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Record W2943213813 · doi:10.33423/jabe.v21i1.663

Okun’s Law at the Florida MSA Level

2019· article· en· W2943213813 on OpenAlexvenueno aff
Johnert Robert Stinespring, Jeremy Adam Luciene

Bibliographic record

VenueJournal of Applied Business and Economics · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicUnemployment and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsOkun's lawSpillover effectEconomicsUnemployment rateUnemploymentEconometricsPanel dataStatisticsMathematicsMacroeconomics

Abstract

fetched live from OpenAlex

This paper applies a fixed effects panel data spatial econometric model of Okun’s law to FloridaMetropolitan Statistical Areas (MSAs) over the period 1990 to 2015. The regression coefficients are used to estimate the impact of increases in output on unemployment and each MSA’s potential rate of output growth. Results indicate that Okun’s coefficient ranges from -0.19 to -0.24 at the MSA level. Between one-half to two-thirds of changes in local unemployment occur as a spillover effect from neighboring MSA output growth. Each MSA’s potential real output growth rate averages between 1.7 to 2.7 percent.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.694

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.030
GPT teacher head0.177
Teacher spread0.148 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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