insignificant) coefficients of the expected sign as well. These results suggest that an empirically based measure of the risk of being unable to work from home would weigh each of these components differently than was done early in the crisis, at least for some upper middle income countries. Moreover, in addition to the occupational characteristics, we found that in Jordan was positively affected by the share of workers with university degrees and was higher for large firms (Table 4-3). In Georgia, was significantly EEIIssh higher for medium and large firms but was not related to the share of employees with tertiary education, EEIIssh possibly because of that country’s high percentages of post-secondary-educated PFPS workers.

We conclude that where greater ICT use for delivery, work, and other business functions was an option, it expanded easily in these countries and that without that option, more jobs would likely have been lost in the short run. However, on net over the medium term, firms had to maintain sales levels if they were to sustain their workforces, wherever certain workers performed their duties. Although some jobs can be done remotely within a firm, only limited services firms can operate fully remotely, and without the necessary labor contributions of key occupations and without adequate levels of demand, this ability does not translate to job retention in all sectors. Therefore, ICT options had limited explanatory power in their firms’ ability to maintain sales. ICT-based work may have saved jobs for female workers who may have seen increased care responsibilities as schools closed. However, as discussed above, for the countries and sectors examined here, the ability to work from home was not a significant influence on the scale of formal firms’ net adjustments to their permanent workforce. This also suggests that in analyzing job market dynamics more generally, it is important to consider the necessary combination of tasks/occupations within a firm and the complementarities between them.

_Table 4-3 : Determinants of Share of Workforce Working from Home, Highlights_

|Dependent variable:<br>share of firm’s workforce workingremotely|Jordan|Georgia|
|---|---|---|
|Percentage ofpermanent full-time workers with universitydegree|0.0901*|0.0101|
||(0.0527)|(0.0205)|
|Firm size,categorical = 2,medium|3.566|7.174***|
||(3.106)|(3.695)|
|Firm size,categorical =3,large|9.264*|11.44***|
||(4.856)|(3.799)|
|Physicallydemandingand manual task index(higher = more intense)|1.636|-1.309|
||(1.149)|(2.492)|
|In-person face-to-face task index(higher = more risk)|-2.483|-1.908|
||(2.202)|(3.819)|
|ICT task index(higher = less ICT use with low internet)|-4.999*|-6.503*|
||(2.674)|(3.360)|
|Observations|436|535|
|Adjusted R-squared|0.145|0.042|

Notes: Full regression results are reported in Appendix 2 in the longer version of this paper.

# **5. Projecting Job Losses and Validating the Method**

In addition to understanding the causes of PFPS job losses, we propose to use our results for the following purposes: (1) assess the degree to which the identified causes can explain the realized distribution of job losses by demographic group; and (2) quantify job loss risk computations. To accomplish both, we therefore 23