6
our data set by considering all variables statistically and economically significant in explaining the
wage gap (Tab. 2-9).
Table 2. OLS estimation results of the earnings equation for employees (male and female samples)
Variable |
Earnings equation (employees) | |||
Female |
Male | |||
Coefficient |
T-value |
Coefficient |
T-value | |
CONSTANT |
6.451078 |
80.249 |
6.516929 |
89.401 |
Educational performance___________________ |
0.000950 |
4.031 |
0.001423 |
5.301 |
lambda________________________________ |
0.383831 |
6.319 |
0.414517 |
7.583 |
experience________________________________ |
-0.036356 |
-6.557 |
0.016100 |
1.465 |
experience2_______________________________ |
0.004010 |
5.722 |
-0.001348 |
-1.141 |
Sciences________________________________ |
0.189980 |
4.621 |
0.075470 |
2.594 |
Pharmacy____________________________ |
0.290410 |
6.725 |
0.150891 |
5.120 |
Natural sciences____________________________ |
0.130703 |
3.820 |
0.062717 |
1.939 |
Engineering_______________________________ |
0.360510 |
7.515 |
0.253378 |
7.238 |
Architecture___________________________________ |
0.183003 |
3.857 |
0.121772 |
3.219 |
Agricultural studies___________________________ |
0.138144 |
3.090 |
0.058937 |
1.561 |
Economics, business and statistics___________ |
0.246893 |
6.207 |
0.168118 |
5.653 |
Political sciences and sociology______________ |
0.203020 |
4.692 |
0.074503 |
2.384 |
Law________________________________ |
0.075950 |
2.721 |
0.042657 |
1.507 |
Humanities________________________________ |
0.131889 |
3.114 |
-0.077757 |
-2.105 |
Foreign languages_________________________ |
0.162961 |
3.879 |
0.016601 |
0.391 |
Teachers college__________________________ |
0.111559 |
2.432 |
0.056077 |
1.162 |
Psychology______________________________ |
0.082282 |
1.645 |
0.043418 |
0.979 |
Hours worked (Q2 21)____________________ |
0.008788 |
8.024 |
0.007432 |
6.449 |
University of North____________________________ |
-0.052636 |
-3.973 |
-0.011993 |
-0.804 |
University of Center_________________________ |
-0.010915 |
-0.766 |
0.043994 |
2.943 |
d Liceo_____________________________________ |
-0.017863 |
-1.904 |
0.002172 |
0.235 |
d Previously entered another degree course |
0.005927 |
0.490 |
0.012281 |
0.894 |
d Studied in the hometown__________________ |
0.000538 |
0.068 |
0.003824 |
0.440 |
d Moved to attend university_________________ |
0.034356 |
3.784 |
0.032285 |
3.018 |
d Working student_________________________ |
0.096551 |
7.163 |
0.076887 |
6.731 |
Training_______________________________________ |
-0.090338 |
-6.441 |
-0.107812 |
-7.538 |
Married_____________________________________ |
0.005629 |
0.685 |
0.080510 |
6.880 |
Children____________________________________ |
-0.009754 |
-0.620 |
0.091021 |
4.602 |
d Father’s university degree_________________ |
0.030699 |
2.294 |
0.007577 |
0.509 |
d Father’s high school degree________________ |
0.018705 |
1.967 |
0.004630 |
0.421 |
d Mother’s degree_________________________ |
-0.002357 |
-0.162 |
0.009058 |
0.567 |
d High school______________________________ |
-0.005666 |
-0.598 |
0.011621 |
1.083 |
d Father’s occupation: manager_____________ |
0.015552 |
1.036 |
0.030424 |
1.903 |
d Father’s occupation: executive cadre_______ |
-0.003928 |
-0.280 |
0.027497 |
1.839 |
d Father’s occupation: white collar____________ |
-0.000960 |
-0.086 |
0.001874 |
0.154 |
d Mother’s occupation: executive cadre______ |
0.011883 |
0.812 |
-0.021382 |
-1.375 |
d Mother’s occupation: white collar___________ |
0.019560 |
1.942 |
0.006837 |
0.628 |
Erasmus_____________________________ |
0.031319 |
2.666 |
0.052507 |
4.020 |
Firm size____________________________________ |
0.089913 |
6.131 |
0.074524 |
3.547 |
d Attended private courses at university______ |
0.020647 |
0.995 |
0.001685 |
0.066 |
d Father employed________________________ |
0.004156 |
0.230 |
-0.000877 |
-0.041 |
d Father self-employed_____________________ |
0.023489 |
2.481 |
0.023034 |
2.074 |
Industrial sector________________________________ |
0.022993 |
2.527 |
0.037864 |
4.124 |
Paid training__________________________________ |
-0.145341 |
-5.817 |
-0.120088 |
-4.218 |
Region dummies________________________ |
X |
X | ||
Number of observations___________________ |
3744 |
3709 | ||
Rbar-squared____________________________ |
0.1480 |
0.1460 | ||
F_________________________________________ |
11.805 (0.00) |
11.596 (0.0^5)~ | ||
Average wage women (ln)________________ |
7.1099409 | |||
Average wage men (ln)____________________ |
7.2269904 |
More intriguing information
1. Thresholds for Employment and Unemployment - a Spatial Analysis of German Regional Labour Markets 1992-20002. An Estimated DSGE Model of the Indian Economy.
3. Agricultural Policy as a Social Engineering Tool
4. The name is absent
5. The Context of Sense and Sensibility
6. The Impact of Optimal Tariffs and Taxes on Agglomeration
7. The name is absent
8. Factores de alteração da composição da Despesa Pública: o caso norte-americano
9. Making International Human Rights Protection More Effective: A Rational-Choice Approach to the Effectiveness of Ius Standi Provisions
10. Transgression et Contestation Dans Ie conte diderotien. Pierre Hartmann Strasbourg