possibility of working from home was more important for women than for men in helping avoid job losses.<sup>14</sup> Next, we explore the role of the presence of children at home. We therefore add to the model an indicator of the presence of children aged between 5 and 18 in the household, its interaction with the dummy _Women_ , and the number of children in the household.<sup>15</sup> Column 2 in Table 4 shows the results that indicate that women with children at home were more likely to lose their jobs than men with similar characteristics who also have children at home.

In order to understand whether any differential role of WFH by gender is explained by childcare needs at home we run model 2 separately for households with and without children and report the results in columns 3 and 4 in Table 4.<sup>16</sup> Notice that the coefficient of the interaction between WFH and gender is negative, large in absolute value and very significant only in households with children, while in households without children the coefficient is still negative but smaller in absolute value and not statistically significant. This implies that the role of WFH in avoiding female job losses occurs mainly in households with children, possibly due to childcare needs. For instance, in households with children, women with jobs incompatible with remote work were almost 14 percentage points more likely to lose their jobs than men with similar characteristics and whose jobs were also incompatible with remote work. In contrast, in households without children the difference is only 5 percentage points.

To check the robustness of the results, Table 5 reports the estimates based on a balanced sample, i.e., the sample that includes only the individuals who were interviewed in all the 3 waves of the HFPS. Likewise, Table 6 reports the results for a model that includes fixed effects by region. In both cases the results in Table 4 hold.<sup>17</sup> Finally, we evaluate whether the mechanism underlying the gender differences in the impact of WFH on job losses is related to occupational segregation by gender. For this, we estimate a variant of model 2 that includes fixed effects by pre-pandemic occupation using the 2-digit ISCO classification. Table 7 reports these results. The coefficient associated to the interaction between gender and WFH is still negative and significant in the sample of households with children, which means that even among women in the same occupation, those with children were the ones for whom the ability for WFH was pivotal. This suggests

> 14Table A.3 reports the results of estimating a variant of model 2 where instead of the adjusted WFH index we separately incorporate the job characteristics and internet access. Our main results hold, as well as when we differentiate between the different types of internet access (Wi-Fi or mobile network).

> 15Unfortunately, the HFPS does not allow for a more complete characterization of the composition and structure of the household. For instance, we do not have information for children younger than 5 nor can we identify which household members are the parents; neither their marital status nor if there are elderly people in the home.

> 16The regressions for households with and without children are similar, in particular they include the same controls, so in principle the results are comparable. As is usual when analyzing heterogeneities, the omission of an unobservable control could generate a larger bias in one of the samples, distorting the results of the comparison.

> 17It would also have been relevant to check the robustness of the results after controlling for the prepandemic economic sector, but unfortunately this variable has 60 percent missing observations in the HFPS.

12