Skip to main content

Advertisement

SpringerLink
Log in

Confounding Bias in the Relationship Between Problem Gambling and Crime

  • Original Paper
  • Published:
Journal of Gambling Studies Aims and scope Submit manuscript

Abstract

Although the relationship between problem gambling and criminal behavior has been widely researched, concerns over the causal nature of this association remain. Some argue that problem gambling does not lead to crime; instead, the same background characteristics that predict problem gambling also predict criminal behavior. Yet, studies suggestive of a spurious association often rely on small, non-random, and cross-sectional samples; thus, the extent to which the findings are generalizable to the broader population is unknown. With this in mind, the present study uses data from The National Longitudinal Study of Adolescent to Adult Health and a series of propensity score weighting and matching techniques to examine the role of confounding bias in the relationship between problem gambling and criminal behavior in young adulthood. On the surface, results show a positive and significant relationship between problem gambling and a range of criminal behaviors. However, after statistically balancing differences in several background measures between problem gamblers and non-problem gamblers, such as low self-control, past substance use, and juvenile delinquency, we find no significant relationship between problem gambling and crime. These patterns are consistent across several propensity score weighting and matching algorithms. Our results therefore parallel those in support of the “generality of deviance” framework, whereby a similar set of covariates known to be associated with criminal behavior also predict problem gambling.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. For more information about the Add Health data, see https://www.cpc.unc.edu/projects/addhealth.

  2. Coefficients (i.e., \(\bar{\beta }\)) from the imputed datasets are combined as follows: \(\bar{\beta } = \frac{{\sum_{i = 1}^{m} \beta_{i} }}{m}\), where \(\beta_{i}\) is the coefficient for the respective covariate \(i\) for each imputed dataset, and \(m\) is the number of imputed datasets. Standard errors for the combined estimates are based on the combination of within- and between-imputation variance: \(Variance_{within} = \frac{{\sum_{i = 1}^{m} SE_{i}^{2} }}{m}\) and \(Variance_{between} = \frac{{\sum_{i = 1}^{m} (\beta_{i} - \bar{\beta })^{2} }}{m - 1}\), where \(m\) is the number of imputed datasets, \(SE_{i}\) is the standard error of the respective covariate \(i\), \(\beta_{i}\) is the parameter estimate of the respective covariate for each imputed dataset, and \(\bar{\beta }\) is the average of the parameter estimates across all of the imputed datasets. The standard error for the combined estimate (i.e., \(\bar{\beta }\)) is as follows: \(SE_{{\bar{\beta }}} = \sqrt {Variance_{within} + Variance_{between} + \frac{{Variance_{between} }}{M}}\), where \(m\) is again the number of imputed datasets.

  3. Supplemental analyses (available upon request) examined alternative operationalization strategies, such as crime counts and frequency measures, and the results were substantively similar to those presented here.

  4. We restrict our analytic sample to those who fall within the region of common support (i.e., the range of propensity scores where problem gamblers and non-problem gamblers overlap). Following this restriction, there are 421 treated respondents and 11,260 controlled respondents.

References

  • Abbott, M. W., & McKenna, B. G. (2005). Gambling and problem gambling among recently sentenced women in New Zealand prisons. Journal of Gambling Studies, 21(4), 559–581.

    PubMed  Google Scholar 

  • Abbott, M. W., McKenna, B. G., & Giles, L. C. (2005). Gambling and problem gambling among recently sentenced male prisoners in four New Zealand prisons. Journal of Gambling Studies, 21(4), 537–558.

    PubMed  Google Scholar 

  • Adolphe, A., Khatib, L., Van Golde, C., Gainsbury, S. M., & Blaszcynski, A. (2019). Crime and gambling disorders: A systematic review. Journal of Gambling Studies, 35(2), 395–414.

    CAS  PubMed  Google Scholar 

  • Agnew, R. (1992). Foundation for a general strain theory of crime and delinquency. Criminology, 30(1), 47–87.

    Google Scholar 

  • Agnew, R. (2001). Building on the foundation of general strain theory: Specifying the types of strain most likely to lead to crime and delinquency. Journal of Research in Crime and Delinquency, 38(4), 319–361.

    Google Scholar 

  • Akers, R. L. (1973). Deviant behavior: A social learning approach. Belmont, CA: Wadsworth.

    Google Scholar 

  • Austin, P. C. (2009). Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research. Communications in Statistics-Simulation and Computation, 28(6), 1228–1234.

