This short note offers a microfoundation for the Nash-in-Kalai model I propose elsewhere.
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.
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 extend Dorn and Guo (2022)'s characterization of bounds for Tan's marginal sensitivity model to considerably more general assumptions and estimands.