The Nature of Risk Preferences: Evidence from Insurance Choices

Abstract

We use data on insurance deductible choices to estimate a structural model of risky choice that incorporates "standard" risk aversion (diminishing marginal utility for wealth) and probability distortions. We find that probability distortionscharacterized by substantial overweighting of small probabilities and only mild insensitivity to probability changes-play an important role in explaining the aversion to risk manifested in deductible choices. This finding is robust to allowing for observed and unobserved heterogeneity in preferences. We demonstrate that neither Kőszegi-Rabin loss aversion alone nor Gul disappointment aversion alone can explain our estimated probability distortions, signifying a key role for probability weighting.(JEL D01, D03, D12, D81, G22) * For helpful comments, we thank seminar and conference participants at Berkeley, Collegio

Fungibility and Consumer Choice: Evidence from Commodity Price Shocks*

“…To our knowledge, ours is the first paper to estimate Köszegi and Rabin’s (2006) and Bordalo, Gennaioli, and Shleifer’s (2012) models using data on retail purchases. In that sense, our paper contributes to a growing literature that structurally estimates the parameters of psychological models of decision-making using field data (Laibson, Repetto, and Tobacman 2007; Conlin, O’Donoghue, and Vogelsang 2007; Barseghyan et al forthcoming; Crawford and Meng 2011; Grubb and Osborne 2012; DellaVigna, List, and Malmendier 2012). Ours is among the first of these papers to compare the predictions of more than one psychological model.…”

Probability and Risk: Foundations and Economic Implications of Probability-Dependent Risk Preferences

“…A more plausible explanation for this kind of widely observed behavior is that people overweight the probability of damage, resulting in a high degree of risk aversion over low-probability events. Barseghyan et al (2010) test this hypothesis by analyzing consumers' deductible choices in auto and home insurance policies. Their sample comprises 4,170 households that purchased new policies from a large US property and casualty insurance company in 2005 or 2006.…”

From Aggregate Betting Data to Individual Risk Preferences

“…Our contributions here are most closely related to the literature on estimating and evaluating theories of individual risk preferences, and also to the literature on identification of random utility models. Most of it has focused on insurance choices (see, e.g., Cohen and Einav (2007), Sydnor (2010); and Barseghyan et al (2013) and Barseghyan, Molinari, and Teitelbaum (2016)) and gambling behavior (see, e.g., Andrikogiannopoulou and Papakonstantinou (2016)). There is also a sizable literature that directly elicits individual risk preferences through survey questions (see, e.g., Barsky et al (1997), Bonin, Dohmen, Falk, Huffman, and Sunde (2007), Dohmen, Falk, Huffman, Sunde, Schupp, and Wagner (2011)) and correlates these measures with other economic behaviors.…”

“…Results indicate that this parameter varies between 2 (for the first decile) and 25 (for the last decile), and that this heterogeneity is poorly explained by demographic variables. Distributions of risk aversions have also been estimated using data on television games (Beetsma and Schotman (2001)), insurance markets (Cohen and Einav (2007), Barseghyan, Molinari, O'Donoghue, and Teitelbaum (2013), Barseghyan, Molinari, and Teitelbaum (2016)), or risk sharing within closed communities (Chiappori, Samphantharak, Schulhofer-Wohl, and Townsend (2014)). Chiappori and Paiella (2011) observed the financial choices of a sample of households across time, and used these panel data to show that while a model with constant relative risk aversion well explains each household's choices, the corresponding coefficient is highly variable across households (its mean is 4 2, for a median of 1 7).…”