Political Risk, Political Polarization, and Earnings Management
Bibliographic record
Abstract
ABSTRACT We examine how exposure to firm‐level political risk (PRISK) affects corporate earnings management (EM). We present robust evidence that heightened PRISK leads to an increase in real earnings management (REM); however, we do not find consistent evidence supporting an increase in accrual earnings management (AEM). Our results remain robust when employing difference‐in‐differences (DiD), two‐stage least squares (2SLS), propensity score matching (PSM), and additional methods designed to address endogeneity concerns. Additionally, we document that firm‐level political polarization exposure (PPE) intensifies the PRISK‐REM relationship. More interestingly, the financial market is relatively forgiving to firms that engage in REM activities in the face of both high PRISK and high PPE. Our findings provide novel insights into how PRISK shapes the financial reporting quality, and the critical role PPE plays in this relationship.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".