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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.

References

  • Bénabou, R., & Tirole, J. (2002). Self-confidence and personal motivation. The Quarterly Journal of Economics, 117(3), 871–915.

    Article  Google Scholar 

  • Benjamin, D. K., & Dougan, W. (1997). Individuals’ estimates of the risks of death: part I—A reassessment of the previous evidence. Journal of Risk and Uncertainty, 15(2), 115–133.

    Article  Google Scholar 

  • Benjamin, D. K., Dougan, W. R., & Buschena, D. (2001). Individuals’ estimates of the risks of death: part II—New evidence. Journal of Risk and Uncertainty, 22(1), 35–57.

    Article  Google Scholar 

  • Bettman, J. R., Luce, M. F., & Payne, J. W. (1998). Constructive consumer choice processes. Journal of Consumer Research, 25(3), 187–217.

    Article  Google Scholar 

  • Bruine de Bruin, W., & Carman, K. G. (2012). Measuring risk perceptions: what does the excessive use of 50% mean? Medical Decision Making, 32(2), 232–236.

    Article  Google Scholar 

  • Crimmins, E. M., Kim, J. K., Langa, K. M., & Weir, D. R. (2011). Assessment of cognition using surveys and neuropsychological assessment: the health and retirement study and the aging, demographics, and memory study. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 66B(suppl 1), i162–i171.

    Article  Google Scholar 

  • Deery, H. A., & Fildes, B. N. (1999). Young novice driver subtypes: relationship to high-risk behavior, traffic accident record, and simulator driving performance. Human Factors: The Journal of the Human Factors and Ergonomics Society, 41(4), 628–643.

    Article  Google Scholar 

  • Dejoy, D. M. (1992). An examination of gender differences in traffic accident risk perception. Accident Analysis and Prevention, 24(3), 237–246.

    Article  Google Scholar 

  • Del Missier, F., Mantyla, T., & Bruine de Bruin, W. (2012). Decision-making competence, executive functioning, and general cognitive abilities. Journal of Behavioral Decision Making, 25(4), 331–351.

    Article  Google Scholar 

  • Del Missier, F., Mantyla, T., & Bruine de Bruin, W. (2010). Executive functions in decision making: an individual differences approach. Thinking & Reasoning, 16(2), 69–97.

    Article  Google Scholar 

  • DellaVigna, S., & Pollet, J. M. (2009). Investor inattention and Friday earnings announcements. The Journal of Finance, 64(2), 709–749.

    Article  Google Scholar 

  • Dickert, S., Kleber, J., Peters, E., & Slovic, P. (2011). Numeracy as a precursor to pro-social behavior: the impact of numeracy and presentation format on the cognitive mechanisms underlying donation decisions. Judgment and Decision Making, 6(7), 638–650.

    Google Scholar 

  • Dillard, A. J., Midboe, A. M., & Klein, W. M. P. (2009). The dark side of optimism: unrealistic optimism about problems with alcohol predicts subsequent negative event experiences. Personality and Social Psychology Bulletin, 35(11), 1540–1550.

    Article  Google Scholar 

  • Dionne, G., Fluet, C., & Desjardins, D. (2007). Predicted risk perception and risk-taking behavior: the case of impaired driving. Journal of Risk and Uncertainty, 35(3), 237–264.

    Article  Google Scholar 

  • Dominitz, J., Manski, C. F., Heinz, J. (2002). Social security expectations and retirement savings decisions. National Bureau of Economic Research Working Paper Series, No. 8718.

  • Fang, H., Keane, M. P., & Silverman, D. (2008). Sources of advantageous selection: evidence from the medigap insurance market. Journal of Political Economy, 116(2), 303–350.

    Article  Google Scholar 

  • Fehr-Duda, H., Bruhin, A., Epper, T., & Schubert, R. (2010). Rationality on the rise: why relative risk aversion increases with stake size. Journal of Risk and Uncertainty, 40(2), 147–180.

