The effect of cultural environment on entrepreneurial decisions
Bibliographic record
Abstract
Purpose This paper empirically examines whether the cultural environment plays a role in entrepreneurial decisions in Europe, the United States, Canada and Australia. Design/methodology/approach To explore this issue, we use data from the Adult Population Survey of 2010–2015 provided by the Global Entrepreneurship Monitor (GEM). To calculate the cultural factor, we utilize additional information from the GEM National Expert Survey data and estimate a probit model to measure the effect of culture based on an unobserved latent variable of satisfaction, measured through a dichotomous variable identifying entrepreneurs. Findings Results show a positive and statistically significant relationship between the cultural factor and the individual choice of entrepreneurial activity. Our findings are subjected to a range of robustness checks. We extend this analysis to an examination of cultural values as predictors of entrepreneurship status in collectivist and individualist countries. Our results point to collectivist and individualist roles as being among the mechanisms through which the cultural environment may operate. Originality/value This is the first empirical work that clusters a wide range of variables provided by the GEM NES data to obtain a cultural indicator, and then applies this indicator to the GEM APS micro-data. Policy-makers should consider these results in order to promote entrepreneurship through culture in collectivist and Mediterranean countries, but use other channels in individualist and Anglo-Saxon countries.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".