Credit Access and the Firm–Government Connection: Is There Any Link?
Bibliographic record
Abstract
Access to credit for businesses is an unresolved issue, especially in developing countries and transition economies. There has been a lot of research exploring factors affecting firms’ credit accessibility. Particularly, factors related to borrowers and lenders are always placed under consideration. However, besides those factors, institutional elements could also play an important role in guiding companies’ operations. In countries where the economy lacks transparency and low-level development is limited, informal institutional factors can have potential impacts. In this paper, we focus on exploring the relationship between firm–government links and credit access, thereby offering managerial implications through utilizing cross-sectional data sets at the firm level, with an initial sample of 26,849 observations from 38 countries at different levels of development around the world. The results show a positive correlation of firm–government connection with credit access. Moreover, this relationship may vary depending on the market in which the business primarily operates. Specifically, firms working internationally are less influenced by links with governments and tend to rely more on their own characteristics and conditions.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".