Bibliographic record
Abstract
Remuneration paid by public administrations in Belgium amounted to one-quarter of their primary expenditure in 2010. Because current conditions call for fiscal consolidation, it is important to examine whether this component of spending could be a source of budget savings, including by adjusting wages. The article focuses principally on wage gaps between the public sector and the private sector. The authors are especially interested in the situation in Belgium as compared with those of nine other euro area countries (Germany, Austria, Spain, France, Greece, Italy, Ireland, Portugal and Slovenia), a study of which using microeconomic data was recently completed. In most of the countries analysed, wages are higher in the public sector than in the private sector, regardless of whether macroeconomic or microeconomic data are used. With the former, it is not possible to consider differences in the characteristics of the populations working in each of the two sectors. Indeed, the public sector – notably in Belgium – is more composed of women, older workers, and those with a higher level of education, but public sector employees work fewer hours and are less likely to occupy a managerial position. To take these factors – gender, seniority or age, education level, and managerial duties – into consideration when comparing wages between the two sectors, microeconomic data from the EU-SILC survey were used. The observations the authors were able to make based on the microeconomic and macroeconomic data overwhelmingly corroborate each other. The analysis shows that the countries where the average wage gap is the biggest in favour of the public sector are also the countries experiencing the toughest budget woes. In most of the countries studied, the salary advantage enjoyed by public sector workers holds for every sub-set of workers, although to differing extents. For example, wage gaps are the widest for women, for lower levels of income, for those who do not have supervisory functions, and in the branches of administration and education. The impact of education level on wage gaps varies from one country to the next. The wage gap between sectors in Belgium is one of the narrowest of any country studied, regardless of what data are used. According to macroeconomic data, it slightly favours the private sector. According to microeconomic data, in which wages are adjusted to control for individual characteristics, for many groups of workers the gaps are so limited that they are not statistically significant.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.089 | 0.031 |
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".