Determinants of Access to Bank Financing in SMEs in Mexico
Bibliographic record
Abstract
Several empirical studies indicate that the lack of financing is one of the main barriers that affects the economic growth of small and medium enterprises (SMEs). The main objective of this investigation was to determine to what extent the economic sector, the enterprise size, the characteristics inherent to the enterprise, the legal status, the variables linked to the performance of the enterprise, and the attributes of the owner influence the access to the bank financing of SMEs in Mexico. Using a discrete-response probit regression model, the impact of enterprise characteristics on the probability of obtaining a bank loan was determined. The data collected are from the Enterprise Surveys of Mexico, carried out by the World Bank. The sample of 1480 enterprises is representative by enterprise size, by economic sector, and by region. The research has a quantitative approach with a correlational scope, and a nonexperimental and transectional design. One of the main results highlights that the determinants with the greatest influence on access to bank financing are: the age, the small size, foreign participation, and the manufacturing sector. These results are consistent with other empirical studies, as well as with the pecking-order theory and the financial life-cycle theory.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".