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Results of Regressions of Average Hourly Earnings on Gender and Education Binary Variables and Other Characteristics Using 2012 Data from the Current Population Survey Dependent variable: average hourly earnings (AHE). Regressor College (X₂) Female (X₂) Age (X₁) Northeast (X₁) Midwest (X₂) South (X) Intercept Summary Statistics and Joint Tests F-statistic for regional effects=0 SER R² A 8.31 (0.23) <-3.85 (0.23) 17.02 (0.17) 9.79 0.162 7440 (2) 8.32 (0.22) -3.81 (0.22) 0.51 (0.04) 1.87 (1.18) 9.68 0.180 7440 (3) 8.34 (0.22) -3.80 (022) 0.52 (0.04) 0.18 (0.36) -1.23 (0.31) i. Individual significance of X4, X5, and X6 : A. Specify the null and alternative hypotheses. -0.43 (0.30) 2.05 (1.18) 7.38 9.67 0.182 7440 (a) Test the individual significance of College and Female at the significance level of 5% in all three regressions. (b) Note that there can be other variables that influence AHE but are not in- cluded in the regressions. This possibility of omitting variables can cause bias in the coefficients of College and Female. Assess the following state- ment: "in all of the regression, the coefficient on Female is negative, large, and statistically significant. This provides strong statistical evidence of gender discrimination in the US labor market." Present statistical argu- ments for your evaluation. (c) Discuss how to modify the regressions to allow for the effect of college education to differ by gender in Regression (1). (d) Find the intercept and the slope of College for males who live in Midwest in Regression (3). (e) Find the intercept and the slope of College for females who live in Midwest in Regression (3). (f) Does Regression (3) allow the regional effects differ by gender? (In other words, does Regression (3) allow the gender gap to vary with region?) (g) Test both individual and joint significance of regional dummies at the 5% significance level.

Results of Regressions of Average Hourly Earnings on Gender and Education Binary Variables and Other Characteristics Using 2012 Data from the Current Population Survey Dependent variable: average hourly earnings (AHE). Regressor College (X₂) Female (X₂) Age (X₁) Northeast (X₁) Midwest (X₂) South (X) Intercept Summary Statistics and Joint Tests F-statistic for regional effects=0 SER R² A 8.31 (0.23) <-3.85 (0.23) 17.02 (0.17) 9.79 0.162 7440 (2) 8.32 (0.22) -3.81 (0.22) 0.51 (0.04) 1.87 (1.18) 9.68 0.180 7440 (3) 8.34 (0.22) -3.80 (022) 0.52 (0.04) 0.18 (0.36) -1.23 (0.31) i. Individual significance of X4, X5, and X6 : A. Specify the null and alternative hypotheses. -0.43 (0.30) 2.05 (1.18) 7.38 9.67 0.182 7440 (a) Test the individual significance of College and Female at the significance level of 5% in all three regressions. (b) Note that there can be other variables that influence AHE but are not in- cluded in the regressions. This possibility of omitting variables can cause bias in the coefficients of College and Female. Assess the following state- ment: "in all of the regression, the coefficient on Female is negative, large, and statistically significant. This provides strong statistical evidence of gender discrimination in the US labor market." Present statistical argu- ments for your evaluation. (c) Discuss how to modify the regressions to allow for the effect of college education to differ by gender in Regression (1). (d) Find the intercept and the slope of College for males who live in Midwest in Regression (3). (e) Find the intercept and the slope of College for females who live in Midwest in Regression (3). (f) Does Regression (3) allow the regional effects differ by gender? (In other words, does Regression (3) allow the gender gap to vary with region?) (g) Test both individual and joint significance of regional dummies at the 5% significance level.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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Results of Regressions of Average Hourly Earnings on Gender and Education Binary Variables and Other Characteristics Using 2012 Data from the Current Population Survey Dependent variable: average hourly earnings (AHE). Regressor College (X₂) Female (X₂) Age (X₁) Northeast (X₂) Midwest (X₂) South (X) Intercept Summary Statistics and Joint Tests F-statistic for regional effects=0 SER R² R 8.31 (0.23) -3.85 (0.23) 17.02 (0.17) 9.79 0.162 7440 (2) 8.32 (0.22) -3.81 (0.22) 0.51 (0.04) 1.87 (1.18) 9.68 0.180 7440 (3) 8.34 (0.22) -3.80 (0.22) 0.52 (0.04) 0.18 (0.36) -1.23 (0.31) i. Individual significance of X4, X5, and X₁: A. Specify the null and alternative hypotheses. B. Calculate the T-statistics for the three dummies. -0.43 (0.30) 2.05 (1.18) 7.38 9.67 0.182 7440 (a) Test the individual significance of College and Female at the significance level of 5% in all three regressions. (b) Note that there can be other variables that influence AHE but are not in- cluded in the regressions. This possibility of omitting variables can cause bias in the coefficients of College and Female. Assess the following state- ment: "in all of the regression, the coefficient on Female is negative, large, and statistically significant. This provides strong statistical evidence of gender discrimination in the US labor market." Present statistical argu- ments for your evaluation. (c) Discuss how to modify the regressions to allow for the effect of college education to differ by gender in Regression (1). (d) Find the intercept and the slope of College for males who live in Midwest in Regression (3). (e) Find the intercept and the slope of College for females who live in Midwest in Regression (3). (f) Does Regression (3) allow the regional effects differ by gender? (In other words, does Regression (3) allow the gender gap to vary with region?) (g) Test both individual and joint significance of regional dummies at the 5% significance level.
