- Results Of Regressions Of Average Hourly Earnings On Sex And Education Binary Variables And Other Characteristics Using 1 (360.58 KiB) Viewed 34 times
a. Is the college–high school earnings difference estimated from
this regression statistically significant at the 5% level?
Construct a 95% confidence interval of the difference.
b. Is the male–female earnings difference estimated from this
regression statistically significant at the 5% level? Construct a
95% confidence interval for the difference. Using the regression
results in column
(3): a. Do there appear to be important regional differences?
Use an appropriate hypothesis test to explain your answer.
b. Juanita is a 28-year-old female college graduate from the
South. Molly is a 28-year-old female college graduate from the
West. Jennifer is a 28-year-old female college graduate from the
Midwest. Construct a 95% confidence interval for the difference in
expected earnings between Juanita and Molly.
ii. Explain how you would construct a 95% confidence interval
for the difference in expected earnings between Juanita and
Jennifer. (Hint: What would happen if you included West and
excluded Midwest from the regression?)
Results of Regressions of Average Hourly Earnings on Sex and Education Binary Variables and Other Characteristics Using 2015 Data from the Current Population Survey Dependent variable: average hourly earnings (AHE). Regressor (1) (2) (3) College (X1) 10.47 10.44 10.42 (0.29) (0.29) (0.29) Female (X) -4.69 -4.56 -4.57 (0.29) (0.29) (0.29) Age (X) 0.61 0.61 (0.05) (0.05) Northeast (X4) 0.74 (0.47) Midwest (X) -1.54 (0.40) South (X) -0.44 (0.37) Intercept 18.15 0.11 0.33 (0.19) (1.46) (1.47) Summary Statistics and Joint Tests F-statistic testing regional effects=0 9.32 SER 12.15 12.03 12.01 R 0.165 0.182 0.185 n 7178 7178 7178