Results of the Bank’s survey of wage-setting in Belgian firms
Bibliographic record
Abstract
The analysis presented is the outcome of a survey conducted by the Bank and forming the Belgian component of an initiative launched by the Wage Dynamics Network (WDN), in order to accompany the empirical analysis based on individual employees’ wage data obtained, for instance, from administrative data banks. The survey contains questions on the wage-setting process, the existence of downward rigidity and the reasons for it, the reaction of firms to shocks, and the frequency and timing of wage and price adjustments. The survey reveals that almost all firms in Belgium are covered by a sector agreement, and just over a quarter apply an additional collective wage agreement at the firm level. Such firm-level collective agreements are more common in large firms. The results also show that just over half of firms apply a wage indexation mechanism with a threshold index, while just under half operate in an environment where indexation takes place at fixed intervals. The latter system is more common in large firms, so that the weighted results indicate that this mechanism applies to the majority of employees. The level of wages of new employees depends mainly on what is specified in collective agreements and on the wage level of comparable employees in the firm. However, the wages which the firm actually pays to its staff may deviate from the pay scales specified in the sectoral agreements. In a significant number of firms, especially for white-collar workers and skilled staff, actual wages paid exceed the sectoral pay scales. Such a wage cushion, forming a buffer between the actual wages and the collectively agreed lower limits, is more common in large firms. Overall, firms seldom respond to adverse shocks by cutting basic wages or using alternative ways of reducing labour costs per employee. Certainly in large firms, costs are reduced mainly via the employment channel, i.e. by reducing the number of primarily permanent staff, and to a lesser extent temporary workers. Reductions in non-wage costs are also important, while variable pay components are only cut in a small number of cases. Only a quarter of firms state that they adjust their prices more than once a year. Time-dependent price adjustments, in which the time of the adjustment does not depend on economic conditions (as opposed to state-dependent adjustments), occur in 22 p.c. of firms and are noticeably common in the business service sector. Combined with the low frequency of price adjustments, this indicates price rigidity in that sector. The frequency and timing of wage adjustments are closely linked to the indexation mechanism applied. Most firms adjust their wages no more than once a year. Time-dependent wage adjustments in a specific month apply to 61 p.c. of firms, and – like price adjustments – wage adjustments are concentrated in the month of January. Another peak occurs in July, and there is some concentration at the beginning of the second and fourth quarters, particularly in the case of wage adjustments.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".