Global Automation Atlas
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 materia
Researchers analyzed 18,797 work tasks in 124 economies, finding that feasible automation depends on task content and country-level conditions. The exposed share of tasks ranges from 3.3% to 61.6%, varying with income and development. Women are disproportionately employed in occupations with substitution-facing exposure, according to the study.
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The study's findings on task automation exposure underscore the need for nuanced policymaking, as the varying levels of exposure across economies and occupations necessitate targeted strategies. Notably, the disproportionate impact on women highlights the importance of considering gender disparities in workforce development programs, lest efforts to mitigate automation's effects exacerbate existing inequalities.