Inference Under Stability of Risk Preferences

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. We leverage the assumption that preferences are stable across contexts to partially identify and conduct inference on the parameters of a structural model of risky choice. Working with data on households' deductible choices across three lines of insurance coverage and a model that nests expected utility theory plus a range of non-expected utility models, we perform a revealed preference analysis that yields household-specific bounds on the model parameters. We then impose stability and other structural assumptions to tighten the bounds, and we explore what we can learn about households' risk preferences from the intervals defined by the bounds. We further utilize the intervals to (i) classify households into preference types and (ii) recover the single parameterization of the model that best fits the data. Our approach does not entail making distributional assumptions about unobserved heterogeneity in preferences. Terms of use: Documents in EconStor may

The Cost of Legal Restrictions on Experience Rating

“…This finding adds to a growing body of evidence that riskiness is a trans‐substantive characteristic of individuals (e.g., Barksy et al ; Dohmen et al ; Golden et al ). It also complements existing research suggesting that risk aversion, though not completely stable across contexts (Barseghyan et al ), also has a latent, domain‐general component (e.g., Einav et al ; Barseghyan et al )…”

“…This finding adds to a growing body of evidence that riskiness is a trans-substantive characteristic of individuals (e.g., Barksy et al 1997;Dohmen et al 2011;Golden et al 2016). It also complements existing research suggesting that risk aversion, though not completely stable across contexts (Barseghyan et al 2011), also has a latent, domain-general component (e.g., Einav et al 2012;Barseghyan et al 2016). 22 Next, we show that conditioning on claims experience leads to material refinements of predicted claim rates, with the average downward revisions ranging from 7 percent to 14 percent and the average upward revisions ranging from 10 percent to 28 percent.…”

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).…”

“…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). 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)).…”