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Short-term fluctuations in incidental happiness and economic decision-making: experimental evidence from a sports bar

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Abstract

We develop a new experimental paradigm to study how emotions affect decision-making. We use it to investigate the impact of short-term fluctuations in incidental happiness on economic decisions. Experimental subjects watch an NFL football game in a sports bar. At various commercial breaks, we measure subjects’ happiness and observe their decisions regarding charitable giving, willingness to pay for a consumer good, risk taking, and trust. We find that events in the game impact the incidental happiness of our subjects, and these changes lead to predictable changes in choices. We provide a simple model that rationalizes how subjects’ behavior varies with incidental happiness and provides insight into how mood can be tractably included in economics models. Our experimental paradigm can be leveraged by other researchers interested in exploring the impact of emotions on behavior.

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Notes

  1. While individuals who are angry are advised to “sleep on it” before making decisions out of anger, individuals are rarely advised to contemplate how incidental happiness might influence their purchase or charitable giving behaviors.

  2. In what follows, we use “mood” to refer to incidental happiness. We use mood in the colloquial sense (e.g., being in a good mood or a bad mood). When we say that mood improves, we mean that incidental happiness has increased.

  3. We see direct effects of the events of the game on economic behavior as being unlikely. Importantly, subjects report not having placed bets on the game, the only obvious channel through which events in the game might directly affect economic choices. We return to this point in Sect. 4.

  4. The latter measure is more highly correlated with incidental happiness, but requires a bit stronger of an assumption to satisfy the exclusion restriction, as discussed in Sect. 4.

  5. Our work is related to the model in Kimball and Willis (2006), which includes happiness in a model of life-cycle utility maximization. In that framework, happiness is considered as a shock to lifetime utility. In our experiment, however, we see an effect on economic decisions of short-term incidental happiness fluctuations induced by events that have no effect on lifetime utility. Consequently, we find that happiness can affect behavior even when it does not impact long-term outcomes.

  6. We are not the only researchers to recognize that sports can be a useful way to vary emotions of sports fans. A paper by Lambsdorffl et al. (2015) uses soccer as an emotional prime. However, their set-up is quite different from ours on a number of dimensions, the most prominent difference is that subjects only make one decision and do so before the game begins.

  7. Because of the dynamic nature of the sports bar, subjects interact with each other and may react to these expressions of emotions as well. While this could be viewed as a limitation, we also see it as a benefit as it heightens the realism of the emotional experience. We find that the average happiness of other subjects does not influence a subject’s own happiness, likely due to happiness being positively correlated with other subjects who favor a given team and negative correlated with other subjects who favor the opposing team. If we focus on the average happiness of other subjects who favor the same team, we find a significantly positive effect on a subject’s happiness reported in Table A12. Including other subjects’ happiness in our IV regressions, presented in Table A13, does not change our results.

  8. This latter point is aided by the fact that we recruit subjects who are rooting for both teams playing in the game, which ensures that events in the game that change the probability each team will win are likely to make one set of subjects happier and another set less happy.

  9. While variation in emotions can be cleanly induced in the laboratory (e.g., by exposing subjects to a video clip), changes in behavior may only arise among subjects who would not have endogenously chosen to expose themselves to such emotions (e.g., if a response to a sad movie clip only arises among individuals who take efforts to avoid exposure to such stimuli). Such endogenous avoidance could undermine the empirical relevance of laboratory findings to settings outside of the lab. Our setting avoids this potential external validity concern.

  10. The project was funded by NBC Sports as part of a larger program investigating the impact of sports viewing on decision-making.

  11. Subjects were told verbally: “You can be \(100\%\) confident that the money will be donated.” Donations were made after both sessions of the study were run.

  12. Tan and Forgas (2010) considers the lottery ticket choice in a dictator game setting and thus interprets their results as happy subjects being less generous, in contrast to some of the papers on charitable giving cited above.

  13. In related work, Andrade and Ariely (2009) finds that happier subjects are willing to accept less fair offers in an ultimatum game, possibly suggesting more concern with social efficiency which could be associated with more trusting.

  14. Results including surprise and excitement are considered in Sect. 4.2.1. We also explore excitement and surprise in related work (see Kessler et al. 2017).

