The Risk of Gambling Problems in the General Population: A Reconsideration

  • Glenn W. Harrison
  • Morten I. Lau
  • Don RossEmail author
Original Paper


We examine the manner in which the population prevalence of disordered gambling has usually been estimated, on the basis of surveys that suffer from a potential sample selection bias. General population surveys screen respondents using seemingly innocuous “trigger,” “gateway” or “diagnostic stem” questions, applied before they ask the actual questions about gambling behavior and attitudes. Modeling the latent sample selection behavior generated by these trigger questions using up-to-date econometrics for sample selection bias correction leads to dramatically different inferences about population prevalence and comorbidities with other psychiatric disorders. The population prevalence of problem or pathological gambling in the United States is inferred to be 7.7%, rather than 1.3% when this behavioral response is ignored. Comorbidities are inferred to be much smaller than the received wisdom, particularly when considering the marginal association with other mental health problems rather than the total association. The issues identified here apply, in principle, to every psychiatric disorder covered by standard mental health surveys, and not just gambling disorder. We discuss ways in which these behavioral biases can be mitigated in future surveys.


Gambling disorder Prevalence studies Sample selection bias Bias correction Econometrics Diagnostic stem questions Comorbidities 



We are grateful to the U.S. National Institute on Alcohol Abuse and Alcoholism, the British Gambling Commission, the Victorian Responsible Gambling Foundation, the Victorian Department of Justice and Regulation and Statistics Canada for providing access to survey data, and to the Danish Social Science Research Council (Project #12-130950) for financial support.

Compliance with Ethical Standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical Standards

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Supplementary material

10899_2019_9897_MOESM1_ESM.docx (1.4 mb)
Supplementary material 1 (DOCX 1466 kb)


