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Record W3123675962 · doi:10.1017/s0770451800006138

Labor supply dynamics, unemployment and experience in the labor market

2004· preprint· en· W3123675962 on OpenAlexaff
Étienne Wasmer

Bibliographic record

VenueRecherches économiques de Louvain · 2004
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsEconomicsLabour economicsUnemploymentShock (circulatory)WageEfficiency wageSupply shockBoomLabor demandDemand shockExcess supplyHuman capitalSupply and demandInequalitySplit labor market theorySecondary labor marketLabor relationsMonetary economicsMarket economyMacroeconomics

Abstract

fetched live from OpenAlex

Summary In the last decades, OECD labor markets faced important labor supply changes with the arrival of women and the cohorts of the baby-boom. Using a survey where workers declare their true employment experience, this paper argues that these supply trends imply more inexperienced workers. It then investigates the consequences of this fact on the skill composition of the labor force, between-groups wage inequality and the level of unemployment. The main result is that a labor market with wage rigidities may not recover from such a temporary labor supply shock: with a younger and less experienced labor force, there is higher unemployment among low-experience workers, they do not accumulate enough on-the-job human capital, this reduces in the long-run the supply of skilled (experienced) workers and the demand for unskilled workers. This intertemporal multiplication of supply shocks generates multiple equilibria, and the rigid economy is stuck to the bad equilibrium even after the shock. In a competitive labor market, in contrast, wage inequality and notably, the wage return to experience becomes higher but there is no persistence of the supply shock.

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)0.007
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: Empirical
Teacher disagreement score0.251
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
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 head0.096
GPT teacher head0.303
Teacher spread0.207 · 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.

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

Citations0
Published2004
Admission routes1
Has abstractyes

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