An SVAR Model of Fluctuations in U.S. Migration Flows and State Labor Market Dynamics
Bibliographic record
Abstract
Large internal migration flows are typically viewed as evidence of flexible U.S. labor markets adjusting to asymmetrical regional demand shocks. Yet, amenity‐induced migration flows suggest that they may not necessarily facilitate adjustment to demand shocks and instead may be destabilizing. This paper employs a structural vector autoregression model with long‐run identifying restrictions to account for both labor‐demand and labor‐supply shocks in examining the role of migration in U.S. regional labor‐market fluctuations. The results reveal that less than one‐half of innovations in state migration flows are responses to labor‐demand shocks. It is not until the third period that migrants fill a majority of demand‐induced jobs in a typical state, while it takes about 7 to 8 years for migration flows to fully adjust to labor‐demand shocks. The extent of the migration response also has implications for how much state and local economic development policies benefit original residents.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".