Votes or Money? Theory and Evidence from the US Congress.
Bibliographic record
Abstract
This paper investigates the relationship between the size of interest groups in terms of voter representation and the interest group's campaign contributions to politicians.We uncover a robust hump-shaped relationship between the voting share of an interest group and its contributions to a legislator.This pattern is rationalized in a simultaneous bilateral bargaining model where the larger size of an interest group affects the amount of surplus to be split with the politician (thereby increasing contributions), but is also correlated with the strength of direct voter support the group can offer instead of monetary funds (thereby decreasing contributions).The model yields simple structural equations that we estimate at the district level employing data on individual and PAC donations and local employment by sector.This procedure yields estimates of electoral uncertainty and politicians effectiveness as perceived by the interest groups.Our approach also implicitly delivers a novel method for estimating the impact of campaign spending on election outcomes: we find that an additional vote costs a politician between 100 and 400 dollars depending on the district.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.003 |
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".