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Seasonal Cycles in Crime, and Their Variability

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Abstract

Seasonal crime patterns have been a topic of sustained criminological research for more than a century. Results in the area are often conflicting, however, and no firm consensus exists on many points. The current study uses a long time series and a large areal sample to obtain more detailed seasonality estimates than have been available in the past. The findings show that all major crime rates exhibit seasonal behavior, and that most follow similar cycles. The existence of seasonal patterns is not explainable by monthly temperature differences between areas, but seasonality and temperature variations do interact with each other. These findings imply that seasonal fluctuations have both environmental and social components, which can combine to create different patterns from one location to another.

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Notes

  1. Consistent with the bulk of research in all areas of the social sciences, we define seasonality as any cyclical pattern that repeats itself at regular intervals. The finer variation in monthly data allow a more detailed study of seasonal patterns than do quarterly aggregations into spring, summer, autumn, and winter. The relatively course quarterly aggregations may in fact obscure information about how seasonality occurs.

  2. The temperature data are monthly means as recorded by the U.S. Weather Service. They were retrieved from http://www.eachtown.com on the World Wide Web, and were current as of June 5, 2011.

  3. To streamline the presentation, all tables omit the eighty-seven city-specific fixed effects. Similarly to reduce clutter, the tables include only trend variables for time (grand mean centered) and its squared values. For some crimes, time polynomials up to the twelfth-order in fact significantly contributed to the models. This is unsurprising given the large sample size, and polynomials higher than the second-order did not substantively affect the other estimates.

  4. Collinearity does not pose significant problems for the analysis. A single collinearity index for the entire set of eleven dummy variables is more helpful than are individual measures for each month. The study therefore evaluated collinearity using condition indices instead of the more familiar variance inflation factors. For the Table 1 models, the largest condition indices were twenty-two or less, below the value of thirty that Belsley et al. (1980) suggest as a threshold for concern. Much of the collinearity that did exist was due to the day-of-week measures. Dropping these from the analysis did not appreciably change the conclusions about seasonality, but it decreased the condition indices to eight or less.

  5. These are Rogers robust standard errors, which allow for unspecified forms of within-city serial correlation. Newey-West robust standard errors resulted in inferences that were substantively similar but not absolutely identical. Both types of robust standard errors provide consistent estimates under very general assumptions about heteroscedasticity and autocorrelation structures. More detailed assumptions about the errors (as exist, for example, in an ARIMA model) would increase estimation efficiency if the assumptions were true. The large sample size makes efficiency a relatively unimportant consideration here, however. Peterson (2009) offers a thorough technical discussion of Rogers, Newey-West, and other robust standard errors for panel data. Stock and Watson (2007) consider the same material more intuitively and in a more general context.

  6. Several of the contemporary studies that claim to find a winter property crime peak actually analyze robbery rates (e.g., Landau and Fridman 1993; Michael and Zumpe 1983). The FBI classifies robbery as a violent crime, and the purely property-related offenses of burglary, larceny, and motor vehicle theft all peak in August.

  7. The analysis centered the monthly temperature variable about its grand mean to reduce collinearity with the other model components. Perhaps as a consequence, condition indices for the models including temperatures were only marginally higher than for their counterparts in Table 1.

  8. In this connection, Kleck (1991) argues that assaults and homicides are less alike in their circumstances and motivations than criminologists often believe. He challenges especially the notion that weapon availability is often the only difference between the two crimes. Alternatively, Block (1984) argues that neither homicides nor assaults truly follow seasonal patterns. Instead, according to her explanation, assaults are more likely to come to police attention during the summer, so creating a false seasonality that depends on recording practices. Dodge (1988) found that aggravated assault reports by National Crime Survey respondents did have seasonal structure, however, and this is inconsistent with the operation of a recording artifact. More generally, the link between assaults and homicides has implications for many questions in criminology, and it deserves closer investigation than the current paper can give it.

  9. The analysis used the centered monthly temperature variable to construct the interaction terms. The condition indices for the interaction models were all between 24 and 25. These are higher than for the other models, but still not large enough to suggest problematic collinearity levels.

  10. A complication in evaluating the directional changes is that the coefficients in Table 3 use a mean-centered temperature variable for both the main and interaction effects. Many cities therefore have negative average temperatures in the early months of the year, and directional changes can occur even when the main and interaction effects have the same signs.

  11. These models included city population as an exposure variable.

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Authors and Affiliations

  1. School of Criminal Justice, University at Albany, 135 Western Avenue, Albany, NY, 12222, USA

    David McDowall, Colin Loftin & Matthew Pate

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  1. David McDowall
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  2. Colin Loftin
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  3. Matthew Pate
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Correspondence to David McDowall.

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McDowall, D., Loftin, C. & Pate, M. Seasonal Cycles in Crime, and Their Variability. J Quant Criminol 28, 389–410 (2012). https://doi.org/10.1007/s10940-011-9145-7

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