Market Reforms in the Time of Imbalance
Bibliographic record
Abstract
We study the consequences of product and labor market reforms in a two-country model with endogenous producer entry and labor market frictions.We focus on the role of business cycle conditions and external constraints at the time of reform implementation (or of a credible commitment to it) in shaping the dynamic effects of such policies.Product market reform is modeled as a reduction in entry costs and takes place in a non-traded sector that produces services used as input in manufacturing production.Labor market reform is modeled as a reduction in firing costs and/or unemployment benefits.We find that business cycle conditions at the time of deregulation significantly affect adjustment.A reduction of firing costs entails larger and more persistent adverse short-run effects on employment and output when implemented in a recession.By contrast, a reduction in unemployment benefits boosts employment and output by more in a recession compared to normal times.The impact of product market reforms is less sensitive to business cycle conditions.Credible announcements about future reforms induce sizable short-run dynamics, regardless of whether the announcement takes place in normal times or during an economic downturn.Whether the immediate effect is expansionary or contractionary varies across reforms.Finally, lack of access to international lending in the wake of reform can amplify the costs of adjustment.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".