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Do Affective States Influence Risk Preferences?

Evidence from Incentive-Compatible Experiments

  • Original Article
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Schmalenbach Business Review Aims and scope

Abstract

Recent discussions in decision sciences and economics stress the potential impact of affect on decision outcomes. In this study, we conducted incentive-compatible laboratory experiments (N = 253) to investigate whether affect causes temporary fluctuations in risk preferences. In particular, we employed film clips to induce participants into joyful, fearful and sad affective states and subsequently elicited risk preferences by asking the participants to make choices among different lotteries. The financial consequences of the lottery choices varied randomly among the fixed-, low-, and high-stakes treatment groups. We find only weak evidence that affective states influence risk preferences. In particular, we find some evidence that sadness leads to risk aversion, but we find no effects for joy and fear. Our findings question recent claims in the literature that the relationship between affect and risk preferences is strong and unambiguous.

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Notes

  1. Affect is often employed as an umbrella term that refers to the current moods and emotions of individuals. Moods are generally described as low-intensity, diffuse, and relatively enduring affective states that often arise for no particularly salient reason. Compared with moods, emotions are considered to be more intense and short-lived affective states that generally have a definite cause and clear cognitive content (Davidson 1994; Gray and Watson 2001). The distinctions between moods and emotions are largely theoretical rather than empirical in nature. In research practice, identical methods are often used to induce both moods and emotions (Fredrickson 2002). In this paper, we use the terms “affect”, “affective state”, and “mood” interchangeably. Nevertheless, we believe that the definition of mood – low-intensity, diffuse, and not object-directed – is more appropriate to our empirical examination than the definition of emotion – intense, object-directed, and with clear cognitive content.

  2. Pham (2007) presents a critical review on the empirical evidence on the relationship between emotion and rationality.

  3. According to Thomson Reuter’s web of science, 188 articles have been published on the relationship between affect (emotion, mood) and risk references in the social sciences since 1980 until 2014.

  4. The Journal of Business & Psychology edited a special issue addressing null results and several articles in this special issue served as excellent examples when null results can be meaningful.

  5. Overall, the internal reliabilities for extroversion (Cronbach’s α = 0.74), agreeableness (α = 0.23), conscientiousness (α = 0.71), emotional stability (α = 0.69), and openness (α = 0.52) were not satisfactory. Therefore, we conducted a rotated factor analysis and extracted four components with eigenvalues that were greater than 1, that is, extroversion, emotional stability, conscientiousness, and openness. Agreeableness was not found to be a separate factor and was not included as a control variable in the analysis. The principal component analysis (PCA) considers all available information, whereas sum scores of the items for one personality dimension would ignore the personality differences among participants. The factor scores and the sum scores were nearly perfectly correlated for four of the five personality dimensions examined (rs > 0.90, ps < 0.001).

  6. We pre-tested film clips from “Silence of the Lambs” and “Halloween” as suggested by Hewig et al. (2005) to induce fear in a sample of 50 students. These film clips did not significantly induce a fearful state in our participants. Thus, we chose to use a film clip from “Paranormal Activity” that successfully induced the desired fearful state.

  7. We used factor scores for the analysis because they consider all the available information, whereas sum scores of the items for one mood type would ignore the mood differences among participants that arose from changes in fear or sadness. We included the same PANAS-X items in the principal component analysis (PCA) factor scores as in the sum scores. Varimax rotation is applied. The factor scores and sum scores were nearly perfectly correlated for all three of the examined moods (r > 0.95, p < 0.001). The results are robust to using sum scores instead of factor scores.

  8. d = 0.20 denotes a small effect, d = 0.50 a medium effect, and d = 0.80 a large effect (Cohen 1988).

  9. Detailed correlation results available upon request.

  10. No evidence against poolability was produced by a poolability analysis across the factor of gender.

  11. We only consider α = 0.05 given that none of our results survives the conservative Bonferroni correction. Statistical power was calculated using formulas derived from first principals, which yields results that are identical to those obtained from using standard software packages such as GPOWER (Erdfelder et al. 1996).

  12. Ibid.

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Authors and Affiliations

  1. Private University Seeburg Castle, Seeburgstraße 8, 5201, Seekirchen am Wallersee, Austria

    Theresa Treffers

  2. Department of Complex Trait Genetics, Free University of Amsterdam, Amsterdam, The Netherlands

    Philipp D. Koellinger

  3. Institute for Behavior and Biology (EURIBEB), Erasmus University Rotterdam, Rotterdam, The Netherlands

    Philipp D. Koellinger

  4. Research Center for Information, Organisation and Management, Munich School of Management, Ludwig-Maximilians-Universität, Munich, Germany

    Arnold Picot

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  1. Theresa Treffers
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  2. Philipp D. Koellinger
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  3. Arnold Picot
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Correspondence to Theresa Treffers.

Additional information

We thank the Erasmus Research Institute of Management (ERIM) and the German Research Foundation (PI 97 /15-1) for financial and organizational support.

Appendix

Appendix

We estimated regressions using the model specifications in Table 4 and only the incentive-compatible low- and high-financial-stakes observations from our sample in studies 1 and 2 (N = 148). There are no significant effects of moods on risk preferences after correction for multiple testing.

Table 6 OLS regressions on risk preferences with incentive-compatible observations
Full size table

In Table 7, we used the excluded observations from study 1 (N = 188) and calculated regressions with identical model specifications as for models 1 and 2 in Table 4. These observations were obtained from participants in study 1 who had completed one or two other tasks between the completion of their mood induction and their risk-preference assessment. Previous studies (Kim and Kanfer 2009) have demonstrated that mood induction becomes weaker as the time period between the induction and task measurement becomes longer. It is therefore unsurprising that none of the mood treatments had a significant effect on risk preferences in the data from these excluded observations. Indeed, none of the experimental treatments had a significant effect in these data after correcting for multiple testing.

Table 7 OLS regressions on risk preferences with the observations excluded from study 1
Full size table

In Table 8, we used the pooled data from study 1 (N = 278) and conducted regression analyses with the model specifications of models 1 and 2 in Table 4. We find no significant mood effects on risk preferences.

Table 8 OLS regressions on risk preferences with pooled observations from study 1
Full size table

Table 9 reports the regression results by financial stakes when using the pooled data from study 1 (N = 278) and model specifications identical to those in Table 5. None of the experimental treatments has a significant effect in these data after correcting for multiple testing.

Table 9 OLS regressions on risk preferences by stakes with observations from study 1
Full size table

In Table 10, we used the pooled high-stakes observations from study 1 and from study 2 (N = 92) to conduct a regression analysis with model specifications identical to those in Model 1 in Table 4. We do not find significant effects of moods on risk preferences.

Table 10 OLS regressions on risk preferences with pooled high-stakes observations from studies 1 and 2
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Treffers, T., Koellinger, P.D. & Picot, A. Do Affective States Influence Risk Preferences?. Schmalenbach Bus Rev 17, 309–335 (2016). https://doi.org/10.1007/s41464-016-0018-3

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