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Digital archive of theses discussed at the University of Pisa

 

Thesis etd-06202020-102249


Thesis type
Tesi di laurea magistrale
Author
BALDI, ELISABETTA
URN
etd-06202020-102249
Thesis title
Gender earnings differentials in the Italian Labour Market, an empirical analysis on 2016 Bank of Italy's microdata
Department
ECONOMIA E MANAGEMENT
Course of study
ECONOMICS
Supervisors
relatore Prof. Corsini, Lorenzo
Keywords
  • gender discrimination
  • gender wage gap
  • labour economics
  • labour market
Graduation session start date
06/07/2020
Availability
Withheld
Release date
06/07/2060
Summary
This thesis work aims at estimating the earning differentials between woman and men in the Italian labour market, in 2016. The gender earning differentials are estimated through a different set of methods, first considering just the employees, then also self-employed and unemployed individuals. The first method consists in running an Ordinary least square regression on log earnings of a dummy female and other control variables, which represent individual and work characteristics; this method gives an estimation of the gender effect on earnings, other things being equal. The second method consists in performing an Oaxaca-Blinder decomposition of earning; this procedure allows to derive the unadjusted log earning gap, and to decompose it into two parts: one that is explained by the differences in characteristics of the two gender groups, and one that is not explained by those differences, hence it could represent, at least in part, discrimination (against women, in this case). Finally, I apply an Heckman procedure to both methods, to account also for the phenomenon of self-selection in the labour market. According to the empirical results of this thesis work, the existence of a gender earning differential, at the expense of women, is unquestionable: depending on the method of estimation, and on the different set of variables, the size of the estimated gap fluctuates from a minimum of 2,2% to a maximum of 25,9%, however it is always present and statistically significant.
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