Two instrumental variables (IV) are used. The first one is the trust score from the EIU database. This indicator considers how confident respondents are that their activity online is private. The score takes values from 0 to 100, where 100 is the most inclusive environment (The Economist Intelligence Unit, 2021a). The existence of such environment would encourage women and girls to access the internet, closing the gender gap in accessing internet with no direct effect on labor force participation. The second IV is the percentage of schools with internet access. This variable is used to control for internet availability. Increasing internet availability in an area would increase women’s access to the internet (Kusumawardhani et al, 2023).

# **4. Estimated Results**

The estimated coefficients of the different models are presented in Table 2. The first column represents the results of the GLS model ( _Model 1_ ) of the Internet Inclusion Index as the main independent variable. The Internet Inclusion Index has a positive significant impact on the gender gap in the labor force. This means that an inclusive internet environment is associated with higher ratio of female-to-male labor force participation.

Considering the gender gap in accessing the internet ( _Model 2_ ), the results show that when men have greater access to the internet than women (indicated by a higher positive value of the index of gender gap in access to internet), the female to male labor force participation ratio decreases. However, this negative effect is not significant in the GLS model. In the 2SLS model ( _Model 3_ ), in contrast, when instrumenting the gender gap in internet access with the trust score and the percentage of schools with internet access, this negative effect is significant. These results align with the proposition that, accessing the internet is a necessary but not sufficient condition for women to access economic opportunities. It worth noting that the results of the first stage of the 2SLS model<sup>5</sup> showed that the higher the trust score value and the percentage of schools with internet access, the lower the value of the gender gap in internet access reflecting women’s higher access compared to men. However, this negative effect is significant only for the percentage of schools with internet access.

Other key drivers of the female-to-male labor force participation ratio include the structure of economic growth. In Models 1 and 2, the higher the service share in GDP, the higher a country’s female labor force participation. This significant positive effect confirms that the services sector is one of the main employers for women in the MENA region. A similar positive significant effect is found for the industrial sector. These two variables became insignificant when the endogeneity of the gender gap in internet access is controlled for. Additionally, the higher the share of trade in GDP, the higher the ratio of female labor force participation to their male counterparts in all models.

With respect to education, the findings show that a high level of educational attainment would result in a decrease of the female-to-male labor force participation ratio. This negative effect is significant only for the first model. This unexpected result might confirm what is known in the literature as “The Education Paradox in the MENA region” (Assaad et al., 2018). The progress achieved in educational achievement in the MENA region, mainly for women, did not translate into an increase in female labor force participation.

> 5 See Annex 1 for the results of the first stage and the test of identification.

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