Does Race Affect Access to Government Services

Inequality in Police Service Provision:

Evidence from Residential Burglary Investigations ∗

Rebecca Goldstein†

July 9, 2020

∗I am grateful to Laura Vittorio, Captain John Leavitt, and Sergeant Aaron Wine of the Tucson Police De- partment for invaluable assistance with this project. I am thankful for comments from Abhay Aneja, Steve Ansolabehere, Pamela Ban, David Deming, Ryan Enos, Jonathan Gould, Jennifer Hochschild, Kaneesha John- son, Mayya Komisarchik, Jens Ludwig, Devah Pager, Deepak Premkumar, Jim Snyder, Jessica Trounstine, Chris Warshaw, Bruce Western, Hye Young You, and workshop and panel participants at the 2018 American Political Science Association Annual Meeting, Harvard, Boston University, and UC Berkeley, and for the ex- cellent research assistance of Jack Demuth, Whitney Driver, and Sean Gerhart. This project received support from the Pershing Square Fund for Research on the Foundations of Human Behavior and the Multidisci- plinary Program on Inequality and Social Policy. This manuscript has not been peer-reviewed; please do not cite without author’s permission. †Assistant Professor, UC Berkeley School of Law (Jurisprudence & Social Policy Program). Email: rgold-

[email protected]. Website: https://rebeccasgoldstein.com.

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Abstract

When a crime victim calls the police for help, what type of response do they re-

ceive? While scholars have extensively documented racial inequalities in the police’s

punitive functions, this paper considers police as service providers. It leverages uniquely

granular data on over 2,500 residential burglary investigations in Tucson, Arizona to

consider the predictors of investigative thoroughness. Contrary to conventional wis-

dom about police behavior, demographics of victims or officers do not predict inves-

tigative thoroughness. Instead, the most important predictor of investigative thor-

oughness is whether the burglary featured a forced entry into the residence, since

forced entry cases feature more evidence and thus provide greater likelihood of case

clearance. However, the probability of forced entry differs significantly by neighbor-

hood, meaning that the seemingly neutral decision to maximize clearance rates has

unequal consequences.

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1. Introduction

When a victim of a crime calls the police for help, what type of response do they receive?

Does the quality of police service provision vary depending on the race, gender, income,

age, or neighborhood of the victim? Scholars have extensively documented racial inequal-

ities in the police’s punitive functions – pursuing, searching, and arresting criminal sus-

pects (Knowles, Persico, and Todd 2001; Beckett, Nyrop, and Pfingst 2006; Persico and

Todd 2008; Golub, Johnson, and Dunlap 2007; Antonovics and Knight 2009; Gelman, Fa-

gan, and Kiss 2007; Mitchell and Caudy 2017) – and traffic ticketing (Baumgartner et al.

2017; West 2018; Goncalves and Mello 2020). Much less scholarly attention has been de-

voted to inequalities in police services rendered to victims of crimes or disturbances, de-

spite the fact that there are many more crime victims than there are arrests every year

(Truman and Morgan 2016; DOJ 2016).

This paper is the first to use incident-level data to consider whether there are inequal-

ities – based on race, gender, income, age, or neighborhood – in the quality of policing

services rendered to victims of crimes. It finds that demographic characteristics do not

dictate what sorts of burglary investigations victims receive. The main determinant of

investigative thoroughness is instead whether the burglary featured a forced entry into

the residence. Forced entry burglaries, however, are disproportionately concentrated in

better-off neighborhoods of the city, and so even though these neighborhoods’ residents

are not directly discriminated against conditional on their neighborhood, unconditional

social inequalities in service provision remain.

Scholarship analyzing inequality in police service provision is rare in part because

incident-level data is usually difficult to access. In this paper, I leverage a novel and

uniquely granular data set from the Tucson Police Department (TPD). By partnering with

the TPD, I obtained data on every residential burglary in Tucson in calendar year 2016

(over 2,500 burglaries), including information on the nature of the incident; the races and

genders of each civilian and police officer present at each burglary scene; and the activ-

ities officers undertook at the scene. I also use the address information of each incident

to link incidents to American Community Survey data on neighborhood demographic

characteristics. I then compare three measures of service quality for residential burglary

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investigations – the amount of time spent at the scene, whether or not officers dusted for

fingerprints, and whether or not a burglary detective was ever assigned to the case – to all

of these incident- and neighborhood-level characteristics.

Contrary to conventional wisdom, burglary victims of different races and genders do

not receive different levels of thoroughness in their investigations, and officers of different

races and genders do not provide different levels of investigative thoroughness. Instead,

I find that, conditional on a host of contextual variables, the main determinant of inves-

tigative thoroughness is whether the burglary featured a forced entry into the residence.

Officers spend 11 more minutes (just over 20% more time) at the scene of forced entry

burglaries, they are 66% more likely to dust for fingerprints, and over 45% more likely to

assign a detective to the case, conditional on relevant situational and demographic vari-

ables.

Making a causal claim about the relationship between forced entry and investigative

thoroughness is challenging because forced entries are not randomly assigned to resi-

dences. To overcome the potential for selection bias, I employ three sets of fixed effects:

date-level (to account for crime seasonality, police resource differences on weekends and

holidays, and any other unobserved time-varying differences), hour-level (to account for

any differences between daytime and nighttime incidents, between officer work shifts, and

any other unobserved patterns related to time of day), and Census block group-level (to

account for any cross-sectional confounding at the level of the Census block group, such as

neighborhood socioeconomic characteristics). These regressions also include a full battery

of controls for incident-level confounders, including level of urgency at the time of the 911

call (indexed by a dispatcher-assigned priority level), the number and demographics of

the victims present, and the number and demographics of the officers who responded. In

the Appendix, I carry out additional tests to address the possibility that racial minorities

call the police only in relatively serious incidents and I find that they do not.

Even though racial minorities are not discriminated against in the quality of investi-

gation that they receive, thoroughly investigated cases are not distributed equally among

Tucson’s neighborhoods. The probability of a forced entry differs significantly by type of

neighborhood and type of residence, and is particularly low in high-poverty areas and

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multi-unit residences. Forced entry is 10% less likely in high poverty (40% poverty or

more) versus low poverty neighborhoods (p < 0.01), and 15% less likely in an apartment

versus in a single-family residence (p < 0.01). Unconditional on any background vari-

ables, officers spend 7 fewer minutes (about 8% less time) at residential burglaries that

take place in high poverty compared to low poverty neighborhoods (p = 0.03). Inequal-

ities do exist, then, because of the distribution of forced- and unforced-entry burglaries

across different types of residences.

These findings show how formally neutral criteria can create inequalities in service

provision. Even though TPD officers are using a criterion (forced entry) that is facially

neutral with respect to race and socioeconomic class to decide where to focus their inves-

tigation efforts, unconditional social inequalities are nonetheless present.

Although these results run contrary to a large amount of research about the racially

discriminatory way police behave in their punitive roles (Gelman, Fagan, and Kiss (2007);

Braun, Rosenthal, and Therrian (2018)), they are consistent with the theoretical predic-

tion that inequalities in public service provision will emerge only for substitutable public

goods (Besley and Coate 1991). Furthermore, although economic analysis of crime has

traditionally focused on the incentives of would-be offenders (Becker 1968; Freeman 1999;

Pinotti 2017), these results are consistent with research showing that police officers, too,

respond to incentives (in their case, the performance-based incentives used to evaluate

police officer performance (Mas 2006; Carpenter 2014)).

This paper makes several contributions to the research literature. First, it contributes

to the scholarly understanding of racial bias in policing, which has so far focused entirely

on the police’s punitive activities and roles, even though crime victims significantly out-

number arrestees (Truman and Morgan 2016; DOJ 2016). Police not behaving in a racially

discriminatory manner in their service provision role considerably complicates our un-

derstanding of race and policing in light the large literatures on racial bias in traffic stops,

pedestrian stops, and arrests (Golub, Johnson, and Dunlap 2007; Goel et al. 2016; Ritter

2017; Braun, Rosenthal, and Therrian 2018). Second, the fact that there are not condition-

ally worse quality burglary investigation services provided to low-income, heavily mi-

nority neighborhoods provides empirical support for the classic Besley and Coate (1991)

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model, which predicts that inequalities in public service provision will emerge only for

substitutable public goods. Third, the unconditional inequality in thoroughness of bur-

glary investigations provided in poorer and wealthier neighborhoods illustrates how ser-

vice provision inequalities can emerge out of a socioeconomically neutral bureaucratic

incentive structure. Finally, the fact that police officers exercise most effort on cases that

they are most likely to solve provides evidence that street-level bureaucrats, just like other

bureaucrats, are focused on maximizing their performance on observable and quantifiable

measures which influence their reputations, and in consequence, their future professional

prospects (Holmström 1999; Carpenter 2014) – in short, that police officers respond to in-

centives in choosing where to focus their efforts, just as members of the public respond to

incentives in choosing whether, when, and where to commit crimes (Di Tella and Schar-

grodsky 2004).

