How do Chinese urban investment bonds affect its economic resilience? Evidence from double machine learning
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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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".