How Do Inventors’ Political Preferences Affect Innovation?
Bibliographic record
Abstract
Team production in innovation has become increasingly important. Simultaneously, political polarization has been increasing over time. This paper examines how inventor political ideology affects innovation team formation and subsequent team innovative productivity. To examine these questions, we match North Carolina-resident inventors in the USPTO patent database to their voter registration records, which contain individuals’ political affiliation and aspects of their voting behavior. We also geolocate each patent assignee to create a risk-set of potential co-inventors in each organization location and in each county. Using a variety of estimation techniques, we describe both the regularities in the data and results from econometric analysis. We estimate that 62 percent of NC inventors are U.S. citizens, the vast majority of whom are male, white, and middle-aged. Republican (Democratic) inventors are overrepresented (underrepresented) in NC relative to the underlying distribution of voters in the state. Citizen-inventors are highly politically engaged, more so on almost every dimension than a similar sample of citizens. Republican inventors pursue different technologies than Democratic inventors. In econometric estimations we show that there is political homophily within co-invention teams: Democrats (Republicans) tend to form teams with other Democrats (Republicans). We also assess the performance of innovative teams in terms of conversion of patent applications into granted patents. In both reduced-form estimation and estimation that instruments for endogenous team formation, ideologically homogenous teams tend to underperform ideologically heterogeneous teams, although this effect falls below conventional thresholds of statistical significance when firm fixed effects are included.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".