How does oversight affect police? Evidence from the police misconduct reform

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How does oversight affect police? Evidence from the police misconduct reformR Wei Long Department of Economics, Tulane University, 304 Tilton Hall, 6823 St. Charles Avenue, New Orleans, LA 70118, United States

a r t i c l e

i n f o

Article history: Received 19 February 2019 Revised 7 October 2019 Accepted 9 October 2019 Available online xxx JEL classification: K42 Keywords: Consent decree Investigation Synthetic control Treatment effect

a b s t r a c t This paper assesses how Department of Justice (DOJ) investigations affect policing behaviors. From 2010 – 2013, nine local police agencies were investigated for civil rights violations and police misconduct and most reached settlements with the DOJ. These settlements mandate extensive reforms such as retraining of officers, collaboration with community representatives and modernization of procedures related to the use of force. Using both the synthetic control method and the panel data approach proposed by Hsiao et al. (2012), I find a significant reduction in misdemeanor arrests of blacks during that period. However, I do not observe a meaningful impact on felony arrests and arrests of whites. These findings suggest that the investigations indeed affected officers’ policing behaviors, but I find no evidence of universal de-policing effects in the investigated agencies. © 2019 Elsevier B.V. All rights reserved.

1. Introduction Since 2014, a sequence of controversial police-related deaths of blacks who were killed by the police or died in police custody have frequently become widely publicized and sparked massive protests against police misconduct. For example, the deaths of Michael Brown (Ferguson, MO), Laquan McDonald (Chicago, IL) and Freddie Gray (Baltimore, MD) drew national attention and spurred investigations by the Department of Justice (DOJ) of the local police departments’ practices of civil rights violations and excessive use of force. Authorized by 42 U.S.C. §14141 as part of the Violent Crime Control and Law Enforcement Act passed in 1994, the civil rights division of the DOJ has initiated multiple investigations into local law enforcement agencies over the past decade. They have found that violation of the Constitution and other federal laws, including discriminatory policing and excessive use of force, was pervasive in most of the investigated agencies. Attributing these findings to deficiencies within departments and the lack of supervisory mechanism, the DOJ proposed extensive reforms. Mandated by the so-called consent decree, the investigated agencies were required to enforce federal statutes prohibiting discriminatory policing and to restore trust between local communities and law enforcements. In this paper, I examine the effect on arrests, a daily routine task of police officers, of the DOJ’s investigations of police misconduct in six local police agencies before 2014. Specifically, using data on arrests from the FBI’s Uniform Crime Report (UCR), I adopt two methods to implement counterfactual studies and then compare the actual arrest rate in an investigated agency with the predicted arrest rate in the absence of the investigation: the synthetic control method (SCM) of R The author would like to thank the Co-Editor Joachim Winter and two anonymous reviewers for their helpful suggestions. The author gratefully acknowledges the financial support from the Carol Lavin Bernick Faculty Grant and the Kurzius Family Endowed Fund at Tulane University. All errors are my own. E-mail address: [email protected]

https://doi.org/10.1016/j.jebo.2019.10.003 0167-2681/© 2019 Elsevier B.V. All rights reserved.

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Abadie et al. (2010) and the panel data approach of Hsiao et al. (2012, HCW hereafter). Both methods yield similar results and suggest that the DOJ’s investigation led to a significant decrease in total non-violent misdemeanor arrests (vandalism, liquor laws violations, etc.) in two of the six investigated agencies during 2010 – 2013. However, the effect is heterogeneous across different racial groups: the reduction in misdemeanor arrests is driven mainly by fewer arrests of blacks, which is statistically significant in four investigated agencies compared with their control agencies; the effect on arrests of whites is relatively smaller and statistically insignificant. In addition, I find no meaningful impact on felony arrests (homicide, robbery, aggravated assault, etc.) of whites and blacks. A sequence of robustness tests shows that the findings are robust and that the reduction in the misdemeanor arrests of blacks is indeed caused by the investigations. This study makes three significant contributions. First, it contributes to the literature on how oversight and regulation affect policing behaviors. In the contentious debate regarding the extent to which public oversight has led to changes in policing behaviors, Rushin and Edwards (2017) find a significant increase in some crime rates in the investigated police departments relative to the unaffected ones, and they attribute the uptick in crime to the de-policing effect caused by the introduction of public scrutiny or external regulation. However, they do not examine the direct effect on policing behaviors. Shjarback et al. (2017) investigate traffic stops among Missouri police departments and find evidence of de-policing after the shooting in Ferguson. Wallace et al. (2018) employ a RCT to investigate body-worn camera-induced passivity and do not find evidence of a de-policing effect. However, neither of these studies directly apply to the DOJ’s investigations and they are limited to certain states (Missouri and Washington). In this study, I directly assess the effect of oversight on arrests and I add more local agencies to the sample. Second, this study complements an extensive literature on policing behaviors (Anwar and Fang, 2006; Donohue and Levitt, 2001, and Mas, 2006) by analyzing the specific effect on arrests. That is, it examines how scrutiny from the public affects policing in general (DeAngelo and Hansen, 2014; Heaton, 2010; Shi, 2009; Cheng and Long, 2018). Third, the findings here contribute to the growing debate on whether interventions by the Federal government, such as consent decrees, indictments and more stringent restrictions, reach too far, hampering the “safety and protection of the public” and even interfering with recruiting efforts by the local police.1 For example, the former Attorney General Jeff Sessions argued that these agreements could violate the sovereignty of local police agencies, “reduce morale” among police officers and lead to more violent crime. Even though his claim has been contested by many researchers and academics, he still signed a memorandum to impose three stringent requirements for the use of consent decrees in a major last-minute act in 2018 (Fortin, 2018). In addition, a recent Pew Research Center report (Morin et al., 2017) surveys a nationally representative sample of 80 0 0 police officers and finds that 93% of the officers have become more concerned about their safety, 76% are more reluctant to use force when it is appropriate, and 72% are less willing to stop and question people who seem suspicious. The findings of the significant reduction in misdemeanor arrests in this study echo the survey results and could be attributed in part to the consent decree’s de-policing effect. However, these findings do not validate the assertions that there is an universal retreat from daily routines in the investigated agencies. Most importantly, considering the allegations of over-policing and discriminatory policing against minority groups, we should expect the reduction in misdemeanor arrests of black due to the enforcement of federal laws and consent decrees. The rest of the paper is organized as follows. The next section briefly presents the background of the DOJ’s investigation and discusses the potential channels through which it affects the behaviors of police officers. In Section 3, I briefly review and compare the SCM and HCW methods. I introduce the data and the selection of control units in Section 4 and Section 5 presents the results of the main analysis. Section 6 presents a series of robustness checks and I discuss the results in Section 7. Section 8 concludes and additional results for the empirical analysis are provided in the appendix. 2. Background and mechanism In 1994, Congress passed Law Enforcement Misconduct Statute 42 U.S.C. §14141 as part of the Violent Crime Control and Law Enforcement Act of 1994 (VCCLEA). Under this statute, it is unlawful for a police agency to engage in “a pattern or practice of conduct by law enforcement officers or by officials or employees of any governmental agency with responsibility for the administration of juvenile justice or the incarceration of juveniles that deprives persons of rights, privileges, or immunities secured or protected by the Constitution or laws of the United States.”2 Thus, the statute authorizes the U.S. Attorney General to seek “equitable relief” to force police agencies to eliminate the pattern or practice. In addition, the DOJ is also authorized to open an investigation pursuant to Title VI of the Civil Rights Act of 1964, 42 U.S.C. §20 0 0d (Title VI), and the Omnibus Crime Control and Safe Streets Act of 1968, 42 U.S.C. 3789d (Safe Streets Act). Similar to Section 14141, Title VI and its implementing regulations and the Safe Streets Act prohibit law enforcement practices that have a disparate impact based on protected status, such as race or ethnicity, unless these practices are necessary to achieve legitimate, nondiscriminatory objectives. Each month, the DOJ receives complaints and referrals about alleged police misconduct from a wide variety of sources: individuals, organizations, and advocacy groups working on police and civil rights issues; attorneys and parties in civil litigation involving police departments; and so force. The collected evidence informs the DOJ’s case selection. However, the 1 One example is the New Orleans Police Department (NOPD). In the wake of the reforms in the NOPD, the size of the police force has dwindled through attrition and a hiring freeze, from 1500 to 1150. Recruits are dropping out of the city police academy to work for the state police, where they don’t have to worry about body cameras and can earn more money. See Kelly et al. (2015). 2 §14141 Cause of action.

