This paper empirically investigates how sentences to be assigned at trial impact plea bargaining. The analysis is based on the model of bargaining with asymmetric information by Bebchuk, 1984. I provide conditions for the nonparametric identification of the model, propose a consistent nonparametric estimator, and implement it using data on criminal cases from North Carolina. Employing the estimated model, I evaluate how different sentencing reforms affect the outcome of criminal cases. My results indicate that lower mandatory minimum sentences could greatly reduce the total amount of incarceration time assigned by the courts, but may increase conviction rates. In contrast, the broader use of non‐incarceration sentences for less serious crimes reduces the number of incarceration convictions, but has a very small effect over the total assigned incarceration time. I also consider the effects of a ban on plea bargains. Depending on the case characteristics, over 20 percent of the defendants who currently receive incarceration sentences would be acquitted if plea bargains were forbidden.

The impact of insurer competition on welfare, negotiated provider prices, and premiums in the U.S. private health care industry is theoretically ambiguous. Reduced competition may increase the premiums charged by insurers and their payments made to hospitals. However, it may also strengthen insurers' bargaining leverage when negotiating with hospitals, thereby generating offsetting cost decreases. To understand and measure this trade-off, we estimate a model of employer-insurer and hospital-insurer bargaining over premiums and reimbursements, household demand for insurance, and individual demand for hospitals using detailed California admissions, claims, and enrollment data. We simulate the removal of both large and small insurers from consumers' choice sets. Although consumer welfare decreases and premiums typically increase, we find that premiums can fall upon the removal of a small insurer if an employer imposes effective premium constraints through negotiations with the remaining insurers. We also document substantial heterogeneity in hospital price adjustments upon the removal of an insurer, with renegotiated price increases and decreases of as much as 10% across markets.

I introduce a model of undirected dyadic link formation which allows for assortative matching on observed agent characteristics (homophily) as well as unrestricted agent-level heterogeneity in link surplus (degree heterogeneity). Like in fixed effects panel data analyses, the joint distribution of observed and unobserved agent-level characteristics is left unrestricted. Two estimators for the (common) homophily parameter, ?0, are developed and their properties studied under an asymptotic sequence involving a single network growing large. The first, tetrad logit (TL), estimator conditions on a sufficient statistic for the degree heterogeneity. The second, joint maximum likelihood (JML), estimator treats the degree heterogeneity \{Ai0\}i = 1N as additional (incidental) parameters to be estimated. The TL estimate is consistent under both sparse and dense graph sequences, whereas consistency of the JML estimate is shown only under dense graph sequences.