Liquidity gaps in financing the SME sector in an emerging market: evidence from Poland
Bibliographic record
Abstract
Purpose Access to finance appears to be the largest challenge for entrepreneurial firms from the small to medium‐sized enterprise (SME) sector in Poland. To address this concern, the government embarked on a program to yield financial and know‐how assistance to the SME sector. The purpose of this paper is to evaluate public intervention in this area. Design/methodology/approach The study focuses on the analysis of primary data. The sampling frame for the study consisted of 278,088 firms from the SME sector in the Warsaw region. The sample size was equal to 500 firms from the SME sector. Questionnaires from 262 respondents were included in the study, for an effective response rate of 52 percent. Findings The study concludes that there are still pronounced liquidity gaps for firms in the SME sector in Poland and that the government programs are not effective in closing these liquidity gaps. Originality/value Problems with access to capital continue to be a challenge to developing a vibrant SME sector in Poland and a lack of access to capital is consistently quoted as the major obstacle to the development of the SME sector in Poland. The paper offers three policy recommendations in relation to closing liquidity gaps in the SME sector.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".