Financial reporting and disclosure practices in China
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
We study financial reporting and disclosure practices in China using survey methods similar to prior studies of U.S. firms (i.e., Graham et al., 2005; Dichev et al., 2013). Comparing earnings features, motives to manage and smooth earnings, and voluntary disclosure practices between the two countries, we reveal three major differences. First, Chinese firms exhibit a stronger preference for predictive, relative to verifiable, attributes of earnings that can signal stable firm performance to their stakeholders. Second, smooth earnings are desired by various stakeholders and can be achieved through coordination among connected stakeholders, which is conceptually different from earnings management. Third, Chinese firms consider public disclosure as less relevant in the reduction of the cost of capital. In addition, Chinese firms do not have a bias towards conservative reporting. We explain and reconcile these differences as resulting from some unique institutional features of China. Our study provides novel field evidence that contributes to, expands, and directly corroborates existing empirical studies.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 it