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10 things we know about race and policing in the U.S.

Days of protests across the United States in the wake of George Floyd’s death in the custody of Minneapolis police have brought new attention to questions about police officers’ attitudes toward black Americans, protesters and others. The public’s views of the police, in turn, are also in the spotlight. Here’s a roundup of Pew Research Center survey findings from the past few years about the intersection of race and law enforcement.

How we did this

Most of the findings in this post were drawn from two previous Pew Research Center reports: one on police officers and policing issues published in January 2017, and one on the state of race relations in the United States published in April 2019. We also drew from a September 2016 report on how black and white Americans view police in their communities. (The questions asked for these reports, as well as their responses, can be found in the reports’ accompanying “topline” file or files.)

The 2017 police report was based on two surveys. One was of 7,917 law enforcement officers from 54 police and sheriff’s departments across the U.S., designed and weighted to represent the population of officers who work in agencies that employ at least 100 full-time sworn law enforcement officers with general arrest powers, and conducted between May and August 2016. The other survey, of the general public, was conducted via the Center’s American Trends Panel (ATP) in August and September 2016 among 4,538 respondents. (The 2016 report on how blacks and whites view police in their communities also was based on that survey.) More information on methodology is available here .

The 2019 race report was based on a survey conducted in January and February 2019. A total of 6,637 people responded, out of 9,402 who were sampled, for a response rate of 71%. The respondents included 5,599 from the ATP and oversamples of 530 non-Hispanic black and 508 Hispanic respondents sampled from Ipsos’ KnowledgePanel. More information on methodology is available here .

Majorities of both black and white Americans say black people are treated less fairly than whites in dealing with the police and by the criminal justice system as a whole. In a 2019 Center survey , 84% of black adults said that, in dealing with police, blacks are generally treated less fairly than whites; 63% of whites said the same. Similarly, 87% of blacks and 61% of whites said the U.S. criminal justice system treats black people less fairly.

Black adults are about five times as likely as whites to say they’ve been unfairly stopped by police because of their race or ethnicity (44% vs. 9%), according to the same survey. Black men are especially likely to say this : 59% say they’ve been unfairly stopped, versus 31% of black women.

White Democrats and white Republicans have vastly different views of how black people are treated by police and the wider justice system. Overwhelming majorities of white Democrats say black people are treated less fairly than whites by the police (88%) and the criminal justice system (86%), according to the 2019 poll. About four-in-ten white Republicans agree (43% and 39%, respectively).

Nearly two-thirds of black adults (65%) say they’ve been in situations where people acted as if they were suspicious of them because of their race or ethnicity, while only a quarter of white adults say that’s happened to them. Roughly a third of both Asian and Hispanic adults (34% and 37%, respectively) say they’ve been in such situations, the 2019 survey found.

Black Americans are far less likely than whites to give police high marks for the way they do their jobs . In a 2016 survey, only about a third of black adults said that police in their community did an “excellent” or “good” job in using the right amount of force (33%, compared with 75% of whites), treating racial and ethnic groups equally (35% vs. 75%), and holding officers accountable for misconduct (31% vs. 70%).

In the past, police officers and the general public have tended to view fatal encounters between black people and police very differently. In a 2016 survey  of nearly 8,000 policemen and women from departments with at least 100 officers, two-thirds said most such encounters are isolated incidents and not signs of broader problems between police and the black community. In a companion survey of more than 4,500 U.S. adults, 60% of the public called such incidents signs of broader problems between police and black people. But the views given by police themselves were sharply differentiated by race: A majority of black officers (57%) said that such incidents were evidence of a broader problem, but only 27% of white officers and 26% of Hispanic officers said so.

Around two-thirds of police officers (68%) said in 2016 that the demonstrations over the deaths of black people during encounters with law enforcement were motivated to a great extent by anti-police bias; only 10% said (in a separate question) that protesters were primarily motivated by a genuine desire to hold police accountable for their actions. Here as elsewhere, police officers’ views differed by race: Only about a quarter of white officers (27%) but around six-in-ten of their black colleagues (57%) said such protests were motivated at least to some extent by a genuine desire to hold police accountable.

White police officers and their black colleagues have starkly different views on fundamental questions regarding the situation of blacks in American society, the 2016 survey found. For example, nearly all white officers (92%) – but only 29% of their black colleagues – said the U.S. had made the changes needed to assure equal rights for blacks.

A majority of officers said in 2016 that relations between the police in their department and black people in the community they serve were “excellent” (8%) or “good” (47%). However, far higher shares saw excellent or good community relations with whites (91%), Asians (88%) and Hispanics (70%). About a quarter of police officers (26%) said relations between police and black people in their community were “only fair,” while nearly one-in-five (18%) said they were “poor” – with black officers far more likely than others to say so. (These percentages are based on only those officers who offered a rating.)

An overwhelming majority of police officers (86%) said in 2016 that high-profile fatal encounters between black people and police officers had made their jobs harder . Sizable majorities also said such incidents had made their colleagues more worried about safety (93%), heightened tensions between police and blacks (75%), and left many officers reluctant to use force when appropriate (76%) or to question people who seemed suspicious (72%).

• Criminal Justice
• Discrimination & Prejudice
• Racial Bias & Discrimination

Drew DeSilver is a senior writer at Pew Research Center .

Michael Lipka is an associate director focusing on news and information research at Pew Research Center .

Dalia Fahmy is a senior writer/editor focusing on religion at Pew Research Center .

What the data says about crime in the U.S.

Fewer than 1% of federal criminal defendants were acquitted in 2022, before release of video showing tyre nichols’ beating, public views of police conduct had improved modestly, black americans differ from other u.s. adults over whether individual or structural racism is a bigger problem, violent crime is a key midterm voting issue, but what does the data say, most popular.

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ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

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Solving racial disparities in policing

Colleen Walsh

Harvard Staff Writer

Experts say approach must be comprehensive as roots are embedded in culture

“ Unequal ” is a multipart series highlighting the work of Harvard faculty, staff, students, alumni, and researchers on issues of race and inequality across the U.S. The first part explores the experience of people of color with the criminal justice legal system in America.

It seems there’s no end to them. They are the recent videos and reports of Black and brown people beaten or killed by law enforcement officers, and they have fueled a national outcry over the disproportionate use of excessive, and often lethal, force against people of color, and galvanized demands for police reform.

This is not the first time in recent decades that high-profile police violence — from the 1991 beating of Rodney King to the fatal shooting of Michael Brown in 2014 — ignited calls for change. But this time appears different. The police killings of Breonna Taylor in March, George Floyd in May, and a string of others triggered historic, widespread marches and rallies across the nation, from small towns to major cities, drawing protesters of unprecedented diversity in race, gender, and age.

According to historians and other scholars, the problem is embedded in the story of the nation and its culture. Rooted in slavery, racial disparities in policing and police violence, they say, are sustained by systemic exclusion and discrimination, and fueled by implicit and explicit bias. Any solution clearly will require myriad new approaches to law enforcement, courts, and community involvement, and comprehensive social change driven from the bottom up and the top down.

While police reform has become a major focus, the current moment of national reckoning has widened the lens on systemic racism for many Americans. The range of issues, though less familiar to some, is well known to scholars and activists. Across Harvard, for instance, faculty members have long explored the ways inequality permeates every aspect of American life. Their research and scholarship sits at the heart of a new Gazette series starting today aimed at finding ways forward in the areas of democracy; wealth and opportunity; environment and health; and education. It begins with this first on policing.

Harvard Kennedy School Professor Khalil Gibran Muhammad traces the history of policing in America to “slave patrols” in the antebellum South, in which white citizens were expected to help supervise the movements of enslaved Black people.

Photo by Martha Stewart

The history of racialized policing

Like many scholars, Khalil Gibran Muhammad , professor of history, race, and public policy at the Harvard Kennedy School , traces the history of policing in America to “slave patrols” in the antebellum South, in which white citizens were expected to help supervise the movements of enslaved Black people. This legacy, he believes, can still be seen in policing today. “The surveillance, the deputization essentially of all white men to be police officers or, in this case, slave patrollers, and then to dispense corporal punishment on the scene are all baked in from the very beginning,” he  told NPR  last year.

Slave patrols, and the slave codes they enforced, ended after the Civil War and the passage of the 13th amendment, which formally ended slavery “except as a punishment for crime.” But Muhammad notes that former Confederate states quickly used that exception to justify new restrictions. Known as the Black codes, the various rules limited the kinds of jobs African Americans could hold, their rights to buy and own property, and even their movements.

“The genius of the former Confederate states was to say, ‘Oh, well, if all we need to do is make them criminals and they can be put back in slavery, well, then that’s what we’ll do.’ And that’s exactly what the Black codes set out to do. The Black codes, for all intents and purposes, criminalized every form of African American freedom and mobility, political power, economic power, except the one thing it didn’t criminalize was the right to work for a white man on a white man’s terms.” In particular, he said the Ku Klux Klan “took about the business of terrorizing, policing, surveilling, and controlling Black people. … The Klan totally dominates the machinery of justice in the South.”

When, during what became known as the Great Migration, millions of African Americans fled the still largely agrarian South for opportunities in the thriving manufacturing centers of the North, they discovered that metropolitan police departments tended to enforce the law along racial and ethnic lines, with newcomers overseen by those who came before. “There was an early emphasis on people whose status was just a tiny notch better than the folks whom they were focused on policing,” Muhammad said. “And so the Anglo-Saxons are policing the Irish or the Germans are policing the Irish. The Irish are policing the Poles.” And then arrived a wave of Black Southerners looking for a better life.

In his groundbreaking work, “ The Condemnation of Blackness: Race, Crime, and the Making of Modern Urban America ,” Muhammad argues that an essential turning point came in the early 1900s amid efforts to professionalize police forces across the nation, in part by using crime statistics to guide law enforcement efforts. For the first time, Americans with European roots were grouped into one broad category, white, and set apart from the other category, Black.

Citing Muhammad’s research, Harvard historian Jill Lepore  has summarized the consequences this way : “Police patrolled Black neighborhoods and arrested Black people disproportionately; prosecutors indicted Black people disproportionately; juries found Black people guilty disproportionately; judges gave Black people disproportionately long sentences; and, then, after all this, social scientists, observing the number of Black people in jail, decided that, as a matter of biology, Black people were disproportionately inclined to criminality.”

“History shows that crime data was never objective in any meaningful sense,” Muhammad wrote. Instead, crime statistics were “weaponized” to justify racial profiling, police brutality, and ever more policing of Black people.

This phenomenon, he believes, has continued well into this century and is exemplified by William J. Bratton, one of the most famous police leaders in recent America history. Known as “America’s Top Cop,” Bratton led police departments in his native Boston, Los Angeles, and twice in New York, finally retiring in 2016.

Bratton rejected notions that crime was a result of social and economic forces, such as poverty, unemployment, police practices, and racism. Instead, he said in a 2017 speech, “It is about behavior.” Through most of his career, he was a proponent of statistically-based “predictive” policing — essentially placing forces in areas where crime numbers were highest, focused on the groups found there.

Bratton argued that the technology eliminated the problem of prejudice in policing, without ever questioning potential bias in the data or algorithms themselves — a significant issue given the fact that Black Americans are arrested and convicted of crimes at disproportionately higher rates than whites. This approach has led to widely discredited practices such as racial profiling and “stop-and-frisk.” And, Muhammad notes, “There is no research consensus on whether or how much violence dropped in cities due to policing.”

Gathering numbers

In 2015 The Washington Post began tracking every fatal shooting by an on-duty officer, using news stories, social media posts, and police reports in the wake of the fatal police shooting of Brown, a Black teenager in Ferguson, Mo. According to the newspaper, Black Americans are killed by police at twice the rate of white Americans, and Hispanic Americans are also killed by police at a disproportionate rate.

Such efforts have proved useful for researchers such as economist Rajiv Sethi .

A Joy Foundation Fellow at the Harvard  Radcliffe Institute , Sethi is investigating the use of lethal force by law enforcement officers, a difficult task given that data from such encounters is largely unavailable from police departments. Instead, Sethi and his team of researchers have turned to information collected by websites and news organizations including The Washington Post and The Guardian, merged with data from other sources such as the Bureau of Justice Statistics, the Census, and the Centers for Disease Control and Prevention.

A Joy Foundation Fellow at the Harvard Radcliffe Institute, Rajiv Sethi is investigating the use of lethal force by law enforcement officers,

Courtesy photo

They have found that exposure to deadly force is highest in the Mountain West and Pacific regions relative to the mid-Atlantic and northeastern states, and that racial disparities in relation to deadly force are even greater than the national numbers imply. “In the country as a whole, you’re about two to three times more likely to face deadly force if you’re Black than if you are white” said Sethi. “But if you look at individual cities separately, disparities in exposure are much higher.”

Examining the characteristics associated with police departments that experience high numbers of lethal encounters is one way to better understand and address racial disparities in policing and the use of violence, Sethi said, but it’s a massive undertaking given the decentralized nature of policing in America. There are roughly 18,000 police departments in the country, and more than 3,000 sheriff’s offices, each with its own approaches to training and selection.

“They behave in very different ways, and what we’re finding in our current research is that they are very different in the degree to which they use deadly force,” said Sethi. To make real change, “You really need to focus on the agency level where organizational culture lies, where selection and training protocols have an effect, and where leadership can make a difference.”

Sethi pointed to the example of Camden, N.J., which disbanded and replaced its police force in 2013, initially in response to a budget crisis, but eventually resulting in an effort to fundamentally change the way the police engaged with the community. While there have been improvements, including greater witness cooperation, lower crime, and fewer abuse complaints, the Camden case doesn’t fit any particular narrative, said Sethi, noting that the number of officers actually increased as part of the reform. While the city is still faced with its share of problems, Sethi called its efforts to rethink policing “important models from which we can learn.”

Fighting vs. preventing crime

For many analysts, the real problem with policing in America is the fact that there is simply too much of it. “We’ve seen since the mid-1970s a dramatic increase in expenditures that are associated with expanding the criminal legal system, including personnel and the tasks we ask police to do,” said Sandra Susan Smith , Daniel and Florence Guggenheim Professor of Criminal Justice at HKS, and the Carol K. Pforzheimer Professor at the Radcliffe Institute. “And at the same time we see dramatic declines in resources devoted to social welfare programs.”

“You can have all the armored personnel carriers you want in Ferguson, but public safety is more likely to come from redressing environmental pollution, poor education, and unfair work,” said Brandon Terry, assistant professor of African and African American Studies and social studies.

Kris Snibble/Harvard file photo

Smith’s comment highlights a key argument embraced by many activists and experts calling for dramatic police reform: diverting resources from the police to better support community services including health care, housing, and education, and stronger economic and job opportunities. They argue that broader support for such measures will decrease the need for policing, and in turn reduce violent confrontations, particularly in over-policed, economically disadvantaged communities, and communities of color.

For Brandon Terry , that tension took the form of an ice container during his Baltimore high school chemistry final. The frozen cubes were placed in the middle of the classroom to help keep the students cool as a heat wave sent temperatures soaring. “That was their solution to the building’s lack of air conditioning,” said Terry, a Harvard assistant professor of African and African American Studies and social studies. “Just grab an ice cube.”

Terry’s story is the kind many researchers cite to show the negative impact of underinvesting in children who will make up the future population, and instead devoting resources toward policing tactics that embrace armored vehicles, automatic weapons, and spy planes. Terry’s is also the kind of tale promoted by activists eager to defund the police, a movement begun in the late 1960s that has again gained momentum as the death toll from violent encounters mounts. A scholar of Martin Luther King Jr., Terry said the Civil Rights leader’s views on the Vietnam War are echoed in the calls of activists today who are pressing to redistribute police resources.

“King thought that the idea of spending many orders of magnitude more for an unjust war than we did for the abolition of poverty and the abolition of ghettoization was a moral travesty, and it reflected a kind of sickness at the core of our society,” said Terry. “And part of what the defund model is based upon is a similar moral criticism, that these budgets reflect priorities that we have, and our priorities are broken.”

Terry also thinks the policing debate needs to be expanded to embrace a fuller understanding of what it means for people to feel truly safe in their communities. He highlights the work of sociologist Chris Muller and Harvard’s Robert Sampson, who have studied racial disparities in exposures to lead and the connections between a child’s early exposure to the toxic metal and antisocial behavior. Various studies have shown that lead exposure in children can contribute to cognitive impairment and behavioral problems, including heightened aggression.

“You can have all the armored personnel carriers you want in Ferguson,” said Terry, “but public safety is more likely to come from redressing environmental pollution, poor education, and unfair work.”

Policing and criminal justice system

Alexandra Natapoff , Lee S. Kreindler Professor of Law, sees policing as inexorably linked to the country’s criminal justice system and its long ties to racism.

“Policing does not stand alone or apart from how we charge people with crimes, or how we convict them, or how we treat them once they’ve been convicted,” she said. “That entire bundle of official practices is a central part of how we govern, and in particular, how we have historically governed Black people and other people of color, and economically and socially disadvantaged populations.”

Unpacking such a complicated issue requires voices from a variety of different backgrounds, experiences, and fields of expertise who can shine light on the problem and possible solutions, said Natapoff, who co-founded a new lecture series with HLS Professor Andrew Crespo titled “ Policing in America .”

In recent weeks the pair have hosted Zoom discussions on topics ranging from qualified immunity to the Black Lives Matter movement to police unions to the broad contours of the American penal system. The series reflects the important work being done around the country, said Natapoff, and offers people the chance to further “engage in dialogue over these over these rich, complicated, controversial issues around race and policing, and governance and democracy.”

Courts and mass incarceration

Much of Natapoff’s recent work emphasizes the hidden dangers of the nation’s misdemeanor system. In her book “ Punishment Without Crime: How Our Massive Misdemeanor System Traps the Innocent and Makes America More Unequal ,” Natapoff shows how the practice of stopping, arresting, and charging people with low-level offenses often sends them down a devastating path.

“This is how most people encounter the criminal apparatus, and it’s the first step of mass incarceration, the initial net that sweeps people of color disproportionately into the criminal system,” said Natapoff. “It is also the locus that overexposes Black people to police violence. The implications of this enormous net of police and prosecutorial authority around minor conduct is central to understanding many of the worst dysfunctions of our criminal system.”

One consequence is that Black and brown people are incarcerated at much higher rates than white people. America has approximately 2.3 million people in federal, state, and local prisons and jails, according to a 2020 report from the nonprofit the Prison Policy Initiative. According to a 2018 report from the Sentencing Project, Black men are 5.9 times as likely to be incarcerated as white men and Hispanic men are 3.1 times as likely.

Reducing mass incarceration requires shrinking the misdemeanor net “along all of its axes” said Natapoff, who supports a range of reforms including training police officers to both confront and arrest people less for low-level offenses, and the policies of forward-thinking prosecutors willing to “charge fewer of those offenses when police do make arrests.”

She praises the efforts of Suffolk County District Attorney Rachael Rollins in Massachusetts and George Gascón, the district attorney in Los Angeles County, Calif., who have pledged to stop prosecuting a range of misdemeanor crimes such as resisting arrest, loitering, trespassing, and drug possession. “If cities and towns across the country committed to that kind of reform, that would be a profoundly meaningful change,” said Natapoff, “and it would be a big step toward shrinking our entire criminal apparatus.”

Retired U.S. Judge Nancy Gertner cites the need to reform federal sentencing guidelines, arguing that all too often they have been proven to be biased and to result in packing the nation’s jails and prisons.

Stephanie Mitchell/Harvard file photo

Sentencing reform

Another contributing factor in mass incarceration is sentencing disparities.

A recent Harvard Law School study found that, as is true nationally, people of color are “drastically overrepresented in Massachusetts state prisons.” But the report also noted that Black and Latinx people were less likely to have their cases resolved through pretrial probation ­— a way to dismiss charges if the accused meet certain conditions — and receive much longer sentences than their white counterparts.

Retired U.S. Judge Nancy Gertner also notes the need to reform federal sentencing guidelines, arguing that all too often they have been proven to be biased and to result in packing the nation’s jails and prisons. She points to the way the 1994 Crime Bill (legislation sponsored by then-Sen. Joe Biden of Delaware) ushered in much harsher drug penalties for crack than for powder cocaine. This tied the hands of judges issuing sentences and disproportionately punished people of color in the process. “The disparity in the treatment of crack and cocaine really was backed up by anecdote and stereotype, not by data,” said Gertner, a lecturer at HLS. “There was no data suggesting that crack was infinitely more dangerous than cocaine. It was the young Black predator narrative.”

The First Step Act, a bipartisan prison reform bill aimed at reducing racial disparities in drug sentencing and signed into law by President Donald Trump in 2018, is just what its name implies, said Gertner.

“It reduces sentences to the merely inhumane rather than the grotesque. We still throw people in jail more than anybody else. We still resort to imprisonment, rather than thinking of other alternatives. We still resort to punishment rather than other models. None of that has really changed. I don’t deny the significance of somebody getting out of prison a year or two early, but no one should think that that’s reform.”

 Not just bad apples

Reform has long been a goal for federal leaders. Many heralded Obama-era changes aimed at eliminating racial disparities in policing and outlined in the report by The President’s Task Force on 21st Century policing. But HKS’s Smith saw them as largely symbolic. “It’s a nod to reform. But most of the reforms that are implemented in this country tend to be reforms that nibble around the edges and don’t really make much of a difference.”

Efforts such as diversifying police forces and implicit bias training do little to change behaviors and reduce violent conduct against people of color, said Smith, who cites studies suggesting a majority of Americans hold negative biases against Black and brown people, and that unconscious prejudices and stereotypes are difficult to erase.

“Experiments show that you can, in the context of a day, get people to think about race differently, and maybe even behave differently. But if you follow up, say, a week, or two weeks later, those effects are gone. We don’t know how to produce effects that are long-lasting. We invest huge amounts to implement such police reforms, but most often there’s no empirical evidence to support their efficacy.”

