Financial Sector Development and Open Economy for Income Inequality Reduction: A Panel Fixed Model Analysis
Bibliographic record
Abstract
This study utilizes a panel fixed model to analyze the impact of financial sector development and commercial openness on income disparity of 40 developing countries over the period between 1995 and 2016. The empirical results suggest that there is a relationship between financial sector development, trade openness and income inequality. We establish that, in Latin America, the financial sector development increases income inequality while in Subsaharian Africa, we show the existence of an inverted U-shaped relationship between financial development and income inequality. Trade openness increases income inequality in the 40 selected countries. The increasing of 1 percent of trade openness leads the rise of 0,077 and 0,068 percent of income inequality in Latin America and Subsaharian Africa respectively. To alleviate income inequality, the government should (1) more develop financial sector and socially wide-ranging over period, important to welfares for both the rich and poor, and (2) diversify its commercial and industrial base beyond primary products in order to export high value-added products to generate more resources, better distribute them between rich and poor, and create more job opportunities.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".