Credit information sharing and investment efficiency: Cross‐country evidence
Bibliographic record
Abstract
Abstract Credit information sharing allows creditors to obtain borrowers' relevant credit information, and it can improve borrowers' investment outcomes that are funded by debt. Using reforms to European countries' public credit registries (PCRs) to capture mandated information sharing among creditors, we examine the impact of such sharing on firms' investment efficiency. We find that information sharing enhances firms' investment efficiency, which we measure by their investment‐q sensitivity. This finding is consistent with credit information sharing enabling creditors to better screen borrowers to mitigate adverse selection and enhancing borrower discipline to avoid a bad credit record, which leads to the borrower making more efficient investments. We also document that the information sharing effect is more pronounced when firms rely more on debt financing, when the shared credit information is more accessible, when firms' information environment is more opaque, and when there is a greater information monopoly in the banking system. We offer supplementary evidence that the effect is also more salient when PCRs have characteristics that suggest more effective credit information sharing. Overall, our paper offers new insight into whether and how information sharing in credit markets enhances firms' investment efficiency. More broadly, it highlights how making more borrower information available to creditors can have important economic spillover effects on firm outcomes.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".