Simulated example data for assessing race bias in traffic stop outcomes
Usage
data(raceprofiling)
Format
A data frame with 5000 observations on the following 10 variables.
id
an ID for each traffic stop
nhood
a factor indicating the neighborhood in which the stop
occurred.
reason
The reason for the stop, mechanical/registration
violations, dangerous moving violation, non-dangerous moving violation
resident
an indicator whether the driver is a resident of the
city
age
driver's age
male
an indicator whether the driver was male
race
the race of the driver, with levels A, B,
H, W
hour
the hour of the stop (24-hour clock)
month
and ordered factor indicating in which month the stop
took place
citation
an indicator of whether the driver received a citation
Source
This is simulated data to demonstrate how to use twang to adjust
estimates of racial bias for important factors. This dataset does not represent
real data from any real law enforcement agency.
References
G. Ridgeway (2006). “Assessing the effect of race bias in
post-traffic stop outcomes using propensity scores,” Journal of
Quantitative Criminology 22(1).