Access by MSMEs to Finance in the Southern and Eastern Mediterranean: What role for credit guarantee schemes?
Bibliographic record
Abstract
Micro-, small- and medium-sized enterprises (MSMEs) in the Southern and Eastern Mediterranean suffer from credit constraints. Given their contribution to employment and growth, similarly as in other regions, policy-makers have developed credit guarantee schemes (CGSs) in order to facilitate small companies’ access to debt capital. CGSs are risk-sharing mechanisms under which a guarantor ensures the lender against a share of the possible losses it incurs when extending a loan. Despite the maturity of some schemes, knowledge of the schemes’ functioning, operating environment as well as performance in guaranteeing loans for MSMEs is scarce. Building on a previous study of CGSs in the region, this paper extends the available knowledge on the region’s schemes, building on the results of an exclusive questionnaire to gain insights into these mechanisms. First, the paper reviews the conditions of MSMEs’ access to finance in the Southern and Eastern Mediterranean; second, it presents the results of the questionnaire across a number of dimensions ranging from ownership to financial performance; and third, the paper presents some avenues for future policy research in this area.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".