Policy Uncertainty and Accounting Quality
Bibliographic record
Abstract
ABSTRACT Using data from 19 countries over the 1990–2015 period, we examine how economic policy uncertainty (EPU) affects accounting quality. We find that accounting quality, measured based on Nikolaev's (2018) model, increases during periods of high policy uncertainty. This relation is confirmed by the negative association between EPU and performance-adjusted discretionary accruals in a multivariate setting, and it extends to various alternative measures of earnings properties. We also find that the positive relation between EPU and accounting quality is more pronounced for government-dependent firms and firms with higher political risk. Additional analyses based on institutional investors' trading behavior, media freedom, and press circulation suggest that market participants' attention is a mechanism through which EPU affects accounting quality. Further, we find evidence that high accounting quality can mitigate the negative effects of EPU on corporate investment and valuation. Data Availability: All data are publicly available from sources indicated in the text.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".