raceprofiling {twang}

Traffic stop data

Description

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).

Examples

data(raceprofiling)

# the first five lines of the dataset
raceprofiling[1:5,]