Meirou Guan 12/18/2020
## YEAR SERIAL CBSERIAL HHWT
## Min. :2006 Min. : 1 Min. :1.000e+00 Min. : 1.0
## 1st Qu.:2006 1st Qu.: 337994 1st Qu.:7.107e+05 1st Qu.: 55.0
## Median :2019 Median : 688870 Median :2.019e+12 Median : 78.0
## Mean :2013 Mean : 690847 Mean :1.053e+12 Mean : 96.7
## 3rd Qu.:2019 3rd Qu.:1041472 3rd Qu.:2.019e+12 3rd Qu.: 112.0
## Max. :2019 Max. :1428037 Max. :2.019e+12 Max. :2377.0
##
## STRATA HHINCOME FOODSTMP NFAMS
## Min. : 10001 Min. : -39996 1:5605098 Min. : 1.000
## 1st Qu.: 80336 1st Qu.: 37200 2: 604196 1st Qu.: 1.000
## Median : 200001 Median : 68700 Median : 1.000
## Mean : 347675 Mean : 93132 Mean : 1.085
## 3rd Qu.: 370636 3rd Qu.: 115000 3rd Qu.: 1.000
## Max. :7777722 Max. :2907600 Max. :20.000
## NA's :232004
## NCHILD NCHLT5 SEX AGE
## Min. :0.0000 Min. :0.00000 male :3031782 Min. : 0.00
## 1st Qu.:0.0000 1st Qu.:0.00000 female:3177512 1st Qu.:20.00
## Median :0.0000 Median :0.00000 Median :41.00
## Mean :0.5006 Mean :0.09371 Mean :40.58
## 3rd Qu.:1.0000 3rd Qu.:0.00000 3rd Qu.:59.00
## Max. :9.0000 Max. :9.00000 Max. :96.00
##
## MARST RACE
## Married,spouse present:2609324 White :4815054
## Married,spouse absent : 102366 \tBlack/African American/Negro : 599475
## Separaate : 86940 Chinese : 75780
## divorced : 537564 \tAmerican Indian or Alaska Native: 58660
## widowed : 342635 japanese : 16668
## never married/single :2530465 (Other) : 0
## NA's : 643657
## asian black_afr other_race white
## Min. :0 Min. :0.0 Min. :0 Min. :0.0
## 1st Qu.:0 1st Qu.:0.0 1st Qu.:0 1st Qu.:1.0
## Median :0 Median :0.0 Median :0 Median :1.0
## Mean :0 Mean :0.1 Mean :0 Mean :0.9
## 3rd Qu.:0 3rd Qu.:0.0 3rd Qu.:0 3rd Qu.:1.0
## Max. :1 Max. :1.0 Max. :0 Max. :1.0
## NA's :643657 NA's :643657 NA's :643657 NA's :643657
## HISPAN CITIZEN RACAMIND RACASIAN
## Min. :0.0000 Min. :1 Min. :1.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:2 1st Qu.:1.000 1st Qu.:1.000
## Median :0.0000 Median :2 Median :1.000 Median :1.000
## Mean :0.2508 Mean :2 Mean :1.017 Mean :1.057
## 3rd Qu.:0.0000 3rd Qu.:3 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :4.0000 Max. :3 Max. :2.000 Max. :2.000
## NA's :5464304
## RACBLK RACOTHER RACWHT HCOVANY HCOVPRIV
## Min. :1.000 Min. :1.000 Min. :1.000 yes : 256565 yes : 995436
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000 no :2982988 no :2244117
## Median :1.000 Median :1.000 Median :2.000 NA's:2969741 NA's:2969741
## Mean :1.105 Mean :1.048 Mean :1.798
## 3rd Qu.:1.000 3rd Qu.:1.000 3rd Qu.:2.000
## Max. :2.000 Max. :2.000 Max. :2.000
##
## HCOVPUB EDUC educ_adv educ_as
## yes :2022204 1 years of college:1856275 Min. :0.0 Min. :0.0
## no :1217349 4 years of coll : 877049 1st Qu.:0.0 1st Qu.:0.0
## NA's:2969741 2 yeasr of college: 711231 Median :0.0 Median :0.0
## 5+ years of coll : 534094 Mean :0.2 Mean :0.1
## grade 5,6,7 or8 : 473989 3rd Qu.:0.0 3rd Qu.:0.0
## grade9 : 472525 Max. :1.0 Max. :1.0
## (Other) :1284131 NA's :399153 NA's :399153
## educ_bach educ_hs educ_nohs educ_smcoll
## Min. :0.0 Min. :0.0 Min. :0.0 