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Ayobami Laniyonu, Ph.D., research lecturer at the University of Toronto, discusses his research on how race and policing increasingly relies on large administrative datasets collected by police officers to estimate racial disparities in police contact, use of force, and racially discriminatory behavior. Many of these datasets, however, rely on officer perception of a stopped person’s race, which extant research suggests may be inconsistent with how those individuals self-identify. Utilizing a multi-dimensional data set which allows us to compare officers’ racial classification of stopped persons to those same persons’ racial self-identification, we characterize rates of racial ‘mismatch’ in administrative police records.

We find that racial mismatch occurs in 8.5% of the police data and is especially pronounced among Hispanic and Asians/Pacific Islanders. We find that officer classification of Hispanics as non-Hispanic White is the most common form of racial mismatch in our sample and that the substantive consequences of this particular form of incongruence are significant: officer classification of Hispanics as non-Hispanic White may lead analysts to incorrectly conclude that Hispanics are no more likely to be cited by police than non-Hispanic Whites.