Strategy and innovation scholars have long emphasized the positive role of market demand in driving innovation within a technological domain. This study sheds light on an indirect negative spillover effect of market demand on technological progress: whereas increased downstream market demand within a domain generally drives increased technological progress in that domain (i.e., the demand-relevant domain), it may also adversely affect the technological progress of firms in adjacent domains. This occurs because the increased technological progress within the demand-relevant domain, driven by the downstream market demand, can intensify competition for skilled knowledge workers—a critical innovation resource whose supply is often inelastic in the short term. Empirically, I test these arguments by exploiting an unexpected environmental policy shock—the zero emission vehicle (ZEV) mandate—which led to an exogenous increase in demand for electric vehicle (EV) technologies. Following the ZEV mandate, I find evidence of increased inventive activities in the EV domain by EV firms. However, firms in adjacent (non-EV) domains were more likely to lose knowledge workers to EV firms following the ZEV mandate. Consequently, these affected firms produced 22% fewer inventions, particularly in their core technological areas, and became 19% less likely to explore new technological areas. Notably, affected firms in growing technological domains, such as renewable energy, and smaller, younger firms were more adversely (or at least equally) impacted.

Using panel data on 251,511 patent inventors matched with voter registration records containing partisan affiliation, we provide the first large-scale look into the partisanship of American inventors. We document that the modal inventor is Republican and that the partisan composition of inventors has changed in ways that are not reflective of partisan affiliation trends amongst the broader population. We then show that the partisan affiliation of inventors is associated with technological invention related to guns and climate change, two issue areas associated with partisan divide. These findings suggest that inventor partisanship may have implications for the direction of inventive activity.

Policymakers and firms have become increasingly dependent on academic research for upstream scientific progress, especially in domains where technical challenges in downstream invention generate demand for additional research. This study examines which academic scientists produce research in a domain when demand for upstream scientific research in that domain increases. In particular, I examine how scientists’ propensity to produce research in that domain varies by their research productivity and intellectual distance from the domain, and how their response patterns shape the direction of research within that domain. Empirically, I exploit an unexpected environmental policy shock—the Zero Emission Vehicle (ZEV) mandate—which led to an exogenous increase in demand for upstream scientific research related to electric vehicle (EV) technologies. Following the ZEV mandate, there was an increase in the number of scientists producing EV research. However, I find that as scientists’ intellectual distance from the EV domain increased, more-productive scientists became disproportionately less likely to produce EV research than less-productive scientists. Notably, when more-productive scientists from intellectually more distant domains chose to produce EV research, they generated more novel and more impactful EV research outcomes than other scientists, including more-productive scientists from closer domains. These findings suggest a potential asymmetry in how academic scientists respond to increased demand for upstream scientific research in a domain: while more-productive scientists from intellectually more distant domains have significant potential to contribute novel and impactful research outcomes in that domain, they tend to be underrepresented among those who actually produce research in the domain.

Emerging technologies require company involvement in open science during the industry’s incubation stage because company and university capabilities are complementary. Such involvement also helps companies position themselves for future commercialization. Yet company participation in science has declined, at least in part because of concerns that disclosed knowledge can benefit rivals. We evaluate whether early signals of commercial interest, decoupled from technological progress, can shift companies’ assessment of the benefits and spillover costs of open science, increasing innovation while technological uncertainty remains high. We study company knowledge production in the context of quantum computing, an emerging technology with continued uncertainty about its technological feasibility. We interpret the 2011 lease of a quantum machine to Lockheed Martin as an early signal of commercial potential. Following that event, we document a sharp increase in company innovation, measured by scientific publications and patents. This increase was driven by large established companies and accompanied by collaboration with university scientists, including the movement of university scientists into these companies. At first, company scientific publications were paired with patents, suggesting that open science efforts occur alongside actions to protect intellectual property. After 2016, this relationship weakened, particularly for companies that had already accumulated several quantum computing patents.

Understanding factors affecting the direction of innovation is a central aim of research in the economics of innovation. Progress on this topic has been inhibited by difficulties in measuring distance and movement in knowledge space. We describe a methodology that infers the mapping of the knowledge landscape based on text documents. The approach is based on an unassisted machine learning technique, Hierarchical Dirichlet Process (HDP), which flexibly identifies patterns in text corpora. The resulting mapping of the knowledge landscape enables calculations of distance and movement, measures that are valuable in several contexts for research in innovation. We benchmark and demonstrate the benefits of this approach in the context of 44 years of USPTO data.