Digital infrastructure and the cost of debt: evidence from “BroadBand China” policy
Bibliographic record
Abstract
Purpose This paper explores the linkage of digital infrastructure to the cost of debt. Design/methodology/approach This study uses the implementation of the “Broadband China” policy that improves digital infrastructure as an exogenous shock and exploits the difference-in-differences method (DID). Findings Empirical analyses show that digital infrastructure leads to increased firms’ borrowing costs, which is robust to several robustness checks. In addition, we find that this unfavourable effect can be attributed to intensified market competition led by digital infrastructure construction. Cross-sectional analysis shows that this effect is greater for non-SOEs and smaller firms. Finally, we offer additional evidence of the unfavourable effect by showing that digital infrastructure construction leads to decreased fundamentals. Originality/value Our paper unveils how digital infrastructure construction affects firms’ business strategy in using private debts and extends the determinants of firms’ borrowing costs.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".