Min. :0.0
## 1st Qu.:0.0 1st Qu.:0.0 1st Qu.:0.0 1st Qu.:0.0
## Median :0.0 Median :0.0 Median :0.0 Median :0.0
## Mean :0.2 Mean :0.3 Mean :0.2 Mean :0.2
## 3rd Qu.:0.0 3rd Qu.:1.0 3rd Qu.:0.0 3rd Qu.:0.0
## Max. :1.0 Max. :1.0 Max. :1.0 Max. :1.0
## NA's :399153 NA's :399153 NA's :399153 NA's :399153
## female DEGFIELD
## Min. :0.0000 61\tMedical and Health Sciences and Services: 161258
## 1st Qu.:0.0000 education asministration and teaching : 106332
## Median :1.0000 engineering : 64913
## Mean :0.5117 \tFine Arts : 63285
## 3rd Qu.:1.0000 55\tSocial Sciences : 61147
## Max. :1.0000 (Other) : 371045
## NA's :5381314
## DEGFIELD2 EMPSTAT LABFORCE
## Min. : 0.0 employed :2924184 Min. :1.0
## 1st Qu.: 0.0 Unemployed : 153409 1st Qu.:1.0
## Median : 0.0 Not in labor force:1951836 Median :2.0
## Mean : 1.2 NA's :1179865 Mean :1.6
## 3rd Qu.: 0.0 3rd Qu.:2.0
## Max. :64.0 Max. :2.0
## NA's :2969741 NA's :1179865
## OCC WKSWORK1 WKSWORK2 UHRSWORK
## Min. : 0 Min. : 0.00 Min. :1.0 Min. : 1.0
## 1st Qu.: 0 1st Qu.: 0.00 1st Qu.:5.0 1st Qu.:35.0
## Median :1040 Median :10.00 Median :6.0 Median :40.0
## Mean :2571 Mean :23.75 Mean :5.1 Mean :38.5
## 3rd Qu.:4720 3rd Qu.:52.00 3rd Qu.:6.0 3rd Qu.:43.0
## Max. :9920 Max. :52.00 Max. :6.0 Max. :99.0
## NA's :2931308 NA's :2931308
## AVAILBLE INCTOT INCWAGE FTOTINC
## 2 : 48260 Min. : -19998 Min. : 0 Min. : -39996
## 3 : 162891 1st Qu.: 7900 1st Qu.: 0 1st Qu.: 32900
## 4 : 281350 Median : 24000 Median : 10000 Median : 63600
## 5 :4536928 Mean : 39299 Mean : 28426 Mean : 88264
## NA's:1179865 3rd Qu.: 50000 3rd Qu.: 40000 3rd Qu.: 110000
## Max. :1629000 Max. :717000 Max. :2907600
## NA's :1097165 NA's :1179865 NA's :241100
## POVERTY TRANTIME
## Min. : 0.0 Min. : 1
## 1st Qu.:171.0 1st Qu.: 10
## Median :332.0 Median : 20
## Mean :315.2 Mean : 27
## 3rd Qu.:501.0 3rd Qu.: 30
## Max. :501.0 Max. :200
## NA's :3503584
part_time <- as.numeric(dat_use1$UHRSWORK<35)
part_time<-is.na(part_time)
part_time<-as.factor(part_time)
full_time <-as.numeric(dat_use1$UHRSWORK<=35)&(dat_use1$UHRSWORK<50)
full_time<-as.factor(full_time)
long_time<-as.numeric(dat_use1$UHRSWORK<=50)
long_time<-as.factor(long_time)Model1_ft<-glm(full_time~AGE + I(AGE^2) +SEX+NFAMS+NCHLT5+NCHILD+HISPAN+RACAMIND+RACASIAN+RACBLK+RACOTHER+RACWHT+educ_nohs+educ_hs+educ_smcoll+educ_bach+educ_as+educ_adv+MARST,family = binomial,(data=dat_use1))
summary(Model1_ft)##
## Call:
## glm(formula = full_time ~ AGE + I(AGE^2) + SEX + NFAMS + NCHLT5 +
## NCHILD + HISPAN + RACAMIND + RACASIAN + RACBLK + RACOTHER +
## RACWHT + educ_nohs + educ_hs + educ_smcoll + educ_bach +
## educ_as + educ_adv + MARST, family = binomial, data = (data = dat_use1))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6863 -0.7348 -0.5066 -0.4093 2.4938
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.079e-01 7.947e-02 -5.132 2.86e-07 ***
