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Record W3122178951

The Intergenerational Effects Of Worker Displacement

2005· preprint· en· W3122178951 on OpenAlexaboutno aff
Ann Huff Stevens, Marianne Page, Philip Oreopoulos

Bibliographic record

VenueRePEc: Research Papers in Economics · 2005
Typepreprint
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEarningsUnemploymentShock (circulatory)Demographic economicsDistribution (mathematics)SociologyEconomicsLabour economicsPsychologyPolitical scienceEconomic growthMedicineFinance
DOInot available

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 and small increases in mobility and divorce. 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. This work was completed while Oreopoulos was a Statistics Canada Research Fellow and member of the Family and Labour Studies Division of Statistics Canada. The financial support of the National Science Foundation is gratefully acknowledged. We also wish to thank Miles Corak, and seminar participants at Brown University, MIT, Princeton University, Stanford University, Yale University, the University of California Berkeley, UCLA, the University of Toronto and the NBER summer institute for their helpful comments.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.821

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.002
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 head0.048
GPT teacher head0.426
Teacher spread0.378 · 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 designNot applicable
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

Citations55
Published2005
Admission routes1
Has abstractyes

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