  1. Abbott, M. W., & Volberg, R. A. (2000). Taking the pulse on gambling and problem gambling in New Zealand: A report on phase one of the 1999 national prevalence survey. New Zealand: Department of Internal Affairs, Government of New Zealand.Google Scholar
  2. Algeria, A. A., Petry, N. M., Hasin, D. S., Liu, S.-M., Grant, B. F., & Blanco, C. (2009). Disordered gambling among racial and ethnic groups in the US: Results from the national epidemiologic survey on alcohol and related conditions. CNS Spectrums, 14, 132–142.CrossRefGoogle Scholar
  3. American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders III revision (DSM-III-R). Washington, DC: APA Press.Google Scholar
  4. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders IV (DSM-IV). Washington, DC: APA Press.Google Scholar
  5. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders 5 (DSM 5). Washington, DC: APA Press.CrossRefGoogle Scholar
  6. Australian Productivity Commission. (1999). Australia’s gambling industries: Inquiry report. Canberra: Australian Government Productivity Commission.Google Scholar
  7. Bärnighausen, T., Bor, J., Wandira-Kazibwe, S., & Canning, D. (2011a). Correcting HIV prevalence estimates for survey nonparticipation using Heckman-type selection models. Epidemiology, 22(1), 27–35.PubMedCrossRefPubMedCentralGoogle Scholar
  8. Bärnighausen, T., Bor, J., Wandira-Kazibwe, S., & Canning, D. (2011b). Interviewer identity as exclusion restriction in epidemiology. Epidemiology, 22(3), 446.PubMedPubMedCentralCrossRefGoogle Scholar
  9. Billi, R., Stone, C. A., Abbott, M., & Yeung, K. (2015). The Victorian Gambling Study (VGS): A longitudinal study of gambling and health in Victoria, 2008–2012: Design and methods. International Journal of Mental Health and Addiction, 13, 274–296.CrossRefGoogle Scholar
  10. Billi, R., Stone, C. A., Marden, P., & Yeung, K. (2014). The Victorian gambling study: A longitudinal study of gambling and health in Victoria, 2008–2012. North Melbourne: Victorian Responsible Gambling Foundation.Google Scholar
  11. Blaire, G., Imai, K., & Zhou, Y.-Y. (2015). Design and Analysis of the randomized response technique. Journal of the American Statistical Association, 110(511), 1304–1319.CrossRefGoogle Scholar
  12. Blanco, C., Hasin, D. S., Petry, N., Stinson, F. S., & Grant, B. F. (2006). Sex differences in subclinical and DSM-IV pathological gambling: Results from the national epidemiologic survey on alcohol and related conditions. Psychological Medicine, 36, 943–953.PubMedCrossRefPubMedCentralGoogle Scholar
  13. Blaszczynski, A., Dumlao, V., & Lange, M. (1977). ‘How much do you spend on gambling?’ Ambiguities in survey questionnaire form. Journal of Gambling Studies, 13(3), 237–252.CrossRefGoogle Scholar
  14. Caetano, R. (2001). Non-response in alcohol and drug surveys: A research topic in need of further attention. Addiction, 96, 1541–1545.PubMedCrossRefPubMedCentralGoogle Scholar
  15. Chaix, B., Bilaudeau, N., Thomas, F., Havard, S., Evans, D., Kestens, Y., et al. (2011). Neighborhood effects on health: Correcting bias from neighborhood effects on participation. Epidemiology, 22(1), 18–26.PubMedCrossRefPubMedCentralGoogle Scholar
  16. Currie, S. R., Miller, N., Hodgins, D. C., & Wang, J. L. (2009). Defining a threshold of harm from gambling for population health surveillance research. International Gambling Studies, 9(1), 19–38.CrossRefGoogle Scholar
  17. De Luca, G. (2008). SNP and SML estimation of univeriate and bivariate binary-choice models. Stata Journal, 8(2), 190–220.CrossRefGoogle Scholar
  18. De Luca, G., & Perotti, V. (2011). Estimation of ordered response models with sample selection. Stata Journal, 11(2), 213–239.CrossRefGoogle Scholar
  19. Dickerson, M. G., Baron, E., Hong, S.-M., & Cottrell, D. (1996). Estimating the extent and degree of Gambling related problems in the Australian population: A national survey. Journal of Gambling Studies, 12, 161–178.PubMedCrossRefPubMedCentralGoogle Scholar
  20. DiNardo, J., McCrary, J., & Sanbonmatsu, L. (2006). Constructive proposals for dealing with attrition: An empirical example. NBER working paper.Google Scholar
  21. Ferris, J., & Wynne, H. (2001). The Canadian Problem Gambling Index final report. Ottawa: Canadian Center on Substance Abuse.
  22. Gallant, A. Ronald, & Nychka, D. W. (1987). Semi-nonparametric maximum likelihood estimation. Econometrica, 55(2), 363–390.CrossRefGoogle Scholar
  23. Geneletti, S., Mason, A., & Best, N. (2011). Commentary: Adjusting for selection effects in epidemiologic studies: Why sensitivity analysis is the only ‘solution’. Epidemiology, 22(1), 36–39.PubMedCrossRefPubMedCentralGoogle Scholar
  24. Gerstein, D., Hoffman, J., Larison, C., Engelman, L., Murphy, S., Palmer, A., et al. (1999). Gambling impact and behavior study: Report to the National Gambling Impact Study Commission. Chicago: National Opinion Research Center at the University of Chicago.Google Scholar
