Description
Version-of-record in Crime & Delinquency
The release of video showing a Chicago Police Department (CPD) officer fatally shooting 17-year-old Laquan McDonald resulted in widespread outrage and scrutiny of the CPD. This study examines whether the video’s release influenced arrest and traffic stop rates in Chicago. It ...
The release of video showing a Chicago Police Department (CPD) officer fatally shooting 17-year-old Laquan McDonald resulted in widespread outrage and scrutiny of the CPD. This study examines whether the video’s release influenced arrest and traffic stop rates in Chicago. It adds to the existing depolicing literature through its focus on the CPD’s specialized units contrasted with officers assigned to patrol. The results indicate that arrest rates decreased significantly after the video was released, but only for officers assigned to specialized units. The video release had no significant impact on traffic stop rates. Additional analyses demonstrate that the significant reductions in arrest rates for specialized units were somewhat more pronounced in police districts with less than a majority of Black residents.
Keywords: depolicing, Ferguson effect, specialized units, arrests, traffic stops, race
November 24, 2015, represented a turning point in the Chicago Police Department (CPD) with the court-ordered release of the dashcam footage of CPD officer Jason Van Dyke firing 16 shots that fatally wounded 17-year-old Laquan McDonald. The shocking footage, and the city’s year-long effort to cover it up, catalyzed a crisis of confidence that led to widespread protests, the firing of CPD superintendent Garry McCarthy, a second-degree murder conviction for Van Dyke, a Department of Justice (DOJ) investigation, and a consent decree that remains in effect.
The Laquan McDonald video release joins a long list of controversies in the world of policing, often centered around mistreatment of people of color and persistent doubts about equal justice. Since Michael Brown was killed in Ferguson, MO, in 2014, observers of American policing have argued the profession has experienced an ongoing crisis of legitimacy, with persistent debates over the role of police, the level of public funding they should receive, and even whether they should be abolished (Vaughn, Peyton, and Huber 2022). In addition, heightened scrutiny has been placed on police officers and their activity, with arguments being raised that they will depolice, or pull back, from their law enforcement functions to decrease the likelihood of any misconduct being captured on video that may draw the public’s ire (Torres and Reling 2020).
While depolicing in the aftermath of key events has been examined in academic literature for years (e.g., Prendergast, 2001), those studies often analyze aggregate metrics of activity (Gau, Paoline, and Paul 2022) that cannot distinguish how policing behavior varies across categories of officers. The existing literature tends to assume that depolicing following a pivotal event is driven by patrol officers pulling back from high-discretion enforcement decisions to reclaim their legitimacy or decrease the likelihood of being involved in a viral incident of misconduct (Slocum et al., 2019). However, the empirical work does little to assess this assumption.
This study advances and tests a rationale that the patrol-centered depictions of depolicing ignore the reality that much of the activity generated in police departments, especially in larger municipalities, is by specialized units of officers. These units are specially trained to reduce violent crime, the drug trade, and criminal gangs (Chaflin et al., 2021; National Policing Institute, 2024; Sweeney & Gorner, 2022), but also generate controversy and legal scrutiny in Chicago (Charles, 2022), and elsewhere (Lassiter, 2021; Winston & BondGraham, 2023), over the use of aggressive and potentially dangerous tactics and methods. As such, after a high-profile event occurs, the activity of officers assigned to those specialized units may be curtailed out of concerns for self-legitimacy or the dangerous nature of their work. In addition, the policing outputs of specialized units may decrease as command staff responds to the crisis by reallocating those teams to order-maintenance functions or directing them to prioritize or deemphasize other types of enforcement in specific parts of the city. Those changes in the activity of specialized units may differ significantly from changes in patrol officer activity, as the latter group of officers is tasked with responding to routinized calls for service or may react differently to high levels of public and media scrutiny following an important event (Nix & Pickett, 2017).
Using administrative data from the CPD on arrests and traffic stops, in addition to contextual data on CPD police districts, this study builds on work that has already found a depolicing effect in Chicago (e.g., Devi & Fryer, 2020; Muchow et al., 2023) in the aftermath of the release of the Laquan McDonald murder footage. Going beyond aggregate metrics of law enforcement activity, we give due attention to the role of specialized units in depolicing, which has not been explored in this line of inquiry. Specifically, we aim to answer a primary research question: To what extent was depolicing after the release of the video footage driven by reductions in activity by specialized units versus patrol officers? To answer this question, we empirically differentiate enforcement activity of patrol officers from the activity of specialized units and test the argument that the effect of the video release is more likely to manifest in reductions in output among the CPD’s specialized units, which undertake most of the proactive and high-risk law enforcement activity in the city. The study also utilizes supplemental analysis to further explore changes in police activity by specialized units and patrol officers in majority and minority Black police districts in Chicago to provide a full accounting of how policing changed in the city in the aftermath of that seminal event.
This study begins with a summary of relevant scholarly literature related to, separately, depolicing and specialized police units. It then develops a framework for a study that examines changes in the activity of specialized units, contrasted with patrol officers, in the aftermath of high-profile incidents of police misconduct.
Scholars have long been interested in how policing activity responds after the occurrence of a high-profile event or what Meyer (Meyer 1982) described as an environmental jolt. For instance, researchers have examined changes in officer activity following riots (Prendergast 2001; Shi 2009)(Prendergast 2001; Shi 2009), lawsuits (Novak, Smith, and Frank 2003), consent decrees (Chanin and Sheats 2018) Davis et al., 2002; Stone et al., 2009), and labor disputes (Pfuhl 1983). Overall, findings are mixed, with some work indicating that officers disengage, or lay low, during times of strife (e.g., Prendergast, 2001; Shi, 2009) and other work indicating no (Chanin & Sheats, 2017; Davis et al., 2002) or temporary (Stone et al., 2009) depolicing effects, as captured by administrative metrics, in the aftermath of those events.
Since the police-involved killing of Michael Brown in 2014 in Ferguson, MO, numerous studies have been published in this space, often testing what is referred to as the “Ferguson Effect.” Coined by St. Louis police chief Samuel Dotson (Byers, 2014), this perspective not only described a linkage between negative publicity and a reduction in police activity but also a residual effect of increased crime rates through an emboldening of possible offenders. Empirical studies of the Ferguson effect have utilized surveys (Gau et al., 2022; Hoffman et al., 2021;(Marier and Fridell 2020; Nix and Pickett 2017)(Marier and Fridell 2020; Nix and Pickett 2017) or interviews (Deuchar et al., 2019; VerBruggen, 2021) with police personnel and have typically found officers are cognizant of public pressure post-Ferguson and subsequently have lower morale and reduced motivation to be proactive in their work.
