Figure 1: Log Wage Regression Results Variable | Obs Mean Std. Dev. Min Max id I 1900 2500.921 1491.346 3 5218 nearc2 | 1900 .44 .4965176 0 1 nearc4 | 1900 .6878947 .4634745 0 1 educ 1900 13.62895 2.592716 18 age | 1900 27.92211 3.087326 24 34 fatheduc 1900 10.11368 3.691888 18 motheduc 1900 10.61526 3.021056 18 ethnic 1900 .1589474 .3657233 1 urban | 1900 .7284211 .444891 1 south 1900 .3784211 .485121 0 1 hwage 1900 588.2095 262.1315 100 2404 g lhwage=log (hwage) g age2=age^2 OLS estimates of the model Ln(wage) = b + bage ++bage + beduc + b4ethnic + b5south + burban + u reg lhwage age age2 educ ethnic south urban Source | SS df MS Number of obs = Model 92.1566811 6 15.3594469 .144132301 Residual | 272.842445 1893 Total | 364.999126 1899 .192205964 F( 6, 1893) = Prob > F 1900 106.56 = 0.0000 R-squared Adj R-squared Root MSE = 0.2525 = 0.2501 = .37965 lhwage Coef. Std. Err. t P>|t| [95% Conf. Interval] age | .1413237 age2 | -.0017252 educ ethnic south .031883 -.1704538 -.1118315 .0579648 .0010094 .0034976 .0254862 .0191644 urban | .1712394 cons | 3.209143 .0200696 .8223219 2.44 -1.71 9.12 0.000 -6.69 0.000 -5.84 0.000 8.53 0.000 3.90 0.000 0.015 0.088 .0276421 .2550053 -.0037048 .0002544 .0250234 .0387425 -.2204379 -.149417 -.1204697 -.0742459 .1318786 .2106003 1.59639 4.821895