  25. Harrison, G. W. (2017). Behavioral responses to surveys about nicotine dependence. Health Economics, 26, 114–123.Google Scholar
  26. Harrison, G. W., Il, H., & Lau, M. (2014). Risk attitudes, sample selection and attrition in a longitudinal field experiment. CEAR working paper 2014-04. Center for the Economic Analysis of Risk, Robinson College of Business, Georgia State University. Review of Economics and Statistics (forthcoming).Google Scholar
  27. Harrison, G. W., Jessen, L. J., Lau, M., & Ross, D. (2018). Disordered gambling prevalence: Methodological innovations in a general Danish population survey. Journal of Gambling Studies, 34, 225–253.PubMedCrossRefPubMedCentralGoogle Scholar
  28. Harrison, G. W., & Ng, J. M. (2016). Evaluating the expected welfare gain from insurance. Journal of Risk and Insurance, 83(1), 91–120.CrossRefGoogle Scholar
  29. Heckman, J. J. (1976). The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. Annals of Economic and Social Measurement, 5, 475–492.Google Scholar
  30. Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47(1), 153–162.CrossRefGoogle Scholar
  31. Hernán, M. A., Hernández-Diaz, S., & Robins, J. M. (2004). A structural approach to selection bias. Epidemiology, 15(5), 615–625.PubMedCrossRefPubMedCentralGoogle Scholar
  32. Kessler, R. C., & Pennell, B.-E. (2015). Developing and selecting mental health measures. In T. P. Johnson (Ed.), Handbook of health survey methods. New York: Wiley.Google Scholar
  33. Lee, L.-F. (1983). Generalized econometric models with selectivity. Econometrica, 51, 507–512.CrossRefGoogle Scholar
  34. Lesieur, H. R. (1994). Epidemiological surveys of pathological gambling: Critique and suggestions for modification. Journal of Gambling Studies, 10(4), 385–398.PubMedCrossRefPubMedCentralGoogle Scholar
  35. Lesieur, H. R., & Blume, S. B. (1987). The South Oaks Gambling Screen (SOGS): A new instrument for the identification of pathological gamblers. American Journal of Psychiatry, 144(9), 1184–1188.PubMedCrossRefPubMedCentralGoogle Scholar
  36. Lesieur, H. R., Blume, S. B., & Zoppa, R. M. (1986). Alcoholism, drug abuse, and gambling. Alcoholism, Clinical and Experimental Research, 10(1), 33–38.PubMedCrossRefPubMedCentralGoogle Scholar
  37. Maddala, G. S. (1983). Limited-dependent and qualitative variables in econometrics. New York: Cambridge University Press.CrossRefGoogle Scholar
  38. Narrow, W. E., Rae, D. S., Robins, L. N., & Reiger, D. A. (2002). Revised prevalence estimates of mental disorders in the United States: Using a clinical significance criterion to reconcile 2 surveys’ estimates. Archives of General Psychiatry, 59, 115–123.PubMedCrossRefPubMedCentralGoogle Scholar
  39. Nower, L., Martins, S., Lin, K.-H., & Blanco, C. (2013). Subtypes of disordered gamblers: Results from the national epidemiologic survey on alcohol and related conditions. Addiction, 108(4), 789–798.PubMedPubMedCentralCrossRefGoogle Scholar
  40. Orr, L., Feins, J. D., Jacob, R., Beecroft, E., Sanbonmatsu, L., Katz, L. F., et al. (2003). Moving to opportunity interim impacts evaluation. Final Report. U.S. Department of Housing and Urban Development, 2003.Google Scholar
  41. Petry, N., Stinson, F. S., & Grant, B. F. (2005). Comorbidity of DSM-IV oathological gambling and other psychiatric disorders: Results from the national epidemiological survey on alcohol and related conditions. Journal of Clinical Psychiatry, 66(5), 564–674.PubMedCrossRefPubMedCentralGoogle Scholar
  42. Pietrzak, R. H., Morasco, B. J., Blanco, C., Grant, B. F., & Petry, N. M. (2007). Gambling level and psychiatric and medical disorders in older adults: Results from the national epidemiologic survey on alcohol and related conditions. American Journal of Geriatric Psychiatry, 15, 301–313.PubMedCrossRefPubMedCentralGoogle Scholar
  43. Rachlin, H. (1990). Why do people gamble and keep gambling despite heavy losses? Psychological Science, 1, 294–297.CrossRefGoogle Scholar
  44. Rachlin, H. (2000). The science of self-control. Cambridge, MA: Harvard University Press.Google Scholar
  45. Rachlin, H., Safin, V., Arfer, K. B., & Yen, M. (2015). The attraction of gambling. Journal of the Experimental Analysis of Behavior, 103(1), 260–266.PubMedCrossRefPubMedCentralGoogle Scholar
  46. Rothman, K. J., Greenland, S., & Lash, T. L. (2012). Modern epidemiology (3rd ed.). New York: Lippincott, Williams & Wilkin.Google Scholar
  47. Schellinck, T., Schrans, T., Bliemel, M., & Schellinck, H. (2015a). Construct development for the focal adult gambling screen (FLAGS): A risk measurement for gambling harm and problem gambling associated with electronic gambling machines. Journal of Gambling Issues, 30, 140–173.CrossRefGoogle Scholar
  48. Schellinck, T., Schrans, T., Bliemel, M., & Schellinck, H. (2015b). Instrument development for the focal adult gambling screen (FLAGS-EGM): A measurement of risk and problem gambling associated with electronic gambling machines. Journal of Gambling Issues, 30, 174–200.CrossRefGoogle Scholar
