Loss Aversion Under Prospect Theory: A Parameter-Free Measurement

Abstract

International audienceA growing body of qualitative evidence shows that loss aversion, a phenomenon formalized in prospect theory, can explain a variety of field and experimental data. Quantifications of loss aversion are, however, hindered by the absence of a general preference-based method to elicit the utility for gains and losses simultaneously. This paper proposes such a method and uses it to measure loss aversion in an experimental study without making any parametric assumptions. Thus, it is the first to obtain a parameter-free elicitation of prospect theory's utility function on the whole domain. Our method also provides an efficient way to elicit utility midpoints, which are important in axiomatizations of utility. Several definitions of loss aversion have been put forward in the literature. According to most definitions we find strong evidence of loss aversion, at both the aggregate and the individual level. The degree of loss aversion varies with the definition used, which underlines the need for a commonly accepted definition of loss aversion

A parametric analysis of prospect theory’s functionals for the general population

“…Our estimates are closer to linearity as compared to the parametric studies of Harrison and Rutström (2009) and Donkers et al (2001), who found (α,β) = (.71, .72) and (.61, .61) respectively, which suggests that their parametric specifications may be inappropriate for separating utility from probability weighting. The estimates confirm diminishing sensitivity, both with respect to losses and to gains (Tversky and Kahneman 1992;Abdellaoui 2000;Abdellaoui et al 2007b), and we cannot reject equal curvature in both domains in favor of the more recent hypothesis of partial reflection (Wakker et al 2007). These results are qualitatively similar to those obtained by Booij and van de Kuilen (2007).…”

“…Their properties are described extensively in Abdellaoui et al (2007a). Unfortunately, a commonly accepted definition of loss aversion does not exist in the literature (Abdellaoui et al 2007b). The framework that we employ, used by Tversky and Kahneman (1992), defines loss aversion implicitly as:…”

“…An index can then be constructed by taking the mean or median values of the relevant values of x, but this is not an arbitrary choice making comparison between measurements difficult. Hence, we have to be careful with comparing loss aversion estimates (see Abdellaoui et al (2007b) for a more extensive discussion).…”

“…These studies deal with the simultaneity problem by either assuming a parametric form for these functions (Tversky and Kahneman 1992;Camerer and Ho 1994;Tversky and Fox 1995;Donkers et al 2001;Harrison and Rutström 2009;Abdellaoui et al 2008) or by exploiting a particular design that permits them to be disentangled non-parametrically (Wakker and Deneffe 1996;Abdellaoui 2000;Bleichrodt and Pinto 2000;Abdellaoui et al 2007b). …”

Risk Aversion, Loss Aversion, and the Demand for Insurance

“…An empirical study of Guo's model is beyond the aim of the present paper. However, we refer the interested reader to papers that have estimated similar models, finding values of λ either in the range of 1.4-5 (see Table 1 in Abdellaoui et al (2007)) or slightly above 1 Murphy and ten Brincke (2018); Nilsson et al (2011). For a careful and critical review, see, e.g., Harrison and Swarthout (2016).…”