Beyond work gauging police perceptions of depolicing and the Ferguson effect, studies have also utilized administrative data from police departments to assess changes in the quantity (Capellan et al., 2020; Morgan & Pally, 2016; Slocum et al., 2019) or quality (Roman et al., 2023; Shjarback et al., 2017) of policing after high-profile events. Quantity-based studies typically use arrest data to examine changes in these aggregate metrics after environmental jolts. For example, Morgan and Pally (2016) found significant reductions in many categories of arrest in Baltimore, MD, including low-level offenses like disorderly conduct and driving violations and more serious offenses like murder and deadly weapons violations, after the death of Freddie Gray while in police custody in 2015. In addition, Slocum et al.’s (2019) study of seven years of data from the St. Louis, MO, Metropolitan Police Department found fewer enforcement actions were taken by police in the time period after Michael Brown was killed in 2014. Finally, in a study conducted in Chicago, Muchow et al. (2023) found evidence of a “Laquan effect” that resulted in significant reductions in felony and misdemeanor arrest rates, beginning immediately after the video was released. Overall, this collection of studies has found evidence of a Ferguson effect manifesting in fewer arrests, although the duration of those reductions has varied from a matter of months (Morgan & Pally, 2016; Muchow et al., 2023) to years (Slocum et al., 2019).
Researchers have expanded beyond arrests to assess other metrics of policing quantity, including traffic and street stops, in addition to searches (see Capellan et al., 2020; Shjarback et al., 2017). Especially relevant are three studies that have assessed traffic and street stops in Chicago. Devi and Fryer (2020), for example, assessed the impact of federal and state “pattern-or-practice” investigations on crime and policing in Chicago and a number of other cities. The authors found investigations that began shortly after viral incidents of deadly force, like the murder of Laquan McDonald in Chicago, led to a reduction in police stops, which was related to increases in homicide and total crime. Cassell and Fowles (2018) concluded similarly that a reduction in street stops in Chicago, brought on by an agreement with the American Civil Liberties Union (ACLU), explained increases in homicides and shootings in the city. Muchow et al. (2023), in contrast, found no evidence of a significant reduction in traffic stop rates after the Laquan McDonald video was released.
Importantly, studies of depolicing have often argued that changes in officer activity after an environmental shock are also based on the racial composition of the jurisdiction or the race of the member of the public. As Slocum et al. (2019) argued, officers may perceive enforcement actions directed towards members of racial or ethnic minority groups as costlier in terms of their legitimacy or the heightened potential to create a viral incident of misconduct. As such, the depolicing effect may be racialized, and reductions in activity more pronounced in jurisdictions or communities with larger populations of Black residents (see Cheng & Long, 2018; Shi, 2009; Shjarback et al., 2017).
In studies that have found evidence of a Ferguson effect in different contexts, the mechanism for a reduction in police activity has typically been underpinned through individual-level processes or what we describe as self-preservation perspectives. Slocum et al. (2019), for example, described the manifestation of the Ferguson effect as stemming from an officer’s instrumental concerns. In other words, officers were constantly weighing the possible benefits and costs of engaging in proactive work. On the one hand, proactive behavior can be viewed in terms of its benefits, which may include a pathway to promotion, commendation, or an improvement in public safety. Conversely, proactive behavior can be viewed in terms of its costs, which may include citizen complaints, injury, lawsuits, and disciplinary action. During times of intense public scrutiny of the police that may follow a high-profile environmental shock, that calculus may shift more towards officers seeing the costs of proactive work versus its benefits. As a result, officers make rational decisions to disengage with community members, which is reflected in fewer stops, searches, and arrests. Slocum et al. (2019) also argued that large-scale public displays of anger and dissatisfaction with police following officer-involved shooting deaths may be interpreted by officers as a challenge to their legitimacy, and they respond by adjusting their actions to reclaim their legitimacy. Collectively, these rationales suggest that during times of strife, when proactive behavior is seen as fraught with costs or strongly opposed by the public, officers respond by undertaking fewer enforcement actions, especially in places with significant populations of racial or ethnic minorities.
The self-preservation mechanisms outlined above, and the overall depolicing literature, are largely focused on the actions or perceptions of patrol officers (Torres et al., 2018). The view of depolicing as stemming from the actions of patrol officers is not a surprise, as the enduring image of policing in America has typically centered on solitary beat cops on patrol who are dispatched and empowered to make decisions in how to respond that range from doing nothing to making an arrest (Van Maanen, 1973).
Yet, many cities across the nation, including Chicago, have utilized specialized units of officers for at least a century to respond to a variety of situations, including labor strikes and social movements (see Brecher, 1997; Donner, 1992; Gourevitch, 2015; Mitrani, 2013). Varied terminology has been used to refer to those units in different contexts, including special weapons and tactics (SWAT) teams (Klinger & Rojek, 2008), paramilitary units (Kraska & Kappeler, 1997), tactical teams (Jenkins et al., 2021), and specialized units (Burke, 2022). A recent guide for best practices (National Policing Institute, 2024, p. 1) defined specialized units as officially designated components of a law enforcement agency requiring specialized training, skills, and mission.
The presence of these units has allowed departments to quickly deploy officers to respond to unusual circumstances in a rapidly changing society. Racially charged incidents during the 1940s and later during the 1960s further demonstrated the need for this type of rapid response (Klinger & Rojek, 2008). In addition, a confluence of rising crime rates and improved technology in the 1960s and 1970s made specialized units more ubiquitous (see International Association of Chiefs of Police, 2011; Kraska & Kappeler, 1997; Kraska & Paulsenb, 1997). As the perceived threats of violence, terrorism, and guns increased, so too did the need for law enforcement to be better equipped, trained, and organized. Police departments responded by selecting and training groups of specialized officers who could function as coordinated units without depleting the agency’s ability to respond to routine calls for service.
The deployment of specialized units has risen over time in the United States (Kraska 2007), but there is also evidence that the breadth of their responsibilities has increased to include proactive patrol, crackdowns, and other crime and violence suppression strategies (Jenkins et al. 2021). In Chicago, for example, media depictions of department operations have highlighted specialized units as responsible for most of the arrests in CPD’s 22 patrol districts (Sweeney & Gorner, 2022) through their focus on crime patterns related to robberies, shootings, and drug activity. The use of specialized teams in these pursuits reflects a more data-driven approach to policing, in which patrol officers can respond to recurring calls for service while specialized units are freed to be allocated to crime hot spots to suppress violence, primarily through arrests, stops, and citations (Burke, 2022).
While research has found that utilizing specialized units in these ways can result in lower levels of crime and violence (Groff et al. 2015), they still are controversial as they illustrate the collateral consequences of mass enforcement (Chalfin, LaForest, and Kaplan 2021) that result primarily in people of color being pulled over, arrested, and ticketed. Further, a number of these units have been disbanded and their members fired or criminally charged for their conduct. This list includes the Special Operations Section (SOS) in Chicago, which was dissolved in 2007. Eleven members of that unit eventually pleaded guilty to criminal charges for committing home invasions and stealing from members of the public (Charles, 2022). These examples of misconduct have led scholars like (Roziere and Walby 2018) to argue that the use of these special weapons and tactics teams in more “routine” forms of policing represents a failed public policy that should be curtailed.
