Gender stereotyping and wage discrimination among Italian graduates



4
education level, brothers and/or sisters; (v) personal characteristics: including, date of birth, sex,
marital status, children, country of domicile, country of birth, residence.

Table 1. Average earnings and employment probability by gender and field of study

Field of study

Average monthly
earnings

Average employment
probability

Male students

Female students

Male students

Female students

Sciences

1252.36

1065.03

069

066

Pharmacy

1280.79

1137.91

074

076

Natural sciences

1232.25

1062.48

065

059

Medicine

1468.22

1234.35

045

027

Engineering

1391.70

1287.06

092

083

Architecture

1221.35

1054.29

087

082

Agricultural studies

1141.59

905.72

0.77

070

Economics, Business and Statistics

1349.92

1169.86

083

077

Political Science and Sociology

1300.48

1096.71

078

082

Law

1172.35

1018.93

060

0.51

Humanities

1107.00

948.09

069

075

Foreign languages

1204.67

1048.28

085

080

Teachers college

1062.94

961.70

081

079

Psychology

1078.69

832.67

072

070

Health

1098.13

882.75

078

074

Total

1299.28

1080.96

072

063

Table 1 reports average monthly earnings and employment probability 3 years after graduation by
gender and field of study. Monthly earnings in 2007 are in euros and net of taxes and social security
contributions. The average earnings are 1299 and 1081 euros per month for the male and the female
sub sample, respectively. The average employment probability 3 years after graduation is 0.72 and
0.63 for male and female candidates, respectively.

In the empirical analysis of Section 2, we estimate the earnings equation for male and female
samples.

2 - Earnings Equations

The following earnings equation was estimated for full-time employees:

ln(w ) = α + β1 edperf + β'2E + β'3X+ β'4 Z + ε

(1)




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