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.