The Shaping Mechanism of Institutional Quality Differences on the Long-Term Economic Growth Path and Its Theoretical Explanation
Bibliographic record
Abstract
This paper explores how the long-term economic growth paths of countries are shaped by differences in institutional quality and the underlying theoretical mechanisms, and constructs a comprehensive analytical framework for the impact of institutional quality on economic growth after reviewing relevant literature in institutional economics, new growth theory, and comparative institutional analysis. The research shows that many aspects such as property rights protection, transaction costs, technological innovation incentives, factor accumulation and allocation efficiency are the ways in which institutional quality affects the economic growth path, among which high-quality institutions can reduce uncertainty, provide reasonable incentive structures to promote the effective operation of markets, support innovation activities to achieve sustainable growth, On the contrary, low-quality institutions may lead to rent-seeking, misallocation of resources and thus fall into a growth trap. Further analysis reveals that institutional evolution is path-dependent and that both historical initial conditions and cultural factors act on the direction of institutional change, resulting in a diverse growth trajectory. This study presents a theory of institutional co-evolution from the perspectives of new institutional economics, endogenous growth theory, and comparative political economy, which explains the dynamic interaction between institutional bundles and economic performance, as well as the situation where differences in institutional quality affect innovation, human capital accumulation, and resource allocation efficiency, resulting in sustained development differences among countries. This theoretical framework opens up a new perspective for understanding long-term economic growth disparities and offers policy implications for institutional reform in developing countries.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".