Inequality Dynamics in Malta: Cracks, Blips and Long-Term Trends
Bibliographic record
Abstract
This study analyses changes in income inequality in Malta and its driving factors between 2005 and 2018. The study employs and analyses data collected by Malta’s National Statistics Office, which conforms with the European Union Survey on Income and Living Conditions. Education and labour status are identified as key drivers behind income inequality changes over the period under review. While the Gini coefficient remained relatively stable between 2005 and 2018, the Lorenz curve moved further away from the line of equality at the upper end of the income distribution, showing modest increases. Over the 2014-2018 period, Government intervention has been mildly neutralizing through social transfers but not through taxes. Social transfers provided a greater safety net to citizens than they did during the 2005-2009 period, whereas tax reforms have abraded some tax progressivity. We also find that inequality was mostly attributed to differences in the individual’s qualifications, hours worked, occupations, and household employment structure and size, highlighting an important role for policy to further reduce the barriers to economic inclusion.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".