Are There Non-linear Effects of Banking Relationships and Ownership Concentration on Operational Performance? Empirical Evidence from Portuguese SMEs Using Cross-section Analysis and Panel Data
Bibliographic record
Abstract
This paper provides new evidence for the relationship between the stability of the banking relationship, ownership concentration and operating profitability, supporting non-linear effects between those variables in the context of small and medium enterprises (SMEs). From a sample of 4,163 Portuguese SMEs and cross-section data and panel data, we found evidence for a U-shaped quadratic relationship between the stability of the banking relationship and operational performance. This result indicates that the consolidation of new banking relationships, the difficulties experienced by SMEs in overcoming the problems of adverse selection and moral hazard reflect negatively on their operating profitability. However, when the banking relationship is solidified, and banking institutions acquire information, supervision and monitoring costs decrease, credit constraints are lower and contractual conditions are tailored to the needs of the company, with positive impacts on operating profitability. In turn, the quadratic specification established between ownership concentration and operating profitability suggests that the expropriation hypothesis prevails for low levels of control rights and the supervision hypothesis prevails for high levels.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".