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Nonlinear pricing is prevalent in industries such as health care, public utilities, and telecommunications. However, this pricing scheme introduces bias into estimating elasticities for welfare analysis or policy changes. I develop a local elasticity estimation method that uses nonlinear price schedules to isolate consumers' expenditure choices from selection and simultaneity biases. This method improves over previous approaches by using commonly-available observational data and requiring only a single general monotonicity assumption. Using claims-level data on health insurance with two nonlinearities, I am able to measure two separate elasticities, and find that elasticity declines from − 0.26 to− 0.09 by the second nonlinearity. These estimates are then used to calculate moral hazard deadweight loss. This method enables estimation of many policies with nonlinear pricing which previous tools could not address.

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.

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. We analyze the firm's decision to manage this risk. Using data on the insurance decisions of publicly-traded firms, 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. These financial characteristics explain more of the hedging decision relative to commonly-cited state insurance mandates. We also show that hedging health risk mitigates investment-cash flow sensitivities.

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.

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.