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Do digital technologies reduce racially biased reporting? Evidence from NYPD administrative data.

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Area of Science:

  • Criminology
  • Sociology
  • Public Policy

Background:

  • Research on police-citizen interactions is limited by biased administrative data, which underreports observed but uninvestigated civilians.
  • Reporting biases within police investigations, distinct from sampling biases, have received less empirical attention.
  • Digital monitoring tools may offer a way to mitigate reporting biases in law enforcement data.

Purpose of the Study:

  • To investigate reporting biases in administrative police data.
  • To examine whether digital monitoring tools, specifically smartphones, help mitigate these reporting biases.
  • To assess the impact of digital tools on the reporting of police-citizen interactions.

Main Methods:

  • Utilized a staggered difference-in-differences estimation.
  • Examined changes in police-citizen interaction reports following the New York City Police Department's adoption of smartphones.
  • Analyzed the characteristics of reported stops, including productivity, citizen race, and location.

Main Results:

  • A significant increase in reported citizen stops was observed after smartphone deployment.
  • The increase in reports was driven by greater documentation of "unproductive" stops.
  • Reports of stops involving non-White citizens and those in high-crime/non-White areas also increased significantly.

Conclusions:

  • Prior research may have underestimated the extent of racial bias in policing due to reporting biases.
  • The implementation of digital monitoring tools can help mitigate reporting biases in law enforcement data.
  • Digital tools provide a more comprehensive, though still potentially biased, record of police-citizen encounters.