# **3.1 Impact of Internet job search on employment**

Relying on panel data from the 2012 and 2018 survey rounds of the ELMPS, I examine the effect of Internet job search on employment and estimate various regression specifications. First, I estimate equation (1) where I examine the impact of Internet job search by the unemployed in 2012 on the probability of finding a job within a 5 year timeframe following their unemployment spell. E E E E E E 1 2 (1) ii,2013−2017<sup>� U</sup> ii,2012 ii,2012 ii,2012 ii PP(EI estimate the impact of Internet job search in 2012 on the probability of finding a job in the 5<sup>E</sup> �= ββ IIUUIIEEIIUUEEII jjEEjj ssEEssIIssh + BB XX + εε years timeframe conditional on being unemployed in 2012 (relying on the market definition of labor force participation). The regression controls for home Internet access in 2012 in addition to the vector , which is a vector of individual characteristics in 2012. The control variables include: individual’s age, a dummy variable for being male, a dummy variable for being married, ii,2012 XX three dummies for the individual’s educational attainment (primary or preparatory education, secondary education, either general or vocational, and above secondary education, either postsecondary institute or university and above, and the reference category is no educational degree, either illiterate individuals or literate individuals without any diploma), a dummy variable for rural areas, as well as dummies for the various geographical regions (Greater Cairo, Alexandria and Suez cities, Urban Lower Egypt, Urban Upper Egypt, Rural Lower Egypt, and Rural Upper Egypt).

I also employ a first difference specification in equation (2) relying on the panel dimension of the data. The dependent variable is a first-difference in dummy variable indicators for being employed between 2018 and 2012, while the independent variable of interest thus becomes the firstdifference in dummy variable indicators for using the Internet for job search between the years 2018 and 2012 controlling for the change in home Internet access. The regression also includes the same set of pre-determined controls in equation (1), first differenced between 2018 and 2012. Relying on this first-difference specification is equivalent to the inclusion of individual fixed effects, thus differences out all time-invariant unobservable characteristics. The main estimated coefficient of interest 1 captures the effect of the change in the use of Internet for job search on the change in the probability of being employed between the two years. A positive coefficient αα would therefore mean that an increase in Internet job search is associated with an increase in the probability of employment.

ΔE E E E 1 2 (2) ii ii ii ii = αα ΔIIUUIIEEIIUUEEII jjEEjj ssEEssIIssh + αα ∆XX + νν Finally, I estimate a fixed effects model relying on equation (3). The dependent variable in equation (3) is a dummy variable indicator equal to one if the individual is employed in the corresponding year relying on the market definition of labor force participation. The main independent variable of interest is a dummy variable indicator equal to one if the individual reports using the Internet for job search in the corresponding year. The regressions also include i , which is a vector of time-varying control variables and it includes age, a dummy variable for being XX 9