Earnings functions attempt to find the determinants of earnings, using both continuous and binary variables. One of the central questions analyzed in this relationship is the returns to education. (a) Collecting data from 253 individuals, you estimate the following relationship (robust standard errors in parenthesis) In(Earn) = 0.54 + 0.083 Educ, R2: 0.20, SER = 0.445 (0.14) (0.011) where In(Earn) is the log of average hourly earnings and Educ is years of education. What is the effect on earnings of an additional year of schooling? If you had a strong belief that years of high school education were different from college education, how would you modify the equation? Similarly, how would you test the hypothesis that an extra year of HS has a different effect on earnings than an extra year of college? (b) You read in the literature that there should also be returns to on-the-job training. To approximate on-the-job training, researchers often use a potential experience variable, which is defined as Exper = Age Educ 6. You incorporate the experience variable into your original regression and obtain the following results (again, robust standard errors in parenthesis): In(Earn) = -0.01 + 0.101 Educ + 0.033 Exper 0.0005 Exper2, R2: 0.34, SER = 0.405 (0.16) (0.012) (0.006) (0.0001) What would be the effect of an additional year of experience for a person who is currently 40 years old and had 12 years of education? What about for a person who is 60 years old with the same education background? (c) Test for the statistical significance of each of the coefficients of the added variables (i.e., Exper and Experz). Why in your opinion has the coefficient on education changed so little relative to the model in part (a)