Intergovernmental organizations and entrepreneurship: understanding the relationship between the supranational, national, and individual level
Bibliographic record
Abstract
Abstract Intergovernmental organizations (IGOs), such as the World Trade Organization, the United Nations Trade and Development and the World Bank, promote stability, security and development for member states and their citizens via supranational institutional influences. However, their influence on individuals, especially their entrepreneurial business activities, is unclear. As policymakers decide when more (or less) IGO involvement best serves their countries and citizens, we must better understand the connection of the supranational, national, and individual levels. Thus, we study how IGO membership influences entrepreneurial opportunities and focus on two activities that impact a country’s economy differently: formal and informal entrepreneurship. Moreover, we identify how national institutional ecologies build the bridge between the supranational and the individual level and mediate the relationships. Using a sample of 68 countries, their entrepreneurial environment, and their connection to IGOs, we find that IGO memberships enhance opportunities for entrepreneurship. Moreover, IGOs promote formal entrepreneurial activities while discouraging informal entrepreneurial activities, mediated by the country’s institutional ecology. We combine insights from international relations, institutional theory, and strategic entrepreneurship to highlight how institutions at different levels influence entrepreneurial opportunities and discuss the policy implications of our findings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".