Questions 1. Fit an educational attainment function using your dataset. Regress S on ASV ABC, SM and SF, and interpret the regression results. Perform t tests on the coefficients of the variables in the education attainment function. 2. Perform a F test of the explanatory power of the equation you obtained in Question 1. Calculate the F statistic using R2 and verify it is the same as the F statistic in your Stata output. 3. Regress the logarithm of EARNINGS on S and EXP. Interpret the regression results, perform t tests on the coefficients and F test of the explanatory power of the model. 4. Regress logarithm of EARNINGS on S, EXP, MALE, ETHHISP and ETHBLACK. Interpret the regression results and perform t tests on the coefficients. 5. Redo Question 4 making ETHBLACK the reference category. What are the impacts of change of reference on the interpretation of the coefficients and the statistical tests (t tests of the coefficients and F test of the model)? 6. Define a slope dummy variable as the product of MALE and S. Regress the logarithm of EARNINGS on S, EXP, ETHHISP, ETHBLACK, MALE, and the slope dummy variable. Interpret the equation and perform appropriate statistical tests (t tests of the coefficients and F test of the model). Is the effect of education on earnings different for males and females? 7. The composite measure of cognitive ability, ASV ABC, in the dataset was constructed as a weighted average of the scores of tests of arithmetic reasoning, ASV ABAR, word know- ledge, ASV ABWK, and paragraph comprehension, ASV ABPC, with ASV ABAR being given double weight. Show mathematically that, when fitting the educational attainment function S = 1 + 2SM + 3SF + 4ASV ABC + u, instead of the model using the individual scores S = 1 + 2SM + 3SF + 1ASV ABAR + 2ASV ABWK + 3ASV ABPC + u, one is implicitly imposing the restrictions 1 = 22 and 1 = 23. Perform a test of these restrictions using your dataset. 8. Fit a wage equation with EARNINGS as the dependent variable and S, EXP and MALE as the explanatory variables. Perform a Goldfeld-Quandt test for heteroskedasticity in the S dimension. 9. Fit a wage equation using the same specification as in Question 8. Perform a White test for heteroskedasticity. 10. Perform an OLS regression of the logarithm of hourly earnings on S, EXP, ASV ABC, MALE, ETHBLACK and ETHHISP using your dataset and an IV regression using SM, SF, and SIBLINGS as instruments for ASV ABC. Perform a Durbin-Wu-Hausman test to evaluate whether ASV ABC appears to be subject to measurement error.