The remainder of this paper proceeds in four parts. First, I discuss what existing theo-

retical and empirical research on public service provision, bureaucratic behavior, inequal-

ity, and policing would lead us to expect about where police are likely to direct inves-

tigative resources. Second, I describe the policing context in Tucson and burglary inves-

tigations in general. Third, I present results from the analysis of over 2,500 residential

burglaries. Finally, I draw conclusions and suggest directions for future research.

2. Policing: Public goods, bureaucratic behavior, and inequality

What does the literature on public goods provision suggest about how police are likely to

allocate investigative resources? The classic Besley and Coate (1991) model predicts that

inequalities in public services will be greatest where it is possible for wealthy citizens to

purchase higher-quality private goods to substitute for lower-quality public goods. In-

deed, urban public services, such as education and housing, feature extreme racial and

socioeconomic inequalities in part for this reason (Duncan and Murnane 2011; Krivo and

Kaufman 2004), and police-provided services which are privately substitutable, such as

community security services, are sometimes replaced by private services in wealthy neigh-

borhoods (Trounstine 2015). But it is hard to imagine a private substitute for burglary

investigation services, and so the Besley and Coate model would not predict racial or

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socioeconomic inequalities to emerge in their provision. Indeed, other non-substitutable

police services, such as emergency response, in fact feature much greater resources de-

voted to high-crime, high-poverty neighborhoods (Walker, Spohn, and DeLone 2012; Ci-

han, Zhang, and Hoover 2012).

The scholarly literature on crime as a market-driven phenomenon focuses primarily

on the incentives of would-be offenders (Ehrlich 1973; Kelly 2000; Pinotti 2017). But the

level of crime is driven not just by the behavior of would-be offenders – police officer

behavior also shapes the equilibrium level of crime (Persico 2002; Chalfin and McCrary

2018; McCrary and Premkumar 2019). Existing economic analysis of officer incentives

has attempted to explain well-known patterns of police bias against racial minorities in

the context of arrests, pedestrian stops, and traffic stops (Persico 2002; West 2018; Braun,

Rosenthal, and Therrian 2018; Goncalves and Mello 2020). A smaller literature describes

the incentives of police officers, often with a focus on opportunities that the police occa-

sionally have to directly collect money for their agencies through seizures of cash from

criminal enterprises (Benson, Rasmussen, and Sollars 1995; Mast, Benson, and Rasmussen

2000; DeAngelo, Gittings, and Ross 2018).

But these analyses examine the police only in their role as pursuers of criminal sus-

pects, not in their role as service providers to witnesses and victims. Accusations of po-

lice bias against racial minorities, though, often include descriptions of police who do

not serve minority community members effectively when those community members are

crime victims (Natapoff 2006; Leovy 2015). Just as scholars have aimed to detect the

presence of racial bias in police stopping and arresting activity (Knowles, Persico, and

Todd 2001; Grogger and Ridgeway 2006; Persico and Todd 2006; Mitchell and Caudy 2017;

Goncalves and Mello 2020), this paper aims to detect any such parallel bias in police ser-

vice provision activity.

In exploring this question, a foundational premise is that police are incentivized – just

as other bureaucrats are – to be seen as high-performing in the eyes of their superiors

(Lipsky 1980). Theory and empirical evidence on incentives in bureaucracies would pre-

dict that police officers will be incentivized to focus their efforts on activities which con-

tribute to achievement on the performance measures on which they are primarily eval-

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uated (Niskanen 1971; Sigelman 1986; Carpenter and Krause 2012; Lemos and Minzner

2014). The most basic measure of police performance is the clearance rate, the number of

crimes that are “cleared” divided by the total number of crimes (Mas 2006; Rayman 2013).

The advent of data-driven policing that is directly targeted at lowering the rate of crime

and increasing the rate of crime clearance has made clearing crimes especially important

for police leadership’s evaluation of police rank-and-file and for city leadership’s evalu-

ation of police leadership (Rayman 2013). Police leadership care about department (and

thus officer) clearance rates because bureaucrats accrue influence and power within and

on behalf of their agencies by maintaining a reputation as highly competent and impar-

tially technocratic – evaluations that are typically also made using observable performance

measures (Carpenter 2014).

For these reasons, research on incentives in bureaucracies would predict police to fo-

cus on cases with the highest probability of clearance. With respect to burglaries, crimi-

nology research distinguishes between forced entry burglaries (such as those involving a

picked lock or broken window) and unforced entry burglaries (such as those involving en-

try through a door or window left open). Forced entry burglaries provide more analyzable

evidence are thus more solvable as compared to unforced entries (Coupe 2016; Shannon

and Coonan 2016; Killmier, Mueller-Johnson, and Coupe 2019). The incentive structure

that police face in investigating burglaries, then, can be expected to encourage them to

focus scarce investigatory resources on forced entry burglaries.

Several other areas of literature suggest the contrary conclusion: that there would be

inequality in police service provision. But existing work is not conclusive on the ques-

tion, and existing lines of work do not necessary translate to inequalities across service

provision activities.

First, and most directly, the little existing work on police service provision provides

evidence of inequalities in outcomes related to service provision. That research shows

that police are less likely to clear homicides when the victim is Black or Hispanic than

when the victim is white (Roberts and Lyons 2011; Fagan and Geller 2018). While some

scholars and journalists believe this is due to police’s deliberate lack of effort in investi-

gating homicide cases with Black and Hispanic victims (Leovy (2015)), others argue that

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this disparity is more due to a lack of cooperation with homicide investigators in commu-

nities where police-community relations are strained (Roberts 2015; Mancik, Parker, and

Williams 2018). These studies illustrate the distinction between investigative thorough-

ness and clearance: if factors beyond investigators’ control largely determine clearance

rates, clearance is not an accurate reflection of investigative thoroughness. One advan-

tage of the present study is that I measure investigative thoroughness directly, without the

complications inherent in using clearance as a proxy for thoroughness.

Second, the literature on inequalities in other areas of policing show significant dispar-

ity in police treatment of suspects of different races. Nonwhites are stopped (in vehicles

and on foot) more than whites, nonwhite suspects more likely than white suspects to be ar-

rested for similar crimes, and nonwhite suspects are more likely to report experiencing ag-

gressive or unfair treatment by police (Carr, Napolitano, and Keating 2007; Walker, Spohn,

and DeLone 2012; Epp, Maynard-Moody, and Haider-Markel 2014). At least some of these

inequalities may be rooted in well-documented, unconscious associations between Black-

ness and criminality (Eberhardt et al. 2004).

But this literature is focused solely on the police’s roles stopping suspicious pedes-

trians and vehicles or arresting criminal suspects, rather than on their roles as service

providers. Inequalities in one context might not translate to the other. A key feature of

pedestrian and vehicle stops is that those contexts require police to make nearly instant

judgments about an individual’s likelihood of possessing drugs or weapons. A wealth of

psychological evidence shows that unintentional biases are magnified under “snap judg-

ment” decision conditions (Payne 2006; Freeman and Johnson 2016). Another situational

characteristic that can magnify the reliance on heuristics such as racial stereotypes is anx-

iety. When police are stopping persons and vehicles in search of contraband, they tend to

be alert to potentially dangerous situational developments (Woods 2018). Psychological

studies show that anxiety inhibits typical information processing and can increase the use

of stereotypes in decision making (Wilder 1993; Hilton and Von Hippel 1996; Hamilton

and Sherman 2014).

Third, and most broadly, other public services feature extreme racial and socioeco-

nomic inequalities. If police investigative services are understood as a public service, it

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is reasonable to think that it – like many other public services – might be distributed un-

equally on the basis of race and/or socioeconomic class, although the absence of a private

equivalent to many police services (including burglary investigations) provides reason to

think that inequalities of the sort present in the context of education, housing, and other

public goods will not apply to the service provision context.

In summary, literature on public goods provision would lead us not to expect racial or

class inequalities in the provision of non-substitutable public goods, including residential

burglary investigation. Literature on incentives in bureaucracies would lead us to expect

police officers to direct resources in ways that promote their success on the performance

measures on which they are evaluated – here, attempts to clear as many crimes as possible

would lead police to focus on forced entry burglaries. But the literatures on racial disparity

in homicide clearance, racial discrimination by police officers, and inequalities in urban

public service provision would lead us to expect that race and class inequalities will exist

in police service provision as well.

