measures are then applied to data from the U.S. Bureau of Labor Statistics (BLS), and to data from several countries from the International Labour Organization (ILO). They find that lower-income countries have a lower prevalence of jobs that can be done at home.

Several articles have implemented different adaptations of DN2020. For example, Gottlieb, Grobovsek, and Poschke (2020) use labor force surveys from 57 countries and point out that lowincome countries have a high share of self-employed agricultural workers. Their ability to work from home impacts the overall labor market effect of COVID-19 in lower-income countries. The DN2020 measure is based on the data from the United States, where farms are typically large and more reliant on hired labor. As a result, DN2020 assumes that only 8.3% of all agricultural workers can work from home. In poor countries farms are much smaller and a large share of agricultural workers is self-employed, which could imply that farming may be possible from home if plots are located very close to home (or perhaps, more relevant, may be feasible while respecting social distancing guidelines). Assuming that all these self-employed agricultural workers can do their jobs from home leads to a negative association between home-based work and GDP per capita. However, suing PIAAC data, Hatayama, Viollaz and Winkler (2020) show that the positive correlation between home-based work and GDP per capita remains positive when using countryspecific measures of tasks instead of O*NET. Their sample includes countries at different levels of development such as Ecuador and Peru (where agricultural employment represents almost a third of total employment) and the United States and Germany (where the corresponding figure is about 1 percent).<sup>3</sup> The Appendix to this paper provides extended arguments regarding these measures and shows that the ability of agricultural workers to work from home is limited and, moreover, tends to increase, rather than to fall, with income per capita.<sup>4</sup>

> construct an index with three intensity categories (low, medium and high). They apply the index to the American Community Survey. Mongey, Pilossoph, and Weinberg (2020) use the same O*NET questions as Dingel and Neiman (2020) but construct a continuous index of working from home using the intensity scores as Leibovici et al. (2020). 3 Their sample also includes lower income economies covered by the STEP surveys, where the positive correlation between home-based work and GDP per capita also holds. However, these surveys exclude rural areas in most cases, thereby excluding most agricultural workers.

> 4 Given that agriculture remains an important source of employment in low-income countries, the assumptions on the ability of self-employed agricultural workers to work from home is an important determinant of the relationship between labor market vulnerability to COVID-19 and development. This paper follows the DN2020 approach, but we recognize that the treatment of agricultural workers is very important. One of our main conclusions, that ICT constraints are more likely to bind in low income countries, does not depend on the assumptions about the ability of the self-employed agricultural workers to work from home.

4