  15. We ran the first session of our experiment on a separate floor of the sports bar that was designated for our study. We ran the second session of our experiment in a different sports bar and had the entire back end of the bar, which was isolated from the rest of the patrons. In both sessions, we ensured that all screens subjects could see were showing the specific game we were analyzing. However, other patrons cheering for other games in other parts of the bars could have theoretically distracted subjects. In addition, subjects completed the experiment on web-based software (written specifically for this experiment) that was accessible on tablets that could be used from anywhere in the bar. This had the advantage of allowing subjects to sit wherever they wanted in our designated sections and to watch the football game as they would have otherwise; however, we did not force subjects to stay seated, so they could leave the bar to smoke or to go to the bathroom and thus miss an opportunity to enter data.

  16. In a survey at the start of the study, we asked subjects about their attitudes towards sports and football. They were asked: “Do you like watching sports in general?” using a 7-point Likert scale where 1 is “Not very much” and 7 is “More than all other types of entertainment” and “Do you like watching football in particular?” using a Likert scale where 1 is “Not very much” and 7 is “Football is my favorite sport to watch.” The mean responses were 5.77 (standard deviation 0.16) and 5.61 (standard deviation 0.17), respectively.

  17. Most similar to our model is Kimball and Willis (2006) in which happiness is considered as a shock to life-time utility (as opposed to the short-term incidental happiness explored here); see also our discussion in footnote 5. Bewley (2009) and Oswald et al. (2015) provide models for studying the effect of emotions on effort decisions while Loewenstein and O’Donoghue (2004) study the impact of emotions in a game between affective and deliberative selves; this strategic interaction between selves is absent in our model. Oswald et al. (2015) look at the effect of happiness on worker effort and derive some comparative statics. Their utility function fits within our model. The main contribution of our model relative to the previous literature is to provide a framework to study the impact of mood on decision-making across a number of different tasks in which there may be other-regarding preferences. This allows us to provide conditions on the utility function that generate comparative statics across the different tasks.

  18. The condition \(u_{12}>0\) is the equivalent to the assumptions made in the model of Bewley (2009).

  19. Both Bewley (2009) and Oswald et al. (2015) make a similar assumption on the additive relationship between material payoffs and mood.

  20. This assumption is less innocuous than a similar assumption made in our analysis of charitable giving since in that case the recipient of charity was an entity completely separate from the donor. In this case, it is possible that a change in mood by the decision maker may also alter her attitude about the goods she buys. This will make our results ambiguous. Such ambiguity may help to explain why we get significant results for the charitable giving task but why results of the effect of mood on willingness to pay for a consumer good are properly signed but not statistically significant (see results in Section 4).

  21. This condition is in fact stronger than we need. In the case of \(u_{12}>0\) (\(u_{12}<0\)), \(A(w,\sigma _i)\) decreasing (increasing) in \(\sigma _i\) is sufficient.

  22. The minimum number of entries was 10 and the maximum was 18. Not every subject entered data every time it was requested. Subjects may have been otherwise occupied (e.g., eating or in the restroom) when asked to enter data. In addition, subjects were technically able to enter data at times other than when we asked them to do so. Nevertheless \(55\%\) of subjects submitted exactly 15 reports, and \(91\%\) of subjects submitted either 14, 15, or 16 reports. Due to technical issues, the first entry for the first game was done with paper and pencil, which we had prepared in the event of such technical issues; all other responses were entered through the web interface on the tablets.

  23. A violation of the exclusion restriction would require game events to have some effect on material well-being. The main concern would be about gambling (e.g., if subjects had bet on a certain team and so their likelihood of winning money fluctuated over the course of the game). Our survey explicitly asked subjects whether they were gambling on the game and all subjects reported that they were not. A more obscure potential confound would arise if the outcome of today’s game affects whether I will watch football in future weeks, assuming my alternative entertainment options are more or less costly than watching football. As discussed in Sect. 4.1, however, such a potential concern is mitigated by the fact that the teams playing in our games are not subjects’ favorite football teams and so their presence or absence in future playoff games is unlikely to affect whether they watch future games. Note also that if the outcome of today’s game affects my happiness because it changes my anticipation of future happiness—but not my material well-being—this would not challenge our exclusion restriction, since contemporaneous decisions are still being affected by changes in contemporaneous happiness.