3. Data: Policing in Tucson, Arizona

The primary data for this project are detailed records for every residential burglary that

took place in Tucson, Arizona in calendar year 2016. Burglary is defined by Arizona statute

as “entering or remaining unlawfully in or on a residential structure with the intent to

commit any theft or any felony therein” (A.R.S. §13-1507). The data includes the street

address of the residence, the division (one of four geographic areas) in which it took place,

the priority level that the police dispatcher placed on the call,1 the time the call was placed,

the time that officers arrived, the time that officers left, the number and ages, races, and

genders of everyone at the scene, and detailed information on the nature of the incident

and the activities undertaken by the officers at the scene. Officers also record how many

officers were present at the scene and their badge identification numbers, along with how

many victims were present at the scene, and their races and genders. The TPD provided

employee records information which allowed me to merge in information on the ages,

1The priority levels range from 1, meaning an emergency call for which dispatched officers would use

lights and sirens in arriving, to 4, meaning lowest priority.

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races, and genders of the officers present at each scene. The TPD system later adds infor-

mation on whether a detective was eventually assigned to the case, which is a decision

made by the burglary sergeant in the division in which the burglary took place.

For purposes of comparability of incidents, I exclude any residential burglary which

was initially reported as a larceny (a theft which does not involve an unlawful entrance)

and is later re-coded as a burglary based on further investigation, since the initial phase

of a larceny investigation is much less thorough than that of a burglary investigation. I

also exclude incidents reported using the TPD web interface rather than a phone call or an

alarm system, since they reflect a much lower level of urgency on the part of the individual

in need of police service. These exclusions leave 2,771 burglaries.2 All these burglaries

needed to be addressed at least partially in the year’s 365 days by Tucson’s 870 sworn

officers, reflecting a very high level of capacity constraint. These burglaries are shown at

the locations they occurred in Figure 1.

The criteria I use to define a thorough burglary investigation are (1) the amount of time

police spent at the scene, (2) whether the police dusted for fingerprints, and (3) whether a

detective was eventually assigned to the case. Conversations with TPD researchers, patrol

officers, burglary detectives, and burglary sergeants revealed that the standard practice at

all burglary scenes is to conduct a thorough interview with the victim or victims and to

dust for fingerprints in the location where fingerprints are most likely to have been left

by the perpetrator (which depends on the victim’s description of missing items and the

perpetrator’s likely point of entry). Other potential measures of thoroughness, such as

whether or not DNA was collected, whether full lists of stolen items were recorded, or

whether the police recorded stolen items’ serial numbers, were not widely applicable to

all cases: DNA is only collected if the perpetrator appears to have left a sample of bodily

fluid, serial numbers are only recorded for items that have serial numbers (such as high-

value electronics and firearms), and full lists of stolen items are sometimes not possible to

record at a scene because victims are often too distressed to know exactly what has been

taken. Sergeants assign detectives based primarily on their perception of the solvability

of the case. In practice, sergeants view cases as solvable when there is potential physical

2The FBI’s Uniform Crime Reporting data for Tucson in 2016 records 4,138 burglaries; the 2,771 in the final

data are only residential (rather than commercial) burglaries that meet the above criteria.

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evidence which prosecutors could later use to definitively link a suspect to a burglary.

In classifying burglaries as forced or unforced entries, I follow both TPD and the FBI’s

Uniform Crime Reporting (UCR) Program, both of which distinguish between the two

types of burglaries (DOJ 2014). TPD classify as forced entry any burglaries in which the

perpetrator entered the residence through a locked door or window, whether by breaking

the window or door, by using any tool to force or pry open the window or door, or by

using a tool to disable the lock on a window or door. In many cases, a forced entry will

be obvious, as it has caused damage to the window or door and/or the surrounding area

of the residence. In some cases, if there are no signs of forced entry, patrol officers need to

decide whether the victim is being truthful in their account that they locked all doors and

windows, and the entry is classified as forced if officers believe the victim’s account and

unforced if they do not.

In addition to the police-provided information about each burglary, I linked the ad-

dresses of the burglaries to American Community Survey (ACS) data (2011-2015 esti-

mates) on Census block group demographics. Census block groups are the smallest level

of aggregation at which the Census Bureau reports demographic data; in dense urban ar-

eas, they typically correspond to individual square city blocks. The average population

of a Census block group in Tucson is 1,540. Summary statistics on the 2016 residential

burglaries and the Census block groups in which they took place is presented in Table 1.

Tucson is an ideal setting for this study because of the very high level of racial diversity

(47% white non-Hispanic, 42% Hispanic, 5% Black, 3% Native American, and 3% Asian,

as of the 2010 Census) as well as socioeconomic diversity (25% of persons in Tucson live

in poverty as of 2015, and the median household income was below the national median

at $37,149) means that there is a great deal of variation in types of neighborhoods and

individuals that the police serve.

As Figure 1 and Figure 2 show, burglaries are more prevalent in denser and poorer

areas of the city. The retirement communities of the west part of Tucson and the wealthy

neighborhoods of the most northern part of Tucson experienced few residential burglaries

in 2016. Burglaries were most concentrated in the poor neighborhoods immediately east

of Interstate 10, which divides the city’s eastern and western parts.

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Table 1 displays summary statistics for key variables of interest. Where the number of

observations is less than 2,771, the relevant data is missing for some of the burglaries. Of-

ten this is due to officers not recording that information in their narrative reports. Missing

data on time spent at the scene is a result of patrol officers editing their narrative reports

after submitting them, which causes the time to default to zero, and failing to manually

input the time stamp again.

4. Results and Discussion

4.1. Race and investigative thoroughness

Table 2 presents the results of OLS regressions predicting the three indicators of inves-

tigative thoroughness. Columns (1), (3), and (5) display the coefficients on the share of

white officers and victims at a scene when predicting the log of time spent at the scene

(in minutes), and the binary probability of the police collecting fingerprints and assign-

ing a detective to the case, without any additional controls. Columns (2), (4), and (6) add

fixed effects for TPD’s four (geographic) police divisions, to control for differences in ca-

pacity between different divisions, and fixed effects for month of the year, to control for

the basic seasonality of crime patterns. This minimum set of controls, especially in the

odd-numbered columns, illustrates that the comparatively large standard errors on the

coefficients for share of white officers and share of white victims are not an artifact of

collinearity in the control variables (Chatterjee and Simonoff 2013). Other incident- and

victim-level variables included in Table 3, such as 911 call priority level, the indicator that

the residence is an apartment, the number of victims, the number of officers, and the age

of victims, are omitted here because they are plausibly post-treatment with respect to the

race of the victims.

Table 2 shows that the relationships between the share of white victims or officers and

investigative thoroughness is substantively small, and for the most part not statistically

significant at conventional levels, even when only a minimum set of control variables are

included in the regression. Furthermore, the signs on the significant coefficients reflect a

result contrary to the conventional wisdom about race and policing: that police officers

spend slightly less time with groups of victims that are more heavily white (specifically,

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that they spend about 7% less time, or 4 fewer minutes, with all white groups of victims

compared to all nonwhite groups of victims).

These findings on race and investigative thoroughness – despite being “null” in the

frequentist sense – are enormously informative in the Bayesian sense. Abadie (2018) ar-

gues that readers of scientific journals are agents in a limited-information Bayesian setting.

Readers have a prior belief about the value of an estimand which the author seeks to esti-

mate, and an author’s result is informative if “it has the potential to substantially change

the [prior] beliefs of the agents” – that is, the result is informative if the prior and pos-

terior distributions of the estimator substantially differ. Abadie shows that, even when

prior distributions are diffuse, the prior and posterior distributions of the estimator very

often differ more when the result is not statistically significant than when the result is

statistically significant, especially in large samples.

In the present case, even using a diffuse prior distribution (the standard normal prior),

the prior probability of rejection of the null hypothesis of zero mean difference is extremely

high (0.97). This fact further emphasizes how informative it is to fail to reject the null hy-

pothesis of zero difference in investigation thoroughness between burglaries with different

shares of white victims at the scene. Details of this calculation are included in Appendix

B.

This result is also extremely surprising in the context of existing research on racial bias

in policing. The bulk of existing research on this topic3 uses data on either pedestrian stops

or traffic stops, or on police-involved fatalities, to assess the extent of racial bias. Most

of the studies of pedestrian stops find that racial minorities are more likely than whites

to be stopped (Gelman, Fagan, and Kiss 2007) and that officers appear to use a lower

threshold of suspicion when deciding to stop nonwhites compared to whites (Goel et al.

2016), although some find limited evidence of bias in arrests conditional on stops (Coviello

and Persico 2015). Previous studies of traffic stops also mostly find significant bias against

minority drivers; for example, Baumgartner et al. (2017) uses data on over 18 million traffic

stops made in North Carolina between 2000 and 2016 and finds that, conditional on all

3The literature on racial bias in policing is vast and spans many disciplines in the social sciences and

humanities, but here I am referring to quantitative, empirical research using police administrative records,

rather than qualitative, game theoretic, or lab experimental research.

