Salience, Myopia, and Complex Dynamic Incentives: Evidence from Medicare Part D

Dalton, Christina Marsh; Gowrisankaran, Gautam; Town, Robert J.

doi:10.1093/restud/rdz023

Cited by 25 publications

(42 citation statements)

References 50 publications

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“…() and Dalton et al. (), also estimate moral hazard effects using structural, WYDM models, but differ from my research in several ways: (i) These models do not allow for endogenous health transitions or endogenous insurance selection, and (ii) analysis is limited to prescription drug decisions of Medicare enrollees. Most importantly, neither paper compares estimated moral hazard effects across models featuring both annual and within‐year medical care decision making, which is the primary objective of this research.…”

Section: Introductionmentioning

confidence: 87%

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Insurance‐induced Moral Hazard: A Dynamic Model of Within‐year Medical Care Decision Making Under Uncertainty

Cronin

2018

Int Economic Review

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This study quantifies the moral hazard effect of health insurance on medical expenditure by estimating a dynamic model of within-year medical care consumption that allows for insurance selection, endogenous health transitions, and individual uncertainty about medical care prices in an environment where insurance has nonlinear cost-sharing features. The results suggest that moral hazard accounts for 53.1%, on average, of total annual medical expenditure when insured. This estimate is significantly different, and generally larger, than that produced by an alternative model that is representative of the annual medical care decision-making models commonly found in the literature. CRONIN Allowing an individual's medical care optimization problem to evolve endogenously over the course of a health insurance year has implications for the analysis of moral hazard. For example, if medical care consumption affects future health transitions, then a generous insurance plan, relative to a less generous plan, may lead an individual to consume more medical care early in the year, experience health improvement, and spend less late in the year. Additionally, if an individual's medical care decisions are determined in part by his forecast of future health and medical expenditure, then health insurance can affect spending patterns through its impact on the forecast horizon in addition to its impact on out-of-pocket prices. The cumulative effect that these mechanisms have on moral hazard is ex ante ambiguous, which motivates this empirical investigation.The principal innovation of this research is the development and estimation of a WYDM model, which is motivated by theoretical (Grossman, 1972;Keeler et al., 1977) and empirical (Gilleskie, 1998;Blau and Gilleskie, 2008;Khwaja, 2010) models of health production and medical care demand. An individual's optimization problem consists of an annual health insurance decision, followed by a sequence of monthly medical care decisions made over the course of a health insurance year. I model monthly medical care decisions to allow the unique benefits and costs associated with the timing of unexpected illness and dynamic insurance cost-sharing features to impact behavior within the model. Within each month, an individual responds to an endogenous, stochastic health event by consuming medical care. The anticipated primary benefit of medical care consumption is improved future health. The anticipated primary cost is financial (i.e., a decrease in the current consumption of nonmedical goods). When health insurance has dynamic cost-sharing features (i.e., a deductible and maximum out-of-pocket expenditure [MOX] level), an additional benefit of medical care consumption is lower future out-of-pocket prices. 4 Importantly, the model assumes that individuals are forward looking, meaning they consider the impact that their actions have on the probability of future outcomes (e.g., health and prices) when making decisions. Evidence of such forward-looking health behavior can be found in Arcidiacono et a...

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Section: Introductionmentioning

confidence: 87%

“…(), Dalton et al. (), and Lin and Sacks (). In all of these studies, insurance possession is either randomized or assumed to be exogenously determined, limiting the scope of policy experiments that can be studied.…”

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confidence: 99%

Insurance‐induced Moral Hazard: A Dynamic Model of Within‐year Medical Care Decision Making Under Uncertainty

Cronin

2018

Int Economic Review

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“…To our knowledge, there are only a handful papers that explore such an identification argument to estimate a consumer's discount factor or her incentive to consider future payoffs (Chevalier and Goolsbee (2009), Ishihara and Ching (2012), Chung, Steenburgh, and Sudhir (2013), Lee (2013), Ching, Erdem, and Keane (2014), , Dalton, Gowrisankaran, and Town (2019), De Groote and Verboven (2019)). These papers investigate sales force compensation schemes, rewards programs, consumer learning, and how the price of used goods affects the demand for new goods.…”

Section: Review Of Literaturementioning

confidence: 99%

Identification and Estimation of Forward-Looking Behavior: The Case of Consumer Stockpiling

Ching

Osborne

2020

Marketing Science

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Understanding how forward-looking consumers respond to price promotions in storable goods markets is an important area of research in empirical marketing and industrial organization. In prior work, researchers have assumed that consumers in these markets are very forward-looking, and calibrated their weekly discount factors to levels around 0.9995. This calibration has been used because earlier research has assumed that a consumer’s storage cost is a continuous function of inventory, which rules out exclusion restrictions that can be used to identify the discount factor. We show that by properly modeling storage cost as a step function of inventory (because storage cost depends on the number of packages stored, instead of the actual amount of inventory), natural exclusion restrictions arise that allow for the discount factor to be point identified. In an application to a storable good category, we find that weekly discount factors are very heterogeneous across consumers, and are on average 0.71. We show through a counterfactual exercise that if one used a model that fixed the discount factor to be consistent with the standard calibrated value, one would overpredict the effect of increased promotional depth for a product on its quantity sold by 18% in the short term, and 15% in the long term.

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“… Dalton, Gowrisankaran, and Town (forthcoming) used a similar argument to identify salience and myopia in a dynamic discrete choice model with parametric utility applied to Medicare Part D data. …”

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confidence: 99%

Identifying the discount factor in dynamic discrete choice models

Abbring

Daljord

2020

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Empirical research often cites observed choice responses to variation that shifts expected discounted future utilities, but not current utilities, as an intuitive source of information on time preferences. We study the identification of dynamic discrete choice models under such economically motivated exclusion restrictions on primitive utilities. We show that each exclusion restriction leads to an easily interpretable moment condition with the discount factor as the only unknown parameter. The identified set of discount factors that solves this condition is finite, but not necessarily a singleton. Consequently, in contrast to common intuition, an exclusion restriction does not in general give point identification. Finally, we show that exclusion restrictions have nontrivial empirical content: The implied moment conditions impose restrictions on choices that are absent from the unconstrained model.

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Salience, Myopia, and Complex Dynamic Incentives: Evidence from Medicare Part D

Cited by 25 publications

References 50 publications

Insurance‐induced Moral Hazard: A Dynamic Model of Within‐year Medical Care Decision Making Under Uncertainty

Insurance‐induced Moral Hazard: A Dynamic Model of Within‐year Medical Care Decision Making Under Uncertainty

Identification and Estimation of Forward-Looking Behavior: The Case of Consumer Stockpiling

Identifying the discount factor in dynamic discrete choice models

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