Even the early studies around the effectiveness of body cameras suggest the devices do little to change “officers’ patterns of behavior,” said Smith, though she cautions that researchers are still in the early stages of collecting and analyzing the data.

And though police body cameras have caught officers in unjust violence, much of the general public views the problem as anomalous.

“Despite what many people in low-income communities of color think about police officers, the broader society has a lot of respect for police and thinks if you just get rid of the bad apples, everything will be fine,” Smith added. “The problem, of course, is this is not just an issue of bad apples.”

Efforts such as diversifying police forces and implicit bias training do little to change behaviors and reduce violent conduct against people of color, said Sandra Susan Smith, a professor of criminal justice Harvard Kennedy School.

Community-based ways forward

Still Smith sees reason for hope and possible ways forward involving a range of community-based approaches. As part of the effort to explore meaningful change, Smith, along with Christopher Winship , Diker-Tishman Professor of Sociology at Harvard University and a member of the senior faculty at HKS, have organized “ Reimagining Community Safety: A Program in Criminal Justice Speaker Series ” to better understand the perspectives of practitioners, policymakers, community leaders, activists, and academics engaged in public safety reform.

Some community-based safety models have yielded important results. Smith singles out the Crisis Assistance Helping Out on the Streets program (known as CAHOOTS ) in Eugene, Ore., which supplements police with a community-based public safety program. When callers dial 911 they are often diverted to teams of workers trained in crisis resolution, mental health, and emergency medicine, who are better equipped to handle non-life-threatening situations. The numbers support her case. In 2017 the program received 25,000 calls, only 250 of which required police assistance. Training similar teams of specialists who don’t carry weapons to handle all traffic stops could go a long way toward ending violent police encounters, she said.

“Imagine you have those kinds of services in play,” said Smith, paired with community-based anti-violence program such as Cure Violence , which aims to stop violence in targeted neighborhoods by using approaches health experts take to control disease, such as identifying and treating individuals and changing social norms. Together, she said, these programs “could make a huge difference.”

At Harvard Law School, students have been  studying how an alternate 911-response team  might function in Boston. “We were trying to move from thinking about a 911-response system as an opportunity to intervene in an acute moment, to thinking about what it would look like to have a system that is trying to help reweave some of the threads of community, a system that is more focused on healing than just on stopping harm” said HLS Professor Rachel Viscomi, who directs the Harvard Negotiation and Mediation Clinical Program and oversaw the research.

The forthcoming report, compiled by two students in the HLS clinic, Billy Roberts and Anna Vande Velde, will offer officials a range of ideas for how to think about community safety that builds on existing efforts in Boston and other cities, said Viscomi.

But Smith, like others, knows community-based interventions are only part of the solution. She applauds the Justice Department’s investigation into the Ferguson Police Department after the shooting of Brown. The 102-page report shed light on the department’s discriminatory policing practices, including the ways police disproportionately targeted Black residents for tickets and fines to help balance the city’s budget. To fix such entrenched problems, state governments need to rethink their spending priorities and tax systems so they can provide cities and towns the financial support they need to remain debt-free, said Smith.

Rethinking the 911-response system to being one that is “more focused on healing than just on stopping harm” is part of the student-led research under the direction of Law School Professor Rachel Viscomi, who heads up the Harvard Negotiation and Mediation Clinical Program.

Jon Chase/Harvard file photo

“Part of the solution has to be a discussion about how government is funded and how a city like Ferguson got to a place where government had so few resources that they resorted to extortion of their residents, in particular residents of color, in order to make ends meet,” she said. “We’ve learned since that Ferguson is hardly the only municipality that has struggled with funding issues and sought to address them through the oppression and repression of their politically, socially, and economically marginalized Black and Latino residents.”

Police contracts, she said, also need to be reexamined. The daughter of a “union man,” Smith said she firmly supports officers’ rights to union representation to secure fair wages, health care, and safe working conditions. But the power unions hold to structure police contracts in ways that protect officers from being disciplined for “illegal and unethical behavior” needs to be challenged, she said.

“I think it’s incredibly important for individuals to be held accountable and for those institutions in which they are embedded to hold them to account. But we routinely find that union contracts buffer individual officers from having to be accountable. We see this at the level of the Supreme Court as well, whose rulings around qualified immunity have protected law enforcement from civil suits. That needs to change.”

Other Harvard experts agree. In an opinion piece in The Boston Globe last June, Tomiko Brown-Nagin , dean of the Harvard Radcliffe Institute and the Daniel P.S. Paul Professor of Constitutional Law at HLS, pointed out the Court’s “expansive interpretation of qualified immunity” and called for reform that would “promote accountability.”

“This nation is devoted to freedom, to combating racial discrimination, and to making government accountable to the people,” wrote Brown-Nagin. “Legislators today, like those who passed landmark Civil Rights legislation more than 50 years ago, must take a stand for equal justice under law. Shielding police misconduct offends our fundamental values and cannot be tolerated.”

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17 Racial Profiling

Robin S. Engel, School of Criminal Justice, University of Cincinnati

Derek Cohen is a Doctoral Student at the University of Cincinnati.

• Published: 01 April 2014
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The use of the term “racial profiling” gained popularity in the mid-1990s and originally referred to the reliance on race as an explicit criterion in “profiles” of offenders that some police organizations issued to guide police officer decision making. This essay traces the evolution of racial profiling, both in terms of terminology and police practice, from the “War on Drugs” in the 1980s to present day. The essay highlights changes in policies, legislation, litigation, and data collection across the country as mechanisms to control the use of racial profiling, particularly in terms of stops and stop outcomes (e.g., citations, arrests, searches, and seizures). It also critiques the research methods and statistical analyses often used by researchers who conduct studies of the prevalence, causes, and consequences of racially biased policing. The essay concludes by issuing a new call to action for future research in the area of racial profiling. Rather than seeking incremental improvements in data collection and methodology, this essay argues for a fundamental reconceptualization of research on race and policing.

T he history of race and policing in the United States is long and troubled. For centuries, the police in America were used as instruments of the state to enforce discriminatory laws and uphold the status quo of the time ( Richardson 1974 ; Monkonen 1981). The discriminatory treatment by police of minorities—and blacks in particular—was reflective of a socially unjust and biased society ( Kerner Commission 1968 ). While the systematic targeting and biased treatment endured by minorities at the hands of American police has been well documented ( Williams and Murphy 1990 ), significant progress has been made in the last several decades toward equity and legitimacy in American policing ( Walker 2003 ; Warren and Tomaskovic-Devey 2009 ; Bayley and Nixon 2010 ). Nevertheless, the legacy that this troubled past brings to modern policing bears repeating. A current concern in American society remains the use of race or ethnicity by police as reason for some form of coercive action. This police practice—often referred to as racial profiling—is widely recognized by politicians, the public, and even the police themselves as inherently problematic. Yet reducing this problem to a single term—racial profiling—simultaneously reduces the nuances surrounding the multifaceted and complicated issues regarding police, race, and crime in America.

In this essay, we first begin with a brief historical overview of the ground previously traveled as it relates to policing, race, and research. In our discussion, we note the historical application of racially-biased police practices as a result of policies arising from the “War on Drugs” in the 1980s. Thereafter we describe the use and definition of the term “racial profiling” and trace the resulting changes in policies, legislation, litigation, and data collection across the country. The changes in data collection in particular resulted in the development of a body of research designed specifically to determine racial/ethnic disparities in police treatment during pedestrian and traffic stops. We summarize this body of research, including a focus on stops and stop outcomes (e.g., citations, arrests, searches, and seizures), and offer a critique of the research methods and statistical analyses often used by researchers. Finally, we issue a new call to action for future research in the area of racial profiling. Rather than seeking incremental improvements in data collection and methodology, we argue for a fundamental reconceptualization of research on race and policing. While we note that this essay is predominately focused on the experiences and research surrounding one racial group (blacks) within one country (United States), it is widely applicable to other minority groups within the United States, as well as minority groups within other countries. Indeed, it seems that the core components of the American story of racial bias, policing, and research is widely generalizable across cultures.

This essay represents a broad examination of racial profiling in the United States, both from historical and contemporary perspectives. Section 17.1 of the essay describes the history of racial profiling, as it was originally developed as a tactic to detect and apprehend drug couriers along the I-95 corridor of the Eastern Seaboard. Section 17.1 also describes the initial efforts to collect data on racial profiling, as well as identifying the evolving definition of the term. Section 17.2 reviews the literature on racially-biased policing starting with the classic ethnographic work that first identified issues related to race and policing; it then examines the more recent empirical evidence on the extent to which racial and ethnic disparities have appeared evident during vehicle and pedestrian stops, citation outcomes, and searches and seizure. Section 17.3 offers a new collective research agenda to help us begin to determine if the observed racial disparities in stops, citations, and searches and seizures amounts to racial/ethnic bias and discrimination. That section also draws some conclusions as to the state of the science on racially-biased policing.

A number of conclusions can be drawn:

Despite large-scale data collection efforts, the extent of racially-biased policing in the United States remains largely unknown

Despite calls from researchers to reform institutional practices, increase accountability and supervision, and engage in better data collection, the evidence regarding the actual impact of such recommendations on racially-biased policing is nearly non-existent.

While many agencies can readily identify racial/ethnic disparities, they often cannot detect bias, and further cannot determine why these disparities exist or how to effectively reduce them.

As police agencies continue to promote and advance practices that have demonstrated effectiveness (e.g., hot spots and other types of focused policing strategies), it is likely that racial/ethnic disparities in stops and stop outcomes will continue or perhaps even increase based on differential offending patterns and saturation patrols in predominately minority areas.

More, and better, research is necessary if we are serious about both the role of science in policing and the need to reduce racial/ethnic bias in policing.

17.1 History of Racial Profiling

While the troubled history of race and policing in the United States is lengthy and complex, a more recent focus on racial profiling emerged in the last two decades. The use of the term “racial profiling” gained popularity in the mid-1990s and originally referred to the use of race as an explicit criterion in “profiles” of offenders that some police organizations issued to guide police officer decision-making ( Engel, Calnon, and Bernard 2001 ; Harris 2006 ). These profiles were used as part of a larger strategy for the “War on Drugs” from the 1980s and 1990s that led to dramatic changes in criminal justice strategies nationally, including the aggressive targeting of drug offenders at the street level and increased rates of incarceration and sentence length ( Scalia 2001 ; Harris 2006 ; Tonry 2011 ). Racial profiling specifically referred to criminal interdiction practices based on drug-courier profiles that were identified and provided to law enforcement officers through federal, state, and local law enforcement training. As part of police efforts to interdict drug trafficking on the nation’s highways, police agencies developed guidelines or “profiles” to help officers identify characteristics of drug couriers that could be used to target drivers and vehicles. This training sometimes identified subjects’ race and ethnicity as part of a larger “profile” of drug courier activity. The focus of this training was on Interstate highways on the East Coast, particularly around the I-95 corridor that linked Miami, Florida with cities and drug distribution points in the major mid-Atlantic and Northeastern cities ( Harris 1999 ; Engel, Calnon, and Bernard 2002 ). Based on this profile, police would make pretextual stops ( Whren v. U.S. , 517 U.S. 806 [1996]) and attempt to establish a legal basis to search for contraband.

Another police tactic resulting from the “War on Drugs” was the increased use of pedestrian stops, along with stop and frisk tactics ( Terry v. Ohio, 392 U.S. 1 [1968]) to maximize the number of police-citizen encounters with individuals believed to be involved in criminal behavior. These targeted enforcement strategies were especially felt by young minority males, who were disproportionately subject to police surveillance and imprisonment for drug offenses ( Kennedy 1997 ; Walker 2001 ; Harris 2002 ; Tonry 2011 ). The controversy surrounding the aggressive use of traffic and pedestrian stops by police still exists today ( Fagan 2004 ; Gelman, Fagan, and Kiss 2007 ; Ridgeway and MacDonald 2009 ).

17.1.1 The Rise of Data Collection

High-profile litigation efforts in the states of New Jersey ( New Jersey v. Soto , 734 A.2d 350 [1996]) and Maryland ( Wilkins v. Maryland State Police , MJG 93-468 [1993]) alleging racial profiling by law enforcement agencies brought a discussion of these practices to the forefront of American public debate ( GAO 2000 ; Buerger and Farrell 2002 ; Harris 2002 ). Based on the notoriety and successful litigation involving these claims of racial profiling, the public, media, and politicians began to exert pressure on law enforcement to address perceived racial/ethnic bias, particularly as related to traffic stops ( Walker 2001 ; Barlow and Barlow 2002 ; Novak 2004 ). As a result of this pressure, law enforcement agencies and politicians across the country began erecting policies and legislation designed to “eliminate” racial profiling practices by local, state, and federal law enforcement agencies ( Harris 2002 , 2006 ; Tillyer, Engel, and Wooldredge 2008 ). These policies were often focused on traffic stops and included mandates to collect data regarding driver and passenger demographics from every traffic stop (regardless of disposition). The data collection efforts originally designed to uncover racial/ethnic disparities in vehicle stops were initiated by litigation, legislative mandate, and proactive action by law enforcement agencies to address community concerns ( Ramirez, McDevitt, and Farrell 2000 ; Davis 2001 ; Davis, Gillis, and Foster 2001 ; Tillyer, Engel, and Cherkauskas 2010 ). As noted by Tillyer et al. (2010) , by 2009, thirty-nine states had passed some form of legislation regarding racial profiling. Specifically, eleven states enacted legislation that prohibited racial profiling, five states mandated traffic-stop data collection, and twelve states both prohibited racial profiling and mandated data collection, while eight states had bills under consideration and three states had other forms of racial profiling policies.

The heavy focus on data collection during traffic stops was based in part on the original definition of racial profiling, but also because of the recognition that traffic stops are the most frequently occurring type of police-citizen interaction and can be initiated for a wide variety of reasons including legal violations, departmental policy requirements, and officer discretion ( Skolnick 1966 ; Walker 2001 ; Meehan and Ponder 2002 ; Alpert, MacDonald, and Dunham 2005 ). Analyses of the Police Public Contact Survey demonstrate that of the 19 percent of citizens surveyed who reported having some form of contact with police, the majority of these citizens (56 percent) indicated that contact occurred as the result of a traffic stop ( Durose, Schmitt, and Langan 2007 ). In addition, police officers have wide and often unfettered discretion when determining when to initiate traffic stops and the outcomes that motorists receive as a result of those stops ( Wilson 1968 ; Ramirez, McDevitt, and Farrell 2000 ; Lundman and Kaufmann 2003 ; Engel and Calnon 2004b ; Novak 2004 ; Engel 2005 ).

When initial claims of racial profiling during traffic stops were leveled against police, it was clear that law enforcement agencies across the country were poorly prepared to demonstrate, document, or defend their current practices. Quite simply, most law enforcement agencies did not routinely collect information about all motorists who were stopped by police, nor did they collect basic demographic information about those who were stopped (including race/ethnicity) ( Ramirez, McDevitt, and Farrell 2000 ). While many agencies did routinely collect information about citations and arrests, this information could not be compared to the population of all motorists stopped by police that did not result in further official action. Likewise, the population of drivers “eligible” to be stopped for traffic violations was also unknown. Described as the “benchmark” problem, the need to compare traffic stops to those eligible to be stopped created a new stream of research across the country ( Walker 2001 ; Engel and Calnon 2004b ). Unfortunately, as noted in more detail below, over two decades of subsequent research produced very little, as the benchmark problem has never been adequately addressed by the research community.

17.1.2 Defining Racial Profiling

The initial narrow focus on “racial profiling” did not adequately address a much larger issue in American police-community relations. Specifically, claims of inappropriate police targeting of minorities for purposes of enhanced criminal apprehension and punishment have been recognized throughout American history. While the term “racial profiling” referred directly to the specific policies and practices in the 1990s of targeting minorities traveling on interstates for increased scrutiny to obtain drug seizures, concerns of racial bias and illegitimate practices by police have existed for many decades. Therefore, researchers recognized the need to broaden the conversation by calling for examinations of all forms of police bias. A more comprehensive definition allowed policy makers, practitioners, and academics to better focus on issues of racial bias beyond drug profiles during traffic stops.

As noted by Fridell and Scott (2005) the term racial profiling has evolved over time. Despite the rather narrow definition of profiling that began with policing drug trafficking, the growing public consensus became that any and all decisions made by officers based solely or partially on the race of the suspect were considered racial profiling. It was this change from a narrowly defined term of profiling to an all-encompassing term that led Fridell et al. (2001) to first introduce the new term. They argued that some past definitions of profiling may have been too restrictive, focusing exclusively on “sole” reliance on race. They noted that police decision making is rarely based on any sole factor, including race. Furthermore, in focus groups with citizens and police officers, Fridell et al. (2001) noted that citizens defined profiling as encompassing any and all demonstrations of racial bias in policing and viewed it as widespread. On the other hand, for police officers “profiling” connoted only the narrow definition of sole reliance on race; therefore, they viewed it as a much rarer occurrence. The differing definitions of profiling led to defensiveness and frustration as the two groups talked past each other, thus the development of the new term, racially biased policing, which Fridell and her colleagues defined as follows: “Racially biased policing occurs when law enforcement inappropriately considers race or ethnicity in deciding with whom and how to intervene in an enforcement capacity.”

As noted by Engel (2008) , economists and other academics have identified two different types of police racial/ethnic bias: 1) “taste discrimination” or “disparate treatment” and 2) “statistical discrimination” or “disparate impact” ( Becker 1957 ; Arrow 1973 ). The difference between these two concepts is based on the individual intentions of police officers—in the former, racial/ethnic discrimination is the direct result of intentional police bias, while in the later, racial/ethnic discrimination is the result of factors other than individual police bias (i.e., deployment patterns, differences based on deployment patterns, offending behavior, etc.) ( Knowles, Perisco, and Todd 2001 ; Ayres 2002 , 2005 ; Persico and Castleman 2005 ). Accurately measuring and classifying these two general types of police bias, however, have proved difficult for researchers. A summary of the evidence regarding racially biased policing is reviewed below.

17.2 The Evidence

Initial systematic research of the police began in the 1950s when a few ethnographic studies reported the realities of policing and the use of discretion. These studies described police agencies and culture (e.g., Wilson 1968 ; Van Maanen 1974 ; Reiss 1983 ; Manning 1997 ); police-citizen encounters (e.g., Skolnick 1966 ; Reiss 1971 ; Muir 1977 ); and the use of coercive power during interactions, particularly with minorities (e.g., Westley 1953 , 1970 ; Skolnick 1966 ; Bayley and Mendelsohn 1969 ; Bittner 1970 ). From this beginning, a body of research emerged that exposed issues surrounding racial bias, abuse of force, corruption, and poor police-community relations ( Bernard and Engel 2001 ). Much of this work was informed by an implicit assumptions that police decision making was inherently biased and exposure of these practices was necessary for reform. Given the tenor of the times, these assumptions are hardly surprising. More importantly, these assumptions created a lasting legacy that is seldom directly challenged in current studies of police decision making.

Over time, a more quantitative body of research developed that examined coercive outcomes of police-citizen encounters (i.e., citations, arrests, use of force) and whether citizens’ characteristics influenced these outcomes. This research evolved from simple bivariate comparisons of police decisions and citizen characteristics (e.g., Pivilian and Briar 1964 ; Black and Reiss 1970 ; Black 1971 ), to the use of multivariate statistical techniques designed to explore the effects of extra-legal factors on police decision making, after controlling for legal factors (e.g., Smith and Visher 1981 ; Smith, Visher, and Davidson 1984 ; Worden 1989 ; Klinger 1994 ; Mastrofski et al. 2000 ). The body of research that emerged compared the impact of legal to extra-legal factors, including the effect of race on police decision making ( Sherman 1980 ; Riksheim and Chermak 1993 ; National Research Council 2004 ).

Although researchers made significant methodological and statistical advances from the 1970s through the 1990s, the actual research questions being asked remained relatively constant. With only a handful of exceptions, this work focused on police decisions to use specific coercive sanctions, including citations, arrests, and use of force. The focus of this research was to determine whether police used their considerable discretion in a morally defensible manner. Summary reviews of this body of research generally indicate that despite differences in measures and methods, a majority of the studies demonstrate legal factors have the largest impact over police behavior ( Gottfredson and Gottfredson 1988 ; Riksheim and Chermak 1993 ; Klinger 1994 ; National Research Council 2004 ). Research has also demonstrated that to a lesser extent, some extra-legal factors impact officer decision making even when legal factors are controlled for; in particular, citizen demographics (including race/ethnicity) have been identified as correlated with some coercive outcomes ( Riksheim and Chermak 1993 ; National Research Council 2004 ).

Based on this larger literature examining police behavior, a growing area of more narrowly focused research has emerged in the last two decades to inform our understanding of “racial profiling” during traffic stops in particular. This research considers stop and search practices (e.g., Fagan and Davies 2000 ; Gould and Mastrofski 2004 ; Alpert, MacDonald, and Dunham 2005 ; Warren and Tomaskovic-Devey 2009 ), and more nuanced decision making points, including the development and interpretation of cues of suspicion ( Alpert, MacDonald, and Dunham 2005 ), and decisions to patrol certain areas ( Tomaskovic-Devey, Mason, and Zingraff 2004 ).

Traffic stop research generally examines two types of police decision making situations: 1) the decision to initiate a traffic stop, and 2) the resolution/disposition of that traffic stop ( Ramirez, McDevitt, and Farrell 2000 ; Smith and Alpert 2002 ). However, given the inherent methodological limitations of examining racial disparities in stop decisions, recent research has focused nearly exclusively on identifying and explaining racial/ethnic disparities in traffic stop outcomes ( Tillyer, Engel, and Wooldredge 2008 ; Tillyer and Engel 2012 ). The findings and limitations of research on stops and post-stop outcomes are reviewed in greater detail below.

