Results of Regressions of Average Hourly Earnings on Gender and Education Binary Variables and Other Characteristics Using 1998 Data from the Curren Population Survey. Dependent variable: average hourly earnings (AHE). Regressor College (X₁) Female (X₂) Age (X3) Northeast (X4) Midwest (X5) South (X₁) A. True. OB. False. Intercept Summary Statistics and Joint Tests F-statistic for regional effects=0 SER R² n (1) 5.42 (0.24) -2.63 (0.22) 12.62 (0.15) 6.27 0.195 3462 (2) 5.34 (0.24) -2.96 (0.22) 0.24 (0.02) 4.23 (1.09) 6.22 0.104 3462 (3) 5.32 (0.24) -2.96 (0.22) 0.24 (0.02) 0.56 (0.21) 0.59 (0.29) -0.22 (0.23) 3.51 (1.06) 6.11 6.21 0.118 3462 Note: The numbers in parentheses below each estimated coefficient are the estimated standard errors. valuate the following statement: "In all of the regressions, the coefficient on Female is negative, large, and statistically significant. This provides rong statistical evidence of gender discrimination in the U.S. labor market." int: Consider two identical workers that differ only in gender, and think about the causal relationship between earnings and gender.
Results of Regressions of Average Hourly Earnings on Gender and Education Binary Variables and Other Characteristics Using 1998 Data from the Curren Population Survey. Dependent variable: average hourly earnings (AHE). Regressor College (X₁) Female (X₂) Age (X3) Northeast (X4) Midwest (X5) South (X₁) A. True. OB. False. Intercept Summary Statistics and Joint Tests F-statistic for regional effects=0 SER R² n (1) 5.42 (0.24) -2.63 (0.22) 12.62 (0.15) 6.27 0.195 3462 (2) 5.34 (0.24) -2.96 (0.22) 0.24 (0.02) 4.23 (1.09) 6.22 0.104 3462 (3) 5.32 (0.24) -2.96 (0.22) 0.24 (0.02) 0.56 (0.21) 0.59 (0.29) -0.22 (0.23) 3.51 (1.06) 6.11 6.21 0.118 3462 Note: The numbers in parentheses below each estimated coefficient are the estimated standard errors. valuate the following statement: "In all of the regressions, the coefficient on Female is negative, large, and statistically significant. This provides rong statistical evidence of gender discrimination in the U.S. labor market." int: Consider two identical workers that differ only in gender, and think about the causal relationship between earnings and gender.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Question
Transcribed Image Text:insists of information on
full-time full-year orkers. The highest educational achi vement of each worker was either a high school diploma or a
bachelor's degree. The worker's age 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. West is the omitted region. A detailed description of the variables used in the data set is available
mere.
Results of Regressions of Average Hourly Earnings on Gender and Education Binary
Variables and Other Characteristics Using 1998 Data from the Curren Population Survey.
Dependent variable: average hourly earnings (AHE).
Regressor
College (X₁)
Female (X₂)
OA. True.
OB. False.
Age (X3)
Northeast (X4)
Midwest (X5)
South (X₂)
ercept
Summary Statistics and Joint Tests
F-statistic for regional effects=0
(1)
5.42
(0.24)
- 2.63
(0.22)
12.62
(0.15)
6.27
0.195
3462
(2)
5.34
(0.24)
-2.96
(0.22)
0.24
(0.02)
4.23
(1.09)
(3)
5.32
(0.24)
-2.96
6.22
0.104
3462
(0.22)
0.24
(0.02)
0.56
(0.21)
0.59
(0.29)
-0.22
(0.23)
3.51
(1.06)
SER
R²
0.118
n
3462
Note: The numbers in parentheses below each estimated coefficient are the estimated
standard errors.
6.11
6.21
Evaluate the following statement: "In all of the regressions, the coefficient on Female is negative, large, and statistically significant. This provides
strong statistical evidence of gender discrimination in the U.S. labor market."
Hint: Consider two identical workers that differ only in gender, and think about the causal relationship between earnings and gender.
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