Kraska (2007) has also been a frequent critic of the use of tactical police, arguing that they have adopted military-style language, equipment, and culture in the execution of raids for contraband and even routine patrol work in crime hot spots. Normalizing these approaches, Kraska (2007) argues, elevates the risk of botched raids, injuries for community members and police officers, and expensive litigation judgments while also collectively undermining relations with members of the community. Indeed, this debate intensified in 2023, as the killing of Tyre Nichols in Memphis, TN, by officers working in the city’s Street Crimes Operation to Restore Peace in Our Neighborhoods (SCORPION) unit resulted in convictions for the officers (Yousif, 2024) and renewed criticism over the collateral damage of deploying these specialized units, and the level of independence and autonomy they have in carrying out their mission.
Despite these controversies and their increasing role in contemporary policing, academic research on these units or the officers who occupy these positions has been sparse. A small number of studies has examined how specialized units are deployed (Jenkins et al., 2021), depicted in the media (Rantatalo 2016), what characterizes sworn personnel who occupy these positions (Heusler and Sutter 2020), or what happens when they are abated (Valasik et al., 2016). However, research has rarely drawn empirical distinctions between the law enforcement activity of specialized teams and patrol officers, especially related to metrics like arrests or stops.
In the depolicing literature, allusions to specialized units are also sparse. Cassell (2020), for example, described a reduction in policing outputs in the wake of George Floyd’s murder in Minneapolis, MN, as stemming from a redeployment of officers away from antigun initiatives and towards protest management and order maintenance, which could presume specialized units. Yet, empirical assessments of overall changes in law enforcement activity by specialized units, juxtaposed against changes in activity by patrol officers, have not been undertaken in the existing depolicing literature nor the broader policing literature.
Given the knowledge gap outlined above, this study bridges academic literature on depolicing with work on the expanding utilization of specialized units in generating law enforcement activity in American cities. Specifically, it seeks to assess to what extent depolicing in Chicago after the release of the Laquan McDonald video was driven by changes in activity by specialized units contrasted against activity by patrol officers.
The current study advances and assesses an argument that in response to an environmental jolt, like the release of this video in Chicago, depolicing is more likely to manifest in a reduction in policing outputs generated by officers assigned to specialized units than officers assigned to patrol. Given that specialized units typically undertake potentially dangerous and high-risk work with a great deal of independence in their expanding role as crime fighters (Burke, 2022), we argue that they may be especially attuned to the instrumental concerns embedded in the thesis of the Ferguson effect. Specifically, they may be more likely, or able, to withdraw from generating arrests and stops as they see the rising “costs” of this work during periods of crisis and the potential to be embroiled in controversy or a viral incident of misconduct. While officers assigned to patrol certainly respond to the same instrumental concerns during crisis periods, the nature of their work is different as they are constantly dispatched to respond to the recurring stream of calls for service and may not have the same level of independence possessed by officers in specialized units.
That a reduction in enforcement activity may manifest more prominently among sworn personnel in specialized units, as opposed to patrol officers, also draws from notions of organizational adaptation. Specifically, Meyer’s (1982) seminal work finds that organizational adaptation to environmental jolts filters through an initial responsive phase in which slack resources, including personnel, are temporarily assigned to other tasks or duties. In police organizations, specialized units of officers could represent the only “slack” personnel resources that exist, as patrol officers are needed to respond to the constant stream of calls for service that are received by the department and that persist, or even increase, during crisis periods. Cassell (2020) alluded to this reality in Minneapolis, MN, following the murder of George Floyd, as antigun operations had to be curtailed as officers were reassigned to manage the protests and maintain order during that crisis period.
Further, reflecting the growing literature related to depolicing and how it can vary based on the demographics of people living in different communities or contexts, this study explores a possible heterogeneous pattern of changing police activity in Chicago through contrasting arrests and stops by specialized units and patrol officers in majority-Black and minority-Black police districts in the city. Building on empirical work in the depolicing literature that has found reductions in policing activity were more pronounced in jurisdictions with larger populations of Black residents (see Cheng & Long, 2018; Shjarback et al., 2017) and a long history in Chicago and elsewhere of specialized units being assigned to communities with large populations of racial and ethnic minorities, we may see a greater initial decline in enforcement activity in those police districts in the city than in other districts where they might not be as concentrated.
This study makes multiple contributions to the existing literature. For one, it is the first study, to our knowledge, to describe and execute a strategy for operationalizing policing activity undertaken by specialized units throughout an entire police department and examining how it changed after an environmental shock. As these units become more widespread, it is imperative that studies of policing, in the aftermath of high-profile events or otherwise, not only acknowledge their role but also have the ability to measure their activity and understand how it relates to issues of transparency, accountability, or their overall effectiveness in accomplishing their stated missions.
Second, our research design and analytic strategy account for important events in Chicago that took place before and after the release of the video, similar to the concepts of “after-shocks” and “pre-shocks” identified by Slocum et al. (2019). Controlling for those factors, such as adoption of body-worn cameras (BWCs) in the city and a contemporaneous agreement with the ACLU that influenced street stops, enables us to better isolate the effects of the video release on arrest rates and traffic stops rates generated by, respectively, specialized units and patrol officers and how they vary based on the racial composition of police districts in the city.
On a practical level, we see the results of this study as relevant to public policy debates concerning the role of police in responding to increases in crime, like those that occurred in many cities on the heels of high-profile incidents of misconduct or even what occurred during the pandemic. Ongoing debates over whether communities need more or fewer officers during crime spikes ignore the distinction between the role of patrol and specialized units and how or if the latter should be utilized or commissioned to respond to challenges when faced with rising levels of crime and disorder, especially given the controversy they often generate. More broadly, as video of police officers becomes more widespread through the expansion of body-worn cameras and personal smartphones, we see, unfortunately, a greater likelihood that police departments will face environmental jolts like the one analyzed here. As such, the implications of studies like this one are vast as they can demonstrate how officers assigned to different units, and the law enforcement organization itself, respond in the aftermath of key events. The data and methods used to make those contributions and conduct this study are described in the following section.
To examine how the public release of the video showing Laquan McDonald’s murder influenced arrests and stops in Chicago by specialized units and patrol officers, we examined the period from January 1, 2015, to December 31, 2016. This time span approximates roughly one year before and one year after the video release. We relied on multiple data sources to conduct our analysis, which will be described in more depth below and organized by the measures they were used to operationalize.
We focused our attention on two distinct measures of policing activity: arrests and traffic stops. CPD responded to our Freedom of Information Act (FOIA) request for details on all custodial arrests made between 2015 and 2016. Using information on the date and geographic location where the arrest occurred, we produced weekly counts of the number of arrests made in each CPD district.
