wages.RdData on the labor-market experience of male high school dropouts.
A data frame with 6402 observations on the following 15 variables.
respondent id - a factor with 888 levels.
natural log of wages expressed in 1990 dollars.
years of experience in the work force
equals 1 if respondent has obtained a GED as of the time of survey, 0 otherwise
labor force participation since obtaining a GED (in years) - before a GED is earned postexp = 0, and on the day a GED is earned postexp = 0
factor - equals 1 if subject is black, 0 otherwise
factor - equals 1 if subject is hispanic, 0 otherwise
highest grade completed - takes integers 6 through 12
hgc - 9, a centered version of hgc
local area unemployment rate for that year
These data are originally from the 1979 National Longitudinal Survey on Youth (NLSY79) that can be found here https://www.bls.gov/nls/nlsy79.htm.
Singer and Willett (2003) used these data for examples in chapter (insert info. here) and the data sets used can be found on the UCLA Statistical Computing website: https://stats.idre.ucla.edu/other/examples/alda/
Additionally the data were discussed by Cook and Swayne (2003) and the data can be found on the GGobi website: http://ggobi.org/book.html.
Singer, J. D. and Willett, J. B. (2003), Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence, New York: Oxford University Press.
Cook, D. and Swayne, D. F. (2007), Interactive and Dynamic Graphics for Data Analysis with R and GGobi, Springer.
#> 'data.frame': 6402 obs. of 15 variables: #> $ id : Factor w/ 888 levels "31","36","53",..: 1 1 1 1 1 1 1 1 2 2 ... #> $ lnw : num 1.49 1.43 1.47 1.75 1.93 ... #> $ exper : num 0.015 0.715 1.734 2.773 3.927 ... #> $ ged : int 1 1 1 1 1 1 1 1 1 1 ... #> $ postexp : num 0.015 0.715 1.734 2.773 3.927 ... #> $ black : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ... #> $ hispanic : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 1 1 ... #> $ hgc : int 8 8 8 8 8 8 8 8 9 9 ... #> $ hgc.9 : int -1 -1 -1 -1 -1 -1 -1 -1 0 0 ... #> $ uerate : num 3.21 3.21 3.21 3.3 2.89 2.49 2.6 4.8 4.89 7.4 ... #> $ ue.7 : num -3.79 -3.79 -3.79 -3.71 -4.11 ... #> $ ue.centert1 : num 0 0 0 0.08 -0.32 ... #> $ ue.mean : num 3.21 3.21 3.21 3.21 3.21 3.21 3.21 3.21 5.1 5.1 ... #> $ ue.person.cen: num 0 0 0 0.08 -0.32 ... #> $ ue1 : num 3.21 3.21 3.21 3.21 3.21 3.21 3.21 3.21 4.89 4.89 ...#> id lnw exper ged #> 1204 : 13 Min. :0.708 Min. : 0.001 Min. :0.0000 #> 3440 : 13 1st Qu.:1.591 1st Qu.: 1.609 1st Qu.:0.0000 #> 7373 : 13 Median :1.842 Median : 3.451 Median :0.0000 #> 9968 : 13 Mean :1.897 Mean : 3.957 Mean :0.2719 #> 10392 : 13 3rd Qu.:2.140 3rd Qu.: 5.949 3rd Qu.:1.0000 #> 12043 : 13 Max. :4.304 Max. :12.700 Max. :1.0000 #> (Other):6324 #> postexp black hispanic hgc hgc.9 #> Min. : 0.0000 0:4783 0:4859 Min. : 6.000 Min. :-3.00000 #> 1st Qu.: 0.0000 1:1619 1:1543 1st Qu.: 8.000 1st Qu.:-1.00000 #> Median : 0.0000 Median : 9.000 Median : 0.00000 #> Mean : 0.9076 Mean : 8.948 Mean :-0.05248 #> 3rd Qu.: 0.1168 3rd Qu.:10.000 3rd Qu.: 1.00000 #> Max. :12.2600 Max. :12.000 Max. : 3.00000 #> #> uerate ue.7 ue.centert1 ue.mean #> Min. : 1.790 Min. :-5.2050 Min. :-15.310 Min. : 2.890 #> 1st Qu.: 5.390 1st Qu.:-1.6050 1st Qu.: -2.900 1st Qu.: 6.070 #> Median : 7.000 Median : 0.0000 Median : -0.500 Median : 7.150 #> Mean : 7.733 Mean : 0.7297 Mean : -1.015 Mean : 7.734 #> 3rd Qu.: 9.400 3rd Qu.: 2.3000 3rd Qu.: 0.600 3rd Qu.: 8.650 #> Max. :23.700 Max. :16.7050 Max. : 13.005 Max. :17.130 #> NA's :402 NA's :406 #> ue.person.cen ue1 #> Min. :-8.403000 Min. : 2.890 #> 1st Qu.:-1.350500 1st Qu.: 6.200 #> Median :-0.142000 Median : 8.300 #> Mean :-0.000011 Mean : 8.762 #> 3rd Qu.: 1.133000 3rd Qu.:10.200 #> Max. :13.421000 Max. :23.700 #>