Transcribed Image Text:Results of Regressions of Average Hourly Earnings on Gender and Education Binary Variables and Other Characteristics Using 2012 Data from the Current Population Survey Dependent variable: average hourly earnings (AHE). Regressor College (X₂) Female (X₂) Age (X₁) Northeast (X₂) Midwest (X₂) South (X) Intercept Summary Statistics and Joint Tests F-statistic for regional effects=0 SER R² R 8.31 (0.23) -3.85 (0.23) 17.02 (0.17) 9.79 0.162 7440 (2) 8.32 (0.22) -3.81 (0.22) 0.51 (0.04) 1.87 (1.18) 9.68 0.180 7440 (3) 8.34 (0.22) -3.80 (0.22) 0.52 (0.04) 0.18 (0.36) -1.23 (0.31) i. Individual significance of X4, X5, and X₁: A. Specify the null and alternative hypotheses. B. Calculate the T-statistics for the three dummies. -0.43 (0.30) 2.05 (1.18) 7.38 9.67 0.182 7440 (a) Test the individual significance of College and Female at the significance level of 5% in all three regressions. (b) Note that there can be other variables that influence AHE but are not in- cluded in the regressions. This possibility of omitting variables can cause bias in the coefficients of College and Female. Assess the following state- ment: "in all of the regression, the coefficient on Female is negative, large, and statistically significant. This provides strong statistical evidence of gender discrimination in the US labor market." Present statistical argu- ments for your evaluation. (c) Discuss how to modify the regressions to allow for the effect of college education to differ by gender in Regression (1). (d) Find the intercept and the slope of College for males who live in Midwest in Regression (3). (e) Find the intercept and the slope of College for females who live in Midwest in Regression (3). (f) Does Regression (3) allow the regional effects differ by gender? (In other words, does Regression (3) allow the gender gap to vary with region?) (g) Test both individual and joint significance of regional dummies at the 5% significance level.
C. Specify the critical values you refer to. Use the statistical table provided in class. D. Conclude regarding the individual significance. ii. Joint significance of X4, X5, and X₁ : A. Specify the null and alternative hypotheses. B. Which regression is restricted and unrestricted models? C. Calculate the F-statistic by specifying the formula you use. Be specific with degrees of freedom. D. Specify the critical values you refer to. Use the statistical table provided in class. E. Conclude regarding the joint significance (h) Which regression (among (1), (2), and (3)) do you believe is the best model and why? Clearly present the relevant statistics you refer to to reach the conclusion. (i) Suppose you believe regression (3) is the best model and decide to use (3) to estimate the impact of college education on Average Hourly Earnings. As you observe the bias seems to be corrected in (3), compared with (1) and (2), you state that "the true population coefficient of College is 8.34." Is this statement is True or False? Why?
Transcribed Image Text:C. Specify the critical values you refer to. Use the statistical table provided in class. D. Conclude regarding the individual significance. ii. Joint significance of X4, X5, and X₁ : A. Specify the null and alternative hypotheses. B. Which regression is restricted and unrestricted models? C. Calculate the F-statistic by specifying the formula you use. Be specific with degrees of freedom. D. Specify the critical values you refer to. Use the statistical table provided in class. E. Conclude regarding the joint significance (h) Which regression (among (1), (2), and (3)) do you believe is the best model and why? Clearly present the relevant statistics you refer to to reach the conclusion. (i) Suppose you believe regression (3) is the best model and decide to use (3) to estimate the impact of college education on Average Hourly Earnings. As you observe the bias seems to be corrected in (3), compared with (1) and (2), you state that "the true population coefficient of College is 8.34." Is this statement is True or False? Why?
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