Estimating Risk Preferences in the Field

Barseghyan, Levon; Molinari, Francesca; O’Donoghue, Ted; Teitelbaum, Joshua C.

doi:10.1257/jel.20161148

Cited by 112 publications

(76 citation statements)

References 134 publications

(203 reference statements)

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“…However, the relationship between past experiences with losses and insurance take-up is not monotonic. Michel-Kerjan et al (2012) study zip-code-level claims data from the National Flood 29 For a detailed discussion of these findings in particular and models of insurance choices more generally, see Barseghyan et al (2016). …”

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confidence: 99%

Behavioral Household Finance

Beshears

Choi

Laibson

et al. 2018

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This chapter provides an overview of household finance. The first part summarizes key facts regarding household financial behavior, emphasizing empirical regularities that are inconsistent with the standard classical economic model and discussing extensions of the classical model and explanations grounded in behavioral economics that can account for the observed patterns. This part covers five topics: consumption and savings, borrowing, payments, asset allocation, and insurance. The second part addresses interventions that firms, governments, and other parties deploy to shape household financial outcomes: education and information, peer effects and social influence, product design, advice and disclosure, choice architecture, and interventions that directly target prices or quantities. This chapter is divided into two parts, each of which is further divided into several sections. The first part summarizes key facts regarding household financial behavior, emphasizing empirical regularities that are inconsistent with the standard classical economic model and discussing both extensions of the classical model and explanations grounded in behavioral economics that can account for the observed patterns. This part covers five topics: (I) consumption and savings, (II) borrowing, (III) payments, (IV) asset allocation, and (V) insurance. The second part addresses interventions that firms, governments, and other parties deploy to shape household financial outcomes: (VI) education and information, (VII) peer effects and social influence, (VIII) product design, (IX) advice and disclosure, (X) choice architecture, and (XI) interventions that directly target prices or quantities. The final section of the paper (XII) concludes. John BeshearsWe offer broad coverage of the household finance literature, but we limit the scope of our discussion along some dimensions. We focus on the U.S. institutional context and on empirical work based on U.S. data, although we do bring evidence from other wealthy countries to bear when germane and occasionally reference evidence from developing countries. We address household asset allocation but do not draw out its implications for asset pricing, which are covered by the asset pricing chapter in this handbook. Although household decisions regarding health care are relevant to household finance, we largely omit this literature from our chapter because it is covered in depth in the chapter on behavioral health economics. Finally, there is some overlap between our section on financial product design and the chapter on behavioral industrial organization; we refer readers to that chapter for related material on that topic. Part 1: Facts I. Consumption and SavingsBeginning with the seminal work of Modigliani and Brumberg (1954) and Friedman (1957), economists have embraced the view that households choose to save and borrow to smooth consumption over the lifecycle. Intuitively, if agents have concave utility functions over consumption, then they should spread consumption across time to optimally...

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confidence: 99%

Behavioral Household Finance

Beshears

Choi

Laibson

et al. 2018

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“…The main contribution of this article is to leverage the data in Chetty et al (2016), which documents an increasing gap in life expectancy based on income, to test if pooling is still valuable to Simon and Heather in the U.S. today, versus a few decades ago. 3 We know today that at the chronological age of 50, taxpayers in the lowest income percentile (have much higher mortality rates and) are expected to live 10-15 years less than taxpayers in the highest income percentile. And yet, they are all forced to participate in the same (mandatory) social security program.…”

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confidence: 99%

Swimming with wealthy sharks: longevity, volatility and the value of risk pooling

Milevsky

2019

Journal of Pension Economics and Finance

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Who values life annuities more? Is it the healthy retiree who expects to live long and might become a centenarian, or is the unhealthy retiree with a short life expectancy more likely to appreciate the pooling of longevity risk? What if the unhealthy retiree is pooled with someone who is much healthier and forced to pay an implicit loading? To answer these and related questions this paper examines the empirical conditions under which retirees benefit (or may not) from longevity risk pooling by linking the economics of annuity equivalent wealth to actuarially models of aging. I focus attention on the Compensation Law of Mortality which implies that individuals with higher relative mortality (e.g., lower income) age more slowly and experience greater longevity uncertainty. Ergo, they place higher utility value on the annuity. The impetus for this research today is the increasing evidence on the growing disparity in longevity expectations between rich and poor.

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Section: A3 Monotonicitymentioning

confidence: 99%

“…For instance, if the underlying model is subjective expected utility theory, monotonicity restricts subjective beliefs to be monotone transformations of objective risk. This is less restrictive, however, than the usual approach taken in the literatureassuming that subjective beliefs correspond to objective risk (see Barseghyan, Molinari, O'Donoghue, and Teitelbaum (2015b)). While we always consider monotonicity in the first instance (and generally view the results under monotonicity as our main results), we often proceed to consider four additional shape restrictions on Ω(·), adding them to the model sequentially in order of increasing strength.…”

Section: Introductionmentioning

confidence: 99%

Inference under stability of risk preferences

Barseghyan

Molinari

Teitelbaum

2016

Quantitative Economics

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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.

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Estimating Risk Preferences in the Field

Cited by 112 publications

References 134 publications

Behavioral Household Finance

Behavioral Household Finance

Swimming with wealthy sharks: longevity, volatility and the value of risk pooling

Inference under stability of risk preferences

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