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.
I show that Nash bargaining weights are unidentified in the presence of uncertainty over nontransferable utility frontiers and propose the Nash-in-Kalai model to enable identification and GMM estimation.
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.
I quantify the impact of predictable increases in benchmark-linked prices between negotiations. There can be real effects in the presence of staggered contracting and time discounting. Using panel data on hospital–insurer contracts from West Virginia, I find both occur.
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 (including new results for global regression rates) 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.