cranvas (version 0.8.5)

wages: Wages of male high-school dropouts

Description

The data was collected to track the labor experiences of male high-school dropouts. The men were between 14 and 17 years old at the time of the first survey.

Format

Number of subjects: 888; Number of variables: 15; Number of observations, across all subjects: 6402

id

id numbers for each subject

lnw

natural log of wages, adjusted for inflation, to 1990 dollars

exper

length of time in the workforce (in years). This is treated as the time variable, with $t = 0$ for each subject starting on their first day at work. The number of time points and values of time points for each subject can differ

ged

when/if a graduate equivalency diploma is obtained

black

categorical indicator of race is black

hispanic

categorical indicator of race is hispanic

hgc

highest grade completed

uerate

unemployment rates in the local geographic region at each measurement time

Source

Singer, J. D. & Willett, J. B. (2003), Applied Longitudinal Data Analysis, Oxford University Press, Oxford, UK. It is a subset of data collected in the National Longitudinal Survey of Youth (NLSY) described at http://www.bls.gov/nls/nlsdata.htm.

Examples

library(cranvas)

## Not run: 
# 
# data(wages)
# qwages <- qdata(wages)
# qtime(exper, lnw, qwages, group = id)  # Too slow right now
# 
# ## this is a very simple linear regression assuming each man has the same slope,
# ## which may not be entirely reasonable
# fit <- lm(lnw ~ exper + id, data = wages)
# wage2 <- subset(wages, !duplicated(wages$id), c(id, hispanic))
# ## predict wages at year 0 and 6 respectively
# wage2$lnw0 <- predict(fit, data.frame(id = unique(wages$id), exper = 0))
# wage2$lnw6 <- predict(fit, data.frame(id = unique(wages$id), exper = 6))
# qwage2 <- qdata(wage2, color = hispanic)
# 
# qhist(lnw0, data = qwage2, main = "Wages on the first day")
# qhist(lnw6, data = qwage2, main = "Wages after 6 years")
# ## End(Not run)