## AGE -1.395e-01 1.291e-03 -107.998 < 2e-16 ***
## I(AGE^2) 1.721e-03 1.427e-05 120.657 < 2e-16 ***
## SEXfemale 1.164e+00 3.436e-03 338.588 < 2e-16 ***
## NFAMS 6.438e-02 3.931e-03 16.378 < 2e-16 ***
## NCHLT5 5.966e-02 4.131e-03 14.442 < 2e-16 ***
## NCHILD 9.276e-02 1.718e-03 53.985 < 2e-16 ***
## HISPAN -2.135e-02 2.326e-03 -9.179 < 2e-16 ***
## RACAMIND 1.715e-01 1.437e-02 11.938 < 2e-16 ***
## RACASIAN 1.329e-01 1.278e-02 10.401 < 2e-16 ***
## RACBLK 9.263e-02 1.256e-02 7.376 1.64e-13 ***
## RACOTHER 9.391e-02 1.415e-02 6.638 3.19e-11 ***
## RACWHT 2.265e-01 1.201e-02 18.865 < 2e-16 ***
## educ_nohs 1.442e-01 3.493e-02 4.129 3.64e-05 ***
## educ_hs -1.011e-01 3.456e-02 -2.926 0.00344 **
## educ_smcoll -1.564e-01 3.461e-02 -4.518 6.23e-06 ***
## educ_bach 1.007e-01 5.754e-03 17.506 < 2e-16 ***
## educ_as -2.174e-01 3.482e-02 -6.244 4.27e-10 ***
## educ_adv -4.788e-01 3.476e-02 -13.774 < 2e-16 ***
## MARSTMarried,spouse absent 1.771e-01 1.200e-02 14.762 < 2e-16 ***
## MARSTSeparaate 1.011e-01 1.096e-02 9.225 < 2e-16 ***
## MARSTdivorced -1.525e-01 5.228e-03 -29.169 < 2e-16 ***
## MARSTwidowed 2.262e-03 1.147e-02 0.197 0.84370
## MARSTnever married/single 2.522e-01 4.823e-03 52.300 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2558649 on 2592107 degrees of freedom
## Residual deviance: 2403311 on 2592084 degrees of freedom
## (707405 observations deleted due to missingness)
## AIC: 2403359
##
## Number of Fisher Scoring iterations: 4
Model2_pt<-glm(part_time~AGE + I(AGE^2) +SEX+NFAMS+NCHLT5+NCHILD+HISPAN+RACAMIND+RACASIAN+RACBLK+RACOTHER+RACWHT+educ_nohs+educ_hs+educ_smcoll+educ_bach+educ_as+educ_adv+MARST,family = binomial,(data=dat_use1))
summary(Model2_pt)##
## Call:
## glm(formula = part_time ~ AGE + I(AGE^2) + SEX + NFAMS + NCHLT5 +
## NCHILD + HISPAN + RACAMIND + RACASIAN + RACBLK + RACOTHER +
## RACWHT + educ_nohs + educ_hs + educ_smcoll + educ_bach +
## educ_as + educ_adv + MARST, family = binomial, data = (data = dat_use1))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0997 -0.6740 -0.5002 -0.3275 2.8235
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.5009783 0.0695003 7.208 5.67e-13 ***
## AGE -0.1475392 0.0011525 -128.019 < 2e-16 ***
## I(AGE^2) 0.0021473 0.0000124 173.194 < 2e-16 ***
## SEXfemale 0.7280268 0.0030270 240.508 < 2e-16 ***
## NFAMS -0.0675786 0.0039106 -17.281 < 2e-16 ***
## NCHLT5 0.3887253 0.0039625 98.102 < 2e-16 ***
## NCHILD -0.0274958 0.0016530 -16.633 < 2e-16 ***
## HISPAN -0.0219298 0.0021245 -10.322 < 2e-16 ***
## RACAMIND 0.3852242 0.0118457 32.520 < 2e-16 ***
## RACASIAN 0.1431116 0.0117267 12.204 < 2e-16 ***
## RACBLK 0.1878489 0.0112318 16.725 < 2e-16 ***
## RACOTHER -0.1217869 0.0127004 -9.589 < 2e-16 ***
## RACWHT -0.0825186 0.0108237 -7.624 2.46e-14 ***
## educ_nohs 0.1376291 0.0236674 5.815 6.06e-09 ***
## educ_hs -0.6055949 0.0234678 -25.805 < 2e-16 ***
## educ_smcoll -0.9808451 0.0235699 -41.614 < 2e-16 ***
## educ_bach 0.3530568 0.0063737 55.393 < 2e-16 ***
## educ_as -1.2488389 0.0239308 -52.186 < 2e-16 ***
## educ_adv -1.7726011 0.0239506 -74.011 < 2e-16 ***
## MARSTMarried,spouse absent 0.5922170 0.0094722 62.521 < 2e-16 ***
## MARSTSeparaate 0.2618371 0.0093244 28.081 < 2e-16 ***
## MARSTdivorced 