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DOJ does not bring a case based on every report received. In other words, for sporadic bad incidents or the actions of the occasional bad officer, the DOJ does not take further action. It assesses whether the allegation potentially could constitute a pattern or practice violation, and if one is detected, the DOJ opens an investigation into those agencies that exhibit such long-term and systematic police misconduct. To date, investigations have focused on allegations of excessive force, unlawful search and seizure issues, and racial or ethnic discrimination in traffic or pedestrian stops. The DOJ has also addressed unlawful responses to individuals who observe, record, or object to police actions.3 The specific procedure of how the DOJ selects cases is beyond the scope of this study, even though some studies (e.g., Rushin, 2014) argue that the DOJ has historically under enforced Section 14141 due to resource limitations. Between 2010 and 2015, the DOJ initiated 15 investigations of local police agencies. Table 1 documents the names of the 15 agencies, the year in which their investigations were opened, and the summary of allegations according to the complaint letters filed with the DOJ.4 Almost all investigated police agencies were accused of violating the U.S. Constitution and federal laws by engaging in a practice of discriminatory policing and excessive use of force against civilians, especially against blacks. For example, the findings on the Newark Police Department (NPD) report that the department “stops black individuals at a greater rate than it stops white individuals” and “black residents of Newark are at least 2.5 times more likely to be subjected to a pedestrian stop or arrested than white individuals.” The findings further conclude that “Newark’s black residents bear the brunt of the NPD’s pattern of unconstitutional policing” because “the NPD engages in a pattern of making stops in violation of the Fourth Amendment.”5 The DOJ’s investigation reports attribute the practice of police misconduct to deficiencies within the departments, the absence of supervisory mechanisms, and the lack of accountability. After the investigations, the DOJ negotiates a settlement with the police agency, the local government, and the communities or racial groups directly affected by the police misconduct. The agreement usually contains a detailed plan to ensure that the offending police agency complies with the Constitution and federal laws. Once an agreement is reached among the related parties, it is sent to a federal judge for review and approval, and a third party monitoring group is assembled to oversee the reform process. Table 1 shows that all fifteen investigated agencies have reached a settlement with the DOJ and six of them have agreed to accept a consent decree overseen by judges. How do these DOJ’s investigations affect the alleged police agencies? Specifically, how do the announcement of an investigation and the continuing scrutiny from the federal government change the behaviors of police officers in these agencies? Intuitively, there should be two contrasting channels through which the behavior of police officers is impacted. On the one hand, scrutiny and thorough investigations by the federal government will substantially increase officers’ expected costs of confronting suspects, especially suspects from minority racial groups. If an investigated agency indeed engaged in a practice of violating civil rights and federal laws, records and evidence collected during the investigation can be used to indict the involved officers. As officers become increasingly concerned that their confrontations with suspects, legally justified or not, might be negatively stereotyped and turned into another controversial publicized event, one can expect that the “depolicing effect” caused by the investigation will drive them to withdraw from routine policing activities. On the other hand, it is also possible that the investigations introduce an “incentive effect.” That is, intense scrutiny by both the public and thrid party monitoring groups should be able to prevent officers from shirking their duties and incentivize them to comply with federal regulations and improve their performance. Given that the de-policing effect and the incentive effect represent two opposing channels, taken together, the net effect of these investigations on police behavior is ambiguous ex ante. 3. Empirical strategy In this section, I briefly review and compare the SCM and the HCW method. Suppose we have J + 1 police departments or sheriff offices over t = 1, 2, . . . , T0 , T0 + 1, . . . , T periods. Without loss of generality, we assume the first agency receives the DOJ’s investigation at T0 + 1, while the remained J agencies are not affected by the investigation. Let yjt denote the agency j’s outcome variable (e.g., arrest rate per 10 0 0 populations) at time t, and y1jt and y0jt respectively denote the outcome variable of agency j at time t with and without treatment. Therefore, the treatment effect for the investigated agency at time t can be formulated as

1t = y11t − y01t , for t = T0 + 1, T0 + 2, . . . , T . However, we can not simultaneously observe y11t and y01t in the real world and the goal of the ˆ 1t , an estimate of the treatment effect, over the post-treatment period by replicating the SCM and HCW method is to find  investigated agency during the pre-treatment period (t = 1, 2, . . . , T0 ). The SCM intends to reproduce the counterfactual of the investigated agency in the absence of the investigation. To this end, it calculates a convex of combination of the control agencies by finding a vector of weights W = {w2 , . . . , wJ+1 } with J+1 w j = 1 such that it minimizes the distance in the pre-treatment period in terms of j=2



(X1 − X0 W) V(X1 − X0 W ),

(1)

3

Department of Justice Civil Rights Divisions.. The complete complaint letters and investigation reports can be accessed through the DOJ’ s website. 5 Investigation of the Newark Police Department. The full report can be downloaded from this link: https://www.justice.gov/sites/default/files/crt/legacy/ 2014/07/22/newark_findings_7- 22- 14.jpg. 4

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New Orleans Police Department, LA Alamance County Sheriff’s Department, NC∗ Seattle Police Department, WA∗∗ Newark Police Department, NJ∗∗ Portland Police Bureau,OR∗ Los Angeles County Sheriff’s Department, CA∗ Missoula Police Department, MT∗ University of Montana Office of Public Safety, MT∗ Albuquerque Police Department, NM∗ Cleveland Division of Police, OH∗∗ Ferguson Police Department, MO∗∗ Evangeline Parish Sheriff’s Department, LA∗ Ville Platte Police Department, LA∗ Baltimore Police Department, MD∗∗ Chicago Police Department, IL∗

Year opened

Allegations

2010 2010 2011 2011 2011 2011 2012 2012 2012 2013 2014 2015 2015 2015 2015

Police misconduct that violate the Constitution and federal law; discriminatory policing based on racial, ethnic, and LGBT bias. Engagement in discriminatory policing and unconstitutional searches and seizures. Engagement in a practice of excessive force that violates the Constitution and federal law; discriminatory policing. Engagement in a practice of discriminatory policing, unjustified force and retaliation against individuals. Engagement in a practice of using excessive force, particularly against people with mental illness. Unconstitutional conduct by deputies at two stations located in the Antelope Valley cities of Lancaster and Palmdale. Engagement in sex discrimination in violation of the Violent Crime Control and Law Enforcement Act of 1994. Engagement in sex discrimination in violation of the Violent Crime Control and Law Enforcement Act of 1994. Engagement in a practice of use of excessive force in violation of the Fourth Amendment and Section 14141. Engagement in a practice of using excessive force in violation of the Fourth Amendment of the US Constitution; structural deficiencies. Engagement in a practice of violations of the Constitution and federal statutory law; discriminatory policing against African Americans. Engagement in a practice of unconstitutional conduct; coerced confessions and improper criminal convictions. Engagement in a practice of unconstitutional conduct; coerced confessions and improper criminal convictions. Engagement in a practice of unlawful stops, searches, and arrests which disproportionately harm African Americans. Engagement in a practice of unlawful conduct and systematic deficiencies that cause the pattern or practice.

Note: Allegations are summarized by the author based on the complaint letters filed to the Department of Justice. Agencies in bold are included in the sample. reached a settlement with the DOJ. ∗ ∗ denotes that the investigated agency and the DOJ has reached a joint motion for entry of a consent decree.