    Article  Google Scholar 

  • Finkelstein, A., & McGarry, K. (2006). Multiple dimensions of private information: evidence from the long-term care insurance market. American Economic Review, 96(4), 938–958.

    Article  Google Scholar 

  • Frederick, S. (2005). Cognitive reflection and decision making. The Journal of Economic Perspectives, 19(4), 25–42.

    Article  Google Scholar 

  • Frederick, S., & Loewenstein, G. (2008). Conflicting motives in evaluations of sequences. Journal of Risk and Uncertainty, 37(2/3), 221–235.

    Article  Google Scholar 

  • Gan, L., Gong, G., Hurd, M., McFadden, D. (2004). Subjective mortality risk and bequests. National Bureau of Economic Research Working Paper Series, No. 10789.

  • Gmel, G., Labhart, F., Fallu, J.-S., & Kuntsche, E. (2012). The association between drinking motives and alcohol-related consequences—room for biases and measurement issues? Addiction, 107(9), 1580–1589.

    Article  Google Scholar 

  • Griffin, D., & Tversky, A. (1992). The weighing of evidence and the determinants of confidence. Cognitive Psychology, 24(3), 411–435.

    Article  Google Scholar 

  • Gul, F., & Pesendorfer, W. (2001). Temptation and self-control. Econometrica, 69(6), 1403–1435.

    Article  Google Scholar 

  • Hakes, J., & Viscusi, W. K. (1997). Mortality risk perceptions: a Bayesian reassessment. Journal of Risk and Uncertainty, 15(2), 135–150.

    Article  Google Scholar 

  • Hansen, W. B., Raynor, A. E., & Wolkenstein, B. H. (1991). Perceived personal immunity to the consequences of drinking alcohol: the relationship between behavior and perception. Journal of Behavioral Medicine, 14(3), 205–224.

    Article  Google Scholar 

  • Hudomiet, P., & Willis, R. J. (2012). Estimating second order probability beliefs from subjective survival data. National Bureau of Economic Research Working Paper Series, No. w18258.

  • Hurd, M. D., & McGarry, K. (2002). The predictive validity of subjective probabilities of survival. The Economic Journal, 112(482), 966–985.

    Article  Google Scholar 

  • Hurd, M. D., & Rohwedder, S. (2003). The retirement-consumption puzzle: Anticipated and actual declines in spending at retirement. National Bureau of Economic Research Working Paper Series, No. 9586.

  • Jolls, C., Sunstein, C. R., & Thaler, R. (1998). A behavioral approach to law and economics. Stanford Law Review, 50(5), 1471–1550.

    Article  Google Scholar 

  • Jonah, B. A. (1986). Accident risk and risk-taking behaviour among young drivers. Accident Analysis & Prevention, 18(4), 255–271.

    Article  Google Scholar 

  • Katsikopoulos, K. V., & Gigerenzer, G. (2008). One-reason decision-making: modeling violations of expected utility theory. Journal of Risk and Uncertainty, 37(1), 35–56.

    Article  Google Scholar 

  • Khwaja, A., Silverman, D., Sloan, F., & Wang, Y. (2009). Are mature smokers misinformed? Journal of Health Economics, 28(2), 385–397.

    Article  Google Scholar 

  • Khwaja, A., Sloan, F., & Chung, S. (2007). The relationship between individual expectations and behaviors: mortality expectations and smoking decisions. Journal of Risk and Uncertainty, 35(2), 179–201.

    Article  Google Scholar 

  • Klayman, J., Soll, J. B., González-Vallejo, C., & Barlas, S. (1999). Overconfidence: it depends on how, what, and whom you ask. Organizational Behavior and Human Decision Processes, 79(3), 216–247.

    Article  Google Scholar 

  • Korobkin, R. B., & Ulen, T. S. (2000). Law and behavioral science: removing the rationality assumption from law and economics. California Law Review, 88(4), 1051–1144.

