Avoiding the Curves: Direct Elicitation of Time Preferences

Abstract

We propose and test a new method for eliciting curvature-controlled discount rates that are invariant to the form of the utility function. Our method uses a single elicitation task and has the advantage of obtaining individual discount rates without knowledge of risk attitude or parametric assumptions about the form of the utility function. We compare our method to the Andersen et al. (2008) double elicitation technique in which the utility function and discount rate are jointly estimated. We use a laboratory experiment to perform a within-subjects comparison of discount rates from these two methods and find consistent results, which is reassuring given the wide range of estimates in the literature. In addition, the estimated discount rates in our study are "plausibly low" in contrast to the vast majority of discount rate studies. Our results are robust to relaxing the expected utility assumption of linearity in the probabilities, as we find little evidence of probability weighting in our data. In a second experiment, we find that discount rates are not sensitive to the length of the horizon, but are sensitive to the length of the front-end delay, suggesting present bias. We estimate average discount rates to be 12.2 percent in the first experiment and 11.3 percent in the second experiment when the front-end delay is at least two weeks.

The pricing kernel puzzle: survey and outlook

“…Closely related is the work by Bartunek and Chowdhury (1997) who use power utility and Benth, Groth, and Lindberg (2010) who use exponential utility instead; both papers calibrate to options data. While the equity premium literature equates the forward looking physical probability distribution with the historical distribution and determines the intertemporal rate of substitution solely through the risk aversion coefficient, Andersen, Fountain, Harrison, and Rutstroem (2014) suggest to elicit physical probabilities, too, and Sprenger (2012a, 2012b) and Laury, McInnes, Swarthout, and Von Nessen (2012) additionally estimate time preferences. 2 2 An interesting observation reconciling the two estimation methods (surveys/experiments vs. market based) can be found in Haug, Hens, and Woehrmann (2013) who argue that the typical inclusion of background risk in market studies leads to larger risk aversion coefficients than in surveys or experiments which tend to ignore background risk.…”

Do lottery payments induce savings behavior? Evidence from the lab

“…See, for example, Coble and Lusk (2010), Andersen, Harrison, Lau, and Rutström (2011a) and Laury et al (2012). The latter paper also examines non-linear probability weighting and fails to find evidence against linear probability weights, but this could be due to their use of intermediate probabilities.…”

“…Rather, our paper should be seen as showing that savings can be increased by making the delayed payment risky. Nevertheless, it is worth mentioning that lab and field experiments can give consistent results when a similar estimation method is used such as Laury, McInnes, and Swarthout (2012) and Andersen et al (2008), though in general it is common for point estimates to vary across lab and field experiments and to vary significantly even within field experiments. In this paper, we offer a mechanism -non-linear probability weighting-for the attractiveness of PLS.…”

Procedures for eliciting time preferences

“…Incentivized work on methods for measuring time preferences has studied alternative ways to jointly measure a person's discount rate and utility function curvature (e.g. Andreoni and Sprenger (2012), Andreoni et al (2015), and Laury et al (2012)), but has ignored the possibility that the elicitation procedure used might a↵ect inferences about discounting even when restricted to the domain of dated rewards.…”