We retrieved information on traffic stops from the Illinois Department of Transportation (IDOT), which responded to our FOIA request for details on all vehicular stops made by CPD officers between 2015 and 2016. We focused on traffic stops because pedestrian stops were immediately affected by a legal settlement between the CPD and the American Civil Liberties Union (ACLU) in 2015. To redress issues raised by the ACLU’s lawsuit alleging legal violations in pedestrian stops, CPD updated its legal and administrative procedures for investigatory stops. Effective January 1, 2016, Special Order S04-13-09 required officers to provide information related to “pat-downs” and expanded the amount of suspect and circumstantial information that officers could provide about an investigatory stop. Anecdotal reports suggest that the added paperwork may have deterred officers from making pedestrian stops (Gorner, 2019), which has been corroborated by research documenting declines in stops after the order went into effect (Cassell & Fowles, 2018). Using the date and location of each recorded incident, we produced district-level weekly counts of traffic stops.
We converted arrests and traffic stops into rates using district-level population estimates interpolated from census tract-level data sourced from the 2011-2015 and 2012-2016 5-year ACS datasets. Specifically, we imported spatial boundaries for CPD districts and census tracts and calculated the degree of tract-district overlap, deriving weights that represent the tract composition of each district. We applied these tract-level weights to derive annual measures of district population size. We divided arrest and traffic stop totals by these yearly population estimates and multiplied by 10,000 to measure arrests/stops per 10,000 residents living in a given district.
The arrest and traffic stop data we collected from CPD and IDOT included information on arresting/stopping officers that allowed us to distinguish the activities of patrol officers from specialized units. We define specialized units as groups of officers designed and directed to reduce violent crime through proactive enforcement, with a special emphasis on violence stemming from firearms, the drug trade, and criminal gangs. This type of proactive enforcement is a secondary responsibility of patrol officers, who, by contrast, are primarily tasked with responding to emergency calls for service.
We distinguished patrol officers from specialized units using information provided by CPD, who responded to our FOIA request for information on car numbers in operation during our study period. Car numbers identify different types of officers and are used internally by CPD for tracking and official reporting purposes. Using descriptions provided by CPD, we designated car numbers as patrol if labeled as “patrol officers,” as well as those described differently but who serve similar functions (e.g., squadrol, bicycle patrol officers, traffic enforcement officers).1 Specialized units consist of those not classified as patrol whose job duties involve complex investigations or street-level enforcement with the goal of reducing public violence. We relied on several sources to make these distinctions, including academic work detailing specialized units in Chicago (Lemmer et al., 2008; Rosenbaum & Stephens, 2005), CPD annual reports (Chicago Police Department, 2017), and several FOIA responses from Chicago’s Office of Public Safety Administration containing job descriptions.2 This approach follows that of Burke (2022) in his study of specialized units in Chicago. Car numbers were then matched to each arrest and stop made over our study period and used to distinguish activities conducted by patrol officers from specialized units. We excluded officers not classified as patrol or specialized, which resulted in the omission of 6.1 percent of arrests and 9.3 percent of traffic stops made over our study period. To help clarify the designations made above and the overall operationalization of activity as being undertaken by patrol officers or specialized units, a supplemental appendix appears at the end of this report that provides the ten most active car numbers by officer type alongside the relative share of arrests and stops they conducted. A complete list of the car number descriptions and corresponding designations of patrol or specialized units is too voluminous to append to this report but is available upon request.
In addition, we control for other variables that may directly affect policing activity. We collected information on district sociodemographic characteristics, demand for police services, historical crime rates, and the implementation of body-worn cameras.
We used information from the U.S. Census American Community Survey 5-year estimates to measure annual district-level sociodemographic characteristics. Because districts do not map directly onto census tracts, we used areal interpolation to derive district-level population estimates. Specifically, we imported spatial boundaries for CPD districts and census tracts and calculated the degree of tract-district overlap, deriving weights that represent the tract composition of each district. Using these weights, we produced a series of district-level characteristics, including population size, the percent of residents male age 15 to 24, and constructed an index of concentrated disadvantage through principal components analysis of the following five characteristics: (1) percent of households receiving public assistance; (2) percent living below the federal poverty line; (3) percent unemployed; (4) percent of single, female-headed households; (5) percent under age 18. All variables loaded positively onto one construct; the eigenvalue was 4.03, and the Cronbach's alpha was 0.83. To ease interpretation, we standardized this variable to have a mean of zero and standard deviation of one.
We also requested data via FOIA from CPD on weekly calls for police service dispatched to each district over our study period. We used information on high-priority calls, which involve “an imminent threat to life, bodily injury, or major property damage/loss,” to derive a calls for service per resident rate. This variable accounts for changes in the demand for police services over our study period. We retrieved information on violent index crimes reported to CPD in 2014, the year before our study period, per 1,000 residents. We interacted this violent index crime rate with a weekly time trend to adjust for district-level changes in crime over our study period. Lastly, because our study period coincided with CPD’s rollout of body-worn cameras, we collected information on the date cameras were adopted in each district. CPD’s deployment of body-worn cameras began in June 2016 and was implemented in seven of CPD’s 22 districts by the end of our study period. As some scholars have argued, the introduction of this technology produces an accountability mechanism that can affect patterns of arrest (Braga et al., 2020). We adjust for their adoption to account for any shifts in policing activity attributable to their use.
This study also examines a potential racialized depolicing effect by partitioning the analysis into majority-Black and minority-Black districts. We estimated the share of Black residents living in each district over the study period and used this measure to distinguish majority-Black districts, where at least 50 percent of residents identified as Black, from minority-Black districts with population shares below 50 percent.3
To assess whether the release of the video documenting Laquan’s murder influenced patrol officer and specialized unit activity in Chicago, we examine weekly trends in arrest rates and traffic stop rates before and after the video release. We compare changes in each outcome relative to the week before the video was released. In this setup, the week prior to the video serves as a plausible counterfactual of what officer activity might have looked like in the absence of the video.
We use an event study design specified by the following equation:
(1)
where
We include several controls to account for other, potentially confounding, sources of variation in policing activity over our study period. First, we incorporate the time-varying district-level vector,
Table 1 displays basic descriptive statistics for our outcomes and control variables for the full study period as well as before and after the video release of Laquan’s murder. At first glance, we can see that patrol officers, on average, make fewer arrests than specialized units, but carry out more traffic stops. Comparisons before and after the video release provide mixed evidence of a decline in policing activity following this pivotal event. While arrest rates decreased significantly after the video for both patrol and specialized officers, traffic stop rates increased. Weekly arrest rates for patrol officers dropped from an average of 2.7 arrests per 10,000 residents before the video to 2.3 thereafter. Arrest rates among specialized units followed a similar pattern, decreasing from an average of 4.6 arrests per 10,000 residents before the video to a rate of 3.6 after. Conversely, traffic stops made by patrol officers increased from 2.5 stops per 10,000 residents before the video to 4.0 stops after and nearly quadrupled among specialized units, growing from a rate of 1.3 stops per 10,000 residents before the video release to 4.1 stops thereafter. However, these descriptive results do not take into consideration alternative sources of variation in policing activity that may confound these comparisons.
