The Real Effects of Implicit Government Guarantee: Evidence from Chinese State-Owned Enterprise Defaults
Bibliographic record
Abstract
We study the effects of implicit government guarantee (IGG) on Chinese state-owned enterprises (SOEs). We find that SOEs reduce their investments by 2.4% of book assets, on average, relative to matched non-SOEs after the first SOE default in China’s onshore bond markets in 2015. The investment reduction concentrates among SOEs that are financially constrained, yet SOEs financed by large state banks are hardly affected. Bondholders require more stringent default protection in newly issued SOE bonds. We also find that the investment reduction is more pronounced for SOEs with severe agency problems and that SOEs experienced more positive market reactions to acquisition announcements after 2015. Our results suggest that the reduction in IGG has confounding effects on Chinese firms. Although the weakening of IGG may help mitigate overinvestment, it exacerbates financial constraints of those with limited access to alternative sources of financing. This paper was accepted by Lucas Schmid, finance. Funding: Z. Zhang acknowledges the funding support by Research Grants Council of Hong Kong [Project 11503318]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4483 .
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".