Labor Market Shocks and Monetary Policy
Bibliographic record
Abstract
We develop a heterogeneous-agent New Keynesian model featuring a frictional labor market with on-the-job search to quantitatively study the role of worker flows in inflation dynamics and monetary policy. Motivated by our empirical finding that the historical negative correlation between the unemployment rate and the employer-to-employer (EE) transition rate up to the Great Recession disappeared during the recovery, we use the model to quantify the effect of EE transitions on inflation in this period. We find that the four-quarter inflation rate would have been 0.6 percentage points higher between 2016 and 2019 if the EE rate increased commensurately with the decline in unemployment. We then decompose the channels through which a change in EE transitions affects inflation. We show that an increase in the EE rate leads to an increase in the real marginal cost, but the direct effect is partially mitigated by the equilibrium decline in market tightness through aggregate demand that exerts downward pressure on the marginal cost. Finally, we study the normative implications of job mobility for monetary policy responding to inflation and labor market variables according to a Taylor rule, and find that the welfare cost of ignoring the EE rate in setting the nominal interest rate is 0.2 percent in additional lifetime consumption.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".