The Effect of the Minimum wage on the Income Distribution of Developingband Developed Countries
Bibliographic record
Abstract
Appropriate distribution of income and reduction of inequality are one of the government’s goals. Different policies and tools are used to achieve this and the minimum wage is one of them. In this study, the effects of minimum wages on income distribution, due to the most similarities in development and per capita income were studied in 7 selected developing countries including (Pakistan, Armenia, Iran, Belarus, Chile, Egypt, Georgia) and 7 selected developed/development countries including (USA, France, Japan, Sweden, Netherlands, Belgium, and Canada) with generalized minimum squares from the years 2000 to 2020. The results show that, in developing and developed countries, an increase in the minimum wage, improves income distribution and the effects of this policy in developed countries are much greater than in developing countries. Therefore, developing and developed countries should increase the minimum wage in proportion to the rate of inflation to progress and improve income distribution.Appropriate distribution of income and reduction of inequality are one of the government’s goals. Different policies and tools are used to achieve this and the minimum wage is one of them. In this study, the effects of minimum wages on income distribution, due to the most similarities in development and per capita income were studied in 7 selected developing countries including (Pakistan, Armenia, Iran, Belarus, Chile, Egypt, Georgia) and 7 selected developed/development countries including (USA, France, Japan, Sweden, Netherlands, Belgium, and Canada) with generalized minimum squares from the years 2000 to 2020. The results show that, in developing and developed countries, an increase in the minimum wage, improves income distribution and the effects of this policy in developed countries are much greater than in developing countries. Therefore, developing and developed countries should increase the minimum wage in proportion to the rate of inflation to progress and improve income distribution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".