defined as a dummy variable indicator equal to one if the individual reported in 2012 that job search was a purpose of Internet use. Relying on the 2018 data, I track individuals who were interviewed in 2012 and identify whether they found a job following their unemployment spell in 2012. The ELMPS 2018 provides retrospective information on employment status and job mobility, which allows identifying if any individual changed his employment status/job and the year of new status or new job. This makes it possible to determine whether the unemployed individuals in 2012 found a job, and if so also the year in which they started the new job. In Table 4, I examine the impact of Internet job search in 2012 on the probability of being employed within a 5 years timeframe, conditional on being unemployed in 2012. Unemployed individuals are defined according to the labor market definition of labor force participation, so individuals who engage in subsistence economic activities are considered as unemployed according to this definition. Table 4 therefore reports results estimating equation (1). I include the control variables gradually to check the sensitivity of the results with respect to the covariates included in the regression specification. For instance, in column (1), I control for home Internet access, gender, and marital status. In column (2), I additionally control for age and educational attainment dummies, while in column (3) I also control for living in rural versus urban areas. Finally, column (4) includes the full set of control variables in addition to controlling for geographical regions’ dummies. Controlling for Internet access at home, I find that unemployed individuals in 2012 who use the Internet for job search are significantly and consistently more likely to find a job within 5 years. Using the Internet for job search increased the likelihood that a previously unemployed found a job by 10 percentage points—a particularly large increase, given that the mean of the dependent variable is 0.038.

The results presented in Table 4 may be concealing heterogenous effects. In Table 5, I therefore investigate heterogeneous effects by gender, area of residence, age, and education. I estimate separate regressions for men and women in columns (1) and (2), respectively. Indeed, the results in Table 5 suggest that Internet job search is effective only for the subsample of unemployed men, as I do not find any significant effect for the subsample of unemployed women in 2012. It is worth mentioning that the magnitude of the estimated coefficient for men is much larger, as I find that using the Internet for job search is associated with a 15 percentage points increase in the probability of employment within 5 years among unemployed men in 2012. The differential impact of Internet job search by gender might be due to several potential reasons. There are structural differences between men’s and women’s unemployment and employment in Egypt and these differences viewed together help rationalize the differential results obtained by gender. First, marriage is concieved to be one of the main barriers to women’s employment in Egypt, as women find it difficult to find jobs that help them combine work and family life. As shown, in the descriptive statistics, the incidence of marriage among unemployed women is found to be higher than among unemployed men. This reasoning brings in the second argument linked to the importance of public sector employment for women. Egyptian women have a strong preference for the public sector, which offers flexible working hours and allows them to more easily balance work and family. This is reflected in the descriptive statistics on the search methods employed by women, which show 11