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Record W3125583747

Spatial Equilibrium with Unemployment and Wage Bargaining: Theory and Estimation

2013· preprint· en· W3125583747 on OpenAlexafffund
Paul Beaudry, David Green, Benjamin Sand

Bibliographic record

VenueRePEc: Research Papers in Economics · 2013
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsYork UniversityUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEconomicsUnemploymentMonopolyWage bargainingWageStructural estimationEstimationEconometricsLabour economicsMicroeconomicsMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

In this paper, we present a spatial equilibrium model where search frictions hinder the immediate reallocation of workers both within and across local labor markets. Because of the frictions, firms and workers find themselves in bilateral monopoly positions when determining wages. Although workers are not at each instant perfectly mobile across cities, in the baseline model we assume that workers flows are sufficient to equate expected utility across markets. We use the model to explore the joint determination of wages, unemployment, house prices and city size (or migration). A key role of the model is to clarify conditions under which this type of spatial equilibrium setup can be estimated. We then use U.S. data over the period 1970–2007 to explore the fit and quantitative properties of the model. Our main goal is to highlight forces that influence spatial equilibria at 10-year intervals.

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)0.004
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.004
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.034
GPT teacher head0.269
Teacher spread0.235 · 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 designSimulation or modeling
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

Citations0
Published2013
Admission routes2
Has abstractyes

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