The anatomy of a troll? Patent licensing business models in the light of patent reassignment data

We examine patent licensing business models of non-practicing entities that generate revenue by selling, licensing, or litigating patents. They may also pursue R&D activities, invent new technologies, or provide services to inventors or product companies. We describe their business models and patent market behavior and then compare their litigation strategies against product companies using a matched sample of highly comparable patents. The main differences among patent licensing firms stem from their technological capabilities, patent portfolio sizes, and external relationships. We find that licensing firms with technological capabilities often pursue litigation until decision and engage in forum shopping. In contrast, litigation incidence, parties involved, and outcomes are primarily determined by patent characteristics, not entity types. Licensing business models drive the acquisition of certain types of patents that influence the outcomes of the patent system. We argue that patent policy should strengthen mechanisms to discover invention quality rather than focus on the amount of litigation or types of entities.     

Examining Estimates of Intervention Effectiveness Using Sensitivity Analysis

Making causal inferences from a quasi‐experiment is difficult. Sensitivity analysis approaches to address hidden selection bias thus have gained popularity. This study serves as an introduction to a simple but practical form of sensitivity analysis using Monte Carlo simulation procedures. We examine estimated treatment effects for a school‐based support intervention designed to address student strengths and needs in academic and nonacademic areas by leveraging partnerships with community agencies. Middle school (Grades 6–8) statewide standardized test scores in mathematics and English language arts (ELA) were examined for students in a large urban district who participated in City Connects during elementary school. Results showed that the estimated treatment effects in both subjects were reduced slightly with the inclusion of U, a hypothesized unobserved binary variable. However, simulated effects fell within one‐sided 90% confidence intervals for original treatment effects, suggesting only a mild sensitivity to hidden bias. Moreover, almost identical estimated treatment effects were observed when the magnitude of the mathematical difference between each pair of the conditional probabilities of U given the treatment indicator Z was the same.