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.

Arguments

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

Run this code
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)

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