PANEL DATA ANALYSIS MAKING EFFECT THE VARIABLES ON INCOME DISTRIBUTION INJUSTICE
Bibliographic record
Abstract
Income distribution injustice is one of the serious and complex problems in development of a country. In a country, for economic development to spread over social basis, it is necessary for the gains en-gendered by growth to be shared with all society. Thanks to this, a country also provides economic growth as well as welfare increase. Spread of economic growth over social basis is closely related to a fair income distribution. In the study carried out, income distribution injustice is associated with Gini Coefficient. While that Gini Coefficient approaches zero means that country incomes are fairly dis-tributed to every sector of society, that it approaches to one expresses that incomes are collected in certain sectors and that income justice in that country becomes worse. In this study, utilizing the1990-2020 annual data of the countries such as USA, Brazil, Canada, Finland and United Kingdom, it was aimed to identify that income distribution injustice are affected from which of the variables such as employment rate, per capita income, fertility rate, urbanization and investment rate and how. As dependent variable of the study, Gini Coefficient was used. In the study, utilized panel data analysis, according to the results of long term PDOLS predictor analysis, it was concluded that an increase of one unit occurring in per capita income, gross capital formation (investment) and employment rate affected Gini Coefficient in positive direction, that fertility rate was insignificant, and that urbaniza-tion negatively affected it.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".