∗

denotes that the investigated agency has

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Table 1 List of police agencies investigated by the Department of Justice under §14141.

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Fig. 1. SCM: the actual and predicted paths of Type II (misdemeanor) arrests.

where X1 denotes a K × 1 matrix of K predictors for the investigated agency during the pre-treatment period, X0 denote a K × J matrix of the K predictors for the J control agencies, and V denotes a symmetric and positive semidefinite diagonal matrix which is estimated to measure the discrepancy between X1 and X0 W. The values of the elements on the diagonal of V represent the relative importance among the K predictors, while the values of w2 , w3 , . . . , wJ+1 reflect the relative weight of the corresponding control agency. In some applications, the predictor matrix X0 and X1 can be partitioned into covariates Z0 and Z1 and values of outcome variable during the pre-treatment period Y0 and Y1 . In this circumstance, the matrix V can be formulated as



V=

VZ 0

0 VY



so that function (1) becomes



(Z1 − Z0 W ) VZ (Z1 − Z0 W ) + (Y1 − Y0 W ) VY (Y1 − Y0 W ).

(2)

Furthermore, the SCM assumes that the outcome variable is governed by the following factor model

y0jt = θt Z j + λt μ j + δt + ε jt , where zj is a vector of observable covariates, μj are unobservable agency-specific factor loadings, δ t denotes the time fixed effect, and the error terms ε jt are zero mean unobservable transitory shocks at the agency level. Suppose there exists a Please cite this article as: W. Long, How does oversight affect police? Evidence from the police misconduct reform, Journal of Economic Behavior and Organization, https://doi.org/10.1016/j.jebo.2019.10.003

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Fig. 2. SCM: gaps (actual - predicted) plots of Type II (misdemeanor) arrests.

series of weights w∗2 , . . . , w∗J+1 such that J+1 

w∗j y j1 = y11 ,

j=2

J+1  j=2

w∗j y j2 = y12 ,

...

,

J+1  j=2

w∗j y jT0 = y1T0 ,

and

J+1 

w∗j z j = z1 ,

(3)

j=2

then Abadie et al. (2010) prove that under regular conditions, the mean of the difference in outcome variable between the investigated and control agencies will be close to zero and the estimated treatment effect can be written as

ˆ 1t = y1t − 

J+1 

w∗ y jt , t = T0 + 1, T0 + 2, . . . , T .

j=2

In practice, we minimize Eq. (2) to select a set of weights and then construct the synthetic control so that Eq. (3) approximately holds. Similar to the SCM, the HCW method proposed by Hsiao et al. (2012) also intends to predict y01t but attempts to estimate 1t by considering the dependency among cross-sectional units. Specifically, the HCW method assumes that y0jt is governed Please cite this article as: W. Long, How does oversight affect police? Evidence from the police misconduct reform, Journal of Economic Behavior and Organization, https://doi.org/10.1016/j.jebo.2019.10.003

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Fig. 3. SCM: the actual and predicted paths of Type II (misdemeanor) arrests of whites.

by a factor model formulated as

y0jt = γ j + λt μ j + ε jt ,

(4)

where γ j are the individual fixed effect, μj is a vector of constants that are heterogenous among agencies, λt denotes time-varying unobservable common factors, and ε jt still represents error terms. Hsiao et al. (2012) suggest that y01t could be predicted by the outcome variables of the control agencies, i.e., y01t =  + β y−1,t + 1t with y−1,t = (y2t , y3t , . . . , yJ+1,t ) and t = 1, 2, . . . , T0 . In practice, HCW uses R2 or the adjusted R2 to select the best OLS estimate for y01t using j out of the J control agencies, denoted as M(j)∗ . Then, we choose M(m)∗ from M (1 )∗ , M (2 )∗ , . . . , M (J )∗ by comparing the values of their information criterion such as Akaike information criteria (AIC). Gardeazabal and Vega-Bayo (2017) systematically compare the two methods and summarize two important differences. First, the SCM mandates that the weights must be non-negative and add up to one so that extrapolation outside the convex hull of covariates for the untreated units is not allowed. Second, the HCW method solely relies on the outcome variable to find the best approximation of y01t . In certain circumstances, the two differences restrict the application of the two methods in empirical studies. For example, HCW could be problematic because it does not provide clear criteria to select the best control agencies, while the SCM uses any possible covariates that help explain the outcome variable as potential predictors, not only the outcome variable during the pre-treatment period. On the other hand, due to the restrictions on weights, the SCM becomes inapplicable when extrapolation is needed for good matching. In other words, the synthetic control does not Please cite this article as: W. Long, How does oversight affect police? Evidence from the police misconduct reform, Journal of Economic Behavior and Organization, https://doi.org/10.1016/j.jebo.2019.10.003

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Fig. 4. SCM: gaps (actual - predicted) plots of Type II (misdemeanor) arrests of whites.

always have a good match and is not recommended when the fit of the pre-treatment period is poor. In this circumstance, HCW can be used as an alternative because it does not impose similar restrictions on weights. In this paper, I implement both the SCM and HCW method to estimate the treatment effect of the DOJ’s investigation on local police agencies. Specifically, I compare the two methods’ estimates of the Average Treatment Effect on the Treated 1 T 1 0 6 (ATET) over the post-treatment period, defined as T −T t=T0 +1 (y1t − y1t ). Simulation results in Gardeazabal and Vega0 Bayo (2017) show that both methods perform equally well when the post-treatment period and the number of control units are small, which fits the situation in this analysis. I will first implement the SCM to estimate the treatment effect. However, as we will see in Section 5, in some cases the synthetic counterfactual does not exhibit a good match during the pre-treatment period. As an alternative, I use the HCW method by constructing a control group which includes only those that have non-negative weights in the SCM. In other words, I use the SCM as a matching procedure to find control units similar to the treated unit. For the inference of the estimated effect, in both methods, I follow the strategy introduced by Abadie et al. (2010) to calculate the empirical p-value through randomizing the treatment to each agency and re-estimating the ATET.

6

I thank one anonymous reviewer for suggesting this.

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Fig. 5. SCM: the actual and predicted paths of Type II (misdemeanor) arrests of blacks.

4. Data Of the 15 investigated agencies listed in Table 1, I first exclude the five agencies (Ferguson, Evangeline, Ville Platte, Baltimore, and Chicago) whose investigations were announced after 2013.7 Since 2014, allegations of police bias and police violence have risen across the country, and police-community relation have deteriorated in the past few years due to a series of publicized police shooting events. As a matter of fact, the shooting events that happened in Ferguson and Chicago directly incurred DOJ’s investigations. These incidents exacerbated racial tensions and sparked the Black Lives Matter movement (Day, 2015; Luibrand, 2015). Anger toward the police and the associated spillover effect could compound and overestimate the treatment effect of the DOJ’s investigation. Cheng and Long (2018) find evidence of the spillover effect of these events and observe a substantial de-policing effect in predominant black cities in the United States after 2014. For similar reasons, I also exclude Cleveland from the analysis due to the shooting of Tamir Rice in 2014 and the massive protests that followed. For the rest of the 9 agencies, I additionally exclude New Orleans, Seattle, and the University of Montana Office of Public Safety. Regarding New Orleans, Hurricane Katrina in 2005 led to a lingering influence on the behavior of local police officers and the problem of missing data. For Seattle, the arrest data in 2012 are missing, and the investigation against the University of Montana Office of Public Safety is part of the investigation against the Missoula Police Department. Thus, based on those 7

In addition, UCR arrest data are not available in Evangeline Parish, Ville Platte and Chicago during the sample period.