0.0243889 0.0045305 5.383 7.31e-08 ***
## MARSTwidowed 0.2648205 0.0085757 30.880 < 2e-16 ***
## MARSTnever married/single 0.4586492 0.0043808 104.695 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3319305 on 3264137 degrees of freedom
## Residual deviance: 2944675 on 3264114 degrees of freedom
## (35375 observations deleted due to missingness)
## AIC: 2944723
##
## Number of Fisher Scoring iterations: 5
Model3_lt<-glm(long_time~AGE + I(AGE^2) +SEX+NFAMS+NCHLT5+NCHILD+HISPAN+RACAMIND+RACASIAN+RACBLK+RACOTHER+RACWHT+educ_nohs+educ_hs+educ_smcoll+educ_bach+educ_as+educ_adv+MARST,family = binomial,(data=dat_use1))
summary(Model3_lt)##
## Call:
## glm(formula = long_time ~ AGE + I(AGE^2) + SEX + NFAMS + NCHLT5 +
## NCHILD + HISPAN + RACAMIND + RACASIAN + RACBLK + RACOTHER +
## RACWHT + educ_nohs + educ_hs + educ_smcoll + educ_bach +
## educ_as + educ_adv + MARST, family = binomial, data = (data = dat_use1))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.8314 0.3216 0.3857 0.5511 0.8084
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.329e+00 1.080e-01 40.092 < 2e-16 ***
## AGE -8.330e-02 1.761e-03 -47.304 < 2e-16 ***
## I(AGE^2) 9.368e-04 1.946e-05 48.135 < 2e-16 ***
## SEXfemale 1.064e+00 4.595e-03 231.616 < 2e-16 ***
## NFAMS 2.565e-02 5.603e-03 4.578 4.69e-06 ***
## NCHLT5 1.338e-02 5.059e-03 2.644 0.008187 **
## NCHILD -6.309e-03 2.164e-03 -2.916 0.003551 **
## HISPAN 8.191e-02 3.257e-03 25.151 < 2e-16 ***
## RACAMIND -1.510e-01 1.895e-02 -7.968 1.61e-15 ***
## RACASIAN 4.260e-02 1.704e-02 2.500 0.012430 *
## RACBLK -8.083e-04 1.700e-02 -0.048 0.962083
## RACOTHER -9.642e-02 1.909e-02 -5.052 4.37e-07 ***
## RACWHT -2.382e-01 1.611e-02 -14.780 < 2e-16 ***
## educ_nohs -1.238e-01 5.019e-02 -2.466 0.013660 *
## educ_hs -1.537e-01 4.965e-02 -3.096 0.001961 **
## educ_smcoll -2.505e-01 4.971e-02 -5.040 4.65e-07 ***
## educ_bach 3.380e-01 6.462e-03 52.306 < 2e-16 ***
## educ_as -1.687e-01 5.001e-02 -3.373 0.000742 ***
## educ_adv -6.578e-01 4.975e-02 -13.220 < 2e-16 ***
## MARSTMarried,spouse absent -6.271e-02 1.512e-02 -4.149 3.34e-05 ***
## MARSTSeparaate 5.594e-02 1.572e-02 3.558 0.000373 ***
## MARSTdivorced 5.555e-03 6.722e-03 0.826 0.408559
## MARSTwidowed 1.159e-01 1.974e-02 5.874 4.26e-09 ***
## MARSTnever married/single 2.084e-01 6.522e-03 31.951 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1744636 on 2592107 degrees of freedom
## Residual deviance: 1669120 on 2592084 degrees of freedom
## (707405 observations deleted due to missingness)
## AIC: 1669168
##
## Number of Fisher Scoring iterations: 5
## Loading required package: stargazer
##
## Please cite as:
## Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.2. https://CRAN.R-project.org/package=stargazer
