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Population Size, Change, and Crime in U.S. Cities

Journal of Quantitative Criminology volume 22, pages 341–367 (2006)Cite this article

Abstract

The sometimes noted contradiction between cross-sectional and longitudinal relationships concerning city population size and crime rates is reexamined using more complex analytic procedures, controlling for extraneous variables, and allowing for non-monotonic relationships. Instead of a simple cross-sectional relationship between population size and crime rates, the more sophisticated analysis reveals either no association or a quadratic relationship. Similarly, instead of a simple lack of longitudinal relationship or a negative one, the more complicated analysis shows a non-monotonic pattern for three of six offenses. However, we contend that these divergent patterns for cross-sectional relative to longitudinal data are not necessarily indicative of an “anomaly.” Instead, they represent different aspects of a dynamic process in need of more extensive theorizing. Finally, the cross-sectional results showing that city size and crime rates are either not linked or when linked are in a non-monotonic pattern call into question one of the accepted relationships in criminology that have long guided thinking about crime.

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Notes

  1. Some studies have used logged population as a predictor, which implies some non-monotonicity.

  2. An additional argument concerning changes in population suggests an association between population changes and crime rates consistent with the linear cross-sectional patterns often observed, though the coefficients would be somewhat attenuated. Mayhew and Levinger (1976) focus on interaction possibilities, contending that crime, especially violent crime, is a probabilistic product of human contact. The more often humans interact, the more likely is somebody to be offended, harmed, or exploited. Therefore, crime can be predicted from a simple multiplicative function of interaction possibilities. Because increases in population produce even larger increases in interaction possibilities, population growth should lead to spiraling amounts of crime while population declines should lead to reductions in crime at a decreasing rate. Thus the pattern will be a positive association with an increasing slope, which should be manifest as a modest underlying positive coefficient.

  3. However, there does not appear to be hard evidence of whole cities being organized around and supportive of criminal norms. Certainly, criminal subcultures sometimes exist within larger communities but they stand in opposition to generally held conventional norms (see Tittle and Paternoster 2000, Chapter 4). In addition, there are instances of cities with corrupt governments that tolerate some forms of criminal activity as well as cities that are largely controlled by criminal syndicates. Yet, in neither instance is it likely that the community structure and intertwined informal organization are basically criminally oriented. Moreover, even if such cities do exist, they are probably not numerous enough to affect the generally predicted relationships here being described.

  4. We are grateful to Terry Miethe for generously sharing this beginning data set with us. See Miethe et al. 1991.

  5. Firebaugh and Gibbs discuss problems surrounding the estimation of a ratio variable model, where the dependent variable is constructed with population size and this variable is included as an independent variable. The model resembles:

    $$ \frac{\# Crimes}{Population}= Population + Controls $$

    Our models differ distinctly from the general form discussed by Firebaugh and Gibbs. In the models we estimate, the dependent variable is the difference between two ratios:

    $$ \left[\frac{\#Crimes_{T}}{Population_{T}}\right]-\left[ \frac{\#Crimes_{T-1}}{Population_{T-1}}\right] $$

    with the key independent variable being relative change in population size, a measure that does not enter into the construction of the dependent variable.

  6. Though the original data set with which we are working included some of these measures, to be safe, we extracted from ICPSR the exact figures used by Land et al. for 1960, 1970, and 1980 to which we added comparable figures for 1990.

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Authors and Affiliations

  1. Department of Sociology, Washington State University, Wilson Hall, Pullman, WA, 00164-4020, USA

    Thomas Rotolo

  2. Department of Sociology and Anthropology, North Carolina State University Campus, Campus Box 8107, Raleigh, NC, 27695-8107, USA

    Charles R. Tittle

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  1. Thomas Rotolo
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Correspondence to Charles R. Tittle.

Appendix

Appendix

 

Table 4 Regression coefficients expressing the cross-sectional relationships between crime rates, population size and population size-squared, with controls included, by year
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Table 5 Estimates from pooled cross-section time series regression models (N = 348)
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Table 6 Estimates from pooled cross-section time series regression models (N = 348)
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Table 7 Estimates from pooled cross-section time series regression models (N = 348)
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Rotolo, T., Tittle, C.R. Population Size, Change, and Crime in U.S. Cities. J Quant Criminol 22, 341–367 (2006). https://doi.org/10.1007/s10940-006-9015-x

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Keywords

  • City
  • Population
  • Size
  • Crime
  • Change
  • Crime rate