Published/Forthcoming
Published/Forthcoming
Identifying Scale and Scope Economies Using Product Market Data
with Guillermo Marshall and Scott Orr
We propose an empirically tractable method to estimate economies of scale and scope. We start from a micro-founded model of production by a multi-product firm and generate an estimating equation for the parameters governing scale and scope economies, together with the distribution of within-firm productivity. A strength of the method is that the parameters can be estimated using product market data (i.e., quantities, prices, demand shifters) along with production facility information, rather than cost-side accounting data. We apply this approach to the U.S. beer industry and evaluate the impact of scale and scope economies on merger analysis.
Common Subcontracting and Airline Prices
with Gaurab Aryal, Dennis J. Campbell, and Federico Ciliberto
In the US airline industry, independent regional airlines fly passengers on behalf of several national airlines across different markets, giving rise to common subcontracting. On the one hand, we find that subcontracting is associated with lower prices, consistent with the notion that regional airlines tend to fly passengers at lower costs than major airlines. On the other hand, we find that common subcontracting is associated with higher prices. These two countervailing effects suggest that the growth of regional airlines can have anticompetitive implications for the industry.
Global Concavity and Optimization in a Class of Dynamic Discrete Choice Models
with Yiding Feng and Denis Nekipelov, Proceedings of the 37th International Conference on Machine Learning, Online, PMLR 119, 2020 (Journal)
Discrete choice models with unobserved heterogeneity are commonly used Econometric models for dynamic Economic behavior which have been adopted in practice to predict behavior of individuals and firms from schooling and job choices to strategic decisions in market competition. These models feature optimizing agents who choose among a finite set of options in a sequence of periods and receive choice-specific payoffs that depend on both variables that are observed by the agent and recorded in the data and variables that are only observed by the agent but not recorded in the data. Existing work in Econometrics assumes that optimizing agents are fully rational and requires finding a functional fixed point to find the optimal policy. We show that in an important class of discrete choice models the value function is globally concave in the policy. That means that simple algorithms that do not require fixed point computation, such as the policy gradient algorithm, globally converge to the optimal policy. This finding can both be used to relax behavioral assumption regarding the optimizing agents and to facilitate Econometric analysis of dynamic behavior. In particular, we demonstrate significant computational advantages in using a simple implementation policy gradient algorithm over existing “nested fixed point” algorithms used in Econometrics.
Valuing Pharmaceutical Drug Innovations
with Gaurab Aryal, Federico Ciliberto, and Leland Farmer
Revise and Resubmit at the Journal of the European Economic Association (arXiv) (SSRN) (PDF)
We propose a methodology to estimate the market value of pharmaceutical drugs. Our approach combines an event study with a model of discounted cash flows and uses stock market responses to drug development announcements to infer the values. We estimate that, on average, a successful drug is valued at $1.62 billion, and its value at the discovery stage is $64.3 million, with substantial heterogeneity across major diseases. Leveraging these estimates, we also determine the average drug development costs at various stages. Furthermore, we explore applying our estimates to design policies that support drug development through drug buyouts and cost-sharing agreements.
The Effect of Federal Research Funding on University STEM Education
with Emily Cook and Devaki Ghose
We examine how federal science and engineering research funding - though intended to advance research - affects education access. Using data from 1971–2016, we find that federal grants account for 18.7% of doctoral and 7.5% of undergraduate STEM degrees annually, and 6.3% of doctoral and 4.2% of undergraduate programs across 200 U.S. research universities. Impacts are concentrated in biology and engineering, aligning with the priorities of major funders such as HHS, NSF, and DOD.
Competition and Attrition in Drug Development
(PDF)
With fewer than 10% of new drugs reaching the market, the drug development process is notorious for its high attrition rate. It is well-known that drugs get discontinued after clinical failures. However, surveys suggest that firms also withdraw drugs for commercial reasons. Disentangling the sources of attrition is necessary for predicting the impact of government policy on pharmaceutical innovation. This paper separately estimates the two components of attrition using a continuous-time dynamic model of the drug development process. I find that commercial withdrawals account for 8.4% of all discontinuations, and up to 35% for some diseases. Without commercial withdrawals, the rate at which new drugs reach consumers would be 23% higher. Large subsidies for clinical trials help realize some of that gain, but the effect is small. Regulatory adjustments that marginally lower the probability of late-stage clinical failures can achieve the same results.
The Emergence of Computer Science Programs in the US, with Emily Cook and Devaki Ghose
The Expected Herfindahl-Hirschman Index: A Concentration Measure for Innovation Pipelines, with Gaurab Aryal, Federico Ciliberto, and Margaret Kyle