--Insert Table 1 Here--
In what follows, we use an event study design to formally assess whether the video affected policing activity among patrol officers and specialized units in Chicago. Such an approach is critical in assessing the impact of the video release as it enables us to precisely gauge the timing and magnitude of changes in policing activity at fine temporal intervals surrounding this important event. This feature allows us to differentiate between the likely effect of the video release and other proximate events, which might otherwise be obscured by methods that aggregate longer periods of time before and after the video release.
To investigate whether the video release of Laquan’s murder affected policing activity of patrol officers and specialized units in Chicago, we estimated equation (1) for arrest rates and stop rates for both officer types. The results of these analyses are presented in Table 2 and displayed graphically in Figure 1. The biweekly coefficients shown therein estimate the percent change in each outcome relative to the week before the video was released.
--Insert Table 2 Here--
--Insert Figure 1 Here--
The results presented in columns (1) and (2) predict changes in arrest rates for patrol officers and specialized units, respectively, and are plotted graphically in the top panel of Figure 1. We can see that before the video release, arrest rates were statistically similar to patterns witnessed the week before the video release for patrol officers, while arrest rates for specialized hovered above pre-video levels. Yet, immediately following the video release, arrests made by specialized units dropped 23 percent and continued to decline for up to three months thereafter. Arrest rates for patrol officers, on the other hand, did not respond immediately to the video. Beginning eight weeks after its release, patrol officers made 14 percent fewer arrests. These results accord with the trends observed in Table 1, which showed significant decreases in arrest rates after the video for both patrol officers and specialized units. However, unlike Table 1, we can see when and the extent to which arrests changed over time. Upon closer inspection of the top panel of Figure 1, it’s apparent that the decrease in arrest rates for patrol officers was not likely a response to the video release but a reaction to the ACLU agreement—denoted by the second dashed vertical line—that went into effect immediately before patrol arrest rates broke from their pre-video trend.
Turning to columns (3) and (4) of Table 2, plotted graphically in the bottom panel of Figure 1, we see that traffic stop rates did not change in the immediate aftermath of the video release. While stop rates among specialized units dipped below their pre-video levels starting two weeks after its release, these declines did not reach conventional levels of statistical significance. What is clear is that stop rates increased dramatically eight weeks after the Laquan video for both patrol officers and specialized units and continued on an upward trajectory for the remainder of the study period. A visual inspection of these shifts—shown in the bottom panel of Figure 1—suggests that this growth in traffic stops was unlikely due to the video but rather a response to the ACLU agreement. The dramatic escalation in traffic stop rates we see here likely reflects a substitution effect, wherein officers—representing both patrol and specialized units—came to rely on vehicular stops when pedestrian stops fell under increased scrutiny after the terms of the agreement went into effect (Gorner, 2019; ACLU, 2019).
The results presented above provide partial evidence of a depolicing effect, involving a significant reduction in arrest rates by specialized units but do not take into consideration whether district-level characteristics moderate these responses. To assess whether officers in majority-Black communities responded differently to the video release, we performed disaggregated analyses for majority-Black and minority-Black districts. The results are presented in Table 3 and displayed graphically in Figures 2 and 3.
--Insert Table 3 Here--
--Insert Figure 2 Here--
--Insert Figure 3 Here--
Focusing first on patrol officer responses, we see little evidence of heterogenous Laquan effects by district racial composition. As shown in column (1) of Table 3 and graphically in the top panel of Figure 2, arrest rates declined in majority-Black districts, but as we saw in Table 2, these drops did not manifest until eight weeks after the video release. That is, they likely stemmed from the ACLU agreement rather than the Laquan video. As seen in column (2) of Table 3 and the bottom panel of Figure 2, stop rates did not deviate from pre-video levels in majority- or minority-Black districts until after the ACLU agreement, which primarily occurred in minority-Black districts.
Our assessment of heterogenous effects on the activity of specialized units yielded some evidence of differential effects across majority- and minority-Black districts. The results shown in column (3) of Table 3, displayed graphically in the top panel of Figure 3, reveal that arrests made by specialized units dipped below pre-video rates immediately after the video release in both majority- and minority-Black districts. These declines were statistically different from pre-video levels for six weeks following the video release in minority-Black districts and remained below, though not statistically different from, pre-video levels for the remainder of the study period. Though not statistically different from pre-video levels four weeks after the video release, arrest rate declines in majority-Black districts were statistically distinguishable from pre-video levels two- and six-weeks post-video but stabilized at pre-video levels shortly thereafter. The results shown in column (4) of Table 3, displayed graphically in the bottom panel of Figure 3, reveal that traffic stops made by specialized units declined in minority-Black districts four weeks after the video release. These declines were not seen in districts where the majority of residents were Black. Yet these declines were short-lived, eventually giving way to a rapid increase in stop rates after the ACLU agreement. The implications of these findings are discussed in the following section.
Despite the passage of a decade since Laquan McDonald was murdered and nine years since the release of the video depicting his death, the implications of this tragedy are still felt today. In terms of the CPD, it continues to implement a multitude of required reforms related to policies, training, and practices in many critical areas, including use of force, accountability, and officer wellness under a federal consent decree that began in 2019 (https://www.chicagopoliceconsentdecree.org/). While the progress of those reforms and their broader impact continue to be assessed and debated today (Lehman, 2023), this study focused its attention on the critical period after the video was released and how metrics of arrest rates and stop rates changed in the city. Even though a sizable empirical literature related to depolicing and the “Ferguson effect” has already assessed changes in policing activity following a variety of environmental shocks, this study is the first to consider the role of specialized units of officers, who are trained to conduct missions related to guns, drugs, and violence in the city, but also generate controversy over their tactics and methods.
Our primary research question asked to what extent depolicing after the release of the video footage was driven by reductions in activity by specialized units versus patrol officers. Using two years of data related to arrests and stops provided by the CPD and IDOT, and a novel methodology that coded each of those enforcement activities as being undertaken by patrol officers or those assigned to a specialized unit, the analysis finds that depolicing in Chicago was driven by reductions in activity by specialized units, as their arrest rates declined significantly following the video release. This main finding is consistent with other work that has found a depolicing effect, as measured by a reduction in arrests, in Chicago (Muchow et al., 2023) and other large cities (Morgan & Pally, 2016). However, we also see this main finding as providing a counter to the prevailing view of depolicing as being driven by reductions in activity by patrol officers. To that point, arrest rates for patrol officers did not change significantly at any point up to six weeks after the video release, a finding we see as consistent with the contours of their job being more disposed towards responding to the unending stream of calls for service that characterize a city like Chicago, which can limit the extent to which they conduct proactive police work related to carrying out arrests.
