(25 pts.) Earnings mctions attempt to nd the determinants of earnings, using both continuous and binary variables. One of the central questions analyzed in this relationship is the returns to education. 1.1 Collecting data from 253 individuals, you estimate the following relationship 111E511): 0.54 + 0.083 x Educ, R2: 0.20, SER = 0.445 (0.14) (0.011) where Earn is average hourly earnings and Educ is years of education. a. What is the effect of an additional year of schooling? (3 pts.) 1.2 You read in the literature that there should also be returns to on-the-job training. To approximate on-the-j ob training, researchers often use the so-called Mincer or potential experience variable, which is dened as Exper = Age Educ ~ 6. a. Explain the reasoning behind this approximation (i.e. Exper = Age Educ 6). (3 pts.) You incorporate the experience variable into your original regression mam; -0.01 + 0.101 x Educ + 0.033 x Exper - 0.0005 x Experz , (0.16) (0.012) (0.006) (0.0001) R2 = 0.34, SER = 0.405 b. Is the effect of an additional year of experience on earnings constant regardless of the value of the years of experience? Explain. (3 pts.) 0. What is the effect of an additional year of experience for a person who is 40 years old and had 12 years of education? (3 pts.) 1.3 You want to nd the effect of introducing two variables, gender and marital status. Accordingly you specify a binary variable that takes on the value of one for females and is zero otherwise (Female), and another binary variable that is one if the worker is married but is zero otherwise (Married). Adding these variables to the regressors results in: 121%; 0.21 + 0.093 x Educ + 0.032 x Exper 0.0005 XExper2 (0.16) (0.012) (0.006) (0.0001) - 0.289 X Female + 0.062 Married, (0.049) (0.056) R2 = 0.43, SER = 0.378 a. In percentage terms, how much less do females earn per hour, controlling for education and experience? (3 pts.) b. How much more do married people make? (3 pts.) c. What is the percentage difference in earnings between a single male and a married female? (3 pts.) 1.4 In your final specification, you allow for the binary variables to interact. The results are as follows: InEarn = 0.14 + 0.093 x Educ + 0.032 x Exper - 0.0005 x Exper2 (0.16) (0.011) (0.006) (0.001) - 0.158 x Female + 0.173 x Married - 0.218 x (Female x Married), (0.075) (0.080) (0.097) R2 = 0.44, SER = 0.375 a. Interpret the coefficient of the interaction term. (4 pts.)