Knowledge that Transforms

Pharmaceutical firms enjoy market exclusivity for new drugs from concurrent patent protection and exclusivity of the clinical trials data submitted for market approval. Patent invalidation during drug development renders data exclusivity the sole source of protection and shifts the period of market exclusivity. In instrumental variables regressions, we quantify the effect of a one-year reduction in expected market exclusivity on the likelihood of drug commercialization. The effect is largely driven by patent invalidations early in the drug development process and by the responses of large originators. We provide estimates of the responsiveness of R&D investments to market exclusivity expectations.

We exploit the variation in admission cutoffs across colleges at a leading Indian university to estimate the causal effects of enrolling in a selective college on cognitive attainment, economic preferences, and Big Five personality traits. Using a regression discontinuity design, we find that enrolling in a selective college improves university exam scores of the marginally admitted women and makes them less overconfident and less risk averse, while men in selective colleges experience a decline in extraversion and conscientiousness. We find differences in peer quality and rank concerns to be driving our findings.

While economic theories indicate that market power by downstream firms can potentially counteract market power upstream, antitrust policy is opaque as to whether to incorporate countervailing market power in merger analyses. We use detailed national claims data from the health care sector to evaluate whether countervailing insurer power does indeed limit hospitals' exercise of market power. We estimate willingness-to-pay models to evaluate hospital market power across analysis areas. We find that countervailing market power is important: a typical hospital merger would raise hospital prices 4.3% at the 25th percentile of insurer concentration but only 0.97% at the 75th percentile of insurer concentration.

Many studies have shown that consumption responds to the arrival of predictable income (excess sensitivity). This paper uses a buffer stock model of consumption to understand what causes excess sensitivity and to test which parameterization is consistent with empirical excess sensitivity estimates. Using high-frequency granular data from a personal finance app, I find that while liquidity constraints are a proximate cause, preferences are the ultimate cause of excess sensitivity. Furthermore, it finds that for feasible parameters, a quasi-hyperbolic version of the model is more consistent with the level of excess sensitivity relative to a standard exponential model.

Health-based drinking water violations affect about one in twelve Americans annually, yet the benefits of drinking water regulation are not well understood. I exploit plausibly exogenous variation in water quality violation timing to estimate the impacts on avoidance behavior and child outcomes. Using purchases of bottled water and common stomach remedies, emergency room visits for gastrointestinal illness, and school absences, I provide a comprehensive calculation of costs associated with poor drinking water quality. Individuals avoid the negative health impacts of coliform bacteria violations only when informed immediately. Timely public notification is a cost-effective way to induce avoidance behavior and protect health.

We estimate the effect of the 1918 influenza pandemic on income inequality in Italian municipalities. Our identification strategy exploits the exogenous diffusion of influenza across municipalities due to the presence of infected soldiers on leave from World War I operations. The measures of income inequality come from newly digitized historical administrative records on taxpayer incomes. We show that in the short and medium run, income inequality is higher in municipalities more afflicted by the pandemic. The effect is mostly explained by an increase in the share of income held by the rich to the detriment of the other strata of the population.

Default effects are pervasive, but the reason they arise is often unclear. We study optimal policy when the planner does not know whether an observed default effect reflects a welfare-relevant preference or a mistake. Within a broad class of models, we find that determining optimal policy is impossible without resolving this ambiguity. Depending on the resolution, optimal policy tends in opposite directions: either minimizing the number of nondefault choices or inducing active choice. We show how these considerations depend on whether active choosers make mistakes when selecting among nondefault options. We illustrate our results using data on pension contribution defaults.

This paper introduces a structural model for the coevolution of networks and behavior. We characterize the equilibrium of the underlying game and adopt the Bayesian Double Metropolis-Hastings algorithm to estimate the model. We further extend the model to incorporate unobserved heterogeneity and show that ignoring this heterogeneity can lead to biased estimates in simulation experiments. We apply the model to study R&D investment and collaboration decisions in the chemical and pharmaceutical industry and find a positive knowledge spillover effect. Our model also provides a tractable framework for a long-run key player analysis.

We evaluate a program that recruited local entrepreneurs to open and operate new schools in 200 underserved villages in Sindh, Pakistan. School operators received a per student subsidy to provide tuition-free primary education, and half the villages received a higher subsidy for females. The program increased enrollment by 32 percentage points and test scores by 0.63 standard deviations, with no difference across the two subsidy schemes. Estimating a structural model of the demand and supply for school inputs, we find that program schools selected inputs similar to those of a social planner who internalizes all the education benefits to society.

We evaluate electronic monitoring as an alternative to prison for nonviolent offenses. Leveraging plausibly exogenous variation in sentencing outcomes generated by quasi-random assignment of judges, we find electronic monitoring reduces reoffending at both extensive and intensive margins. Compared with prison, electronic monitoring is estimated to reduce the probability of reoffending by 22 percentage points five years after sentencing and by 11 percentage points ten years after sentencing, with the cumulative number of offenses reduced by 40% ten years after sentencing. These results demonstrate that electronic monitoring has sustained crime-reducing effects.