A manager who learns privately about a project over time may want to delay quitting it if recognizing failure/lack of success hurts his reputation. In the banking industry, managers may want to roll over bad loans. How do distortions depend on expected project quality? What are the effects of releasing public information about quality? A key feature of banks is that managers learn about project quality from bad news, i.e., a default. We show that in such an environment, distortions tend to increase with expected quality and imperfect information about quality. Results differ if managers instead learn from good news.
We provide a general framework for investigating partial identification of structural dynamic discrete choice models and their counterfactuals, along with uniformly valid inference procedures. In doing so, we derive sharp bounds for the model parameters, counterfactual behavior, and low-dimensional outcomes of interest, such as the average welfare effects of hypothetical policy interventions. We characterize the properties of the sets analytically and show that when the target outcome of interest is a scalar, its identified set is an interval whose endpoints can be calculated by solving well-behaved constrained optimization problems via standard algorithms. We obtain a uniformly valid inference procedure by an appropriate application of subsampling. To illustrate the performance and computational feasibility of the method, we consider both a Monte Carlo study of firm entry/exit, and an empirical model of export decisions applied to plant-level data from Colombian manufacturing industries. In these applications, we demonstrate how the identified sets shrink as we incorporate alternative model restrictions, providing intuition regarding the source and strength of identification.
Reclassification risk is a major concern in health insurance where contracts are typically one year in length but health shocks often persist for much longer. We theoretically characterize optimal long-term insurance contracts with one-sided commitment, and use our characterization to provide a simple computation algorithm for computing optimal contracts from primitives. We apply this method to derive empirically-based optimal long-term health insurance contracts using all-payers claims data from Utah, and then evaluate the potential welfare performance of these contracts. We find that optimal long-term health insurance contracts that start at age 25 can eliminate over 94% of the welfare loss from reclassification risk for individuals who arrive on the market in good health, but are of little benefit to the worst age-25 health risks. As a result, their ex ante value depends significantly on whether pre-age-25 health risk is otherwise insured. Their value also depends on individuals’ expected income growth.
This paper develops a strategy with simple implementation and limited data requirements to identify spatial distortion of supply from demand -or, equivalently, unequal access to supply among regions- in transportation markets. We apply our method to ride-level, multi-platform data from New York City (NYC) and show that for smaller rideshare platforms, supply tends to be disproportionately concentrated in more densely populated areas. We also develop a theoretical model to argue that a smaller platform size, all else being equal, distorts the supply of drivers toward more densely populated areas due to network effects. Motivated by this, we estimate a minimum required platform size to avoid geographical supply distortions, which informs the current policy debate in NYC around whether ridesharing platforms should be downsized. We nd the minimum required size to be approximately 3.5M rides/month for NYC, implying that downsizing Lyft or Via-but not Uber{can increase geographical inequity.