We use worker-level data on the task content of jobs to measure the ability to work-fromhome (WFH) in developing countries. We show that the ability to WFH is low in developing countries and document significant heterogeneity across and within occupations, and across worker characteristics. Our measure suggests that educated workers, wage employees and women have a higher ability to WFH. Using data from Brazil, Costa Rica and Peru, we show that our measure is predictive of actual WFH both in terms of overall levels and variation with occupation and individual characteristics, as well as employment outcomes. Our measure can thus be used to predict WFH outcomes in developing countries.

We use an accounting framework to evaluate the aggregate impact of a common lockdown policy for 85 countries. We find that poorer countries devote more labor to essential activities that are unaffected by the lockdown, while richer countries can more easily substitute non-essential employment with work from home. The lockdown generates an employment response that is U-shaped in income: it drops by 32% in the poorest quintile of the distribution, by 36% in the middle quintile, and by 31% in the richest quintile. Annualized GDP declines by 39% in the bottom three quintiles and by 31% in the richest quintile. Agriculture, an essential sector, is key in sustaining employment and economic activity in poorer countries.

We use worker-level data on the task content of jobs to measure the ability to work-fromhome (WFH) in developing countries. We show that the ability to WFH is low in developing countries and document significant heterogeneity across and within occupations, and across worker characteristics. Our measure suggests that educated workers, wage employees and women have a higher ability to WFH. Using data from Brazil, Costa Rica and Peru, we show that our measure is predictive of actual WFH both in terms of overall levels and variation with occupation and individual characteristics, as well as employment outcomes. Our measure can thus be used to predict WFH outcomes in developing countries.

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