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available background variables, Black drivers were significantly more likely to be stopped

and significantly less likely to be found with contraband compared to white drivers. Most

studies of police-involved fatalities also find that African-Americans are more likely than

whites to be killed by the police (Ross 2015; Knox, Lowe, and Mummolo 2019), although

agreement on this score is not universal (Fryer Jr 2019).

4.2. Forced entry and investigative thoroughness

Table 3 presents the results of fixed effects regressions predicting the three indicators of

investigative thoroughness. Columns (1), (3), and (5) show the coefficient of the binary

variable indicating a forced entry on the log of minutes spent at the scene, on the probabil-

ity of prints having been taken, and on the probability of a detective having been assigned,

with no additional controls. Columns (2), (4), and (6) make full use of the rich microdata

by employing controls for all available victim, officer, and incident characteristics, along

with three sets of fixed effects: block group, calendar date, and hour of the day.

Comparing the odd-numbered and even-numbered Columns reveals that, while some

of the variation that is explained by forced entry is explained by the battery of victim-

, incident-, and officer-level controls, and by the three sets of fixed effects, forced entry

nevertheless has a statistically significant and substantively large effect on investigative

thoroughness even when all of these variables are taken into account. The estimates in

Columns (2), (4), and (6) reflect that officers spend 21% more time at the scenes of forced

entry burglaries, are 18 percentage points more likely to dust for fingerprints, and are 12

percentage points more likely to assign a detective to the case when the burglary featured

a forced entry compared to an unforced entry, conditional on all of the available situational

and demographic variables, and on block group, date, and hour fixed effects. The means

of these three dependent variables for unforced entry cases are 55 minutes, 27% likelihood

of collecting fingerprints, and 25% likelihood of assigning a detective, and so these coeffi-

cients represent officers spending 11 more minutes at the scene, along with a 66% increase

in the likelihood of collecting fingerprints and a 47% increase in the likelihood of assigning

a detective.

The key identifying assumption for a causal interpretation of the relationship between

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forced entry and investigative thoroughness is that within Census block group, calendar

date, and hour of the day, forced and unforced entry are randomly assigned to residences.

This assumption is plausible for three reasons, corresponding to the three sets of fixed ef-

fects. First, Census block groups are small: in Tucson, on average, they are composed of

just 1,540 individuals, or 616 households. The assumption that these fixed effects capture

neighborhood-level variation which would be related to the incidence of forced versus

unforced entry (such as the residences’ level of physical security or any other socioeco-

nomic characteristics) is thus not a strong one. Second, crime and police capacity are

time-varying in many ways: according to the seasons, according to whether it is a week-

day, a weekend day, or a holiday, and even, some have argued, according to the timing

of public benefits distribution (Foley 2011). Fixed effects for the individual calendar day,

then, account for all of that variation and any other unobservable time variation at the

level of the day or longer. Finally, crime and police capacity vary greatly according to the

time of day – because of light and darkness, because of when individuals are likely to be in

their homes versus in their workplaces, and because of changing police shifts. Allowing

effects to be estimated within the specific hour of the incident, then, accounts for all of this

variation.

These results are consistent with the large body of literature on bureaucratic decision-

making argues bureaucrats are focused on maximizing their performance on observable,

quantifiable measures which influence their reputations, and in consequence, their future

professional prospects (Carpenter (2014)). These results conform to a model of police of-

ficers as bureaucrats maximizing their main performance measure – clearance rates – just

as other types of bureaucrats seek to maximize their own performance measures Balla and

Gormley Jr (2017); Brodkin (2008). An officer, in other words, reasons that a forced en-

try is more likely to result in a cleared case than is an unforced entry, regardless of the

demographics of the victim.

4.3. Unconditional inequality in service provision

Although Tucson residents receive similar quality burglary investigations conditional on

whether the burglary featured a forced or unforced entry, they do not receive similar qual-

14

ity burglary investigations overall. Service provision is unconditionally unequal.

Because the regression models in Table 3 account for many types of variation that could

affect investigative thoroughness, the results mask neighborhood- and residence-level dif-

ferences in the incidence of forced and unforced entry. In particular, there are marked

unconditional differences in the rates of forced and unforced entry in different types of

residences and different types of neighborhoods; Table 4 shows that apartments, high

poverty neighborhoods, and high renter share (more than two-thirds rented residences)

neighborhoods are over-represented among unforced entry cases and under-represented

among forced entry cases (forced entry cases are 65% of overall cases).

Although it is impossible to tell with certainty why this is the case, the clearest expla-

nation is that landlords on the private rental market have much less incentive to invest in

security measures for their properties than owners of their own residences do (Hamilton-

Smith and Kent 2005). In addition, Tucson burglary officials speculated that in multi-

family residences it is easier to case many residences quickly for unlocked doors or win-

dows, and there is an economy of scale for a burglar in learning a way in to a housing

complex with a large number of units. This mechanism would be consistent with crimino-

logical literature which finds that event dependence for repeat and near-repeat burglaries

(that is, burglaries which occur more than once within a year at the same residence or

within the same small cluster of residences) is greater where the distance between homes

is smaller (Short et al. (2009)). Taken together, this evidence demonstrates that officers’

seemingly neutral decisions to attempt to maximize clearance rates in fact has distribu-

tional consequences.

15

5. Conclusion: Bureaucratic Incentives, Resource Allocation, and Inequality

Policing, like other public services, involves the provision of services to a diverse set of

residents with differing interests and needs. Local police departments provide a public

service to residents by responding to calls for service. Scholars of policing in many disci-

plines have focused extensively on racial inequalities in the police’s punitive functions –

pursuing and arresting criminal suspects (Knowles, Persico, and Todd 2001; Persico and

Todd 2006; Grogger and Ridgeway 2006; Golub, Johnson, and Dunlap 2007; Persico and

Todd 2008; Beckett, Nyrop, and Pfingst 2006; Antonovics and Knight 2009; Gelman, Fagan,

and Kiss 2007; Mitchell and Caudy 2015; Legewie 2016; Horrace and Rohlin 2016; Baum-

gartner et al. 2017). And research that leverages incident-level data to measure inequalities

focuses on either traffic ticketing patterns (DeAngelo and Owens 2017; West 2018, 2019)

or racial patterns in criminal victimization (DeAngelo, Gittings, and Pena 2018). These

methods have not, however, been previously used to explore inequalities in police ser-

vices rendered to victims and witnesses of crimes or disturbances, despite the crucial role

of victim service provision in the police function (Goldstein 1977; Friedman 2020).

In this paper, I show that police officers in Tucson, Arizona primarily use a simple

rule – whether or not the burglary featured a forced entry into the residence – in decid-

ing how thoroughly to investigate a residential burglary. Conditional on basic seasonal

controls, demographic characteristics of victims and officers are not significantly associ-

ated with additional investigative thoroughness, but officers do devote greater investiga-

tive resources to forced entry burglaries. Officers spend 11 more minutes at the scenes of

forced entry burglaries, they are 18 percentage points more likely to dust for fingerprints,

and they are 12 percentage points more likely to eventually assign a detective to the case

when the burglary featured a forced entry, conditional on all of the available situational

and demographic variables, and on block group, date, and hour fixed effects.

Are these results likely generalizable to cities other than Tucson? While it is impossible

to say for certain, residential burglaries take place in every city and investigating them is

a core component of police training across the United States (Weisel 2002; Antrobus and

Pilotto 2016). Clearance rates, too, are a key metric of police performance in every depart-

ment (Mas 2006; Sonnichsen 2007; Pare 2014). For these reasons, it is not unreasonable

16

to expect the police incentive to clear residential burglaries to shape police behavior in

many cities. Further research is necessary to determine how the clearance incentive in-

teracts with other incentives – including, perhaps, the incentive to provide more racially

or socioeconomically advantaged citizens with higher-quality public services (Keefer and

Khemani 2005).

The findings in this study beg the question: why do street-level bureaucrats exhibit

different levels of bias in different contexts? As applied to police officers: why they might

they be less likely to discriminate against racial minorities in the residential burglary in-

vestigation context than they are in the vehicle stop, pedestrian stop, and use-of-force

contexts? Three areas of literature provide plausible answers.

A first hypothesis requiring further research, for which this paper provides prelim-

inary support, is that police behave differently when providing substitutable and non-

substitutable public goods. The article’s result is consistent with the famous Besley and

Coate (1991) prediction that inequalities in public service provision will be greater for non-

substitutable public goods. Not all police-provided services are non-substitutable: private

patrol is directly available to citizens from private firms (Trounstine 2015), and wealthy

citizens routinely call on civil lawyers or therapists for mediation and arbitration services

for which poorer citizens routinely call the police (Friedman 2020). Residential burglary

investigation, by contrast, is non-substitutable, and so this article’s finding that racial and

socioeconomic inequalities are not present, conditional on contextual variables, is consis-

tent with Besley and Coate (1991).

