Lockdowns imposed around the world to contain the spread of the COVID-19 pandemic are
having a differential impact on economic activity and jobs. This paper presents a new index
of the feasibility to work from home to investigate what types of jobs are most at risk. We
estimate that over 97.3 million workers, equivalent to about 15 percent of the workforce, are
at high risk of layoffs and furlough across the 35 advanced and emerging countries in our
sample. Workers least likely to work remotely tend to be young, without a college education,
working for non-standard contracts, employed in smaller firms, and those at the bottom of the
earnings distribution, suggesting that the pandemic could exacerbate inequality. Crosscountry
heterogeneity in the ability to work remotely reflects differential access to and use of
technology, sectoral mix, and labor market selection. Policies should account for
demographic and distributional considerations both during the crisis and in its aftermath.
provides economic analysis and policy advice with the aim of promoting sustainable and equitable development. The Institute began operations in 1985 in Helsinki, Finland, as the first research and training centre of the United Nations University. Today it is a unique blend of think tank, research institute, and UN agency-providing a range of services from policy advice to governments as well as freely available original research. The Institute is funded through income from an endowment fund with additional contributions to its work programme from Finland, Sweden, and the United Kingdom as well as earmarked contributions for specific projects from a variety of donors.
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. JEL Classification Numbers: E24, J16, J23, J24, O33
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