Nonlinear relationship between financial inclusion and inclusive economic development in developed economies: evidence from panel smooth transition regression model
Bibliographic record
Abstract
Purpose The purpose of this study is to investigate the nonlinear association between financial inclusion and inclusive economic growth (IEG) in developed economies. A Block of G7 countries (Germany, Japan, Canada, France, Italy, the UK and the US) are considered in this study. Design/methodology/approach For analysis, the authors have employed the “Panel Smooth Transition Regression model.” Annual data consists of the period from 1995 to 2019. Findings This research makes a unique contribution to literature with reference to G7 countries, being a pioneering attempt to apply the panel threshold regression model to analyze the relationship between financial inclusion and IEG by applying more rigorous and advanced econometric techniques. Originality/value The results indicate that total labor force available in a country, gross fixed capital formation and financial inclusion are positive and significant in lower regimes, but as it moves toward the higher regime, the labor force available in a country becomes less impactful. However, an increase has been observed in financial inclusion in the higher regime. The complete sample generally exhibits a positive yet significant relationship between financial inclusion and inclusive economic development.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".