How do Chinese urban investment bonds affect its economic resilience? Evidence from double machine learning
Why this work is in the frame
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Bibliographic record
Abstract
This paper employs the double machine learning model to investigate the impact of urban investment bonds on economic resilience. To deal with a broad set of macroeconomic and industry variables, LASSO is used for model estimation. The sample consists of 239 Chinese cities that issued debt and loan instruments between 2016 and 2021. The results show that 1) urban investment bonds have a positive, inverted U-shaped effect on economic resilience; 2) the ability to recover from an economic shock plays an important role in constructing the Chinese economic resilience index. The heterogeneity analysis reveals that the impact of urban investment bonds on economic resilience varies according to cities’ locations, industrial structure, and financial structure. Furthermore, the mechanism analysis demonstrates that urban investment bonds enhance economic resilience by promoting infrastructure development. These findings provide helpful guidance for China and other developing countries to ensure financing security and maintain robust economic growth. • Identifying the effect of China’s UIB on its economic resilience. • Employing the entropy method to construct the Chinese ERI. • Implementing an analysis of the importance of various economic indicators on constructing China’s ERI. • Applying the novel DML model for exploring the effect of China’s UIB on its ERI. • Providing a helpful guidance for both China and other developing countries to improve their economic resilience.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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