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San Francisco banned this racially biased police practice. The results are remarkable

11 0
13.03.2026

A policy enacted in 2024 restricts S.F. police officers from stopping drivers solely for low-level violations such as hanging an item on the rearview mirror or expired registration tags. The result is a huge drop in stops for Black and Latino drivers.

For decades, police in San Francisco and around the country have routinely conducted “pretext stops,” in which officers pull drivers over for low-level traffic violations, such as having an air freshener on their rearview mirror, as an excuse to fish for evidence of unrelated crimes. 

While officers strategically use these low-level traffic stops to pull over anyone they are suspicious of, pretext stops rarely result in the discovery of contraband or illegal activity. Less than 1% of pretext stops in San Francisco discover a gun, yet for decades, this strategy has undergirded a method of policing that has overwhelmingly targeted communities of color and led to fatal consequences. Nationally, from 2017 to 2021, police killed more than 400 unarmed civilians in these types of stops — a rate of more than one death a week. 

In an effort to curtail this biased, ineffective and dangerous policing practice, the San Francisco Police Commission passed Department General Order 9.07 in February 2024. The policy restricts officers from pulling people over for a subset of low-level traffic violations that are most regularly used for pretext stops, such as hanging an item on the rearview mirror, expired registration tags or failure to illuminate rear license plates. The state of California also passed a similar (but less expansive) law that prohibits officers from using expired registration tags as the sole basis of a stop. Both of these policies went into effect in July 2024. 

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The idea behind these policy changes was simple: reduce the over-policing of communities of color and prevent routine traffic encounters from escalating into dangerous — or even deadly — interactions, all without compromising public safety. According to my recently published research in the Berkeley Public Policy Journal, San Francisco’s limits on low-level traffic stops are doing exactly that.

Using police department stop data from January 2022 through September 2025, my analysis shows that San Francisco and California’s pretext stop restrictions led to dramatic, statistically significant reductions in stops for Black, Latino and white drivers in the city — all without generating changes in drug, gun or contraband discoveries. These effects were immediate. In the first week after San Francisco’s policy took effect, there was a 68% decrease in weekly stops of Latino drivers. Weekly stops of Black drivers dropped nearly 50%. The large reductions in weekly stops due to the policies reversed the previous trend, which was steady increases in pretext stops across all racial groups, which held constant even after accounting for changes in crime rates, police staffing levels and driving activity in the city.  

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The only stop outcome that dropped significantly was the rate of searches conducted on those who were pulled over for pretext violations. And when broken down by race, the data reveals where that drop really came from — Black and Latino drivers, who were the only racial groups to see statistically significant reductions in their rate of searches.

Put plainly, the pretext stop policies did not result in fewer drug, gun or contraband discoveries, nor did they result in the apprehension of fewer criminals. They did, however, lead to significantly fewer stops and subsequent searches of Black and Latino drivers. Considering what we know about these interactions often escalating into dangerous and even fatal encounters for people of color, these large reductions in weekly stops are a remarkable policy accomplishment. They are unwritten headlines of police violence that was not allowed to occur. 

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It’s worth reiterating that pretext stops were never an effective policing tactic. Drug, gun, contraband and crime discoveries were — and remain — extremely rare occurrences; so much so that none of these outcomes shifted by more than a single percentage point after the policies were implemented. This, perhaps more than anything else, is evidence that pretext stops weren’t making communities safer — they were giving police a tool to disproportionately stop, search and harass Black and Latino drivers, and when that tool was taken away, the only thing lost was the bias itself. It’s precisely because these stops overwhelmingly targeted people of color while returning negligible public safety benefits that these reforms were adopted. By limiting low‑level traffic stops, San Francisco has effectively reduced the risks these encounters pose for drivers and passengers, particularly people who are Black or brown. 

Guest opinions in Open Forum and Insight are produced by writers with expertise, personal experience or original insights on a subject of interest to our readers. Their views do not necessarily reflect the opinion of The Chronicle editorial board, which is committed to providing a diversity of ideas to our readership.

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Effective public safety utilizes strategies that have been proven to work and discards those that have not. San Francisco’s pretext stop reform is a model for other cities in California to follow. The city is proving that we can make progress toward ending the over-policing of communities of color and minimizing the unnecessary escalation of low-level traffic stops without compromising public safety. But these improvements won’t just happen by themselves — it takes leadership and a real commitment to evidence-based policymaking.

As such, local and state leaders should follow San Francisco’s lead and restrict ineffective pretext stops, while shifting toward proven, evidence-based public safety strategies that actually improve quality of life. 

Parambir Dhillon is a master of public affairs student at UC Berkeley’s Goldman School of Public Policy. His research interests include alternative approaches to public safety and machine learning applications in the criminal legal system. 


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