The data set consists of information on 4200 full-time full-year workers. The highest educational achievement for each worker was either a high school diploma or a bachelor'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 high school)

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

Age = age (in years)

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

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

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

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

Using the regression results in column (3):Workers in the Northeast earn $ ? more per hour than workers in the West, on average, controlling for other variables in the regression.

Workers in the South earn $? less per hour than workers in the West, on average, controlling for other variables in the regression.

)

Do there appear to be important regional differences?

A.

No, because the difference in wages is minimal.

B.

Yes, because wages are not consistent across the region.

C.

Yes, because workers in the Northeast earn significantly more than workers in the South.

D.

No, because workers in the Midwest earn more per hour than workers in the Northeast, while workers in the South earn less per hour than workers in the West.

Results of Regressions of Average Hourly Earnings on Gender and Education Binary Variables and Other Characteristics Using Data from the Current Population Survey:

Dependent variable: average hourly earnings (AHE).