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An expected utility maximizer walks into a bar...

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Abstract

We conducted field experiments at a bar to test whether blood alcohol concentration (BAC) correlates with violations of the generalized axiom of revealed preference (GARP) and the independence axiom. We found that individuals with BACs well above the legal limit for driving adhere to GARP and independence at rates similar to those who are sober. This finding led to the fielding of a third experiment to explore how risk preferences might vary as a function of BAC. We found gender-specific effects: Men did not exhibit variations in risk preferences across BACs. In contrast, women were more risk averse than men at low BACs but exhibited increasing tolerance towards risks as BAC increased. Based on our estimates, men and women’s risk preferences are predicted to be identical at BACs nearly twice the legal limit for driving. We discuss the implications for policy-makers.

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Notes

  1. Casinos commonly provide complimentary alcohol to individuals seated at gaming tables.

  2. Most recently available data from the Bureau of Justice Statistics: http://bjs.ojp.usdoj.gov/content.../pub/pdf/dwiocs.pdf

  3. National Highway Traffic Safety Administration: http://www.nhtsa.gov/Research/Human+Factors/Alcohol+Impairment

  4. National Institute for Alcohol Abuse and Alcoholism Statistical Tables: http://pubs.niaaa.nih.gov/publications/Surveillance92/CONS09.pdf

  5. National Income and Product Accounts table 2.4: http://www.bea.gov/national/consumer_spending.htm

  6. This number is likely a dramatic underestimate of the total dollar value of risky choices made while intoxicated. For example, we cannot find data on the amount of alcohol sold each year at bars, a large proportion of which is sold to individuals with non-zero BACs. And these are but a limited class of risky decisions made by intoxicated individuals.

  7. We should note, however, that these laws rarely appear to be enforced which may reflect any number of considerations with regard to this apparent conviction.

  8. Both of these statistics come from the NHTSA 2005 research report titled “Preventing Over-consumption of Alcohol - Sales to the Intoxicated and “Happy Hour” (Drink Special) Laws.”

  9. Although, as reported and discussed in the online Appendix A (available on the author’s web site http://www.decisionsrus.com/documents/Appendix_JRU.pdf), we find potential, but very weak, evidence for a minuscule female-specific effect on adherence to GARP at very high BACs.

  10. Note that our empirical strategy is to measure BAC and use it as a continuously varying proxy for what these classes of models typically assume are discrete states.

  11. Cook (2007) provides an excellent summary of this vast literature.

  12. While the budgets we employ come from the Harbaugh et al. (2001) design, we use computer-based graphically presented bundles similar to the mechanism employed by Choi et al. (2007). At a more procedural level, we took extensive pains to limit stereotyped choice behaviors, like corner solving, which limit the power of some studies that use these approaches.

  13. Three excellent reviews of this literature are Cox and Harrison (2008), chapters 10 and 11 in Glimcher et al. (2009), and Kahneman and Tversky (2000).

  14. Two counter-examples are Kagel et al. (1980), which explores the relationship between work performance and marijuana consumption, and Krishnamurti et al. (2011) which explores the choices of drunk people in the ultimatum game.

  15. This study administers a benzodiazepene agonist which shares the same principal pharmacological mechanism as alcohol.

  16. Only two out of 321 potential participants failed this test.

  17. Participants were informed during the instructional phase of the experiment that they would need to sign a receipt for their payment but that their signature did not need to be legible.

  18. An amount constantly proportional to blood alcohol concentration (over a range of BAC up to 0.400%) is exhaled with each breath (O’Daire 2009)

  19. The difference between our breathalyzer and ones used by law enforcement agents for evidentiary testing is that our breathalyzer has no memory nor can it be attached to a printer.

  20. Table 14 in the online Appendix A (available on the author’s web site http://www.decisionsrus.com/documents/Appendix_JRU.pdf) describes all of the budget details (prices and wealth levels).

  21. Harbaugh et al. (2001) report average AEIs of 0.93, 0.96, and 0.94 in their populations of students in second-grade, sixth-grade, and undergraduate classrooms. Andreoni and Miller (2002) report that only four of their 176 undergraduate subjects had AEIs less than unity which, based on our calculations from their Table II, gives an average AEI of 0.998.

