firms have mostly increased their demand on _male_ employment, while slightly declining that on females. So, in addition to these types of firms’ enormous contribution to the gender gap (see stylized facts above), their effect post-COVID is to further entrench this gap. Again, this is not surprising as women tend to be concentrated in low-tech, traditional sectors such as clothing.

A note on the dependent variable is warranted. Ideally one would use the change in female employment share in total employment post COVID as dependent variable. This would take into account relative changes in female employment compared to those of men given a particular firm size. Nevertheless, using this dependent variable turns all results insignificant.

# _Heterogeneity Analysis Results_

Table 4 and Tables 3-4 in Annex 3 give our first set of heterogeneous effects of other firm characteristics. The interaction of the _female dominated sectors_ with the various types of shocks produced the expected results in terms of increasing the gender gaps (regressions 3 and 6, Table 4 and Tables 3-4 in Annex 3). It did so through either reducing female employment (e.g., regression 1 Table 3) or increasing male percentage change in employment (regressions 4 and 5, Table 4 and Tables 3-4 in Annex 3) without a corresponding increase for females. Demand, supply and the combined shock increased the gender gap in the two female dominated sectors of clothing and leather compared to the other non-female dominated sectors. When interacting the shocks with the female dominated sector variable it becomes clear that the worsening of the gender gap is driven by firms in female dominated sectors that are hit by the supply and combined COVID-19 shock. As a robustness check we ran the same regressions for the top 5% female dominated sectors.<sup>12</sup> Results remain robust if slightly weaker.

When additionally controlling for the number of informal workers we find that the higher the number of informal employees in a firm pre-COVID, the greater the increase in the number of ‘male’ workers compared to females post-COVID (Tables 5-7 in Annex 3, regression 2), leading to a very slight deterioration of the gender gap (Tables 6-7 in Annex 3, regression 3). Nevertheless, for firms with informal workers that have additionally experienced a shock (i.e. the interaction term) this effect more or less cancels this employment effect out, even though that the overall estimated effects of informality are not substantially large. There is no contradiction here with the fact that the informal sector, which is made up strictly of informal firms, is a ‘survival sector’ that has provided ‘helping hand employment’ to the type of employment harder hit by the pandemic.

# _Propensity Score Matching_

The propensity score matching results (Table 5) basically confirm the baseline results in tables.<sup>13</sup> The COVID shock, be it a demand one, a supply one, or combined has led to a decline in overall employment and in the percentage change of employment. This is true of both sexes. Nevertheless, the reduction in ‘male’ employment has been on average over three times greater than that for women. This relative weaker direct employment impact on women is primarily due to the very limited number of women in employment in manufacturing to start

> 12 In the EIFBS data these are leather, clothing, furniture and wood products, textiles, other manufacturing and food.

> 13 The balance tests for the covariates are provided in Tables 8-9 in Annex 3. In all cases, the standardized mean differences (i.e., median bias) are less than the recommended threshold of 5%.

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