Abstract
We examine whether exposure to a more or less risky environment affects people’s subsequent risk-taking behavior. In a laboratory setting, all subjects went through twelve rounds of multiple-price-list decisions between a risky alternative and a safe alternative. In the first six rounds, subjects were randomly assigned to a high-, moderate-, or low-risk environment, which differed in the variances of the lotteries they were exposed to. In the last six rounds, subjects in all treatments made decisions on an identical set of lotteries. We found that subjects who had experienced a riskier environment exhibited a higher degree of risk aversion. Our experimental design allows us to conclude that this effect is driven by the risk environment per se, rather than the realized outcomes of the risk. This finding has important theoretical and policy implications.
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Notes Fig. 2 displays the average number of Safe choices for each lottery (characterized by x) in rounds 7–12 by treatment, for choices with no more than one switch point
Notes Fig. 3 illustrates the average reaction time in each round of the main task. Reaction time is the elapsed time between the presentation of the lottery and sure outcome options and the submission of the final decisions. The average reaction time gradually declines over rounds; however, a significant jump occurs in round 7 of the LM, LM′, HM and HM′ groups
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Notes
Besides, Mengel et al. (2016) argue that for those field studies demonstrating the link between macroeconomic shocks or financial crises and risk-taking behavior, it is difficult to establish whether such effects are due to an increase in risk aversion or to updated priors or other reasons.
In Holt and Laury (2002), subjects choose between a riskier lottery and a less risky lottery for each row. In our experiment, subjects chose between a lottery and a sure outcome.
Specifically, given x, the better outcome of the lottery in the round, in any row i, where i ∈ {1, 2,…, 13}, the following equation shows how the value of z i was determined.
$$z_{i} = \left\{ {\begin{array}{*{20}l} {400 - x + \frac{200 - (400 - x)}{10} \cdot i,} \hfill & {if\;i \le 10} \hfill \\ {200 + \frac{(x - 200)}{5} \cdot (i - 10),} \hfill & {if\;i > 10} \hfill \\ \end{array} } \right.$$For instance, given x = 210, the sure outcomes zi’s are 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 202, 204, 206, respectively. See also Table 1 for the more details. The Online Appendix B shows all the 12 rounds of the main decision-making task in the HM treatment.
For each of the first six rounds in treatment LM′, we drop the first row of the menu, based on that of treatment LM, so that subjects would make one fewer lottery choice and switch earlier accordingly. For each of the first six rounds in treatment HM′, compared to that in treatment HM, we added a row at the top of the choice menu with the sure outcome being small enough to ensure that subjects would choose the lottery option in the first row. This way, each of the first six rounds in treatment LM′ contains 12 rows, while each of the first six rounds in treatment HM′ contains 14 rows. Refer to Table 2 for more details.
Subjects in the HM, MM, and LM groups made a total of 156 binary decisions (13 decisions in one round × 12 rounds). Subjects in the HM′ group made 162 binary decisions, while subjects in the LM′ group made 150 decisions.
Our aim is to identify whether a high-risk or low-risk prior environment has an effect on subsequent risk-taking behaviors. Thus, we recruited more subjects for the HM and LM treatments than the MM treatment. HM′ and LM′ are treatments for robustness check.
The hourly rate for an undergraduate student assistant in Singapore is between $6–$9. Hence, given the length of a session, the incentive offered in this study is more than twice as much as other available options.
Otherwise, at least one of the following properties of rationality is violated: (1) completeness, (2) transitivity, and (3) more is better.
Experimental studies which use the multiple price list method to elicit risk or time preferences find that some subjects switch back and forth from row to row, a phenomenon known as multiple switch points (e.g. Andersen et al. 2006). Holt and Laury (2002) document that roughly 10 percent of the observations exhibited multiple switching from an undergraduate student subject pool.
Meanwhile, the average numbers of Safe choices in the HM′ group appear to be higher than in the HM group.
The lottery-averaged number of Safe choices means a subject’s average number of Safe choices in the last six rounds. For reasons of comparability, we excluded the subjects who made any irrational decisions in the last six rounds for the analysis in the last column of Table 4.
For the lottery with x = 300, subjects chose slightly more Safe options in the first six rounds than in the last six rounds (on average 6 vs. 5.4).
Of our subjects, 56% were male, 91% were Chinese, 23% were Economics or Business majors, and 17% had previous experience in financial investment.
Introducing interaction terms between period dummies and High or Low to columns (1) and (2) or introducing interaction terms between period dummies and the four treatment dummies to columns (3) and (4) does not change the results qualitatively. In addition, most coefficients of the period dummies and interaction terms remain insignificant.
For each column, the coefficient of HM′ is also significantly different from the coefficients of LM (p values < 0.05) and LM′ (p values < 0.003).
Given a subject’s unique switch point for a lottery, we can calculate the subject’s expected certainty equivalence for the lottery. Certainty equivalence indicates the guaranteed return that a subject is willing to accept instead of taking the lottery, which could provide a different angle on subjects’ risk-taking behavior. To complement the above analysis, we calculated expected certainty equivalence for each subject and each lottery in the second half of the session, and ran regressions with certainty equivalence as the dependent variable. The Online Appendix D details the analysis and reports the results, which support the above findings.
Relatedly, from a functional-evolutionary perspective, Ohman (1986) shows that fear originates in a predatory defense system whose function is to allow animals to avoid and escape predators. This suggests that the emotions associated with high risk, like fear and stress, are also evolutionarily adaptive strategies conducive to survival in a high-risk environment.
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Acknowledgements
We thank the editor, two referees, Ian Krajbich, Lionel Page, Joseph Tao-yi Wang, Songfa Zhong, seminar and conference participants in Nanyang Technological University, National Cheng-chi University, National Taiwan University, National Tsing-hua University, and the 12th International Conference of Western Economic Association International for helpful comments. He acknowledges financial support from Nanyang Technological University (HASS Start-up grant).
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Earlier versions of this paper were circulated under the titles of “Exposure to Risk and Risk Aversion: A Laboratory Experiment” and “Bigger Risk, More Risk Aversion: Experimental Evidence”.
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He, TS., Hong, F. Risk breeds risk aversion. Exp Econ 21, 815–835 (2018). https://doi.org/10.1007/s10683-017-9553-0
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DOI: https://doi.org/10.1007/s10683-017-9553-0