Second, police are incentivized to maximize performance on measurable performance

metrics, in this case the crime clearance rate. This incentive is described by with the classic

characterization of police as street-level bureaucrats (Lipsky 1980; Hill 2003), the relatively

low-level officials who exercise enormous discretion in allocating public resources. Lipsky

(1980) observed that street-level bureaucrats tend to focus on cases or clients that are the

easiest, the most straightforward, or are the most likely to lead to a positive outcome for

which they can take credit – a phenomenon he calls “creaming.” “Confronted with more

clients than can readily be accommodated,” Lipsky writes, “bureaucrats often choose (or

skim off the top) those who seem most likely to succeed in terms of bureaucratic success

17

criteria.” This insight raises the possibility that discrimination by street-level bureaucrats

may be more prevalent in contexts where the cases which are administratively simplest to

handle are also the cases where clients are more racially or socioeconomically advantaged.

Third, different contexts implicate different psychological responses. Burglary investi-

gations can be done slowly and methodically, and victims of residential burglaries are not

perceived threats to officer safety. This contrasts with the pedestrian and vehicle stop con-

texts, in which many officers are trained to be alert to the possibility that civilians might

be armed or otherwise dangerous (Woods 2018). We might therefore expect officers to

rely less on racial stereotypes when carrying out a residential burglary investigation than

when stopping persons or vehicles.

Each of these hypotheses open the door for further research on the determinants of

discrimination – or the lack thereof – by police in different context. A focus on the nature

of the goods that police provide, the incentives they face, and the psychological pressures

they encounter can provide us with a more textured view of police discrimination.

Finally, the distributional consequences illustrated here represent an example of biased

outcomes emerging from neutral rules (Jolls (2001)). Even if and when public officials do

not intend to discriminate, the incentive structure that they face can lead to disparate im-

pacts on disadvantaged communities. Outside the policing context, public officials are

incentivized to locate hazardous waste or other undesirable sites on cheap land or in com-

munities unable to mobilize opposition, and these incentives can lead to undesirable sites

being located in minority communities (Foreman 2011; Sherman 2012). Public officials

are similarly incentivized to hire government contractors with relevant experience or who

promise to deliver a project at low costs, but even seemingly neutral criteria such as these

can have disparate impacts on minority contractors (Sonn 1992). And public universi-

ties are incentivized to admit students with high standardized test scores, since having

students with higher test scores increases their ranking and prestige, but reliance on test

scores can have a disparate impact on minority and female applicants (Jencks and Phillips

2011). In each of these instances, a legitimate goal, and a legally unobjectionable incentive

structure, can lead government officials or entities to act in ways that replicate existing

racial, social, and economic inequalities.

18

6. Appendix

6.1. Appendix A: addressing selection

One reason to believe that racial minorities may be calling about more serious burglaries

would be if they place a disproportionate share of high priority burglary calls. The priority

level fixed effects which feature in the main analysis should eliminate the possibility that

any differences of this sort would bias the results, but Appendix Figure A1 below confirms

that whites do not place a disproportionate share of low priority calls relative to their share

of the Tucson population.

Figure A1: Share white among victims and in the Block group at each priority level

Appendix Table A1 shows that, even when call priority level is not taken into account,

the demographics of the victims and the neighborhood are not significant predictors of

investigative thoroughness. This suggests that call priority level, even though it bears a

statistically significant relationship to investigative thoroughness in the full model, is not

explaining any of the same variation in thoroughness outcomes as victim or neighborhood

demographics.

19

Table A1: Predicting investigative thoroughness without priority level

Dependent variable:

Time (mins) (log) Prints collected Detective assigned

(1) (2) (3)

Forced entry 0.186∗∗∗ 0.175∗∗∗ 0.116∗∗∗

(0.045) (0.026) (0.025) Share white officers 0.019 0.020 0.028

(0.051) (0.029) (0.030) Share white victims −0.049 −0.036 −0.010

(0.044) (0.027) (0.026) Apartment −0.059 −0.044 −0.098∗∗∗

(0.054) (0.028) (0.027) Victim ages (mean) −0.0001 −0.001 −0.001

(0.001) (0.001) (0.001) Share male officers 0.042 0.046 0.061∗

(0.063) (0.032) (0.033) Share male victims −0.005 −0.015 0.024

(0.047) (0.026) (0.025) Total victims −0.006 0.030∗ 0.028

(0.025) (0.016) (0.018) Total officers 0.120∗∗∗ 0.010 0.035∗∗∗

(0.011) (0.007) (0.005) East 0.319 0.151∗ 0.036

(0.198) (0.085) (0.099) West −0.027 0.033 −0.041

(0.094) (0.056) (0.060) Midtown 0.341∗ 0.042 0.020

(0.193) (0.080) (0.089) Constant 4.080∗∗∗ −0.748∗∗∗ −0.273

(0.459) (0.176) (0.193)

Block group fixed effects? Yes Yes Yes Date fixed effects? Yes Yes Yes Hour fixed effects? Yes Yes Yes Observations 2,508 2,641 2,641 R2 0.424 0.354 0.355

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01 Robust standard errors are clustered at the Block

group level and are included in parentheses. Omitted division category: South.

20

An ordered probit regression predicting priority level, presented in Appendix Table A2,

confirms that there is no statistically significant relationship between victim or neighbor-

hood demographics and assigned priority at conventional levels. Block group fixed effects

are omitted from this regression in order to see what, if any, relationship there is between

Block group demographics and priority level. Police division fixed effects are included to

account for some geographic variation.

Table A2: Predicting priority level (ordered probit regression)

Dependent variable:

Priority level (1-4)

Forced entry −0.494∗∗∗ (0.128)

Share Hispanic victims −0.214 (0.154)

Share Black victims −0.268 (0.272)

Hispanic share (Block group) −0.532 (0.342)

Black share (Block group) −1.815 (1.251)

Poverty share (Block group) −0.437 (0.479)

Renter share (Block group) 0.714∗∗

(0.332) Apartment 0.491∗∗∗

(0.142) Victim ages (mean) 0.003

(0.004) Share male victims 0.542∗∗∗

(0.130) Total victims −0.197∗∗∗

(0.069) East −0.525∗∗

(0.243) West −0.621∗∗∗

(0.212) Midtown −0.703∗∗∗

(0.227)

Date fixed effects? Yes Hour fixed effects? Yes

Observations 2,556

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01 Omitted division category: South.

21

Another reason to suspect that nonwhites are being discriminated against relative to

whites in cases of the same seriousness would be if nonwhites and whites received dif-

ferent treatment in the subset of forced entry cases. Table A3, though, shows that victim

and officer demographics are not predictive of thoroughness in the subset of forced entry

cases.

Table A3: Predicting investigative thoroughness among forced entry cases

Dependent variable:

Time (mins) (log) Prints collected Detective assigned

(1) (2) (3)

Share white officers 0.005 0.012 0.021 (0.070) (0.045) (0.048)

Share white victims −0.072 −0.037 −0.055 (0.061) (0.041) (0.042)

Apartment −0.045 −0.024 −0.124∗∗∗ (0.072) (0.048) (0.044)

Victim ages (mean) 0.002 −0.0004 −0.001 (0.002) (0.001) (0.001)

Share male officers 0.066 0.046 0.054 (0.080) (0.048) (0.049)

Share male victims 0.044 0.001 0.060 (0.069) (0.039) (0.040)

Total victims −0.009 0.020 0.039 (0.042) (0.026) (0.025)

Total officers 0.110∗∗∗ 0.003 0.031∗∗∗

(0.017) (0.009) (0.007) East 0.520 0.220 0.142

(0.371) (0.150) (0.152) West −0.027 0.071 0.011

(0.154) (0.125) (0.115) Midtown 0.526 0.087 0.153

(0.359) (0.144) (0.164) Constant 5.707∗∗∗ −0.830∗ −0.765∗

(0.775) (0.430) (0.459)

Block group fixed effects? Yes Yes Yes Date fixed effects? Yes Yes Yes Hour fixed effects? Yes Yes Yes Observations 1,618 1,705 1,705 R2 0.554 0.469 0.467

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01 Robust standard errors are clustered at the Block

group level and are included in parentheses. Omitted division category: South.

Finally, if residents of heavily minority Tucson neighborhoods were calling the police

less than residents of heavily white neighborhoods, that would provide suggestive evi-

dence that racial minorities were under-reporting crimes (or under-reporting some types

of crimes) to the police. To test this, I downloaded publicly available data on every 911

22

call made in Tucson in 2016. These calls are geo-located to the level of the 100-block. I

used GIS to match these locations to Census Block groups and merged in demographic

information from the American Community Survey. I included only calls that came from

citizen telephones (landlines and cellphones) and excluded officer-originated 911 calls. In

the regression table that follows, the unit of analysis is the Block group, the dependent

variables are the logged total number of 911 calls received in 2016, the logged number of

calls regarding burglaries,4 and the logged number of calls regarding suspicious persons.

