Sources of fluctuations in hours worked for Canada, Germany, Japan and the U.S.: a sign restriction VAR approach
Bibliographic record
Abstract
This study empirically examines the sources of fluctuations in hours worked in Canada, Germany, Japan and the U.S. It is particularly motivated by Galí’s (1999) VAR study, which demonstrates that a positive technology shock reduces hours worked, at least in the short run. However, in the present study, a technology shock is identified without recourse to Galí’s long-run restriction, which has been subject to active controversy. Furthermore, this study uncovers other important sources of fluctuations in hours worked to reflect the concern, raised by numerous studies, that technology shocks leave most variations in hours worked unexplained. Specifically, there are six shocks underlying our model, and they are identified using a set of sign restrictions. The empirical results confirm that in all four countries, a positive technology shock significantly reduces hours worked. This technology shock, along with labor supply and demand shocks, accounts for most of the short-term variations in hours worked. As the forecasting horizon increases, technology and demand shocks become less important, whereas labor supply shocks contribute to explaining the bulk of long-run variations in hours worked. Finally, the empirical relevance of Galí’s long-run identification restriction is tested and the results are related to those obtained using the sign restriction model.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".