The relationship between entrepreneurial activity, the business cycle and economic openness
Bibliographic record
Abstract
We investigate the interplay between entrepreneurial activity, the business cycle and unemployment in relation to the openness of the economy. Also, we explore to what extent the observation frequency (quarterly versus annual data) influences estimation results. Following empirical literature, we estimate a pooled VAR model of the three macro-economic variables. Using both quarterly and annual data for 19 OECD countries over the period 1998-2007, we find that in the short run (after one quarter), a country’s entrepreneurial activity is stimulated when its business cycle is lagging the world’s business cycle, whereas in the medium run (after one to two years), entrepreneurial activity is stimulated when its business cycle is leading the world’s business cycle. This suggests that a country’s business cycle position relative to the world’s cycle creates different types of entrepreneurial opportunities depending on the time horizon considered. These results apply to relatively open economies only which suggests that economic openness plays a role for entrepreneurial opportunities related to a country’s cyclical performance.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".