    Google Scholar 

  • Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46(3), 399–424.

    PubMed  PubMed Central  Google Scholar 

  • Baier, C. J., & Wright, B. R. (2001). “If you love me, keep my commandments”: A meta-analysis of the effect of religion on crime. Journal of Research in Crime and Delinquency, 38(1), 3–21.

    Google Scholar 

  • Banks, J. (2017). Gambling, crime and society. London: Palgrave Macmillan.

    Google Scholar 

  • Barnes, G. M., Welte, J. W., Hoffman, J. H., & Dintcheff, B. A. (2005). Shared predictors of youthful gambling, substance use, and delinquency. Psychology of Addictive Behaviors, 19(2), 165–174.

    PubMed  Google Scholar 

  • Barnes, G. M., Welte, J. W., Tidwell, M. C. O., & Hoffman, J. H. (2015). Gambling and substance use: Co-occurrence among adults in a recent general population study in the United States. International Gambling Studies, 15(1), 55–71.

    PubMed  PubMed Central  Google Scholar 

  • Beaver, K. M., Ratchford, M., & Ferguson, C. (2009). Evidence of genetic and environmental effects on the development of low self-control. Criminal Justice and Behavior, 36(11), 1158–1172.

    Google Scholar 

  • Bergen, A. E., Newby-Clark, I. R., & Brown, A. (2012). Low trait self-control in problem gamblers: Evidence from self-report and behavioral measures. Journal of Gambling Studies, 28(4), 637–648.

    PubMed  Google Scholar 

  • Bernburg, J. G., & Krohn, M. D. (2003). Labeling, life chances, and adult crime: The direct and indirect effects of official intervention in adolescence on crime in early adulthood. Criminology, 41(4), 1287–1318.

    Google Scholar 

  • Blaszczynski, A. P., & McConaghy, N. (1994). Antisocial personality disorder and pathological gambling. Journal of Gambling Studies, 10(2), 129–145.

    CAS  PubMed  Google Scholar 

  • Brownstein, N., Kalsbeek, W. D., Tabor, J., Entzel, P., Daza E., & Harris, K. M. (2010). Non-response in wave IV of the national longitudinal study of adolescent health. Retrieved February 28, 2020, from https://www.cpc.unc.edu/projects/addhealth/documentation/guides/W4_nonresponse.pdf.

  • Campbell, C., & Marshall, D. (2007). Gambling and crime. In G. Smith, D. Hodgins, & R. Williams (Eds.), Research and measurement issues in gambling studies (pp. 541–564). New York: Academic Press.

    Google Scholar 

  • Canale, N., Scacchi, L., & Griffiths, M. D. (2016). Adolescent gambling and impulsivity: Does employment during high school moderate the association? Addictive Behaviors, 60, 37–41.

    PubMed  Google Scholar 

  • Clark, C., Nower, L., & Walker, D. (2013). The relationship of ADHD symptoms to gambling behavior in the USA: Results from the national longitudinal study of adolescent health. International Gambling Studies, 13(1), 37–51.

    Google Scholar 

  • Clark, C., & Walker, D. M. (2009). Are gamblers more likely to commit crime? An empirical analysis of a nationally representative survey of U.S. young adults. International Gambling Studies, 9(2), 119–134.

    Google Scholar 

  • D’Amico, E. J., Edelen, M. O., Miles, J. N., & Morral, A. R. (2008). The longitudinal association between substance use and delinquency among high-risk youth. Drug and Alcohol Dependence, 93(1–2), 85–92.

    PubMed  Google Scholar 

  • DeLisi, M., & Vaughn, M. G. (2016). Correlates of crime. In A. R. Piquero (Ed.), The handbook of criminological theory (pp. 18–36). Malden, MA: Wiley.

    Google Scholar 

  • Dowling, N. A., Merkouris, S. S., Greenwood, C. J., Oldenhof, E., Toumbourou, J. W., & Youssef, G. J. (2017). Early risk and protective factors for problem gambling: A systematic review and meta-analysis of longitudinal studies. Clinical Psychology Review, 51, 109–124.

    CAS  PubMed  Google Scholar 

  • Eitle, D., & Taylor, J. (2010). General strain theory, BIS/BAS levels, and gambling behavior. Deviant Behavior, 32(1), 1–37.

    Google Scholar 

  • Feigelman, W., Gorman, B. S., & Lesieur, H. (2006). Examining the relationship between at-risk gambling and suicidality in a national representative sample of young adults. Suicide and Life-Threatening Behavior, 36(4), 396–408.

