Decentralisation of the minimum wage setting in Russia: Causes and consequences
Bibliographic record
Abstract
Abstract In this article, we study the minimum wage setting reform in Russia that aimed to decentralise the fixing of the minimum wage and to increase the involvement of social partners into this process. The old system of minimum wage setting was based on a single nationwide minimum wage which was differentiated across regions and occupations via a cumbersome framework of coefficients. The new system is a mixture of the government-set minimum wage at the federal level and collective agreements at the regional level. We show that the system of minimum wage setting has become more flexible. The reform succeeded in raising the real value of the minimum wage and increasing earnings of low-paid workers without causing significant negative effects in terms of employment. The reform did not lead to greater regional variation of minimum wages. Nevertheless, it introduced some new imbalances: an unintended consequence of the reform was the emergence of separate regional wage sub-minima for private and public sector workers in many regions. The major challenge in coming years is to strengthen the institutions of collective bargaining, introduce evidence-based evaluation and boost the capacities of government and non-government monitoring agencies.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".