Cycles in Crime and Economy: Leading, Lagging and Coincident Behaviors

Abstract

In the last decades, the interest in the relationship between crime and business cycle has widely increased. It is a diffused opinion that a causal relationship goes from economic variables to criminal activities, but this causal effect is observed only for some typology of crimes, such as property crimes. In this work we examine the possibility of the existence of some common factors (interpreted as cyclical components) driving the dynamics of Gross Domestic Product and a large set of criminal types by using the nonparametric version of the dynamic factor model. A first aim of this exercise is to detect some comovements between the business cycle and the cyclical component of some typologies of crime, which could evidence some relationships between these variables; a second purpose is to select which crime types are related to the business cycle and if they are leading, coincident or lagging. Italy is the case study for the time span 1991:1–2004:12; the crime typologies are constituted by the 22 official categories classified by the Italian National Statistical Institute. The study finds that most of the crime types show a counter-cyclical behavior with respect to the overall economic performance, and only a few of them have an evident relationship with the business cycle. Furthermore, some crime offenses, such as bankruptcy, embezzlement and fraudulent insolvency, seem to anticipate the business cycle, in line with recent global events.

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Notes

  1. 1.

    A static version of DFM was proposed by Stock and Watson (2002). For the parametric DFM see Sargent and Sims (1977) and Stock and Watson (1993).

  2. 2.

    It is important to note that in Italy, unlike the United States, such crime does not include intentional damages, like vandalism.

  3. 3.

    This form of illegal activity is generally associated with false accounting by managers in order to divert resources for personal use and gain.

  4. 4.

    SAF offences are connected with the sales of adulterated foodstuffs. They include any undesirable adulteration in foodstuffs or reduction or extraction of any natural quality or utility from foodstuffs in order to maintain health and convenience of the general public.

  5. 5.

    We use (2m + 1) values for ω, with \(\omega_k=1-\frac{|k|}{m+1}\), with \(k=-m, \ldots,m\) and \(m=\hbox{round}(\frac{\sqrt{T}}{4})\), as suggested in Forni et al. (2000).

  6. 6.

    To save space we do not show these results, that are available on request.

  7. 7.

    Correlations from lag (lead) 6 to 12 are less than 0.01, so, to save space, we do not report them.

  8. 8.

    We are in debt to an anonymous referee for this interpretation.

  9. 9.

    Such choice of a threshold value for the common component correlations and for the variance ratio is quite subjective. The values used here are proposed by Fiorentini and Planas (2003).

  10. 10.

    The analysis has been performed using different threshold values of the explained variance; remarkably, the results do not change up to a threshold value of 65%, showing a good level of robustness.

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Acknowledgements

We would like to thank the three anonymous referees for their detailed reading of the paper and many comments which led to a clearer focus of the paper. We also thank Christophe Planas, Alessio Scano and Marco Vannini for their useful suggestions. Financial support from Italian MIUR under Grants 20087Z4BMK_002 and 2006137221_001 are gratefully acknowledged

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Correspondence to Edoardo Otranto.

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Detotto, C., Otranto, E. Cycles in Crime and Economy: Leading, Lagging and Coincident Behaviors. J Quant Criminol 28, 295–317 (2012). https://doi.org/10.1007/s10940-011-9139-5

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Keywords

  • Business cycle
  • Crime
  • Common factors
  • Dynamic factor models