Charles Hodgson Publications

Policies that mandate disclosure of innovative project outcomes aim to increase innovation by limiting wasteful duplicative innovation. Yet, such policies change not only the ex-post information environment but also firms' ex-ante innovation incentives. Firms may slow down their own innovation efforts in anticipation of increased disclosure by others. We examine the innovation-related impacts of the 2017 FDA Final Rule amendment, which mandates disclosure of clinical trial results for pharmaceutical firms. We show that the policy hastened and increased disclosure of results for clinical trials post-completion, but also increased the time to completion of clinical trials, the time between early phases of clinical trials, and delays in development-related investments. We provide evidence consistent with mandated disclosure leading firms to wait to learn from their competitors. Our results suggest that mandating disclosure may slow innovation when there is value to waiting.

We propose a new way to obtain identification results using order statistics as finite mixtures with two key properties: i) the weights are known integer numbers; and ii) the elements of the mixture are the distributions of the maximum over a subset of the original random variables. We leverage Exponentiated Distributions (ED), which extend extreme value theory results. ED are max-stable, and we show that finite mixtures of ED are linearly independent. This enables us to derive non-parametric identification results and extend commonly known results using Gumbel and Fréchet distributions, both examples of ED. The results have broad applications in auctions, discrete-choice, and other settings where maximum or minimum choices play a central role. We illustrate the usefulness of our results by proposing new estimators for auctions with bidder-level heterogeneity.

We develop and estimate a model of consumer search with spatial learning. Consumers make inferences from previously searched objects to unsearched objects that are nearby in attribute space, generating path dependence in search sequences. The estimated model rationalizes patterns in data on online consumer search paths: search tends to converge to the chosen product in attribute space, and consumers take larger steps away from rarely purchased products. Eliminating spatial learning reduces consumer welfare by 12%: cross-product inferences allow consumers to locate better products in a shorter time. Spatial learning has important implications for product recommendations on retail platforms. We show that consumer welfare can be reduced by unrepresentative product recommendations and that consumer-optimal product recommendations depend on both consumer learning and competition between platforms.

While the mechanism design paradigm emphasizes notions of efficiency based on agent preferences, policymakers often focus on alternative objectives. School districts emphasize educational achievement, and transplantation communities focus on patient survival. It is unclear whether choice-based mechanisms perform well when assessed based on these outcomes. This paper evaluates the assignment mechanism for allocating deceased donor kidneys on the basis of patient life-years from transplantation (LYFT). We examine the role of choice in increasing LYFT and compare realized assignments to benchmarks that remove choice. Our model combines choices and outcomes in order to study how selection affects LYFT. We show how to identify and estimate the model using instruments derived from the mechanism. The estimates suggest that the design in use selects patients with better post-transplant survival prospects and matches them well, resulting in an average LYFT of 9.29, which is 1.75 years more than a random assignment. However, the maximum aggregate LYFT is 14.08. Realizing the majority of the gains requires transplanting relatively healthy patients, who would have longer life expectancies even without a transplant. Therefore, a policymaker faces a dilemma between transplanting patients who are sicker and those for whom life will be extended the longest.

We develop a model of consumer search with spatial learning in which sampling the payoff of one product causes consumers to update their beliefs about the payoffs of other products that are nearby in attribute space. Spatial learning gives rise to path dependence, as each new search decision depends on past experiences through the updating process. We present evidence of spatial learning in data that records online search for digital cameras. Consumers’ search paths tend to converge to the chosen product in attribute space, and consumers take larger steps away from rarely purchased products. We estimate the structural parameters of the model and show that these patterns can be rationalized by our model, but not by a model without spatial learning. Eliminating spatial learning reduces consumer welfare by 12%: cross-product inferences allow consumers to locate better products in a shorter time. Spatial learning has important implications for the power of search intermediaries. We use simulations to show that consumer-optimal product recommendations are that are most informative about other products.