Income Inequality and Economic Growth: An Analysis Using a Panel Data
Bibliographic record
Abstract
A long time ago, economic growth was the main indicator of countries’ economic health. However, since the 1970s, the analysis of the relationship between economic growth and other economic phenomena such as inequality has begun to grow (Sundrum, 1974). Much of the literature on the link between economic growth and income inequality is based on Kuznets revolutionary theory. The purpose of our article is to suspect the causality relationship between growth and inequality. To do this, we used data from 189 countries for the period between 1990 and 2015. We estimated a global model and three other of each category of countries in terms of development. In the global model, economic growth is insignificant even if its sign is positive. The same result appears in the developing country model and the moderately developed countries one. However, in the developed countries model, economic growth is negatively and statistically related to inequality. The Kuznets curve is approved in our study only when using human development indicator in the place of growth. Growth explain inequality’s movement in our study only in the model of developed countries and its coefficient is negative.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".