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(1957) and the mainstream literature on the gender pay gap we interpret unexplained sex disparities
in pay that persist even with extensive controls for individuals and jobs characteristics as due to
discrimination. Gender discrimination arises when the assessment of productivity is affected by
stereotypes, that is non-conscious hypotheses, beliefs or expectations that influence our judgments
of others (Valian 1998).
We hypothesize that the effects of gender stereotypes as “a woman after pregnancy is a resource
for the company lost” or “think manager, think male” are an important cause of statistical
discrimination which is realized in the unexplained component of the gender pay gap. Following
Heilman (1997) and Hunt et al. (2002) we identify some contexts in which stereotypes are more
likely to occur and we verify that the most likely the stereotype, the higher the unexplained
component of the gender pay gap. Finally, we show that an excellent educational performance
serves to counteract the gender bias in on-the-job evaluations, even if being excellent at school does
not ensures that a woman will be rewarded as an equivalently performing man.
1 - Data
Our data come from the Survey on Labour Market Transitions of University Graduates carried out
in 2007 by the Italian National Statistical Office. The Survey is the result of interviewing Italians
who graduated from university in 2004 three years after graduation. The retrospective information
gathered allows us to analyze both employment probabilities and earnings at the beginning of their
career (Tab. 1). The graduate population consists of 167,886 individuals (68,939 males and 98,947
females). The ISTAT survey is based on a 16% sample of these students and is stratified on the
basis of degree course taken and by the sex of the individual student. The response rate is about
69.5%, yielding a data-set containing information on 26,570 graduates. The data contain
information on educational curriculum, occupational status and the student’s family background and
personal characteristics.
In particular, the principal variables contained in the data set can be divided into the following five
main groups. (i) University career and high school background: including, kind of high school
attended, high school mark, other education, university, subject, duration, degree score,
accommodation, work during university, post graduate studies; (ii) work experience: including,
previous experience, experience in actual work, type of work, net monthly wage; (iii) search for
work: including, kind of work desired, willingness to work abroad, preference overworking hours,
minimum net monthly wage required; (iv) family information: including, parents’ work, parents’