    Article  Google Scholar 

  • Kruger, J. (1999). Lake Wobegon be gone! The ‘below-average effect’ and the egocentric nature of comparative ability judgments. Journal of Personality and Social Psychology, 77(2), 221–232.

    Article  Google Scholar 

  • Lacey, J. H., Kelley-Baker, T., Furr-Holden, D., Voas, R. B., Romano, E., Torres, P., et al. (2009). 2007 National roadside survey of alcohol and drug use by drivers: Alcohol results. National Highway Traffic Safety Administration.

  • Lachman, M., & Spiro, A. (2002). Critique of cognitive measures in the health and retirement study (HRS) and the asset and health dynamics among the oldest old (AHEAD) study (HRS data monitoring committee report). Bethesda: National Institute on Aging.

    Google Scholar 

  • Lancaster, T. (1979). Econometric methods for the duration of unemployment. Econometrica, 47(4), 939–956.

    Google Scholar 

  • Lancaster, T., & Chesher, A. (1983). An econometric analysis of reservation wages. Econometrica, 51(6), 1661–1676.

    Article  Google Scholar 

  • Levitt, S. D., & Porter, J. (2001). How dangerous are drinking drivers? Journal of Political Economy, 109(6), 1198–1237.

    Article  Google Scholar 

  • Lundborg, P. (2007). Smoking, information sources, and risk perceptions—new results on Swedish data. Journal of Risk and Uncertainty, 34(3), 217–240.

    Article  Google Scholar 

  • Lundborg, P., & Lindgren, O. (2002). Risk perceptions and alcohol consumption among young people. Journal of Risk and Uncertainty, 25(2), 165–183.

    Article  Google Scholar 

  • Mandayam, S., Jamal, M. M., & Morgan, T. R. (2004). Epidemiology of alcoholic liver disease. Seminars in Liver Disease, 24(3), 217–232.

    Article  Google Scholar 

  • Mann, R. E., Smart, R. G., Stoduto, G., Adlaf, E. M., Vingilis, E., Beirness, D., et al. (2003). The effects of drinking-driving laws: a test of the differential deterrence hypothesis. Addiction, 98(11), 1531–1536.

    Article  Google Scholar 

  • Manski, C. F. (2004). Measuring expectations. Econometrica, 72(5), 1329–1376.

    Article  Google Scholar 

  • Manski, C. F., & Molinari, F. (2010). Rounding probabilistic expectations in surveys. Journal of Business & Economic Statistics, 28(2), 219–231.

    Article  Google Scholar 

  • McCullough, A. J., O’Shea, R. S., & Dasarathy, S. (2011). Diagnosis and management of alcoholic liver disease. Journal of Digestive Diseases, 12(4), 257–262.

    Article  Google Scholar 

  • McFadden, D. (1999). Rationality for economists? Journal of Risk and Uncertainty, 19(1–3), 73–105.

    Article  Google Scholar 

  • Mothers Against Drunk Driving (2012). State statistics. http://www.madd.org/drunk-driving/campaign/state-stats/. Accessed February 24, 2012.

  • Naveau, S., Giraud, V., Borotto, E., Aubert, A., Capron, F., & Chaput, J. C. (1997). Excess weight risk factor for alcoholic liver disease. Hepatology, 25(1), 108–111.

    Article  Google Scholar 

  • Ofstedal, M. B., Fisher, G. G., & Herzog, A. R. (2005). Documentation of cognitive functioning measures in the health and retirement study. Ann Arbor: University of Michigan.

    Book  Google Scholar 

  • Peters, E., Hart, P. S., & Fraenkel, L. (2011). Informing patients: the influence of numeracy, framing, and format of side effect information on risk perceptions. Medical Decision Making, 31(3), 432–436.

    Article  Google Scholar 

  • Peters, E., Västfjäll, D., Slovic, P., Mertz, C. K., Mazzocco, K., & Dickert, S. (2006). Numeracy and decision making. Psychological Science, 17(5), 407–413.

