Do Political Connections Induce More or Less Opportunistic Financial Reporting? Evidence from Close Elections Involving <scp>SEC</scp>‐Influential Politicians*
Bibliographic record
Abstract
ABSTRACT This study explores close US congressional elections involving politicians who have influence over the SEC to examine the effect of firms' political connections on their financial reporting. This question is important in understanding the overall effect of political connections on financial reporting. Our difference‐in‐differences tests reveal no evidence that firms experiencing a relative increase in political connections report more opportunistically after close elections in anticipation of preferential treatment by the SEC in its enforcement actions. In contrast, we find evidence that these firms report less opportunistically in response to an increase in their connections with SEC‐influential politicians. Further tests show that our findings are unlikely to be driven by capital market pressure, managerial equity incentives, or corporate governance. Overall, our results are consistent with political connections mitigating opportunistic reporting through enhanced scrutiny by the SEC of politically connected firms' financial reporting. Our findings provide new insights into the interactions among political connections, SEC oversight, and financial reporting by showing how politically connected firms alter their financial reporting in anticipation of differential treatment by the SEC.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".