Inequality and Poverty in Greece: Changes in Times of Crisis
Bibliographic record
Abstract
The Greek crisis was the deepest and longest ever recorded in an OECD country in the postwar period. The output declined by over a quarter, the disposable income by more than 40%, while the unemployment rate exceeded 27%. This paper explores the effects of the crisis on the level and the structure of aggregate inequality and poverty using the data of EU-SILC for the period 2007-2014. The results show that inequality rose but the magnitude of the change varies across indices. The recorded increases are larger when the indices used are relatively more sensitive to changes close to the bottom of the income distribution. Unlike claims often made in the public discourse, the elderly improved their relative position in the income distribution while there was substantial deterioration in the relative position of the enlarged group of the unemployed. The contribution of disparities between educational groups to aggregate inequality declined while that of disparities between socio-economic groups rose. All poverty indicators suggest that poverty increased substantially, especially when “anchored” poverty lines are used. Substantial changes are observed regarding the structure of poverty. Despite an increase in the population share of households headed by pensioners, their contribution to aggregate poverty declined considerably, with a corresponding increase in the contribution of households headed by unemployed persons. These changes are starker when distribution-sensitive poverty indices are utilised.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".