Internal Control over Financial Reporting and Resource Extraction: Evidence from China*
Bibliographic record
Abstract
ABSTRACT We examine whether the strength of internal control over financial reporting (internal control) reduces the expropriation of resources from the firm by managers and controlling shareholders. Although we have ample evidence from prior literature that internal controls reduce errors in financial reports, it is less clear that they can curb resource extraction, because management may fail to enforce these controls. Exploiting the setting of China, where we have a rich internal control data set and established measures of resource extraction, we provide evidence consistent with internal controls curbing resource extraction on average. In particular, we document a negative association between internal control strength and resource extraction. We also find that the association between internal control strength and resource extraction is weaker in settings where we expect management to have fewer incentives to enforce these controls: within state‐owned firms and within non‐state‐owned firms that have a powerful controlling shareholder. We interpret these results as suggesting that internal controls must both exist and be enforced by management for the controls to safeguard assets. Although the analyses are conducted using Chinese data, we expect the spirit of our findings to generalize to other settings—management can “window dress” internal control procedures while still engaging in undesirable behavior.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".