Urban Law Enforcement in Canada: An Empirical Analysis

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  • Urban Law Enforcement in Canada: An Empirical AnalysisAuthor(s): William J. Furlong and Stephen L. MehaySource: The Canadian Journal of Economics / Revue canadienne d'Economique, Vol. 14, No. 1(Feb., 1981), pp. 44-57Published by: Wiley on behalf of the Canadian Economics AssociationStable URL: http://www.jstor.org/stable/134839 .Accessed: 10/06/2014 14:08

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  • Urban law enforcement in Canada: an empirical analysis W I L L I A M J. F U R L O N G / Queen's University

    S T E P H E N L. M E H A Y/ San Jose State University

    Abstract. This paper examines the issue of crime deterrence as supplied by a single urban law enforcement agency. A simultaneous model consisting of the supply of offences, police production function, and police deployment function is specified and estimated.Account is also taken of crime spillovers among observations and the daily mobility patterns of the population. The deterrent impact of police output is confirmed, but the link between police inputs and the production of deterrence is found to be weak. Consideration of the population's mobility significantly reduces the effect on crime rates of socio-economic factors such as unemployment.

    L'application de la loi dans une ville au Canada: une analyse empirique. Ce m6moire etudie les effets dissuasifs sur les criminels des activites d'un seul corps de police. Les auteurs elaborent un modele specifiant une fonction d'offre d'actes criminels, une fonction de production des services policiers et une fonction de deploiement du personnel policier, et puis ils calibrent leur modele. Les auteurs tiennent compte des effets de retomb6e d'un quartier a l'autre ainsi que des patterns journaliers de mobilite de la population a l'int6rieur de la communaut6 urbaine. Le m6moire vient confirmer l'effet dissuasif de l'activit6 policiere mais le lien entre le taux d'activit6 policiere et le niveau de dissuasion est faible. Une prise en compte de la mobilit6 de la population tend a r6duire de fagon consid6rable l'effet des facteurs socio-6conomiques comme le chomage sur le taux de criminalite.


    The purpose of this study is to examine empirically the existence and strength of the deterrent effects of urban police activities in a major Canadian metropolitan area. The hypothesis that criminal justice sanctions deter crime by altering the structure of incentives facing potential offenders was formulated initially by Becker (1968) and extended by Ehrlich (1973). The

    Financial assistance provided by the solicitor-general of Canada is gratefully acknowledged. The authors would also like to acknowledge the helpful comments of Richard Arnott and members of the Queen's University Economics Workshop. An earlier version of this paper was presented at the Southern Economic Association meetings in Washington, D.C.

    Canadian Journal of Economics/Revue canadienne d'Economique, XIV, No. 1 February/fevrier 1981. Printed in Canada/lmprime au Canada.

    0008-4085 / 81 / 0000-0044 $01.50 ?) 1981 Canadian Economics Association

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  • Urban law enforcement in Canada / 45

    deterrent hypothesis has been examined extensively in previous econometric studies and, in general, the results have supported the predictions derived from the economic model of offender behaviour. Nonetheless, interpretation of the results of these studies has been clouded by numerous problems.1

    A major problem in assessing the deterrent effect of the police is that the bulk of the econometric crime literature has used aggregated cross-sectional data for estimation purposes, often the state, county, or SMSA level for u.s. jurisdictions. The only Canadian crime deterrence studies (Avio and Clark, 1976, 1978) also used aggregated observations; provincial and Ontario census division, respectively. The level of aggregation in the data affects the accuracy of estimates of the effect of municipal police on crime2 and the implications for resource allocation at that level. Those studies which have used disaggregated data have produced conflicting results,3 and no such disaggregated studies of police deterrent effects have been conducted for Canadian cities.

    This study extends and improves previous econometric analyses of crime deterrence in several ways. First, we employ a disaggregated data base composed of districts located within a single Canadian metropolitan area, Montreal. Second, our data reflect both the underlying pattern of resource deployment across districts (neighbourhoods) and the distribution of man- power between the two major crime deterrence police programs, patrol and investigation. The disaggregated data should enable us to establish the full deterrence link between alternative police deployment strategies and crime. It should also allow us to test a richer set of hypotheses concerning police resource allocation. Specifically, a three-equation simultaneous model is formulated to examine the impact of alternative manpower levels and deployment on clearance rates and the effect of the latter on property crime rates.

    We augment and modify existing specifications of the crime deterrence model to incorporate additional factors and to sharpen the focus on the police. Because we are examining police services in a single metropolitan area, potential crime spillovers across neighbourhoods may bias empirical estima- tion of police deterrence effects. While previous cross-sectional studies have largely ignored the crime spillover problem,4 we explicitly integrate crime displacement effects into the model. A second source of bias in previous

    1 For a survey and critical analysis of numerous econometric studies of crime see National Research Council (1978) and Taylor (1978).

    2 Aggregated data will yield accurate results only if all micro units that comprise the aggregate react identically to changes in the variables; the estimated effect of local police will be an average of the true effects for all local units in the aggregate.

    3 While a more direct deterrence link between the police and crime has been examined and supported by some previous studies (Thaler, 1977; Chapman, et al., 1975), it has also been either rejected or only weakly supported by numerous others (Pogue, 1975; Carr-Hill and Stern, 1973; Greenwood and Wadycki, 1973; Swimmer, 1974; Mathur, 1978).

    4 The major exception is a study by Mehay (1977).

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  • 46 / William J. Furlong and Stephen L. Mehay

    empirical estimates of deterrence is the omission of the effect of daily commutation patterns in metropolitan areas. In this study an alternative measure of district population, based on both daytime and night-time population, is used to adjust variables expressed in per capita terms.


    The simultaneous system consists of offence supply, police output and manpower deployment equations. With a given level of resources, the chosen deployment strategy will affect police output, measured as clearances, in each district. Changes in the number of clearances in each district will alter crime rates which, in turn, will influence the police agency, either directly or indirectly through pressure from district residents, to revise the deployment of resources. Hence, the level of crime, police output, and geographic deployment of resources are assumed to be simultaneously determined. The model is estimated with data from the Montreal Urban Community (Muc). The MUC Police Department is composed of thirty-nine police districts, or stations, thirty-eight of which comprise the sample observations for this study.5 We shall discuss each equation of the model in turn.

    The supply of offences The individual decision to participate in illegal activities is assumed to depend on the expected utility of the net monetary gain where all relevant risk factors and potential rewards (monetary and psychic) are considered, including the opportunity cost of forgone legal earnings.6 The offence supply equations are specified to reflect the various economic risk and gain factors:

    n cRj = Bo + BlcLCj + B2 In uNj + B3 In Yj + B4 In YGj

    + B5 In vLj + B6 In SLj + B7 DFj + el,(1) where

    cR1j = rate of offences of type c in districtj,

    CL,j = clearance rate for offence type c in districtj,

    UNj = male unemployment rate inj,

    Yj = average household income inj,


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