Income inequality and its driving forces in transitional countries: evidence from Armenia, Azerbaijan and Georgia
Bibliographic record
Abstract
The purpose of this study is to measure and compare income inequality and its driving forces in the low-income countries of the Caucasus by drawing on micro-data from nationally representative household surveys in Armenia, Georgia, and Azerbaijan. Inequality in the region of the Caucasus is very high. The Gini coefficient for the regions as a whole reached 55%. Azerbaijan has the lowest income inequality, followed by Armenia and Georgia. Among predictors, graduate and postgraduate education has the strongest positive effect on income in all countries. By contrast, the positive effect of technical vocational education is relatively smaller and can be observed only in Azerbaijan and Georgia. In addition to formal education, knowledge of English and computers also has a separate positive effect in all countries. An increase in age, and therefore an increase in years of experience, has a low positive impact on the increase in income in all countries. By contrast, being a female has the strongest negative effect on income across the region. Living in rural areas and reporting poor health is associated with having lower income.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".