The exact mechanism by which the CPD’s specialized units significantly reduced their arrest activity cannot be delineated by the analyses conducted here. When examining the roles of officers assigned to specialized units, their work is certainly more mission-specific and output-oriented than patrol (National Policing Institute, 2024). At the same time, these units typically operate with a great deal of independence and relatively limited supervision. This reality contrasts with the working environment of officers assigned to patrol, who are constantly dispatched to respond to the recurring stream of calls for service received by the Department. As such, given the environments in which specialized units of officers operate, coupled with the fact that they generate the vast majority of the arrest activity in the CPD, we argue that they may be more attuned to the instrumental concerns (Slocum et al., 2019) embedded in the thesis of the Ferguson effect, and in turn be more likely to see the possible costs of their often dangerous, controversial, and high-risk work during periods of crisis.
Similarly, officers assigned to specialized units may respond to the public outcry by reducing their enforcement activity in an effort to reclaim their legitimacy (Nix & Pickett, 2017; Nix & Wolfe, 2017). Officers working in specialized units may be especially attentive to these processes, as they may see their mission and tactics viewed with more hostility during periods of crisis when media coverage is particularly unfavorable towards this form of law enforcement. Rantatalo (2016, p. 107) found evidence of this view among officers assigned to a specialized police tactical unit, with semi-structured interviews that found that when police were depicted negatively in terms of their aggression and violence, respondents then constructed their occupational identities in opposition to such values—in essence, distancing themselves from those depictions and reframing their identities as being less aggressive and more accessible to the community.
It is also possible that the significant reduction in arrest rates by specialized units in the aftermath of the video release reflects an organizational adaptation of the CPD. Specifically, the significant reduction in arrest rates by these units after the video release may reflect them being pulled away from their assigned missions and reassigned to order maintenance functions or directed to prioritize other types of activity (see Cassell, 2020). As discussed earlier in this report, officers assigned to these units may represent the only slack resources at the department’s disposal and, thus, are more likely to be reassigned or have their priorities changed during crisis periods. Especially in an agency like the CPD, with a number of specialized units that often perform similar tasks and have overlapping missions, the ability of the Department to adjust those slack resources in the throes of crisis may be contributing to the significant reduction in arrest activity as indicated by the analyses conducted here.
Depolicing as an organizational adaptation to crisis receives very little attention in the existing academic work. Future studies should consider the possibility that reductions in outputs after an environmental shock may reflect temporary reassignments of personnel or even more concerted efforts by the department to “look and act right” (Crank & Langworthy, 1996, p. 215) as they try to adapt to influential constituencies during crisis periods by directing specialized units to pull back from some law enforcement activity. In fact, from the standpoint of many community members, this form of adaptation may be a positive development and a form of relief from aggressive police units and their reliance on enforcement actions like arrest, which create collateral consequences related to criminal records, net widening, and other hardships (Nix et al., 2017).
Regardless of the exact mechanism that underpins the significant reduction in arrest rates by specialized units, another key takeaway here is the relatively short-lived depolicing phenomenon in Chicago after the video release. Depending on the statistical model and level of disaggregation, significant reductions in arrest rates, seen solely in the models for those officers assigned to specialized units, continued to manifest only 6 to 12 weeks after the video release and then reverted to pre-video levels. This finding is consistent with other empirical work (e.g., Chanin & Sheats, 2018; Morgan & Pally, 2016; Muchow et al., 2023) that has found the duration of a Ferguson effect, or a depolicing effect in general, encompasses a matter of months, as opposed to being a years-long phenomenon (see Slocum et al., 2019).
As far as the supplemental analysis that disaggregated the models by the racial composition of the police districts, we found slight evidence of a racialized effect, but not in the direction of our prevailing assumptions. Specifically, the results of this study suggest that reductions in arrest rates, and to a small degree, stop rates, by officers assigned to specialized units were more consistent and pronounced in minority Black police districts. This finding runs counter to results from studies conducted by Shjarback et al. (2017), Cheng and Long (2018), and Shi (2009), all of which found that reductions in activity were more sizeable in jurisdictions or communities with larger populations of Black residents. The racialized effect here is not robust, but it could again illustrate organizational adjustments in these units that materialized in the aftermath of the video release, in which some were reassigned or pulled away from minority Black districts, which historically have less crime and violence.
While the video release did manifest in significantly lower arrest rates by officers in specialized units, it did not result in any significant change in stop rates for either group of officers in the main statistical models found in Table 2. The null finding is in line with Muchow et al.’s (2023) analysis in Chicago but is at odds with Shjarback et al.’s (2017) study of a number of law enforcement agencies in Missouri, which found significant declines in traffic stop rates post-Ferguson, in particular in places with larger shares of Black residents. Similar to Muchow et al. (2023), we argue that this null finding likely indicates that traffic stops are less scrutinized by the general public compared to arrests and, thus, less susceptible to the instrumental concerns that underpin the thesis of the Ferguson effect.
In a different vein, seeing the significant increase in traffic stop rates following the ACLU agreement indicates how quickly key events can alter the trajectory of various outputs in a police department (Cassell & Fowles, 2018). This ACLU effect, which was more pronounced in minority Black police districts, again underscores the importance of disaggregating analyses by race to ascertain any disproportionate impact of key events on different populations of people in the city. It also illustrates the importance of using an event study design, like what was utilized here, in order to delineate very finite periods of time. Invariably, there is noise embedded in police administrative data, subject to a variety of events in the city or even nationwide. As such, designs that aggregate long periods of time together into pre- and post- periods around a seemingly pivotal event may obscure other environmental jolts that make direct comparisons of those two periods difficult.
We would be remiss without acknowledging multiple limitations to this study. For one, it did not disaggregate the metrics of policing quantity beyond assessing overall arrest and stop rates. Certainly, within those categories come additional distinctions between felony and misdemeanor arrests or weapons and narcotics stops, just to name two examples. Of note, scholars conducting empirical work in this space have started to make those empirical distinctions in their work, with Muchow et al.’s (2023) supplemental analyses that focused on drug and firearm arrests being one example. Given that this piece was the first one to empirically distinguish between specialized units and patrol officers, additional theoretical ground must be traversed to develop testable rationales for why specific metrics of activity by specialized units or patrol officers would decrease after a pivotal event. A second limitation is that roughly 6% of arrests and 9% of traffic stops during the study period were excluded from the analyses, as it was impossible to discern from the existing data whether they were conducted by patrol officers or those assigned to specialized units. Also, the generalizability of the findings here is also limited by our focus on Chicago, which is one of the largest municipal departments in the United States and thus an outlier to the vast majority of agencies that have less than 25 full-time sworn personnel and no dedicated specialized units (Gardner & Scott, 2022).
With these limitations in mind, the following section maps out various implications of the study and its findings for research and practice. For one, it is important to acknowledge that despite recent examples of abuses of power wielded by specialized units in Chicago (Charles, 2022), they are still widely utilized to generate substantial policing activity in the CPD. Many scholars have alluded to the expanded role of specialized units in municipal police departments (Burke, 2022; Kraska, 2007), but being able to quantify their output makes clear their outsized role in generating arrests and stops in the city of Chicago. The descriptive statistics provided in this study indicate that officers assigned to these specialized units generated arrest rates that are almost double the arrest rates of officers assigned to patrol. In terms of traffic stop rates, the activity of patrol officers largely outpaces that of officers assigned to specialized units, yet the post-video release period saw virtual parity in the two groups with regard to this metric.