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Fig. 6. SCM: gaps (actual - predicted) plots of Type II (misdemeanor) arrests of blacks.

anomalies, the assessment of the treatment effect of the federal investigations is limited to six local police agencies (bold in Table 1) during 2005 – 2013. The empirical approach requires measuring quarterly arrest rates (number of arrestees per 10 0 0 city/county populations) for the treatment and control agencies. To do so, I obtain the FBI’s Uniform Crime Report (UCR) data on arrest from the Inter-university Consortium for Political and Social Research (ICPSR). Under the framework of UCR, each police agency has its unique originating agency identifier (ORI), and I use these identifiers to aggregate their data at the quarterly level. In these data, for each arrest, the information of year, month, race of arrestee, and offense code is given. Based on the UCR’s hierarchical coding system, I categorize arrests into either Type I felony arrests or Type II misdemeanor arrests. The former contains the UCR’s Type I index crimes such as homicide, rape, robbery, aggravated assault, burglary, larceny, and auto theft, and the latter contains non-violent offenses such as disorderly conduct, vandalism, liquor laws violations, and the like. Comparatively, the Type II offense is less severe, and a police officer usually has the discretionary power to decide whether to make an arrest or not. More importantly, if police officers indeed withdraw from proactive policing because of public oversight, the de-policing effect should be more salient on Type II arrests because they are part of police officers’ daily routines and are more common than Type I arrests. For the covariates required by the SCM, I collect data from the American Community Survey (ACS, 2005 – 2013) for the demographic and socioeconomic factors, including population, black population share, percentage of males, percentage of population aged 25–64, percentage of population with a high school Please cite this article as: W. Long, How does oversight affect police? Evidence from the police misconduct reform, Journal of Economic Behavior and Organization, https://doi.org/10.1016/j.jebo.2019.10.003

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Fig. 7. HCW: the actual and predicted paths of Type II (misdemeanor) arrests.

diploma, percentage of population with a bachelor’s degree, poverty rate, and median household income (in 2015 dollars). The control agencies for each investigated agency are documented in Table A1 of the appendix.

5. Main results 5.1. Synthetic control method I first implement the SCM to examine the treatment effect of the DOJ’s investigation on total misdemeanor arrests and demonstrate the actual (solid black) and the predicted (dashed blue) paths of the six investigated agencies in Fig. 1. In each panel, the vertical dashed line indicates the quarter in which the DOJ’s investigation was opened. For each investigated agency, the selected control agencies and their corresponding weights (in parentheses) are documented in Table 2. For all six investigated agencies, the SCM performs reasonably well in tracking the actual paths before the investigations were started. In particular, the predicted paths even successfully track the substantial drops in misdemeanor arrests in Alamance County (Panel (a)) and Albuquerque (Panel (f)) before the treatment. Since all predicted paths closely follow the patterns of the actual paths during the pre-treatment period, I can observe whether the two paths substantially diverge after the investigations. Please cite this article as: W. Long, How does oversight affect police? Evidence from the police misconduct reform, Journal of Economic Behavior and Organization, https://doi.org/10.1016/j.jebo.2019.10.003

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Group

Selected Control Agencies and Weights (in parentheses)

Alamance County # of control = 89

Total White

Camden (0.163), Carteret (0.066), Craven (0.063), Duplin (0.098), Halifax (0.359), Nash (0.180), Wilson (0.070) Sampson (0.189), Camden (0.258), Carteret (0.172), Cleveland (0.084), Craven (0.151), Duplin (0.016), Moore (0.044), New Hanover (0.007), Stokes (0.023), Yancey (0.054) Camden (0.047), Duplin (0.147), Halifax (0.084), Nash (0.553), New Hanover (0.073), Wilson (0.096) Mount Holly (0.297), Camden (0.101), Linden (0.009), Mount Ephraim (0.137), Oaklyn (0.017), Allenhurst (0.015), Deal (0.047), Barrington (0.194), Lakehurst (0.016), Ocean Gate (0.072) Edgewater (0.027), Fair Lawn (0.054), Lodi (0.172), Mount Laurel (0.179), New Hanover (0.140), Berlin (0.029), Mount Ephraim (0.052), Belleville (0.248), Deal (0.059), Madison (0.013), Lakehurst (0.002), New Brunswick (0.021) Camden (0.126), East Orange (0.238), Westville (0.135), Deal (0.307), Penns Grove (0.114), Salem (0.081) Philomath (0.078), Bend (0.018), Corvallis (0.092), Klamath Falls (0.003), Coburg (0.024), Eugene (0.003), Tigard (0.046), Roseburg (0.016), Salem (0.144), Gresham (0.240), Ashland (0.211), Amity (0.125) Philomath (0.058), Beaverton (0.111), Prineville (0.029), Hillsboro (0.041), Bend (0.077), Coburg (0.050), Eugene (0.029), Albany (0.027), Lebanon (0.027), Gervais (0.021), Turner (0.001), Woodburn (0.201), Fairview (0.266), Amity (0.057) Monmouth (0.461), Coburg (0.482), Eugene (0.056), Gresham (0.001) Alameda (0.038), Colusa (0.353), Mono (0.112), Sonoma (0.365), Kern (0.059), Trinity (0.026), Merced (0.046) Alameda (0.048), Butte (0.153), Colusa (0.107), Mono (0.108), San Bernardino (0.053), San Diego (0.004), Sonoma (0.313), Merced (0.088), Trinity (0.127) Alameda (0.631), Contra Costa (0.321), Solano (0.047) Plains (0.085), Red Lodge (0.134), Kalispell (0.125), Havre (0.378), Livingston (0.112), Libby (0.165) Plains (0.114), Red Lodge (0.222), Kalispell (0.271), Hamilton (0.036), Libby (0.356) Great Falls (0.226), Glendive (0.075), Kalispell (0.065), Whitefish (0.222), Cut Bank (0.043), Havre (0.030), Hamilton (0.042), Billings (0.205), Libby (0.094) Raton (0.039), Village of Angel Fire (0.034), Texico (0.048), Hatch (0.072), Las Cruces (0.199), Santa Rosa (0.047), Lovington (0.259), Espanola (0.065), Farmington (0.023), Cuba (0.012), Truth Or Consequences (0.009), Estanicia (0.102), Los Lunas (0.091) Cimarron (0.024), Texico (0.009), Hatch (0.096), Las Cruces (0.199), Sunland Park (0.025), Santa Rosa (0.029), Eunice (0.054), Lovington (0.227), Espanola (0.055), Truth Or Consequences (0.021), Estanicia (0.129), Bosque Farms (0.056), Los Alamos (0.078) Roswell (0.432), Clovis (0.064), Las Cruces (0.386), Tucumcari (0.003), Aztec (0.070), Santa Fe (0.033), Cuba (0.011)

Newark # of control = 71

Black Total White

Portland # of control = 115

Black Total White

Los Angeles County # of control = 52

Missoula # of control = 48

Albuquerque # of control = 57

Black Total White Black Total White Black Total White Black

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Note: For Alamance County and Los Angeles County, the corresponding control units are also counties. Total, White and Black respectively denote total misdemeanor arrests, misdemeanor arrest of whites and misdemeanor arrests of blacks. Cities/counties in bold are selected as control units for the HCW method.

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Table 2 Investigated agencies and their corresponding control agencies selected by the SCM.

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Fig. 8. HCW: gaps (actual - predicted) plots of Type II (misdemeanor) arrests.

