Regional Economic Environment, Compensation Marketization and Financing Platform Performance—An Empirical Analysis Based on Data of China
Why this work is in the frame
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Bibliographic record
Abstract
Based on the basic theory of labor market and compensation incentive, this paper deduces the impact of compensation marketization on the performance of financing platform, discusses the differential effect of compensation marketization on the performance of financing platforms under different regional conditions, and puts forward the research hypothesis. This paper collected the financial data of 115 financing platforms in Jiangsu and Anhui provinces of China from 2005 to 2020 for empirical analysis. Our research finds that compensation marketization can improve the performance of financing platforms, and this effect will be larger if the financing platform is in a more developed region than in a less developed region. After further research we find that compensation marketization promotes performance by increasing capital turnover and reducing cost stickiness. Also executives’ political background may lead to a higher effect of compensation marketization on financing platform performance.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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