Credit information sharing and firm innovation: Evidence from the establishment of public credit registries
Bibliographic record
Abstract
Abstract Lenders are reluctant to finance firms' innovation activities because such activities tend to be opaque, with a high likelihood of negative outcomes that could hamper loan repayment. We posit that public credit registries (PCRs), which play an important role in credit information sharing in many countries, can facilitate financing by reducing adverse selection and moral hazard and increasing bank competition. Using the staggered establishment of PCRs in different countries and an international firm–patent data set, we find that credit information sharing positively affects firm innovation, especially in firms that experience a larger increase in bank debt financing after the establishment of a PCR. This finding is consistent with the notion that credit information sharing promotes firm innovation by easing bank debt financing frictions. We also find a stronger effect in countries that experience a large increase in bank competition after the establishment of a PCR—consistent with increased bank competition serving as a channel through which credit information sharing facilitates bank debt financing, thereby generating a positive effect on firm innovation. The positive effect is more pronounced when the established PCR has features that promote credit information sharing. It is also more pronounced for opaque firms and firms in innovation‐intensive industries, indicating that credit information sharing helps to reduce financing frictions. Finally, we posit and find evidence that firm efficiency in transforming innovation inputs into outputs improves after the establishment of a PCR. Overall, our paper offers novel insights into how credit information sharing facilitates firm innovation.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".