17.2.1 Traffic and Pedestrian Stops

Initial research examining racial profiling relied on the use of traffic stop studies to determine racial/ethnic disparities in officers’ decisions to stop motorists. These initial studies reported differences in aggregate rates of stops across racial groups and often interpreted these disparities as evidence of racial discrimination (e.g., Lamberth 1994 , 1997 ). After these initial studies, dozens of published studies and agency reports followed that reported the degree to which police agencies over-stop minority drivers, relative to white drivers ( Fridell 2004 ; Tillyer, Engel, and Wooldredge 2008 ). Over time, however, researchers were more careful to note that while these studies demonstrated racial/ethnic disparities in traffic stops, it could not be determined if these disparities actually represented racial bias by police. Researchers lacked the ability to determine why disparities existed. Rather, researchers focused on establishing a standard basis for determining that particular demographic groups were overrepresented in police stops, by comparing the percentage of drivers of a particular racial/ethnic group to the percentage that are expected to be stopped assuming no bias (i.e., a benchmark) ( Zingraff et al. 2000 ; Engel, Calnon, and Bernard 2002 ; McMahon et al. 2002 ; Smith and Alpert 2002 ; Fridell 2004 ; Rojek, Rosenfeld, and Decker 2004 ; Schafer, Carter, and Katz-Bannister 2004 ; Gaines 2006 ; Tillyer, Engel, and Wooldredge 2008 ).

Benchmark comparisons represent researchers’ attempts to isolate race as an explanatory factor for disparity in traffic stops from the driving quality explanation and other possible alternative factors, including driving quantity, driving location, time of travel, etc. ( Engel, Calnon, and Bernard 2002 ). However, this approach has considerable limitations, the most important of which is the inability to identify and measure a scientifically valid benchmark for comparison purpose ( Walker 2001 ; Engel and Calnon 2004a ; Fridell 2004 ; Tillyer, Engel, and Wooldredge 2008 ; Ridgeway and MacDonald 2009 ). In this effort to rule out factors other than racial discrimination in traffic stop research, social scientists utilized several different data sources to measure comparison groups, some of which were readily available and others that involved initiating new data collection. The most common types of benchmark data include: Residential Census populations, “adjusted” Census populations, official accident data, DMV records of licensed drivers, citizen surveys, internal departmental comparisons, observations of roadway usage, and assessments of traffic violating behavior (for review, see Fridell et al. 2001 ; Walker 2001 ; Engel and Calnon 2004a ; Fridell 2004 ). Each type of data has strengths and limitations as a representative measure of motorists at risk of being stopped by police. Importantly, no benchmark data has demonstrated the ability to adequately measure all the risk factors associated with the likelihood of being stopped and no consensus exists regarding which benchmarks are the most accurate ( Engel and Calnon 2004b ).

Early studies into disparate stop practices often used census data and other official records as the relevant denominator. For example, Verniero and Zoubek (1999) sought to uncover racial bias in the state of New Jersey by comparing the percentage of minority motorists stopped compared to their percentage in the residential population or eligible driving population. Similar analyses were conducted using data from Cincinnati, Ohio ( Browning et al. 1994 ). Both studies reported that minority drivers constituted a greater proportion of stops compared to their representation in the residential population. Later studies marginally improved on this method by using the driving-eligible portion of the population. For example, in an analysis of traffic stops in Richmond, Virginia, Smith and Petrocelli (2001) found that when compared to the driving-eligible population minority motorists were overrepresented in the stop data, concluding that minority motorists were 46 percent more likely to be stopped than nonminority motorists. This finding was echoed by Meehan and Ponder (2002) ; using the alternative measure of observed roadway composition in a mostly-white suburban community, the authors found that the minority drivers were three times more likely to be stopped by the police. Disparities between driving population and stoppage rates were also observed when using spatially-weighted benchmark to account for confounding issues presented by cross-jurisdictional commuters ( Rojek, Rosenfeld, and Decker 2004 ). In an analysis of the greater St. Louis, Missouri area, the authors found concentrated areas of small instances of disparate stop practices, with blacks being more likely to be stopped, searched, and arrested than white and Hispanic motorists in the areas in which a relationship was found.

Disparity via a disproportionate share of stops has been observed in studies of the San Jose and Sacramento Police Departments’ traffic practices as well. During the period of study from July to September 1999, Hispanics represented 43 percent of motorists stopped by police while accounting for just 31 percent of San Jose’s population ( Withrow 2004 ). Similarly, using Census data as a benchmark Greenwald (2001) it was shown that during a one-year period black motorists were stopped by the Sacramento police at greater frequency than justified by their percentage in the general population.

Census data, however, are limited in their ability to measure alternative explanations of racial disparities including factors influencing drivers’ risk of being stopped (e.g., where and when they drive, frequency of driving, what and how they drive) ( Engel and Calnon 2004a ; Gaines 2006 ). The Census’ lack of measures of alternative explanatory factors, however, did not prevent some of the initial studies of traffic stops from prematurely interpreting disparity as discrimination and attributing racial disparities in stops and/or stop outcomes to unmeasured officers’ racial prejudice ( Engel, Calnon, and Bernard 2002 ). Most researchers in the field began to realize, however, that the hypothesis that police are racially biased in their stopping decisions is just one of numerous possible hypotheses or explanations for disparity in stops. Without measuring alternative explanatory factors, researchers cannot determine whether differences in traffic stops and stop outcomes reflect disparity or discrimination ( Engel, Calnon, and Bernard 2002 ).

One suggested reason for these reported racial disparities among stopped motorists is the use of traffic stops as a pretext for criminal or drug interdiction purposes. Some support has been found for this hypothesis in studies of suburban communities ( Meehan and Ponder 2002 ; Novak 2004 ), highlighting that while the correlation of race and the decision to stop is weak, minorities are more likely to be stopped at night and to reside in areas outside where the stop has taken place. This has given rise to a conflict theory-oriented explanation of police behavior (i.e., that police officers disproportionately target minorities when found outside of the areas where they typically reside or travel). Likewise, Petrocelli, Piquero, and Smith (2003) demonstrated that contextual variables, such as a neighborhood’s percentage of black and UCR Part I crime rate, influence the number of stops performed in the area. Subsequently, searches of black suspects in these high-search areas resulted in fewer arrests or summons being issued. This general trend has been shown in self-reported data sources as well. A telephone survey of licensed drivers in North Carolina illustrated differential practices between the local police departments and the North Carolina State Highway Patrol (NCSHP). The decision of local police officers to issue tickets is related to driver age, race, and traffic history, while NCSHP ticketing decisions were driven both by legal factors (i.e., speeding) as well as quasi-legal factors (i.e., driver age and homeownership status) ( Warren et al. 2006 ; Miller 2008 ). Similar (though insignificant) disparities were found by Gaines (2006) in Riverside, California. Reviewing all traffic stops made in 2003, the author established that stops made by traffic units showed no evidence of racial bias, while stops made by patrol and investigative units exhibited slight, statistically insignificant bias. Further, Gaines found that that the stop data correlated strongly with race variables found in neighborhood crime data and received police reports.

Similar findings have been observed in studies of pedestrian stops as well. Using internal benchmarking (i.e., comparing the decisions of one officer to others similarly assigned; Walker 2001 ), Ridgeway and MacDonald (2009) developed a statistical method for identifying potentially problematic officers. These officers were more likely to stop black and Hispanic pedestrians, net of situational characteristics, compared to officers in similar assignments. Of the 2,756 officers whose approximately 500,000 cumulative stops were analyzed, the authors identified only 15 officers (0.54 percent) as significantly more likely to stop minority pedestrians. A multilevel analysis of pedestrian stops in New York City revealed similar patterns. Fagan (2004) found that after controlling arrest rates by race, black and Hispanic pedestrians were stopped more often than white pedestrians. This may be attributable to zero-tolerance policing strategies, the application of which was found to be driven more by neighborhood characteristics including poverty rate, racial makeup, and social disorganization ( Fagan and Davies 2000 ).

Rojek, Rosenfeld, and Decker (2004) sought to correct the problems using Census data as a benchmark by spatially weighting motorists by their residential proximity to the various municipalities under analysis. This was believed to account for the fact that motorists spend more time driving in and around their own neighborhoods, and that race effects could be seen as spurious if observed in a majority-white neighborhood where a disproportionate number of minorities are stopped should that neighborhood abut majority-nonwhite neighborhoods.

Researchers have also developed benchmarks through the use of self-reported citizen surveys. General and purposive surveys have been used in both creating a more accurate composite of individuals’ driving practices as well as recording the nuances of their interaction with the police (e.g., Lundman and Kaufman 2003 ; Miller 2008 ). Citizen surveys offer researchers the benefit of circumnavigating official data collection protocols and observational reports that may fail to capture key variables (such as perceived cause for the stop or officer demeanor) or erroneously categorize the demographic information of the stopped motorist. However, survey response data is prone to errors in recollection, desirability bias, and false reporting ( Engel and Calnon 2004a ).

A shared problem of these various benchmarks, however, is that they do not account for possible racial variations in driving behavior. For example, the differential offending hypothesis holds that certain racial groups may drive more frequently, more aggressively, or in locations with more police presence, and are therefore more likely to attract the attention of law enforcement. The literature offers measured support for this hypothesis; several studies have shown that certain minority subgroups are likely to engage in aggressive driving behaviors at a higher rate and to greater severity than white drivers ( Lange, Blackman, and Johnson 2001 ; Lange, Johnson, and Voas 2005 ; Tillyer and Engel 2012 ).

In summary, the available analyses of traffic stop data have rather consistently demonstrated racial disparities in stopping patterns ( Engel and Johnson 2006 ; Warren et al. 2006 ; Tillyer, Engel, and Wooldredge 2008 ). However, the methodological and analytical problems associated with this body of research are now widely recognized, including the inherent limitations associated with using benchmarks to determine racial disparities in vehicle stops ( Walker 2001 ; Engel, Calnon, and Bernard 2002 ; Engel and Calnon 2004a ; Fridell 2004 ; Tillyer, Engel, and Wooldredge 2008 ; Ridgeway and MacDonald 2009 ). As a result, research emphasis shifted away from examining officers’ initial decisions to stop motorists and more toward officers’ decisions during the stop (e.g., issuing citations, making arrests, and conducting searches). The study of traffic stop outcomes allowed for the use of more robust analytical techniques including multivariate analysis ( Tillyer, Engel, and Wooldredge 2008 ).

17.2.2 Traffic and Pedestrian Stop Outcomes

Many researchers examining racial bias by police have reinvigorated the study of post-stop outcomes, including citations, arrests, and searches. This shift in focus may be due in part to the inherent methodological and statistical problems associated with examining racial disparities in traffic and pedestrian stops. Additionally, some have argued that racial/ethnic bias may be more likely to manifest itself after an initial stop is made and officers interact with citizens ( Ramirez, McDevitt, and Farrell 2000 ; Alpert et al. 2006 ). As noted previously, research examining arrests dominated the policing literature in the 1970s and 1980s. Academics interested in examining racial profiling simply applied the widely used statistical techniques of multivariate regression modeling used in previous examinations of systematic social observation data to current studies using traffic and pedestrian stop data.

17.2.2.1 Citations and Arrests

Rather than focusing on stop or search decisions, the earliest exploration of racially-biased police practices examined the effect of race in the issuance of formal sanctions, such as citations and arrests. The evidence generated regarding the impact of drivers’ race over the likelihood of citations has been mixed. While most studies have reported that drivers’ race has a significant impact over citations, the direction of these reported findings have been both positive and negative ( Tillyer and Engel 2012 ). While some studies have demonstrated that minority drivers were more likely to be cited compared to whites ( Smith et al. 2003 ; Engel, Cherkauskas, and Tillyer 2007 ; Ingram 2007 ), other research suggests that black drivers were less likely to be cited ( Alpert Group 2004 ; Engel et al. 2007 ; Lovrich, et al. 2007 ; Tillyer and Engel 2012 ). These results also varied across racial groups. For example, Alpert et al. (2006) reported that Hispanics, Asian, and Native American drivers were more likely to be cited, while black drivers were less likely to be cited, compared to whites. As a result, there appears to be little consistency regarding the reported influence of race/ethnicity over the likelihood of being issued citations during traffic stops. As concerned in the context of possible police bias, this mixed evidence correlates with differing hypotheses regarding the likely direction of the effect ( Tillyer and Engel 2012 ). Some have suggested that minorities are more likely to be cited once stopped as a form of enhanced punishment. Others have suggested that minorities may be more likely than whites to be stopped as a pretext for criminal interdiction purposes, and then are released with a warning.

Studies examining the impact of race on arrests during traffic and pedestrian stops have been slightly more consistent. Tillyer and Engel (2012) reported that most traffic stop studies found that minority drivers were between 1.5 and 2.6 times more likely to be arrested compared to similarly situated white drivers ( Smith and Petrocelli 2001 ; Withrow 2004 ; Alpert et al., 2006 ). A few studies, however, have reported no racial disparities in arrest (e.g., Alpert Group 2004 ; Engel et al. 2006 ; Tillyer and Engel 2012 ). Other studies suggest that arrest decisions are impacted by both citizen and officer race. For example, Brown and Frank’s (2006) analysis of police-citizen encounters in Cincinnati, Ohio found that after controlling for characteristics of the officer and citizen along with contextual effects, black officers were more likely to arrest black citizens, while white officers are equally likely to arrest both black and white citizens.

In sum, the body of science surrounding racially-biased policing is generally seen as inconclusive ( National Research Council 2004 ). Most recently however, in a meta-analysis of 40 arrest studies using 23 different datasets, Kochel, Wilson, and Mastrofski (2011) systematically computed an effect size for the effect of race in arrest decisions net of offense severity, demeanor, intoxication, and other factors. The researchers reported “with confidence that the results are not mixed. Race matters.” This declaration was based on observed effect sizes ranging from 1.32 to 1.52 (498). The study concluded that blacks were 30 percent more likely be arrested compared to whites, even after controlling for other factors. The authors noted that although previous policing experts have described the collective research findings as “mixed” regarding the effects of race (e.g., Riksheim and Chermak 1993 ; National Research Council 2004 ; Rosich 2007 ), their comprehensive review of the available research, however, is necessarily limited by the quality of the individual studies reviewed. Due to the nature of meta-analysis as a technique, the quality of the meta-analytic results is based on the quality of the individual studies included in the meta-analysis ( Gendreau and Smith 2007 ). Further, their analyses cannot systematically explain why, how, and when race matters in arrest decisions, only that it does.

Specific to traffic and pedestrian stops, the research available generally shows an inconsistent impact of race over the likelihood of issued citations. In contrast, the impact of race over the likelihood of arrest during traffic and pedestrian stops appears to be more consistent, demonstrating racial disparities in arrest decisions. Further, the bulk of the available research demonstrates that minorities (and especially blacks) continue to be arrested at much higher rates than their representation in the general population (Engel and Swartz, forthcoming). Whether this disparity is the result of police bias, however, remains a point of contention throughout the research community.

17.2.2.2 Searches and Seizures

Beyond the decision to stop a minority motorist, racial profiling could potentially manifest itself in officers’ decisions to search. While extant Fourth Amendment precedent limits the utility of contraband discovered outside of reasonable or warranted searches, officers may seek consent to search an individual’s person, effects, or automobile. The relationship between race and search likelihood has been observed across a multitude of jurisdictions, using qualitative and quantitative analyses on both official and unofficial data sources. The bulk of scholarship examining traffic searches suggests that minority motorists are more likely to be searched compared with other racial groups ( Rojek, Rosenfeld, and Decker 2004 ; Withrow 2004 ; Engel and Johnson 2006 ; Roh and Robinson 2009 ; cf. Smith and Petrocelli 2001 ; Paoline and Terrill 2005 ; Schafer et al. 2006 ).

Engel and Johnson’s (2006) review of agency reports from twelve different state highway police/patrol agencies demonstrated consistently higher rates of minority searches compared to white drivers stopped for traffic offenses. For example, using data from the Washington State Patrol, Pickerell, Mosher, and Pratt (2009) found that black and Hispanic drivers were more likely to be searched compared to white drivers, regardless of the reason for the search. Close and Mason’s (2007) examination of traffic stop data from the Florida Highway Patrol also showed that black and Hispanic drivers were more likely to be searched compared to white drivers, irrespective of the search type.

Examining interaction effects using college campus data, Moon and Corley (2007) found that black male students were more likely to be searched compared to their white counterparts. Based on results from propensity score matching, Ridgeway (2006) reported that black motorists were twice as likely as whites to be searched based on probable cause in Oakland, California. And most recently, Rojek, Rosenfeld, and Decker (2012) reported that young, black males were more likely to be searched compared to young white males during traffic stops in St. Louis, Missouri and Cincinnati, Ohio. Likewise, analyses of survey data from the Police Public Contact Survey indicated that younger drivers, male drivers, and minority drivers all reported higher rates of search compared with other drivers ( Engel and Calnon 2004a ). Similar racial disparities in search rates have been reported in survey and qualitative research ( Brunson 2007 ; Brunson and Weitzer 2009 ).

Despite these consistent findings, Tillyer, Klahm, and Engel (2012) identified several limitations of the analyses examining racial disparities in search rates. First, many studies did not separate mandatory from discretionary searches. Mandatory searches (e.g., searches incident to arrest, inventory searches, etc.) are required by departmental policy and should not be included in analyses designed to examine officer discretion. Second, as with other post-stop analyses, the statistical analyses of searches often are misspecified due to the omitted variable problem (cf. Mustard 2003 ; Gelman, Fagan, and Kiss 2007 ). Third, examinations of officer search behavior is often based on pooled variance models ( Lundman 2003 ; Alpert, Dunham, and Smith 2007 ; Moon and Corley 2007 ), without taking into account the nested nature of traffic stop data that requires the use of hierarchical models. Finally, as with studies of citations and arrests, research examining searches is often not guided by a theoretical framework necessary to understand the reasons for racial disparities in search rates ( Engel, Calnon, and Bernard 2002 ; Tomaskovic-Devey, Mason, and Zingraff 2004 ; Engel and Johnson 2006 ).

The current discussion regarding racial profiling has also shifted from examinations of stops, benchmarks, and search rates, to examinations of contraband seizures during searches. A “hit rate” commonly refers to the percentage of searches conducted by police that result in discoveries of contraband ( Engel 2008 ). In addition to criminologists and legal scholars, economists have entered the racial profiling debate by publishing articles using police vehicle and pedestrian stop, search and seizure data in an effort to determine racial and ethnic discrimination at the hands of the police. Specifically, economists have argued that a statistical comparison of search success rates can be used to distinguish between statistical discrimination and officer bias ( Knowles, Persico, and Todd 2001 ; Ayres 2002 ; Persico and Castleman 2005 ; Persico and Todd 2006 ). The economic perspective explicitly suggests that if one racial/ethnic group is found to be more involved in criminal activity, members of that racial/ethnic group should be subjected to increased police scrutiny in an effort to maximize police resources and increase the rates of discovering contraband. Therefore, under these economic principles, a difference in search rates across racial/ethnic groups is tolerable if the rates of recovering contraband across racial groups are statistically equivalent ( Knowles, Persico, and Todd 2001 ; Anwar and Fang 2006 ).

To identify racial/ethnic discrimination, the analytical strategy utilized is a statistical comparison of search outcomes across racial/ethnic groups, commonly referred to as the “outcome test” ( Ayres 2002 ). If the hit rates are different across racial/ethnic groups, economists argued this is evidence of discrimination ( Knowles, Persico and Todd 2001 ; Ayres 2002 ; Hernandez-Murillo and Knowles 2004 ; Persico and Castleman 2005 ; Anwar and Fang 2006 ; Persico and Todd 2006 ). Using the outcome test method, most studies have reported that first, minorities are more likely to be searched, and second, when searched, minorities are less likely to be found with contraband compared to whites. However, the use of the outcome test as a tool to determine police discrimination has been met with sharp criticism. As noted by criminologists and economists, many of the underlying assumptions required by the statistical model do not coincide with what is known about decision-making during police-citizen encounters, and further the underlying conditions necessary to support the outcome test cannot be met (Anwar and Fang 2004; Dharmapala and Ross 2004 ; Hernandez-Murillo and Knowles 2004 ; Engel 2008 ; Engel and Tillyer 2008 ; Antonovics and Knight 2009 ).

Nevertheless, the accumulating evidence that minorities are more likely to be searched, but less likely to be discovered with contraband begs the question why ? Why are minority citizens searched more frequently for discretionary reasons, but less frequently found to be carrying contraband? The outcome test assumes the response is officer bias, but there are many other potential contributing factors ( Engel 2008 ). Questioning why racial disparities exist demonstrates the severe limitation of this body of research—a nearly exclusive focus on outcome , rather than process . Studying the process of officer decision making is crucial to fully developing an understanding of the relationship between citizen race/ethnicity and police behavior. Unpacking and understanding the process of officer decision making is the next great challenge in understanding police discretion and is rooted in the existence of officer suspicion (e.g., Alpert Group 2004 ; Alpert, MacDonald, and Dunham 2005 ).

17.3 The Future

As noted above, the empirical body of evidence available has clearly demonstrated the routine existence of racial and ethnic disparities in stops, citations, arrests, and searches in police agencies across the country. What remains in debate, however, is whether these racial/ethnic disparities are indicative of officer bias. Unfortunately, our research methods and statistical analyses thus far cannot determine officers’ motivation and intent, and therefore cannot determine racial bias and discrimination. While researchers can identify patterns and trends of disparities, we cannot readily determine the causes of these disparities. This is, of course, a critical limitation of social scientific research in this area. And, as a result, our current research cannot readily assist police agencies with the difficult task of developing policies and procedures designed to reduce racial disparities.

