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Record W1540637492

Life After the High-tech Downturn: Permanent Layoffs and Earnings Losses of Displaced Workers

2007· preprint· en· W1540637492 on OpenAlexaboutno aff
Marc Frenette

Bibliographic record

VenueRePEc: Research Papers in Economics · 2007
Typepreprint
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLayoffEarningsRecessionLabour economicsDisplaced workersManufacturing sectorAttritionHigh techDemographic economicsBusinessEconomicsUnemploymentEconomic growthGeographyFinance
DOInot available

Abstract

fetched live from OpenAlex

The high-tech sector was a major driving force behind the Canadian economic recovery of the late 1990s. It is well known that the tide began to turn quite suddenly in 2001 when sector-wide employment and earnings halted this upward trend, despite continued gains in the rest of the economy. As informative as employment and earnings statistics may be, they do not paint a complete picture of the severity of the high-tech meltdown. A decline in employment may result from reduced hiring and natural attrition, as opposed to layoffs, while a decline in earnings among high-tech workers says little about the fortunes of laid-off workers who did not regain employment in the high-tech sector. In this study, I use a unique administrative data source to address both of these gaps in our knowledge of the high-tech meltdown. Specifically, the study explores permanent layoffs in the high-tech sector, as well as earnings losses of laid-off high-tech workers. The findings suggest that the high-tech meltdown resulted in a sudden and dramatic increase in the probability of experiencing a permanent layoff, which more than quadrupled in the manufacturing sector from 2000 to 2001. Ottawa-Gatineau workers in the industry were hit particularly hard on this front, as the permanent layoff rate rose by a factor of 11 from 2000 to 2001. Moreover, laid-off manufacturing high-tech workers who found a new job saw a very steep decline in earnings. This decline in earnings was well above the declines registered among any other groups of laid-off workers, including workers who were laid off during the "jobless recovery" of the 1990s. Among laid-off high-tech workers who found a new job, about four out of five did not locate employment in high-tech, and about one out of three moved to another city. In Ottawa-Gatineau, many former high-tech employees found jobs in the federal government. However, about two in five laid-off high-tech workers left the city.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.136
Threshold uncertainty score0.271

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.044
GPT teacher head0.396
Teacher spread0.352 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

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