Myopia and Complex Dynamic Incentives: Evidence from Medicare Part D (with Gautam Gowrisankaran and Robert Town), Review of Economic Studies , Vol. 87, No. 2, March 2020, pp 822-869.
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Standard Medicare Part D drug insurance provides limited coverage in a "donut hole" region, making the purchase problem dynamic. We develop a discontinuity-based test for myopia using enrollees who arrived near the coverage gap early in the year. We find that there are fewer and cheaper purchases immediately after reaching the gap, providing evidence in favor of myopia. We structurally estimate a dynamic drug purchase model and find complete myopia. For a nationally representative sample, "behavioral hazard" increases enrollee spending by 41%. Entirely eliminating the gap would increase insurer spending 31%, compared to 6% for generic-only gap coverage.
Better Together: Coexistence of For-profit and Nonprofit Firms with an Application to the U.S. Hospice Industry (with W. David Bradford), Journal of Health Economics, Vol 63, January, pp 1-18, 2019
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Many markets maintain a nontrivial mix of both nonprofit and for-profit firms, particularly in health care industries such as hospice, nursing homes, and home health. What are the effects of coexistence versus dominance of one ownership type? We show how the presence of both ownership types can lead to greater diversity in consumer types served, even if both firms merely profit-maximize. This is the case where firms serve consumers for multiple consumption durations, but where donations are part of a nonprofit firm objective function and happen after services have been provided. This finding is strengthened if the good or service has value beyond immediate consumption or the direct consumer. We show these predictions empirically in the hospice industry, using data containing over 90 percent of freestanding U.S. hospices, 2000-2008. Nonprofit and for-profit providers split the patient market according to length of stay, leading to a wider range of patients being served than in the absence of this coexistence.
Why Do Firms Use Insurance to Fund Worker Health Benefits? The Role of Corporate Finance (with Sarah Holland), Journal of Risk and Insurance , Vol. 86, No. 1, pp 183-212, 2019. First online publication: March 23, 2017.
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When a firm offers health benefits to workers, it exposes the firm to the risk of making payments when workers get sick. A firm can either pay health expenses out of its general assets, keeping the risk inside the firm, or it can purchase insurance, shifting the risk outside the firm. Using data on insurance decisions, we find that smaller firms, firms with more investment opportunities, and firms that face a convex tax schedule are more likely to hedge the risk of health benefit payments. We show how firms trade of the benefits that come from financing and investment characteristics with the costs of regulation when choosing insurance.
Cost versus Control: Understanding Ownership Through Outsourcing in Hospitals (with Patrick Warren), Journal of Health Economics, Vol 48, July, pp 1-15. 2016
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For-profit hospitals in California contract out to a much greater extent than either public hospitals or private nonprofit hospitals. To explain why, we build a model in which the outsourcing decision is a trade-off between net revenues and "quality", any factor of interest to the hospital manager over-and-above its impact on net-revenues. Since nonprofit firms must consume profits indirectly through perquisites, they trade off differently from for-profit firms. This difference is exaggerated when "quality" is particularly important or the firm is hit with a favorable fixed-cost shock. We test these predictions in a panel of California hospitals, finding evidence for each. These results suggest that a model of public or nonprofit make-or-buy decisions should be more than a simple relabeling of a model derived in the for-profit context.
Moral Hazard, Adverse Selection and Health Expenditures: A Semiparametric Analysis (with Patrick Bajari, Han Hong, and Ahmed Khwaja), RAND Journal of Economics, Vol. 45, No. 4, pp. 747-763, 2014.
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Honorable Mention: 23rd Arrow Award for best paper in health economics, iHEA 2015.
Theoretical models predict asymmetric information in health insurance markets may generate inefficient outcomes due to adverse selection and moral hazard. However, previous empirical research has found it difficult to disentangle adverse selection from moral hazard in health care. We empirically study this question by a using unique data set with confidential information from a large self insured employer to estimate a structural model of the demand for health insurance and medical care. We propose a two-step semi-parametric estimation strategy that builds on the work on identification and estimation of auction models. We find significant evidence of moral hazard and adverse selection.
Estimating Demand Elasticities Using Nonlinear Pricing, International Journal of Industrial Organization, Vol. 37, pp. 178-191, 2014.
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(Previous version: Estimating Health Expenditure Elasticities Using Nonlinear Pricing, November 2009) Step-by-step directions and code for implementing the estimation method [Code]
Stata ado-file [locpolyslope]Nonlinear pricing is prevalent in industries such as health care, public utilities, and telecommunications. However, nonlinear pricing in these sectors causes bias when estimating elasticities for welfare analysis or policy changes. This paper develops a local elasticity estimation method that takes advantage of nonlinear price schedules to isolate consumers' expenditure choices from selection and simultaneity biases. This method improves over previous approaches because it uses commonly-available observational data and requires a single general monotonicity assumption. Using detailed claims-level health insurance data with two nonlinearities, this paper measures declining elasticities from -0.26 to -0.09 along initial expenditure ranges, in contrast to previous work which aggregates over a skewed spending distribution. These estimates are then used to calculate moral hazard deadweight loss. This method enables estimation on many policies with nonlinear pricing which previous tools could not address.
Working Papers
"Initial Learning and Eventual Substitution: A Behavior Study in Medicare Part D," (with Yi Zhong), [PDF]
Large out-of-pocket cost regions have been gaining traction in insurance plans as a way to influence enrollee behavior and control medical costs. In this paper, we analyze great dimensional richness in learning and behavioral change for Medicare Part D enrollees responding to nonlinear changes in their out-of-pocket costs. In particular, we identify a previously unaddressed learning region : initial purchases. Enrollees fill more often during initial purchases and respond more sharply to a large price shock than they do later in the pricing schedule. We identify enrollee reactions to the price schedule using a discontinuity approach that simultaneously controls for underlying disease mix and enrollee expected end-of-year price. Besides initial purchases, we deconstruct the large spending drop at the "doughnut hole" threshold, and show the decrease is largely driven by dropping branded drugs. Enrollees reduce out-of-pocket costs by simply filling the same mix of drugs less frequently instead of dropping fills in certain disease categories. There is some substitution to generic options, but we show that linking these patterns into disease-level substitution ability reveals substantial heterogeneity.
Selected Work in Progress:
"Disruptions to In-person Medical Visits by Medical Specialty and Socioeconomic Status from Initial to 'New Normal' Periods
of the COVID-19 Pandemic," with Jia Li and Weixin Liu, December 2021.
"Bargaining and Entry in Nursing Home and Hospice Partnerships," with Siying Liu, October 2018.
"Effects of a Physician Ranking System in Hospitals," with Jia Li, February 2018.
"The Geography of Employer-Sponsored Health Insurance," with Sara Holland, July 2018.
Presentation Schedule: 2nd Annual North Carolina Health Economics Colloquium (NCHEC)
Presentation Schedule: 1st Annual NCHEC
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