In the majority of studies examining bias-based policing, academics typically acknowledge these limitations of their research designs and statistical techniques, while simultaneously noting the importance of their work as adding to the accumulating body of research. Academics then often advocate for more of the same types of research to continue this incremental advancement in knowledge. In contrast to these recommendations, we believe a significant departure from the existing line of research in this area is necessary to advance the field. It appears to us that the current research has exhausted its value, particularly to practitioners struggling to reduce racial/ethnic disparities in police outcomes. Incremental increases in knowledge based on analyses of new data from traffic stop studies, or slight changes in the measurement of variables in yet one more multivariate statistical equation used to model stops, citations, arrests, and searches will not address the overarching methodological limitations that plague this line of research. We agree with Piquero (2009, 376) that “there should be a high priority of focused theoretical and research efforts that use multiple methods to generate a careful description and understanding of police-citizen encounters, as well as the myriad of factors that influence both police and citizen decisions.” We further note that repeated application of the statistical technique du jour (e.g., outcome tests, propensity score matching, hierarchical linear modeling, etc.) also will not solve the underlying benchmark problem, nor will it address why racial/ethnic disparities persist despite multiple forms of police intervention. After over two decades of focused research on racial profiling, the research community is no closer to assisting practitioners in the reduction of racial/ethnic disparities than we were in the early 1990s.

While patterns of racial/ethnic disparities have been routinely identified and confirmed, research dedicated to understanding why these patterns exist has been limited. Despite the abundance of academic study devoted to this topic, researchers have limited theories to explain the mounting evidence of racial/ethnic disparities. Although some researchers developed partial theories to explain these disparities post hoc (e.g., Parker et al. 2004 ; Tomaskovic-Devey, Mason, and Zingraff 2004 ; Warren et al. 2006 ; Smith and Alpert 2007 ), these theories have not been adequately tested ( Tillyer and Engel 2012 ). As a result, researchers continue to struggle with determining why disparities exist in coercive outcomes during police-citizen encounters (Engel and Swartz, forthcoming).

Importantly, there is also little evidence available to suggest that the frequency of racial disparities have been reduced as a result of research efforts, or based on changes in police policies, procedures, and training. National estimates of the rates of police-citizen contacts with minorities have remained relatively stable over time ( Langan et al. 2001 ; Durose, Scmitt, and Langan 2005 , 2007 ), suggesting there have not been significant reductions in racial disparities despite years of attention by legislatures, police administrators, academics, and the public. The only other study attempting to examine this issue suffers from severe methodological constraints. Although Warren and Tomaskovic-Devey (2009) reported that racial disparities in hit rates decreased as a result of media attention and changes in legislation in North Carolina, they did not control for any other rival explanations—including changes in specific training, supervisory oversight, departmental policies, among many others—and further were unable to properly establish time ordering to demonstrate cause and effect (Piquero 2009). Therefore, despite this important first step forward taken by Warren and Tomaskovic-Devery (2009) in an attempt to address these issues, the impact of specific attempts to reduce officer bias remains untested.

Further, we believe that even using new statistical techniques in racial profiling research that are promising (e.g., Ridgeway and MacDonald 2010 ) will not result in significant progress until theories of police discretion grounded in the daily work of police officers are developed and applied to this research (Engel and Swartz, forthcoming). As clearly articulated by Piquero (2009, 372),

the science of racial profiling research rests on a weak data and knowledge base, and although there has been some important methodological/statistical progress, we are likely not yet in a position to reach definitively any strong set of conclusions concerning whether racial profiling exists (even if we can arrive at some working definition and operationalization of it) and most certainly which set of policies can diminish and/or eliminate the explicit/sole use of race/ethnicity in police decision making.

Therefore, the challenges that remain for both research and practice are considerable.

17.3.1 A New Research Agenda

It is based on this review of the state of research in bias-based policing that we call for new approaches and changes in our current research agenda. In short, we argue that it is time to advance research that will better aid practitioners interested in reducing racial/ethnic disparities. A similar argument was made fifteen years ago by Sherman (1998) when he advocated for the use of science to help the police find humane crime fighting practices rather than simply look for failures in policing. That Sherman felt compelled to justify the moral soundness of police crime reduction research illustrates how radical this idea was at the time. This also led to the changing of the core research questions that were being asked by prominent researchers in the field and promoted the evidence-based movement in policing ( Weisburd and Neyroud 2011 ). In the same vein, we argue that rather than continually documenting racial/ethnic disparities in police stops and stop outcomes (e.g., citations, arrests, searches), academics should pursue and advance research specifically designed to reduce racial/ethnic disparities in police decision making and then test the results. How do we know, for example, that the changes in police policies, procedures, and training recommended by researchers have any impact on police behavior? Police agencies across the country spent millions of dollars on changes to policies and training designed to eliminate racial profiling—has it had an impact? The research community has been silent on these critical issues. Many academics, private companies, former practitioners, etc. provide training for police agencies to reduce racial profiling; yet are these trainings effective?

The research questions that must be addressed are: (1) why racial disparities persist in the outcomes of police-citizen encounters, and (2) what works to reduce these disparities. To address these questions, we propose the advancement of a research agenda that includes the implementation of stronger research designs, greater use of mixed methods and qualitative research, and increased use of panel-wave and longitudinal data.

First, the selection of strong research designs is critical, though the selection of the appropriate design should be guided by the questions asked rather than simply relying on external standards. The considerable advances in police effectiveness research, compared to racial profiling research, is due in part to the use of strong quasi-experimental designs, and when appropriate, randomized controlled trials. There is no reason these designs cannot be applied to study the impact of policies, procedures, and training on racial bias. The type of research proposed might include pre- or post-tests of the impact of different policies, procedures, and training implemented by police agencies, or even quasi-experimental designs where some officers, units, etc. are provided specialized training and others are not. Changes in officers’ attitudes, levels of racial disparities in stops, post-stop outcome measures, and citizens’ perceptions could all be measured outcomes. This is not to suggest that other research designs have no value. To the contrary, many important topics cannot be examined with randomized controlled trials or even strong quasi-experiments. But to say that there is a place for all systematic methods is not to say that any method can be fruitfully applied to any question, or that all research methods are created equal. Concerns over police discrimination against minorities can be, and must be, translated into falsifiable and therefore testable hypotheses.

Moreover, research has demonstrated that citizens’ experiences during police-citizen encounters significantly influence their attitudes toward law enforcement ( Brandl, Frank, Worden, and Bynum 1994 ; Weitzer and Tuch 2004 , 2005 ; Engel 2005 ). Given that citizens’ contact with police is most likely to occur as the result of a traffic stop ( Durose, Schmitt, and Langan 2007 ), coupled with the impact that these contacts have on the formation of citizen attitudes, perceptions of racial/ethnic disparities within this context could seriously undermine police legitimacy ( Tillyer and Engel 2012 ). Therefore, researchers might also focus on testing the impact of particular focused policing strategies on citizens’ perceptions of racial bias and police legitimacy. Additionally, police research that examines effectiveness (often measured as reductions in crime) might also include measures of changes in disparate outcomes as an indicator of success. In short, we argue that our collective research agenda should be expanded to focus on research that will assist in the reduction of racial/ethnic disparities, and increases in legitimacy and transparency in policing (e.g., Weitzer and Tuch 2004 , 2005 ; Tyler 2006 ) rather than simply continuing to document racial/ethnic disparities in stops and stop outcomes.

Second, prior to the development of testable interventions, studies of police bias will inevitably rely on non-experimental approaches. But to better understand racial bias, we clearly need to better use mixed method and qualitative examinations of police decision making. In this regard, direct observation of the police, their decision making, and their interactions with the public is critical. Some promising research in this area includes rich qualitative work (e.g., Kennedy 1997 ; Brunson and Miller 2006 ; Brunson and Stewart 2006 ; Engel et al. 2007 ) that helps provide context and may stimulate further theoretical development. This work successfully expands the initial research questions asked in the biased-based policing literature to incorporate police legitimacy research.

It should now be clear to the research community that black box evaluations and secondary data analyses without detailed descriptions of the original data collection effort are seldom helpful. It is essential to capture information about the process so that practitioners can implement and researchers can replicate. For example, a recent study showed that police removal of homeless encampments may have lowered crime ( Berk and MacDonald 2010 ), but the results are unlikely to help policy makers because of inadequate descriptions of the intervention, outcomes, setting, and mechanisms ( Eck 2010 ). Further, this study led to uninformed speculations on the nature of the intervention ( White 2010 ), which in turn led police administrators to question “who will police the criminologists” ( Beck, Bratton, and Kelling 2011 ). Though this example comes from the police effectiveness literature, the same sort of problems exist in racial profiling research when secondary data sets are used and the researchers neither directly observed the police nor understand the details of how the data were collected, and consequently cannot sufficiently describe or understand police decision making.

Longitudinal studies, such as those using panel or time-series designs, show promise in addressing many of the methodological shortcomings found in the current body of research. Unfortunately, these types of data are very limited and attempts to compile an applicable dataset would introduce a host of measurement issues, as independent data sources vary widely. One promising research endeavor is the National Institute of Justice’s recently funded National Police Research Platform ( Rosenbaum, Schuck, and Cordner 2011 ). Providing researchers with longitudinal data on police practice will allow analyses to model the effects of changes in training protocol, workforce changes, and officer perception over time ( National Research Council 2004 ).

17.3.2 Conclusion

Fridell and Scott (2005) have suggested that police administrators need to be concerned about three ways that issues of police racial bias might manifest themselves: “Bad apple” officers, well-intentioned officers in need of guidance, and institutional practices or policies that might inadvertently contribute to the problem. Each involves different types of agency responses to identify and correct the problems. Fridell and Scott identified seven areas that police agencies can consider for responses to racially biased policing: (1) institutional practices and priorities; (2) accountability and supervision; (3) recruitment and hiring; (4) education and training; (5) minority community outreach; (6) policies prohibiting racially biased policing; and (7) data collection. Within each of these categories are a set of reasonable recommendations that, on their face, are believed to reduce police bias. Yet the evidence regarding the actual impact of these recommendations is nearly non-existent. A closer look at the available research surrounding these recommendations demonstrates that we are often operating in the dark. For example, only a handful of studies have considered the impact of recruitment, training, and education, on police behavior generally, and even fewer have focused specifically on reducing biased policing (e.g., Worden 1990 ; Sun 2002 ; Engel and Worden 2003 ). The same critique regarding the lack of evidence can be applied to each of the categories noted by Fridell and Scott (2005) .

Fridell and Scott concluded their article by arguing that “law enforcement has never been better situated to address these issues” (2005, 359) and that “the police are more capable than ever of effectively detecting and addressing police racial bias” (2005, 343). However, based on the lack of available evidence to guide practitioners, we are less optimistic at this stage. While many agencies can readily identify racial/ethnic disparities, they often cannot detect bias, and they further cannot determine why these disparities exist or how to effectively reduce them. Further, as police agencies continue to promote and advance practices that have demonstrated effectiveness (e.g., hot spots and other types of focused policing strategies), it is likely that racial/ethnic disparities in stops and stop outcomes will continue or perhaps even increase based on differential offending patterns and saturation patrols in predominately minority areas ( Engel, Smith, and Cullen 2012 ). This is not to suggest that Fridell and Scott’s identified categories to reduce police bias are inaccurate; we agree that these areas are ripe for reform and that changes in these areas have the potential to reduce racial bias in policing. Rather we argue that specific guidance based on social scientific evidence regarding the types of training, practices, recruitment, policies, supervision, education, etc. to reduce police bias currently does not exist. This is where, we believe, our research community should focus if we are serious about both the role of science in policing and the need to reduce racial/ethnic bias in policing.

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Criminal Law: Racial Profiling by Police Essay

Despite the measures taken to address the inequality issue, the issue of racial profiling remains a part and parcel of the modern world. The phenomenon is referred to as the use of race as the main pretext for the police to take actions such as interrogation or make decisions such as the choice of the key suspects. As a rule, when detecting the primary factors affecting the changes in the racial profiling rates among the representatives of the law enforcement, one brings up the concepts of race and social class, stating that these variables affect the perception of the issue among the members of the police.

The link between the tendencies for racial profiling among the members of the police and the concept of race as it is represented in modern society is quite evident. Although tremendous efforts were made to address the racial discrimination problem, it still exists on several levels, from households to political institutions. The prejudices that swarm in the modern interpretation of race impact the judgments made by the members of the police.

Similarly, the concept of the social class that is currently viewed as acceptable in society, affects the increase in racial profiling rates among the police members. As long as the representatives of ethnic and racial minorities are associated with a specific social class that is viewed as a threat to the wellbeing of the rest of the society, the issue will remain unresolved.

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IvyPanda. (2021, April 8). Criminal Law: Racial Profiling by Police. https://ivypanda.com/essays/criminal-law-racial-profiling-by-police/

"Criminal Law: Racial Profiling by Police." IvyPanda , 8 Apr. 2021, ivypanda.com/essays/criminal-law-racial-profiling-by-police/.

IvyPanda . (2021) 'Criminal Law: Racial Profiling by Police'. 8 April.

IvyPanda . 2021. "Criminal Law: Racial Profiling by Police." April 8, 2021. https://ivypanda.com/essays/criminal-law-racial-profiling-by-police/.

1. IvyPanda . "Criminal Law: Racial Profiling by Police." April 8, 2021. https://ivypanda.com/essays/criminal-law-racial-profiling-by-police/.

Bibliography

IvyPanda . "Criminal Law: Racial Profiling by Police." April 8, 2021. https://ivypanda.com/essays/criminal-law-racial-profiling-by-police/.

• Racial Profiling by Police: Effects and Possible Remedies
• The Concept of Criminal Interrogation
• Debate on the Racial Profiling in the USA
• Juvenile and Special Category Offenders' Rights
• Conflict & Crime Control vs. Consensus & Due Process Model
• Prison Life: Understanding and Opinions
• Ewing v. California: Law Case
• Criminal Justice Administration and Police Functions

Racial Profiling in Police Agencies

How it works

Racism, discrimination, prejudgment, and hatred are the effects that tarnish police agencies throughout the country. Blacks who are pulled over by cops are scared that the judicial system is on no occasion by their side. Racial profiling places a fault in the confidence, amongst the youngest generation of black people, of law enforcement agencies. Racial profiling is the time a person or set of persons is prejudged against in a specific manner because of the color of their skin. Scholars have come to an agreement on the reasons and boundaries, of racial profiling, there is not a solution that has been generally recognized to work.

The African-Americans of this country have been mistreated for generations because of their race. Racial profiling is damaging to the African-American population and culture. Racial profiling by law enforcement agencies makes it problematic for blacks because of the unfairness that originates after being stopped, questioned, or detained by a Caucasian cop. There isn’t an instantaneous response as to why blacks are chosen by law enforcement.

Even though the fourth amendment grants cops the right to search, cops know of shortcuts through the legal system that allow law enforcement to employ traffic stops for investigation. Arizona allows racial profiling through a house bill, SB 1070, in which the color of somebody’s skin is a factor to question nationality and cops are allowed to request identification ( Bell, Hopson, Craig, & Robinson, 2014, p.34). Research has proven that African American people are scared when stopped by a cop and wonder if they will be able to drive away. There have been numerous accounts of racial profiling, and nothing has been done about it. Such abuse violates trust in law enforcement officials who promise to protect and to serve.

The mass media demonstrates the fact that African Americans are continuously being mistreated. This is not a post-racial society within the United States. Research proposes additional indication of racial profiling is needed to prove that it exists. There is no equivalent defense or equivalent righteousness if the judicial system engages in polices that are based on a concept that the only way to reduce crime is to target people of color on the feeling that they are more likely to be doing something illegal (Senator Corzine, Browne, 2001). The mass media also displays the exclamations of black communities when the kids of the community have been mistreated by the police department with little to no belief that the police department will be responsible.

The African American public strongly believe that there isn’t any kind of justice or righteousness for the African American community; a lot of people have decided to take the law into their own hands by pillaging and abolishing their own towns hoping to be listened to by the police. Research shows that blacks are being discriminated against by law enforcement. Blacks are devastatingly denoted as criminals, where as whites are naturally underrepresented as criminals ( Bell, Hopson, Craig, & Robinson, 2014, p. 2). Every time an African American is harassed, unlawfully investigated or even murdered families and communities are being ripped apart. There is a line of inequality that separates blacks and white.

Blacks have protested that racial prejudice is correspondingly within the Police department throughout the United States. Bolton (2003, p.1) thinks that racial prejudice exists within the precinct to stop blacks from getting promoted to higher ranking jobs. Blacks were being made to retire or quit being police officers. According to individual discussions blacks were mistreated by racial slurs, puns and isolation. Bolton (2003), believes that black cops are forced out of their jobs, because white officers believe that blacks don’t belong in commanding rolls (2003). If there isn’t unison within the police department then this must be damaging to the black community and any progression for black officers.

Our country has tried in the regulation of racial profiling, but it still has a lot of ground to cover. In the legislature a bill is being approved in Rhode Island that will hold law enforcement responsible for their actions. The police vehicles will be equipped with video and audio recorders. The bill will include that attorneys will have the right to view all recordings from the stops that were made. Previous to the passing of a young black male, Michael Brown, Hillary Clinton started a movement to put a stop to racial profiling by passing a bill at all three levels of government. Browne (2001) recognizes that this bill forms a federal ban against racial profiling, secondly, it delivers capital for the requalification of police officers on how to cease and stop the situation of racial profiling, and third, it holds law enforcement agencies accountable for their racial actions. A lot of people would reason that this bill is sufficient for the police department to see that racial profiling is not okay, but racial profiling endures in policing to this day. Browne (2014, p.1) states, “Hillary Clinton denounced the practice of racial profiling as “very divisive”, adding that innocent citizens should not be subjected to different treatment simply because of their race ethnicity or native origin” (p.1). The further people who are tangled with the subject of racial profiling, such as politicians, people of status and fam, the more likely the country is guaranteed to make changes in laws against racial profiling.

After the passing of Michael Brown, President Obama approved a law that necessitates police to wear body cameras. According to Hudson, (2015, p.1) Palestine, Texas is forcing their officers to fasten there vests with body cameras and retrain their officers to distinguish peoples are the ones to protect and serve. In Fayetteville, North Carolina, the police are in exercise to aid them in adopting behaviors to communicate with the people for an optimistic meeting. These exercises is going to benefit them with being dutiful, and with their body movements on their method talking to a citizen in optimisms that this will recover affairs and remove subliminal prejudice.

Training and deterrence programs must be executed. If the problem with racial profiling is not taken to righteousness, then the insurrections will endure in pursuit of justice (Murphy, 2015). Discrimination and preconception give the impression to be the reasons backing racial profiling by law enforcement. The police department will repeatedly have the justice system on their side on their side, when it emanates to supervising people, weapons, and the right to kill if threatened. In reality, law enforcement needs to be partitioned outside their physical skills and firearm training. The justice system will make sure that the police department has all the gear they require to carry out the laws to keep them safe, but to not distinguish that it needs more than just a firearm to be a cop.

Someplace throughout recent history the country has forgotten equality. This country has been in dissent for an amount of time on righteousness and fairness. Survived experiences have showm indication that racial profiling is an problem and pending the laws are passed, then the United States will remain separated. Racial profiling lasts to be the origin of war in America deprived of the other nations interfering. Browne (2001) claims that if decrees are approved, then racial profiling be stopped by law enforcement agencies. It is not ever acceptable to hate somebody because of their skin color; ample energy is required to reeducate cops on how well they connect with unalike beliefs, race, and general civilization.

Research has exposed the power that police officers have, the historical representations of power throughout slavery, reconstruction, Jim Crow, the U.S. civil rights movement, and beyond, all affected the mindset of the black community. The burgling, destroying, and undesirable gripe have cost this country a great quantity of money to reconstruct a community. Some peoples reacted because they felt righteousness is not on their side. Research has exposed that in the upcoming years regulations will be in place to defend minorities but didn’t give an instant answer to the problem. The United States can only try to put an end to racial profiling. Prejudgment, discrimination, and hatred are cultured so there isn’t an exact way to fix this problem.

• Bell, G.C., Hopson, M.C., Craig, R., & Robinson N.W., Exploring black and white accounts of 21st –century racial profiling, Qualitative R
• Harris, David. Profiles in Injustice: Why Racial Profiling Cannot Work. Chicago: W W Norton & Co Inc , 2001. Carranza

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• Published: 04 May 2020

A large-scale analysis of racial disparities in police stops across the United States

• Emma Pierson 1 ,
• Camelia Simoiu 2 ,
• Jan Overgoor 2 ,
• Sam Corbett-Davies 1 ,
• Daniel Jenson 2 ,
• Amy Shoemaker   ORCID: orcid.org/0000-0002-4008-4486 2 ,
• Vignesh Ramachandran 2 ,
• Phoebe Barghouty 2 ,
• Cheryl Phillips 3 ,
• Ravi Shroff 4 &
• Sharad Goel   ORCID: orcid.org/0000-0002-6103-9318 2  

Nature Human Behaviour volume  4 ,  pages 736–745 ( 2020 ) Cite this article

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• Computer science
• Criminology

We assessed racial disparities in policing in the United States by compiling and analysing a dataset detailing nearly 100 million traffic stops conducted across the country. We found that black drivers were less likely to be stopped after sunset, when a ‘veil of darkness’ masks one’s race, suggesting bias in stop decisions. Furthermore, by examining the rate at which stopped drivers were searched and the likelihood that searches turned up contraband, we found evidence that the bar for searching black and Hispanic drivers was lower than that for searching white drivers. Finally, we found that legalization of recreational marijuana reduced the number of searches of white, black and Hispanic drivers—but the bar for searching black and Hispanic drivers was still lower than that for white drivers post-legalization. Our results indicate that police stops and search decisions suffer from persistent racial bias and point to the value of policy interventions to mitigate these disparities.

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More than 20 million Americans are stopped each year for traffic violations, making this one of the most common ways in which the public interacts with the police 1 , 2 , 3 , 4 . Due to the decentralized nature of policing in the United States—and a corresponding lack of comprehensive and standardized data—it is difficult to rigorously assess the manner and extent to which race plays a role in traffic stops 5 . The most widely cited national statistics come from the Police–Public Contact Survey (PPCS) 1 , which is based on a nationally representative sample of approximately 50,000 people who report having been recently stopped by the police. In addition to such survey data, some local and state agencies have released periodic reports on traffic stops in their jurisdictions, and have also made their data available to outside researchers for analysis 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 . While useful, these datasets provide only a partial picture. For example, there is concern that the PPCS, like nearly all surveys, suffers from selection bias and recall errors. Data released directly by police departments are potentially more complete but are available only for select agencies, are typically limited in what is reported and are inconsistent across jurisdictions.

