We examine how the confluence of competition and upstream innovation influences downstream firms’ profit-maximizing strategies. We focus on personal computers and use two novel data sets to describe the dramatic fall in both price (27% at an annual rate) and sales of a computer over its product cycle. Further, we document that computers are typically sold for only four months before being replaced by a higher-quality product. To explain these facts, we develop and calibrate a vintage capital model that combines a competitive market structure with an exogenous rapid rate of innovation.

We propose a new approach to estimating central bank preferences, including the implicit inflation target, that requires no priors on the underlying macroeconomic structure nor observation of monetary policy actions. Our approach entails directly estimating the central bank’s objective function from the sentiment expressed by policymakers in their internal meetings. We apply the approach to the objective function of the U.S. Federal Open Market Committee (FOMC). The results challenge two key aspects of conventional wisdom regarding FOMC preferences. First, the FOMC had an implicit inflation target of approximately 1\frac12% on average over our baseline 2000–11 sample period, significantly below the commonly assumed value of 2. Second, the FOMC’s loss depends strongly on output growth and stock market performance and less so on their perception of current economic slack.

We analyze the effects of competition on price dispersion in the airline industry, using panel data from 1993:Q1 through 2006:Q3. Competition has a negative effect on price dispersion, in line with the textbook treatment of price discrimination. This effect is pronounced for routes with consumers characterized by relatively heterogeneous elasticities of demand. On routes with a homogeneous customer base, the effects of competition on price dispersion are smaller. Our results contrast with those of Borenstein and Rose, who found that price dispersion increases with competition. We reconcile the different results by showing that the cross-sectional estimator suffers from omitted-variable bias.

Who bears the consequences of administrative problems in health care? We use data on repeated interactions between a large sample of U.S. physicians and many different insurers to document the complexity of health care billing, and estimate its economic costs for doctors and consequences for patients. Observing the back-and-forth sequences of claim denials and resubmissions for past visits, we can estimate physicians’ costs of haggling with insurers to collect payments. Combining these costs with the revenue never collected, we estimate that physicians lose 18% of Medicaid revenue to billing problems, compared with 4.7% for Medicare and 2.4% for commercial insurers. Identifying off of physician movers and practices that span state boundaries, we find that physicians respond to billing problems by refusing to accept Medicaid patients in states with more severe billing hurdles. These hurdles are quantitatively just as important as payment rates for explaining variation in physicians’ willingness to treat Medicaid patients. We conclude that administrative frictions have first-order costs for doctors, patients, and equality of access to health care. We quantify the potential economic gains—in terms of reduced public spending or increased access to physicians—if these frictions could be reduced and find them to be sizable.