Data and Code for "Tax-Exempt Lobbying: Corporate Philanthropy as a Tool for Political Influence"
Bibliographic record
Abstract
We analyze the role of charitable giving as a means of political influence, a channel that has been heretofore unexplored in the political economy literature. For philanthropic foundations associated with Fortune 500 and S&P500 corporations, we show that grants given to charitable organizations located in a congressional district increase when its representative obtains seats on committees that are of policy relevance to the firm associated with the foundation. This pattern parallels that of publicly disclosed Political Action Committee (PAC) spending. As further evidence on firms' political motivations for charitable giving, we show that a member of Congress's departure is associated with a short-term decline in charitable giving to his district, and we again observe similar patterns in PAC spending. Charities directly linked to politicians through personal financial disclosure forms filed in accordance with Ethics in Government Act requirements similarly exhibit patterns that are consistent with political dependence. Our analysis suggests that firms may deploy their charitable foundations as a form of tax-exempt influence seeking. Based on a stylized model of political influence, our most conservative estimates imply that around 7 percent of total U.S. corporate charitable giving can be interpreted as politically motivated, an amount that is economically significant: it is 2.5 times larger than annual PAC contributions and about 36 percent of total federal lobbying expenditures. Given the lack of formal electoral or regulatory disclosure requirements, charitable giving may be a form of political influence that goes mostly undetected by voters and shareholders, and which is subsidized by taxpayers.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.022 |
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".