The data set consists of information on 3900 full-time full-year workers. The highest educational achievement for each worker was either a high school diploma or abachelor's degree. The worker's ages ranged from 25 to 45 years. The data set also contained information on the region of the country where the person lived, marital status, and number of children. For the purposes of these exercises, let

AHE = average hourly earnings (in 2005 dollars)

College = binary variable (1 if college, 0 if highschool)

Female = binary variable (1 if female, 0 ifmale)

Age = age (in years)

Ntheast = binary variable (1 if Region = Northeast, 0 otherwise)

Midwest = binary variable (1 if Region = Midwest, 0otherwise)

South = binary variable (1 if Region = South, 0otherwise)

West = binary variable (1 if Region = West, 0otherwise)

 

Dependent variable: average hourly earnings (AHE). Regressor (1) (2) (3) College (X,) Female (X2) 5.19 5.21 5.17 - 2.51 -2.49 -2.49 Age (X3) Northeast (X4) 0.28 0.28 0.66 Midwest (X5) 0.57 South (X6) -0.26 Intercept 12.06 4.18 3.56 Summary Statistics SER 5.96 5.91 5.90 R? 0.167 0.181 0.184 R? 0.167 0.180 0.183 3900 3900 3900 Using the regression results in column (2): On average, a worker earns $ per hour for each year that he or she ages. Is age an important determinant of earnings? O A. No, because the difference in wages is minimal. O B. No, because older workers do not earn significantly more than younger workers. O C. Yes, because wages are not consistent across different age groups. O D. Yes, because younger workers earn more than older workers.