- Load, inspect, and clean (if necessary) the income data provided on Desire to Learn.
- Specify and run an econometric model that explains the variation in yearly earnings
(from work) among the people in the sample. Present the regression output in a proper table. You must provide a STATA log documenting your complete program. Interactive work is not allowed. The program must run smoothly without errors from start to finish.
- Did you create any additional variables from the ones existing? Explain.
- Do you think your model suffers from omitted variable bias? Explain (you may consider variables not provided in the dataset. Remember though that if I gave you all the variables I used in data creation – which I did, then there is no omitted variable bias. I guess I’m asking what other variables you would include in a real life, real data project)
- Do you think your model suffers from heteroskedasticity? Explain (a formal white test is expected, of course)
- Do you think your model suffers from multicollinearity? Explain (a formal vif test is expected, of course)
- What other specifications, other than the one you chose, were “close candidates” in your search for the optimal specification? Why did you not choose them?
- Why do you think your chosen model has the best specification (given the data)?
- Scatter plot the predicted values from your regression versus the residuals. Do you see any patterns that cause you to worry about your model?
- Did you transform any of the variables (for example by taking a log, a root, a square etc)? Why or why not?
- Did you consider interaction terms? If yes, which? For those who were not included in the final regression, why not?
- Discuss your regression results in detail. For each of the following, discuss how they compare to your expectations:
- Signs of all coefficients
- Magnitudes of all coefficients
- Statistical significance of the regression as a whole
- Discuss, variable by variable, including variables you included in your model as well as those you chose to omit (if any), what was your decision to omit or include based on.
- Does it seem, based on your results, that there is discrimination of some sort in the marketplace? Why or why not?
- Explain your choice of the dependent variable.
Appendix – variable description for final dataset
Gender – this variable is equal to one for males, zero for females.
Race – this variable is equal to one for Caucasians, zero for others.
Experience – this variable measures years of experience in the workplace.
Age – this variable measures the current age of the person.
Industry – 1 for a ‘skilled labor’ industry, 0 for unskilled labor. This does not mean that
the data has only 2 industries – only that they are categorized.
Schooling – this variable measures the total schooling (in years) of the person.
Total income – this variable measures total income (from all sources) of the person last year. Other income – this variable measures only the non-work income of the person last year.
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