The large role of these teams should be a call for researchers to focus more work on specialized units and the officers assigned to these positions, especially in light of the history of abuses of power that have manifested. In particular, this work could focus on more thorough evaluations of the effectiveness of these units in decreasing various forms of violence and how their tactics are perceived and viewed by members of the community. Through conducting these analyses, research becomes more actionable for police departments as they try to understand the most effective ways to reduce crime while also being accountable to the communities that they serve.
Further, this work could include interviews with officers who fill these units (Rantatalo, 2016) or surveys that either specifically include officers from specialized units or have a question or item that allows their responses to be distinguished from their counterparts in patrol. Work of this nature could help distill the extent to which officers who occupy these units conduct their jobs, interact with community members, evaluate their instrumental concerns, and weigh proactive work during times of strife. We also encourage more research that explores the organizational components of depolicing and how command-level decisions are shifted in the aftermath of pivotal events. Specifically, we suggest that future research utilizes interviews with command staff and/or assessments of contemporaneous correspondence between command staff via emails or other official directives that can help assess this organizational response to monumental events.
For police departments, we understand the imperative that currently exists to adequately respond to crime and violence while also being community-focused, transparent, and open to approaches that are less focused on the use of stops and arrests. How specialized units of officers fit into those conversations is difficult and reflects the incongruencies that police departments face in using these militarized units while also trying to be oriented toward community policing (Kraska, 2007). While this study’s focus does not relate to evaluating the effectiveness of CPD’s specialized units, we still echo and add to many of the recommendations in the recently published report by the National Policing Institute (2024) to ensure they are accomplishing their missions while also being attentive to the needs of the community. To that end, we suggest assessment tools that move beyond arrests and stops to include perceptions of community members about procedural justice, safety, fear of crime, or disorder in the community. Through an expansion of these measures, police departments will get a better sense of the effectiveness of their specialized units and how those metrics change during key periods of time.
In a similar vein, keeping the community informed about the presence of specialized units, their missions, and the ways in which they are evaluated is key to getting buy-in from community members. As the National Policing Institute (2024) report mentions, when agencies disband and form specialized units without community engagement, it leaves the public confused and leaves the department open to criticism that it is not sufficiently transparent. Further, as specialized units have become more ubiquitous and institutionalized, they are also seen as the “solution” when more complex problems arise. This approach can ignore the presence of other possible remedies to these complex problems that may be more appropriate, including social workers or conflict-violence interruption (CVI) experts.
Regardless of whether these recommendations are heeded, it is clear from this study that specialized units of police officers are prominently involved in the CPD’s approach to law enforcement and that they pulled back significantly in performing arrests in the aftermath of the Laquan McDonald video release. While this study was limited in ascertaining whether those reductions in arrest rates were driven by individual-level changes in how those officers viewed the costs and benefits of their work or organizational-level shifts in assignments or priorities, these findings again demonstrate the complexity of how policing responds to environmental shocks.
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Overall (n=2,288) | Before video release (n=1,034) | After video release (n=1,254) | |||||||
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Mean | SD | Mean | SD | Mean | SD | p-value | |||
Outcomes | |||||||||
Patrol officers | |||||||||
Arrest rate | 2.490 | 1.726 | 2.723 | 1.948 | 2.299 | 1.493 | <0.001 | ||
Traffic stop rate | 3.306 | 4.425 | 2.461 | 3.117 | 4.003 | 5.162 | <0.001 | ||
Specialized units | |||||||||
Arrest rate | 4.074 | 4.528 | 4.611 | 4.943 | 3.630 | 4.103 | <0.001 | ||
Traffic stop rate | 2.870 | 6.821 | 1.332 | 3.199 | 4.138 | 8.540 | <0.001 | ||
Controls | |||||||||
Population | 123,111 | 52,217 | 123,193 | 52,337 | 123,043 | 52,138 | 0.946 | ||
Percent male 15 to 24 | 5.244 | 1.052 | 5.293 | 1.083 | 5.204 | 1.024 | 0.046 | ||
Concentrated disadvantage index (CDI) | 0.006 | 0.996 | 0.044 | 1.002 | -0.026 | 0.990 | 0.093 | ||
Calls for service per capita | 4.275 | 2.638 | 4.118 | 2.554 | 4.405 | 2.700 | 0.009 | ||
Violent index crime rate (2014) | 0.006 | 0.005 | 0.006 | 0.005 | 0.006 | 0.005 | 1.000 | ||
Body worn cameras | 0.076 | 0.264 | 0.000 | 0.000 | 0.138 | 0.345 | <0.001 |
Notes: Table displays weekly district-level averages and are not weighted by population. P-values from two-sided t-test for differences in means before and after the video release of Laquan’s murder.
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
Arrest rate | Stop rate | |||
Patrol | Specialized | Patrol | Specialized | |
> 12 weeks before | -0.03 (0.04) | 0.10 (0.06) | 0.28** (0.10) | 0.14 (0.12) |
12 weeks before | 0.02 (0.05) | 0.17* (0.07) | 0.21* (0.10) | 0.02 (0.08) |
10 weeks before | -0.03 (0.06) | 0.18* (0.07) | 0.07 (0.11) | -0.00 (0.09) |
8 weeks before | 0.02 (0.06) | 0.21** (0.07) | 0.08 (0.11) | 0.00 (0.09) |
6 weeks before | 0.10 (0.06) | 0.22*** (0.05) | 0.19 (0.12) | 0.03 (0.08) |
4 weeks before | 0.03 (0.04) | 0.11* (0.05) | 0.14 (0.09) | 0.07 (0.07) |
2 weeks before | 0.01 (0.05) | 0.19*** (0.04) | 0.16* (0.07) | 0.00 (0.04) |
2 weeks after | -0.06 (0.05) | -0.23*** (0.04) | 0.06 (0.07) | -0.03 (0.05) |
4 weeks after | 0.03 (0.07) | -0.17* (0.07) | 0.02 (0.08) | -0.17 (0.08) |
6 weeks after | -0.03 (0.07) | -0.31*** (0.06) | -0.13 (0.11) | -0.14 (0.09) |
8 weeks after | -0.14* (0.07) | -0.27** (0.08) | 0.47* (0.18) | 0.71* (0.27) |
10 weeks after | -0.14* (0.06) | -0.21* (0.08) | 0.41* (0.18) | 0.78* (0.28) |
12 weeks after | -0.15* (0.06) | -0.16* (0.07) | 0.56** (0.18) | 0.78* (0.28) |
> 12 weeks after | -0.10 (0.06) | -0.11 (0.06) | 0.57** (0.18) | 0.85** (0.27) |
Pop (x 10,000) | 0.00 (0.13) | 0.18 (0.11) | -0.08 (0.34) | 0.61 (0.55) |
% male 15-24 | 0.11 (0.08) | -0.04 (0.15) | -0.01 (0.29) | 0.01 (0.50) |
CDI | 0.05 (0.23) | -0.08 (0.30) | 0.01 (0.89) | -1.16 (1.70) |
Calls for service | 0.08*** (0.01) | 0.03 (0.02) | 0.01 (0.02) | 0.01 (0.02) |
Crime trend | -0.13 (0.09) | 0.00 (0.08) | -0.17 (0.34) | -0.67 (0.36) |
BWCs | 0.02 (0.05) | 0.05 (0.06) | 0.01 (0.19) | 0.01 (0.22) |
Observations | 2,288 | 2,288 | 2,288 | 2,288 |
Dep var mean | 2.49 | 4.07 | 3.31 | 2.87 |
R-squared | 0.86 | 0.91 | 0.93 | 0.85 |
Notes: * p<0.05, ** p<0.01, *** p<0.001. The estimates presented in this table predict the inverse hyperbolic sine of weekly arrest rates and traffic stop rates. Untransformed dependent variable means are reported below coefficient estimates. All models include district and month fixed effects. Standard errors are in parentheses and clustered at the district level. The corresponding point and confidence interval estimates presented in this table are plotted in Figure 1. Results are weighted by district population.