##
## ======================================================================
## Dependent variable:
## -------------------------------------------
## part_time full_time long_time
## (1) (2) (3)
## ----------------------------------------------------------------------
## AGE -0.148*** -0.139*** -0.083***
## (0.001) (0.001) (0.002)
##
## I(AGE2) 0.002*** 0.002*** 0.001***
## (0.00001) (0.00001) (0.00002)
##
## SEXfemale 0.728*** 1.164*** 1.064***
## (0.003) (0.003) (0.005)
##
## NFAMS -0.068*** 0.064*** 0.026***
## (0.004) (0.004) (0.006)
##
## NCHLT5 0.389*** 0.060*** 0.013***
## (0.004) (0.004) (0.005)
##
## NCHILD -0.027*** 0.093*** -0.006***
## (0.002) (0.002) (0.002)
##
## HISPAN -0.022*** -0.021*** 0.082***
## (0.002) (0.002) (0.003)
##
## RACAMIND 0.385*** 0.171*** -0.151***
## (0.012) (0.014) (0.019)
##
## RACASIAN 0.143*** 0.133*** 0.043**
## (0.012) (0.013) (0.017)
##
## RACBLK 0.188*** 0.093*** -0.001
## (0.011) (0.013) (0.017)
##
## RACOTHER -0.122*** 0.094*** -0.096***
## (0.013) (0.014) (0.019)
##
## RACWHT -0.083*** 0.227*** -0.238***
## (0.011) (0.012) (0.016)
##
## educ_nohs 0.138*** 0.144*** -0.124**
## (0.024) (0.035) (0.050)
##
## educ_hs -0.606*** -0.101*** -0.154***
## (0.023) (0.035) (0.050)
##
## educ_smcoll -0.981*** -0.156*** -0.251***
## (0.024) (0.035) (0.050)
##
## educ_bach 0.353*** 0.101*** 0.338***
## (0.006) (0.006) (0.006)
##
## educ_as -1.249*** -0.217*** -0.169***
## (0.024) (0.035) (0.050)
##
## educ_adv -1.773*** -0.479*** -0.658***
## (0.024) (0.035) (0.050)
##
## MARSTMarried,spouse absent 0.592*** 0.177*** -0.063***
## (0.009) (0.012) (0.015)
##
## MARSTSeparaate 0.262*** 0.101*** 0.056***
## (0.009) (0.011) (0.016)
##
## MARSTdivorced 0.024*** -0.152*** 0.006
## (0.005) (0.005) (0.007)
##
## MARSTwidowed 0.265*** 0.002 0.116***
## (0.009) (0.011) (0.020)
##
## MARSTnever married/single 0.459*** 0.252*** 0.208***
## (0.004) (0.005) (0.007)
##
## Constant 0.501*** -0.408*** 4.329***
## (0.070) (0.079) (0.108)
##
## ----------------------------------------------------------------------
## Observations 3,264,138 2,592,108 2,592,108
## Log Likelihood -1,472,338.000 -1,201,655.000 -834,560.000
## Akaike Inf. Crit. 2,944,723.000 2,403,359.000 1,669,168.000
## ======================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
Model4_pt<-glm(part_time~AGE + I(AGE^2) +SEX+female*NFAMS+female*NCHLT5+NCHILD*female+MARST:female,family = binomial,(data=dat_use1))
summary(Model4_pt)##
## Call:
## glm(formula = part_time ~ AGE + I(AGE^2) + SEX + female * NFAMS +
## female * NCHLT5 + NCHILD * female + MARST:female, family = binomial,
## data = (data = dat_use1))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2795 -0.7126 -0.5515 -0.2796 3.8775
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 6.985e-01 2.559e-02 27.299 <2e-16 ***
## AGE -1.517e-01 1.124e-03 -134.920 <2e-16 ***
## I(AGE^2) 2.137e-03 1.212e-05 176.356 <2e-16 ***
## SEXfemale 1.069e-01 8.489e-03 12.587 <2e-16 ***
## female NA NA NA NA
## NFAMS -5.356e-03 5.001e-03 -1.071 0.284
## NCHLT5 -2.639e-01 1.023e-02 -25.807 <2e-16 ***
## NCHILD -4.798e-01 3.297e-03 -145.520 <2e-16 ***
## female:NFAMS 9.062e-02 7.057e-03 12.842 <2e-16 ***
## female:NCHLT5 6.539e-01 1.090e-02 59.967 <2e-16 ***
## female:NCHILD 6.188e-01 3.707e-03 166.918 <2e-16 ***
## female:MARSTMarried,spouse absent 