(a) Graph wage (average hourly earnings) against educ (years of education), and superimpose the linear regression line on the graph. What do you observe?
(b) Run a regression of wage against educ. Interpret the coefficient on educ. Test the hypothesis that educ has no effect on wage. What do you conclude?
(c) Now run a regression of wage against controls for educ, exper (years of potential experience), expersq (years of potential experience squared), gender, and marital status. Interpret each coefficient. Interpret the t-statistic for each variable.
(d) The impact of educ on wage has been estimated in part (b) and in part (c). Which estimate do you think is more accurate? Why?
(e) Test the joint significance of the exper and expersq variables? Interpret the result. Explain why the significance of these two variables should be tested jointly.
(f) Now expand the model in part (c) to allow for the possibility that the effects of education and experience may differ by gender. Estimate the model and interpret the magnitudes and statistical significance of all coefficients.
(g) For the model estimated in part (f), test the overall statistical significance of gender. (h) For the model estimated in part (f), test the significance of the entire model. Interpret the result.
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