  22. Although Sippel (1997) reports the proportions of his two samples (1/12 and 3/30) above an AEI threshold of 0.950. As can be inferred from Table 4, we find a slightly smaller proportion of our sample to have AEIs above that threshold.

  23. Technically our experiment tests for violations of a slightly weaker condition: Betweenness (Dekel 1986; Camerer and Ho 1994). However, because betweenness is a necessary condition for independence, to any extent that our empirical results suggest failures of betweenness, they also imply failures of independence although of course the reverse is not necessarily the case. However, because our experimental design employed three-state lotteries, any true test of independence (as opposed to betweenness) would greatly increase the complexity of the choice-situations: subjects would need to compare two three-state lotteries instead of one degenerate and one three-state lottery. For this reason we adopted the test of betweenness described here to examine the stability of the independence axiom.

  24. Henceforth, we use the term “lottery” to refer to the non-certain option for brevity.

  25. For a complete description of the unique colors and prize values used in this experiment see the online Appendix B, available on the author’s web site http://www.decisionsrus.com/documents/Appendix_JRU.pdf

  26. These comparisons are problematic due to differences in the number of choice-situations, prize spaces, specific probabilities, and real vs. hypothetical choices.

  27. Our experiment is an example of what Camerer and Ho (1994) label as “on-border” types of tests for independence because our choice-situations include lotteries on an edge of the simplex.

  28. Most studies listed in the “Gain” column of Camerer and Ho’s Fig. 4 report that, generally, about 70% of individuals violate independence. We find that for our subjects (who made choices in 15 CSPs) nearly all of them make some form of IAV.

  29. We would like to thank an anonymous referee for pointing out these lines of interpretation.

  30. In fact we take the “increase in error rates” interpretation to a literal extreme in our modeling of risk preferences because we allow the error dispersion parameter \((\kappa )\) in our logit model to vary systematically with BAC.

  31. We have also calibrated models with a constant absolute risk aversion (CARA) Bernoulli but find significantly better fits with CRRA in this dataset.

  32. When we constrain the error dispersion parameter \((\kappa )\) to be constant across BAC and sex, we find significantly reduced maximized log-likelihood values. Moreover, based on the findings from our IA experiment, the dispersion-parameter estimate for BAC \((\kappa _{1})\) bears the expected signs.

  33. For a policy-maker, whether this apparent change in risk preferences is the result of selection, or is causally related to BAC, is less of an issue if less risk averse women are disproportionately opting into higher BACs. It would not change the fact that the population of drunk women is substantially less risk averse than their sober counter-parts.

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Acknowledgments

All authors gratefully acknowledge valuable input from John Rotrosen, M.D., Bill Harbaugh, Mark Dean and Daniel Martin. Matthew Kelly and Michael Laba generously permitted us access to their bar and patrons. Begoña Fernandez Diaz, Eric DeWitt, Maggie Grantner, Dino Levy, and Linnaea Ostroff provided valuable assistance with participant queuing. This work was supported by the U.S. National Institutes of Health (5R01NS054775 and 5F32MH084431).

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Authors and Affiliations

  1. Department of Economics, University of Zürich, Blümlisalpstrasse 10, 8006, Zürich, Switzerland

    Daniel R. Burghart

  2. Center for Neuroeconomics, New York University, 4 Washington Place, Room 809, New York, NY, 10003, USA

    Paul W. Glimcher

  3. Institute of Cognitive Neuroscience, University College London, 26 Bedford Way, London, WC1H0AP, UK

    Stephanie C. Lazzaro

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  1. Daniel R. Burghart
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  2. Paul W. Glimcher
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  3. Stephanie C. Lazzaro
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Correspondence to Daniel R. Burghart.

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Burghart, D.R., Glimcher, P.W. & Lazzaro, S.C. An expected utility maximizer walks into a bar.... J Risk Uncertain 46, 215–246 (2013). https://doi.org/10.1007/s11166-013-9167-7

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