Risk Preferences and Demand Drivers of Extended Warranties

Abstract

The objective of this paper is to understand what drives consumers to buy extended warranties and pay high premia for them. We primarily focus on the role of risk preferences and disentangle and study their relative importance. Empirical and behavioral research on insurance is at odds with whether diminishing returns (curvature of the utility function), or loss aversion and non-linear probability weighting lead to the observed consumer behavior. This is primarily due to the inability of standard choice data to separate curvature of the utility function, loss aversion and non-linear probability weights, and the need to rely on strong parametric assumptions. We design two conjoint studies (consistent with simultaneous and sequential decision making) with choices over washing machines (with and without extended warranty) where failure probabilities and repair costs are given to subjects. Using stated choice data from the survey, consumer preferences, degree of curvature, loss aversion and probability weights can be non-parametrically identied. We nd that loss aversion is signicantly more important than curvature and probability weights in explaining extended warranty choices. These ndings are robust to dierent specications of the utility function and risk preferences. Importantly, failure to decompose risk averse behavior into that arising from curvature, loss aversion and probability weighting leads to lower washer and warranty prices, and under predicts the rate of change of warranty prices with varying repair costs. We test theory on complementary good pricing and nd that estimates from the sequential choice survey rationalize the high premium consumers pay for extended warranties. Finally, forcing dierent retailers to sell washers and extended warranties increases (decreases) the washer (warranty) price, and makes consumers worse-o. A 5% discount on the washer price makes consumers indierent to the proposed policy intervention.

Mental Accounting, Access Motives, and Overinsurance*

“…Similarly, both loss aversion and probability weighting can help explain the purchase of insurance against modest risks such as warranties (Rabin and Thaler, 2001;Cutler and Zeckhauser, 2004;Michel, 2018), and empirical evidence has been found in support of these approaches (Jindahl, 2014). Nevertheless, there is also empirical evidence calling into question whether this can be the whole story.…”

The premium as informational cue in insurance decision making

“…However, this potential value of the premium has been rarely investigated in the insurance decision making literature. The literature is traditionally concerned with testing the hypothesis of expected utility maximization when the loss probability is given (e.g., Cutler and Zeckhauser 2004;Kunreuther et al 2013;Jindal 2015). Those investigations typically have a focus on applying prospect theory (Kahneman and Tversky 1979) and relatedly, reference dependence and loss aversion (Köszegi and Rabin 2006;Sydnor 2010) as well as the overweighting of small probabilities (Barseghyan et al 2013; see also Fox 1995 andWakker 1995); see Ho et al (2006) for some further discussion and Jindal (2015) for recent development.…”

“…The literature is traditionally concerned with testing the hypothesis of expected utility maximization when the loss probability is given (e.g., Cutler and Zeckhauser 2004;Kunreuther et al 2013;Jindal 2015). Those investigations typically have a focus on applying prospect theory (Kahneman and Tversky 1979) and relatedly, reference dependence and loss aversion (Köszegi and Rabin 2006;Sydnor 2010) as well as the overweighting of small probabilities (Barseghyan et al 2013; see also Fox 1995 andWakker 1995); see Ho et al (2006) for some further discussion and Jindal (2015) for recent development. There is a similar emphasis in a more general stream of literature on decision behavior; in that literature, the decision maker's estimates of the probabilities of observing different outcomes are usually taken as given, and the focus is on how the estimated probabilities are combined or are translated into decision weights (e.g., Abdellaoui et al 2011;Kilka and Weber 2001;Tversky and Fox 1995;Tversky and Wakker 1995).…”

Optimal extended warranty strategy: uniform or nonuniform pricing?

“…This finding is consistent with the argument presented by Brooks and White (1996), and can also be widely observed in reality. For example, in the vehicle industry, consumers who buy the extended warranties after the product purchasing time have to pay a higher price (Jindal, 2014).…”