3.184e-01 1.301e-02 24.474 <2e-16 ***
## female:MARSTSeparaate 1.191e-01 1.128e-02 10.564 <2e-16 ***
## female:MARSTdivorced -3.394e-01 5.743e-03 -59.100 <2e-16 ***
## female:MARSTwidowed 2.710e-01 9.264e-03 29.257 <2e-16 ***
## female:MARSTnever married/single -4.900e-02 5.528e-03 -8.864 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3378501 on 3299512 degrees of freedom
## Residual deviance: 3094682 on 3299498 degrees of freedom
## AIC: 3094712
##
## Number of Fisher Scoring iterations: 6
model5<-lm(log1p(INCWAGE)~AGE + I(AGE^2) +SEX+female*HISPAN+female*RACAMIND+female*RACASIAN+female*RACBLK+female*RACOTHER+female*RACWHT, data=dat_use1)
summary(model5)##
## Call:
## lm(formula = log1p(INCWAGE) ~ AGE + I(AGE^2) + SEX + female *
## HISPAN + female * RACAMIND + female * RACASIAN + female *
## RACBLK + female * RACOTHER + female * RACWHT, data = dat_use1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.278 -4.621 1.890 3.030 10.162
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.046e+00 1.540e-01 19.777 <2e-16 ***
## AGE 3.771e-01 1.830e-03 206.061 <2e-16 ***
## I(AGE^2) -4.963e-03 2.014e-05 -246.468 <2e-16 ***
## SEXfemale -2.280e+00 2.060e-01 -11.071 <2e-16 ***
## female NA NA NA NA
## HISPAN -6.553e-02 5.031e-03 -13.024 <2e-16 ***
## RACAMIND -1.259e+00 3.137e-02 -40.136 <2e-16 ***
## RACASIAN 8.239e-01 2.867e-02 28.737 <2e-16 ***
## RACBLK -1.242e+00 2.813e-02 -44.132 <2e-16 ***
## RACOTHER -9.753e-03 3.101e-02 -0.315 0.753
## RACWHT 4.654e-01 2.695e-02 17.267 <2e-16 ***
## female:HISPAN -1.023e-01 6.982e-03 -14.645 <2e-16 ***
## female:RACAMIND 5.740e-01 4.377e-02 13.116 <2e-16 ***
## female:RACASIAN -5.471e-01 3.972e-02 -13.775 <2e-16 ***
## female:RACBLK 1.594e+00 3.900e-02 40.867 <2e-16 ***
## female:RACOTHER -4.389e-01 4.340e-02 -10.113 <2e-16 ***
## female:RACWHT -5.749e-02 3.743e-02 -1.536 0.125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.496 on 3299497 degrees of freedom
## Multiple R-squared: 0.07011, Adjusted R-squared: 0.07011
## F-statistic: 1.659e+04 on 15 and 3299497 DF, p-value: < 2.2e-16
#OLS REGRESSION WITH SOME INTERACTIONS WITH FEMALE
model6<-lm(log1p(INCWAGE)~AGE + I(AGE^2) +SEX+NFAMS+female*NCHLT5+female*NCHILD+female*HISPAN+female*RACAMIND+female*RACASIAN+female*RACBLK+female*RACOTHER+female*RACWHT+female*educ_nohs+female*educ_hs+female*educ_smcoll+female*educ_bach+female*educ_as+female*educ_adv+female*MARST+female*UHRSWORK, data=dat_use1)
summary(model6)##
## Call:
## lm(formula = log1p(INCWAGE) ~ AGE + I(AGE^2) + SEX + NFAMS +
## female * NCHLT5 + female * NCHILD + female * HISPAN + female *
## RACAMIND + female * RACASIAN + female * RACBLK + female *
## RACOTHER + female * RACWHT + female * educ_nohs + female *
## educ_hs + female * educ_smcoll + female * educ_bach + female *
## educ_as + female * educ_adv + female * MARST + female * UHRSWORK,
## data = dat_use1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.1021 0.1089 0.6856 1.1521 6.3167
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.632e+00 1.112e-01 68.614 < 2e-16 ***
