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Criminals and the Price System: Evidence from Czech Metal Thieves

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Abstract

Objectives

This paper tests the economic theory of criminal behavior. Specifically, it looks at “the carrot” side of the theory, studying how thieves react to changes in monetary gains from crime.

Methods

Using a unique crime-level dataset on metal theft in the Czech Republic, we study thieves’ behavior in a simple regression framework. We argue that variation in metal prices represents a quasi-experimental variation in gains from crime. It is because (1) people steal copper and other nonferrous metals only to sell them to scrapyard and (2) prices at scrapyards are set by the world market. This facilitates causal interpretation of our regression estimates.

Results

We find that a 1% increase (decrease) in the re-sale price causes metal thefts to increase (decrease) by 1–1.5%. We show that the relationship between prices and thefts is very robust. Moreover, we find that thieves’ responses to price shocks are rapid and consistent.

Conclusion

Our results are in line with the economic model of crime, wherein criminal behavior is modeled as a rational agent’s decision driven by the costs and benefits of undertaking criminal activities. Our estimates are also consistent with recent results from the United Kingdom, suggesting these patterns are more general.

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Notes

  1. See “A Guide to the LME,” London Metal Exchange, PDF file, 2014, at http://www.lme.com/~/media/Files/Brochures/A Guide to the LME, last accessed on June 28, 2016. See also Watkins and McAleer (2004).

  2. See also Aruga and Managi (2011), Draca et al. (2015), and Labys et al. (1971).

  3. For more extensive and insightful discussion of the price-theft hypothesis in the realm of non-ferrous metal markets see also Sidebottom et al. (2011).

  4. This development is consistent with general decline in property crime in Western Europe (Aebi and Linde 2010, 2012; Tonry 2014).

  5. See “Thieves Steal Local Bridge,” CBS Pittsburgh, Online, October 7, 2011, at http://pittsburgh.cbslocal.com/2011/10/07/thieves-steal- bridge-in-lawrence-county (last accessed on June 28, 2016); “Czech metal thieves dismantle 10-ton bridge,” The Telegraph, Online, April 30, 2012, at http://www.telegraph.co.uk/news/newstopics/howaboutthat/9235705/Czech-metal-thieves-dismantle-10-ton-bridge.html (last accessed on June 28, 2016); and “Thieves Steal Entire Bridge in Western Turkey,” Time, Online, March 21, 2013, at http://newsfeed.time.com/2013/03/21/thieves-steal-entire-bridge-in-western-turkey (last accessed on June 28, 2016).

  6. See “Copper Thefts Threaten U.S. Critical Infrastructure,” Federal Bureau of Investigation, Criminal Intelligence Section, Online, September 15, 2008, at http://www.fbi.gov/stats-services/publications/copper-thefts (last accessed on June 28, 2016) and resources therein. For policy papers on costs of metal thefts, further background, and potential measures see Bennett (2008, 2012b, 2012a); Kooi (2010); and Lipscombe and Bennett (2012).

  7. “Evidenčně? statistický systém kriminality” in Czech.

  8. The reason why the other yards could not provide us with the data, which their personnel gave most often, was simply that the prices change very frequently and they do not keep records. However, the personnel often stated that their prices are determined by the market.

  9. The amendment was published on December 22, 2008 as Directive no. 478/2008.

  10. Although metal theft has been a continuous public concern and proposals for an intervention were discussed frequently in media and politics, it was only in March 2015 that scrapyards were prohibited from buying scrap metals and other items for cash. A referee noted that scrapyards became obliged to identify sellers and keep a record of individual transactions. However this obligation was already present the Directive 383/2001, which is the general directive that regulates the operation of establishments dealing with waste, that was effective from January 1, 2002 (a year before our the first year of our data). In particular, scrapyards were required to keep a record of the kind and amount of the items bought as well as the name, address, and the serial number of the Identity Document of the seller. This obligation applied to a list of items that contained all nonferrous metals as well as their mixtures and cables.

  11. See Murray (1994) for an excellent non-technical introduction into nonstationary processes and cointegration.

  12. See Davidson and MacKinnon (2003, ch. 14.6) for an overview and discussion of testing for cointegration.

  13. The two series are plotted in Fig. 5 in the Appendix. We note that it is not clear whether property crime should be controlled for or not. It is possible that offenders may substitute metal thefts and other property, depending on their relative valuation. In that case, property crimes would be affected by copper prices, and regressions controlling for property crimes would underestimate the effect of prices on metal thefts. Note, however, that the average number of property crimes per month in our data is 18,400 while the average number of metal thefts is 280, so the bulk of variation in property crime will probably be unrelated to substitution from metal thefts (see Table 3). More importantly, one might argue that not including property crimes would lead to overestimating the effect of prices on thefts, as new metal thefts may represent substitutes for other opportunities and not new crimes. We lean towards the latter approach and prefer the regressions controlling for property crimes and bicycle theft in order to net out these potential substitution effects and control for general crime trends. We further delve the issue of substitution in Sect. 4.4.

