REGIONAL AND SECTORAL EVIDENCE OF THE MACROECONOMIC EFFECTS OF LABOR REALLOCATION: A PANEL DATA ANALYSIS
Bibliographic record
Abstract
This article revisits the sectoral shifts hypothesis by examining unemployment fluctuations for 48 U.S. states over the period 1990:M01–2011:M12. We develop a panel approach that incorporates dynamics, parameter heterogeneity, aggregate factors, and cross‐sectional dependence (CSD). Our findings provide support for a positive and significant effect of the employment dispersion index on unemployment. This outcome is robust under alternative specifications and measures of employment dispersion. The empirical evidence corroborates the presence and relevance of CSD and heterogeneity among states. The results show that, once unobserved common factors and cross‐state heterogeneity are taken into account, labor reallocation has a significant effect on unemployment that is half the size of the estimate when cross‐sectional dependence is not taken into account. (JEL E24, E32, J21, R23, C23)
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".