I chose these latter two types of calls because they reflect quite serious and quite minor

crime concerns, respectively.

Table A4: Predicting the number of 911 calls in 2016 (Block group level)

Dependent variable:

Total 911 (log) Burglary (log) Suspicious person (log)

(1) (2) (3)

Share Hispanic −0.112 −0.224 −0.306 (0.283) (0.212) (0.238)

Share Black 2.638∗∗ 1.291 1.795∗

(1.129) (0.845) (0.950) Share other 0.189 0.286 0.263

(1.072) (0.802) (0.902) Share 18 to 24 0.230 1.006∗∗ 0.151

(0.577) (0.432) (0.486) Share 65 or over −1.946∗∗∗ −1.550∗∗∗ −1.510∗∗∗

(0.585) (0.438) (0.492) Median HH income (log) −0.658∗∗∗ −0.527∗∗∗ −0.551∗∗∗

(0.153) (0.114) (0.129) Total population (log) 0.110 0.155∗ 0.166∗

(0.115) (0.086) (0.097) Constant 12.155∗∗∗ 6.996∗∗∗ 8.166∗∗∗

(1.755) (1.313) (1.477)

Observations 410 410 410 R2 0.161 0.209 0.143

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

Appendix Table A4 shows that the strongest negative determinants of 911 call volume

are the share of the Block group over 65 years old and the median household income in

the Block group. The racial makeup of the Block group is mostly not associated with

911 call volume at conventional significance levels, although the share Black in the Block

4In principle 911 calls regarding burglaries could be compared to the TPD’s burglary records to calculate

the 911 reporting rates for burglaries. Unfortunately, because the 911 data does not distinguish between

residential and commercial burglaries, such a comparison is not possible with the data to which I was granted

access.

23

group is positively associated with total 911 call volume at the 0.05 level. Of course, this

could be because of a greater baseline amount of crime in mostly Black neighborhoods

that is being reported at the same rate as crime elsewhere. To the extent that it is because

of over-reporting of crime by residents of mostly Black neighborhoods, that phenomenon

is documented in some of the literature on sociological determinants of 911 use (Davis

and Henderson 2003; Bosick et al. 2012; Baumer and Lauritsen 2010; Desmond and Valdez

2013).

24

6.2. Appendix B: the significance of “null” results

Abadie (2018) argues that, in a limited-information Bayesian framework, where readers

have a prior over the value of an estimand Θ, “a statistical result [is] informative when

it has the potential to substantially change the beliefs of the agents over a large range

of values for θ.” It follows that rejection of the point null is only informative when the

prior probability of rejection is low (that is, below 0.5), and that failure to reject the null

is more informative than rejection of the null whenever the prior probability of rejection

is greater than 0.5. In the simple case where an agent has a prior θ ∼ N(µ, σ2) on θ,

with σ2 > 0, there are n independent observations of θ, x1, ..., xn distributed N(θ, 1), and

θ̂n = 1 n Σ

n i=1 xi ∼ N(θ,

1 n ), with statistical significance conventionally defined as

√ n|θ̂n|

greater than some threshold c > 0 (usually c is 1.96), the prior probability of rejection is

given by:

Pr( √

n|θ̂n| > c) = Φ √

nµ − c √

1 + nσ2 + Φ − √

nµ − c √

1 + nσ2

.

This quantity, importantly, does not depend on θ, but rather on depends heavily on

the sample size (n). In the present case, where n = 2, 771, non-significance is extremely

informative. Using a standard normal prior (µ = 0 and σ2 = 1) on any regression coeffi-

cients θ and a standard significance threshold of c = 1.96 reveals that the prior probability

of rejection in this case is 0.97.

25

References

Abadie, Alberto. 2018. Statistical non-significance in empirical economics. Technical report

National Bureau of Economic Research.

Antonovics, Kate, and Brian Knight. 2009. “A New Look at Racial Profiling: Evidence from

the Boston Police Department.” Review of Economics and Statistics 91 (1): 163-177.

Antrobus, Emma, and Andrew Pilotto. 2016. “Improving forensic responses to residen-

tial burglaries: Results of a randomized controlled field trial.” Journal of Experimental

Criminology 12 (3): 319–345.

Balla, Steven J, and William T Gormley Jr. 2017. Bureaucracy and democracy: Accountability

and performance. CQ Press.

Baumer, Eric P, and Janet L Lauritsen. 2010. “Reporting crime to the police, 1973–2005:

A multivariate analysis of long-term trends in the National Crime Survey (NCS) and

National Crime Victimization Survey (NCVS).” Criminology 48 (1): 131–185.

Baumgartner, Frank R, Derek A Epp, Kelsey Shoub, and Bayard Love. 2017. “Targeting

young men of color for search and arrest during traffic stops: Evidence from North

Carolina, 2002–2013.” Politics, Groups, and Identities 5 (1): 107–131.

Becker, Gary S. 1968. “Crime and punishment: An economic approach.” In The economic

dimensions of crime. Springer.

Beckett, Katherine, Kris Nyrop, and Lori Pfingst. 2006. “Race, drugs, and policing: Under-

standing disparities in drug delivery arrests.” Criminology 44 (1): 105–137.

Benson, Bruce L, David W Rasmussen, and David L Sollars. 1995. “Police bureaucracies,

their incentives, and the war on drugs.” Public Choice 83 (1-2): 21–45.

Besley, Timothy, and Stephen Coate. 1991. “Public provision of private goods and the re-

distribution of income.” The American Economic Review 81 (4): 979–984.

26

Bosick, Stacey J, Callie Marie Rennison, Angela R Gover, and Mary Dodge. 2012. “Report-

ing violence to the police: Predictors through the life course.” Journal of Criminal Justice

40 (6): 441–451.

Braun, Michael, Jeremy Rosenthal, and Kyle Therrian. 2018. “Police discretion and racial

disparity in organized retail theft arrests: evidence from Texas.” Journal of empirical legal

studies 15 (4): 916–950.

Brodkin, Evelyn Z. 2008. “Accountability in street-level organizations.” Intl Journal of Pub-

lic Administration 31 (3): 317–336.

Carpenter, Daniel. 2014. Reputation and Power: Organizational Image and Pharmaceutical Reg-

ulation at the FDA. Princeton University Press.

Carpenter, Daniel P, and George A Krause. 2012. “Reputation and public administration.”

Public administration review 72 (1): 26–32.

Carr, Patrick J, Laura Napolitano, and Jessica Keating. 2007. “We never call the cops and

here is why: A qualitative examination of legal cynicism in three Philadelphia neigh-

borhoods.” Criminology 45 (2): 445–480.

Chalfin, Aaron, and Justin McCrary. 2018. “Are US cities underpoliced? Theory and evi-

dence.” Review of Economics and Statistics 100 (1): 167–186.

Chatterjee, Samprit, and Jeffrey S Simonoff. 2013. Handbook of regression analysis. Vol. 5 John

Wiley & Sons.

Cihan, Abdullah, Yan Zhang, and Larry Hoover. 2012. “Police response time to in-progress

burglary: A multilevel analysis.” Police Quarterly 15 (3): 308–327.

Coupe, Richard Timothy. 2016. “Evaluating the effects of resources and solvability on bur-

glary detection.” Policing and Society 26 (5): 563–587.

Coviello, Decio, and Nicola Persico. 2015. “An economic analysis of Black-White dispari-

ties in the New York Police Department’s stop-and-frisk program.” The Journal of Legal

Studies 44 (2): 315–360.

27

Davis, Robert C, and Nicole J Henderson. 2003. “Willingness to report crimes: The role of

ethnic group membership and community efficacy.” Crime & Delinquency 49 (4): 564–

580.

DeAngelo, Gregory, and Emily G Owens. 2017. “Learning the ropes: General experience,

task-specific experience, and the output of police officers.” Journal of Economic Behavior

& Organization 142: 368–377.

DeAngelo, Gregory J., R. Kaj Gittings, and Amanda Ross. 2018. “Police Incentives, Policy

Spillovers, and the Enforcement of Drug Crimes.” Review of Law & Economics 14 (March).

DeAngelo, Gregory, R Kaj Gittings, and Anita Alves Pena. 2018. “Interracial face-to-face

crimes and the socioeconomics of neighborhoods: Evidence from policing records.” In-

ternational review of law and economics 56: 1–13.

Desmond, Matthew, and Nicol Valdez. 2013. “Unpolicing the urban poor: Consequences

of third-party policing for inner-city women.” American sociological review 78 (1): 117–

141.

Di Tella, Rafael, and Ernesto Schargrodsky. 2004. “Do police reduce crime? Estimates using

the allocation of police forces after a terrorist attack.” American Economic Review 94 (1):

115–133.

DOJ. 2014. “Burglary.” Crime in the United States, 2013, Department of Justice, Federal Bureau

of Investigation. https://ucr.fbi.gov/crime-in-the-u.s/2013/crime-in-the-u.s.