    Article  Google Scholar 

  • Plassman, B. L., Newman, T. T., Welsh, K. A., Helms, M., et al. (1994). Properties of the telephone interview for cognitive status: application in epidemiological and longitudinal studies. Neuropsychiatry, Neuropsychology, & Behavioral Neurology, 7(3), 235–241.

    Google Scholar 

  • Rust, J., & Phelan, C. (1997). How social security and medicare affect retirement behavior in a world of incomplete markets. Econometrica, 65(4), 781–831.

    Article  Google Scholar 

  • Sloan, F., & Platt, A. (2011). Information, risk perceptions, and smoking choices of youth. Journal of Risk and Uncertainty, 42(2), 161–193.

    Article  Google Scholar 

  • Sloan, F., Reilly, B., & Schenzler, C. (1995). Effects of tort liability and insurance on heavy drinking and drinking and driving. Journal of Law and Economics, 38(1), 49–77.

    Article  Google Scholar 

  • Smith, V. K., Taylor, D. H., & Sloan, F. A. (2001). Longevity expectations and death: can people predict their own demise? American Economic Review, 91(4), 1126–1134.

    Article  Google Scholar 

  • Snowberg, E., & Wolfers, J. (2010). Explaining the favorite-longshot bias: Is it risk-love or misperceptions? National Bureau of Economic Research Working Paper Series, No. 15923.

  • Stephens, M. (2004). Job loss expectations, realizations, and household consumption behavior. Review of Economics and Statistics, 86(1), 253–269.

    Article  Google Scholar 

  • Sunstein, C.R. (1997). Behavioral analysis of law. The University of Chicago Law Review, 1175–1195.

  • United States Department of Transportation. (1992). Driving under the influence: A report to congress on alcohol limits. Washington, D.C.: National Highway Traffic Safety Administration.

    Google Scholar 

  • van der Klaauw, W., & Wolpin, K.I. (2005). Social security and the retirement and savings behavior of low income households. Mimeo, University of North Carolina-Chapel Hill and University of Pennsylvania.

  • Viscusi, W. K. (1990). Do smokers underestimate risks? Journal of Political Economy, 98(6), 1253–1269.

    Article  Google Scholar 

  • Viscusi, W. K. (2002). Smoke-filled rooms: A postmortem on the tobacco deal. Chicago: University of Chicago Press.

    Book  Google Scholar 

  • Viscusi, W. K., & Hakes, J. K. (2003). Risk ratings that do not measure probabilities. Journal of Risk Research, 6(1), 23–43.

    Article  Google Scholar 

  • Watson, P. E., Watson, I. D., & Batt, R. D. (1981). Prediction of blood-alcohol concentrations in human-subjects: updating the Widmark Equation. Journal of Studies on Alcohol, 42(7), 547–556.

    Google Scholar 

  • Weinstein, N. D. (1980). Unrealistic optimism about future life events. Journal of Personality and Social Psychology, 39(5), 806–820.

    Article  Google Scholar 

  • Weinstein, N. D. (1987). Unrealistic optimism about susceptibility to health problems: conclusions from a community-wide sample. Journal of Behavioral Medicine, 10(5), 481–500.

    Article  Google Scholar 

  • Weinstein, N. D., & Klein, W. M. (1995). Resistance of personal risk perceptions to debiasing interventions. Health Psychology, 14(2), 132–140.

    Article  Google Scholar 

  • Zeckhauser, R. J., & Viscusi, W. K. (2008). Discounting dilemmas: editors’ introduction. Journal of Risk and Uncertainty, 37(2/3), 95–106.

    Article  Google Scholar 

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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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  1. Department of Economics, Duke University, 213 Social Sciences Building, Box 90097, Durham, NC, 27708, USA

    Frank A. Sloan, Lindsey M. Eldred, Tong Guo & Yanzhi Xu

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  1. Frank A. Sloan
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  2. Lindsey M. Eldred
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Correspondence to Frank A. Sloan.

Appendix

Appendix

Table 10 Cognition questions: Health and Retirement Study and Survey of Alcohol and Driving
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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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