control for education, another key driver of female labor force participation, educational attainment measured by average years of schooling at the country level, is included in the model.

Formal and informal institutions affect the gender roles in society and women’s decision to enter the labor market, especially in the MENA region. Three variables from the World Bank Women, Business and the Law dataset are used to control for such institutions. Regarding legislations, known as formal institutions, two variables are included in the model. The first formal institution variable is the number of days of paid paternity leave. Giving men access to paid paternity leave may challenge the traditional roles, as it suggests men and women will share care work responsibilities. So, in countries where the legislation states more days of paid paternity leave, women’s participation in labor market is expected to increase. The second formal institution variable is captured in a dummy variable that equals one if the law prohibits discrimination in access to credit based on gender, 0 otherwise. The informal institution included in the model is women’s freedom of mobility as a measure of values and norms. This dummy variable equals one if a woman can travel outside her home in the same way as a man, 0 otherwise.

The data used is a pooled cross section dataset of 13 MENA countries for years from 2018, to 2021. The dataset is constructed from the World Development Indicators, the Economist Intelligence Unit database of the Inclusive Internet index and the World Bank Women, Business and the Law database. The constructed dataset is used to estimate equation 1 using the generalized least square (GLS) method to control for heteroskedasticity of error terms. The error terms are clustered at the country level.

Year dummies are included in the model to capture the effect of the years of the COVID-19 pandemic. Three dummy variables are included for the years 2019, 2020, and 2021.

Another version of the model is estimated using the gender gap in internet access<sup>4</sup> as an explanatory variable instead of the Internet Inclusion Index ( _Model 2_ ). A higher positive value of this gap means that male internet access exceeds female access. Model 2 is estimated using the GLS method. However, as internet users are not randomly assigned and as education, economic growth, and geography affect access to the internet, the gender digital divide is likely to be endogenous to labor market outcomes (Dettling, 2013; Gurumurthy and Shami, 2014). Thus, Model 2 is re-estimated using a two stages least square (2SLS) approach to control for endogeneity ( _Model 3_ ). The identification assumption is that the existence of national strategies that promote safety and trust encourages women to use the internet, resulting in a reduction in the gender digital gap. This reflects an understanding that safety and security concerns are barriers for women’s use of the internet.

> 4 The measure of the gender gap in internet access is computed as (% male access - % female access / % male access). Positive values indicate that male access exceeds female access. A smaller or negative gap indicates greater female connectivity.

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