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 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 | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".