Decentralising for local information? Evidence from state‐owned listed firms in China
Bibliographic record
Abstract
Abstract This study investigates the effect of decentralisation of SOEs on stock price crash risk. In so doing, we test two competing hypotheses. Under the Political Influence Hypothesis, decentralisation aggravates local government's expropriation of minority shareholders (type II agency conflict), and thus increases crash risk. Under the Local Information Hypothesis, decentralisation decreases monitoring distance (type I agency conflict), strengthens external monitoring and thus decreases crash risk. We find robust evidence supporting the Political Influence Hypothesis. Cross‐sectional analyses show that our baseline results are more pronounced when firms are decentralised to the provincial level and politicians have greater incentives to pursue their political objectives. We further show that bad news hoarding and risk‐taking are two potential channels through which SOE decentralisation increases crash risk. Taken together, our results imply that the decentralisation exacerbates the type II agency conflict rather than ameliorates the type I agency conflict in SOEs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".