  14. See, e.g., Ayres and Donohue (2003); Cameron (1987); Detotto and Pulina (2013); Koskela and Viren (1997); Levitt (1998); and Lott and Mustard (1997).

  15. The choice of leads and lags follows Stock and Watson (1993) who, in their Monte Carlo simulations, used two leads and lags for samples of size 100; our sample size is 120. Using different a number of leads and lags yields qualitatively similar results (see Sect. 4.5).

  16. Note that \(\hat{\epsilon _t}\) is an estimate of \(\epsilon _t\) containing measurement error. Therefore, our estimates of \(\gamma _1\) will be biased towards zero. We are grateful to Giovanni Mastrobuoni for pointing this out to us.

  17. Note that using the 10% level, the Augmented Dickey–Fuller tests fail to reject nonstationarity of residuals from specifications (1), (7) and (8), so these results should be interpreted with caution.

  18. See “2012 trading volumes,” Online, at https://www.lme.com/metals/reports/monthly-volumes/annual/2012/ (last accessed on July 7, 2016).

  19. We are grateful to Nikolas Mittag for raising some of these points.

  20. For discussion of market forces and dynamic aspects of illicit trade see also d’Este (2014). Looking at the effects of pawnshop availability on property crime in the US, he finds an elasticity of property thefts to pawnshops of between 0.8 and 1.5.

  21. In a review of 102 studies, Guerette and Bowers (2009) find no systematic evidence of displacement of criminal activity following an intervention. About one fourth of studies find some displacement, yet it is never complete. However, about the same number of studies found diffusion of benefits.

  22. We are grateful to David de Meza for pointing this out to us and suggesting a more direct test we report below. See also the discussion in footnote 13.

  23. See “In Russia, Stealing Is a Normal Part of Life,” Los Angeles Times, Online, September 21, 1998, at http://articles.latimes.com/1998/sep/21/news/mn-25012 (last accessed on June 28, 2016).

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Acknowledgements

We wish to thank the following for their helpful comments: two anonymous reviewers, Richard Boylan, Jan Broulík, Pawel Bukowski, Pavel čížek, Brendan Dooley, Jakub Drápal, Libor Dušek, Jitka Dušková, Oren Gazal-Ayal, Martin Guzi, Petr Koráb, Peter Huber, Marek Litzman, Guido Maretto, Giovanni Mastrobuoni, David de Meza, Nikolas Mittag, Marie Obidzinski, Daniel Pi, Amos Witztum, and participants at the 2016 Conference on Empirical Legal Studies in Europe at the University of Amsterdam, the 2015 conference of the Society for Institutional and Organizational Economics at Harvard University, the 2015 conference of the European Association of Law and Economics at the University of Vienna, the 2015 Law and Economics Workshop at Erasmus University Rotterdam, the 2015 Young Economists’ Meeting at Masaryk University in Brno, and the 2014 Annual Meeting of the German Law and Economics Association at Ghent University. We are grateful to Arnošt Danihel, Vladimír Stolín, and Bohuslav Zúbek from the Police Presidium of the Czech Republic for providing us with crime-level data on metal thefts. Parts of this paper were written between April and July 2014 while Josef Montag was a visiting researcher at the Tilburg Law and Economics Centre at Tilburg University; he gratefully acknowledges the centre’s hospitality, support, and valuable discussions with TILEC’s faculty and researchers. We also thank Annie Barton for careful editing. All remaining errors are our own responsibility.

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Authors and Affiliations

  1. University of J. E. Purkynĕ, Pasteurova 1, 40096, Ústí nad Labem, Czech Republic

    Tomáš Brabenec

  2. International School of Economics, Kazakh-British Technical University, 59 Tole Bi Street, Almaty, Kazakhstan, 050000

    Josef Montag

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  1. Tomáš Brabenec
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Correspondence to Josef Montag.

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Josef Montag: The data and code producing results reported in this paper are available at http://sites.google.com/site/josefmontag or upon request.

Appendix

Appendix

See Fig. 5.

Fig. 5
figure 5

Property crime and bicycle theft (that qualify as crime) reported to the Czech Police (z-scores of quarterly averages). Data are deseasoned, mean centered, and divided by respective standard deviations

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Brabenec, T., Montag, J. Criminals and the Price System: Evidence from Czech Metal Thieves. J Quant Criminol 34, 397–430 (2018). https://doi.org/10.1007/s10940-017-9339-8

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