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Estimating police effectiveness with individual victimisation data

Author

Listed:
  • Ben Vollaard

    (CPB Netherlands Bureau for Economic Policy Analysis)

  • Pierre Koning

    (CPB Netherlands Bureau for Economic Policy Analysis)

Abstract

In this paper, we present evidence on the effect of greater numbers of police personnel on victimisation of crime and experience of nuisance. We make use of individual data from a Dutch victimisation survey unique in its size, duration and scope. By using individual victimisation data we provide evidence on the effects of police on nuisance rather than 'hard crime' only, we circumvent measurement error common to police statistics, and we are able to control for both individual and municipality characteristics. We find significantly negative effects of higher police levels on property crime, violent crime and nuisance. The estimated elasticities are in line with the literature based on police statistics. Urban police forces are more effective than rural police forces for most types of crime and nuisance. Additionally, we find experience of nuisance mostly to be a characteristic of the municipality in which someone lives, with little variation across individuals in a municipality, whereas victimisation of violent crime varies across individuals rather than municipalities. For property crime, individual and municipality characteristics are about equally important. Finally, we provide evidence that greater police protection allows people to move around more freely, which is an additional benefit of higher police levels not reflected in a decline in victimisation rates.

Suggested Citation

  • Ben Vollaard & Pierre Koning, 2005. "Estimating police effectiveness with individual victimisation data," CPB Discussion Paper 47, CPB Netherlands Bureau for Economic Policy Analysis.
  • Handle: RePEc:cpb:discus:47
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    References listed on IDEAS

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    1. H. Naci Mocan & Hope Corman, 2000. "A Time-Series Analysis of Crime, Deterrence, and Drug Abuse in New York City," American Economic Review, American Economic Association, vol. 90(3), pages 584-604, June.
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    3. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    4. Levitt, Steven D, 1997. "Using Electoral Cycles in Police Hiring to Estimate the Effect of Police on Crime," American Economic Review, American Economic Association, vol. 87(3), pages 270-290, June.
    5. Klick, Jonathan & Tabarrok, Alexander, 2005. "Using Terror Alert Levels to Estimate the Effect of Police on Crime," Journal of Law and Economics, University of Chicago Press, vol. 48(1), pages 267-279, April.
    6. Tauchen, Helen & Witte, Ann Dryden & Griesinger, Harriet, 1994. "Criminal Deterrence: Revisiting the Issue with a Birth Cohort," The Review of Economics and Statistics, MIT Press, vol. 76(3), pages 399-412, August.
    7. Ann Dryden Witte & Robert Witt, 2001. "What we spend and what we get: Public and private provision of crime prevention and criminal justice," Fiscal Studies, Institute for Fiscal Studies, vol. 22(1), pages 1-40, March.
    8. Philipson, Tomas J & Posner, Richard A, 1996. "The Economic Epidemiology of Crime," Journal of Law and Economics, University of Chicago Press, vol. 39(2), pages 405-433, October.
    9. Karin Wittebrood & Marianne Junger, 2002. "Trends in violent crime: a comparison between police statistics and victimization surveys," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 59(2), pages 153-173, August.
    10. Rafael Di Tella & Ernesto Schargrodsky, 2004. "Do Police Reduce Crime? Estimates Using the Allocation of Police Forces After a Terrorist Attack," American Economic Review, American Economic Association, vol. 94(1), pages 115-133, March.
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    Cited by:

    1. Ben Vollaard, 2006. "Evaluating the push for tougher, more targeted policing in the Netherlands; evidence from a citizen survey," CPB Document 119, CPB Netherlands Bureau for Economic Policy Analysis.

    More about this item

    JEL classification:

    • K4 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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