Question

1. Use the LNU dataset that allows you to estimate a wage equation. Estimate a wage equation including a dummy variable for female (FEMALE). Interpret the estimated coefficient for the variable Female. 

2. Estimate the same model adding a dummy variable for public sector (PUBLIC). Interpret the estimated coefficient for the variable PUBLIC. Compare the estimate for FEMALE in this model and the model above where the variable PUBLIC was not included.

3. Estimate the wage equations above separately for Men and Women. Comment on the results you obtain here with the results you obtained when estimating the wage equation using the pooled data for men and women. 

4. Using the years of schooling variable you can define category variables as follows. PRIMARY for those who have less than 12 years of schooling. GYMNASIUM for those who have between 12 and 14 years of schooling and UNIVERSITY for those who have 15 ears or more years of schooling. 

a) Use the years of schooling variable in your wage equation. Estimate that model. Denoting the coefficient for the schooling variable by B1, we can use a t-test to test the hypothesis B1 = 0. What is the F-statistics here? What is the interpretation of this F-statistics?

b) Estimate a wage equation including these category variables for school- ing instead of years of schooling. Interpret the coefficients for the educa- tion dummy variables. Use primary education as the reference category.

c) Formulate a hypothesis involving the education category variables and use a F-test to test it.