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Are people overoptimistic about the effects of heavy drinking?

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Abstract

We test whether heavy or binge drinkers are overly optimistic about probabilities of adverse consequences from these activities or are relatively accurate about these probabilities. Using data from a survey in eight cities, we evaluate the relationship between subjective beliefs and drinking. We assess accuracy of beliefs about several outcomes of heavy/binge drinking: reduced longevity, liver disease onset, link between alcohol consumption and Driving While Intoxicated (DWI), probability of an accident after drinking, accuracy of beliefs about encountering intoxicated drivers on the road, and legal consequences of DWI—ranging from being stopped to receiving fines and jail terms. Overall, there is no empirical support for the optimism bias hypothesis. We do find that persons consuming a lot of alcohol tend to be more overconfident about their driving abilities and ability to handle alcohol. However, such overconfidence does not translate into over-optimism about consequences of high levels of alcohol consumption.

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Notes

  1. For example, Jolls et al. (1998) state: “A common feature of human behavior is overoptimism: People tend to think that bad events are far less likely to happen to them than to others. Thus, most people think that their probability of a bad outcome is far less than others’ probability, although of course this cannot be true for more than half the population.” (p.1524). Korobkin and Ulen (2000) state: “Even when actors know the actual probability distribution of a particular event, their predictions as to the likelihood that that event will happen to them are susceptible to the “overconfidence bias”: the belief that good things are more likely than average to happen to us and bad things are less likely than average to happen to us. Demonstrating that a particular individual is overconfident is difficult to do, because the individual might well differ from the statistically average person in positive ways.” (p. 1091). In our study, we are able to make within sample comparisons, which mitigates the issue addressed in the previous sentence. For studies combining optimism bias with analysis of high levels of alcohol consumption, see Hansen et al. (1991) and Dillard et al. (2009).

  2. A search in Web of Science under the term “optimism bias” yielded 799 results (accessed 10/28/2012).

  3. Further, data from secondary sources for the study cities on some outcomes we analyze in the within sample comparisons are not available (citations for driving over 15 miles per hour above the speed limit and for motor vehicle accidents).

  4. The Lake Wobegon effect is when there is a belief that “my family members and I are better than average.” This is named after a fictional town in Minnesota and is used to represent the natural human tendency to overestimate one’s capabilities. For a critical evaluation of the Lake Wobegon effect, see Kruger (1999).

  5. For a description of the Health and Retirement Study, see http://hrsonline.isr.umich.edu (accessed 10/28/12).

  6. Compared to a mean age of 43.4 for respondents to the SAD.

  7. There are several differences in sample composition between the SAD and HRS. Heavy binge drinkers are over twice as common in the SAD and other drinkers are only half as common in the SAD. The mean CAGE index is over twice as high in the SAD than in the HRS. Forty-six percent of the SAD respondents were married at the time of the survey as compared with 77% of the HRS. Mean educational attainment is almost 3 years higher in the SAD and income over $10,000 higher than HRS. The unemployment rate is five times higher in the SAD than in the HRS, but this difference can be explained by the fact that the HRS is for 1996 and the SAD was conducted in 2010. The fraction of persons out of the labor force is four times higher in the HRS than in the SAD, but this can be explained by the age differences in respondents to the two surveys. Although older on average, HRS respondents had a longer mean financial planning horizon.

  8. Naveau et al. (1997) analyzed data from a cohort study of 1,604 persons with alcoholism and found approximately 20% for alcoholic hepatitis prevalence. In Mandayam et al. (2004), 6–41% of heavy drinkers developed cirrhosis. Lacey et al. (2009) reported a range of cirrhosis prevalence of 1–5.7% in heavy drinkers.

  9. While these results are consistent with past research showing overestimation by smokers of the probability of getting lung cancer (Viscusi 1990) and heart disease (Khwaja et al. 2009), another possibility is that some respondents were thinking about the probability of getting fatty liver conditional on heavy drinking. The objective probability of fatty liver under such circumstances is at least 0.9.

  10. Men’s data are from Watson et al. (1981); women’s data are from United States Department of Transportation (1992).

  11. This overestimate could have occurred because our objective estimates using LP’s method refer to the entire week rather than the weekend in the SAD question.

  12. Washington: Rev. Code Wash. (ARCW) § 46.61.5055; North Carolina: N.C. Gen. Stat. § 20–179; Pennsylvania: 75 Pa.C.S. § 3804; Wisconsin: Wis. Stat. § 346.65.

  13. Other studies reaching this conclusion are Smith et al. (2001), Finkelstein and McGarry (2006), and Fang et al. (2008).

  14. See the quotations in footnote 1.

  15. Another strand of literature refers to systematic overconfidence of one’s own ability. For example, some CEOs may be overconfident about their own managerial ability (on such overconfidence, see e.g., Griffin and Tversky (1992); Sunstein (1997); Klayman et al. (1999); Bénabou and Tirole (2002); DellaVigna and Pollet (2009)).

  16. This issue is discussed by Hurd and McGarry (2002); Manski and Molinari (2010); Bruine de Bruin and Carman (2012).

  17. An alternative to the optimism bias view is that people who engage in risky behaviors are aware of these probabilities. In fact, they learn about the probabilities from direct experiences (Hakes and Viscusi 1997) and from peers who also engage in risky behavior. This is the prevailing view of economists who conduct research on risk perceptions, based for example on smokers’ and drinkers’ perceptions (Viscusi 1990; Lundborg and Lindgren 2002; Lundborg 2007; Khwaja et al. 2007; and Sloan and Platt 2011). This does not eliminate the possibility that specific population subgroups underestimate probabilities of harm from risky behaviors, e.g., young drivers whose risk perceptions become more similar to those of older drivers as they gain driving experience (Jonah 1986; Deery and Fildes 1999). Even if individuals’ risk perceptions are inaccurate, there is an argument that obtaining such information may be costly and the cost will not be incurred for choices irrelevant to the decision maker (Benjamin and Dougan 1997; Benjamin et al. 2001).

  18. We rely on objective estimates using the Levitt and Porter (2001) method above since these estimates are based on objective motor vehicle mortality data rather than subjective beliefs of attorneys and police.

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Acknowledgments

This research was supported by a grant (R01-AA-017913-03) from the National Institute on Alcohol Abuse and Alcoholism, a part of the National Institutes of Health. This sponsor had no involvement in study design, collection, analysis and interpretation of data, in the writing of the manuscript or in the decision to submit the manuscript for publication.

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Correspondence to Frank A. Sloan.

Appendix

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Table 10 Cognition questions: Health and Retirement Study and Survey of Alcohol and Driving
Table 11 Relative likelihood of causing a fatal crash: drinking vs. sober drivers

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Sloan, F.A., Eldred, L.M., Guo, T. et al. Are people overoptimistic about the effects of heavy drinking?. J Risk Uncertain 47, 93–127 (2013). https://doi.org/10.1007/s11166-013-9172-x

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