Supporting Tax Policy Change Through Accounting Discretion: Evidence from the 2012 Elections
Bibliographic record
Abstract
Some corporations attempt to lessen their tax burden through involvement in the legislative process. We identify firms that contributed to congressional candidates who favor reductions in the U.S. corporate statutory tax rate. This support created a temporary incentive to manage effective tax rates (ETRs) up. We document that these firms increased their reported effective tax rate in the two calendar quarters preceding the 2012 election relative to adjacent periods and other firms supporting candidates in the same election. We find that the variation in upward ETR management is correlated with firm-level proxies for potential reputational costs, capital markets costs, and long-run tax burdens. The variation in upward ETR management is also correlated with firm-candidate-level proxies for strength of relationships and competitiveness of election races. Our findings provide new evidence on accounting choices in support of corporate political activity and on the political cost hypothesis in the tax setting. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2842 . This paper was accepted by Shivaram Rajgopal, accounting.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".