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Full-Text Articles in Insurance Law
The Cost Of Legal Restrictions On Experience Rating, Levon Barseghyan, Francesca Molinari, Darcy Steeg Morris, Joshua C. Teitelbaum
The Cost Of Legal Restrictions On Experience Rating, Levon Barseghyan, Francesca Molinari, Darcy Steeg Morris, Joshua C. Teitelbaum
Georgetown Law Faculty Publications and Other Works
We investigate the cost of legal restrictions on experience rating in auto and home insurance. The cost is an opportunity cost as experience rating can mitigate the problems associated with unobserved heterogeneity in claim risk, including mispriced coverage and resulting demand distortions. We assess this cost through a counterfactual analysis in which we explore how risk predictions, premiums, and demand in home insurance and two lines of auto insurance would respond to unrestricted multiline experience rating. Using claims data from a large sample of households, we first estimate the variance-covariance matrix of unobserved heterogeneity in claim risk. We then show …
Do Credit-Based Insurance Scores Proxy For Income In Predicting Auto Claim Risk?, Darcy Steeg Morris, Daniel Schwarcz, Joshua C. Teitelbaum
Do Credit-Based Insurance Scores Proxy For Income In Predicting Auto Claim Risk?, Darcy Steeg Morris, Daniel Schwarcz, Joshua C. Teitelbaum
Georgetown Law Faculty Publications and Other Works
Auto insurers often use credit-based insurance scores in their underwriting and rating processes. The practice is controversial—many consumer groups oppose it, and most states regulate it, in part out of concern that insurance scores proxy for policyholder income in predicting claim risk. We offer new evidence on this issue in the context of auto insurance. Prior studies on the subject suffer from the limitation that they rely solely on aggregate measures of income, such as the median income in a policyholder's census tract or zip code. We analyze a panel of households that purchased auto and home policies from a …
Inference Under Stability Of Risk Preferences, Levon Barseghyan, Francesca Molinari, Joshua C. Teitelbaum
Inference Under Stability Of Risk Preferences, Levon Barseghyan, Francesca Molinari, Joshua C. Teitelbaum
Georgetown Law Faculty Publications and Other Works
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 …
Distinguishing Probability Weighting From Risk Misperceptions In Field Data, Levon Barseghyan, Francesca Molinari, Ted O'Donoghue, Joshua C. Teitelbaum
Distinguishing Probability Weighting From Risk Misperceptions In Field Data, Levon Barseghyan, Francesca Molinari, Ted O'Donoghue, Joshua C. Teitelbaum
Georgetown Law Faculty Publications and Other Works
The paper outlines a strategy for distinguishing rank-dependent probability weighting from systematic risk misperceptions in field data. Our strategy relies on singling out a field environment with two key properties: (i) the objects of choice are money lotteries with more than two outcomes and (ii) the ranking of outcomes differs across lotteries. We first present an abstract model of risky choice that elucidates the identification problem and our strategy. The model has numerous applications, including insurance choices and gambling. We then consider the application of insurance deductible choices and illustrate our strategy using simulated data.
The Nature Of Risk Preferences: Evidence From Insurance Choices, Levon Barseghyan, Francesca Molinari, Joshua C. Teitelbaum, Ted O'Donoghue
The Nature Of Risk Preferences: Evidence From Insurance Choices, Levon Barseghyan, Francesca Molinari, Joshua C. Teitelbaum, Ted O'Donoghue
Georgetown Law Faculty Publications and Other Works
The authors 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. They find that probability distortions--characterized 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. They 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.
Are Risk Preferences Stable Across Contexts? Evidence From Insurance Data, Levon Barseghyan, Jeffrey Prince, Joshua C. Teitelbaum
Are Risk Preferences Stable Across Contexts? Evidence From Insurance Data, Levon Barseghyan, Jeffrey Prince, Joshua C. Teitelbaum
Georgetown Law Faculty Publications and Other Works
Using a unique data set, the authors test whether households' deductible choices in auto and home insurance reflect stable risk preferences. Their test relies on a structural model that assumes households are objective expected utility maximizers and claims are generated by household-coverage specific Poisson processes. They find that the hypothesis of stable risk preferences is rejected by the data. Their analysis suggests that many households exhibit greater risk aversion in their home deductible choices than their auto deductible choices. They find that their results are robust to several alternative modeling assumptions.