Designing Performance Systems in Anticipation of Unintended Consequences: Experiences and Lessons From the Compstat-Based Performance Regime in NYPD

Published date01 July 2021
AuthorYi Lu,M. Blair Thomas,Kaifeng Yang
DOI10.1177/0095399720976532
Date01 July 2021
Subject MatterArticles
https://doi.org/10.1177/0095399720976532
Administration & Society
2021, Vol. 53(6) 907 –936
© The Author(s) 2020
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DOI: 10.1177/0095399720976532
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Article
Designing Performance
Systems in Anticipation
of Unintended
Consequences:
Experiences and Lessons
From the Compstat-
Based Performance
Regime in NYPD
Yi Lu1, Kaifeng Yang2,3, and M. Blair Thomas4
Abstract
Scholars have long documented the unintended consequences of
performance systems, but insufficient attention is paid to the drivers of such
consequences and the ways of mitigating them. This article contributes to
this line of inquiry by analyzing New York Police Department’s (NYPD)
experience with its signature performance system and its affiliated policies.
With multiple data sources, it finds three main drivers of the unintended
consequences, including the excessive performance pressure, the
bureaucracy-public expectation mismatch, and the ambiguity concerning
government performance. The article makes six suggestions regarding
the design of performance management systems so that their potential
unintended consequences can be better identified and mitigated.
1John Jay College of Criminal Justice, New York, NY, USA
2Renmin University of China, Beijing, China
3Florida State University, Tallahassee, USA
4Valdosta State University, GA, USA
Corresponding Authors:
Kaifeng Yang, School of Public Administration and Policy, Renmin University of China,
Haidian District, Beijing 100872, China.
Email: yangkaifeng@ruc.edu.cn
976532AASXXX10.1177/0095399720976532Administration & SocietyLu et al.
research-article2020
908 Administration & Society 53(6)
Keywords
unintended consequences, performance management, accountability,
ambiguity
“Computer statistics” or “comparative statistics,” commonly known as
Compstat, has been popular in the United States, notably within the policing
field. Since the New York Police Department (NYPD) adopted it in 1994,
Compstat, a system that “uses computer-mapping technology and timely
crime analysis to target emerging crime patterns and coordinate police
response” (Bratton & Malinowski, 2008, p. 259), has been arguably respon-
sible for modernizing the NYPD with a new model to fight crime. In the
earlier years of its implementation, Harvard University bestowed Compstat
with its “Innovations in American Government Award” in 1996. Moore
(2013) recognizes the NYPD performance practice as an exemplar in recog-
nizing and creating public value, and Behn (2014) praises it as an effective
leadership strategy for producing results.
In the meantime, reflections have been done on how Compstat is imple-
mented and how good policing should perform, by the police profession
(Willis et al., 2003), oversight bodies (Kelley & McCarthy, 2013), and schol-
ars (Eterno & Silverman, 2012; Weisburd & Braga, 2006). Growing concerns
of the unintended consequences of Compstat have surfaced, notably regard-
ing how NYPD commanders may have misused the tool to gain more control
over officers and exacerbated the mistrust between the police and the com-
munity (Eterno & Silverman, 2010a).
Looking broadly, what do we know about the unintended consequences of
performance systems in general? Indeed, public management literature has
identified various types of unintended consequences of performance systems:
cutting the corners, ratchet, threshold, magic tricks, among many others
(Bevan & Hood, 2006; Courty & Marschke, 2007; Hood, 2006, 2020; Hood
& Piotrowska, 2020; Kelman & Friedman, 2009; Pollitt, 2003; P. Smith,
1995; Taylor, 2020; Terman & Yang, 2016; Van Thiel & Leeuw, 2002). More
generally, Hood and Peters (2004) argued that the New Public Management
(NPM) movement, which includes performance measurement, has unin-
tended or paradoxical effects. They conclude that the value of identifying the
unintended effects is “challenging scholars to work out ways to account for
the surprises that attend apparently rationalistic reforms” (p. 279).
So far, it remains largely unclear how we might better deal with unin-
tended consequences. In this research, we aim to provide some preliminary
answers by analyzing NYPD’s experience with Compstat-based performance

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