The Impact of Taking into Account Information Asymmetries and the Credit Market Structure in the Assessment of Borrowers' Credit Worthiness: Case of Tunisia
Bibliographic record
Abstract
We attempt to assess borrowers’ credit worthiness in three scenarios one heralding prevalence of information asymmetries the second heralding absence of information asymmetries and the third a realistic scenario encompassing both features for the sake of determining which one gives the best outcome in terms of goodness of fit and adequacy of the model specification with the corresponding market structure of the credit market in purview. The added value of this research is that, when we want to estimate any entity, the explanatory variables carrying market imperfections must be manifested and not implicitly invocated because of the endogeneity of market imperfections and the market structure should be specified clearly; otherwise, the estimation is inadequate and the empirical results do not correspond to the theoretical predilections. In other words, the conformity of empirical results with theoretical predilections does not rely on the absence of information asymmetries, whose full recognition does not introduce spuriousness into empirical results. The research is a comparative cross investigation of scenarios highlighting the best fit to borrowers' Creditworthiness assessment and deducing through logical analysis the role payed by information asymmetries and the banking market power in shaping this fit.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".