IDEAS home Printed from
MyIDEAS: Login to save this article or follow this journal

Testing for Racial Profiling in Traffic Stops From Behind a Veil of Darkness

  • Grogger, Jeffrey
  • Ridgeway, Greg

The key problem in testing for racial profiling in traffic stops is estimating the risk set, or "benchmark," against which to compare the race distribution of stopped drivers. To date, the two most common approaches have been to employ Census-based residential population data or to conduct traffic surveys in which observers tally the race distribution of drivers at a certain location. It is widely recognized that residential population data may provide poor estimates of the population at risk of a traffic stop; at the same time, traffic surveys have limitations and may be too costly to carry out on the ongoing basis required by recent legislation and litigation. In this paper, we propose a test for racial profiling that does not require explicit, external estimates of the risk set. Rather, our approach makes use of what we refer to as the "veil of darkness" hypothesis, which asserts that at night, police cannot determine the race of a motorist until they actually make a stop. The implication is that the race distribution of drivers stopped at night should equal the race distribution of drivers at risk of being stopped at night. If we further assume that racial differences in traffic patterns, driving behavior, and exposure to law enforcement do not vary between day and night, we can test for racial profiling by comparing the race distribution of stops made during daylight to the race distribution of stops made at night. We propose a means of weakening this assumption by restricting the sample to stops made during the evening hours and controlling for clock time while estimating day/night contrasts in the race distribution of stopped drivers. We provide conditions under which our estimates are robust to a substantial non-reporting problem present in our data and in many other studies of racial profiling. We propose an approach to assess the sensitivity of our results to departures from our maintained assumptions. Finally, we apply our method to da

(This abstract was borrowed from another version of this item.)

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL:
File Function: full text
Download Restriction: Access to full text is restricted to subscribers.

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Article provided by American Statistical Association in its journal Journal of the American Statistical Association.

Volume (Year): 101 (2006)
Issue (Month): (September)
Pages: 878-887

in new window

Handle: RePEc:bes:jnlasa:v:101:y:2006:p:878-887
Contact details of provider: Web page:

Order Information: Web:

No references listed on IDEAS
You can help add them by filling out this form.

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:bes:jnlasa:v:101:y:2006:p:878-887. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.