To address these challenges, we compiled and analysed a dataset detailing nearly 100 million traffic stops carried out by 21 state patrol agencies and 35 municipal police departments over almost a decade. This dataset was built through a series of public records requests filed in all 50 states. To facilitate future analysis, we have redistributed these records in a standardized form.

Our statistical analysis of these records proceeds in three steps. First, we assess potential bias in stop decisions by applying the ‘veil of darkness’ test developed by Grogger and Ridgeway 21 . This test is based on a simple observation: because the sun sets at different times throughout the year, one can examine the racial composition of stopped drivers as a function of sunlight while controlling for time of day. In particular, we use the discontinuity created by the start (and end) of daylight-saving time (DST), comparing the racial distribution of drivers stopped in the evenings immediately before DST begins, when it is dark, to the distribution after DST begins, when it is light at the same time of day. If black drivers comprise a smaller share of stopped drivers when it is dark and accordingly difficult to determine a driver’s race, that suggests black drivers were stopped during daylight hours in part because of their race. In both state patrol and municipal police stops, we find that black drivers comprise a smaller proportion of drivers stopped after sunset, suggestive of discrimination in stop decisions.

Second, we investigate potential bias in the post-stop decision to search drivers for contraband. To do so, we apply the threshold test recently developed by Simoiu et al. 7 and refined by Pierson et al. 22 . The threshold test incorporates both the rate at which searches occur, as well as the success rate of those searches, to infer the standard of evidence applied when determining whom to search. This approach builds on traditional outcome analysis 23 , 24 , in which a lower search success rate for one group relative to another is seen as evidence of bias against that group, as it suggests that a lower evidentiary bar was applied when making search decisions. Applied to our data, the threshold test indicates that black and Hispanic drivers were searched on the basis of less evidence than white drivers, both on the subset of searches carried out by state patrol agencies and on those carried out by municipal police departments.

Finally, we examine the effects of drug policy on racial disparities in traffic stop outcomes. We specifically compare patterns of policing in Colorado and Washington—two states that legalized recreational marijuana at the end of 2012—to those in 12 states in which recreational marijuana remained illegal. Using a difference-in-differences strategy, we find that legalization reduced both search rates and misdemeanour rates for drug offences for white, black and Hispanic drivers—though a gap in search thresholds persists.

In the process of collecting and analysing millions of traffic stop records across the country, we encountered many logistical and statistical challenges. Based on these experiences, we conclude by offering suggestions to improve data collection, analysis and reporting by law enforcement agencies. Looking forward, we hope this work provides a road map for measuring racial disparities in policing, and facilitates future empirical research into police practices.

Compiling a national database of traffic stops

To assemble a national dataset of traffic stops, we filed public records requests with all 50 state patrol agencies and over 100 municipal police departments. The municipal police departments include those that serve the largest 100 cities in the nation; to achieve geographic coverage, we also included departments that serve some of the largest cities in each state.

To date, we have collected (and released) data on approximately 221 million stops carried out by 33 state patrol agencies, and 34 million stops carried out by 56 municipal police departments, for a total of 255 million records. In many cases, however, the data we received were insufficient to assess racial disparities (for example, the race of the stopped driver was not regularly recorded, or only a non-representative subset of stops was provided). For consistency in our analysis, we further restrict to stops occurring in 2011–2018, as many jurisdictions did not provide data on earlier stops. Finally, we limit our analysis to drivers classified as white, black or Hispanic, as there were relatively few recorded stops of drivers in other race groups. Our primary dataset thus consists of approximately 95 million stops from 21 state patrol agencies and 35 municipal police departments, as shown in Fig. 1 and described in more detail in Supplementary Table 2 .

We analysed data on approximately 95million stops from 21state patrol agencies (blue) and 35municipal police departments (red) across the country.

Because each jurisdiction provided stop data in idiosyncratic formats with varying levels of specificity, we used a variety of automated and manual procedures to create the final dataset. For each recorded stop, we attempted to extract and normalize the date and time of the stop; the county (for state patrol agencies) or police subdivision (for example, beat, precinct or zone, for municipal police departments) in which the stop took place; the race, gender and age of the driver; the stop reason (for example, speeding); whether a search was conducted; the legal justification for the search (for example, ‘probable cause’ or ‘consent’); whether contraband was found during a search; and the stop outcome (for example, a citation or an arrest). As indicated in Supplementary Table 2 , the information we received varies markedly across states. We therefore restricted each of our specific analyses to the corresponding subset of jurisdictions for which we have the required fields. Our complete data-cleaning pipeline is extensive and required subjective decisions, which we describe in more detail in Methods . For transparency and reproducibility, we have released the raw data, the standardized data and code to clean and analyse the records at https://openpolicing.stanford.edu .

Assessing bias in traffic stop decisions

Relative to their share of the residential population, we found that black drivers were, on average, stopped more often than white drivers. In particular, among state patrol stops, the annual per-capita stop rate for black drivers was 0.10 compared to 0.07 for white drivers; and among municipal police stops, the annual per-capita stop rate for black drivers was 0.20 compared to 0.14 for white drivers. For Hispanic drivers, however, we found that stop rates were lower than for white drivers: 0.05 for stops conducted by state patrol (compared to 0.07 for white drivers) and 0.09 for those conducted by municipal police departments (compared to 0.14 for white drivers). In all cases, these numbers are the unweighted average annual per-capita stop rates across the states and cities we analysed, and all estimates have corresponding 95% confidence intervals (CIs) with radius <0.001. We note that these results are consistent with self-reported stop rates by white, black and Hispanic drivers who participated in the national PPCS 1 .

These numbers are a starting point for understanding racial disparities in traffic stops, but they do not, per se, provide strong evidence of racially disparate treatment. In particular, per-capita stop rates do not account for possible race-specific differences in driving behaviour, including amount of time spent on the road and adherence to traffic laws. For example, if black drivers, hypothetically, spend more time on the road than white drivers, that could explain the higher stop rates we see for the former, even in the absence of discrimination. Moreover, drivers may not live in the jurisdictions where they were stopped, further complicating the interpretation of population benchmarks.

Quantifying potential bias in stop decisions is a statistically challenging problem, in large part because one cannot readily measure the racial distribution of those who actually violated traffic laws because the data contain information only on those stopped for such offences. To mitigate this benchmarking problem, Grogger and Ridgeway 21 proposed a statistical approach known as the veil-of-darkness test. Their method starts from the idea that officers who engage in racial profiling are less able to identify a driver’s race after dark than during the day. As a result, if officers are discriminating against black drivers—all else being equal—one would expect black drivers to comprise a smaller share of stopped drivers at night, when a veil-of-darkness masks their race. To account for patterns of driving and police deployment that may vary throughout the day, the test leverages the fact that the sun sets at different times during the year. For example, although it is typically dark at 19:00 during the winter months, it is often light at that time during the summer.

To illustrate the intuition behind this method, in Fig. 2 we examine the demographic composition of drivers stopped by the Texas State Patrol at various times of day. Each panel in the plot shows stops occurring in a specific 15-min window (for example, 19:00–19:15), and the horizontal axis indicates minutes since dusk. We restrict to white and black drivers—because the ethnicity of Hispanic drivers is not always apparent, even during daylight hours—and, following Grogger and Ridgeway, we filter out stops that occurred in the ~30-min period between sunset and dusk, when it is neither ‘light’ nor ‘dark.’ For each time period, the plot shows a marked drop in the proportion of drivers stopped after dusk who are black, suggestive of discrimination in stop decisions.

For each window (19:00–19:15, 19:15–19:30 and 19:30–19:45), we compute the percentage of stops that involved black drivers for a series of 10-min periods before and after dusk. The figure is based on 112,938 stops of black and white drivers (35,270 during 19:00–19:15, 38,726 during 19:15–19:30 and 38,942 during 19:30–19:45), with points sized according to the total number of stops in each bin. The vertical line at t  = 0 indicates dusk, at which point it is generally considered ‘dark’; we remove stops in the ~30-min period between sunset (indicated by the left-most vertical line in each panel) and dusk, as this period is neither ‘light’ nor ‘dark’. The dashed horizontal lines show the overall proportion of stops involving black drivers before and after dark, with 95% CI. For all three depicted time windows, black drivers comprise a smaller share of stopped drivers after dark, when a veil of darkness masks their race, suggestive of racial profiling.

In this basic form, darkness—after adjusting for time of day—is a function of the date. As such, to the extent that driver behaviour changes throughout the year, and that these changes are correlated with race, the test can suggest discrimination where there is none. To account for these potential seasonal effects, we applied a more robust variant of the veil-of-darkness test that restricts to two 60-day windows centred on the beginning and end of DST 25 . On this subset of the data, we fit the following logistic regression model:

where Pr(black ∣ t , g , p , d , s , c ) is the probability that a stopped driver is black at a certain time t , location g and period p (with two periods per year, corresponding to the start and end of DST), with darkness status d ∈ {0, 1} indicating whether a stop occurred after dusk ( d  = 1) or before sunset ( d  = 0), and with the enforcement agency being either state patrol, s ∈ {0, 1} or city police department, c ∈ {0, 1}. In this model, ns 6 ( t ) is a natural spline over time with six degrees of freedom, γ [ g ] is a fixed effect for location g and δ [ p ] is a fixed effect for period p . The location g [ i ] for stop i corresponds to either the county (for state patrol stops) or city (for municipal police department stops), with γ [ g [ i ]] the corresponding coefficient. Finally, p [ i ] captures whether stop i occurred in the spring (within a month of beginning DST) or the fall (within a month of ending DST) of each year, and δ [ p [ i ]] is the corresponding coefficient for this period. For computational efficiency, we rounded time to the nearest 5-min interval when fitting the model.

The main terms of interest in our model are α s and α c , which describe differences in the composition of stopped drivers between daylight and dark, after adjusting for time and location, with α s , α c  < 0 suggesting discrimination against black drivers for state patrols and city departments, respectively. We find α s  = −0.033 (95% CI [−0.039, −0.027], P  < 0.001) and α c  = −0.039 (95% CI [−0.045, −0.033], P  < 0.001), suggesting discrimination among both state patrols and municipal police departments. To gauge the robustness of our results, we fit multiple variations of the veil-of-darkness model described above (for example, using different degrees for the natural spline). In all cases, we found qualitatively similarly results that are suggestive of racial bias against black drivers in stop decisions, as described in Methods and Supplementary Table 1 .

The veil-of-darkness test is a popular technique for assessing disparate treatment but, like all statistical methods, it comes with caveats. Results could be skewed if race-specific driving behaviour is related more to lighting than time of day, leading the test to suggest discrimination where there is none. Conversely, artificial lighting (for example, from street lamps) can weaken the relationship between sunlight and visibility, and so the method may underestimate the extent to which stops are predicated on perceived race. Finally, if violation type is related to lighting, the test could give an inaccurate measure of discrimination. For example, broken tail lights are more likely to be detected at night and could potentially be more common among black drivers 17 , which could in turn mask discrimination. To address this last limitation, one could exclude stops prompted by such violations but our data, unfortunately, do not consistently indicate stop reasons. Despite these shortcomings, we believe the veil-of-darkness test provides a useful, if imperfect, measure of bias in stop decisions.

Assessing bias in search decisions

After stopping a driver, officers may carry out a search of the driver or vehicle if they suspect more serious criminal activity. We next investigate potential bias in these search decisions. Among stopped drivers, we found that black and Hispanic individuals were, on average, searched more often than white individuals. However, as with differences in stop rates, the disparities we see in search rates are not necessarily the product of discrimination. Black and Hispanic drivers might, hypothetically, carry contraband at higher rates than white drivers, and so elevated search rates may result from routine police work even if no racial bias were present. To measure the role of race in search decisions, we apply two statistical strategies: outcome analysis and threshold analysis. To do so, we limit to the eight state patrol agencies and six municipal police departments for which we have sufficient data on the location of stops, whether a search occurred and whether those searches yielded contraband. We specifically consider state patrol agencies in Connecticut, Illinois, North Carolina, Rhode Island, South Carolina, Texas, Washington and Wisconsin; and municipal police departments in Nashville, TN, New Orleans, LA, Philadelphia, PA, Plano, TX, San Diego, CA and San Francisco, CA. We defer to each department’s characterization of ‘contraband’ when carrying out this analysis.

In the eight state patrol agencies we consider, search rates were 4.3% (95% CI 4.2–4.4%) for black drivers, 4.1% (95% CI 4.0–4.1%) for Hispanic drivers and 1.9% (95% CI 1.9–1.9%) for white drivers. In particular, the gap in search rates between black and white drivers was 2.4% (95% CI 2.3–2.5%, P  < 0.001) and the gap in search rates between Hispanic and white drivers was 2.2% (95% CI 2.1–2.2%, P  < 0.001). Similarly, in the six municipal police departments we consider, the search rates were 9.5% (95% CI 9.4–9.6%) for black drivers, 7.2% (95% CI 7.0–7.3%) for Hispanic drivers and 3.9% (95% CI 3.8–3.9%) for white drivers. The gap in municipal search rates between black and white drivers was 5.6% (95% CI 5.5–5.7%, P  <0.001) and the gap in search rates between Hispanic and white drivers was 3.3% (95% CI 3.1–3.5%, P  <0.001). As above, these numbers are unweighted average search rates across our states and cities, respectively.

In these jurisdictions, stopped black and Hispanic drivers were searched about twice as often as stopped white drivers. To assess whether this gap resulted from biased decision-making, we apply the outcome test, originally proposed by Becker 23 , 24 , to circumvent omitted variable bias in traditional tests of discrimination. The outcome test is based not on the search rate but on the ‘hit rate’: the proportion of searches that successfully turn up contraband. Becker argued that even if minority drivers are more likely to carry contraband, in the absence of discrimination, searched minorities should still be found to have contraband at the same rate as searched whites. If searches of minorities are successful less often than searches of whites, it suggests that officers are applying a double standard, searching minorities on the basis of less evidence. Implicit in this test is the assumption that officers exercise discretion in whom to search; therefore, when possible, we exclude non-discretionary searches, such as vehicle impound searches and searches incident to arrest, as those are often required as a matter of procedure, even in the absence of individualized suspicion. We note that outcome tests gauge discrimination only at one specific point in the decision-making process—in this case, the decision to search a driver who has been stopped. In particular, this type of analysis does not capture bias in the stop decision itself.

In Fig. 3 (top row), we plot hit rates by race and location for the states (left) and for the cities (right) for which we have the necessary information. Across jurisdictions, we consistently found that searches of Hispanic drivers were less successful than those of white drivers. However, searches of white and black drivers had more comparable hit rates. The outcome test thus indicates that search decisions may be biased against Hispanic drivers, but the evidence is more ambiguous for black drivers. Aggregating across state patrol stops, contraband was found in 32.0% (95% CI 31.6–32.4%) of searches of white drivers compared to 24.3% (95% CI 23.5–25.2%) of searches of Hispanic drivers and 29.4% (95% CI 28.7–30.0%) of searches of black drivers. In particular, the gap in hit rates between white and Hispanic drivers was 7.6% (95% CI 6.7–8.6%, P  < 0.001), and the gap in hit rates between white and black drivers was 2.6% (95% CI 1.9–3.4%, P  < 0.001). Similarly, aggregating across municipal police departments, contraband was found in 18.2% (95% CI 17.8–18.7%) of searches of white drivers compared to 11.0% (95% CI 10.6–11.5%) of searches of Hispanic drivers and 13.9% (95% CI 13.7–14.2%) of searches of black drivers. In this case, the gap in hit rates between white and Hispanic drivers was 7.2% (95% CI 6.6–7.8%, P  < 0.001) and the gap in hit rates between white and black drivers was 4.3% (95% CI 3.8–4.8%, P  < 0.001). These numbers all indicate unweighted averages across our cities and states, respectively.

a , Data for state patrol are based on 647,251 searches in eight states, with points corresponding to counties. b , Data for municipal police are based on 187,145 searches in six cities, with points corresponding to police precincts. Points are sized by number of searches. The top panels indicate search hit rates, and the bottom panels indicate search thresholds. Across locations, the inferred thresholds for searching black and Hispanic drivers are typically lower than those for searching white drivers, suggestive of bias. Despite these lower inferred search thresholds, hit rates for black drivers are comparable to those for white drivers, ostensibly due to the problem of infra-marginality in outcome tests.

The outcome test is intuitively appealing, but it is an imperfect barometer of bias; in particular, it suffers from the problem of infra-marginality 8 , 26 , 27 . To illustrate this shortcoming, suppose that there are two, easily distinguishable, types of white driver: those who have a 5% chance of carrying contraband and those who have a 75% chance of carrying contraband. Likewise assume that black drivers have either a 5 or 50% chance of carrying contraband. If officers search drivers who are at least 10% likely to be carrying contraband, then searches of white drivers will be successful 75% of the time whereas searches of black drivers will be successful only 50% of the time. Thus, although the search criterion is applied in a race-neutral manner, the hit rate for black drivers is lower than that for white drivers and the outcome test would (incorrectly) conclude that searches are biased against black drivers. The outcome test can similarly fail to detect discrimination when it is present.

Addressing this possibility, Knowles et al. 27 suggested an economic model of behaviour—known as the KPT model—in which drivers balance their utility for carrying contraband with the risk of getting caught, while officers balance their utility of finding contraband with the cost of searching. Under equilibrium behaviour in this model, the hit rate of searches is identical to the search threshold and so one can reliably detect discrimination with the standard outcome test. However, Engel and Tillyer 28 argue that the KPT model of behaviour requires strong assumptions, including that drivers and officers are rational actors and that every driver has perfect knowledge of the likelihood that he or she will be searched.

To mitigate the limitations of outcome tests (as well as limitations of the KPT model), the threshold test has been proposed as a more robust means for detecting discrimination 7 , 22 . This test aims to estimate race-specific probability thresholds above which officers search drivers—for example, the 10% threshold in the hypothetical situation above. Even if two race groups have the same observed hit rate, the threshold test may find that one group is searched on the basis of less evidence, indicative of discrimination. To accomplish this task, the test uses a Bayesian model to simultaneously estimate race-specific search thresholds and risk distributions that are consistent with the observed search and hit rates across all jurisdictions. The threshold test can thus be seen as a hybrid between outcome and benchmark analysis, as detailed in Methods .

As shown in Fig. 3 (bottom row), the threshold test indicates that the bar for searching black and Hispanic drivers is generally lower than that for searching white drivers across the municipal police departments and states we consider. In aggregate across cities, the inferred threshold for white drivers is 10.0% compared to 5.0 and 4.6% for black and Hispanic drivers, respectively. The estimated gaps in search thresholds between white and non-white drivers are large and statistically significant: the 95% credible interval for the Hispanic–white difference is (–6.4, –4.4%) and the corresponding interval for the black–white difference is (–6.1, –4.0%). Similarly across states, the inferred threshold for white drivers is 20.9% compared to 16.0% for black drivers and 13.9% for Hispanic drivers. These differences are again large and statistically significant: the 95% credible interval for the Hispanic–white gap is (–8.4, –5.6%); and the analogous interval for the black–white gap is (–6.5%, –3.1%). As with our outcome results, aggregate thresholds are computed by taking an unweighted average of the city- and state-specific thresholds, respectively.

Compared to by-location hit rates, the threshold test more strongly suggests discrimination against black drivers, particularly for municipal stops. Consistent with past work 7 , this difference appears to be driven by a small but disproportionate number of black drivers who have a high inferred likelihood of carrying contraband. Thus, even though the threshold test finds that the bar for searching black drivers is lower than that for white drivers, these groups have more similar hit rates.

The threshold test provides evidence of racial bias in search decisions. However, as with all tests of discrimination, it is important to acknowledge limits in what one can conclude from such statistical analysis per se. For example, if search policies differ not only across, but also within, the geographic subdivisions we consider, then the threshold test might mistakenly indicate discrimination where there is none. Additionally, if officers disproportionately suspect more serious criminal activity when searching black and Hispanic drivers compared to white drivers (for example, possession of larger quantities of contraband), then lower observed thresholds may stem from non-discriminatory police practices. Finally, we note that thresholds cannot be identified by the observed data alone 7 , and so inferences are dependent on the specific functional form of the underlying Bayesian model, including the prior distributions. (In Methods , however, we show that our main results are robust to relatively large changes to prior distributions).

The effects of legalization of marijuana on stop outcomes

We conclude our analysis by investigating the effects of legalization of recreational marijuana on racial disparities in stop outcomes. We specifically examine Colorado and Washington, two states in which marijuana was recently legalized and for which we have detailed data before and after legalization. In line with expectations, we find that the proportion of stops that resulted in either a drug-related infraction or misdemeanour fell substantially in both states after marijuana was legalized at the end of 2012 (see Supplementary Fig. 2 ). Notably, since black drivers were more likely to be charged with such offences previous to legalization, these drivers were also disproportionately impacted by the policy change. This finding is consistent with past work showing that marijuana laws disproportionately affect minorities 29 .

Because the policy change decriminalized an entire class of behaviour (that is, possession of small amounts of marijuana), it is not surprising that drug offences correspondingly decreased. However, it is less clear, a priori, how the change might have affected officer behaviour more broadly. Investigating this issue, we found that after the legalization of marijuana the number of searches fell substantially in Colorado and Washington (Fig. 4 ), ostensibly because the policy change removed a common reason for conducting searches.

After legalization, there was a substantial drop in search rates. The vertical dashed lines indicate the timing of legalization; the other dashed lines show fitted linear trends pre- and post-legalization. The left panel (Colorado) represents 1,674,619 stops and the right panel (Washington) represents 3,985,677 stops. We excluded data for the fourth quarter of 2012, since that period includes stops both before and after legalization. Additionally, in both states, we excluded searches incident to an arrest and other searches that are conducted as a procedural matter, irrespective of any suspicion of drug possession.

