labour margins, technology channels, AI materiality, labour-market composition and informality, and country-conditioned rationale hypotheses.

Figure 1

Cross-country patterns in task exposure

Automation exposure rises with income, but countries at similar income levels still differ substantially.

Interpretation The same task can receive different exposure labels across countries, so cross-country differences reflect both task composition and country-specific conditions.

Figure 2

Country-level task pathways

Development changes both how much work is exposed and whether exposed work is substitution-only, augmentation-only, or both.

Interpretation Labour margins classify how automation changes the worker's role inside exposed tasks. They are task-level judgments, not estimates of employment effects.

Figure 3

Automation channels and AI materiality

Lower-income exposure is concentrated in rule-based workflow automation. Higher-income exposure spreads across physical, planning, information, and inference channels.

Interpretation The channel mix suggests that automation is shaped by complementary infrastructure, capital, and organisational capacity.

Figure 4

AI materiality, AI function mix, and labour-margin composition

AI-material exposure rises with income and is often attached to shared or augmenting worker roles.

Interpretation AI exposure is not only a replacement margin. In many exposed tasks, AI changes the worker's role inside the task.

Figure 5

Labour-margin exposure, gender composition, and informality

Female employment is more exposed to substitution-only automation in occupation weights. Employment sorting explains most of the gap, while country-specific exposure can offset it; informality is negatively associated with substitution-only exposure after GDP adjustment.

Interpretation Employment sorting helps explain why gender gaps differ across countries, while higher informality is linked to a smaller substitution-only share.

Figure 6

Country covariates and rationale concepts behind exposure differences

Country covariates identify broad patterns in exposure and its labour margins. Heldout same-task rationale comparisons point to production conditions that recur when the same task receives different labels across countries.

Interpretation Broad country conditions help account for exposure patterns, while recurring rationale concepts identify the production settings in which the same task receives different labels.