Does the Underwriter Reputation Affect the Pricing of Local Government Bonds in China?
Bibliographic record
Abstract
As the product of the combination of fiscal and financial, local government bonds should also follow the pricing mechanism of the securities market even under the special financial system in China. This paper uses Heckman's two-stage model to investigate whether the mechanism of underwriter reputation affects the pricing of local government bonds. The empirical results show that local governments tend to choose securities company underwriters with high reputation when they issue bonds with large scale, long maturity, and call right which have high degree of information asymmetry, and this tendency has an obvious time trend. However, high-reputation securities company underwriters failed to play the role of information intermediary to reduce the cost of local governments. On the contrary, implicit guarantees and government interventions induced the commercial banks to depress their quotations even leading to “interest rate upside down”, which resulted in the lack of securities company underwriters. In order to play the mechanism of underwriter reputation to promote the marketization of local government bonds pricing, this paper proposes to eliminate government interference, guide underwriters to strengthen the construction of their reputation, promote the marketization of underwriting fees and strengthen the supervision of underwriters.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".