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dc.contributor.advisorJonathan Gruber and Jerry A. Hausman.en_US
dc.contributor.authorKowalski, Amandaen_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Economics.en_US
dc.date.accessioned2008-12-11T16:55:19Z
dc.date.available2008-12-11T16:55:19Z
dc.date.copyright2008en_US
dc.date.issued2008en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/43729
dc.descriptionThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2008.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionIncludes bibliographical references.en_US
dc.description.abstractThis dissertation consists of an empirical chapter, an econometrics chapter, and a theoretical chapter, all of which advance the study of the price elasticity of expenditure on medical care. In Chapter 1, I estimate the price elasticity of expenditure on medical care across the quantiles of the expenditure distribution. My identification strategy relies on family cost sharing provisions that generate differences in marginal prices between individuals who have injured family members and individuals who do not. I use a new censored quantile instrumental variables (CQIV) estimator, which allows me to examine variations in price responsiveness across the skewed distribution of medical expenditure. The CQIV estimator does not require any parametric assumptions to account for individuals who consume zero medical care. Using CQIV, as well as traditional estimators, I find elasticities that are an order of magnitude larger than those in the literature. My CQIV estimates suggest strong price responsiveness among people who spend the most. I find that the price elasticity of expenditure is approximately -2.3, which is stable across the .65 to .95 quantiles of the expenditure distribution. In Chapter 2, Chernozhukov and Kowalski (2008), we develop a censored quantile instrumental variables (CQIV) estimator. The CQIV estimator handles censoring nonparametrically in the tradition of Powell (1986), and it generalizes standard censored quantile regression (CQR) methods to incorporate endogeneity. Our computational algorithm combines a control function approach with the Chernozhukov and Hong (2002) CQR algorithm. Through Monte-Carlo simulation, we show that CQIV performs well relative to Tobit IV in terms of median bias and interquartile range.en_US
dc.description.abstract(cont.) In Chapter 3, I develop a structural model to estimate the price elasticity of expenditure on medical care. The model relies on deductibles, coinsurance rates, and stoplosses that generate nonlinearities in consumer budget sets. The model generalizes existing nonlinear budget set models by allowing for more than one nonconvex kink. Furthermore, it incorporates censoring as a corner solution. Unlike reduced form models, the model utilizes identification from utility theory, it allows for preference heterogeneity, and it allows for the direct calculation of welfare effects.en_US
dc.description.statementofresponsibilityby Amanda Ellen Kowalski.en_US
dc.format.extent155 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectEconomics.en_US
dc.titleEssays on medical care using Semiparametric and structural econometricsen_US
dc.typeThesisen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Economics
dc.identifier.oclc260021960en_US


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