Are gamblers more likely to commit crimes? An empirical analysis of a nationally representative survey of US young adults

Abstract

We examine the relationship between gambling and criminal behaviour using data from the National Longitudinal Study of Adolescent Health (Add Health). Our data set includes survey responses from 6145 young adults. The results of our empirical analysis are consistent with the gambling literature in which it is suggested that higher gambling losses increase the propensity to commit crime. This study complements the current literature, as our data and empirical analysis allow us to control for many variables that have been neglected in previous studies, including various forms of gambling. Our findings provide useful information on the general relationship between gambling behaviour and criminal behaviour.

Keywords:

• gambling behavior
• crime
• young adults
• surveys

Acknowledgements

We are grateful to Harold Wynne and two anonymous referees for their helpful comments and suggestions. This research uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the U.S. National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due to Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524, USA ([email protected]).

Notes

1. There are different degrees to which a person may have a gambling problem. Because specific classifications (e.g. problem gambling, pathological gambling, probable pathological gamblers) are not the subject of this paper, we simply use the term ‘problem gambling’ to include all degrees of gambling problems.

2. Other instruments are available, such as the DSM-IV-J and SOGS-RA, designed for juveniles and adolescents. However, none of these instruments more closely parallels the Add Health questions than the DSM-IV or SOGS. Several studies that have utilised these alternative instruments examine individuals with mean ages of around 18.5 years (Derevensky & Gupta, 2000; Hardoon, Derevensky and Gupta, 2003). The mean age of the Add Health wave 3 respondents is around 22 years. Only 10% of the respondents were under 20 years old.

3. Researchers have examined how subsets of the DSM-IV and SOGS criteria can be used in lieu of the full versions. However, the few criteria addressed by the Add Health survey are generally not seen as the most important criteria in the DSM-IV or SOGS instruments. For example, ‘preoccupation’ is one of the questions in the Add Health that is also part of the NODS-CLiP, but none of the other Add Health criteria are considered to be among the most important ones. For further discussion of ‘short screens’, see Volberg, Munck and Petry (2008) and Strong, Lesieur, Breen, Stinchfield and Lejeuz (2004).