Public wages, public employment, and business cycle volatility: Evidence from U.S. metro areas
Bibliographic record
Abstract
We revisit the question about whether a larger public sector stabilizes or destabilizes the economy. Based on results from two causal identification approaches, we show that a higher rate of public-sector employment reduces volatility in, i.e. stabilizes, private-sector employment growth, with at most a slight crowding-out of private employment. Public wages, meanwhile, increase private wages but appear not to be destabilizing. The stabilizing effect of public employment with limited crowding out is at odds with standard search and matching models that contain a public sector, which predict 1:1 crowding out and strong destabilization . To improve the performance of such models, we follow Gomes (2015) and add a product market that can replicate what Gomes calls the Business Cycle Wealth Effect. We also point out that the government procures output directly from the private sector . When the model has these two features, then it can generate stabilizing effects of public employment on private employment, with reduced crowding out.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".