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Record W2464739676 · doi:10.1016/j.red.2024.05.001

Public wages, public employment, and business cycle volatility: Evidence from U.S. metro areas

2024· article· en· W2464739676 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueReview of Economic Dynamics · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBusiness cycleWageVolatility (finance)EconomicsPublic sectorMatching (statistics)Labour economicsMonetary economicsMacroeconomicsEconometricsEconomy

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.581
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.079
GPT teacher head0.267
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it