1. The Effect of Race on Wage by Region

2. To what extent were black males paid less than nonblack males in the same region with the same levels of education and experience? Set up linear regression w/ wage controlling for race, education, experience, smsa indicator,

3. *** Linear Model *** (BAD) Call: lm(formula = wage ~ educ + exper + black.ind + smsa.ind + black.ind:region, data = Data.Set.1029, na.action = na.exclude) Residuals: Min 1Q Median 3Q Max -1074 -209.2 -49.55 141.9 18193 Coefficients: Value Std. Error t value Pr(>|t|) (Intercept) -427.9085 13.9288 -30.7211 0.0000 educ 61.2050 0.8920 68.6124 0.0000 exper 10.7860 0.2073 52.0427 0.0000 black.ind -129.6313 11.5057 -11.2667 0.0000 smsa.ind 105.2641 5.6947 18.4845 0.0000 black.indregion1 13.0278 15.4987 0.8406 0.4006 black.indregion2 -9.9147 6.4676 -1.5330 0.1253 black.indregion3 4.6202 7.9833 0.5787 0.5628 Residual standard error: 395.1 on 25623 degrees of freedom Multiple R-Squared: 0.2094 F-statistic: 969.3 on 7 and 25623 degrees of freedom, the p-value is 0

4. *** Summary Statistics for data in: Data.Set.1029 (BAD)*** $$$"Factor Summaries": region V4 V5 MW:6226 Length: 25631 Length: 25631 NE:5949 Class: Class: S:7991 Mode:logical Mode:logical W:5465 $$$"Numeric Summaries": wage educ exper black.ind smsa.ind V1 V2 V3 Min: 50.3900 0.000000 -4.00000 0.000000e+000 0.0000000 3.9197927 -Inf -Inf 1st Qu.: 356.1300 12.000000 9.00000 0.000000e+000 0.0000000 5.8752958 2.197225 2.197225 Mean: 640.1625 13.076275 18.58656 7.756233e-002 0.7428505 6.2743463 -Inf -Inf Median: 567.2300 12.000000 16.00000 0.000000e+000 1.0000000 6.3407649 2.772589 2.772589 3rd Qu.: 826.2100 16.000000 27.00000 0.000000e+000 1.0000000 6.7168490 3.295837 3.295837 Max: 18777.2000 18.000000 63.00000 1.000000e+000 1.0000000 9.8403986 4.143135 4.143135 Total N: 25631.0000 25631.000000 25631.00000 2.563100e+004 25631.0000000 25631.0000000 25631.000000 25631.000000 NA's : 0.0000 0.000000 0.00000 0.000000e+000 0.0000000 0.0000000 194.000000 194.000000 Std Dev.: 444.2833 2.904286 12.42466 2.674868e-001 0.4370711 0.6273408 NA NA *** Summary Statistics for data in: Data.Set.1029 *** $$$"Factor Summaries": region V4 V5 MW:6226 Length: 25631 Length: 25631 NE:5949 Class: Class: S:7991 Mode:logical Mode:logical W:5465 $$$"Numeric Summaries": wage educ exper black.ind smsa.ind V1 V2 V3 Min: 50.3900 0.000000 -4.00000 0.000000e+000 0.0000000 3.9197927 -Inf -Inf 1st Qu.: 356.1300 12.000000 9.00000 0.000000e+000 0.0000000 5.8752958 2.197225 2.197225 Mean: 640.1625 13.076275 18.58656 7.756233e-002 0.7428505 6.2743463 -Inf -Inf Median: 567.2300 12.000000 16.00000 0.000000e+000 1.0000000 6.3407649 2.772589 2.772589 3rd Qu.: 826.2100 16.000000 27.00000 0.000000e+000 1.0000000 6.7168490 3.295837 3.295837 Max: 18777.2000 18.000000 63.00000 1.000000e+000 1.0000000 9.8403986 4.143135 4.143135 Total N: 25631.0000 25631.000000 25631.00000 2.563100e+004 25631.0000000 25631.0000000 25631.000000 25631.000000 NA's : 0.0000 0.000000 0.00000 0.000000e+000 0.0000000 0.0000000 194.000000 194.000000 Std Dev.: 444.2833 2.904286 12.42466 2.674868e-001 0.4370711 0.6273408 NA NA

5. Graphs (Bad) Conical, Outliers?Tail?

6. Graphs (Bad) p.2

7. So, take log transformations of wage, experience+1 Subset Data Good==T So pretend graphs and linear reg. table here

8. Log(wage)=4.047+.0916*Educ+.3242log(exper+1)+.1616(smsa.ind: 1 or 0)-.25(race.ind: 1 or 0) NE: +.0379 NEB: +.0298 S: -.0609 S: +.0072 W: -.0116 W: +.0598 F-stat: 1276, pval=0, Df~25,000

9. Conclusion: There are no regional racial differences after adjusting for education, experience, rural v. urban location, and race. Therefore, we can drop the interactive effect found in the linear regression model because of Occam’s Razor.