Gender stereotyping and wage discrimination among Italian graduates



Table 3. Estimation results of the employment probabilities for employees (male and female
samples)

Variable

___________Employment probabilities (employees)___________

__________Female__________

___________Male___________

Coefficient

T-value

Coefficient

T-value

CONSTANT                ~

-0.40774

-2.880

0.11501

0.770

Educational performance______________

0.00211

2.660

-0.00004

-0.050

Sciences____________________________

0.79611

9.810

0.31538

4.520

Pharmacy________________________

1.10942

18.530

0.51041

7.530

Natural sciences_______________________

0.56726

9.820

0.19276

2.760

Engineering__________________________

1.34976

19.820

1.24828

24.280

Architecture_____________________________

1.24957

18.570

0.87663

11.880

Agricultural studies______________________

0.91999

11.510

0.62628

7.280

Economics, business and statistics_____

0.99523

22.500

0.75041

15.470

Political sciences and sociology_________

1.15891

22.230

0.62422

9.810

Law____________________________

0.39985

9.290

0.21823

4.360

Humanities___________________________

0.99855

17.350

0.33098

4.420

Foreign languages____________________

1.05389

16.320

0.68531

5.190

Teachers college_____________________

1.16688

16.77

0.90959

6.440

Psychology_________________________

0.86643

10.370

0.51204

5.310

University of North_______________________

-0.03092

-0.610

0.05380

0.970

University of Center____________________

-0.03418

-0.750

0.04650

0.930

d Liceo________________________________

-0.18332

-6.140

-0.09455

-2.870

d Moved to attend university____________

0.04979

1.620

0.05416

1.560

Erasmus_________________________

0.00179

0.040

-0.04484

0.930

Married________________________________

0.02327

0.770

0.29326

7.030

Children_______________________________

-0.24011

-5.470

0.18525

2.710

d Father’s university degree____________

0.02472

0.550

-0.05689

-1.140

d Father’s high school degree__________

0.06245

0.100

0.03627

0.920

d Mother’s degree_____________________

-0.01029

-0.210

0.01988

0.370

d High school_________________________

0.00679

0.200

0.03721

0.095

d Father’s occupation: manager________

-0.03102

-0.600

-0.01754

-0.320

d Father’s occupation: executive cadre

0.01070

0.220

-0.06419

-1.250

d Father’s occupation: white collar______

0.02907

0.760

-0.01106

-0.250

d Mother’s occupation: executive cadre

-0.02730

-0.580

-0.01326

-0.260

d Mother’s occupation: white collar______

-0.02826

-0.830

-0.01810

-0.470

d Father employed___________________

0.02325

0.380

0.06934

0.098

d Father self-employed________________

0.08043

2.460

-0.00925

-0.240

d Attended private courses at university

0.24619

2.970

0.08003

0.096

d Working student____________________

0.38804

14.770

0.39104

13.220

Training_________________________________

-0.52419

-15.990

-0.71315

-19.740

Region dummies____________________

______X______

______X______

Number of observations______________

13499

11909

Percent Correctly Predicted_____________

73.8944

78.1678

Moreover, we use whenever possible, the same set of variables to explain the wage gap between all
the population groups considered6.

We note that there is a significant gender difference in graduates earnings: female average earnings
are about 89% of male average earnings. From the separate regression analyses by gender, we
calculate the Oaxaca decomposition and find that only about 12% of the gender gap can be

6 - OLS estimation results of the earnings equations underlying Tables 3-9 are conducted similarly to the earnings
equation presented in Table 2. Calculations are not presented here for brevity, but will be provided by the authors to
anyone who requests.



More intriguing information

1. Evolving robust and specialized car racing skills
2. Parallel and overlapping Human Immunodeficiency Virus, Hepatitis B and C virus Infections among pregnant women in the Federal Capital Territory, Abuja, Nigeria
3. Financial Development and Sectoral Output Growth in 19th Century Germany
4. 03-01 "Read My Lips: More New Tax Cuts - The Distributional Impacts of Repealing Dividend Taxation"
5. Growth and Technological Leadership in US Industries: A Spatial Econometric Analysis at the State Level, 1963-1997
6. Segmentación en la era de la globalización: ¿Cómo encontrar un segmento nuevo de mercado?
7. The name is absent
8. From music student to professional: the process of transition
9. The name is absent
10. Olfactory Neuroblastoma: Diagnostic Difficulty