| The classic issues of moral hazard and adverse selection as they appear in health care are addressed in this dissertation using new tools of analysis.;In the first essay, I construct a new estimator and estimates to measure the price response of patients in health insurance. These estimates allow us to measure the magnitude of moral hazard. Recent health care initiatives attempt to stem rising costs by increasing patients' cost sharing. These initiatives include high deductible plans, the Medicare Drug Plan "doughnut hole," and Health Savings Accounts (HSAs). The success of such initiatives depends on how health expenditures change as patients' reimbursement decreases. Estimating this elasticity is complicated by selection bias, as high expenditure patients can self-select into high reimbursement plans. Additionally, nonlinear reimbursement is prevalent in U.S. insurance contracts and the aforementioned initiatives. Nonlinearities introduce bias when using previous estimation methods by simultaneously determining expenditure and reimbursement rate. This paper develops an elasticity estimation method that controls for selection bias by taking advantage of nonlinear reimbursement rates. Discontinuous reimbursement rates induced by a nonlinearity are used to isolate patients' expenditure choices. Using detailed claims-level data of employer-sponsored health insurance, I find a tight range of elasticities between -0.25 and -0.33 in the range of average U.S. spending. I then use these estimates in a policy experiment measuring moral hazard and calculate the resulting welfare effects. This paper's estimation method may be used on many policies with nonlinear reimbursement which previous tools could not address.;In the second essay, I present joint work with Patrick Bajari, Han Hong, and Ahmed Khwaja wherein we construct an estimator that can address both moral hazard and adverse selection simultaneously. 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 semiparametric estimation strategy that builds on the work on identification and estimation of auction models. We find significant evidence of moral hazard and adverse selection. |
