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α: calibrated so average coauthorship-adjusted count equals average raw count
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.