Agriculture and Non-Agriculture Growth, Inflation and Income Inequality in Developed and Developing Countries
Bibliographic record
Abstract
The aim of this article is to examine how agriculture and non-agriculture growth and inflation affect income inequality. The multivariate panel data approach is used to examine the application of Kuznets hypothesis between income inequality and agriculture and non-agriculture growth and test the existence of nonlinear relationship between income inequality and inflation rate in a large sample of data collected for developed and developing countries. The Hodrick-Prescott (HP) filter is used to separate the cyclical component from the trend component of inflation rate and agriculture and non-agriculture growth. The results demonstrate a significant negative nonlinear relationship between income inequality and the HP filtered inflation rate squared in developed countries. The findings confirm the application of a ‘U-shaped’ of Kuznets curve between income inequality and agriculture growth and between income inequality and non-agriculture growth in developed countries. In addition, the results show Kuznets inverted ‘U-shaped’ curve between agriculture growth and income inequality, and Kuznets ‘U-shaped’ curve between non-agriculture growth and income inequality in developing countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".