-2013/property-crime/burglary-topic-page/burglarymain_final.pdf.

DOJ. 2016. “Crime in the United States, 2015.” Department of Justice, Federal Bureau

of Investigation. https://ucr.fbi.gov/crime-in-the-u.s/2015/crime-in-the-u.s.

-2015/resource-pages/2015-cius-summary_final.pdf.

Duncan, Greg J, and Richard J Murnane. 2011. Whither opportunity?: Rising inequality,

schools, and children’s life chances. Russell Sage Foundation.

Eberhardt, Jennifer L, Phillip Atiba Goff, Valerie J Purdie, and Paul G Davies. 2004. “Seeing

28

black: Race, crime, and visual processing.” Journal of Personality and Social Psychology

87 (6): 876.

Ehrlich, Isaac. 1973. “Participation in Illegitimate Activities: A Theoretical and Empirical

Investigation.” Journal of Political Economy 81 (May): 521–565.

Epp, Charles R, Steven Maynard-Moody, and Donald P Haider-Markel. 2014. Pulled over:

How Police Stops Define Race and Citizenship. Univ. of Chicago Press.

Fagan, Jeffrey, and Amanda Geller. 2018. “Police, Race, and the Production of Capital

Homicides.” Berkeley J. Crim. L. 23: 261.

Foley, C Fritz. 2011. “Welfare payments and crime.” The review of Economics and Statistics

93 (1): 97–112.

Foreman, Christopher H. 2011. The promise and peril of environmental justice. Brookings In-

stitution Press.

Freeman, Jonathan B, and Kerri L Johnson. 2016. “More than meets the eye: Split-second

social perception.” Trends in cognitive sciences 20 (5): 362–374.

Freeman, Richard B. 1999. “Chapter 52 The economics of

crime.” In Handbook of Labor Economics. Vol. 3. Elsevier.

https://linkinghub.elsevier.com/retrieve/pii/S1573446399300432.

Friedman, Barry. 2020. “Disaggregating the Police Function.” U. Pa. L. Rev.(forthcoming

2020-21).

Fryer Jr, Roland G. 2019. “An empirical analysis of racial differences in police use of force.”

Journal of Political Economy 127 (3): 1210–1261.

Gelman, Andrew, Jeffrey Fagan, and Alex Kiss. 2007. “An analysis of the New York City

police department’s “stop-and-frisk” policy in the context of claims of racial bias.” Jour-

nal of the American Statistical Association 102 (479): 813–823.

Goel, Sharad, Justin M Rao, Ravi Shroff et al. 2016. “Precinct or prejudice? Understand-

ing racial disparities in New York City’s stop-and-frisk policy.” The Annals of Applied

Statistics 10 (1): 365–394.

29

Goldstein, Herman. 1977. “Policing a free society.” Policing a Free Society Cambridge, Mass:

Ballinger Pub. Co.

Golub, Andrew, Bruce D Johnson, and Eloise Dunlap. 2007. “The race/ethnicity disparity

in misdemeanor marijuana arrests in New York City.” Criminology & public policy 6 (1):

131–164.

Goncalves, Felipe, and Steven Mello. 2020. “A few bad apples? Racial bias in policing.”

Working Paper available at https://papers.ssrn.com/sol3/papers.cfm?abstract_

id=3627809.

Grogger, Jeffrey, and Greg Ridgeway. 2006. “Testing for racial profiling in traffic stops from

behind a veil of darkness.” Journal of the American Statistical Association 101 (475): 878–

887.

Hamilton, David L, and Jeffrey W Sherman. 2014. “Stereotypes.” In Handbook of social cog-

nition. Psychology Press.

Hamilton-Smith, Niall, and Andrew Kent. 2005. “The prevention of domestic burglary.”

Handbook of crime prevention and community safety: 417–457.

Hill, Heather C. 2003. “Understanding implementation: Street-level bureaucrats’ re-

sources for reform.” Journal of Public Administration Research and Theory 13 (3): 265–282.

Hilton, James L, and William Von Hippel. 1996. “Stereotypes.” Annual review of psychology

47 (1): 237–271.

Holmström, Bengt. 1999. “Managerial incentive problems: A dynamic perspective.” The

Review of Economic Studies 66 (1): 169–182.

Horrace, William C, and Shawn M Rohlin. 2016. “How dark is dark? Bright lights, big city,

racial profiling.” Review of Economics and Statistics 98 (2): 226–232.

Jencks, Christopher, and Meredith Phillips. 2011. The Black-White test score gap. Brookings

Institution Press.

Jolls, Christine. 2001. “Antidiscrimination and accommodation.” Harvard Law Review 115:

642.

30

Keefer, Philip, and Stuti Khemani. 2005. “Democracy, public expenditures, and the poor:

understanding political incentives for providing public services.” The World Bank Re-

search Observer 20 (1): 1–27.

Kelly, Morgan. 2000. “Inequality and crime.” Review of Economics and Statistics 82 (4): 530–

539.

Killmier, Bronwyn, Katrin Mueller-Johnson, and Richard Timothy Coupe. 2019.

“Offender–Offence Profiling: Improving Burglary Solvability and Detection.” In Crime

Solvability Factors. Springer.

Knowles, John, Nicola Persico, and Petra Todd. 2001. “Racial bias in motor vehicle

searches: Theory and evidence.” Journal of Political Economy 109 (1): 203–229.

Knox, Dean, Will Lowe, and Jonathan Mummolo. 2019. “Administrative records mask

racially biased policing.” American Political Science Review: 1–19.

Krivo, Lauren J, and Robert L Kaufman. 2004. “Housing and wealth inequality: Racial-

ethnic differences in home equity in the United States.” Demography 41 (3): 585–605.

Legewie, Joscha. 2016. “Racial profiling and use of force in police stops: How local events

trigger periods of increased discrimination.” American Journal of Sociology 122 (2): 379–

424.

Lemos, Margaret H, and Max Minzner. 2014. “For-Profit Public Enforcement.” Harvard Law

Review 127: 853.

Leovy, Jill. 2015. Ghettoside: A True Story of Murder in America. New York: PenguinRandom-

House.

Lipsky, Michael. 1980. Street-Level Bureaucracy: Dilemmas of the Individual in Public Services.

New York: Russel Sage Foundation.

Mancik, Ashley M, Karen F Parker, and Kirk R Williams. 2018. “Neighborhood context

and homicide clearance: Estimating the effects of collective efficacy.” Homicide studies

22 (2): 188–213.

31

Mas, Alexandre. 2006. “Pay, reference points, and police performance.” The Quarterly Jour-

nal of Economics 121 (3): 783–821.

Mast, Brent D, Bruce L Benson, and David W Rasmussen. 2000. “Entrepreneurial Police

and Drug Enforcement Policy.” : 24.

McCrary, Justin, and Deepak Premkumar. 2019. “Why We Need Po-

lice.” In The Cambridge Handbook of Policing in the United States, ed.

Tamara Rice Lave and Eric J. Miller. 1 ed. Cambridge University Press.

https://www.cambridge.org/core/product/identifier/9781108354721

Mitchell, Ojmarrh, and Michael S Caudy. 2015. “Examining racial disparities in drug ar-

rests.” Justice Quarterly 32 (2): 288–313.

Mitchell, Ojmarrh, and Michael S Caudy. 2017. “Race differences in drug offending and

drug distribution arrests.” Crime & Delinquency 63 (2): 91–112.

Natapoff, Alexandra. 2006. “Underenforcement.” Fordham L. Rev. 75: 1715.

Niskanen, William A. 1971. Bureaucracy & Representative Government. New York: Rout-

ledge.

Pare, Paul-Philippe. 2014. “Indicators of police performance and their relationships with

homicide rates across 77 nations.” International Criminal Justice Review 24 (3): 254–270.

Payne, B Keith. 2006. “Weapon bias: Split-second decisions and unintended stereotyping.”

Current Directions in Psychological Science 15 (6): 287–291.

Persico, Nicola. 2002. “Racial profiling, fairness, and effectiveness of policing.” American

Economic Review 92 (5): 1472–1497.

Persico, Nicola, and Petra E Todd. 2008. “The hit rates test for racial bias in motor-vehicle

searches.” Justice Quarterly 25 (1): 37–53.

Persico, Nicola, and Petra Todd. 2006. “Generalising the hit rates test for racial bias in law

enforcement, with an application to vehicle searches in Wichita.” The Economic Journal

116 (515): F351–F367.

32

Pinotti, Paolo. 2017. “Clicking on Heaven’s Door: The Effect of Immigrant Legalization on

Crime.” American Economic Review 107 (January): 138–168.

Rayman, Graham A. 2013. The NYPD Tapes: A Shocking Story of Cops, Cover-Ups, and

Courage. St. Martin’s Press.