The overall evidence exhibited by Fig. 1 implies that the DOJ’s investigations have led to evident decreases in total misdemeanor arrests by four of the six agencies. In Alamance County, Newark, Missoula, and Albuquerque, the misdemeanor arrest rates dropped almost immediately after the investigations were announced and stayed at a low level throughout the post-treatment periods. Such a persistent treatment effect should be expected given that the DOJ’s investigation usually takes several months to collect solid evidence, and a consent decree needs even longer time for agreement to be reached among different parties. The exceptions are Portland and Los Angeles County. For Portland, even though the predicted path is slightly above the actual path in the first six quarters after the investigation, the predicted path moves downward quickly and becomes lower than the actual path after 2012. Therefore, the overall treatment effect on Portland is not clear. In Los Angeles County, I cannot observe clear divergence between the two paths until 2013. To evaluate the significance of these estimates, I carry out the strategy similar to Abadie et al. (2010) and simulate a distribution of gaps between each control agency and its synthetic control. By doing this, I can check whether the gaps exhibited by the investigated agencies are relatively larger than the control agencies’ gaps. To provide the visual evidence, I plot the paths of gaps between the actual and the predicted paths of the control agencies in Fig. 2, with the black line denoting the investigated agency and the gray lines denoting the controls. Of the six investigated agencies compared with their corresponding controls, Newark (Panel (b)) and Missoula (Panel (e)) exhibit the largest gaps, whereas the gaps of the other four investigated agencies are not very salient. This result indicates that the DOJ’s investigations have led to a Please cite this article as: W. Long, How does oversight affect police? Evidence from the police misconduct reform, Journal of Economic Behavior and Organization, https://doi.org/10.1016/j.jebo.2019.10.003

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Fig. 9. HCW: the actual and predicted paths of Type II (misdemeanor) arrests of whites.

remarkable decline in total misdemeanor arrests in Newark and Missoula in comparison to other local agencies in the same state. Next, I formally test the significance of the ATET estimates discussed in Section 3. Specifically, I compare the ATET estimate of the investigated agency with the placebo ATET estimates of the controls. In column (1) of Table 3, for each investigated agency, I demonstrate its ATET estimate, the rank (in ascending order) of the ATET estimate in the placebo distribution, and the empirical p-value of a one-sided test for the probability of an agency in the placebo distribution whose ATET estimate is lower than that of the investigated agency. In other words, a higher rank and a smaller empirical p-value suggest a more pronounced treatment effect. The results displayed in column (1) of Table 3 are consistent with the observations from Fig. 2: after the DOJ announced the investigation, Newark witnessed the largest decrease (−10.762 per 10 0 0 populations) in total misdemeanor arrests, and the effect is significant at the 5% level. Missoula exhibited the second largest drop (−2.829 per 10 0 0 populations), and its ATET estimate is significant at the 10% level. The other four agencies also exhibited decreases in total misdemeanor arrests on average during the post-treatment effect, but the effect is not significant compared to that of their respective controls. Then, I examine whether the treatment effect on misdemeanor arrests is heterogeneous between whites and blacks. Fig. 3 shows the change in misdemeanor arrests of whites by the six agencies. In general, the six panels in Fig. 3 are similar to their counterparts in Fig. 1 and Newark still exhibits the largest gap among the investigated agencies during the posttreatment period. Next, I implement the same procedures discussed above and plot the gaps plots in Fig. 4. Panels (b) and Please cite this article as: W. Long, How does oversight affect police? Evidence from the police misconduct reform, Journal of Economic Behavior and Organization, https://doi.org/10.1016/j.jebo.2019.10.003

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Table 3 SCM: the estimates of ATET on misdemeanor arrests and inference. (1) Type II

(2) Type II: White

(3) Type II: Black

Alamance County Pre: 2005:Q1 - 2010:Q1, Post: 2010:Q2 - 2013:Q4 ATET Rank (low to high) Emp. p-value

−1.825 19/89 [0.213]

−0.605 34/89 [0.382]

−1.977∗∗ 1/89 [0.011]

Newark Pre: 2005:Q1 - 2011:Q1, Post: 2011:Q2 - 2013:Q4 ATET Rank (low to high) Emp. p-value

−10.762∗∗ 1/71 [0.014]

−2.698∗ 6/71 [0.085]

−7.927∗∗ 1/71 [0.014]

Portland Pre: 2005:Q1 - 2010:Q4, Post: 2011:Q1 - 2013:Q4 ATET Rank (low to high) Emp. p-value

-0.562 39/115 [0.339]

-0.579 56/115 [0.487]

— — —

Los Angeles County Pre: 2005:Q1 - 2011:Q2, Post: 2011:Q3 - 2013:Q4 ATET Rank (low to high) Emp. p-value

-0.153 28/52 [0.538]

-2.174 17/52 [0.327]

-0.089 9/52 [0.173]

Missoula Pre: 2005:Q1 - 2012:Q1, Post: 2012:Q2 - 2013:Q4 ATET Rank (low to high) Emp. p-value

−2.829∗ 4/48 [0.083]

−1.087 11/48 [0.229]

−0.001 27/48 [0.563]

Albuquerque Pre: 2005:Q1 - 2012:Q4, Post: 2013:Q1 - 2013:Q4 ATET Rank (low to high) Emp. p-value

−1.557 22/57 [0.386]

−1.724 16/57 [0.281]

−0.239∗∗ 2/57 [0.035]

Note: The estimates of the average treatment effect on the treated (ATET) over 1 T 1 0 the post-treatment period is defined as T −T t=T0 +1 (y1t − y1t ). Rank denotes 0 where the investigated agency’s ATET estimate ranks in the distribution of the simulated values (lowest to highest). The empirical p-value (Emp. p-value) in square parentheses is a one-sided test of the probability that a random draw from the control group has a value lower than the investigated agency. ∗ , ∗∗ and ∗∗∗ respectively indicate rejection of the null at 10%, 5% and 1%.

(e) in Fig. 4 suggest that, even though Newark and Missoula still exhibit the largest gaps during the post-treatment period, the magnitude of decline in the misdemeanor arrests of whites by both agencies is not as pronounced as that of their total misdemeanor arrests. This finding is also supported by the results summarized in column (2) of Table 3: the magnitude of decline in the misdemeanor arrests of whites becomes smaller in Newark (−2.698 per 10 0 0 populations) and Missoula (−1.087 per 10 0 0 populations), and only the effect in Newark is marginally significant at the 10% level. In general, I do not observe a remarkable decrease in the misdemeanor arrests of whites by any of the six investigated agencies. I finally assess how the DOJ’s investigations affect the misdemeanor arrests of blacks by the six agencies. As before, I draw the actual and predicted paths plots and the gaps plots in Figs. 5 and 6. In Fig. 5, the predicted paths closely track the actual paths before the investigations in five agencies (except for Portland), and three agencies (Alamance County, Newark, and Albuquerque) exhibit large deviation between the predicted and actual paths after the investigations. For Portland, the SCM cannot find a match for the actual path, because the average misdemeanor arrest rate of blacks in Portland during the pre-treatment period is 2.47, whereas the highest average rate in the controls is just 1.40. Therefore, a weighted sum of the misdemeanor arrests of blacks from the controls cannot equal Portland’s, and the predicted path is uniformly below the actual path in Panel (c) of Fig. 5.8 In the next subsection, I will implement HCW to evaluate the effect on the misdemeanor arrests of blacks in Portland. The gaps plots in Fig. 6 indicate that the decrease in the misdemeanor arrests of blacks

8 In principle, a weighted sum of outcomes from controls could equal to the treated unit if the weights were not bounded between 0 and 1. But using those weights could create a synthetic control that is not representative of the treated unit during the pre-treatment period.

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Fig. 10. HCW: gaps (actual - predicted) plots of Type II (misdemeanor) arrests of whites.

by the three agencies is still remarkable compared to their respective controls.9 Column (3) in Table 3 summarizes the observations from these figures and indicates that the average decrease in the misdemeanor arrests of blacks in Alamance County, Newark, and Albuquerque is significant at the 5% level. In summary, the overall evidence demonstrated by the SCM suggests that the DOJ’s investigations indeed affected police officers’ decision in three local agencies on the misdemeanor arrests of blacks, while in most of the investigated agencies, the effect on the misdemeanor arrests of whites is insignificant. 5.2. HCW method In this subsection, I use the HCW method to re-evaluate the effect on misdemeanor arrests. In Section 3, one merit of the HCW method that was pointed out is that it allows for extrapolation to find a good match. As seen in the previous subsection, even though the SCM does reasonably well in synthesizing the pre-treatment paths of most agencies, small deviations can still occasionally be observed during the pre-treatment period. For Portland, the SCM becomes problematic because the predicted path exhibits a poor fit to the actual path during the pre-treatment period. In such circumstances, the implementation of the HCW method becomes necessary and meaningful.

