Back to our wage regression. We run the following model table1 ============ Dependent variable: lwage (4) (1) (2) (3) (5) (6) (7) educ tenure minority hours married urban 0.055*** 0.057*** 0.059*** 0.055*** 0.055*** 0.047*** 0.040*** (0.006) (0.006) (0.006) (0.006) (0.006) (0.007) (0.007) 0.015*** 0.015*** 0.014*** 0.014*** 0.014*** 0.012*** 0.013*** (0.003) (0.003) (0.003) (0.002) (0.002) (0.003) (0.003) -0.209*** -0.219*** -0.208*** -0.231*** -0.239*** -0.169*** -0.164*** (0.037) (0.037) (0.037) (0.036) (0.039) (0.047) (0.043) -0.005** -0.005** -0.005*** -0.005** -0.007*** -0.005** (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) 0.204*** 0.212*** 0.210*** 0.221*** 0.208*** (0.040) (0.039) (0.044) (0.048) (0.044) 0.197*** 0.213*** 0.210*** 0.207*** (0.027) (0.028) (0.031) (0.028) -0.016** -0.017 -0.007 (0.008) (0.010) (0.009) 0.012** 0.013*** (0.006) (0.005) 0.005 (0.006) 0.003*** (0.001) 5.950*** 6.151*** 5.961*** 5.866*** 5.894*** 5.896*** 5.602*** (0.087) (0.122) (0.128) (0.121) (0.135) (0.150) (0.152) brthord meduc feduc IQ Constant 935 Observations R2 Adjusted R2 0.163 0.160 935 0.169 0.166 935 0.192 0.187 935 0.235 0.230 852 0.249 0.243 663 0.251 0.240 784 0.259 0.250 ======= ====== Note: *p