Real Wage Rigidity and the Empirical Pertinence of the Hybrid New Keynesian Phillips Curve in Tunisia
Bibliographic record
Abstract
By incorporating real wage rigidity, the Hybrid New Keynesian Phillips Curve (HNKPC) developed by Blanchard and Galí (2007) provides a relevant framework for analysing inflation dynamics and the effects of monetary policies, particularly in a globalised economy. This study assesses its empirical validity in Tunisia by examining the relationship between inflation, unemployment, and output in both the short and long run, using the Generalised Method of Moments (GMM) and a calibration technique. The findings confirm the relevance of this model for the Tunisian economy, highlighting the central role of adaptive expectations in current inflation dynamics. They also reveal a short-term trade-off between inflation and the welfare-relevant output gap, as well as between inflation and unemployment, whereas in the long run, this trade-off disappears, in line with theoretical predictions. The magnitude of these trade-offs is positively correlated with the degree of real wage rigidity, estimated at approximately 95% due to the structure of the Tunisian labour market. These results underscore the importance of structural reforms to enhance the effectiveness of monetary policies and foster a more robust economic trajectory.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".