ETD

Archivio digitale delle tesi discusse presso l'Università di Pisa

Tesi etd-06202020-102249


Tipo di tesi
Tesi di laurea magistrale
Autore
BALDI, ELISABETTA
URN
etd-06202020-102249
Titolo
Gender earnings differentials in the Italian Labour Market, an empirical analysis on 2016 Bank of Italy's microdata
Dipartimento
ECONOMIA E MANAGEMENT
Corso di studi
ECONOMICS
Relatori
relatore Prof. Corsini, Lorenzo
Parole chiave
  • labour economics
  • labour market
  • gender discrimination
  • gender wage gap
Data inizio appello
06/07/2020
Consultabilità
Non consultabile
Data di rilascio
06/07/2060
Riassunto
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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