1m_robust(formula = 1n_wage educ + exper+ hrswk + married + metro + midwest + southwest + black + asian, data = CPS, se_

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1m Robust Formula 1n Wage Educ Exper Hrswk Married Metro Midwest Southwest Black Asian Data Cps Se 1 (90.85 KiB) Viewed 39 times

1m Robust Formula 1n Wage Educ Exper Hrswk Married Metro Midwest Southwest Black Asian Data Cps Se 2 (106.14 KiB) Viewed 39 times

1m_robust(formula = 1n_wage educ + exper+ hrswk + married + metro + midwest + southwest + black + asian, data = CPS, se_type="stata") Standard error type: HC1 Coefficients: (Intercept) educ exper Estimate Std. Error t value Pr (>t) CI Lower CI Upper DF 1.057195 0.124757 8.4741 8.511e-17 0.812377 1.302013 989 0.085612 0.006819 12.5542 1.192e-33 0.072230 0.098994 989 0.005032 0.001370 3.6723 2.533e-04 0.002343 0.007720 989 0.008623 0.001644 5.2469 1.893e-07 0.005398 0.011848 989 0.092659 0.032804 2.8246 4.829e-03 0.028285 0.157033 989 0.174487 hrswk married metro midwest south 0.037888 4.6054 4.654e-06 0.100137 0.248836 989 -0.088859 0.044017 -2.0188 4.378e-02 -0.175236 -0.002483 989 -0.053901 0.045654 -1.1807 2.380e-01 -0.143490 0.035688 989 0.014750 0.046019 0.3205 7.486e-01 -0.075557 0.105057 989 -0.118906 0.051289 -2.3184 2.063e-02 -0.219553 -0.018259 989 -0.054381 0.090051 -0.6039 5.461e-01 -0.231095 0.122333 989 west black asian Adjusted R-squared: 0.2434 Multiple R-squared: 0.251, F-statistic: 29.26 on 10 and 989 DF, p-value: < 2.2e-16 N > reg3 = 1m_robust (1n_wage educ+ exper+ hrswk + married + metro + midwest + west + black + asian, data = CPS, se_type = "stat + south a")
(c) (5 points) Using the estimation results of the benchmark model, test the hypothesis that the hourly wage is not affected by the geographic location. Explain how you reach your conclusion. (Hint: use package car.) (d) (5 points) Using the estimation results of the benchmark model, test the hypothesis that the wage differential associated with African American is equal to the wage differential associated with Asian American. Explain how you reach your conclusion. (Hint: use package car.) (e) (7 points) How would you modify the benchmark model to estimate the effects on hourly wage of one additional year of education separately for each gender (4 points). How do the effects of education differ between the genders and is the difference statistically significant? (3 points) (f) (5 point) Keoka is an African American woman, working in a metropolitan area. After she obtained her high school diploma, she got a job and started working instead of getting a higher education. She has never been married. Now she has a five-year of experience in the industry and is working full time (40 hours a week). Using the benchmark model, predict her hourly wage. Be careful: the left-hand side variable is ln(wage), but you should predict Keoka's wage.