## AGE 2.945e-02 1.356e-03 21.711 < 2e-16 ***
## I(AGE^2) -4.243e-04 1.506e-05 -28.177 < 2e-16 ***
## SEXfemale -1.477e+00 1.571e-01 -9.405 < 2e-16 ***
## NFAMS -2.040e-02 4.187e-03 -4.874 1.10e-06 ***
## female NA NA NA NA
## NCHLT5 5.908e-03 5.423e-03 1.089 0.27594
## NCHILD 2.479e-02 2.417e-03 10.258 < 2e-16 ***
## HISPAN 1.448e-02 3.254e-03 4.451 8.55e-06 ***
## RACAMIND -2.075e-01 2.136e-02 -9.716 < 2e-16 ***
## RACASIAN 7.841e-02 1.871e-02 4.190 2.78e-05 ***
## RACBLK 5.021e-02 1.862e-02 2.697 0.00699 **
## RACOTHER -2.011e-02 2.032e-02 -0.990 0.32225
## RACWHT -5.136e-02 1.771e-02 -2.901 0.00372 **
## educ_nohs -1.069e-01 4.518e-02 -2.367 0.01796 *
## educ_hs 4.623e-01 4.466e-02 10.353 < 2e-16 ***
## educ_smcoll 7.584e-01 4.477e-02 16.940 < 2e-16 ***
## educ_bach -2.632e-01 7.972e-03 -33.011 < 2e-16 ***
## educ_as 9.455e-01 4.519e-02 20.924 < 2e-16 ***
## educ_adv 1.580e+00 4.496e-02 35.140 < 2e-16 ***
## MARSTMarried,spouse absent -2.696e-01 1.649e-02 -16.347 < 2e-16 ***
## MARSTSeparaate -1.900e-01 1.807e-02 -10.514 < 2e-16 ***
## MARSTdivorced -2.441e-01 7.949e-03 -30.708 < 2e-16 ***
## MARSTwidowed -1.913e-01 2.634e-02 -7.264 3.76e-13 ***
## MARSTnever married/single -2.437e-01 6.629e-03 -36.763 < 2e-16 ***
## UHRSWORK 2.852e-02 2.002e-04 142.490 < 2e-16 ***
## female:NCHLT5 -7.402e-02 7.930e-03 -9.335 < 2e-16 ***
## female:NCHILD -8.598e-02 3.359e-03 -25.594 < 2e-16 ***
## female:HISPAN -4.673e-02 4.698e-03 -9.947 < 2e-16 ***
## female:RACAMIND 6.113e-02 3.043e-02 2.009 0.04456 *
## female:RACASIAN -3.862e-02 2.657e-02 -1.454 0.14608
## female:RACBLK 6.933e-02 2.630e-02 2.636 0.00839 **
## female:RACOTHER -8.149e-02 2.934e-02 -2.778 0.00548 **
## female:RACWHT -2.177e-02 2.514e-02 -0.866 0.38650
## female:educ_nohs 4.232e-01 7.590e-02 5.577 2.45e-08 ***
## female:educ_hs 3.830e-01 7.497e-02 5.108 3.25e-07 ***
## female:educ_smcoll 2.175e-01 7.507e-02 2.898 0.00376 **
## female:educ_bach 5.168e-05 1.114e-02 0.005 0.99630
## female:educ_as 3.408e-01 7.549e-02 4.514 6.36e-06 ***
## female:educ_adv 1.281e-01 7.528e-02 1.701 0.08893 .
## female:MARSTMarried,spouse absent 1.121e-01 2.485e-02 4.512 6.41e-06 ***
## female:MARSTSeparaate 2.938e-02 2.374e-02 1.238 0.21583
## female:MARSTdivorced 2.813e-01 1.062e-02 26.480 < 2e-16 ***
## female:MARSTwidowed 2.149e-01 3.040e-02 7.070 1.55e-12 ***
## female:MARSTnever married/single 2.323e-01 9.073e-03 25.604 < 2e-16 ***
## female:UHRSWORK 3.042e-02 2.865e-04 106.177 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.65 on 2592063 degrees of freedom
## (707405 observations deleted due to missingness)
## Multiple R-squared: 0.07441, Adjusted R-squared: 0.0744
## F-statistic: 4736 on 44 and 2592063 DF, p-value: < 2.2e-16
computer_Inf_service<-(DEGFIELD==21)
