Short-Term Gain or Pain? A DSGE Model-Based Analysis of the Short-Term Effects of Structural Reforms in Labour and Product Markets
Bibliographic record
Abstract
This paper explores the short-term effects of labour and product market reforms through a dynamic general equilibrium model that features endogenous producer entry, equilibrium unemployment and costly job creation and destruction. Unlike in existing work, the link between labour and product market dynamics and the policy factors driving it are modelled explicitly. The analysis yields three main findings. First, it takes time for reforms to pay off, typically at least a couple of years. This is partly because their benefits materialise through firm entry and increased hiring, both of which are gradual processes, while any reform-driven layoffs are immediate. Second, all reforms appear to stimulate GDP already in the short run, but some of them -- such as job protection reforms -- are found to increase unemployment temporarily. Implementing a broad package of labour and product market reforms enables governments to minimise or even alleviate such transitional costs. Third, reforms are not found to have noticeable deflationary effects, suggesting that the inability of monetary policy to deliver large interest rate cuts in their aftermath -- either because of the zero bound on policy rates or because the country belongs to a large monetary union -- may not be a relevant obstacle to reform implementation. Alternative simple monetary policy rules have little impact on the transitional costs from reforms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".