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Record W3124659787 · doi:10.3386/w11587

The Intergenerational Effect of Worker Displacement

2005· report· en· W3124659787 on OpenAlexaffabout
Philip Oreopoulos, Marianne Page, Ann Huff Stevens

Bibliographic record

VenueNational Bureau of Economic Research · 2005
Typereport
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsUniversity of Toronto
FundersNational Science Foundation
KeywordsDisplacement (psychology)Labour economicsDemographic economicsBusinessEconomicsPsychology

Abstract

fetched live from OpenAlex

This paper uses variation induced by firm closures to explore the intergenerational effects of worker displacement.Using a Canadian panel of administrative data that follows almost 60,000 father-child pairs from 1978 to 1999 and includes detailed information about the firms at which the father worked, we construct narrow treatment and control groups whose fathers had the same level of permanent income prior to 1982 when some of the fathers were displaced.We demonstrate that job loss leads to large permanent reductions in family income.Comparing outcomes among individuals whose fathers experienced an employment shock to outcomes among individuals whose fathers did not, we find that children whose fathers were displaced have annual earnings about 9% lower than similar children whose fathers did not experience an employment shock.They are also more likely to receive unemployment insurance and social assistance.The estimates are driven by the experiences of children whose family income was at the bottom of the income distribution, and are robust to a number of specification checks.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.061
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.200
GPT teacher head0.517
Teacher spread0.317 · 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

Citations22
Published2005
Admission routes2
Has abstractyes

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