job losses.

We explore these hypotheses using the following regression model.

_JobLosticw_ is an indicator that takes the value 1 if individual _i_ from country _c_ lost her job in wave _w_ relative to the pre-pandemic period; _Womenicw_ indicates whether individual _i_ is a woman and _WFHic_ 0 is the WFH index adjusted by home internet access corresponding to the job that individual _i_ had before the pandemic;<sup>11</sup> the interaction between _Womenicw_ and _WFHic_ 0 allows for gender differences in the effect of WFH on job losses; vector _Xicw_<sup>_′_includeseducationattainment,ageandsquaredage,andwhether</sup> the individual was a salaried worker or self-employed before the pandemic, which allows controlling for job stability and other characteristics not related with the content of tasks, such as time flexibility;<sup>12</sup> the model also controls for household size and includes wave dummies ( _θw_ ) as well as country fixed effects ( _λc_ ).

We estimate model 2 using the sample of individuals who were employed before the pandemic. Column 1 in Table 4 reports the results.<sup>13</sup> The estimated coefficient for the WFH index is negative and significant, indicating that, even after controlling for all other variables, there is a negative association between the possibilities of doing remote work and the probability of losing a job during the pandemic. The coefficient of the dummy _Women_ is positive and statistically significant, while the interaction of this variable and _WFH_ is negative and also highly significant. This implies that while women were more likely to lose their jobs during the pandemic than men, having a job that can be done from home largely offsets that disadvantage. For instance, women with jobs incompatible with remote work (WFH = 0) were almost 11 percentage points more likely to lose their jobs during the pandemic than men with similar characteristics including similar jobs in terms of WFH. However, if we compare similar women and men in jobs fully compatible with remote work, women were only 2.8 percentage points more likely than men to lose their jobs during the pandemic. This result is in line with our previous discussion: the

> 11Our preferred specification includes this adjusted index that captures at the individual level the combined effect of both the characteristics of the occupation and the availability of technology at home on the ability to do teleworking. We also estimate models including separately the unadjusted WFH index and household internet access. All our main results hold.

> 12Unfortunately, the HFPS does not have information on hours worked. However, from other sources (e.g., SEDLAC) we know that self-employment exhibits a greater dispersion of weekly working hours than salaried work.

> 13Tables A.1 and A.2 show the results of estimating other more parsimonious specifications that gradually add controls. Models in Table A.1 explore conditional gender differences in the probability of experiencing job losses. The results indicate that the probability of job losses is 7.7 percentage points higher for women than for men with similar characteristics—i.e., same age, education, country, etc. Models in Table A.2 add the WFH index as a regressor. Given everything else, workers in jobs fully compatible with remote work (WFH = 1) are 8.5 percentage points less likely to have lost their jobs during the pandemic than workers in jobs incompatible with remote work (WFH = 0).

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