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Record W3124071105

Do layoffs increase transitions to postsecondary education among adults

2016· article· en· W3124071105 on OpenAlexaboutno aff
Wen Ci

Bibliographic record

VenueAnalytical Studies Branch Research Paper Series · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsLayoffMicrodata (statistics)Demographic economicsJob lossDisplaced workersLabour economicsRecessionHuman capitalEconomicsLongitudinal dataGreat recessionAttendanceDemographyUnemploymentPopulationEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

Faced with job loss, displaced workers may choose to return to school to help them reintegrate into the labour force. Job losses in a given local labour market may also induce workers who have not yet been laid off to pre-emptively enrol in postsecondary (PS) institutions, as a precautionary measure. Combining microdata and grouped data, this study examines these two dimensions of the relationship between layoffs and PS enrolment over the 2001-to-2011 period. Using individual-level longitudinal microdata and controlling for the unobserved heterogeneity of workers in a flexible way, the study finds that laid-off male and female workers are two to four percentage points more likely than other men and women to transition to PS education in the year of the layoff or the following year (from a baseline rate of about three per cent). For both sexes, full-time PS enrolment accounts for most of the increase in enrolment. Statistically significant correlations between layoffs and full-time PS attendance are detected between two years before job loss and two years after job loss. The study also takes advantage of the fact that the 2008-2009 recession increased layoff rates in a differentiated way across Canada and, thus, generated exogenous variation in layoff rates at the regional level.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.086
GPT teacher head0.462
Teacher spread0.376 · 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 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

Citations1
Published2016
Admission routes1
Has abstractyes

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