Political Risk, Political Polarization, and Earnings Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it