You estimate the following regression: In(w) = b + b Educ+b Female +b(Female Educ) + b_Exper+ b Exper, where w is average hourly earnings, Female is a female dummy variable, Educ is years of education, Exper is potential experience (number of years since completion of schooling). You estimate the regression on a large sample of data and find that all coefficients are statistically significant at the 1% level. (a) Consider a male with 18 years of education and 3 years of experience. Express the predicted change in In(w) associated with an additional year of experience as a function of the estimated regression coefficients. (b) Let 4ln(w) denote the predicted change you estimated in (a). A 95% confidence interval for Aln(w) would equal Aln(w) 1.96 se (Aln(w)). Rearrange the regression so that se (Aln(w)) is the standard error of an estimated coefficient in the transformed regression. (c) Consider a male with 18 years of education and 4 years of experience. Express the predicted change in In(w) associated with an additional year of experience as a function of the estimated regression coefficients. (d) Is the difference between your answers to (c) and (a) statistically significant at the 5% level? Why or why not? (e) Would your answers to (a) through (d) change if the person were female? Why or why not? (f) A friend of yours uses your sample of data and estimates the following regression: In (w) = 0.6+0.12 Educ+0.33 Male -0.02(Malex Educ) +0.03Exper-0.0005 Exper, where Male is a male dummy variable and the other variables are defined as in your regression. Use your friend's output to estimate all the coefficients in your regression, i.e., find b, 6, 6, 63, 64, and b5. 0 1. 21 (g) Write down the regression that you will estimate if you suspected that the effect of potential experience on earnings was different for men and women.