Because black and Hispanic drivers were more likely to be searched before legalization, the policy change reduced the absolute gap in search rates between race groups; however, the relative gap persisted, with black and Hispanic drivers still more likely to be searched than white drivers post-legalization. We further note that marijuana legalization had secondary impacts for law-abiding drivers, because fewer searches overall also meant fewer searches of those without contraband. In the year after legalization in Colorado and Washington, about one-third fewer drivers were searched with no contraband found than in the year before legalization.

As further evidence that the observed drop in search rates in Colorado and Washington was due to marijuana legalization, we note that in the 12 states where marijuana was not legalized—and for which we have the necessary data—search rates did not exhibit sharp drops at the end of 2012 (Fig. 5 ). To add quantitative detail, we computed a difference-in-differences estimate 30 ; our precise model specification and the fitted coefficients are described in Methods . We found that the race-specific coefficients are large and negative for white, black and Hispanic drivers, which again suggests that the observed drop in searches in Colorado and Washington was caused by the legalization of marijuana in those states.

In the 12 states where marijuana was not legalized—and for which we have the necessary data—search rates did not exhibit sharp declines at the end of 2012, which further suggests that marijuana legalization caused the observed drop in search rates in Colorado and Washington. The plots are based on the following number of stops: 2,038,850 in AZ, 19,012,414 in CA, 3,686,757 in FL, 1,773,546 in MA, 547,115 in MT, 3,500,180 in NC, 4,660,935 in OH, 229,691 in RI, 3,909,950 in SC, 20,494,642 in TX, 250,949 in VT and 741,245 in WI. Dashed lines are as in Fig. 4 .

Despite the legalization of marijuana decreasing search rates across race groups, Fig. 4 shows that the relative disparity between whites and minorities remained. We applied the threshold test to assess the extent to which this disparity in search rates may reflect bias. Examining the inferred thresholds (shown in Supplementary Fig. 3 ), we found that white drivers faced consistently higher search thresholds than minority drivers, both before and after marijuana legalization. The data thus suggest that, although overall search rates dropped in Washington and Colorado, black and Hispanic drivers still faced discrimination in search decisions.

Analysing nearly 100 million traffic stops across the country, we have worked to quantify the often complex relationship between race and policing in the United States. Our analysis provides evidence that decisions about whom to stop and, subsequently, whom to search are biased against black and Hispanic drivers. Our results, however, also point to the power of policy interventions—specifically, legalization of recreational marijuana—to reduce these racial disparities.

These findings lend insight into patterns of policing, but they only partially capture the wider impacts of law enforcement on communities of colour. If, for example, officers disproportionately patrol black and Hispanic neighbourhoods, the downstream effects can be injurious even if individual stop decisions are not directly affected by the colour of one’s skin. Similarly, enforcement of minor traffic violations, like broken tail lights—even if conducted uniformly and without animus—can place heavy burdens on black and Hispanic drivers without improving public safety 17 . We hope the data we have collected and released are useful for measuring, and in turn addressing, these broader effects.

In the course of carrying out this study, we encountered many challenges working with large-scale policing data. We conclude by offering several recommendations for future data collection, release and analysis. As a minimum, we encourage jurisdictions to collect individual-level stop data that include the date and time of the stop; the location of the stop; the race, gender and age of the driver; the stop reason; whether a search was conducted; the search type (for example, ‘probable cause’ or ‘consent’); whether contraband was found during a search; the stop outcome (for example, a citation or an arrest); and the specific violation with which the driver was charged. Most jurisdictions collect only a subset of this information. There are also variables that are currently rarely collected but would be useful for analysis, such as indicia of criminal behaviour, an officer’s rationale for conducting a search and short narratives written by officers describing the incident. New York City’s UF-250 form for pedestrian stops is an example of how such information can be efficiently collected 31 , 32 .

Equally important to data collection is ensuring the integrity of the recorded information. We frequently encountered missing values and errors in the data (for example, implausible values for a driver’s age and invalid racial categorizations). Automated procedures can be put in place to help detect and correct such problems. For example, the recorded race of the driver is often based on the officer’s perception rather than a driver’s self-categorization. While there are perhaps sound reasons for this practice, it increases the likelihood of errors. To quantify and correct for this issue, police departments might regularly audit their data, possibly by comparing an officer’s perception of race to a third party’s judgement based on driver’s licence photos for a random sample of stopped drivers.

Despite the existence of public records laws, several jurisdictions failed to respond to our repeated requests for information. We hope that law enforcement agencies consider taking steps to make data more accessible to both external researchers and the public. Connecticut and North Carolina are at the forefront of opening up their data, providing online portals for anyone to download and analyse this information.

We also hope that police departments start to analyse their data regularly and report the results of their findings. Such analyses might include estimates of stop, search and hit rates stratified by race, age, gender and location; distribution of stop reasons by race; and trends over time. More ambitiously, departments could use their data to design statistically informed guidelines that encourage more consistent, efficient and equitable decisions 31 , 33 , 34 , 35 . Many of these analyses can be automated and rerun regularly with little marginal effort. In conjunction with releasing the data underlying these analyses, we recommend that the analysis code also be released to ensure reproducibility.

Finally, it bears emphasis that the type of large-scale data analysis we have carried out in this paper is but one of many complementary ways to gauge and rectify bias in police interactions with the public. Just as critical, for example, are conversations with both officers and community members, who can often provide more nuance than is possible with our aggregate statistical approach. Collecting, releasing and analysing police data are important steps for increasing the effectiveness and equity of law enforcement practices, and for improving relations with the public through transparency. Ultimately, though, data collection and analysis are not enough. We must act on the results of such efforts if we are to reduce the persistent, discriminatory impacts of policing on communities of colour.

Data collection and standardization

Our primary dataset includes 94,778,505 stops from 21 state patrol agencies and 35 municipal police departments. For more detail on all jurisdictions whose data were used in our analyses, Supplementary Table 2 lists the total number of stops and the range of years in which these stops took place. The table also indicates whether each of several important covariates was available in each jurisdiction: the date and time of the stop; more granular geographic information; the race, age and gender of the stopped driver; and whether a search was conducted and, if so, whether contraband was found. Our data-processing pipeline was extensive. Below we describe some of the key steps in this process, and also note that the complete code required to process the data is available at https://openpolicing.stanford.edu .

For each state and city, we requested individual-level records for traffic stops conducted since 2005, under the state’s public records law and filed with the agency responsible for traffic stop data collection. The 33 states and 56 cities that complied provided the data in various formats, including raw text files, Microsoft Excel spreadsheets and Microsoft Access databases. We converted all data we received to a standard comma-separated, text-file format. The states and cities varied widely in terms of the availability of the different data fields, the manner and detail of how the data were recorded and in recording consistency from year to year, even within the same location.

We standardized available fields when possible. Often, locations provided dictionaries to map numeric values to human-interpretable ones. Aggregated data—as provided by Missouri, Nebraska and Virginia, for example—were disaggregated by expanding the number of rows by the reported count. For some locations, we had to manually map the provided location data to a county or district value. For example, in Washington, counties were mapped by first computing the latitude and longitude of the highway post that was recorded for the stop, and then those coordinates were mapped to a county using a shapefile. The raw data we received from the states and cities, the processed data we used in this analysis and the code to clean and analyse the data are all available for public inspection and reuse.

In many cases, more than one row in the raw data appeared to refer to the same stop. For example, in several jurisdictions each row in the raw data referred to one violation, not one stop. We detected and reconciled such duplicates by matching on a location-specific set of columns. For example, in Colorado we counted two rows as duplicates if they had the same officer identification code, officer first and last name, driver first and last name, driver birth date, stop location (precise to the milepost marker) and stop date and time. This type of de-duplication was a common procedure that we applied to many states and cities.

The raw data provided to us by state and municipal police agencies often contained clear errors. We ran numerous automated checks to detect and correct these where possible, although some errors probably remain due to the complex nature of the data. For example, after examining the distribution of recorded values in each jurisdiction, we discovered a spurious density of stops in North Carolina listed as occurring at precisely midnight. As the value ‘00:00’ was probably used to indicate missing information, we treated it as such. In Pittsburgh, PA, recorded values for ‘sex’ and ‘gender’ did not match 73% of the time in the pedestrian stop data, suggesting data corruption. (Note that we did not include pedestrian stop data in our analysis, but we have released those records for other researchers to use.)

In another example, past work revealed that Texas State Patrol officers incorrectly recorded many Hispanic drivers as white, an error the agency subsequently corrected 36 . To investigate and adjust for this issue, we imputed Hispanic ethnicity from surnames. To carry out this imputation, we used a dataset from the U.S. Census Bureau that estimates the racial and ethnic distribution of people with a given surname for surnames occurring at least 100 times 37 . To increase the matching rate, we performed minor string edits to the names, including removal of punctuation and suffixes (for example, ‘Jr.’ and ‘II’), and considered only the longest word in multi-part surnames. Following previous studies 38 , 39 , we defined a name as ‘typically’ Hispanic if at least 75% of people with that name identified as Hispanic, and we note that 90% of those with typically Hispanic names identified as Hispanic in the 2000 Census.

Among drivers with typically Hispanic names, the proportion labelled as Hispanic in the raw data was quite low in Texas (37%), corroborating past results. For comparison, we considered Arizona and Colorado, the two other states that included driver name in the raw data. The proportion of drivers with typically Hispanic names labelled as Hispanic in the raw data was 70% in Colorado and 79% in Arizona, much higher than in Texas. Because of this known issue in the Texas data, we re-categorized as ‘Hispanic’ all drivers in Texas with Hispanic names who were originally labelled ‘white’ or who had missing race data; this method adds about 1.9 million stops of Hispanic drivers over the period 2011–2015. We did not re-categorize drivers in any other jurisdiction.

Veil-of-darkness analysis

In our veil-of-darkness analysis, we compared stop rates before sunset and after dusk—as is common when applying this test. Specifically, sunset is defined as the point in time where the sun dips below the horizon, and dusk (also known as the end of civil twilight) is the time when the sun is six degrees below the horizon and when it is generally considered to be ‘dark.’ As recommended by Grogger and Ridgeway 21 , we further restricted to stops that occurred during the ‘inter-twilight period’: the range from the earliest to the latest time that dusk occurs in the year. This range is approximately 17:00–22:00, although the precise values differ by location and year. All times in the inter-twilight period are, by definition, light at least once in the year and dark at least once in the year. In Supplementary Table 1 , we report the results of several variations of our primary veil-of-darkness model (for example, varying the degree of the natural spline from 1 to 6). The results were statistically significant and qualitatively similar in all cases.

Threshold test

The threshold test for discrimination was introduced by Simoiu et al. 7 to mitigate the most serious shortcomings of benchmark and outcome analysis. The test is informed by a stylized model of officer behaviour. During each stop, officers observe a myriad of contextual factors—including the age, gender and race of the driver, and behavioural indicators of nervousness or evasiveness. We imagine that officers distil all these complex signals down to a single number, p , that represents their subjective estimate of the likelihood that the driver is carrying contraband. Based on these factors, officers are assumed to conduct a search if, and only if, their subjective estimate of finding contraband, p , exceeds a fixed, race-specific search threshold for each location (for example, county or district). Treating both the subjective probabilities and the search thresholds as latent, unobserved quantities, our goal is to infer them from data. The threshold test takes a Bayesian approach to estimating the parameters of this process, with the primary goal of inferring race-specific search thresholds for each location.

Under this model of officer behaviour, we interpret lower search thresholds for one group relative to another as evidence of discrimination. If, for example, officers have a lower threshold for searching black drivers than white drivers, that would indicate that they are willing to search black drivers on the basis of less evidence than for white drivers—and we would conclude that black drivers are being discriminated against. In the economics literature, this type of behaviour is often called taste-based discrimination 24 as opposed to statistical discrimination 40 , 41 , in which officers might use a driver’s race to improve their estimate that the driver is carrying contraband. Regardless of whether such information increases the efficiency of searches, officers are legally barred from using race to inform search decisions outside of circumscribed situations (for example, when acting on specific and reliable suspect descriptions that include race among other factors). The threshold test, however, aims to capture only taste-based discrimination, as is common in the empirical literature on discrimination.

In our work, we applied a computationally fast variant of the threshold test developed by Pierson et al. 22 , which we fit separately on both state patrol stops and municipal police stops. As described below, we modified the Pierson et al. test to include an additional hierarchical component. For example, while the original Pierson et al. model considered one state, and allowed parameters to vary by county (and race) within that state, our state patrol model considers multiple states, allowing parameters to vary by county (and race) within each state. Relevant information is partially pooled across both counties and states. Similarly, our municipal model considers multiple police departments, allowing parameters to vary by police district (and race) within each department.

To run the threshold test, we assume the following information is observed for each stop, i :

the race of the driver, r i

the region—state (for the state patrol model) or city (for the municipal police model)—where the stop occurred, g i (for example, Texas or Nashville)

the specific county (for the state patrol model) or district (for the municipal police model) where the stop occurred, d i (for example, Harris County, TX or Hermitage Precinct, Nashville)

whether the stop resulted in a search, indicated by S i ∈ {0, 1}, and

whether the stop resulted in contraband recovery, indicated by H i ∈ {0, 1}

To formally describe the threshold test, we need to specify the parametric process of search and recovery, as well as the priors on those parameters. For ease of exposition, we present the model for state patrol stops, where stops occur in counties that are nested within states. The municipal stop model has the same structure, with districts corresponding to counties and cities corresponding to states. We start by describing the latent signal distributions on which officers base their search decisions, which we parameterize by the race and location of drivers. As detailed in Pierson et al. 22 , these signal distributions are modelled as homoskedastic discriminant distributions, a class of logit-normal mixture distributions supported on the unit interval [0, 1]. Discriminant distributions can closely approximate beta distributions, but they have properties that are computationally attractive. Each race- and county-specific signal distribution can be described using two parameters, which we denote by ϕ r , d and δ r , d . In our setting, ϕ r , d ∈ (0, 1) is the proportion of drivers of race r in county d that carry contraband; and δ r , d  > 0 characterizes how difficult it is to identify drivers with contraband 34 .

We impose additional structure on the signal distributions by assuming that ϕ r , d and δ r , d can be decomposed into additive race and location terms. Specifically, given parameters ϕ r , g for each race group r and state g , and parameters ϕ d for each county d , we set

where g [ d ] denotes the state g in which county d lies. Similarly, for parameters δ r , g and δ d , we set

Following Pierson et al. 22 , we use hierarchical priors 42 on the signal distributions that restrict geographical heterogeneity while allowing for base rates to differ across race groups. This choice substantially accelerates model fitting. In particular, we use the following priors:

Next we detail the structure of our search thresholds. In our informal description above, officers search drivers when their estimated likelihood of possessing contraband exceeds a specific race- and county-specific value t r , d ∈ (0, 1). For computational reasons, we map these thresholds from the unit interval to the real line via a monotonic transformation outlined in Pierson et al. 22 . For simplicity, we continue using the notation t r , d to denote these transformed values. As above, we set a hierarchical prior on the threshold values. Specifically, we have

Finally, given this parametric model of signals and thresholds, we describe the underlying data-generating process, which mirrors our informal description above. For each stop i , we draw a signal p i from the associated race- and location-specific discriminant distribution. If the signal p i exceeds the race- and location-specific threshold \({t}_{{r}_{i},{d}_{i}}\) , then a search is conducted and S i  = 1; otherwise there is no search and S i  = 0. If a search is conducted, contraband is found with probability p i , in which case H i  = 1; otherwise, H i  = 0.

The hierarchical structure we employ allows us to make reasonable inferences even for locations with a relatively small number of stops. However, to ensure more statistically robust estimates, we limit our analysis to counties and districts with at least 50 searches per race group. To gauge the sensitivity of our results, we repeated our analysis with priors having 0.5× and 1.5× s.d. of the priors in our main analysis, and found nearly identical results under these transformations.

For both the state patrol and municipal department model, we perform posterior predictive checks to ensure that the models fit the data well. Specifically, for each county (state patrol model) or district (municipal department model) and race group, we compare the observed search and recovery rates to their expected values under the assumed data-generating process, with parameters drawn from the inferred posterior distribution. Such posterior predictive checks are a common approach for identifying and measuring systematic differences between a fitted Bayesian model and the data 42 , 43 . Supplementary Fig. 1 shows the results of these posterior predictive checks. For both the state patrol model and the municipal department model, the prediction errors are minimal and similar across race groups.

Given the inferred race- and location-specific thresholds, \({\hat{t}}_{r,d}\) , we define overall race-specific thresholds \({\hat{t}}_{r}\) for both states and municipal departments in two steps. First, for each state (municipal department) g , we compute an average \({\hat{t}}_{r,g}\) over all its counties (districts), weighting each threshold \({\hat{t}}_{r,d}\) by the proportion of stops in location d . Next, we calculate \({\hat{t}}_{r}\) as the unweighted average of all state thresholds \({\hat{t}}_{r,g}\) (or municipal thresholds) for race group r . In the second step, we take the unweighted average to account for differences in reporting practices across states, although our results are qualitatively similar under alternative averaging schemes.

Effects of legalization of marijuana

Measures legalizing recreational marijuana took effect on 9 December 2012 in Washington, and on 10 December 2012 in Colorado. In Colorado, an additional Senate bill was passed on 28 May 2013 (‘Inferences for Marijuana and Driving Offenses’, HB 13-1325) limiting the amount of tetrahydrocannabinol a person can have in their bloodstream to 5 ng ml –1 . Below we describe the data-processing choices we made when examining the effects of marijuana legalization, and then provide additional detail to supplement the analyses reported in Results .

When calculating the proportion of stops that resulted in drug-related offences, we excluded all alcohol-related violations or those relating to drug paraphernalia or drug-related felonies. More specifically, we excluded the following violation codes in Washington: ‘Drugs paraphernalia—misdemeanor’, ‘DUI—Drugs W/Test’, ‘DUI—Drugs No Test’, ‘Veh Hom—DUI/Drug’, ‘Veh Assault—DUI/Drug’, ‘Drugs paraphernalia—felony’ and ‘Drugs—felony’. And, in Colorado, we excluded the following: ‘Drove Vehicle While Under the Influence of Alcohol or Drugs or Both’, ‘Possession of Drug Paraphernalia’ and ‘Drove (Motor/Off-highway) Vehicle upon Highway When (License/Privilege to Drive) was Restrained for Express Consent or Alcohol/Drug Related Offense’.

Furthermore, when analysing search rates, we excluded impound, incident to arrest, protective frisk and warrant searches, as these types of search are not usually motivated by drug recovery. In Colorado, this means that we include both consent and probable cause searches. However, in Washington, we note that probable cause searches are barred because the state does not have an automobile exception for warrantless searches. For further context, it is acceptable in most states for police officers to conduct a (limited) warrantless search of a vehicle when officers have probable cause to believe that contraband or other evidence of a crime is in a vehicle, a policy known as the ‘automobile exception’ to the warrant requirement for searches. Washington, however, does not recognize this exception. Even if an officer has well-founded probable cause to believe that contraband or other evidence of a crime is in a vehicle, the officer still cannot search the vehicle without a warrant and must instead conduct the search pursuant to another exception to the warrant requirement, usually a driver’s consent.

As discussed in Results , the proportion of stops resulting in a search fell substantially in both Colorado and Washington, while in 12 states where marijuana was not legalized, search rates did not show sharp drops at the end of 2012. To assess this pattern quantitatively, we fit the following difference-in-differences search model on the set of stops in the 14 states we consider here (Colorado, Washington and the 12 non-legalization states):

where Y indicates whether a search was conducted, \({\beta }_{s}^{{\rm{state}}}\) and \({\beta }_{r}^{{\rm{race}}}\) are state and race fixed effects and β time is a time trend, with t a continuous variable in units of years since legalization (for example, t  = 0.5 means 6 months post-legalization). The Z term indicates ‘treatment’ status—that is, Z i  = 1 in Colorado and Washington for stops carried out during the post-legalization period, and Z i  = 0 otherwise. Thus the key parameters of interest are the race-specific coefficients \({\alpha }_{r}^{{\rm{race}}}\) . Table 1 lists coefficients for the fitted model. We found that \({\alpha }_{r}^{{\rm{race}}}\) is large and negative for white, black and Hispanic drivers, suggesting that the observed drop in searches in Colorado and Washington was due to marijuana legalization in those states.

Despite marijuana legalization decreasing search rates for white, black and Hispanic drivers, Fig. 4 shows that the relative disparity between white and non-white drivers remains. In addition, Supplementary Fig. 3 shows that white drivers faced consistently higher search thresholds than black and Hispanic drivers, both before and after marijuana legalization. Supplementary Fig. 3 also shows that the threshold faced by all groups decreases after legalization (although not all drops are statistically significant). There are several possible explanations for this decrease. Officers may not have fully internalized the change of policy, searching people who would have been at risk of carrying contraband before legalization but who were no longer high risk after marijuana became legal. Alternatively, or in addition, officers may have become focused on more serious offences (such as drug trafficking), applying a lower threshold commensurate with the increase in the severity of the suspected crime. Finally, officers may have had more resources after being relieved of the task of policing marijuana possession, freeing them to make searches with a lower chance of finding contraband.

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

All data to reproduce the findings of this study are available at https://openpolicing.stanford.edu .

Code availability

All code to reproduce the findings of this study is available at https://openpolicing.stanford.edu .

