Causal peer effects in police misconduct

Quispe-Torreblanca, Edika; Stewart, Neil

doi:10.1038/s41562-019-0612-8

Cited by 52 publications

(50 citation statements)

References 34 publications

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“…Despite the use of officer fixed effects to address differences in force levels, training receptivity, and general behavior between officers, an analysis of individual force rates may be insufficient, given that individual officers' behavior is subject to peer influence. Recent studies (Ouellet, Hashimi, Gravel, & Papachristos, 2019;Quispe-Torreblanca & Stewart, 2019) have found that officer involvement in excessive use of force complaints or misconduct can be explained by exposure to peers accused of similar; it stands to reason that positive peer influence may also reduce such behaviors. In this case, nontrained officers may be influenced by others who had undergone the training, thus adapting their behavior to mimic trained officers and thereby reducing force levels.…”

Section: Main Results For Department-level Analysismentioning

confidence: 99%

Did de‐escalation successfully reduce serious use of force in Camden County, New Jersey? A synthetic control analysis of force outcomes

Goh¹

2021

Criminology & Public Policy

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Despite the widespread interest that de-escalation training has attracted in law enforcement contexts over the past few years, we know little about its effectiveness in reducing use of force incidents.This study seeks to ascertain the effect of de-escalation training on serious use of force events in Camden, a high-crime and high-poverty city in New Jersey. An analysis of individual officers suggested de-escalation training had no significant effects on serious force, whereas a synthetic control analysis of the entire department suggested that de-escalation training led to a 40% reduction in serious force events. It is suggested that spillover effects between trained and untrained officers may account for the discrepancy. Policy Implications: This study offers evidence that de-escalation training may be more effective at reducing police force than other measures that have been proposed in recent years, such as consent decrees, less lethal weapons, and body-worn cameras. However, the unique environment in which the program was introduced-a high-crime, high-use of force jurisdiction that had previously dissolved and rebuilt its police force-suggests these encouraging results should be tempered with a good dose of caution.

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Section: Main Results For Department-level Analysismentioning

confidence: 99%

Did de‐escalation successfully reduce serious use of force in Camden County, New Jersey? A synthetic control analysis of force outcomes

Goh¹

2021

Criminology & Public Policy

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“…CK's stipulation that incapacitation is ineffective assuming no marked spillover effects is at odds with their own account of research on such effects. As CK discuss, recent research finds a 1% increase in misconduct by an officer's peers increases that officer's future misconduct by 0.8% (Quispe-Torreblanca & Stewart, 2019). CK go so far as to state that this effect size is "consistent with meaningful and behaviorally-important peer network effects" (p. 365).…”

Section: Sacrifice Of Relevance For Certitudementioning

confidence: 94%

“…In this case, it is also used to minimize the importance of spillover effects to their conclusions. In light of research that documents peer effects and network spillovers in multiple departments, including the CPD (Ouellet et al, 2019;Quispe-Torreblanca & Stewart, 2019), we believe it necessary to incorporate and explicitly discuss spillovers across the full range of simulations to appropriately gauge the potential effect of CK's "bad apple" replacement policy. 2 We apply the 80% spillover multiplier found by Quispe-Torreblanca (2019) to every estimate presented in CK's Table 2 (p. 362).…”

Section: Sacrifice Of Relevance For Certitudementioning

confidence: 99%

Bad apples and incredible certitude

Sierra‐Arévalo

Papachristos

2021

Criminology & Public Policy

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Research summary: Chalfin and Kaplan attend to the problem of police misconduct with a series of simulation analyses that leverage data on complaints and uses of force in the Chicago Police Department. They conclude that incapacitating officers has minimal effects on misconduct and that, given political constraints, policy makers may prefer broader reforms around accountability and management to removing "bad apples". In this comment, we argue that this conclusion and its policy implications are characterized by "incredible certitude" driven by a selective focus on a subset of their full simulation results and inadequate incorporation of network spillovers into their analysis.Policy implications: Contrary to CK's conclusions, their preferred estimates of the effect of incapacitating "bad apples" on misconduct are squarely within the range of other interventions aimed at reducing police complaints and use of force. Once network spillovers are accounted for, estimates are up to five times as large. We conclude with a discussion of how even small reductions in misconduct can have outsized benefits as measured in both dollars and human suffering, and argue that the removal of problem officers is a normative good that should be pursued on moral grounds.

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“…Recently, more granular analyses have described the policing environment with greater clarity; however, the application of machine learning analytics has been noteworthy (Helsby et al, 2018). Significant contributions have also been made to the area of organizational- (Huff et al, 2018) and group-level features of misconduct (Quispe-Torreblanca and Stewart, 2019). Nevertheless, the ability to interrogate large data sets for granular insights into misconduct is pivotal to effective policy development and implementation, particularly for individual-level correlates of serious police misconduct.…”

Section: Introductionmentioning

confidence: 99%

A machine learning analysis of misconduct in the New York Police Department

Cubitt

Birch

2021

PIJPSM

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PurposeThere is a paucity of data available relating to the misconduct of police officers in larger policing agencies, typically resulting in case study approaches and limited insight into the factors associated with serious misconduct. This paper seeks to contribute to the emerging knowledge base on police misconduct through analysis of 28,429 complaints among 3,830 officers in the New York Police Department, between 2000 and 2019.Design/methodology/approachThis study utilized a data set consisting of officer and complainant demographics, and officer complaint records. Machine learning analytics were employed, specifically random forest, to consider which variables were most associated with serious misconduct among officers that committed misconduct. Partial dependence plots were employed among variables identified as important to consider the points at which misconduct was most, and least likely to occur.FindingsPrior instances of serious misconduct were particularly associated with further instances of serious misconduct, while remedial action did not appear to have an impact in preventing further misconduct. Inexperience, both in rank and age, was associated with misconduct. Specific prior complaints, such as minor use of force, did not appear to be particularly associated with instances of serious misconduct. The characteristics of the complainant held more importance than the characteristics of the officer.Originality/valueThe ability to analyze a data set of this size is unusual and important to progressing the knowledge area regarding police misconduct. This study contributes to the growing use of machine learning in understanding the police misconduct environment, and more accurately tailoring misconduct prevention policy and practice.

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Causal peer effects in police misconduct

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 52 publications

References 34 publications

Did de‐escalation successfully reduce serious use of force in Camden County, New Jersey? A synthetic control analysis of force outcomes

Did de‐escalation successfully reduce serious use of force in Camden County, New Jersey? A synthetic control analysis of force outcomes

Bad apples and incredible certitude

A machine learning analysis of misconduct in the New York Police Department

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