  24. In the December 29, 2013 game between the Philadelphia Eagles and the Dallas Cowboys, 17 subjects favored the Eagles and 12 subjects favored the Cowboys. In the January 4, 2014 game between the Philadelphia Eagles and the New Orleans Saints, 13 subjects favored the Eagles and 19 favored the Saints.

  25. Subjects were asked to report their favorite football team at the start of the study, and none of the reported favorite teams were playing. The results of the December 29, 2019 Eagles versus Cowboys game did not affect any other team’s playoff chances. The winner of the January 4, 2014 Eagles versus Saints game would face the Seattle Seahawks in the Divisional round of the playoffs. No subjects reported that the Seahawks were their favorite team.

  26. The self-reported probability of the Eagles winning is the self-reported probability of one’s favored team winning for subjects who favor the Eagles, and 1 minus the self-reported probability for subjects who favor the Cowboys in game 1 and the Saints in game 2.

  27. As described in Sect. 2, receivers were asked how much they wanted to return to the sender if they received \(\$24\), \(\$48\), \(\$72\) or \(\$96\). We construct the average proportion returned for these 4 amounts to use as the dependent variable. Running the regressions with each amount individually yields very similar results.

  28. For example, the maximum amount a subject could give to charity was \(\$40\). If a subject donated \(\$25\), the variable would be \(25/40=0.625\). The maximum amount was also \(\$40\) for the WTP for the consumer good and for the risky gamble. The maximum amount that could be transferred in the trust game was \(\$32\). The maximum amount that could be returned in the trust game depended on the initial transfer.

  29. Results without the game-quarter dummies are presented in Table A7 in Appendix A in supplementary material and are very similar.

  30. As discussed in Footnote 3 and discussed below, we rule out gambling on the game as a potential source of such shocks.

  31. Instrumental variable approaches are somewhat uncommon in experimental economics but can be quite useful in the presence of endogenous variables, such as subject earnings (Drouvelis and Marx 2020), or when the experimenter does not have perfect control over a treatment (Kessler 2017).

  32. We thank an anonymous referee for this suggestion.

  33. Models of economic decision-making rarely include emotions as inputs into behavior (see Wälde and Moors (2017) and Wälde (2016) for recent surveys).

  34. See, Edmans et al. (2007) on stock markets dips in response to a country’s elimination from the world cup; Card and Dahl (2011) on spikes in domestic violence when a city’s football team suffers a surprise loss; Otto et al. (2016) on an increase in lottery sales in response to unexpected local sports team wins and sunny days; and Eren and Mocan (2018) on changes in judicial sentencing after a state’s college team unexpectedly loses or wins.

  35. While Card and Dahl (2011) do provide some theoretical structure in their paper, they tailor their model to fit the situation they are trying to describe rather than to provide a general model of incidental happiness on economic decision-making.

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Authors and Affiliations

  1. The Wharton School, University of Pennsylvania, Philadelphia, USA

    Judd B. Kessler

  2. Booth School of Business, University of Chicago, Chicago, USA

    Andrew McClellan

  3. Department of Economics, New York University, New York, USA

    James Nesbit & Andrew Schotter

Authors
  1. Judd B. Kessler
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  2. Andrew McClellan
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  3. James Nesbit
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  4. Andrew Schotter
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Correspondence to Andrew Schotter.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The authors thank Eric Clemons and NBC Sports for organizing recruitment and providing funding for the experiment. They thank Anwar Ruff for writing the software for the study; Ala Avoyan, Elizabeth Schotter, Joseph Briggs, and Severine Toussaert for their help running the experiments; and Rachel Ryley for RA work. They thank Ori Heffetz, Alex Rees-Jones, Homa Zarghamee, and seminar participants at CESS for helpful comments. In addition to organizing recruitment and funding the experiment, NBC Sports, through Eric Clemons, paid $5000 each to Judd B. Kessler and Andrew Schotter for consulting services related to analysis of data from the experiment. However, no party had the right to review the current manuscript or limit the publication of any research findings related to this project.

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Kessler, J.B., McClellan, A., Nesbit, J. et al. Short-term fluctuations in incidental happiness and economic decision-making: experimental evidence from a sports bar. Exp Econ 25, 141–169 (2022). https://doi.org/10.1007/s10683-021-09708-9

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