# Is n250 Description of the variables: Female: I if female: 0 if male AHE: Average Hourly Earnings Ba

Is n250 Description of the variables: Female: I if female: 0 if male AHE: Average Hourly Earnings Bachelor: I if worker has a bochelor&#39;s degree: 0 if worker has a high school degree Estimated standard deviations are given in the parentheses (8) Data from 2015 (6) (5) (2) (3) (4) (1) Dependent Variable АНЕ АНЕ 0.16 In(AHE) In(AHE) 0.16 (0.063) -0.0023 АНЕ 0.531 In(AHE) In(AHE) In(AHE) 0.024 0.139 0.135 Age 0.134 (0.072) -0.0023 (0.06) -0.0018 (0.046) -0.0019 (0.0008) (0.0008) (0.045) (0.046) -0.0019 (0.002) Age (0.001) (0.0011) (0.0012) In(Age) 0.72 (0.06) 0.0002 Female x Age -0.0012 (0.093) -0.0002 (0.0016) (0.091) Female x Age -1 (0.0015) Bachelor x Age -0.041 -0.049 (0.091) (0.093) 0.0008 (0.0016) -0.04 Bachelor x Age? 0.0009 (0.0015) -0.19 Female -0.18 -4.14 -0.18 (0.01) 0.46 -0.18 -0.03 -0.19 (0.01) (0.26) (0.01) (0.02) 0.45 (0.02) (1.33) 0.45 (0.02) 0.023 (0.02) 1.09 (1.36) 0.94 Bachelor 9.85 0.46 0.46 (0.26) (0.01) (0.01) (0.01) (1.34) (1.36) Female x Bachelor 0.023 0.024 0.024 (0.011) (0.023) (0.023) (0.023) SER Adjusted R? 0.19 0.08 0.11 0.17 0.14 0.16 0.21 0.19 1) Based on the regression specification (2), if Age increases from 25 to 26, how are hourly earnings expected to change (all else equal)? A) In(AHE) is predicted to increase by 2.4% B) Earnings are predicted to increase by 24 \$ C) Earnings are predicted to increase by 2.4% D) In(AHE) is predicted to increase by 24 \$ 2. 2) Based on the regression specification (3), if dee increases from 34 to 38, how are earnings expected to change?. A) 7.26 \$ B) 8.34 % C) 0.032 % D) 0.014 \$ 3) Based on the regression specification (4), if Age increases from 33 to 34, how are earnings expected to change? A) 0.7% B) 7.24 \$ C) 0.4% D) 9.04 \$ 4) From regressions specifications (1) and (2), which one is better? A) (1) B) (2) C) (1) and (2) cannot be compared D) They are equally good 5) Do the estimation results in column (4) indicate gender discrimination? A) Yes, females earn less, and the gap is significant at 1 % B) Yes, males earn less, and the gap is significant at 5 % C) No, since the gap is not statistically significant at any level D) Yes, females earn less, but the gap is not significant at any level 6) Test statistic for the joint significance of the parameters next to the variables Female x Age, Female> Age?, Bachelor x Age, Bachelor x Age in regression specification (8) is A) 2.5698 B) 4.7654 C) 0.2478 D) 5.6987 7) Are the parameters in Question 6 jointly significant? At what level of significance? A) Yes, at only 5% B) No C) Yes, at only 10% D) Yes, at even 1% 8) From specification (5), what is the estimated change in AHE if female gets bachelor&#39;s degree compar to when male gets bachelor&#39;s degree? A) 0.023 \$ B) 2.3 % C) 23 \$ D) 2.3 \$ is the effect in Question 8 statistically significant? At what level? What is test statistic? What is the associated p-value? A) Yes, at 5 %, test statistic is 2.09, p-value is 0.0366 B) Yes, at 10 %, test statistic is 4.09, p-value is 0.0369 C) No, test statistic is 2.09, p-value is 0.0366 D) Yes, at 1 %, test statistic is 4.09, p-value is 0.0369 10) Calculate R? for specification (6) A) 0.5632 B) 0.1256 C) 0.1836 D) 0.2147 11) Based on specification (2) what is the estimated effect if female gets a bachelor&#39;s degree? A) AHE are estimated to increase by 5.84 % B) AHE are estimated to increase by 46.0 \$ C) AHE are estimated to increase by 58.4 % D) AHE are estimated to increase by 4.60 S 12) Calculate p-value for the interaction term, Female x Bachelor, in specification (8) A) 0.2984 B) 0.2365 C) 0.4569 D) 0.1236 13) Assume that you wanted to investigate whether or not females and males are affected differently by marriage in earninne fimatio