  49. Schüll, N. D. (2012). Addiction by design: Machine gambling in Las Vegas. Princeton: Princeton University Press.Google Scholar
  50. Sharp, C., Steinberg, L., Yaroslavsky, I., Hofmeyr, A., Dellis, A., Ross, D., et al. (2012). An item response theory analysis of the Problem Gambling Severity Index. Assessment, 19(2), 167–175.PubMedCrossRefPubMedCentralGoogle Scholar
  51. StataCorp. (2013). Stata base reference manual: Version 13. College Station, TX: StataCorp LP.Google Scholar
  52. Statistics Canada, Health Statistics Division. (2004). Canadian Community Health Survey. Cycle 1.2: Mental health and well-being. Ottawa: Statistics Canada, Catalogue #82M0021GPE.Google Scholar
  53. Stewart, M. B. (2004). Semi-nonparametric estimation of extended ordered probit models. Stata Journal, 4(1), 27–39.CrossRefGoogle Scholar
  54. Stewart, M. B. (2005). A Comparison of semiparametric estimators for the ordered response model. Computational Statistics & Data Analysis, 49, 555–573.CrossRefGoogle Scholar
  55. Stone, C., Romild, U., Abbott, M., Young, K., Billi, R., & Volberg, R. (2015). Effects of different screening and scoring thresholds on PGSI gambling risk segments. International Journal of Mental Health and Addiction, 13, 82–102.CrossRefGoogle Scholar
  56. Tam, T. W., & Midanik, L. T. (2000). The effect of screening on prevalence estimates of alcohol dependence and social consequences. Journal of Studies on Alcohol, 61(4), 617–621.PubMedCrossRefPubMedCentralGoogle Scholar
  57. Tam, T. W., Midanik, L. T., Greenfield, T. K., & Caetano, R. (1996). Selection bias in national surveys due to screening: implications from a county general population survey. Addiction, 91(4), 557–564.PubMedCrossRefPubMedCentralGoogle Scholar
  58. Toce-Gerstein, M., Gerstein, D., & Volberg, R. (2003). A hierarchy of gambling disorders in the community. Addiction, 98, 1661–1672.PubMedCrossRefPubMedCentralGoogle Scholar
  59. Van de Ven, W. P. M. M., & Van Praag, B. M. S. (1981). The demand for deductibles in private health insurance: A probit model with sample selection. Journal of Econometrics, 17, 229–252.CrossRefGoogle Scholar
  60. Volberg, R. A. (1996). Prevalence studies of problem gambling in the United States. Journal of Gambling Studies, 12(2), 111–128.PubMedCrossRefPubMedCentralGoogle Scholar
  61. Volberg, R. A., & Steadman, H. J. (1988). Refining prevalence estimates of pathological gambling. American Journal of Psychiatry, 145(4), 502–505.PubMedCrossRefPubMedCentralGoogle Scholar
  62. Volberg, R. A., & Steadman, H. J. (1989). Prevalence estimates of pathological gambling in New Jersey and Maryland. American Journal of Psychiatry, 146(12), 1618–1619.PubMedCrossRefPubMedCentralGoogle Scholar
  63. Volberg, R. A., & Williams, R. J. (2012). Developing a short form of the PGSI. Report to the Gambling Commission. Northampton, MA: Gemini Research.
  64. Wardle, H., Moody, A., Spence, S., Orford, J., Volberg, R., Jotangia, D., et al. (2011). British Gambling Prevalence Survey 2010. London: National Centre for Social Research.Google Scholar
  65. Warner, S. L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60(309), 63–69.PubMedCrossRefPubMedCentralGoogle Scholar
  66. Williams, R. J., & Volberg, R. (2009). Impact of survey description, administration format, and exclusionary criteria on population prevalence rates of problem gambling. International Gambling Studies, 9(2), 101–117.CrossRefGoogle Scholar
  67. Williams, R. J., & Volberg, R. A. (2010). Best practices in the population assessment of problem gambling. Report. Guelph: Ontario Problem Gambling Research Centre.
  68. Williams, R. J., Volberg, R. A., & Stevens, R. M. G. (2012). The population prevalence of problem gambling: Methodological influences, standardized rates, jurisdictional differences, and worldwide trends. Report Prepared for the Ontario Problem Gambling Research Centre & the Ontario Ministry of Health and Long Term Care.
  69. Williams, R. J., & Wood, R. T. (2004). The proportion of gambling revenue derived from problem gamblers: Examining the issues in a Canadian context. Analyses of Social Issues and Public Policy, 4(1), 33–45.CrossRefGoogle Scholar
  70. Wood, R. T., & Williams, R. J. (2007). ‘How much money do you spend on gambling?’ The comparative validity of question wordings used to assess gambling expenditure. International Journal of Social Research Methodology, 10(1), 63–77.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Risk Management and Insurance, Robinson College of BusinessGeorgia State UniversityAtlantaUSA
  2. 2.Copenhagen Business SchoolFrederiksbergDenmark
  3. 3.School of Society, Politics, and EthicsUniversity College CorkCorkIreland
  4. 4.School of EconomicsUniversity of Cape TownCape TownSouth Africa
  5. 5.Center for the Economic Analysis of Risk, Robinson College of BusinessGeorgia State UniversityAtlantaUSA
  6. 6.Durham University Business SchoolDurham UniversityDurhamUK

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