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Acknowledgements

We thank B. Bonilla, W. Kim, J. Nudell, S. Robertson and E. Sagara for their assistance throughout this project; we also thank A. Chohlas-Wood and A. Feller for their helpful feedback. This work was supported in part by the John S. and James L. Knight Foundation and by the Hellman Foundation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Pierson, E., Simoiu, C., Overgoor, J. et al. A large-scale analysis of racial disparities in police stops across the United States. Nat Hum Behav 4 , 736–745 (2020). https://doi.org/10.1038/s41562-020-0858-1

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Racial Profiling: Definition

“Racial Profiling” refers to the discriminatory practice by law enforcement officials of targeting individuals for suspicion of crime based on the individual’s race, ethnicity, religion or national origin. Criminal profiling, generally, as practiced by police, is the reliance on a group of characteristics they believe to be associated with crime. Examples of racial profiling are the use of race to determine which drivers to stop for minor traffic violations (commonly referred to as “driving while black or brown”), or the use of race to determine which pedestrians to search for illegal contraband.

Another example of racial profiling is the targeting, ongoing since the September 11th attacks, of Arabs, Muslims and South Asians for detention on minor immigrant violations in the absence of any connection to the attacks on the World Trade Center or the Pentagon.

Law enforcement agent includes a person acting in a policing capacity for public or private purposes. This includes security guards at department stores, airport security agents, police officers, or, more recently, airline pilots who have ordered passengers to disembark from flights, because the passengers’ ethnicity aroused the pilots’ suspicions. Members of each of these occupations have been accused of racial profiling.

Racial profiling does not refer to the act of a law enforcement agent pursuing a suspect in which the specific description of the suspect includes race or ethnicity in combination with other identifying factors.

Defining racial profiling as relying “solely” on the basis of race, ethnicity, national origin or religion can be problematic. This definition found in some state racial profiling laws is unacceptable, because it fails to include when police act on the basis of race, ethnicity, national origin or religion in combination with an alleged violation of all law. Under the “solely” definition, an officer who targeted Latino drivers who were speeding would not be racial profiling because the drivers were not stopped “solely” because of their race but also because they were speeding. This would eliminate the vast majority of racial profiling now occurring.

Any definition of racial profiling must include, in addition to racially or ethnically discriminatory acts, discriminatory omissions on the part of law enforcement as well. For example, during the eras of lynching in the South in the 19th and early 20th centuries and the civil rights movement in the 1950’s and 1960’s, southern sheriffs sat idly by while racists like the Ku Klux Klan terrorized African Americans. At times, the sheriffs would even release black suspects to the lynch mobs. A recent example would be the complaint by an African American man in Maryland, who after moving into a white community, was attacked and subjected to property damage. Local police failed to respond to his repeated complaints until they arrested him for shooting his gun into the air, trying to disperse a hostile mob outside his home.

Racial Profiling May Be Hazardous To Your Health

Many racial profiling victims walk away with traffic tickets, but too often for others the outcome of racial profiling is death.

Pennsylvania (Brentwood) – On October 12, 1995, Jonny Gammage, a 31 year-old African American male, was killed after being pulled over while driving the Jaguar of his cousin, Pittsburgh Steelers football player Ray Seals, in a predominately white community. Although police claimed that Gammage initiated the struggle, a tow truck driver said he saw one officer start the fight and the others join in kicking, hitting and clubbing Gammage while he lay on the pavement. Three officers were tried for involuntary manslaughter: John Vojtas was acquitted; Lt. Milton Mulholland and Michael Albert had their charges dismissed after two mistrials. Gammage’s family settled a wrongful death civil rights lawsuit against the five officers involved and their police departments for $1.5 million.

New York (Bronx-New York City) – On February 4, 1999, Amadou Diallo, an unarmed 22 year-old immigrant from New Guinea, West Africa, was shot and killed in the narrow vestibule of the apartment building where he lived. Four white officers, Sean Carroll, Kenneth Boss, Edward McMellon and Richard Murphy fired 41 bullets, hitting Diallo 19 times. All four were members of the New York City Police Department’s Street Crimes Unit, which, under the slogan, “We Own the Night,” used aggressive “stop and frisk” tactics against African Americans at a rate double that group’s population percentage. A report on the unit by the state attorney general found that blacks were stopped at a rate 10 times that of whites, and that 35 percent of those stops lacked reasonable suspicion to detain or had reports insufficiently filled out to make a determination. Thousands attended Diallo’s funeral. Demonstrations were held almost daily, along with the arrests of over 1,200 people in planned civil disobedience. In a trial that was moved out of the community where Diallo lived and to Albany in upstate New York, the four officers who killed Diallo were acquitted of all charges.

Ohio (Cincinnati) – On April 7, 2001, in the early morning hours, Timothy Thomas, a 19 year-old African-American, was shot to death by police officer John Roach. Thomas had 14 outstanding misdemeanor warrants, mostly traffic violations, including failure to wear a seat belt. According to a city councilman, he was running away, holding up his baggy pants, and scaled a fence, landing in a driveway where Roach was approaching and shot Thomas. He became the fifth black male in the city to die at the hands of police in a five-month period and the fifteenth since 1995. Two nights of protests left broken windows at City Hall and fires around the city. Witnesses reported that following Thomas’ funeral, six city SWAT team officers shot pellet-filled bags into a peaceful crowd. Two people hit by the pellets filed lawsuits. Under community and city council pressure, both the public safety director and city manager resigned. Officer Roach was indicted on charges of negligent homicide, and obstructing official business, resulting from differences in his version of events.

Roach was acquitted in a bench trial characterized by the judge’s (a former prosecutor) open admiration for Roach, and blaming Timothy Thomas for “making” Roach kill him.

A community coalition, the Cincinnati Black United Front and the ACLU of Ohio filed suit against the city and the Fraternal Order of Police, citing a pattern and practice of discrimination by police, including issuing the type of traffic citations Thomas received to African Americans at twice their population percentage. In April 2002 the case was settled, under terms including the establishment of a civilian complaint review board and the activation of the reporting of collected traffic stop data that had been enacted by city ordinance in 2001. The Department of Justice also intervened and settled with the city, including revision and review of use of force policy.

It is significant to note that research confirms the existence of bias in decisions to shoot. A series of University of California/University of Chicago studies recreated the experience of a police officer confronted with a potentially dangerous suspect, and found that:

• participants fired on an armed target more quickly when the target was African American than when White, and decided not to shoot an unarmed target more quickly when the target was White than when African American;
• participants failed to shoot an armed target more often when that target was White than when the target was African American. If the target was unarmed, participants mistakenly shot the target more often when African American than when White;
• shooting bias was greater among participants who held a strong cultural stereotype of African Americans as aggressive, violent and dangerous, and among participants who reported more contact with African Americans. shooting bias was greater among participants who held a strong cultural stereotype of African Americans as aggressive, violent and dangerous, and among participants who reported more contact with African Americans 1 .

The stories above and hundreds of others present a compelling argument that not only does racial profiling exists, but it is widespread, and has had a destructive effect on the lives of communities of color, and attitudes toward police.

Asian Racial Profiling

Asians, who, according to the U.S. census, number 10 million, or 4 percent of the population, have been victims of racial profiling as well. Wen Ho Lee, a Taiwanese American was targeted and suspected of espionage on the basis of his race. Memos by high-ranking FBI and Department of Energy officials acknowledged that Lee was singled out because he was Chinese, and eight similarly situated non-Chinese were not prosecuted. 2

In Seattle, Washington in July 2001 a group of 14 Asian American youth were stopped by police for jaywalking, claiming that they were kept against the wall for about an hour. The Seattle Times reported that one officer told them he had visited their country while in the army, and asked them repeatedly whether they spoke English. The paper also reported that U.S. Representative David Wu (D-Oregon) was detained entering the headquarters of the Department of Energy, and repeatedly.

In 2001, the Asian Freedom Project of Wisconsin issued a report that found the racial profiling of Hmong communities there, and included the testimony of adults, as well as boys and girls.

The Garden Grove (CA) Police Department settled a “gang” database racial profiling lawsuit by a group of young Asian Americans who said their civil rights were violated when officers photographed them as suspected gang members based merely on their ethnicity and clothing.

Indian Racial Profiling

Indigenous people ( Native Americans) call it “DWI,” with a new twist: “Driving While Indian.” According to the National American Indian Housing Council, there are 2.4 million Indians (including Eskimos and Aleuts) in the U.S. Indians complain about stops and searches by local police and sheriffs on roads leading to and from reservations.

In South Dakota , widespread reports of racial profiling led to hearings before the state legislature, where Indians testified about their being stopped and searched not only based on race but also on religious articles hanging from rearview mirrors, and regional license plates that identified them as living on reservations.

In June 2002 scores of Indians in the state’s Bennett County complained to Department of Justice attorneys, alleging racial profiling at the hands of sheriffs there, including vehicular stops in the absence of reasonable suspicion, the administration of breathalyzer tests without reasonable suspicion, warrantless searches of homes and vehicles, and demanding to see drivers licenses and vehicle registrations while inside bars.

Walking While Black and Brown

Although “Driving While Black/Brown” traffic stops and searches are the form of racial profiling that has received the most media attention, profiling takes place off the roadways as well. Black and Latino pedestrians are regularly stopped and frisked without reasonable cause.

In New York City , the December 1999 report of the New York City Police Departments pedestrian “stop and frisk” practices by the state attorney general provided glaring evidence of racial profiling in the nation’s largest city. Blacks comprise 25.6 percent of the City’s population, yet 50.6 percent of all persons “stopped” during the period were black. Hispanics comprise 23.7 percent of the City’s population yet, 33.0 percent of all “stops” were of Hispanics. By contrast, whites are 43.4 percent of the City’s population, but accounted for only 12.9 percent of all stops. Blacks comprise 62.7 percent of all persons “stopped” by the NYPD’s Street Crime Unit (“SCU”).

In precincts in which blacks and Hispanics each represented less than 10 percent of the total population, individuals identified as belonging to these racial groups nevertheless accounted for more than half of the total “stops” during the covered period. Blacks accounted for 30 percent of all persons “stopped” in these precincts; Hispanics accounted for 23.4 percent of all persons “stopped.”

Finally, precincts where minorities constitute the majority of the overall population tended to see more “stop & frisk” activity than precincts where whites constitute a majority of the population: Of the ten precincts showing the highest rate of “stop and frisk” activity (measured by “stops” per 1,000 residents), in only one (the 10 th Precinct) was the majority of the population white. In seven other precincts, blacks and Hispanics constituted the majority of the population. The remaining two precincts were business districts in Manhattan and Brooklyn in which the daytime racial breakdown of persons within the precinct is unknown.

In roughly half of the police precincts in New York City, the majority of the population living in the precinct is white. However, of these 36 majority-white precincts, only 13 were in the top half of precincts showing most “stops” during the period.

“Gang” Database Racial Profiling

In Orange County California , a database containing the names and photographs of reputed gang members appeared to racially profile. 3 Latinos, Asians and African Americans were more than 90 percent of the 20,221 men and women in the Gang Reporting Evaluation and Tracking system, but made up less than half of Orange County’s population. The disparity attracted the notice of the California Advisory Committee of the U.S. Commission on Civil Rights as well as the ACLU. We asked the county district attorney’s office to establish a civilian oversight board to monitor what we saw as problems with the list.

“Bicycling While Black and Brown”

Youth of color have been victims of racially-motivated bicycling stops, ” In April, 2001, the ACLU joined a suit against Eastpointe, Michigan , representing 21 young African-American men who were stopped by the police while riding their bikes there. The ACLU argued that the bicyclists were stopped in this predominantly white suburb of Detroit because of their race and not because they were doing anything wrong. In a 1996 memorandum to the Eastpointe City Manager, the former police chief stated that he instructed his officers to investigate any black youths riding through Eastpointe subdivisions. Police searched many of young men and, in some cases, seized and later sold their bicycles. Police logs and reports in Eastpointe have identified over 100 incidents between 1995 and 1998 in which African-American youth were detained.

“Bitten While Black and Brown”

A throwback to the grainy ‘60’s black and white television news footage of vicious police dogs attacking peaceful black civil rights protesters is the continued discriminatory use of canine units by police. These dogs, lethal weapons capable of biting at 2000 pounds pressure per square inch, and their handlers have been implicated in a vicious form of racial profiling that has led to legal action:

California (Los Angeles)- The ACLU of Southern California compiled reports on the hundreds of mostly blacks and Latinos who were bitten by Los Angeles Police Department dogs from 1990-1992, charging that the dogs trained to “attack and maul,” were routinely sent out in non-violent situations. In 1997, California state highway patrol canine units stopped almost 34,000 vehicles. Only 2 percent were carrying drugs.

Maryland (Prince Georges County) – The Washington Post reported that in May 2001 federal prosecutors charged a county police officer with releasing her police dog on an unarmed Mexican immigrant as part of a pattern of using and threatening the use of the dog on people of color. Despite being the subject of four lawsuits, twice being guilty of making false statements to a supervisor, and five prior instances of releasing the dog on suspects who weren’t resisting, and being flagged by a departmental “early warning” system, the officer remained undisciplined in any substantive way. In 1999 the Post reported that thirteen police dog excessive force suits had been filed in Prince Georges circuit and federal courts, in addition to five others that ended in judgement for plaintiffs or settlement. Of the total, ten alleged repeated bites of suspects once under police control, or while cuffed or on the ground.

South Dakota (Wagner)- While not involving the use of physical canine force, the issue reached a new low when school officials and police led a large German shepherd drug dog through classrooms in suspicionless drug searches of Yankton Sioux K-12 students, some as young as six years old. In July 2002, the ACLU filed suit in federal court.

Washington (Seattle) -In 1992 the ACLU alleged that police dog handlers used excessive force on suspects. Dogs were trained to attack and bite suspects regardless of their actions, even against alleged shoplifters, gasoline siphoners and jaywalkers. They also reported that in that year, 40 percent of police dog attacks were against African Americans, and that 91 people had received police dog bite injuries requiring hospitalization.

The following states appear to require independent reasonable suspicion for dog searches: Alaska, 4 Illinois, 5 Minnesota, 6 New Hampshire, New York, 8 Pennsylvania, 9 and Washington. 10

“Shopping While Black and Brown”

The targeting of shoppers/business patrons of color for suspicion of shoplifting by private security and other employees has disproportionately affected both working and prominent African-American women. TV talk show host Oprah Winfrey said she was refused buzz-in entry to a store even after seeing white women admitted and making a second attempt. After calling from a pay phone and being assured the store was in fact open, a third try failed as well (New York City) . U.S, Congresswoman Maxine Waters said she was followed around a store and required to show her key at a hotel, unlike whites who entered before her ( New York City) . Professional basketball player and Olympic medalist Sheryl Swoopes was kept waiting to be seated for almost an hour at a restaurant, while whites who arrived after her were seated before her (Houston, Texas) .

Pauline Hampton and her niece, both African-Americans, were shopping at the Dillard Department Store in Overland Park, Kansas , a suburb of Kansas City, with their children. After making several purchases, they went to the cosmetics counter to redeem a coupon. A white security guard accused Hampton of shoplifting, took her shopping bag, and, without consent, searched it, emptying the bag onto the counter. After finding the receipt for the items, he shoved the goods and the empty bag back to her. When she complained about his actions, the guard ordered them to leave, and threatened to call the police and have them forcibly removed. Hampton eventually called her husband to the scene and the situation escalated. They sued, and were awarded a $1.2 million judgement; the U.S. Supreme Court declined to hear Dillard’s appeal.

The store chain, based in Arkansas has also faced dozens of racial profiling lawsuits, claiming harassment and false arrest, in other states including Arkansas, Iowa, and Texas . Evidence produced in one case showed that although 16 percent of its shoppers were African American, 87 percent of the false arrest claims were made by them. In Texas, Dillard settled and paid money to the family of an African American customer who died at a store after being beaten and hog-tied while being detained, and has also settled discrimination suits by employees in Kansas and Missouri.

Other companies sued for racial profiling include Eddie Bauer, Avis Rent A Car, Denny’s Restaurant, The Children’s Place, and Holiday Spa.

Worksite Racial Profiling

The Immigration and Naturalization Service has had a history of disproportionately targeting ethnic groups of color for undocumented labor violations. Like all law enforcement, INS agents must have sufficient evidence of wrong doing to establish probable cause or reasonable suspicion to arrest or detain. They may not carry out their duties in a racially or ethnically discriminatory manner. While ethnicity or nationality are obviously critical elements in immigration violations by themselves, without additional facts there is insufficient basis for law enforcement action.

The New York Times reviewed files of INS raids released as part of the settlement of a garment workers union selective enforcement suit against the agency in New York City. The settlement included a summary that Latinos were 96 percent of the 2,907 people arrested in the 187 worksite raids carried out by the INS in the district, fat greater than their representation in the city’s legal or illegal population. This occurred even where the INS acknowledged that half the workers were not Latino but Asian, including undocumented immigrants.

And while some raids were based on informant information, 80 percent were initiated by agents who cited as primary evidence subjects’ appearance or language without evidence of wrongdoing. Included were skin color, speaking Spanish or English with a Spanish accent, appearing to be of South or Central American descent and wearing clothing “not typical of North Americans.” Such characterizations in major American cities are common to born and naturalized citizens alike.

Undocumented workers were discovered and arrested in all but a few of the reviewed raids, but nearly everyone arrested was Latino.

Suits have also been filed in Arkansas, California, Louisiana, and Ohio claiming racial profiling by the INS. A federal court in Ohio found violations of the rights of Latinos by that states highway patrol’s practice of stopping Latino drivers to question them about their immigration status, including officers even confiscated the green cards of legal migrant workers claiming they were counterfeit. In California, federal courts have found Fourth Amendment violations of Latinos in the stopping of Latinos on the basis of appearance and foreign sounding names.

The Supreme Court has held that INS agents working near the Mexican border may use Spanish ethnicity as a basis for detaining a person, but that it may not be the only basis.

A related issue is the targeting by police, first reported by the ACLU in Florida, of Latinos waiting on public sidewalks for labor employers to appear and select them for work, under the offense of being “visual clutter.”

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• 3d ago Court hearings delayed as Ministry of Justice slows proceedings due to prison overcrowding
• 3d ago Jeremy Hunt and Mel Stride warn against benefits ‘lifestyle choice’
• 3d ago Curbs on sex education use pupils as ‘political football’, school leaders say
• 3d ago Minister says stop and search to be ramped up, dismissing concerns about disproportionate targeting of black men

Labour says MoJ's decision to hold up court hearings shows people 'less safe' under Tories

Labour says the Ministry of Justice’s decision to delay court hearings because of prison overcrowing (see 10.39am ) shows that people are “less safe” under the Tories. That’s a very convenient retort to Rishi Sunak, because only two days ago he gave a major speech arguing that security was a key reason why his party deserved to win the election.

In a statement, Shabana Mahmood , the shadow justice secretary, said:

The Tories continue to make major and unprecedented changes to the justice system without so much as a word to the public. It’s completely unacceptable and the public will be alarmed at this latest panic measures. The government is stalling justice and leaving victims in limbo because of the mess they have created. This comes days after they hid from the public that they’re now letting criminals out of jail earlier than ever before. It is astonishing that lawyers and witnesses, let alone the public, are none the wiser on which cases will actually be affected – how many people will be let out on bail when they should have been remanded, and how will the government ensure public safety is not compromised? This is no way to run the justice system, and it is certainly no way to run the country. Communities will rightly conclude that they’re less safe under Rishi Sunak’s Tory party.

Nick Thomas-Symonds , a shadow Cabinet Office minister, has been giving interviews on behalf of Labour this morning. On the government plan to ramp up stop and search, he was relatively non-committal, saying in some circumstances this was a valuable tool. He told GB News:

The government is completely failing [on knife crime]. We’ve had an 80% increase since 2015 and rises all around the country. That’s the first point. On stop and search, that is intelligence lead and evidence-based and is a really important tool. We’ve had, for example, the Inspectorate of Constabulary, an independent organisation, looking at this saying that what’s essential is that it is done in that targeted way. In a non-targeted way, when it isn’t evidence based, it does lead to a lack of community [cohesion].

This is what the Criminal Law Solicitors’ Association sent to members last night about court hearings being delayed.

Urgent release below re: Operational Early Dawn. pic.twitter.com/GTivtkkF21 — CLSA (@CrimeSolicitors) May 14, 2024

Emergency measure triggered to help with prison population, says Law Society

The Law Society has issued this statement about today’s decision to delay some court hearings because of prison overcrowding.

We have been made aware that from today, the lord chancellor is triggering an emergency measure, Operation Early Dawn, to deal with the worsening problem of the prison population. Many magistrates' court cases will be delayed. Practitioners will not know if their clients’ cases… — The Law Society (@TheLawSociety) May 15, 2024

We have been made aware that from today, the lord chancellor is triggering an emergency measure, Operation Early Dawn, to deal with the worsening problem of the prison population. Many magistrates’ court cases will be delayed. Practitioners will not know if their clients’ cases will be delayed for sure until they get there. This particular impact will be caused by a triage process for defendants being transferred from police custody suites to the magistrates courts and then likely to be transferred to prison. We understand priority will be given to defendants in the most serious cases. We understand that defendants who are not prioritised will be released on police bail. We are awaiting further urgent information from HMCTS and MoJ to clarify the practical implications of this decision, including how they will deal with the issue of the wasted costs members may incur in these circumstances. We will update members in due course.

Court hearings delayed as Ministry of Justice slows proceedings due to prison overcrowding

Some court hearings in England are going to be delayed because of prison overcrowding, the BBC is reporting. In their story Chris Mason and Jennifer McKiernan report:

Courts in England in areas with a lack of prison places will be affected for around a week. A solicitors’ group said many magistrates’ court cases will be affected as officials decide which defendants will be prioritised. The move, called Operation Early Dawn, is expected to impact courts in England for around a week in regions where there is a lack of prison places.

The Ministry of Justice told the BBC that the government is creating thousands more prison places to meet demand. The Criminal Law Solicitors Association said:

We are appalled of the state of our criminal justice system and have been campaigning on this from our inception and whilst we recognise the need for some action, this is a symptom of a systemic problem caused by more than 40 years of neglect of our criminal justice system.