computer_Inf_service<-as.factor(computer_Inf_service)
construction_service<-(DEGFIELD==56)
construction_service<-as.factor(construction_service)
educ_admin_teaching<-(DEGFIELD==31)
educ_admin_teaching<-as.factor(educ_admin_teaching)
medical_health_scie<-(DEGFIELD==61)
medical_health_scie<-as.factor(medical_health_scie)newdata2<-cbind.data.frame(newdata,computer_Inf_service,construction_service,educ_admin_teaching,medical_health_scie)##
## Call:
## lm(formula = log1p(INCWAGE) ~ female)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.760 -5.662 2.914 4.337 7.821
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.760159 0.003219 2099.8 <2e-16 ***
## female -1.098392 0.004475 -245.4 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.015 on 5029427 degrees of freedom
## (1179865 observations deleted due to missingness)
## Multiple R-squared: 0.01183, Adjusted R-squared: 0.01183
## F-statistic: 6.023e+04 on 1 and 5029427 DF, p-value: < 2.2e-16
model8<-lm(log1p(INCWAGE)~female+female*RACAMIND+female*RACASIAN+female*RACBLK+female*RACOTHER+female*RACWHT)
summary(model8)##
## Call:
## lm(formula = log1p(INCWAGE) ~ female + female * RACAMIND + female *
## RACASIAN + female * RACBLK + female * RACOTHER + female *
## RACWHT)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.465 -5.610 2.867 4.338 8.107
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.06913 0.13098 46.336 < 2e-16 ***
## female -3.07715 0.18240 -16.871 < 2e-16 ***
## RACAMIND -0.65094 0.02869 -22.691 < 2e-16 ***
## RACASIAN 0.90712 0.02556 35.494 < 2e-16 ***
## RACBLK -0.79671 0.02491 -31.987 < 2e-16 ***
## RACOTHER 0.73826 0.02670 27.646 < 2e-16 ***
## RACWHT 0.27621 0.02390 11.557 < 2e-16 ***
## female:RACAMIND 0.61127 0.04000 15.281 < 2e-16 ***
## female:RACASIAN -0.02712 0.03553 -0.763 0.44530
## female:RACBLK 1.39739 0.03463 40.353 < 2e-16 ***
## female:RACOTHER -0.32700 0.03741 -8.741 < 2e-16 ***
## female:RACWHT 0.10646 0.03329 3.198 0.00139 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.007 on 5029417 degrees of freedom
## (1179865 observations deleted due to missingness)
## Multiple R-squared: 0.01514, Adjusted R-squared: 0.01514
## F-statistic: 7029 on 11 and 5029417 DF, p-value: < 2.2e-16
NNobs<-length(newdata$LABFORCE)
set.seed(12345)
graph_obs<-(runif(NNobs)<0.5)
dat_graph<-subset(newdata,graph_obs)
plot(SEX~DEGFIELD,pch=10,ylim=c(0,1),data=dat_graph,main="SEX", xlab="DEGFIELD",ylab = "SEX",col=c("blue","gray"))
NNobs<-length(newdata$SEX)
set.seed(12345)
graph_obs<-(runif(NNobs)<0.5)
dat_graph<-subset(newdata,graph_obs)
plot(SEX~EDUC,pch=10,ylim=c(0,1),data=dat_graph,main="SEX", xlab="EDUC",ylab = "SEX",col=c("blue","gray"))