Effects of Sub-National Political Institutions on Localized Innovation: Evidence From United States Counties
Bibliographic record
Abstract
This study investigates the influence of political institutions on innovation at the sub-national level in the United States, an area less explored compared to national contexts. It examines how political affiliations impact innovation support and outcomes, which are crucial indicators of economic development. The analysis focuses on county-level political affiliation, calculated as the mean percentage of Democratic versus Republican votes across five presidential elections. Data from over 94% of US counties were analyzed using hierarchical linear regression. The study evaluates the relationship between political affiliation and four measures of innovation, including support factors like venture capital and business incubators, as well as outcomes such as patents and initial public offerings. The findings indicate that counties leaning Democratic create a more favorable political environment for innovation. These results underscore the significant role of political institutions in fostering innovative business activities at the sub-national level, providing insights for policymakers and stakeholders aiming to enhance local economic growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".