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Record W4323854287 · doi:10.1111/irfi.12415

Economic growth and labor investment efficiency

2023· article· en· W4323854287 on OpenAlex
Amanjot Singh

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

VenueInternational Review of Finance · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsThe King's UniversityWestern University
Fundersnot available
KeywordsInefficiencyEconomicsInvestment (military)Labor intensityLabour economicsSample (material)Monetary economicsMarket economy

Abstract

fetched live from OpenAlex

Abstract We examine the relationship between economic growth and labor investment efficiency. Using a sample of US firms from 1991 to 2019, our findings suggest that labor investment inefficiency increases with the expansion of economic activities. Although economic growth increases labor overinvestment, it also decreases labor underinvestment. The magnitude effect of economic growth is more pronounced for labor overinvestment. Labor investment inefficiency is noticeable during low economic policy uncertainty. Economic growth‐induced labor investment inefficiency is pronounced for (1) large firms, (2) high labor intensity firms, and (3) firms with overinvestment in non‐labor investments. Further, economic growth negatively (positively) influences the firm's future performance for labor overinvested (underinvested) firms. Our findings remain robust to alternative specifications.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.024
GPT teacher head0.259
Teacher spread0.234 · 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