reg Inearn female i. educ age age2 i. race foreignborn if hours>=35 [aw=perwt] (sum of wgt is 95, 588, 231) Source SS df MS Number of obs = 719, 892 F(18, 719873) = 19581 . 62 Model 126074.926 18 7004. 16257 Prob > F = 0 . 0000 Residual 257491 . 906 719, 873 . 35769074 R-squared = 0. 3287 Adj R-squared 0. 3287 Total 383566 . 832 719 , 891 . 532812373 Root MSE = . 59807 learn Coefficient Std. err. t P> | t| [95% conf. interval] female -. 2698774 . 0014338 -188.22 0 . 000 -. 2726876 -. 2670671 educ grade 11 . 0284813 . 0080366 3. 54 0 . 000 . 0127297 . 0442328 12th grade, no dip. . . 068007 . 0069196 9. 83 0 . 000 . 0544449 . 0815692 regular high school . . 1742094 . 0039901 43 . 66 0 . 000 . 1663889 . 1820299ged or alternative. . . 126283 . 0054558 23 . 15 0 . 000 . 1155899 . 1369762 some college, but . . . 2830359 . 0046572 60.77 0 . 000 .2739079 . 2921638 1 or more years of. . . 3355085 . 0041609 80. 63 0 . 000 . 3273533 . 3436636 associate's degree. . . 3857426 . 0043472 88. 73 0 . 000 . 3772222 . 3942629 bachelor's degree . 7361155 . 003964 185 .70 0 . 000 . 7283463 . 7438848 master's degree . 9069178 . 0042469 213.55 0 . 000 . 898594 . 9152416 professional degree . 1 . 245741 . 0058871 211 . 61 0 . 000 1. 234202 1. 257279 doctoral degree 1. 083248 . 0063133 171.58 0 . 000 1. 070874 1 . 095622 age . 0760103 . 000444 171. 21 0 . 000 . 0751402 . 0768805 age2 - . 0007362 5. 24e-06 -140.61 0 . 000 - . 0007465 - . 0007259 race Black non-Hispanic -. 2212195 . 002287 -96.73 0. 000 -. 2257019 -. 2167371 Other non-Hispanic - . 0088337 . 0025347 -3. 49 0 . 000 - . 0138017 - . 0038658 Hispanic -. 1225787 . 002161 -56. 72 0 . 000 -. 1268141 -. 1183432 foreignborn -. 0316623 . 0021381 -14.81 0 . 000 - . 0358529 - . 0274717 cons 8 . 769018 . 0096621 907.57 0 . 000 8 . 750081 8. 787956reg Inearn male i. educ age age2 i. race foreignborn if hours>=35 [aw=perwt] ( sum of wgt is 95, 588, 231) Source SS df MS Number of obs = 719, 892 F (18, 719873) = 19581. 62 Model 126074 . 926 18 7004.16257 Prob > F 0 . 0000 Residual 257491 . 906 719, 873 . 35769074 R-squared E 0. 3287 Adj R-squared 0. 3287 I Total 383566. 832 719, 891 . 532812373 Root MSE = . 59807 Inearn Coefficient Std. err. t P> | t| [95% conf. interval] male 2698774 . 0014338 188.22 0 . 000 . 2670671 . 2726876 educ grade 11 . 0284813 . 0080366 3 . 54 0 . 000 . 0127297 . 0442328 12th grade, no dip. . . 068007 . 0069196 9. 83 0 . 000 . 0544449 . 0815692 regular high school. . 1742094 . 0039901 43 . 66 0 . 000 . 1663889 . 1820299ged or alternative. . . 126283 . 0054558 23 . 15 0. 000 . 1155899 . 1369762 some college, but . . . 2830359 . 0046572 60.77 0 . 000 . 2739079 . 2921638 1 or more years of. . . 3355085 0041609 80. 63 0 . 000 . 3273533 . 3436636 associate's degree. . . 3857426 . 0043472 88. 73 0 . 000 . 3772222 . 3942629 bachelor's degree . 7361155 . 003964 185.70 0 . 000 . 7283463 . 7438848 master's degree . 9069178 . 0042469 213.55 0 . 000 . 898594 . 9152416 professional degree . 1 . 245741 . 0058871 211 . 61 0 . 000 1. 234202 1. 257279 doctoral degree 1. 083248 . 0063133 171.58 0 . 000 1. 070874 1 . 095622 age . 0760103 . 000444 171. 21 0 . 000 . 0751402 . 0768805 age2 - . 0007362 5.24e-06 -140.61 0 . 000 - . 0007465 - . 0007259 race Black non-Hispanic -. 2212195 . 002287 -96.73 0 . 000 -. 2257019 -. 2167371 Other non-Hispanic - . 0088337 . 0025347 -3. 49 0 . 000 - . 0138017 - . 0038658 Hispanic -. 1225787 . 002161 -56.72 0 . 000 -. 1268141 -. 1183432 foreignborn - . 0316623 . 0021381 -14.81 0 . 000 - . 0358529 -. 0274717 _cons 8 . 499141 . 0096868 877.39 0 . 000 8 . 480155 8. 5181274. (18 points) So far, our analysis has simply compared men and women on average. Now, we will add control variables to the regression to compare men and women who are equivalent in some observable ways, such as educational attainment, what type of work they do, and so on. Regress log earnings on the female indicator, education indicators, a quadratic in age (both age and its square, which you will have to create), race / ethnicity indicators, and the foreign-born indicator. For the educ and race variables (note that the educ variable in this data describes levels, not years), use the i . prex to include the necessary indicators; see the slides for an example of this technique. When you estimate the regression correctly, you will nd that R2 = 0.3287. 4.1. How much lower are women's earnings compared to men's earnings, as a positive percentage value, holding xed the other explanatory variables? 4.2. How much more do bachelor's degree holders earn compared to those with a high school diploma, in percent, holding xed the other covariates? 4.3. What is the estimated rate of change of earnings with respect to age, in percent, for someone who is 40 years old? 4.4. At what age are predicted earnings highest? 4.5. Which race-ethnicity group has the highest earnings, holding xed the other covariates? 4.6. Which race-ethnicity group has the lowest earnings, holding xed the other covariates
