Does Rule of Law Affect Economic Growth Positively?
Bibliographic record
Abstract
Efficient institutional structure resolves the uncertainties in the market and the problem of asymmetric information, and thus creates a positive exogeneity, ensures the efficient distribution of the resources and makes a positive impact on the functioning of the economy. In addition to this, especially rule of law forms the basis of the socio-economic development. In the presence of the factors such as prevention of corruption and freedom of expression, institutional structure has a significant impact on economic growth. However, there are empirical studies that state that institutional efficiency boosts economic growth in developed countries, whereas it doesn’t have an impact or has a negative impact on economic growth in developing countries. For all these reasons, the impact of institutional efficiency on economic growth in developed, developing and underdeveloped countries will be analyzed in this study. In this study, the effect of institutional effectiveness on economic growth has been analyzed in both three country groups from 2002 to 2015 by using GMM. Dependent variable is GDP and the independent variables are institutional variables (rule of law, fight against corruption, voice and accountability). Based on our primitive findings we expect that developed institutions effect economic growth positively in develop countries unlike developing 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".