How to reveal people’s preferences Comparing time

Authors: Tamás Csermely Alexander Rabas

Publish Date: 2017/02/01

Volume: 53, Issue: 2-3, Pages: 107-136

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Abstract

The question of how to measure and classify people’s risk preferences is of substantial importance in the field of economics Inspired by the multitude of ways used to elicit risk preferences we conduct a holistic investigation of the most prevalent method the multiple price list MPL and its derivations In our experiment we find that revealed preferences differ under various versions of MPLs as well as yield unstable results within a 30minute time frame We determine the most stable elicitation method with the highest forecast accuracy by using multiple measures of withinmethod consistency and by using behavior in two economically relevant games as benchmarks A derivation of the wellknown method by Holt and Laury American Economic Review 9251644–1655 2002 where the highest payoff is varied instead of probabilities emerges as the best MPL method in both dimensions As we pinpoint each MPL characteristic’s effect on the revealed preference and its consistency our results have implications for preference elicitation procedures in generalRisk is a fundamental concept that affects human behavior and decisions in many reallife situations Whether a person wants to invest in the stock market tries to select the best health insurance or just wants to cross the street he/she will face risky decisions every day Therefore risk attitudes are of high importance for decisions in many economicsrelated contexts A multitude of studies elicit risk preferences in order to control for risk attitudes as it is clear that they might play a relevant role in explaining results — eg De Véricourt et al 2013 in the newsvendor setting Murnighan et al 1988 in bargaining Beck 1994 in redistribution or Tanaka et al 2010 in linking experimental data to household income to name just a few Moreover several papers try to shed light on the causes of riskseeking and riskaverse behavior in the general population with laboratory Harrison and Rutström 2008 internet Von Gaudecker et al 2011 and field experiments Andersson et al 2016 Harrison et al 2007 Since the seminal papers by Holt and Laury 2002 2005 approximately 20 methods have been published which provide alternatives to elicit risk preferences They differ from each other in terms of the varied parameters representation and framing Many of these risk elicitation methods have the same theoretical foundation and therefore claim to measure the same parameter — a subject’s “true” risk preference However there are significant differences in results depending on the method used as an increasing amount of evidence suggests It follows that if someone’s revealed preference is dependent on the measurement method used scientific results and realworld conclusions might be biased and misleadingAs far as existing comparison studies are concerned they usually compare two methods with each other and often use different stakes parameters framing representation etc which makes their results hardly comparable Our paper complements existing experimental literature by making the following contribution Taking the method by Holt and Laury 2002 as a basis we conduct a comprehensive comparison of the multiple price list MPL versions of risk elicitation methods by classifying all methods into nine categories To the best of our knowledge no investigation — including various measures of between and withinmethod consistency — has ever been conducted in the literature that incorporates such a high number of methods To isolate the effect of different methods we consistently use the MPL representation and calibrate the risk intervals to be the same for each method assuming expected utility theory EUT and constant relative risk aversion CRRA while also keeping the riskneutral expected payoff of each method constant and employing a withinsubject design Moreover our design allows us to investigate whether differences across methods can be reconciled by assuming different functional forms documented in the literature such as constant absolute risk aversion CARA decreasing relative risk aversion DRRA decreasing absolute risk aversion DARA increasing relative risk aversion IRRA and increasing absolute risk aversion IARA Additionally we extend our analysis to incorporate EUT with probability weighting and also to incorporate prospect theory PT and cumulative prospect theory CPTWe investigate the withinmethod consistency of each method by comparing the differences in subjects’ initial and repeated decisions within the same MPL method Moreover we assess methods’ selfperceived complexity and shed light on differences and similarities between methods In the end we provide suggestions for which specific MPL representation to use by comparing our results to decisions in two benchmark games that resemble reallife settings investments in capital markets and auctions Therefore we analyze the methods along two dimensions robustness and predictive power and determine which properties of particular methods drive risk attitude and its consistencyWe find that a particular modification of the method by Holt and Laury 2002 derived by Drichoutis and Lusk 2012 2016 has the highest predictive power in investment settings both according to the OLS regression and Spearman rank correlation In addition specific methods devised by Bruner 2009 also perform relatively well in these analyses However the method by Drichoutis and Lusk 2012 2016 clearly outperforms the other methods in terms of withinmethod consistency and is perceived as relatively simple — in the end our study provides the recommendation for researchers to implement this method when measuring risk attitudes using an MPL framework Moreover our results remain qualitatively the same if we relax our assumption on the risk aversion function or if we take probability weighting or alternative theories such as prospect theory or cumulative prospect theory into accountIncentivized risk preference elicitation methods aim to quantify subjects’ risk perceptions based on their revealed preferences We present nine methods in a unified structure — the commonly used MPL format — to our subjects taking one of the most cited methods as a basis Holt and Laury 2002 The MPL table structure is as follows Each table has multiple rows and in each row all subjects face a lottery two columns on one side of the table and a lottery or a certain payoff one or two columns on the other side depending on the particular method Then from row to row one or more of the parameters change The methods differ from each other by the parameter which is changing As the options on the right side become strictly more attractive from row to row a subject indicates the row where he/she wants to switch from the left option to the right option This switching point then gives us an interval for a subject’s risk preference parameter according to Table 11 assuming EUT and CRRA2Note that several other representations of risk elicitation methods exist besides the MPL such as the bisection method Andersen et al 2006 the tradeoff method Wakker and Deneffe 1996 questionnairebased methods Weber et al 2002 willingnesstopay Hey et al 2009 etc but the MPL is favored because of its common usage Andersen et al 2006 consider that the main advantage of the MPL format is that it is transparent to subjects and it provides simple incentives for truthful preference revelation They additionally list its simplicity and the little time it takes as further benefits As far as the specific risk elicitation method in the MPL framework designed by Holt and Laury 2002 is concerned it has proven itself numerous times in providing explanations for several phenomena such as behavior in 2x2 games Goeree et al 2003 market settings Fellner and Maciejovsky 2007 smoking heavy drinking being overweight or obese Anderson and Mellor 2008 consumption practices Lusk and Coble 2005 and many others

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