    PubMed  Google Scholar 

  • Folino, J. O., & Abait, P. E. (2009). Pathological gambling and criminality. Current Opinion in Psychiatry, 22(5), 477–481.

    PubMed  Google Scholar 

  • Ford, C. A., Bearman, P. S., & Moody, J. (1999). Foregone health care among adolescents. The Journal of the American Medical Association, 282(23), 2227–2234.

    CAS  PubMed  Google Scholar 

  • Gainsbury, S. M., Liu, Y., Russell, A., & Teichert, T. (2016). Is all internet gambling equally problematic? Considering the relationship between mode of access and gambling problems. Computers in Human Behavior, 55, 717–728.

    Google Scholar 

  • Gottfredson, M. R. (2006). The empirical status of control theory in criminology. In F. T. Cullen, J. P. Wright, & K. R. Blevins (Eds.), Taking stock: The status of criminological theory (pp. 77–100). New Brunswick, NJ: Transaction.

    Google Scholar 

  • Gottfredson, M. R., & Hirschi, T. (1990). A general theory of crime. Palo Alto, CA: Stanford University Press.

    Google Scholar 

  • Greco, R., & Curci, A. (2017). Does the general strain theory explain gambling and substance use? Journal of Gambling Studies, 33(3), 919–936.

    PubMed  Google Scholar 

  • Grinols, E. L. (2017). Problem gambling, mental health, alcohol and drug abuse: Effects on crime. In E. U. Savona, M. A. R. Kleiman, & F. Calderoni (Eds.), Dual Markets (pp. 321–330). Cham: Springer.

    Google Scholar 

  • Guo, S., & Fraser, M. W. (2015). Propensity score analysis: Statistical methods and applications (2nd ed.). Thousand Oaks, CA: Sage Publications Inc.

    Google Scholar 

  • Haynie, D. L., & Osgood, D. W. (2005). Reconsidering peers and delinquency: How do peers matter? Social Forces, 84(2), 1109–1130.

    Google Scholar 

  • Hirschi, T., & Gottfredson, M. R. (1994). The generality of deviance. In T. Hirschi & M. R. Gottfredson (Eds.), The generality of deviance (pp. 1–22). New Brunswick, NJ: Transaction Publishers.

    Google Scholar 

  • Hoeve, M., Stams, G. J. J., Van der Put, C. E., Dubas, J. S., Van der Laan, P. H., & Gerris, J. R. (2012). A meta-analysis of attachment to parents and delinquency. Journal of Abnormal Child Psychology, 40(5), 771–785.

    PubMed  PubMed Central  Google Scholar 

  • Hoffmann, J. P. (2000). Religion and problem gambling in the U.S. Review of Religious Research, 41(4), 488–509.

    Google Scholar 

  • Johnson, M. K., Crosnoe, R., & Thaden, L. L. (2006). Gendered patterns in adolescents’ school attachment. Social Psychology Quarterly, 69(3), 284–295.

    Google Scholar 

  • Jones, S., & Quisenberry, N. (2004). The general theory of crime: How general is it? Deviant Behavior, 25(5), 401–426.

    Google Scholar 

  • Jun, H. J., Sacco, P., & Cunningham-Williams, R. M. (2019). Gambling in emerging adulthood: The role of adolescent depressive symptoms, antisocial behaviors, and alcohol use. International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-019-00087-0.

    Article  Google Scholar 

  • Katsiyannis, A., Ryan, J. B., Zhang, D., & Spann, A. (2008). Juvenile delinquency and recidivism: The impact of academic achievement. Reading and Writing Quarterly, 24(2), 177–196.

    Google Scholar 

  • Ladouceur, R., Boudreault, N., Jacques, C., & Vitaro, F. (1999). Pathological gambling and related problems among adolescents. Journal of Child and Adolescent Substance Abuse, 8(4), 55–68.

    Google Scholar 

  • Lanehart, R. E., Rodriguez de Gil, P., Kim, E. S., Bellara, A. P., Kromrey, J. D., & Lee, R. S. (2012). Propensity score analysis and assessment of propensity score approaches using SAS procedures. Retrieved August 2, 2019, from https://pdfs.semanticscholar.org/c10a/cf956fd8fd91debda40aa16c18f707303ec7.pdf.

