Explaining female and male entrepreneurship at the country level
Bibliographic record
Abstract
Using Global Entrepreneurship Monitor data for 29 countries this study investigates the (differential) impact of several factors on female and male entrepreneurship at the country level. These factors are derived from three streams of literature, including that on entrepreneurship in general, on female labour force participation and on female entrepreneurship. The paper deals with the methodological aspects of investigating (female) entrepreneurship by distinguishing between two measures of female entrepreneurship: the number of female entrepreneurs and the share of women in the total number of entrepreneurs. The first measure is used to investigate whether variables have an impact on entrepreneurship in general (influencing both the number of female and male entrepreneurs). The second measure is used to investigate whether factors have a differential relative impact on female and male entrepreneurship, i.e. whether they influence the diversity or gender composition of entrepreneurship. Findings indicate that – by and large – female and male entrepreneurial activity rates are influenced by the same factors and in the same direction. However, for some factors (e.g. unemployment, life satisfaction) we find a differential impact on female and male entrepreneurship. The present study also shows that the factors influencing the number of female entrepreneurs may be different from those influencing the share of female entrepreneurs. In this light it is important that governments are aware of what they want to accomplish (i.e. do they want to stimulate the number of female entrepreneurs or the gender composition of entrepreneurship) to be able to select appropriate policy measures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".