The Patent Bazaar: Incentives and Screening in the U.S. Patent System, with Mark Schankerman, October 2022
We develop and estimate a dynamic structural model of the patent screening process. The model incorporates incentives, intrinsic motivation and bargaining structure. We estimate the model using novel negotiation-round-level data on examiner decisions and text data from 24 million patent claims. From the claim text data, we use modern natural language processing methods to develop a new measure of patent distance. Our model estimates imply substantial variation in examiners’ intrinsic motivation relative to examiners’ time costs, with senior examiners less intrinsically motivated than juniors on average. With the estimated model, we calculate changes to timeliness and examination quality resulting from changes to agents’ incentives and the bargaining structure. We find that a reduction in the number of negotiation rounds would improve both timeliness and quality of the patent screening process.
Multivariate Ordered Discrete Response Models, with Tatiana Komarova, September 2022
We introduce multivariate ordered discrete response models that exhibit non-lattice structures. From the perspective of behavioral economics, these models correspond to broad bracketing in decision making, whereas lattice models, which researchers typically estimate in practice, correspond to narrow bracketing. There is also a class of hierarchical models, which nests lattice models. A special case of non-lattice models, hierarchical models correspond to sequential decision making and can be represented as binary decision trees. In each of these three cases, we specify latent processes as a sum of an index of covariates and an unobserved error, with unobservables for different latent processes potentially correlated. This additional dependence further complicates the identification of model parameters in non-lattice models. We provide conditions sufficient to guarantee identification under the independence of errors and covariates, compare these to identification conditions in lattice models, and outline an estimation approach. Finally, we provide simulations and empirical examples, with particular focus on probit specifications.
Simultaneous Sample Selection models
I extend sample selection models by allowing the outcome to affect selection directly. I microfound the model then provide identification and estimation results for semiparametric and parametric models. The simultaneity between the outcome and selection generates additional endogeneity, and, unlike traditional sample selection models, my identification results require an excluded regressor in the outcome equation. Simulations confirm the finite sample performance of the new estimator and show sizeable differences in parameters compared to models that do not account for the direct effect of the outcome on the selection decision. I finish with an application relating to the examination process for patents and patent’s potential quality. I show that traditional sample selection methods understate the positive effect of the inventing firm’s size on patent quality.
Selected Work in Progress
“On the Validity of Leniency Instruments”, with Mark Schankerman