9

Gaps plot for Portland is not displayed here due to the poor match by the SCM.

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One practical issue for the application of HCW is the selection of controls. Because of the large number of controls for each investigated agency, in order to make the HCW method applicable, I follow the procedure outlined in Gardeazabal and Vega-Bayo (2017) and reduce the control group to a subset that includes controls with positive weights in the synthetic control. For example, for Alamance County, Table 2 shows that 13 agencies (in bold) are selected from 89 controls in the synthetic control. Thus, in the implementation of HCW, the control group will include the 13 controls only. Gardeazabal and Vega-Bayo (2017) use this procedure when they compare the performance of the two methods so that the constituents of the control group are selected by an automatic, data-driven method.10 Using the HCW method, I repeat the steps discussed in the previous subsection with the new sets of control. Table 4 documents the selected controls and the corresponding weights. Fig. 7 plots the predicted and actual paths of the six investigated agencies and Table 5 documents the selected controls and the corresponding coefficients. Similar to Fig. 1, immediate divergence can still be observed between the predicted and the actual paths in Newark, Missoula and Albuquerque. One exception is Los Angeles County; unlike the SCM, HCW shows that the total misdemeanor arrests dropped immediately after the investigation and the effect became more pronounced in 2013. The gaps plots in Fig. 8 show that Newark and Missoula still exhibit salient effects compared with their respective controls, while the effect in Los Angeles County and Albuquerque becomes obscure after comparing with their controls’ paths. Column (1) in Table 5 confirms the observations in Fig. 8: the total misdemeanor arrests in Newark and Missoula have declined significantly since the investigations. Even though the effect in Los Angeles County is not significant at any conventional level, the magnitude of decline is the second largest among the six investigated agencies. In summary, for total misdemeanor arrests, the two methods give similar results. Figs. 9 and 10 and Figs. 11 and 12 respectively demonstrate and evaluate the effect on the misdemeanor arrests of whites and blacks. For whites, the predicted paths deviate from the actual paths immediately in five panels (except for Portland), but the gaps plots in Fig. 10 show that only Missoula witnessed a significant decline when compared with its 12 controls. For blacks, the visual evidence from Fig. 11 shows, except in Los Angeles County and Missoula, sharp and immediate declines in the remaining four investigated agencies. In addition, Fig. 12 shows that the magnitude of the decline in the four agencies is remarkable in comparison to their respective controls. The ATET estimates, ranks, and empirical p-values displayed in column (3) of Table 5 confirm the findings from these gaps plots.11 In summary, results obtained by HCW show that four investigated agencies experienced significant declines in the misdemeanor arrests of blacks, while the misdemeanor arrests of whites significantly declined only in one agency. These findings echo the results by the SCM and further indicate that the effect of the DOJ’s investigations is more pronounced on the misdemeanor arrests of blacks. In addition, by comparing the performance of the SCM and HCW, I find that the predicted paths obtained by HCW exhibit a better fit during the pre-treatment periods with the reduced control units. 5.3. Felony arrest In this section, I assess the treatment effect of the DOJ’s investigations on felony arrests. For efficiency’s sake, herein I only implement HCW and summarize the ATET estimates, ranks, and corresponding empirical p-values in Table 6. Column (1) in Table 6 shows that the effect of investigations on total felony arrests is mixed: police officers in Missoula and Albuquerque tended to make more felony arrests, but officers in the other four agencies decreased their felony arrests. However, according to their rankings in the placebo distributions and the empirical p-values, none of the six investigated agencies exhibited a significant drop in felony arrests, which remarkably differs from the misdemeanor arrests scenario since significant decreases can be observed in non-felony arrests in Newark and Missoula after the investigations. Columns (2) and (3) of Table 6 respectively display the ATET estimates on the felony arrests of whites and blacks. For both whites and blacks, the estimated effect is still mixed: both increases (Missoula and Albuquerque) and decreases (Alamance County, Newark, Portland and Los Angeles County) in felony arrests of both races occur. However, the treatment effect is not significant in all six agencies because their empirical p-values are greater than the conventional significance levels. In summary, police officers in these agencies did not significantly change their behaviors in performing felony arrests after the DOJ’s investigations. 6. Robustness check Next, I conduct three tests to evaluate the robustness of the main results. Again, for the sake of efficiency, I will only report the HCW’s results. First, I assume the investigations of these agencies were started two years (eight quarters) earlier. Because the main finding of this study is that misdemeanor arrests of blacks decreased after the investigations, I only demonstrate the results 10

I thank one anonymous reviewer for suggesting this. Following the suggestion of one anonymous reviewer, I additionally used HCW to respectively examine the effect in the six agencies on five types of misdemeanor arrests (vandalism, drug abuse violations, liquor laws, disorderly conduct and all other non-traffic offenses). Because the UCR data do not provide the racial information of arrestees for itemized misdemeanor arrests, for each type, I could only examine the effect on its total. Table A2 in the appendix documents the results. According to Table A2, the total arrests for vandalism and liquor laws violations decreased in all six agencies, although the effects on the other three types are mixed. Of the five types of misdemeanor offenses, arrests for vandalism decreased significantly at the 10% level in Alamance County, arrests for drug abuse violations decreased significantly at the 5% level in Newark, and arrests for liquor laws violations decreased significantly at the 10% level in Missoula. 11

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18

Group

Selected Control Agencies and Weights (in parentheses)

Alamance County # of controls = 13

Total White Black Total

Carteret (0.380), Craven (0.248), Halifax (0.667), Moore (−0.603), Nash (0.427) Camden (0.255), Cleveland (1.170), Craven (0.452), Stokes (−0.370), Yancey (0.164) Duplin (0.146), Halifax (0.339), Nash (0.574), Sampson (−0.325), Wilson (0.089), Yancey (1.331) Fair Lawn (1.426), Mount Laurel (1.343), Mount Ephraim (0.327), Oaklyn (0.688), East Orange (−0.404), Westville (0.204), New Brunswick (−1.522), Penns Grove (0.144) Edgewater (0.075), Fair Lawn (0.176), Lodi (0.319), Barrington (0.078), Camden (−0.072), Mount Ephraim (0.039), Belleville (0.183), Deal (0.021), Linden (−0.264) Lodi (3.502), Mount Laurel (2.828), Belleville (3.609), Westville (0.415), New Brunswick (−1.995), Lakehurst (0.752), Penns Grove (−0.164) Corvallis (0.117), Bend (0.620), Ashland (−0.142), Gervais (−0.101), Salem (0.102), Turner (0.092), Woodburn (0.210), Fairview (0.730), Monmouth (0.103), Beaverton (−0.311), Amity (0.050) Bend (0.187), Ashland (−0.089), Klamath Falls (0.131), Coburg (0.007), Lebanon (0.099), Turner (0.050), Woodburn (0.237) Prineville (−1.984), Roseburg (3.327), Ashland (2.172), Klamath Falls (1.406), Albany (−1.019), Lebanon (−1.087), Gervais (−2.057), Salem (2.469), Gresham (−0.873), Hillsboro (−1.725), Tigard (−4.379), Amity (0.546) Alameda (0.180), Butte (0.636), Colusa (0.234), Mono (0.224), San Diego (0.724), Trinity (0.324) Alameda (0.173), Butte (0.460), Colusa (0.127), Mono (0.145), San Diego (0.501), Trinity (0.231) Butte (1.695), Kern (2.460), Merced (−2.521), San Bernardino (0.602), San Diego (2.476) Red Lodge (0.344), Havre (0.351), Livingston (−0.359), Plains (0.446) Whitefish (−0.379), Cut Bank (0.454), Havre (0.683), Libby (0.648), Livingston (−0.884), Hamilton (−0.260), Plains (0.528), Billings (3.236) Glendive (0.086), Kalispell (0.178), Whitefish (0.238) Las Cruces (0.254), Lovington (0.331), Espanola (0.064), Estancia (0.129) Las Cruces (0.210), Sunland Park (0.124), Lovington (0.276), Espanola (0.036), Estancia (0.082) Roswell (0.177), Clovis (0.043), Santa Rosa (−0.126), Eunice (−0.162), Los Alamos (−0.394), Truth Or Consequences (−0.719), Estancia (0.190)

Newark # of controls = 23

White

Portland # of controls = 22

Black Total White Black

Los Angeles County # of controls = 12 Missoula # of controls = 12 Albuquerque # of controls = 22

Total White Black Total White Black Total White Black

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Note: For Alamance County and Los Angeles County, the corresponding control units are also counties. Total, White and Black respectively denote total misdemeanor arrests, misdemeanor arrests of whites and misdemeanor arrests of blacks.

