more (13.3%) for non-technologically oriented firms and just under 5 times more for firms not providing worker training (17.8%) for their employees.

Despite the very modest changes in female employment post-COVID reported by our survey, looking at these stylized facts of the sector, there seem to be some interesting heterogenous effects at play that require further rigorous investigation.

# **4. Methodology**

Our aim is to assess the heterogeneous impact of COVID-19 on the gender employment gap in Egyptian manufacturing using unique firm-level data. We control for sector, location and firm level characteristics to disentangle the effect of the shock on the employment gap.

# _The Model_

To measure the impact of COVID-19 on the gender employment gap, we run the following two OLS fixed effects specifications:

> _Emp. Gapikg = α0 + Shockikg + α1Xikg + α2 Zikg + μk+ δg + ŋikg                                                     (1)_

_Percentage change in F/M emplikg = α0 + Shockikg + α1Xikg + α2 Zikg + μk+ δg + ŋikg (2)_ where the dependent variable _Emp. Gapikg_ is the change in the difference of the number of male and female employees after COVID ( _Δ in male-female employment AC_ ) in firm _i_ in sector _k_ and governorate _g_ with separate equations estimated for (i) the change in the number of male employees; and (ii) the change in the number of female employees post-COVID. δg and _μk_ are governorate and sector dummies respectively, to control for governorate and sector unobversables, and ŋikg is the error term. The sectors are classified according to the ISIC Rev. 4 at the 2-digit level. The dependent variable in equation (2) is the percentage change in male and female employment, and the difference thereof. The vectors of the independent variables are identical in equations (1) and (2).

Two sets of factors could affect the gender gap. We distinguish between ‘status variables’ or ‘innate characteristics’ and those which are shaped by the behavior of the industrial firm such as good managerial practices or investment in innovation. Xikg and Zikg are vectors of firm’s innate characteristics and behavior, respectively. ‘Innate characteristics’ cover traits such as being informal, solely catering to the domestic market, firm size or being in the private sector. Thus, the former includes controls for firm age, indicators for private ownership, formality, export status, industrial zone and firm size prior to COVID. The latter set of variables control for degree of technology adoption, worker training, innovation, i.e., spending on R&D and good managerial practices prior to COVID-19.

_Shockikg_ is a composite index for firms’ exposure to COVID shocks. We distinguish three types of shocks: _demand shock_ , _supply shock_ and the _combined shock_ . The _demand shock_ is calculated as the sum of two indicators, namely the self-reported decline in the value of the quantity demanded both domestically and in export markets. The _supply shock_ is given as the sum of the self-reported decline in production value and in capacity utilization. The combined shock aggregates the demand and supply shocks. We cluster the standard errors at the sector and governorate level. The OLS fixed effects model allows us to control for a wide range of 10