Does “Good†Governance Promote Economic Growth According to Countries' Conditional Income Distribution
Bibliographic record
Abstract
This study identifies the relative impact of “good†governance on comparative economic growth performance for a large sample of countries classified based on their relative income distributions, namely; low income countries, middle income countries, and high income countries. The data set covers 100 countries throughout the period for 1996 to 2018. The empirical model is estimated with econometric pooled Ordinary Least Squares (OLS), random effects, fixed effects techniques and using the Hausman Test. According to the appropriate fixed effects estimated model, findings suggest that “good†governance generally has a positive and statistically significant effect on economic growth across all countries in the sample. However, results confirm that the impact of “good†governance differs according to conditional income distributions among countries. Indicators of “good†governance for low income countries are more likely to affect economic growth than those for middle and high income countries. Specifically, findings show that the dominant governance indicators for economic growth in low income countries include government effectiveness, political stability, regulatory quality, rule of law, and voice and accountability. Findings also show that control of corruption seems not to influence economic growth for high and low income countries. There are some policy implications that can be drawn for countries to develop a variety of policies toward the role of governance in the economy according to their income distributions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".