Entrepreneurship and national economic growth: the European entrepreneurial deficit
Bibliographic record
Abstract
We develop a quantitative model relating entrepreneurship and economic growth, based on global competitiveness report and global entrepreneurship monitor data for 18 countries in the European Union, plus USA and China, for the years 2003?2005. The model is used to identify policy actions that can be undertaken to achieve the goals set forth in the Lisbon Agenda for the EU. Low GDP growth rates are associated with an entrepreneurial deficit. Higher levels of entrepreneurship need to be encouraged to commercialise the considerable knowledge and technology available in Europe. We find support for a model in which GDP growth is dependent on an exploitation bridge between independent entrepreneurs and corporate entrepreneurs in larger firms. The latter have the resources to mass market radical innovations developed by independent entrepreneurs and to capitalise on the technological spillover effects. In particular, opportunity-based entrepreneurs affect GDP by exploiting national investments in the commercialisation/innovation infrastructure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".