Economic policy uncertainty, information production, and transparency
Bibliographic record
Abstract
This paper investigates how Economic Policy Uncertainty (EPU) influences corporate information environments in Chinese stock markets from 2005 to 2022. Using multiple measures of information transparency based on bid-ask spreads, price impact, and trading illiquidity, we document that elevated EPU leads to enhanced information transparency in the subsequent year. We identify asymmetric effects of EPU on information production: while firms respond to high EPU by increasing disclosure intensity and adopting a more optimistic tone, analysts and media coverage significantly decline. Additionally, EPU weakens the link between firms' information production and transparency outcomes. These findings are robust to an instrumental variable approach that addresses endogeneity concerns, as well as to alternative measures of both EPU and information transparency. Our findings contribute to the literature by revealing the complex mechanisms through which policy uncertainty shapes information environments in emerging markets. • Economic Policy Uncertainty (EPU) improves information transparency in China's stock market, based on inverse bid-ask spread, disclosure quality index, and illiquidity ratio. • While higher EPU encourages managers to issue more detailed and optimistic reports, it reduces coverage from analysts and media. • By refining measurement approaches, this study clarifies the role of EPU in shaping information environments.
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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 | 0.017 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".