Socio-economic causals for entrepreneurial transformation
Bibliographic record
Abstract
Entrepreneurship has become vital for national growth. Therefore, it is essential to explore the factors that enhance entrepreneurial transformation. The literature identifies two main driving forces behind entrepreneurship: necessity and opportunity, which react differently to the socio-economic factors. This study explores the socio-economic determinants of necessity-based entrepreneurship and opportunity-based entrepreneurship. Here the yearly data of 108 countries from 2009 to 2017 is used to formulate a panel data model. Data on entrepreneurship is taken from the Global Entrepreneurship Monitor (GEM). HDI is used as a surrogate measure of socio-economic factors along with several control variables like cost of doing business, economic factors, governance factors and perception factors. Panel Feasible Generalized Least Squares (FGLS) estimation technique accounts for spatial heterogeneity. The panel data estimation shows that human capital improvement enhances the opportunities for entrepreneurial transformation while decreasing necessity-based entrepreneurship due to higher job creation. The findings also suggest that improvement in governance, perceived opportunities, openness, and culture are vital for enhancing opportunity-driven entrepreneurship.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".