THE IMPACT OF MACRO FACTORS ON ENTREPRENEURSHIP AT THE PROVINCIAL LEVEL IN A DEVELOPING COUNTRY
Bibliographic record
Abstract
This study analyzes the effect of macroeconomic, social and institutional factors on provincial entrepreneurship, defined as the aggregate number of newly created enterprises per year in a province within a country. Using a robust fixed-effects regression model with data from 63 provinces in Vietnam between 2014 and 2021, the findings reveal that the GDP growth, trained labor force, reduced time costs for regulatory compliance, lower informal charges and enhanced business support services positively influence provincial entrepreneurship. Conversely, economic openness and poverty rates negatively affect entrepreneurship in the provinces. Therefore, we can confirm the effect of some key macro factors on provincial entrepreneurship, implying that the aggregate entrepreneurship rate varies among provinces in a developing country because of differences in their macro factors. Our study contributes to the literature on provincial entrepreneurship and its macro determinants, providing practical implications for policymakers aiming to foster entrepreneurial activities across their provinces in contexts like Vietnam.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".