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Record W4213450927 · doi:10.1111/caje.12549

Effects of the COVID‐19 pandemic on the Colombian labour market: Disentangling the effect of sector‐specific mobility restrictions

2022· article· en· W4213450927 on OpenAlexvenueaboutno aff
Leonardo Fabio Morales, Leonardo Bonilla‐Mejía, José David Pulido, Luz Adriana Flórez, Didier Hermida, Karen L. Pulido‐Mahecha, Francisco Javier Lasso-Valderrama

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicQuarter (Canadian coin)EconomicsDemographic economicsMargin (machine learning)Job lossLabour economics2019-20 coronavirus outbreakWelfare economicsUnemploymentGeographyEconomic growthMedicineInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

We assess the effect of the COVID-19 pandemic and particularly the sector-specific mobility restrictions on the Colombian labour market. We exploit the sectoral and temporal variation of the restriction policies to identify their effect. Mobility restrictions significantly reduced employment, accounting for approximately a quarter of the total job loss between February and April of 2020. The remaining three quarters of the job losses could be attributed to the disease's regional patterns and other epidemiological and economic factors affecting the whole country. Therefore, we should expect important employment losses even in the absence of such restrictions. We also assess the effect of restrictions on the intensive margin, finding negative, although smaller effects on the number of hours worked and wages. Most of the employment effect is driven by salaried workers, while self-employment was more responsive to the disease spread. Finally, we find that women are disproportionally affected: mobility restrictions account for a third of the recent increase of the gender gap in salaried employment.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.108
GPT teacher head0.204
Teacher spread0.096 · 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 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

Citations20
Published2022
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Economics/Revue canadienne d économiqueSame topicCOVID-19 Pandemic ImpactsFrench-language works237,207