Partisanship and Innovation: Exploring the Link between Partisanship and the Rate and Direction of Inventive Activity

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This project will study the political orientation of scientists and its role in shaping the rate and direction of inventive activity. While research has shown that political orientation influences individual decision-making, we know relatively little about how the political orientation of scientists may shape labor market and collaboration choices, two factors which reflect directly on both the rate and direction of innovation outcomes. The first component of this project addresses data limitations which have thus far prevented wide-scale and systematic study of the political orientation of scientists by linking voter registration records with individual patent inventors and academic researchers. The second component of this project uses these data to (1) provide comprehensive descriptive statistics about variation in political orientation of scientists across knowledge domains, industries, firms, and geographic regions; (2) evaluate the impact of scientists’ political orientation on mobility in knowledge space by evaluating changes in collaboration preferences driven by partisanship and its consequences for innovation outcomes; and (3) evaluate the impact of scientists’ political orientation on mobility in physical space by evaluating changes across geographical regions driven by partisanship and the consequences of such changes for innovation outcomes.To match voter registration records with individual patent inventors and academic researchers, we use data from multiple sources. We obtain voter registration records from L2, a commercial data provider who sources voter information directly from individual states. Data on patent inventors are collected from PatentsView, a USPTO database which disambiguates patent inventor names, and information on academic researchers are collected from Dimensions, a database containing information on more than 106 million academic publications and 1.2 billion citations. To address the primary matching challenge of limited overlapping information between these databases, we build on recent advancements in machine learning and statistical record-linking procedures that work by identifying matches and weighting these matches by the probability of observing such a match at random. To provide a robust analysis of the role of political orientation is innovation outcomes, we employ frontier difference-in-differences statistical techniques that aim to identify causal relationships in field data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.