Political Connections and the <scp>Trade‐Off</scp> Between Real and <scp>Accrual‐Based</scp> Earnings Management*
Bibliographic record
Abstract
ABSTRACT We provide evidence on the effect of political connections on the trade‐off between real and accrual‐based earnings management in the United States. This evidence is important because prior literature documents mixed evidence on whether political connections reduce the threat of SEC enforcement. By studying earnings management with a large sample, our study provides more generalizable insights into the effects of political connections on enforcement. We argue that politically connected firms face a lower threat of enforcement, which reduces the costs of accrual‐based earnings management and alters the trade‐off between real and accrual‐based earnings management. Consistent with our predictions, using a single‐step estimation method as well as a difference‐in‐differences test based on an exogenous shock, we find that connected firms engage in more accrual management and less real earnings management. Our results are driven by firms that have relatively high costs of real earnings management. Furthermore, we find that political connections mitigate the relation between SEC comment letters and earnings management. Overall, the evidence is consistent with politically connected firms facing a lower threat of regulatory enforcement and using this flexibility to increase accrual‐based earnings management and reduce real earnings management that is potentially value destructive. Our study complements and strengthens inferences in prior work that documents evidence of lax SEC enforcement for politically connected firms using small samples. Our findings should be of interest to policy‐makers, regulators, and other professionals that are interested in understanding the effects of political connections.
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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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".