Pharmacy benefit managers dominate the U.S. pharmaceutical market but are controversial and poorly understood. We analyze PBMs as market intermediaries that operate formulary contests in which on-patent brand-drug makers compete for favorable placement by offering rebates off list price. These formulary contests deliver efficiency gains compared to drug makers selling directly to consumers; PBMs capture some of these gains. Our approach answers key questions regarding the determinants of efficiency, rebates, list prices, and PBM market power in the pharmaceutical market. Our analysis also explains how common contracting practices, federal regulations, and incentives within formulary contests can undermine market efficiency.

This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs). The standard multiplier bootstrap inference fails to capture the negative dependence of observations within each pair, and thus, is conservative. The analytical inference involves estimating multiple functional quantities that requires several tuning parameters. In this paper, we propose two bootstrap methods that can consistently approximate the limit distribution of the original QTE estimator and lessen the burden of tuning parameter choice. In particular, the inverse propensity score weighted multiplier bootstrap can be implemented without knowledge of pair identities.

To identify the causal effect of schooling on cognitive skills, we exploit conditionally random variation in the date Swedish males take a battery of cognitive tests in preparation for military service. We find an extra ten days of school instruction raises scores on crystallized intelligence tests (synonyms and technical comprehension tests) by approximately 1% of a standard deviation, whereas extra nonschool days have almost no effect. In contrast, test scores on fluid intelligence tests (spatial and logic tests) do not increase with additional days of schooling but do increase modestly with age.

Quantitative estimates of the impacts of climate change on economic outcomes are important for public policy. We show that the vast majority of estimates fail to account for well-established uncertainty in future temperature and rainfall changes, leading to potentially misleading projections. We reexamine seven well-cited studies and show that accounting for climate uncertainty leads to a much larger range of projected climate impacts and a greater likelihood of worst-case outcomes, an important policy parameter. Incorporating climate uncertainty into future economic impact assessments will be critical for providing the best possible information on potential impacts.

We introduce the class of linear‐rational term structure models in which the state price density is modeled such that bond prices become linear‐rational functions of the factors. This class is highly tractable with several distinct advantages: (i) ensures nonnegative interest rates, (ii) easily accommodates unspanned factors affecting volatility and risk premiums, and (iii) admits semi‐analytical solutions to swaptions. A parsimonious model specification within the linear‐rational class has a very good fit to both interest rate swaps and swaptions since 1997 and captures many features of term structure, volatility, and risk premium dynamics—including when interest rates are close to the zero lower bound.

A risk‐averse manager's overconfidence makes him less conservative. As a result, it is cheaper for firms to motivate him to pursue valuable risky projects. When compensation endogenously adjusts to reflect outside opportunities, moderate levels of overconfidence lead firms to offer the manager flatter compensation contracts that make him better off. Overconfident managers are also more attractive to firms than their rational counterparts because overconfidence commits them to exert effort to learn about projects. Still, too much overconfidence is detrimental to the manager since it leads him to accept highly convex compensation contracts that expose him to excessive risk.

We derive the optimal labor contract for a levered firm in an economy with perfectly competitive capital and labor markets. Employees become entrenched under this contract and so face large human costs of bankruptcy. The firm's optimal capital structure therefore depends on the trade‐off between these human costs and the tax benefits of debt. Optimal debt levels consistent with those observed in practice emerge without relying on frictions such as moral hazard or asymmetric information. Consistent with empirical evidence, persistent idiosyncratic differences in leverage across firms also result. In addition, wages should have explanatory power for firm leverage.

This paper proposes a new simulation‐based approach for optimal portfolio allocation in realistic environments with complex dynamics for the state variables and large numbers of factors and assets. A first illustration involves a choice between equity and cash with nonlinear interest rate and market price of risk dynamics. Intertemporal hedging demands significantly increase the demand for stocks and exhibit low volatility. We then analyze settings where stock returns are also predicted by dividend yields and where investors have wealth‐dependent relative risk aversion. Large‐scale problems with many assets, including the Nasdaq, SP500, bonds, and cash, are also examined.