I study auto-renew share of charges contracts, which are associated with small insurers paying high prices to American hospitals. I demonstrate that under certain conditions, these contracts can lead to Pareto improvements. The key feature of these contracts is the renewal process, which enables the insurers to discipline hospital charges with the threat of contract termination and renegotiation.
We study the introduction of a gainsharing program for cardiologists choosing stents. We find that gainsharing led to reduced hospital spending, likely driven by reduced acquisition prices rather than a shift to lower-cost stents.
The large literature on vertical market bargaining assumes contracts last for one period, but actual hospital-insurer contracts last for multiple years and are negotiated as a multiple of a benchmark price that changes over time. I study dynamic regulations to those benchmark prices by extending the existing single-period approach to vertical market bargaining to enable contracts that are formed at different times.
I show that even when the density of propensity scores may be unbounded near zero, t-statistics based on a thresholded Augmented IPW estimator can remain well-calibrated. I characterize the necessary conditions in terms of black-box rates and minimal smoothness orders, and use the conditions to propose rules of thumb for clipping or trimming rates.
I investigate vertical market contract dynamics by documenting and analyzing a novel panel dataset of hospital--insurer contracts in West Virginia. The largest insurer typically formed three- and five-year contracts. In contrast, smaller insurers generally formed long-lived contracts with faster price growth. By documenting a unique dataset and stark dynamic implications, this research contributes to a larger understanding of vertical market dynamics and helps set the stage for future work.
We propose semiparametric average treatment effect (ATE) bounds estimators with novel robustness properties: double sharpness and double validity.
We extend Dorn and Guo (2022)'s characterization of bounds for Tan's marginal sensitivity model to considerably more general assumptions and estimands.
We propose a meta-learner for conditional average treatment effect (CATE) bounds that can efficiently estimate sharp bounds.
We provide sharp bounds for an existing sensitivity analysis that are valid under minimal conditions and sharp given a consistent quantile regression estimate.