Is It Whom You Know or What You Know? An Empirical Assessment of the Lobbying Process
Bibliographic record
Abstract
What do lobbyists do?Some believe that lobbyists' main role is to provide issue-specific information and expertise to congressmen to help guide the law-making process.Others believe that lobbyists mainly provide the firms and other special interests they represent with access to politicians in their "circle of influence" and that this access is the be-all and end-all of how lobbyists affect the lawmaking process.This paper combines a descriptive analysis with more targeted testing to get inside the black box of the lobbying process and inform our understanding of the relative importance of these two views of lobbying.We exploit multiple sources of data covering the period 1999 to 2008, including: federal lobbying registration from the Senate Office of Public Records, Federal Election Commission reports, committee and subcommittee assignments for the 106th to 110th Congresses, and background information on individual lobbyists.A pure issue expertise view of lobbying does not fit the data well.Instead, maintaining connections to politicians appears central to what lobbyists do.In particular, we find that whom lobbyists are connected to (through political campaign donations) directly affects what they work on.More importantly, lobbyists appear to systematically switch issues as the politicians they were previously connected to switch committee assignments, hence following people they know rather than sticking to issues.We also find evidence that lobbyists that have issue expertise earn a premium, but we uncover that such a premium for lobbyists that have connections to many politicians and Members of Congress is considerably larger.
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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 | 0.028 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".