The Impact of Government Effectiveness on Trade and Financial Openness: The Generalized Quantile Panel Regression Approach
Bibliographic record
Abstract
Purpose: This paper aims to investigate the impact of government effectiveness on trade and financial openness in 35 selected countries around the globe. Design/methodology/approach: A quantitative research approach was applied in the study using the generalized quantile panel regression approach to analyze the impact of identified variables in these selected countries. Panel quantile models with high estimation performance are preferred in the presence of excessive deviations and in cases where the normal distribution is invalid. Findings/results: The empirical findings indicate that selected countries with above-average governmental effectiveness, that is, with a well-established state bureaucracy and a historically strong state tradition, will further increase their activities toward international integration through financial and trade openness. Practical implications: This study aims to provide valuable information that governments and regulatory authorities can benefit from in their decision-making processes. Originality/value: In this study, it is preferred to use the trade openness of countries as the share of exports in total world exports and financial openness as the ratio of capital flows to world flows. In this way, these variables will provide new information to analyze the influence of government effectiveness. Implementing the generalized quantile panel regression technique can also be expressed as an innovation in this field of literature.
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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 | 0.000 |
| 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".