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. The name is absent2. PACKAGING: A KEY ELEMENT IN ADDED VALUE
3. Opciones de política económica en el Perú 2011-2015
4. The name is absent
5. Robust Econometrics
6. Place of Work and Place of Residence: Informal Hiring Networks and Labor Market Outcomes
7. Ein pragmatisierter Kalkul des naturlichen Schlieβens nebst Metatheorie
8. Demographic Features, Beliefs And Socio-Psychological Impact Of Acne Vulgaris Among Its Sufferers In Two Towns In Nigeria
9. The name is absent
10. On s-additive robust representation of convex risk measures for unbounded financial positions in the presence of uncertainty about the market model