Abstract
The “general risk question” (GRQ) has been established as a quick way to meaningfully elicit subjective attitudes toward risk and correlates well with real-world behaviors involving risk. However, little is known about what aspects of attitudes toward financial risk are captured by the GRQ. We examine how answers to the GRQ correlate with different preference motives and biases toward financial risk using an incentivized choice task (n = 1,730). We find that the GRQ has meaningful correlation with loss aversion and attitudes toward variation in financial losses, but much weaker to non-existent correlations with attitudes toward variation in financial gains, likelihood insensitivity, and certainty preferences. These results suggest that practical applications using the GRQ as an index for financial risk preferences may be most appropriate in settings where decisions rest on attitudes toward financial losses.
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Notes
Specifically, the GRQ asks respondents to rate their own willingness to take risks on a scale of 0 (not at all willing) to 10 (very willing). Our translation of Dohmen et al.’s original GRQ (from German) is displayed in Fig. 1.
Specifically, when only considering the gain domain, the model nests expected utility theory, rank dependent expected utility and the dual theory of Yaari (1987). The prospect theory functional also acts very similar to the reference-dependent preference model of Köszegi and Rabin (2007) in surprise gambles which are arguably the correct model for lottery decisions in experiments. The model does, however, not allow for eliciting preference of certain other models, such as regret theory (Bell 1982).
For the exact technical assumptions, refer to Online Appendix A.
v(0) = u(0) needs to be maintained such that a preference for certainty can exist within the framework of cumulative prospect theory.
The order of the risk preference elicitation tables was randomized. Additionally, we randomized whether the safe choices were displayed on the left or on the right and whether payments and probability were vertically increasing or decreasing.
In a slight abuse of terminology, we henceforth refer to these measures of preference motives as “preference scales.” For ease of reference, the calculations for all preference scales are also given in the captions of Fig. 2. We provide detailed proofs supporting our construction of these preference scales in Online Appendix A.
The analysis of those insurance choices and their link to preference measures is the focus of a separate research paper. That paper does not analyze the correlations with GRQ.
In compliance with local laboratory standards regarding hourly wages, university subjects were also paid a flat show-up fee of $6.00. This payment was not disclosed until the end of the experiment. No additional payments were made to mTurk subjects.
This pattern is not a product of randomizing the direction of the GRQ response scale. Both the group that answered on a 0-10 scale and the group that answered on a 10-0 scale show a bimodal pattern of responses.
Specifically, our measure of loss aversion ranges from 0 to 21, while the other preferences are on a 33-point scale (UC measures are 0 to 32, while LI and CP are -16 to 16). GRQ is on an 11-point scale (0 to 10). To address this in our analysis, we standardize GRQ and the preference scales by subtracting the mean and dividing by the standard deviation, denoting these standardized values with the subscript std. We evaluate the correlations and regression coefficients as shifts in standard deviation rather than comparing unit changes of unequal measure.
Callen et al. (2014), for example, report that 69% of their subjects switched in the same row after excluding dominance-violating choices. Our GD or LD tables show this pattern in less than 20% of the cases.
There is a substantial literature showing the gender effect on risk preferences. See Charness and Gneezy (2012) for a meta analysis.
In the experiment, the “Next” button was hidden for 20 seconds on each lottery page to encourage thoughtful consideration of the lottery choices (and to prevent subjects from clicking through as soon as the page loads). The subsample in column (1) thus includes only subjects who always took at least 5 seconds more than the minimum requirement.
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We thank the Alfred P. Sloan Foundation for financial support under grant number G-2016-7312. For helpful feedback and comments, we are indebted to Sebastian Ebert, Ferdinand Vieider, Philipp Wichardt, and seminar participants at Universität Hamburg, the Southern Risk and Insurance Association annual meeting, and the American Risk and Insurance Association annual meeting. All remaining errors are ours.
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Jaspersen, J.G., Ragin, M.A. & Sydnor, J.R. Linking subjective and incentivized risk attitudes: The importance of losses. J Risk Uncertain 60, 187–206 (2020). https://doi.org/10.1007/s11166-020-09327-4
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DOI: https://doi.org/10.1007/s11166-020-09327-4