An Economics section at a large university keeps track of its majors begninnig salaries. We address the question of the value of taking econometrics, based on last year's crop of 50 majors. Let SAL=$ salary, GPA = grade point average on a 4.0 scale, METRICS=1 if student took
econometrics, METRICS = 0 otherwise, SEX=1 if student is a female, otherwise =0.
Consider the following regression
SAL = B1 + B2GPA + B3METRICS + B4METRICS * GPA + et
(a) Based on table 1, what is the marginal effect (benefit) of taking econometrics?
(b) What is the predicted wage difference between a student who took econometrics and one who did not, given that their GPA =3.0?
Consider another regression
SAL = B1+B2GPA+B3METRICS+B4SEX+B5METRICS*SEX+et
(c) Based on table 2, what is the reference group in this model? What is the estimated salary of them?
(d) Does the model suggest salary difference among gender? Justify your answer
(e) Is the value of econometric the same for men and women? Justify your answer.
Source | SS df MS Number of obs = 50
-------------+------------------------------ F( 3, 46) = 45.59
Model | 275083851 3 91694616.9 Prob > F = 0.0000
Residual | 92520533 46 2011315.94 R-squared = 0.7483
-------------+------------------------------ Adj R-squared = 0.7319
Total | 367604384 49 7502130.28 Root MSE = 1418.2
------------------------------------------------------------------------------
salary | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gpa | 1918.108 396.7813 4.83 0.000 1119.428 2716.787
metrics | 8440.214 2399.488 3.52 0.001 3610.296 13270.13
metricsgpa | -1197.261 828.1421 -1.45 0.155 -2864.224 469.7027
_cons | 23379.25 1207.748 19.36 0.000 20948.18 25810.32