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Please cite this article as: W. Long, How does oversight affect police? Evidence from the police misconduct reform, Journal of Economic Behavior and Organization, https://doi.org/10.1016/j.jebo.2019.10.003

Table 4 Investigated agencies and their corresponding control agencies selected by HCW.

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Table 5 HCW: the estimates of ATET on misdemeanor arrests and inference. (1) Type II

(2) Type II: White

(3) Type II: Black

Alamance County Pre: 2005:Q1 - 2010:Q1, Post: 2010:Q2 - 2013:Q4 ATET Rank (low to high) Emp. p-value

−0.089 10/13 [0.769]

−1.490 5/13 [0.308]

−1.124∗ 1/13 [0.077]

Newark Pre: 2005:Q1 - 2011:Q1, Post: 2011:Q2 - 2013:Q4 ATET Rank (low to high) Emp. p-value

−14.479∗∗ 1/23 [0.043]

−2.708 3/23 [0.130]

−16.601∗∗ 1/23 [0.043]

Portland Pre: 2005:Q1 - 2010:Q4, Post: 2011:Q1 - 2013:Q4 ATET Rank (low to high) Emp. p-value

−0.742 11/22 [0.500]

0.683 16/22 [0.727]

−2.068∗∗ 1/22 [0.045]

Los Angeles County Pre: 2005:Q1 - 2011:Q2, Post: 2011:Q3 - 2013:Q4 ATET Rank (low to high) Emp. p-value

−4.231 2/12 [0.167]

−2.325 3/12 [0.250]

−0.503 2/12 [0.167]

Missoula Pre: 2005:Q1 - 2012:Q1, Post: 2012:Q2 - 2013:Q4 ATET Rank (low to high) Emp. p-value

−4.157∗ 1/12 [0.083]

−6.521∗ 1/12 [0.083]

−0.023 5/12 [0.417]

Albuquerque Pre: 2005:Q1 - 2012:Q4, Post: 2013:Q1 - 2013:Q4 ATET Rank (low to high) Emp. p-value

−1.852 7/22 [0.318]

−1.506 7/22 [0.318]

−0.279∗∗ 1/22 [0.045]

Note: The estimates of the average treatment effect on the treated (ATET) over the 1 T 1 0 post-treatment period is defined as T −T t=T0 +1 (y1t − y1t ). Rank denotes where 0 the investigated agency’s ATET estimate ranks in the distribution of the simulated values (lowest to highest). The empirical p-value (Emp. p-value) in square parentheses is a one-sided test of the probability that a random draw from the control group has a value lower than the investigated agency. ∗ , ∗∗ and ∗∗∗ respectively indicate rejection of the null at 10%, 5% and 1%.

of the four agencies (Alamance County, Newark, Portland, and Albuquerque) whose misdemeanor arrests of blacks significantly declined. Fig. 13 displays the results: in the four panels, the predicted paths still closely track the actual paths of misdemeanor arrests of blacks immediately after the placebo investigations, and obvious divergence between the two paths cannot be observed until the real investigations are conducted. This result suggests that the HCW method indeed picks up the causal effect, and the ATET estimates in Table 5 reflect the effect of the DOJ’s investigations. Second, I extend the sample period to the most recent 2016 UCR data and conduct HCW again. As mentioned in Section 2, since 2014, a series of high-profile events in several cities instigated investigations from the DOJ and initiated the Black Lives Matter movement on social media. Scrutiny from the federal government and local neighborhoods has imposed additional pressure on the investigated agencies so that the effect observed before 2014 should not diminish quickly but rather extend into the long term. Fig. 14 plots the predicted and actual paths of misdemeanor arrests of blacks by the six agencies. It is clear that the magnitude of the gaps between the two paths is still salient in Newark, Portland, Los Angeles County, and Albuquerque during 2014–16. Third, I use HCW to examine the robustness of the results by considering agencies that were investigated after 2013. Because the UCR data are not available in Cleveland, Evangeline Parish, Ville Platte and Chicago in the sample period, I only consider the police departments in Ferguson and Baltimore. These two agencies were respectively investigated by the DOJ due to two high-profile events — the deaths of Michael Brown and Freddie Gray — in August 2014 and April 2015. Columns (2) - (3) in Table 7 show that the decrease at both agencies in the misdemeanor arrests of blacks is significant at the 5% level, whereas the decline in the misdemeanor arrests of whites is not significant at any conventional levels. In addition, Please cite this article as: W. Long, How does oversight affect police? Evidence from the police misconduct reform, Journal of Economic Behavior and Organization, https://doi.org/10.1016/j.jebo.2019.10.003

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Table 6 HCW: the estimates of ATET on felony arrests and inference. (1) Type I

(2) Type I: White

(3) Type I: Black

Alamance County Pre: 2005:Q1 - 2010:Q1, Post: 2010:Q2 - 2013:Q4 ATET Rank (low to high) Emp. p-value

−0.821 3/13 [0.231]

−0.560 2/13 [0.153]

−0.276 3/13 [0.231]

Newark Pre: 2005:Q1 - 2011:Q1, Post: 2011:Q2 - 2013:Q4 ATET Rank (low to high) Emp. p-value

−0.741 9/23 [0.391]

−0.159 13/23 [0.565]

−0.031 10/23 [0.435]

Portland Pre: 2005:Q1 - 2010:Q4, Post: 2011:Q1 - 2013:Q4 ATET Rank (low to high) Emp. p-value

−0.670 8/22 [0.364]

−0.351 9/22 [0.409]

−0.372 8/22 [0.364]

Los Angeles County Pre: 2005:Q1 - 2011:Q2, Post: 2011:Q3 - 2013:Q4 ATET Rank (low to high) Emp. p-value

−0.132 6/12 [0.417]

−0.080 7/12 [0.583]

−0.075 3/12 [0.250]

Missoula Pre: 2005:Q1 - 2012:Q1, Post: 2012:Q2 - 2013:Q4 ATET Rank (low to high) Emp. p-value

0.694 8/12 [0.667]

0.812 7/12 [0.583]

0.029 8/12 [0.667]

Albuquerque Pre: 2005:Q1 - 2012:Q4, Post: 2013:Q1 - 2013:Q4 ATET Rank (low to high) Emp. p-value

1.043 19/22 [0.864]

0.635 16/22 [0.727]

0.093 19/22 [0.864]

Note: The estimates of the average treatment effect on the treated (ATET) over 1 T 1 0 the post-treatment period is defined as T −T t=T0 +1 (y1t − y1t ). Rank denotes 0 where the investigated agency’s ATET estimate ranks in the distribution of the simulated values (lowest to highest). The empirical p-value (Emp. p-value) in square parentheses is a one-sided test of the probability that a random draw from the control group has a value lower than the investigated agency. ∗ , ∗∗ and ∗∗∗ respectively indicate rejection of the null at 10%, 5% and 1%. Table 7 The estimates of ATET on misdemeanor (Type II) and felony (Type I) arrests in Ferguson and Baltimore and inference by HCW. (1) Type II

