Supply Shocks, Demand Shocks, and Labor Market Fluctuations
Bibliographic record
Abstract
We use structural vector autoregressions to analyze the responses of worker ‡ows, job ‡ows, vacancies, and hours to shocks.We identify demand and supply shocks by restricting the short-run responses of output and the price level.On the demand side we disentangle a monetary and non-monetary shock by restricting the response of the interest rate.The responses of labor market variables are similar across shocks: expansionary shocks increase job creation, the hiring rate, vacancies, and hours.They decrease job destruction and the separation rate.Supply shocks have more persistent e¤ects than demand shocks.Demand and supply shocks are equally important in driving business cycle ‡uctuations of labor market variables.Our …ndings for demand shocks are robust to alternative identi…cation schemes involving the response of labor productivity at di¤erent horizons and an alternative speci…cation of the VAR.However, supply shocks identi…ed by restricting productivity generate a higher fraction of responses inconsistent with standard search and matching models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".