Pay-to-Play Politics: Informational Lobbying and Contribution Limits When Money Buys Access
Bibliographic record
Abstract
We develop a game-theoretic model of lobbying in which contributions buy access to politicians. The analysis considers the claim that the rich are better off because they have more access to politicians, and that contribution limits reduce the rich-interest advantage, resulting in less-skewed policy. We show that these arguments do not hold when the politician is strategic in granting access. In equilibrium, rich interest groups receive greater access to the politician, but they are also the targets of politician rent seeking. Relatively poor groups tend to be better off in equilibrium. Contribution limits decrease the politician’s ability to extract rents from interest groups, which improves the payoffs of rich interests, and can result in worse policy. Finally, the paper provides a novel (and theoretically justified) argument in favor of contribution limits: they can encourage lobby formation, which results in more evidence disclosure and better policy.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".