Innovation, Entrepreneurship, and Economic Growth: The Moderating Role of Corruption
Bibliographic record
Abstract
This study examines the relationships among a nation's innovation capability, entrepreneurship, and economic growth and investigates the corruption level's impact on those relationships. Based on 2022 data of 44 counties from the Global Innovation Index (GII), Gross domestic product (GDP), Total early-stage entrepreneurial activity (TEA), and the Corruption Perceptions Index (CPI), obtained from the World Intellectual Property Organization, World Bank, Global Entrepreneurship Monitor, and Transparency International, a multiple regression analysis was used to examine the causal relationship between innovation capability, entrepreneurial climate, and economic growth. The results showed that a nation's innovation capacity significantly positively affects the growth of economies. On the one hand, the result found that entrepreneurship is negatively associated with economic growth. This can imply that a significant portion of entrepreneurship in the analyzed countries may be necessity-driven rather than opportunity-driven. Additionally, the study found that corruption moderates the relationship between a nation's innovation capacity and economic growth, such that higher levels of corruption weaken the positive impact of innovation capacity toward economic growth but are not found to significantly moderate the relationship between entrepreneurship and the growth of nation economies. These findings emphasize the significance of addressing corruption to exploit the advantages of innovation capacity for economic growth. Policymakers should focus on improving the entrepreneurial ecosystem to promote opportunity-driven ventures that foster innovation and contribute to long-term economic development. This work is among the few to discover nationally the compound interplay between innovation, entrepreneurship, and corruption. It offers useful insights for policymakers who seek to promote economic growth by improving governance and creating a favorable climate for entrepreneurs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".