Institutions and entrepreneurship: Empirical evidence for OECD countries
Bibliographic record
Abstract
Objective: The objective of the article is to test the bidirectional association of institutions and entrepreneurship in 19 OECD countries over the period of 2014-2016. Research Design & Methods:Most of the previous studies emphasise the role of institutions in entrepreneurial activity, while ignoring the role of entrepreneurship in the building of institutions.We estimate how institutions and entrepreneurship relate to each other and contribute to economic growth.For the estimation, we apply the structural equation modelling (SEM) with panel data.Findings: Estimated results show that the regulatory dimension of institutions and entrepreneurship have a strong bidirectional relationship -as we expected -while the normative dimension of institutions and entrepreneurship have a unidirectional association.These two interrelated factors stimulate economic growth.Implications & Recommendations: Policymakers should create a more friendly regulatory environment for entrepreneurship to flourish.In this process, institutional entrepreneurs also play an important role. Contribution & Value Added:There is a need for research on the bilateral relationship between institutions and entrepreneurship.Most previous articles consider the effect that is transmitted from institutions to entrepreneurship.However, there exists a two-way causal relationship between institutions and entrepreneurship that is worth exploring.In this regard, the greatest contribution of this article is that it is one of the first empirical works devoted to testing the two-way causal relationship between institutions and entrepreneurship.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".