Small Firms, Corruption, and Demand for Credit. Evidence from the Euro Area
Bibliographic record
Abstract
In this paper, we aim to assess how the quality of the institutional environment – identified according to the level of corruption perceived in a country – may affect the access to credit for micro, small, and medium-sized enterprises (MSMEs). Based on a sample of 68,115 observations – drawn from the ECB-SAFE survey – related to MSMEs chartered in 11 euro area countries, we investigate whether the level of corruption affects their demand for bank loans during the period 2009–2014.Overall, we find that the degree of corruption seems to play a role in the applications for bank loans when small firms are under investigation. Interestingly, results highlight that small businesses chartered in highly corrupt countries face a greater probability of self-restraint regarding their loan applications (about 7.4%) than small firms located in low-corruption economies (around 6%). The results are robust to various model specifications and econometric methodologies. Our findings suggest that anti-corruption policies and measures enhancing transparency in the economy may be crucial in reducing the negative spillovers generated by a low-quality institutional environment on the access to credit by small firms.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".