  • Laursen, B., Plaubork, R., Ekholn, O., Larsen, C. V. L., & Juel, K. (2016). Problem gambling associated with violent and criminal behavior: A Danish population-based survey and register study. Journal of Gambling Studies, 32(1), 25–34.

    PubMed  Google Scholar 

  • Leuven, E., & Sianesi, B. (2003). PSMATCH2: Stata module to perform full mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. Boston: Statistical Software Components and Boston College Department of Economics.

    Google Scholar 

  • Little, R. J. A. (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association, 83(404), 1198–1202.

    Google Scholar 

  • Magoon, M. E., & Ingersoll, G. M. (2006). Parental modeling, attachment, and supervision as moderators of adolescent gambling. Journal of Gambling Studies, 22(1), 1–22.

    PubMed  Google Scholar 

  • Martins, S. S., Lee, G. P., Santaella, J., Liu, W., Ialongo, N. S., & Storr, C. L. (2014). Age of first arrest varies by gambling status in a cohort of young adults. The American Journal on Addictions, 23(4), 386–392.

    PubMed  PubMed Central  Google Scholar 

  • McComb, J. L., & Sabiston, C. M. (2010). Family influences on adolescent gambling behavior: A review of the literature. Journal of Gambling Studies, 26(4), 503–520.

    PubMed  Google Scholar 

  • Miller, T., & Vuolo, M. (2018). Examining the antiascetic hypothesis through social control theory: Delinquency, religion, and reciprocation across the early life course. Crime & Delinquency, 64(11), 1458–1488.

    Google Scholar 

  • Mishra, S., Lalumière, M. L., Morgan, M., & Williams, R. J. (2011). An examination of the relationship between gambling and antisocial behavior. Journal of Gambling Studies, 27(3), 409–426.

    PubMed  Google Scholar 

  • Mishra, S., Lalumière, M. L., & Williams, R. J. (2017). Gambling, risk-taking, and antisocial behavior: A replication study supporting the generality of deviance. Journal of Gambling Studies, 33(1), 15–36.

    PubMed  Google Scholar 

  • Moffitt, T. E. (1993). Adolescent-limited and life-course-persistent antisocial behavior: A developmental taxonomy. Psychological Review, 100(4), 674–701.

    CAS  PubMed  Google Scholar 

  • Na, C., & Paternoster, R. (2012). Can self-control change substantially over time? Rethinking the relationship between self- and social control. Criminology, 50(2), 427–462.

    Google Scholar 

  • National Opinion Research Center. (1999). Gambling impact and behavior study: Report to the National Gambling Impact Study Commission. Chicago, IL: NORC at the University of Chicago. Retrieved August 18, 2019, from http://www.norc.org/pdfs/publications/gibsfinalreportapril1999.pdf.

  • Nofziger, S., & Rosen, N. L. (2016). Building self-control to prevent crime. In B. Teasdal & M. S. Bradley (Eds.), Preventing crime and violence (pp. 43–56). Cham: Springer.

    Google Scholar 

  • Osgood, D. W., Johnston, L. D., O’Malley, P. M., & Bachman, J. G. (1988). The generality of deviance in late adolescence and early adulthood. American Sociological Review, 51(1), 81–93.

    Google Scholar 

  • Pastwa-Wojciechowska, B. (2011). The relationship of pathological gambling to criminality behavior in a sample of Polish male offenders. Medicine Science Monitor, 17(11), 669–675.

    Google Scholar 

  • Perrone, S., Jansons, D., & Morrison, L. (2013). Problem gambling and the criminal justice system. Melbourne: Victorian Responsible Gambling Foundation.

    Google Scholar 

  • Rubin, D. (1987). Multiple imputation for non-response in surveys. New York, NY: John Wiley & Sons Inc.

    Google Scholar 

  • Rudd, C., & Thomas, S. D. M. (2015). The prevalence, mental health, and criminal characteristics of potential problem gamblers in a substance using treatment seeking population. International Journal of Mental Health and Addiction, 14(5), 700–714.

    Google Scholar 

  • Sakurai, Y., & Smith, R. G. (2003). Gambling as a motivation for the commission of financial crime: Report no. 256. Canberra: Australian Institute of Criminology.

    Google Scholar 

  • Stinchfield, R. (2004). Demographic, psychosocial, and behavioral factors associated with youth gambling and problem gambling. In J. L. Derevensky & R. Gupta (Eds.), Gambling Problems in Youth: Theoretical and Applied Perspectives (pp. 27–39). New York, NY: Kluwer.

