Gender stereotyping and wage discrimination among Italian graduates



5
where w is the monthly wage, “edperf” is educational performance, E is a vector of educational
dummy variables,
X is a vector of personal characteristics and Z is a vector of regional dummy
variables.

Two dimensions of educational performance are taken into account: degree score and the speed at
which students complete their academic career. In order to take into account both dimensions, we
build up the following measure for educational performance:

dscore

edperf = --------------------------

1 + 010 × years

where “dscore” is the degree mark plus the laude or highest honors when it occurs4.

Table 2 reports results from estimating gender-specific earnings equations controlled for self-
selection. We estimate the sample selection model by means of the Heckman (1979) two-step
procedure. The dependent variable is the natural logarithm of the net monthly wage. We first
consistently estimate the selection equations, binary choice type equations, where the binary
variable simply indicates working or not working. The estimation is conducted by means of probit
maximum likelihood. We then use the estimation results of the first stage to consistently estimate
by OLS the linear earning equations. Our specification incorporates labor market experience5 and
educational performance. In order to capture the impact of differences in regional wages we include
dummies for region of residence. We include also family background variables as the level of
education, the employment status and occupation of the father. We add further information on the
educational attainment and the work experience: work during the university, minimum degree score
needed for present work, obtainment of professional qualification. We try to exploit the richness of

4 - The degree scores in the publicly available data are provided in brackets rather than as a continuous variables. They
fall into four intervals (<79, 80-89, 90-94, 95-99) and for scores higher than 99 the effective value is disposable. We
treat the degree mark as continuous variable by using the midpoint of each range when the value is not available. The
number of years in excess (“years”) used to get the degree is eventually corrected for those having carried out military
service during their university years. Obviously, the degree scores have been normalized to take into account the
different marking scale for each faculty. The final degree score ranges from 66 to 110 (for some universities the
maximum mark awarded is 100). According to each faculty internal ruling a laude (distinction) may be assigned to
candidates with a 110/110 mark for recognition of the excellence of their thesis (in this analysis the 110
cum laude was
transformed to 113).

5 We make use of the age to approximate the labor market experience. We consider also the square of labor market
experience to take into account non linear effects.



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