The characteristics of the top decile wage earners in Croatia
Bibliographic record
Abstract
International research on top incomes predominantly explores variations in income shares at the top, with a broad emphasis on cross-economy comparisons to highlight heterogeneity. In contrast, detailed analyses focusing on the specific attributes of top wage earners within individual countries are less common. Notably, Croatia has been overlooked in these discussions. This paper aims to address this gap by uncovering the distinctive characteristics of the highest decile of wage earners in Croatia, diverging from the more common approach of comparing across multiple economies to instead provide an in-depth look at a single country. Accordingly, the aim of the paper is to reveal the main characteristics of the top decile wage earners in Croatia. For this purpose, the paper uses the probit model and data from the EUSILC 2020 survey. We analyse persons who received employment income and who worked all 12 months of the year. The results show that the top decile wage earners receive about a quarter of the total employment income. The probit analysis shows that gender, age, settlement size, education and economic activity of the main job have a significant impact on belonging to the top decile wage earners group. In other words, men, elderly persons, those living in densely populated areas, those with tertiary education and those working in financial intermediation are significantly more likely to be in the top decile wage earners group than others.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".