(2) Type II: White

(3) Type II: Black

(4) Type I

(5) Type I: White

(6) Type I: Black

−3.609 5/24 [0.208]

−0.544 5/24 [0.208]

−2.387 12/24 [0.500]

0.216 14/20 [0.700]

−0.149 11/20 [0.550]

0.225 19/20 [0.950]

Ferguson Pre: 2005:Q1 - 2014:Q2, Post: 2014:Q3 - 2016:Q4 ATET Rank (low to high) Emp. p-value

−1.166 13/24 [0.542]

−0.749 13/24 [0.542]

−5.733∗∗ 1/24 [0.042]

Baltimore Pre: 2005:Q1 - 2015:Q1, Post: 2015:Q2 - 2016:Q4 ATET Rank (low to high) Emp. p-value

−9.240∗∗ 1/20 [0.050]

−0.772 9/20 [0.450]

−6.348∗∗ 1/20 [0.050]

Note: The estimates of the average treatment effect on the treated (ATET) over the post-treatment period is defined as 1 T 1 0 t=T0 +1 (y1t − y1t ). Rank denotes where the investigated agency’s ATET estimate ranks in the distribution of the simulated T −T0 values (lowest to highest). The empirical p-value (Emp. p-value) in square parentheses is a one-sided test of the probability that a random draw from the control group has a value lower than the investigated agency. ∗ , ∗∗ and ∗∗∗ respectively indicate rejection of the null at 10%, 5% and 1%.

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Fig. 11. HCW: the actual and predicted paths of Type II (misdemeanor) arrests of blacks.

the ATET estimates displayed in column (3) of Table 5, when compared with the results in column (3) of Table 7, suggest that the effects in Ferguson and Baltimore are greater than the effects on four of the five agencies investigated before 2014 (Newark is the lone exception). Such a larger effect could be explained in part by the extra pressure from the Black Lives Matter movement and the nation-wide protests. According to columns (4)–(6), there is no significant change in felony arrests by the Ferguson and Baltimore police departments. 7. Discussion The findings in the previous section show that the DOJ’s investigations led to significant decreases for four of the six agencies in the misdemeanor arrests of blacks. When the DOJ investigates a particular agency for policing misconduct and civil rights violations, police officers in other local agencies might also change their policing behavior and avoid confronting civilians — at least during the investigation period. If such a spillover effect indeed exists, the assumption of no inference between control and treatment units, which is essential for causal inference, is violated.12 In this study, such a spillover effect should be real, especially considering that all these investigation were conducted when President Obama was in office, and

12

Abadie and Gardeazabal (2003) develop this argument and I thank one anonymous reviewer for reminding me of this.

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Fig. 12. HCW: gaps (actual - predicted) plots of Type II (misdemeanor) arrests of blacks.

the DOJ was headed by the Democrats, who called for more supervision on law enforcement.13 Therefore, the misdemeanor arrests of blacks by control agencies would also be likely to decrease, leading to a downward bias (in absolute value) in the estimated treatment effect and making the significant effect more difficult to find. In other words, the estimated treatment effect in this study would be a lower bound and the analysis above would provide a conservative inference about the effect of investigations. Therefore, the potential spillover effect does not significantly affect the main conclusion. Another concern regarding the credibility of the causal inference is that the significant decrease in misdemeanor arrests could be caused by a decrease in misdemeanor offenses rather than the investigation. However, the UCR data only provide records on felony offenses (Type I index crime), not misdemeanor offenses (Type II offenses). Records of misdemeanor offenses are included in the National Incident-Based Reporting System (NIBRS), but none of the six agencies participated in the NIBRS as of 2013 except for the Missoula Police Department, which does not exhibit a statistically significant decline in the misdemeanor arrests of blacks.14 Therefore, I cannot directly assess whether changes in the number of misdemeanor 13 During his tenure at the DOJ, then Attorney General Eric Holder made the law enforcement investigations a higher priority. In 2015, the DOJ asked Congress for a $2.5 million budget increase to add 13 attorneys and six investigators to its Civil Rights Division’s police misconduct team, according to the FY 2016 Performance Budget Congressional Submission. 14 According to NIBRS Participation by State released by the FBI, no agency in California, New Jersey, New Mexico and North Carolina had joined the NIBRS by 2013. The Portland Police Bureau has participated since 2016. The full report can be found at https://ucr.fbi.gov/nibrs/2013/resources/ nibrs- participation- by-state.

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Fig. 13. Placebo treatment: the actual and predicted paths of Type II (misdemeanor) arrests of blacks by HCW with placebo investigation (two years early).

offense affect the number of misdemeanor arrests. Nevertheless, the large and significant decrease in the misdemeanor arrests of blacks in four of the six agencies immediately after the investigations seems unlikely to be driven by a sudden drop in misdemeanor offenses by blacks, especially considering their ranks in the placebo distributions. Based on the current UCR data, I implement HCW to additionally examine how the number of felony offense (violent crime and property crime) changes after the investigations. Table A3 in the appendix shows mixed results, but none are statistically significant at the conventional levels. In addition, the decline in the misdemeanor arrests of blacks could be caused by both the incentive effect and the institutional effect. For the former, out of consideration of personal safety and the higher expected cost of confronting civilians during investigations, police officers might be discouraged from law enforcement activities – proactive policing in particular – in order to avoid getting involved in controversial incidents. For the latter, police officers in the investigated agencies may change their policing behavior just to comply with the rules. For example, on-duty police officers might be required to wear body-worn cameras and to submit more paper work after making pedestrian stops or arrests. To some extent, the institutional effect is related to the incentive effect because an extra workload makes officers more reluctant to make arrests.15 Given the current data, it is difficult to differentiate the pure incentive effect from the effect caused by the institutional changes.

8. Conclusion In this paper, I study the behavioral change of police officers in response to the DOJ’s investigations. Using the SCM and HCW and the arrest data from six investigated agencies between 2005 and 2013, this study provides some causal evidence of the investigations’ influence on officers’ decisions to arrests. The findings are mixed. On the one hand, officers in some local police agencies indeed reduced the misdemeanor arrests of blacks after investigations were opened. This finding is in line with the conclusions in Morgan and Pally (2016) and Cheng and Long (2018), who find substantial declines in arrests and other proactive policing activities after such investigations. On the other hand, I do not find a meaningful effect on

15 Jim Pasco, the executive director of the National Fraternal Order of Police, argued that “officers feel under the consent decree that they are being required to articulate all of their activities.” (Anderson, 2019) In his letter to President Trump, Ed Hutchison, president of the National Police Association, claimed that “the Consent Decree does little to assist or support officers, but overwhelmingly burdens them with increased level of report-writing, confusing and at times contradictory standards of interaction and policing, training that exceeds even the highest amount of training required from legal professionals.” Hutchison (2019).

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Fig. 14. Placebo investigation: the actual and predicted paths of Type II (misdemeanor) arrests of blacks by HCW with extended sample period (2005 2016).

arrests for felony offenses and arrests of whites, which indicates that the investigations did not universally discourage police officers from their daily routines. This study is a small step toward the larger goal of understanding the overall effect of the DOJ’s investigation. Therefore, it still has some limitations, partially due to the availability of data. First, racial information of police and locations of arrests are not included in the UCR data, so I could not examine whether the decrease in the misdemeanor arrests of blacks is driven by white officers working in predominantly black communities. Second, besides misdemeanor arrests, some other measurements of proactive policing activities (e.g., pedestrian stop and self-initiated activity) exist in which police officers have discretionary power. Estimating the potential effect of these activities could help to check the robustness of the main results. In the future, individual-level policing and crime data with more details can help to conduct more thorough research on this important topic.

Declaration of Competing Interest None. Please cite this article as: W. Long, How does oversight affect police? Evidence from the police misconduct reform, Journal of Economic Behavior and Organization, https://doi.org/10.1016/j.jebo.2019.10.003

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