City: Columbia, Missouri

Reporting to: Diversity Equity and Inclusion Administrator

The Challenge

The Columbia Police Department began tracking vehicle traffic stops demographic data in 2000 and has been publicly reporting these data since 2014. High-level analysis of the data over the years shows a racial disparity across all metrics: stops, searches, and outcomes. The 2022 data analysis indicates that Black drivers are 1.8 times more likely to be searched, especially based on drug or alcohol odor, and are 1.6 times more likely to be arrested. Moreover, analyses of traffic stops data in 2018, 2019, and 2020—both internally and by external parties—confirms these findings. While traffic stops declined from 7,772 to 5,370 between 2020 and 2022, the disparity persists. This indicates that interventions to-date, such as implicit bias trainings and additional vehicle stop data (see here and here), have had little impact. Across the state, law enforcement stopped Black drivers at a rate 1.5 times higher than expected by demographic proportions. In Columbia, this disparity is more than twice that rate and 2022 marked the highest disparity level recorded by the Columbia Police Department since tracking began in 2000. Both the City Manager and the Mayor see addressing this issue, and the importance of additional resources and collaboration, as a key priority; they have assembled a cross-departmental team to work on this issue as part of the Bloomberg Harvard City Leadership Initiative’s Data Track.¹

The Columbia Police Chief formed a Vehicle Stops Committee (VSC) in April, 2019. The committee has made several recommendations around enhancing police implicit bias training and vehicle stops data with checkoffs for pretext stops. For example, stopping vehicles for equipment violations may disproportionately impact lower-income drivers, most of whom are Black. The committee called for further categorization of the severity of this type of violation when used as the basis for a vehicle stop. However, these recommendations are not data-informed but from anecdotal evidence. Moreover, there is no evidence-based link established between implicit bias training and more equitable outcomes in traffic stops. Most importantly, there is no context to the raw data to develop interventions to address systemic discrimination.² This is at its core an equity issue. Lessons learned from a data-informed analysis of this problem could be extended to other disparities in obligations such as other types of violations, use of force, and criminal justice. The summer fellow’s work will help ensure the Data Track Team’s recommendations are prioritized, implemented, and planned, as well as planning ongoing intervention evaluations through establishing processes and platforms to collect and use data and analyze performance and outcomes.

A successful summer fellow would also help build capacity and transferable knowledge on the following: maintaining the ongoing collection and analysis of data; preventing bias in the routine collection and use of data; identifying and removing algorithmic bias; measuring progress towards targets; developing a replicable performance management process; and evaluating program implementation.

What You’ll Do

The fellow will work with the Performance Lead (DEI Officer), responsible for implementing the processes for performance management and supporting the mayor in running meetings, to ensure successful implementation of the Data Track Team’s recommendations. Key stakeholders include: Mayor and City Council; City Manager; Chief of Police; Vehicle Stops Committee members; Boone County Sheriff’s Department; Columbia Police Department subject matter expert(s); Columbia Police Department data analyst; Community groups such as Neighborhood Associations and local activist groups; City’s data/performance analysis team; Performance Lead: City’s Diversity, Equity and Inclusion (DEI) Office; and City’s Program Management Office. Given the Data Track Team will have already completed data-informed problem definition, analysis, and implemented performance management meetings by June 2024.

Key Deliverables Include:

1. Implementation roadmap of long-term Data Track Team recommendations identifying:
   • Necessary policy and process changes
   • Re-programmed city resources or new capabilities
   • Key stakeholders/collaborations and roles and responsibilities
   • Funding, authority, etc.
2. Identification of data gaps and recommendations for how to perform solid data collection, analysis, and usage to manage performance and evaluate outcomes.
3. Process evaluation methodology to assess how well the Data Track team’s recommendations are being implemented.
4. Presentation of work to key stakeholders including the Mayor, City Council, and City Manager.

What You’ll Bring

The fellow will be expected to possess the following skills:

• Data Analysis
• Qualitative Interviewing and Analysis
• Mapping (GIS) skills preferred but not required
• Policy Analysis
• Language Fluency (English)
• Writing and editing

Endnotes

¹City Manager Seewood’s previous connection to Ferguson, MO makes disparity in policing a key issue for him.

² For example, beats, shifts, and days of the week associated with vehicle stops could be analyzed to examine whether certain neighborhoods are more heavily patrolled and disproportionately represented in the data.

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