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
Patrol | Specialized | |||
Arrest rate | Stop rate | Arrest rate | Stop rate | |
Panel A: Majority Black districts | ||||
2 weeks after | -0.05 (0.06) | 0.06 (0.04) | -0.13* (0.05) | 0.07 (0.07) |
4 weeks after | -0.06 (0.09) | -0.03 (0.07) | -0.16 (0.13) | 0.04 (0.03) |
6 weeks after | -0.08 (0.11) | -0.04 (0.12) | -0.31* (0.13) | 0.05 (0.10) |
8 weeks after | -0.28* (0.10) | 0.76 (0.49) | -0.26 (0.16) | 1.05 (0.61) |
10 weeks after | -0.23* (0.08) | 0.74 (0.49) | -0.06 (0.15) | 1.06 (0.63) |
12 weeks after | -0.16 (0.09) | 0.87 (0.50) | 0.03 (0.15) | 1.09 (0.70) |
> 12 weeks after | -0.05 (0.08) | 0.95 (0.55) | 0.09 (0.14) | 1.19 (0.77) |
Observations | 936 | 936 | 936 | 936 |
Dep var mean | 3.56 | 3.00 | 7.25 | 4.61 |
R-squared | 0.84 | 0.96 | 0.89 | 0.95 |
Panel B: Minority Black districts | ||||
2 weeks after | -0.06 (0.06) | 0.07 (0.10) | -0.25*** (0.05) | -0.06 (0.08) |
4 weeks after | 0.06 (0.09) | 0.05 (0.11) | -0.20* (0.08) | -0.26* (0.11) |
6 weeks after | -0.02 (0.09) | -0.14 (0.14) | -0.33*** (0.07) | -0.21 (0.12) |
8 weeks after | -0.03 (0.06) | 0.57* (0.22) | -0.23 (0.12) | 0.78* (0.31) |
10 weeks after | -0.04 (0.06) | 0.51* (0.20) | -0.22 (0.12) | 0.87* (0.32) |
12 weeks after | -0.09 (0.05) | 0.65* (0.22) | -0.20 (0.10) | 0.85* (0.29) |
> 12 weeks after | -0.05 (0.06) | 0.66** (0.21) | -0.15 (0.09) | 0.92** (0.27) |
Observations | 1,352 | 1,352 | 1,352 | 1,352 |
Dep var mean | 1.75 | 3.52 | 1.87 | 1.66 |
R-squared | 0.81 | 0.92 | 0.83 | 0.79 |
Notes: * p<0.05, ** p<0.01, *** p<0.001. Table displays estimates from the same model specifications presented in Table 2 conducted separately for districts where 50 percent or more of residents are Black (Panel A) and those with Black population shares less than 50 percent (Panel B). Point and confidence interval estimates for the pre- and post-video release periods are plotted in Figures 2 and 3. Results are weighted by district population.
Total arrest rates
Traffic stop rates
Note: Figures plot the
Total arrest rates
Traffic stop rates
A graph of a line graph Description automatically generated with medium confidence
Note: Figures plot the
Total arrest rates
Traffic stop rates
Note: Figures plot the
Table A1 includes car number descriptions for the ten most active patrol officers and specialized units in our sample. Alongside these descriptions are figures showing the share of all arrests and traffic stops made over our study period, as well as the percentage of arrests and traffic stops made by each officer type.
Table A1. Car number descriptions by officer type
Pct of total activity | Pct of officer type activity | |||
---|---|---|---|---|
Car number description | Arrests | Stops | Arrests | Stops |
Patrol officers | ||||
Beat Officer | 34.9 | 44.4 | 85.6 | 75.6 |
Foot Patrol Officer | 1.7 | 6.5 | 4.1 | 11.0 |
Traffic Enforcement Officer | 1.1 | 3.0 | 2.7 | 5.1 |
3 Wheel/PAPV/Traffic Enforcement | 0.5 | 1.5 | 1.1 | 2.6 |
Sector Beat Officer | 0.4 | 0.0 | 1.1 | 0.0 |
Bike Patrol Officer | 0.4 | 0.0 | 1.0 | 0.1 |
Pickpocket Team Officer | 0.4 | N/A | 0.9 | N/A |
20 Sector Squadrol | 0.3 | 0.9 | 0.8 | 1.5 |
10 Sector Squadrol | 0.3 | 0.5 | 0.6 | 0.9 |
Traffic Enforcement Officers | 0.2 | 0.4 | 0.4 | 0.6 |
Specialized units | ||||
Tactical Team C Officer | 7.4 | 3.2 | 12.5 | 7.7 |
Tactical Team A Officer | 7.2 | 3.8 | 12.2 | 9.3 |
Tactical Team B Officer | 6.8 | 3.0 | 11.5 | 7.3 |
Area Saturation Team Officer | 5.1 | 6.0 | 8.7 | 14.5 |
Rapid Response Officer | 4.8 | 7.1 | 8.2 | 17.3 |
Incident Team Officer | 4.1 | 0.9 | 6.9 | 2.1 |
Narcotics General Enforcement | 3.1 | 6.0 | 5.2 | 14.5 |
Mission Specific Team Officer (Authorization Required) | 3.1 | 1.6 | 5.2 | 3.9 |
Area 3 Gang Enforcement Unit Team | 1.6 | 0.7 | 2.8 | 1.8 |
Detectives And P.O.S, Fugitive Apprehension Unit | 1.5 | N/A | 2.5 | N/A |
Notes: We were unable to calculate percentages for car numbers with no recorded stop data and marked these as not applicable (N/A).