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The Relationship Between Crime and Electronic Gaming Expenditure: Evidence from Victoria, Australia

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Abstract

Gambling in Australia is a significant economic activity. Expenditure on its many forms is sizeable and has undergone sustained periods of expansion. At the same time, the structure of the gambling industry has undergone substantial change, with the use of gaming facilities in local hotels and licensed clubs now representing one of the most predominant forms of gambling. Despite this, and the extensive international literature on the relationships between gambling and crime, there have been relatively few studies which examine the local area effects of gaming establishments on crime in Australia. This study uses a unique set of data from the Australian state of Victoria, a region in which local area expansion of gaming networks has been considerable since 1991, to investigate the relationship between gaming machine expenditure and various types of crime in 1996, 2001 and 2006. One particular focus is that of income-generating crime, defined here as theft, fraud, breaking and entering, forgery, false pretences, larceny and robbery. After controlling for a host of statistical issues, our results indicate a consistent positive and significant relationship between gaming and crime rates, especially income-generating crime rates, at the local level.

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Notes

  1. Only NSW and the Northern Territory have higher per capita spending on gambling.

  2. Of these, 27,461 (91%) were located in hotels and clubs in 2009.

  3. Data from the Victorian Department of Justice show that per capita spending on EGMs increased from $615 in 2000 to $649 in 2009. (http://www.vcgr.vic.gov.au/CA256F800017E8D4/Statistics/FD7EA8DF7FD68F8ECA257067001AB256?Open. Last Accessed 26/11/09).

  4. A report for Gambling Research Australia in 2005 set out a national definition of problem gambling as that which “is characterised by difficulties in limiting money and/or time spent on gambling which leads to adverse consequences for the gambler, others, or for the community” (SACES 2005; i). Severe problem gambling can be ‘pathological gambling’, which is ‘a disorder characterized by a continuous or periodic loss of control over gambling, a preoccupation with gambling and with obtaining money with which to gamble, irrational thinking, and a continuation of the behavior despite adverse consequences’ (Committee on the Social and Economic Impact of Pathological Gambling, National Research Council 1999; 18).

  5. This is a complex area of research. For example, Lynch and Cantor (1992) show that the determinants of household larceny and burglary differ. For the former, exposure seems to be important (i.e. it is a crime of opportunity), while for the latter other factors such as guardianship of the property at night may be more important determinants. This study captures some of the elements of this line of enquiry by including differing forms of crime and by considering how gaming machine availability may interact with local crime rates. Notwithstanding, data constraints do exist with regard to many variables that would serve as proxies for the opportunity to commit criminal acts.

  6. Such problems are discussed in Grinols and Mustard (2006). Moreover, these authors draw attention to incidences where funding for the research was derived from vested interests.

  7. There are two types of disorder, social and physical. Yang (2010) defined social disorder as including the following: disorderly conduct, noise, alcohol and public drinking, gambling, drug-related offenses (not including large scale drug trafficking), and prostitution. Physical disorder was defined as: illegal dumping, litter, graffiti, weeds, vacant buildings, inoperable cars on the street, junk storage, weeds, zoning violations, exterior abatement, substandard housing and minor property damage.

  8. If spatial dependence is not accounted for, the regressions can have unstable parameter estimates and yield unreliable significance tests.

  9. According to Anselin (2006) spatial dependence takes two major forms, spatial lag dependence and spatial error dependence. Spatial lag dependence is modelled by including a function of the dependent variable observed at other locations. With a row-standardized spatial weights matrix, this amounts to including the average of the crime rates in particular in neighboring locations (SLAs) as an additional variable in the regression specification. With respect to spatial error dependence, spatial autocorrelation does not enter as an additional variable in the model, but affects the covariance structure of the error term. In order to test whether there is spatial dependence in our dataset and whether a spatial lag or a spatial error model should be considered, diagnostic tests derived from the residuals of an OLS regression were carried out using the robust Lagrange multiplier test statistic. The test statistic is reported under both spatial lag and spatial error specifications and the proper alternative is most likely the one with the largest significant value. If neither test statistic is significant, it indicates spatial dependence is not present and spatial modelling is not necessary.

  10. Due to the presence of the Crown Casino, the SLA of Melbourne Southbank Docklands was removed from our databases for 2001 and 2006, and in 1996 Melbourne remainder SLA was excluded. This is because a large proportion of the gamblers in the Crown Casino are either tourists or do not live in the surrounding area, hence their gaming expenditure cannot be related to crime rates in the area.

  11. Lynch and Cantor (1992) note that the use of more disaggregated data to provide analysis at a level something akin to a neighborhood is preferable.

  12. Endogeneity problems were identified with the use of Sargan-Hansen statistics in STATA [under conditional homoskedasticity, this endogeneity test statistic is numerically equal to a Hausman test statistic: see Hayashi (2000; 233–234)].

  13. Weak instruments were identified using the Kleibergen-Paap rk Wald F statistic, with the Stock-Yogo test for critical values.

  14. As well as past total drug offenses, we also tested individual past and current offenses such as drug cultivation, manufacturing and trafficking, and drug possession and use, for the best fit for an instrument for total drug offenses. All individual categories of current drug offenses were endogenous with various forms of crime. In some cases, we had to use a lagged version of one individual drug offense (drug cultivation, manufacture, trafficking) as an instrument for current drug offenses.

  15. STATA Version 10 is unable to simultaneously correct for both problems.

  16. Note, the lagged version of drug offenses was used directly as a variable in the regression rather than as an instrument as was the case in the 2SLS models. Although this did solve the endogeneity problem in the spatial models, it introduced a new problem of potential missing variable bias given that the variable representing current drug offenses was not in the regression. In sensitivity testing it was found that when current drug offenses are used instead of lagged drug offenses, the coefficient of current drug offenses is larger than the coefficient of lagged drug offenses in the income-generating crime models (and the coefficient of the gaming variable is smaller—albeit still positive and significant).

  17. This was calculated for 2006 by taking the unstandardized coefficient of gaming in the income-generating crime regression multiplied by both the gaming mean ($383.86) and then by total adult Victorian population.

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Acknowledgments

We gratefully acknowledge the research funds provided for this project by the Department of Justice in Victoria. One of the conditions of this support was that no discussion of the policy implications of our findings would be made in research outputs. We also appreciate the statistical advice received from Dr Alec Zuo and the helpful comments by three anonymous reviewers and the editors at JQC that have significantly improved this paper.

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Authors and Affiliations

  1. Centre for Regulation and Market Analysis, School of Commerce, University of South Australia, Adelaide, SA, 5001, Australia

    Sarah A. Wheeler, David K. Round & John K. Wilson

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  1. Sarah A. Wheeler
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Correspondence to Sarah A. Wheeler.

Appendices

Appendix 1

See Table 3.

Table 3 Classification of 27 Victorian offenses into income- or non-income-generating
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Appendix 2

See Tables 4, 5, and 6.

Table 4 Income- and non-income-generating crime models by category in Victoria in 2006 per 1,000 adult population
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Table 5 Income- and non-income-generating crime models by category in Victoria in 2001 per 1,000 adult population
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Table 6 Income- and non-income-generating crime models by category in Victoria in 1996 per 1,000 adult population
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Wheeler, S.A., Round, D.K. & Wilson, J.K. The Relationship Between Crime and Electronic Gaming Expenditure: Evidence from Victoria, Australia. J Quant Criminol 27, 315–338 (2011). https://doi.org/10.1007/s10940-010-9123-5

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