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Record W3042036470

Labor markets in crisis: The causal impact of Canada's COVD19 economic shutdown on hours worked for workers across the earnings distribution

2020· preprint· en· W3042036470 on OpenAlexfundaboutno aff
Kourtney Koebel, Dionne Pohler

Bibliographic record

VenueEconstor (Econstor) · 2020
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsEarningsShutdownDistribution (mathematics)Shock (circulatory)Coronavirus disease 2019 (COVID-19)EconomicsDemographic economicsLabour economicsMedicineEngineeringFinance
DOInot available

Abstract

fetched live from OpenAlex

We use Statistics Canada's Labour Force Survey to explore the labor market impacts of the novel coronavirus (COVID-19). Specifically, we adopt a unique identification strategy to examine the heterogeneous causal effects of the COVID-19 economic shutdown by governments on hours worked across the earnings distribution in Canada, focusing on individuals who remained employed in March and April. Most early crisis analyses found that workers in the bottom of the earnings distribution experienced a much larger negative shock to hours worked than workers in the top of the earnings distribution. However, some low-income individuals are also working more as a result of the COVID-19 economic shutdown, and this nuance is missed when only considering the net effect. When we condition on whether workers lost or gained hours, we find that workers in the bottom of the earnings distribution experienced not only the largest percentage reduction in hours, but also the largest percentage increase in hours.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.273
Teacher spread0.249 · 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 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

Citations6
Published2020
Admission routes2
Has abstractyes

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