Bibliographic record
Abstract
Despite a large literature on lobbying and information transmission by interest groups, no prior study has measured returns to lobbying.In this paper, we statistically estimate the returns to lobbying by universities for educational earmarks (which now represent 10 percent of federal funding of university research).The returns to lobbying approximate zero for universities not represented by a member of the Senate Appropriations Committee (SAC) or House Appropriations Committee (HAC).However, the average lobbying university with representation on the SAC receives an average return to one dollar of lobbying of $11-$17; lobbying universities with representation on the HAC obtain $20-$36 for each dollar spent.Moreover, we cannot reject the hypothesis that lobbying universities with SAC or HAC representation set the marginal benefit of lobbying equal to its marginal cost, although the large majority of universities with representation on the HAC and SAC do not lobby, and thus do not take advantage of their representation in Congress.On average, 45 percent of universities are predicted to choose the optimal level of lobbying.In addition to addressing questions about the federal funding of university research, we also discuss the impact of our results for the structure of government.
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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 | 0.006 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".