10. The following table is computed using data from 2012 from the Current Population Survey. The data set consists of information on 7440 full-time, full-year workers. The highest educa- || tional achievement for each worker was either a high school diploma or a bachelor's degree. College (X) Dependent Variable: Avg Hourly Earnings (AHE) | (1) | (2) | (3) 8.31 8.32 8.34 The workers' age ranged from 25 to 34 years. The data set also contains information on the (0.23) (0.22) | (0.22) region of the country where the person lived, marital status, and the number of children. For Female (X2) the purposes of the exercises, let: -3.85 -3.81 -3.80 (0.23) (0.22) | (0.22) AHE=average hourly earnings (in 2012 dollars) Age (Xa) 0.51 0.52 College=binary variable (1 if college or more than college education, 0 if less than a college | (0.04) (0.04) education) Northeast (X4) 0.18 (0.36) Female=binary variable (1 if female, 0 if male) Midwest (Xs) -1.23 Age= age (in years) Northeast=binary variable (1 if Region=Northeast, 0 otherwise), including states such as Massachusetts, New Hampshire, and New Jersey (0.31) South (Xa) -0.43 (0.30) Midwest=binary variable (1 if Region=Midwest, 0 otherwise), including states such as Michi- gan, Minnesota, and Missouri South= binary variable (1 if Region = South, 0 otherwise), including states such as Louisiana, latercept 17.02 1.87 2.05 (0.17) | (1.18) | (1.18) Summary Statistics and Joint Tests and Texas P-value for test that coefficients West = binary variable (1 if Region=West, 0 otherwise), including states such as California, on Be = 0, 8, = 0, & ß = 0 0.0001 Oregon and Washington 0.162 0.180 0.181

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10. The following table is computed using data from 2012 from the Current Population Survey.
Dependent Variable: Avg Hourly Earnings (AHE)
The data set consists of information on 7440 full-time, full-year workers. The highest educa-
| (1) | (2) | (3)
tional achievement for each worker was either a high school diploma or a bachelor's degree. College (X)
8.31
8.32
8.34
The workers' age ranged from 25 to 34 years. The data set also contains information on the
(0.23) (0.22) | (0.22)
region of the country where the person lived, marital status, and the number of children. For Female (X2)
the purposes of the exercises, let:
AHE=average hourly earnings (in 2012 dollars)
College=binary variable (1 if college or more than college education, 0 if less than a college
-3.85
-3.81
-3.80
(0.23) (0.22)
(0.22)
Age (X3)
0.51
0.52
(0.04) (0.04)
Northeast (X4)
0.18
education)
Female=binary variable (1 if female, O if male)
(0.36)
Midwest (Xs)
-1.23
Age= age (in years)
Northeast=binary variable (1 if Region=Northeast, 0 otherwise), including states such as
(0.31)
South (Xs)
-0.43
Massachusetts, New Hampshire, and New Jersey
(0.30)
Midwest=binary variable (1 if Region=Midwest, 0 otherwise), including states such as Michi-
gan, Minnesota, and Missouri
South= binary variable (1 if Region = South, O otherwise), including states such as Louisiana,
Intercept
17.02
1.87
2.05
(0.17) | (1.18) | (1.18)
Summary Statistics and Joint Tests
and Texas
p-value for test that coefficients
West = binary variable (1 if Region=West, 0 otherwise), including states such as California,
on B = 0, 8s = 0, & B6 = 0
0.0001
Oregon and Washington
0.162 0.180
0.181
Note: The four regions encompass all states in the U.S.
7440
7440
7440
(a) Using the regression results in column (1): Compute the 99% confidence interval for the
effect of a gender on earnings. Based on the confidence interval, is there statistically
significant evidence that women's average hourly earning is $4.50 less than men's?
(b) Using the regression results in column (1): Interpret the coefficient on college in words.
(c) Using the regression results in column (1): Does a college education have a statistically
significant effect on earnings? Explain and use the appropriate statistical test to explain
your answer.
(d) We are primarily interested in the effect of a college education on earnings. Provide
an example of a variable that could potentially result in omitted variable bias on our
coefficient of interest. Explain why the variable could result in omitted variable bias
and the direction (positive/negative) and magnitude (overestimate/underestimate) of
the bias.
Transcribed Image Text:10. The following table is computed using data from 2012 from the Current Population Survey. Dependent Variable: Avg Hourly Earnings (AHE) The data set consists of information on 7440 full-time, full-year workers. The highest educa- | (1) | (2) | (3) tional achievement for each worker was either a high school diploma or a bachelor's degree. College (X) 8.31 8.32 8.34 The workers' age ranged from 25 to 34 years. The data set also contains information on the (0.23) (0.22) | (0.22) region of the country where the person lived, marital status, and the number of children. For Female (X2) the purposes of the exercises, let: AHE=average hourly earnings (in 2012 dollars) College=binary variable (1 if college or more than college education, 0 if less than a college -3.85 -3.81 -3.80 (0.23) (0.22) (0.22) Age (X3) 0.51 0.52 (0.04) (0.04) Northeast (X4) 0.18 education) Female=binary variable (1 if female, O if male) (0.36) Midwest (Xs) -1.23 Age= age (in years) Northeast=binary variable (1 if Region=Northeast, 0 otherwise), including states such as (0.31) South (Xs) -0.43 Massachusetts, New Hampshire, and New Jersey (0.30) Midwest=binary variable (1 if Region=Midwest, 0 otherwise), including states such as Michi- gan, Minnesota, and Missouri South= binary variable (1 if Region = South, O otherwise), including states such as Louisiana, Intercept 17.02 1.87 2.05 (0.17) | (1.18) | (1.18) Summary Statistics and Joint Tests and Texas p-value for test that coefficients West = binary variable (1 if Region=West, 0 otherwise), including states such as California, on B = 0, 8s = 0, & B6 = 0 0.0001 Oregon and Washington 0.162 0.180 0.181 Note: The four regions encompass all states in the U.S. 7440 7440 7440 (a) Using the regression results in column (1): Compute the 99% confidence interval for the effect of a gender on earnings. Based on the confidence interval, is there statistically significant evidence that women's average hourly earning is $4.50 less than men's? (b) Using the regression results in column (1): Interpret the coefficient on college in words. (c) Using the regression results in column (1): Does a college education have a statistically significant effect on earnings? Explain and use the appropriate statistical test to explain your answer. (d) We are primarily interested in the effect of a college education on earnings. Provide an example of a variable that could potentially result in omitted variable bias on our coefficient of interest. Explain why the variable could result in omitted variable bias and the direction (positive/negative) and magnitude (overestimate/underestimate) of the bias.
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