WAGE DYNAMICS IN EUROPE FINAL REPORT OF THE WAGE DYNAMICS NETWORK (WDN) Executive Summary
Bibliographic record
Abstract
This report summarises the main findings and policy conclusions of the Eurosystem/ESCB Wage Dynamics Network (WDN) since it started operations in July 2006. The objectives of the WDN were i) to identify the sources and features of wage and labour cost dynamics that are most relevant for monetary policy and ii) to clarify the relationship between wages, labour costs and prices both at the firm and macro-economic level. This was partly motivated by the finding in the Inflation Persistence Network (IPN) that cross-sector differences in the frequency of price changes were highly negatively correlated with the labour share, suggesting that stickiness in wages and labour costs may be one of the driving factors behind the slow adjustment of prices. Most of the analysis summarised in this report is based on data that comes from the period before the intensification of the financial crisis in the third quarter of 2008. However, during the past year an attempt was made to update some of the information (such as the survey) and to investigate to what extent the findings can explain the response of the labour market in the current crisis. Against this background, the report first describes some of the medium-term developments in European labour markets focusing on the recent evolution of collective bargaining and wage setting institutions in the EU and the development of the wage structure in a selected number of EU countries. Wage bargaining institutions (Section 2.1) are an important determinant of both wage dynamics and the
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".