Estimating Neighborhood Effects on Turnout from Geocoded Voter Registration Records ∗
Bibliographic record
Abstract
Do voters turn out more or less frequently when surrounded by those like them? While decades of research examined the determinants of turnout, little is known about how the turnout of one voter is influenced by the characteristics of other voters around them. We geocode over 50 million voter registration records in California, Florida, and North Carolina and estimate the effects of racial and partisan composition of small residential neighbor-hoods at the census block level. Through cross-section and panel difference-in-differences estimation, we address the general identification problem of neighborhood research: vot-ers in different neighborhoods cannot be directly compared because both voters ’ individual characteristics and those of their neighborhoods differ. We find that a 10 percentage point increase in the out-group neighborhood proportion yields an approximately 0.5 to 2.5 per-centage point decrease in the turnout probability. These neighborhood effects persist in non-competitive districts, suggesting that mobilization alone cannot explain their existence. ∗We thank Bruce Willsie, the president of Labels & Lists, Inc., for generously providing unlimited access to their database and answering numerous questions. We also thank Bill Guthe and Jonathan Olmsted for geocoding and computational assistance. Eitan Hersh, Marc Meredith, Ali Valenzuela, and seminar participants at Princeton
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".