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Mariya Brussevich, Ms. Era Dabla-Norris, and Salma Khalid

task assignment at the workplace. To remedy this, we apply the method developed by Arntz, Gregory, and Zierahn (2017) to impute the probabilities to worker characteristics and job task descriptions from the PIAAC dataset. 12 To relate automation probabilities at the occupational level to individuals, each observation in the PIAAC data is matched to the occupational codes in ONET for which we have estimates of automation probability from Frey and Osbourne (2017) . Since the PIAAC data only contains 2-digit ISCO codes for occupations, each observation can be

Mariya Brussevich, Ms. Era Dabla-Norris, and Salma Khalid
Using individual level data on task composition at work for 30 advanced and emerging economies, we find that women, on average, perform more routine tasks than men?tasks that are more prone to automation. To quantify the impact on jobs, we relate data on task composition at work to occupation level estimates of probability of automation, controlling for a rich set of individual characteristics (e.g., education, age, literacy and numeracy skills). Our results indicate that female workers are at a significantly higher risk for displacement by automation than male workers, with 11 percent of the female workforce at high risk of being automated given the current state of technology, albeit with significant cross-country heterogeneity. The probability of automation is lower for younger cohorts of women, and for those in managerial positions.