    Google Scholar 

  • Sumter, M., Wood, F., Whitaker, I., & Berger-Hill, D. (2018). Religion and crime studies: Assessing what has been learned. Religions, 9(6), 193.

    Google Scholar 

  • Turner, N. E., McAvoy, S., Ferentzy, P., Matheson, F. I., Myers, C., Jindani, F., et al. (2017). Addressing the issue of problem gambling in the criminal justice system: A series of case studies. Journal of Gambling Issues, 35, 74–100.

    Google Scholar 

  • Turner, N. E., Preston, D. L., Saunders, C., McAvoy, S., & Jain, E. (2009). The relationship between problem gambling to criminal behavior in a sample of Canadian male federal offenders. Journal of Gambling Studies, 25(2), 153–169.

    PubMed  Google Scholar 

  • Uecker, J. E., & Stokes, C. E. (2016). Religious background and gambling among young adults in the United States. Journal of Gambling Studies, 32(1), 341–361.

    PubMed  Google Scholar 

  • Uggen, C., & Wakefield, S. (2008). What have we learned from longitudinal studies of work and crime? In A. M. Liberman (Ed.), The long view of crime: A synthesis of longitudinal research (pp. 191–219). New York, NY: Springer.

    Google Scholar 

  • Vitaro, F., Brendgen, M., Ladouceur, R., & Tremblay, R. E. (2001). Gambling, delinquency, and drug use during adolescence: Mutual influences and common risk factors. Journal of Gambling Studies, 17(3), 171–190.

    CAS  PubMed  Google Scholar 

  • Walker, D. M., Clark, C., & Folk, J. L. (2010). The relationship between gambling behavior and binge drinking, hard drug use, and paying for sex. UNLV Gaming Research and Review Journal, 14(1), 15–26.

    Google Scholar 

  • Welte, J. W., Barnes, G. M., Tidwell, M. C. O., & Hoffman, J. H. (2011). Gambling and problem gambling across the lifespan. Journal of Gambling Studies, 27(1), 49–61.

    PubMed  PubMed Central  Google Scholar 

  • Welte, J. W., Barnes, G., Tidwell, M. O., Hoffman, J. H., & Wieczorek, W. F. (2015). Gambling and problem gambling in the United States: Changed between 1999 and 2013. Journal of Gambling Studies, 31(3), 695–715.

    PubMed  PubMed Central  Google Scholar 

  • Welte, J. W., Barnes, G. M., Tidwell, M. C. O., & Wieczorek, W. F. (2017). Predictors of problem gambling in the U.S. Journal of Gambling Studies, 33(2), 327–342.

    PubMed  Google Scholar 

  • Winters, K. C., Stinchfield, R., & Fulkerson, J. (1993). Patterns and characteristics of adolescent gambling. Journal of Gambling Studies, 9(4), 371–386.

    Google Scholar 

  • Yokotani, K., Tamura, K., Kaneko, Y., & Kamimura, E. (2019). Craving for gambling predicts income-generating offenses: A pathways model of a Japanese prison population. Journal of Gambling Studies. https://doi.org/10.1007/s10899-019-09887-4.

    Article  Google Scholar 

  • Yuan, Y. (2014). Sensitivity analysis in multiple imputation for missing data. Retrieved February 28, 2020, from http://support.sas.com/resources/papers/proceedings14/SAS270-2014.pdf.

  • Zhai, Z. W., Duenas, G. L., Wampler, J., & Potenza, M. N. (2020). Gambling, substance use and violence in male and female adolescents. Journal of Gambling Studies. https://doi.org/10.1007/s10899-020-09931-8.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University at Buffalo, SUNY, Buffalo, USA

    Christopher R. Dennison

  2. State University of New York at Fredonia, Fredonia, USA

    Jessica G. Finkeldey

  3. Ball State University, Muncie, USA

    Gregory C. Rocheleau

Authors
  1. Christopher R. Dennison
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jessica G. Finkeldey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gregory C. Rocheleau
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christopher R. Dennison.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by Grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website. No direct support was received from Grant P01-HD31921 for this analysis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dennison, C.R., Finkeldey, J.G. & Rocheleau, G.C. Confounding Bias in the Relationship Between Problem Gambling and Crime. J Gambl Stud 37, 427–444 (2021). https://doi.org/10.1007/s10899-020-09939-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10899-020-09939-0

Keywords

Search

Navigation