Conflating Lobbying and PACs: The Surprisingly Low Overlap in Organizational Lobbying and Campaign Expenditures
Bibliographic record
Abstract
ABSTRACT This article investigates whether campaign contributions and lobbying are complementary, substitutive, or distinct forms of organizational political engagement. Our study reveals minimal overlap between organizations that engage in lobbying and those that make campaign contributions despite the perception that these activities are interchangeable forms of “money in politics.” Using comprehensive contribution and lobbying report data from 1998 to 2018, we find that most politically active organizations focus exclusively on either lobbying or making campaign contributions. Only a small percentage of organizations engage in both activities. This finding challenges the assumption that these forms of political activity are inherently linked. The majority of organizations engaged in political activity do so exclusively through lobbying. However, the top lobbying groups spend the most money and almost always have affiliated political action committees (PACs). Most lobbying money is spent by a small number of big spenders—organizations that also have affiliated PACs. Organizations that both lobby and make campaign contributions tend to be well resourced and rare.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".