Ritter, Joseph A. 2017. “How do police use race in traffic stops and searches? Tests based

on observability of race.” Journal of Economic Behavior & Organization 135: 82–98.

Roberts, Aki. 2015. “Adjusting rates of homicide clearance by arrest for investigation diffi-

culty: Modeling incident-and jurisdiction-level obstacles.” Homicide studies 19 (3): 273–

300.

Roberts, Aki, and Christopher J Lyons. 2011. “Hispanic victims and homicide clearance by

arrest.” Homicide Studies 15 (1): 48–73.

Ross, Cody T. 2015. “A multi-level Bayesian analysis of racial bias in police shootings at

the county-level in the United States, 2011–2014.” PloS one 10 (11): e0141854.

Shannon, Stephen, and Barry Coonan. 2016. “A solvability-based case screening checklist

for burglaries in Ireland.” European Law Enforcement Research Bulletin (15): 31–41.

Sherman, Daniel J. 2012. Not here, not there, not anywhere: politics, social movements, and the

disposal of low-level radioactive waste. Routledge.

Short, Martin B, Maria R D’orsogna, Patricia J Brantingham, and George E Tita. 2009.

“Measuring and modeling repeat and near-repeat burglary effects.” Journal of Quanti-

tative Criminology 25 (3): 325–339.

Sigelman, Lee. 1986. “The Bureaucrat as Budget Maximizer: An Assumption Examined.”

Public Budgeting & Finance 6 (1): 50–59.

Sonn, Paul K. 1992. “Fighting minority underrepresentation in publicly funded construc-

tion projects after Croson: A Title VI litigation strategy.” The Yale Law Journal 101 (7):

1577–1606.

Sonnichsen, Richard C. 2007. “Measuring police performance.” Monitoring Performance in

the Public Sector: Future Directions from International Experience: 219–235.

33

Trounstine, Jessica. 2015. “The privatization of public services in American cities.” Social

Science History 39 (3): 371–385.

Truman, Jennifer L., and Rachel E. Morgan. 2016. “Criminal Victimization, 2015.” United

States Bureau of Justice Statistics. http://www.bjs.gov/index.cfm?ty=pbdetail&iid=

5804.

Walker, Samuel, Cassia Spohn, and Miriam DeLone. 2012. The color of justice: Race, ethnicity,

and crime in America. Cengage Learning.

Weisel, Deborah Lamm. 2002. Burglary of single-family houses. Vol. 18 US Department of

Justice, Office of Community Oriented Policing Services . . . .

West, Jeremy. 2018. “Racial Bias in Police Investigations.” Working Paper available at

https://people.ucsc.edu/~jwest1/articles/West_RacialBiasPolice.pdf.

West, Jeremy. 2019. “Learning by Doing in Law Enforcement.” Working Paper available at

https://people.ucsc.edu/~jwest1/articles/West_LBDPolice.pdf.

Wilder, David A. 1993. “The role of anxiety in facilitating stereotypic judgments of out-

group behavior.” In Affect, cognition and stereotyping. Elsevier.

Woods, Jordan Blair. 2018. “Policing, Danger Narratives, and Routine Traffic Stops.” Mich.

L. Rev. 117: 635.

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7. Tables

Table 1: Summary statistics

Statistic N Mean St. Dev. Min Max

Forced entry 2,771 0.646 0.478 0 1 Poverty share (Block group) 2,771 0.297 0.173 0 0.749 Hispanic share (Block group) 2,771 0.423 0.258 0.023 0.960 White share (Block group) 2,771 0.458 0.247 0.012 0.964 Black share (Block group) 2,771 0.042 0.050 0 0.339 Victim ages (mean) 2,647 43.270 17.721 0 99 Share white officers 2,771 0.573 0.435 0 1 Share Black officers 2,771 0.039 0.171 0 1 Share Hispanic officers 2,771 0.315 0.406 0 1 Share male officers 2,771 0.783 0.370 0 1 Share white victims 2,771 0.509 0.486 0 1 Share Black victims 2,771 0.051 0.214 0 1 Share Hispanic victims 2,771 0.271 0.434 0 1 Share male victims 2,771 0.495 0.462 0 1 Total victims 2,771 1.355 0.797 1 15 Total officers 2,771 2.168 2.145 1 24 Apartment? 2,771 0.311 0.463 0 1 Priority level (1-4) 2,686 3.603 0.781 1 4 East 2,771 0.212 0.409 0 1 West 2,771 0.275 0.447 0 1 Midtown 2,771 0.254 0.436 0 1 South 2,771 0.202 0.402 0 1 Incident on weekend or holiday? 2,771 0.248 0.432 0 1 Incident hour 2,765 14.689 6.293 1 24 Time spent (mins) 2,626 86.928 78.015 1 910 Prints collected? 2,771 0.399 0.490 0 1 Detective assigned? 2,771 0.344 0.475 0 1

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Table 2: Race of officers, race of victims, and investigative thoroughness

Dependent variable:

Time (mins) (log) Prints collected Detective assigned

(1) (2) (3) (4) (5) (6)

Share white officers 0.039 0.044 0.032 0.041∗ 0.038∗ 0.025 (0.037) (0.040) (0.021) (0.022) (0.021) (0.022)

Share white victims −0.082∗∗ −0.066∗ −0.014 −0.009 −0.026 −0.027 (0.033) (0.035) (0.019) (0.020) (0.019) (0.019)

Constant 4.190∗∗∗ 4.119∗∗∗ 0.388∗∗∗ 0.314∗∗∗ 0.336∗∗∗ 0.421∗∗∗

(0.031) (0.067) (0.018) (0.038) (0.017) (0.037)

Month fixed effects? No Yes No Yes No Yes Division fixed effects? No Yes No Yes No Yes Observations 2,626 2,626 2,771 2,771 2,771 2,771 R2 0.003 0.010 0.001 0.026 0.002 0.020

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

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Table 3: Forced entry and investigative thoroughness

Dependent variable:

Time (mins) (log) Prints collected Detective assigned

(1) (2) (3) (4) (5) (6)

Forced entry 0.254∗∗∗ 0.184∗∗∗ 0.202∗∗∗ 0.176∗∗∗ 0.139∗∗∗ 0.120∗∗∗

(0.046) (0.046) (0.027) (0.025) (0.019) (0.024) Share white officers 0.025 0.011 0.024

(0.051) (0.028) (0.027) Share white victims −0.014 −0.035 −0.009

(0.046) (0.026) (0.025) Apartment −0.044 −0.039 −0.089∗∗∗

(0.055) (0.029) (0.028) Victim ages (mean) −0.0004 −0.001 −0.001

(0.001) (0.001) (0.001) Share male officers 0.014 0.032 0.056∗

(0.066) (0.033) (0.032) Share male victims −0.015 −0.010 0.031

(0.048) (0.025) (0.024) Total victims −0.007 0.027∗ 0.028∗

(0.027) (0.015) (0.015) Total officers 0.115∗∗∗ 0.014∗ 0.032∗∗∗

(0.013) (0.007) (0.007) East 0.383 0.063 −0.014

(0.274) (0.120) (0.116) West −0.030 −0.030 −0.047

(0.107) (0.069) (0.067) Midtown 0.292 −0.077 0.030

(0.334) (0.122) (0.118) Constant 4.007∗∗∗ 3.893∗∗∗ 0.269 −0.850∗∗ 0.254∗∗∗ −0.299

(0.501) (0.501) (0.222) (0.429) (0.015) (0.413)

Priority level fixed effects? No Yes No Yes No Yes Block group fixed effects? No Yes No Yes No Yes Date fixed effects? No Yes No Yes No Yes Hour fixed effects? No Yes No Yes No Yes Observations 2,626 2,434 2,771 2,559 2,771 2,559 R2 0.021 0.432 0.039 0.355 0.020 0.359

Notes: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01 Robust standard errors are clustered at the Block

group level and are included in parentheses. Omitted division category: South.

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Table 4: Representation of economic disadvantage in forced and unforced entry burglaries

Overall mean Share among Share among p-value forced entry unforced entry

Apartment 0.31 0.28 0.37 < 0.01 High poverty neighborhood 0.26 0.24 0.29 < 0.01 High renter share neighborhood 0.39 0.36 0.44 < 0.01

38

8. Figures

Figure 1: Map of burglaries in Tucson in 2016, with Census block group population density information (persons per square mile)

Notes: Graduated colors and legend refer to population density of the Census block group. Shaded areas are the independent municipality of South Tucson, which has its own police department (left), and Davis- Monthan Air Force Base (right).

39

Figure 2: Map of burglaries in Tucson in 2016, with Census block group income informa- tion (median annual income)

Notes: Graduated colors and legend refer to the median annual household income in the Census block group. Shaded areas are the independent municipality of South Tucson, which has its own police department (left), and Davis-Monthan Air Force Base (right).

40