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Record W4294719534 · doi:10.1002/ise3.27

Search, technology choice, and unemployment

2022· article· en· W4294719534 on OpenAlexaff
Constantine Angyridis, Haiwen Zhou

Bibliographic record

VenueInternational Studies of Economics · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsUnemploymentEconomicsIncentiveWageWage bargainingLabour economicsBargaining powerWage rateEfficiency wageUnemployment rateNash equilibriumGeneral equilibrium theoryMicroeconomicsMacroeconomics

Abstract

fetched live from OpenAlex

Abstract Technology variations among countries account for a significant part of their income differences. In this paper, a firm's technology choice is embedded in a search theoretic framework for unemployment. More advanced technology is assumed to have a higher setup cost, but it is more productive. The model is tractable and the following results are derived analytically. An increase in the unemployment benefit leads to an increase in the equilibrium wage rate, giving an incentive to firms to choose a more advanced technology. Thus, this result regarding unemployment insurance in models with wage posting carries through with Nash bargaining as well. As a consequence, the equilibrium unemployment rate increases. Furthermore, an increase in the bargaining power of workers increases the unemployment rate but has an ambiguous impact on the equilibrium level of technology and the wage rate. Finally, an increase in the exogenous job separation rate or the interest rate increases the unemployment rate and decreases the wage rate but does not affect the equilibrium level of technology.

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 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 categoriesnone
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 score0.211
Threshold uncertainty score0.413

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.284
Teacher spread0.225 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

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