The Adjustments of Hours and Real Wages to Technology Shocks: Assessing the Role of Nominal Rigidities
Bibliographic record
Abstract
Nominal rigidities are generally viewed as important for the transmission of monetary policy. We argue that nominal rigidities are important also for the transmission of technology shocks, especially for explaining their effects on hours and real wages. Evidence suggests that a positive technology shock leads to a short-run decline in labor hours and a gradual rise in real wages. We examine the ability of an RBC model augmented with real frictions, a pure sticky-price model, a pure sticky-wage model, and a model combining sticky prices and sticky wages in accounting for this evidence. We find that, according to this metric, the model with nominal wage and price rigidities is more successful than others. This finding is robust and holds true for a relatively small Frisch elasticity of hours and a relatively high frequency of price reoptimization that are consistent with microeconomic evidence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".