Chris Philp , the policing minister, told Sky News this morning that equipment allowing the police to scan people for knives at a distance is almost ready to be rolled out. He explained:

A police officer could scan people walking down the street at a distance of six or 10 feet to see if they’re carrying a knife. So scanning large numbers of people, that’s not ready for deployment today, but I’m hoping by the end of this year it can be deployed experimentally or in a test way.

Jeremy Hunt and Mel Stride warn against benefits ‘lifestyle choice’

Jeremy Hunt , the chancellor, and Mel Stride , the work and pensions secretary, have suggested that too many people are claiming unemployment benefit as a lifestyle choice in a joint article published in the Times. Matthew Weaver has the story here.

The Labour party has not press released anything about stop and search or sex education this morning, but it is attacking the government’s record on sewage. In a response to a BBC story revealing that millions of litres of raw sewage were pumped into Windermere in February, because a water company took 10 hours to address a problem with a broken pump, Steve Reed, the shadow environment secretary, said:

The Conservatives just folded their arms and looked the other way while United Utilities pumped a tidal wave of raw sewage into Britain’s most beautiful lake. It’s time for change. The next Labour government will put the water companies under special measures to force United Utilities to clean up their toxic mess. We will give the regulator tough new powers to make law-breaking water bosses face criminal charges and ban the payment of their multi-million pound bonuses until they clean up their toxic filth.

Curbs on sex education use pupils as ‘political football’, school leaders say

School leaders have accused the government of using children as “a political football” over its proposals to restrict sex education lessons in England, Richard Adams reports.

Minister says stop and search to be ramped up, dismissing concerns about disproportionate targeting of black men

Good morning. It would be nice to think that the government is not entirely focused on re-election, and that not all decisions are being made with partisan considerations to the fore, but it can be hard to sustain that view after a glance at the papers. Today the government has put two issues on the table – both of which might appeal to a Tory election strategist hoping to find something (anything?) that might create a dividing line with Labour .

First, sex education. Gillian Keegan, the education secretary, is expected to publish plans tomorrow, but right-leaning papers have been briefed that the rules for schools in England will be stricter and that they will be told not to teach pupils about gender identity issues.

THE TIMES: Don’t teach pupils about gender ID, schools told #TomorrowsPapersToday pic.twitter.com/kXXxhcc981 — Neil Henderson (@hendopolis) May 14, 2024

DAILY MAIL: Sex education to be banned for under 9s #TomorrowsPapersToday pic.twitter.com/h8PaHzAWJr — Neil Henderson (@hendopolis) May 14, 2024

Our story, by Richard Adams and Pippa Crerar , is here.

The advantage of briefing out a story like this in advance, from the government’s point of view, is that reports get written up on the basis of what sources tell reporters, not on the basis of what a document actually says. But the disadvantage is that this makes it easy for Labour to say it won’t comment until it has seen the detail, but which time the media caravan will have moved on.

And, second, there is an announcement about ramping up stop and search to counter knife crime.

TELEGRAPH: Tories tell police: Bring back stop and search #TomorrowsPapersToday pic.twitter.com/XUUqDnotwr — Neil Henderson (@hendopolis) May 14, 2024

Chris Philp, the policing minister, has been giving interviews about this topic this morning and he told LBC:

I’d like to see officers, of course, use the power lawfully and also respectfully, but it does need to be used more to protect the public and particularly the kind of young men who often end up being victims of knife crime.

When it was put to him that in the past stop and search has been used disproportionately against young black men, he replied:

The sad truth is that young black men are disproportionately victims of knife crime and we’re doing this as much to protect them as anything else.

He also said that stop and search success rates typically have a success rate of 25 to 30%.

That percentage is pretty much the same across something to within 1% across all ethnicities so that gives me quite a high degree of confidence that police are not unreasonably picking on particular parts of the community.

Philp was also on the Today programme where Emma Barnett , who has just joined the programme as a presenter, pointed out that when Theresa May was home secretary she restricted the use of stop and search. She asked why the government was performing a U-turn. In response, Philp said stop and search rates in London have fallen by 44% in the past two years. When Barnett put it to him that May’s policy had failed, Philp dodged the question.

Talking of Theresa May , there was some speculation last night that Labour might announce another defector in time for PMQs today. May told Bethany Dawson from Politico that it would not be her.

Asked if she would stand again as a Conservative, Theresa May tonight said "I am a Conservative, I would stand again as a Conservative," largely quashing the excitement of people thinking of the funniest option for defection. — Bethany Dawson (@bethanymrd) May 14, 2024

Asked if she would stand again as a Conservative, Theresa May tonight said “I am a Conservative, I would stand again as a Conservative,” largely quashing the excitement of people thinking of the funniest option for defection.

Labour sources are saying that hacks on defector watch should stand down today. But the very fact that it did not seem totally absurd for a reporter to ask a former PM if they would defect to another party does say something about the state of Rishi Sunak’s Conservative party.

Here is the agenda for the day.

9.30am: Brian Bell, chair of the Migration Advisory Committee, and other migration experts give evidence to the Commons home affairs committee.

10am: Arlene Foster, the former Northern Ireland first minister, gives evidence to the UK Covid inquiry in Belfast.

12pm: Rishi Sunak faces Keir Starmer at PMQs.

After 12.45pm: MPs begin the first day of the debate on the remaining stages of the criminal justice bill.

3pm: Michael Tomlinson, the minister for illegal migration, gives evidence to the joint committee on human rights.

For technical reasons we are not using the ‘send us a message’ feature any more, and if you want to contact me, please post a message below the line (BTL) or message me on X (Twitter). I can’t read all the messages BTL, but if you put “Andrew” in a message aimed at me, I am more likely to see it because I search for posts containing that word. If you want to flag something up urgently, it is best to use X; I’ll see something addressed to @AndrewSparrow very quickly. I find it very helpful when readers point out mistakes, even minor typos (no error is too small to correct). And I find your questions very interesting too. I can’t promise to reply to them all, but I will try to reply to as many as I can, either BTL or sometimes in the blog.

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Arizona Republicans aren't giving you the full story on their 'border security' bill

Opinion: arizona lawmakers pushing a ballot measure to crack down on illegal immigration aren't telling the truth on what it does..

The Arizona lawmakers pushing for a Texas-style law authorizing local officers to go after illegal border crossers insist nobody will be racially profiled because of it.

That’s a myth and they know it.

There’s no such thing as an immigration crackdown without racial profiling — not in Arizona or any place else in America.

Reality is that most asylum seekers and border crossers fleeing poverty and other calamities come from countries where darker skins predominate.

What the ballot measure would do

Until a few years ago, most people crossing the southern border came from Mexico .

Now they’re mainly from elsewhere , including Central America, Haiti, Venezuela, Cuba, Syria, Iran, Turkey and other war-zone countries like Ukraine.

Thus, putting a target on these immigrants is a target on anyone with dark skin, unless enforcement is geographically limited to the borderline, which the Arizona proposal doesn’t do.  

The Republican-sponsored House Concurrent Resolution 2060 is largely similar to the legislation that Gov. Katie Hobbs recently vetoed. They now want to skip the governor and send it directly to the November ballot to rally voters against Democrats whom they blame for the uptick of border crossers.

The Arizona proposal , which mimics Texas’ immigration law being litigated in federal court, would make it a state crime to enter the country illegally, punishable with jail time and longer prison sentences for repeat offenders.

It’s also stacked with a range of penalties designed to crack down on illegal immigration anywhere in the state — not just at the border, as proponents maintain.

Those include:

• Making it a state crime to submit false documents in applying for federal, state or local benefits,
• Requiring agencies to use the federal E-Verify program to determine public benefits eligibility, and
• Imposing a minimum 10-year prison sentence on adults caught selling fentanyl that results in a death.

Supporters insist it's about border security

“This is truly a border security bill,” Republican Sen. President Warren Petersen told Fox News , insisting that it is different than the infamous Senate Bill 1070 that led to racial profiling of Latinos and which cost Arizona hundreds of millions of tourism dollars and legal fees.

What Petersen says and what the proposal spells out don’t entirely match.

“It allows law enforcement to, if they see somebody crossing the border illegally, they’re able to arrest them, detain them and put them through the judicial process,” Petersen said.

GOP looks for job security: In fake border bill

That’s right. But a crucial detail he and others leave out of their media soundbites is the fact that the proposal doesn’t specifically limit law enforcement along Arizona’s 370-mile shared border with Mexico.

Technically, any law enforcement officer anywhere in the state could turn “any traffic stop into an immigration interrogation,” as Democratic Rep. Analise Ortiz puts it .

Republican Yavapai County Sheriff David Rhodes admitted as much during this week’s legislative hearing, saying there’s a lot of questions to be answered.

Speaking on behalf of the Arizona Sheriff’s Association, Rhodes said border counties would bear the brunt of arresting undocumented immigrants but still wouldn’t say enforcement is strictly limited to the border.

This is important because border enforcement — at the border — is Republicans’ selling point to voters, leaving out the sweeping ramifications this kind of law would inflict on Arizona’s labor market, immigrant families of mixed-immigration status and Latinos in general.

These provisions would target brown people

Nobody can deny that SB 1070 put a target on brown people . Police data and court documents prove it .

Anecdotally, countless U.S. citizens were targeted under the “show me your papers” provision of SB 1070. Some of them told lawmakers as much , yet Republicans dismissed the narrative as nothing more than politicking.

Yet, proponents can’t admit the fact that the legislation as written gives local enforcement anywhere in the state the authority to enforce immigration law and that it would be up to them to carry it out — and how.

No word yet on how much of taxpayers’ money it would take to enforce any of the provisions.

Or on how local law enforcement would differentiate illegal border crossers from legal residents and U.S. citizens making a wrong turn in traffic.

What would give local cops the “probable cause” to question the immigration status of somebody they encounter other the initial suspicions because of their skin color?      

Presumably, none of the supporters have ever been racially profiled and truly believe the practice doesn’t exist. But these people are smart enough to know exactly what has happened under SB 1070.

They know exactly what they’re doing. They’re counting on Arizonans to merely take their word for what they say the ballot measure would do — whether that’s true or not.

Elvia Díaz is editorial page editor for The Arizona Republic and azcentral. Reach her at 602-444-8606 or  [email protected] . Follow her on Twitter,  @elviadiaz1 . 

A Palestinian converted to Judaism. An Israeli soldier saw him as a threat and opened fire

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At first, it seemed like the kind of shooting that has become all too common in the Israeli-occupied West Bank. A Palestinian aroused suspicions and an Israeli soldier killed him.

But then the deceased was identified as David Ben-Avraham, a Palestinian who had made the almost unheard-of decision to convert from Islam to Judaism years earlier.

His unusual journey had taken him across some of the deepest fault lines in the Middle East and led to some unlikely friendships. Most Palestinians saw him as an eccentric outcast, while many Israelis treated him as an unwelcome convert to a religion that doesn’t proselytize.

But in his final moments, he was once again viewed as a Palestinian who was in the wrong place, at a time of widespread anger and suspicion.

A DIVIDED CITY

He was born Sameh Zeitoun in Hebron, home to some 200,000 Palestinians as well as hundreds of Jewish settlers who live in enclaves guarded by Israeli troops. Tensions have run high for decades, often spilling over into violence.

Rights groups have long accused Hebron’s settlers of harassing Palestinian residents, and Palestinians have committed a number of stabbing and shooting attacks against Israelis over the years.

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At its most extreme, the bitter neighbors live just a few meters apart. In some narrow alleys of Hebron’s Old City, metal netting protects Palestinian shoppers from objects thrown by settlers living on the upper floors.

Zeitoun first made contact with Jewish settlers over a decade ago, asking for help converting to Judaism, according to Noam Arnon, a Jewish settler in Hebron who went on to befriend him.

He said Zeitoun was inspired by family stories about his grandfather protecting Jews when riots erupted in 1929, when the Holy Land was under British colonial rule. Palestinians killed dozens of Jewish residents in the city.

“He went further, not only to live as a good neighbor but to join the Jewish community,” Arnon recounted.

A RARE CONVERSION

Conversion to other faiths is deeply frowned upon in Islam. In much of the Muslim world, those who do so are cast out of their communities, sometimes violently. Judaism, unlike Islam and Christianity, has no tradition of proselytization.

Such a conversion is even more fraught in Israel and the Palestinian territories, where religion and nationality usually overlap in a decades-old conflict. Judaism is the faith of most of the soldiers who patrol the territory and the settlers whom Palestinians see as hostile colonizers.

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Arnon said most of the settlers from Hebron’s tight-knit community refused to accept Ben-Avraham. Only Arnon and a few others interacted with him, helping with his conversion application papers.

Religious conversions are rare but legal in areas administered by the semi-autonomous Palestinian Authority. Most are undertaken by Palestinian Christians converting to Islam for marriage.

In Israel, converting to Judaism requires an application to the government-run Conversion Authority. Ben-Avraham submitted two requests in 2018 but did not meet the requirements, according to a government official who was not authorized to speak with media and spoke on condition of anonymity.

With that pathway closed, Ben-Avraham turned to Israel’s insular ultra-Orthodox community and eventually made his conversion official in 2020, according to documents published online.

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In the year before his conversion, Ben-Avraham was detained by the Palestinian Authority’s intelligence unit in Hebron, according to Arnon and a local Palestinian activist, Issa Amro.

The reason for his arrest was never publicly disclosed, but they believe his conversion and open connections with Israelis attracted unwanted attention.

Palestinians can face arrest or even death if they’re seen as collaborating with Israeli authorities. But few would have suspected Ben-Avraham of being an informant because his story was widely known.

Ben-Avraham told the Israeli news site Times of Israel that he was held for two months in solitary confinement and beaten before being released. Around that time, a video emerged showing him holding what appears to be a Quran and pledging his Muslim faith.

Arnon and Amro said his statement was likely made under duress during detention. The PA’s prosecution office said it had no information about his case.

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After his release, Ben-Avraham moved in with Haim Parag, a Jewish friend who lived in Jerusalem. He returned to Hebron infrequently because of safety concerns and continued his Jewish studies. Parag said the pair regularly prayed together at a nearby synagogue.

“He was like a son to me,” he said.

Parag also said he met Ben-Avraham’s wife and some of his children, and that several close family members maintained a relationship with him even after his conversion.

The Zeitoun family declined to speak with the Associated Press, fearing reprisal. In the end, Ben-Avraham left little public record of what drove his personal convictions.

A DEADLY SHOOTING

Ben-Avraham was waiting outside a West Bank settlement for an Israeli bus to take him to Parag’s apartment March 19 when he got into an argument in Hebrew with an Israeli soldier.

Across the West Bank, Jewish settlers live apart from Palestinians in guarded settlements where they’re subject to different laws. Palestinians are generally barred from entering settlements unless they have work permits.

“Are you Jewish?” the soldier shouts in a video that circulated online and appears to have been shot by his body camera.

“Of course,” Ben-Avraham answers.

“What’s your name?” the soldier says.

“David,” he replies.

“David?” the soldier says.

“Ben-Avraham, stupid.”

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The soldier then orders Ben-Avraham to step away from his bag on the ground and raise his hands in the air, before saying sarcastically, “Jewish.”

A second video, apparently taken from a nearby security camera, appears to show two soldiers shooting Ben-Avraham from a close distance as he keels over backward onto the sidewalk.

The army said a small knife was found in Ben Avraham’s bag after the shooting. Parag said he gave him the knife for self-defense.

The Israeli army said it’s investigating the shooting, but rights groups say soldiers are rarely held accountable in such situations.

Israeli forces have been on high alert as the West Bank has seen a surge of violence linked to the war in Gaza. Nearly 500 Palestinians have been killed by Israeli fire since the war’s start, according to the Palestinian Health Ministry. Many have been shot dead in armed clashes during military raids, others for throwing stones at troops, and some who were posing no apparent threat.

Palestinians have also carried out several stabbing and other attacks against Israelis.

Arnon said the shooting was a tragic misunderstanding. Parag, Ben-Avraham’s friend in Jerusalem, accused the soldiers of racial profiling, saying they saw Ben-Avraham for his background and not his unexpected beliefs.

A FUNERAL Even in death, Ben-Avraham’s identity was contested.

Parag and another Israeli friend asked an Israeli court for the body to bury him at a Jewish cemetery, filing a petition against members of the Zeitoun family who wanted a Muslim funeral. Bezalel Hochman, a lawyer representing the two Israelis, said the Tel Aviv family court ruled in their favor.

After his death caused a public outcry, the Interior Ministry granted him Israeli residency, saying it wanted “to fulfill the will and desire of the deceased to be part of the nation of Israel.”

Ben-Avraham was buried in April in a Jewish cemetery on the foothills of Mount Gerizim, near the Palestinian city of Nablus, Parag said. The hilltop is sacred for Samaritans — a small, ancient religious minority that straddles the Palestinian-Israeli divide, just like Ben-Avraham.

No one from the Zeitoun family attended the funeral, said Parag, who’s designing his friend’s gravestone.

He said it will read: “David Ben-Avraham Zeitoun Parag. The Holy Jew.”

Jeffery writes for the Associated Press. AP writer Tia Goldenberg in Jerusalem contributed to this report.

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Arizona sheriff backs state border bill but raises funding worry

( NewsNation ) — The sheriff of Arizona’s Cochise County voiced support for a Republican-backed border security bill that could go to voters next year while pushing back against concerns that it could lead to racial profiling by law enforcement.

Sheriff Mark Dannels said Wednesday on “ NewsNation Now ” he backs the “spirit” and “intent” of the HCR 2060 or “Secure the Border Act” bill, which is similar to Texas’ law giving local police more authority to make immigration arrests. However, he raised concerns about how the state would fund agencies to take on increased immigration enforcement duties.

“The intent of the bill, the spirit of the bill, I support 100%,” Dannels said. “How do we fund it? … That’s where the problem lies.”

The bill has stalled in the Arizona Senate after a Republican member wanted to keep protections in place for the Deferred Action for Childhood Arrivals (DACA) program. Dannels said that issue was slowing the bill’s progress, with the Senate expected to take it up again next week.

While supporting greater immigration enforcement, Dannels rejected the notion it would inevitably involve racial profiling of Hispanics. A local opinion writer wrote this week there’s “no such thing as an immigration crackdown without racial profiling — not in Arizona or any place else in America.”

“I don’t agree with that,” Dannels said, though he acknowledged such concerns stemmed from Arizona’s controversial “show me your papers” law a decade ago. With the new border bill, he said, “if an agency or law enforcement agency … is doing that racial profiling, shame on that leader.”

For the latest news, weather, sports, and streaming video, head to NewsNation.

Largest Latino group backs Biden in battleground Arizona while spotlighting ballot measures

The largest Latino group in the country endorsed President Joe Biden in Arizona on Tuesday and said the group will also work to turn out voters to influence potential ballot measures on abortion, minimum wage and immigration. These measures, the group says, are as important in driving Latino voters to the polls .

Janet Murguía, president of UnidosUS and its political arm, UnidosUS Action Fund, threw the group's support behind Biden and Vice President Kamala Harris and endorsed Rep. Ruben Gallego in his U.S. Senate race and Raquel Terán and Kirsten Engel in their House races. All are Democrats.

Proposed measures in the highly competitive state include asking voters to legalize abortion and to increase the minimum wage. The Legislature also was scheduled to vote Tuesday on whether to put on the ballot a Republican-backed immigration measure with several proposals, including one to create a state law similar to a Texas law that allows police to arrest people in the country illegally and judges to deport them.

"We are looking at this state in a comprehensive way," Murguía said. "Of course the national presidential election is crucial to us, but we see opportunity to leverage the turnout for representation in Congress ... and beyond that it's those key issues that are going to impact our community, minimum wage ... and then the ongoing battle that we're in for women's reproductive health rights."

Arizona Republicans won a state Supreme Court decision to enforce an 1864 near-total abortion ban and had fought efforts to repeal it. The governor has signed a repeal of the law after a handful of Republicans joined Democrats to pass it.

Endorsements, Murguía said, can be leveraged not for just one candidate but also to benefit the community on several fronts across candidates and several issues.

UnidosUS announced its endorsement as the state is nearing a boiling point over immigration. The battle is reminiscent of the atmosphere that reigned more than a decade ago because of the anti-immigrant measure SB 1070, which allowed police in the state to question people about whether they were legally in the country for any reason. Much of that law, known as the "Show Me Your Papers" law, was struck down by the U.S. Supreme Court, which ruled immigration enforcement is a federal, not state, authority.

At the same time, Joe Arpaio, then the sheriff of Maricopa County, was using racial profiling as part of an anti-immigrant campaign that included dressing detained migrants in pink underwear and holding them in tent camps in the hot weather.

UnidosUs joined other organizations in boycotting the state, which led to lost tourism revenue and business sales.

"This ballot measure would take us backward, not forward," Enrique Davis Mazlum, Arizona state director for UnidosUS Action Fund, said about the proposed immigration measure. "If approved, it opens the door to discrimination, including racial profiling for immigrant and Latino Arizonans in places where our community should feel safe, like schools, churches and hospitals."

Murguía said the organization and others are "better prepared to push back" against measures seen as harmful to the Latino community.

"I think we found our voice and our vote matters, and we have a record of success when we come together and use both," she said.

The National Association of Latino Elected and Appointed Officials, NALEO, has projected that 855,000 Arizona voters in the November election, or 1 in 4, will be Latino.

About 814,000 Arizona Latinos voted in 2020, up from 543,000 in 2016, according to UnidosUS' Hispanic Electorate Data Hub.

Biden won Arizona by 10,457 votes in 2020, and Latino voters were seen as critical. His victory flipped the state, which had historically been red. Although Biden won a majority of Hispanic voters, Donald Trump made inroads, and polls suggest he could make a better showing this election.

UnidosUS, with Voto Latino, Mi Familia Vota and Latino Victory Project, plan to spend a combined $50 million on registration, canvassing, media buys and voter turnout. Davis Mazlum said they will particularly target people without strong histories of voting in urban areas and in communities on the U.S.-Mexico border.

For more from NBC Latino,  sign up for our weekly newsletter .

Suzanne Gamboa is a national reporter for NBC Latino and NBCNews.com