The regression shown in column (2) was estimated again, this time using data from 1992 (4000 observations selected at random from the March 1993 CPS, converted into 1998 dollars using the consumer price index). The results are

Comparing this regression to the regression for 1998 shown in column (2), was there a statistically significant change in the coefficient on College?

The data set consists of information on 4000 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 34 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 1998 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)

AHE = 077 + 529College-259Fernale +0AOAge, SER = 585,F-021. (0.18) (0.03) (08) (020)( Results of Regressions of Average Hourly Eamings on Gender and Education Binary Variables and Other Characteristics Using 1998 Data from the Current Population Survey Dependent Variable: average hourly earnings (AHD. College (X) Female (X) Age (%) Northeast (X Midwest (Xs) South (X) 5.48 (021) -262 (020) 0.29 (004) 5.46 (021) 5.44 (0.21) -2.62 (0.20) 0.29 (0.04) 0.69 (0.30) 0.60 (0.28) -0.27 (0.26) .75 -2.64 (0.20 12.69 (0.14) Intercept 4.40 Summary Statistics and Joint Tests F-statistic for regional effects =0 SER R2 6.27 0.176 4000 6.22 0.190 4000 6.10 6.21 0.194 4000