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Record W4214631948 · doi:10.1111/caje.12576

Labour market conditions and adult health in Mexico

2022· article· en· W4214631948 on OpenAlexvenueno aff
Pınar Mine Güneş, Magda Tsaneva

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2022
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsConstruct (python library)Mental healthDemographic economicsPanel dataPhysical healthLabour economicsBaseline (sea)PsychologyPolitical scienceEconometrics

Abstract

fetched live from OpenAlex

Abstract This paper examines the role of local labour market conditions on self‐reported adult physical and mental health and health behaviours in Mexico. We construct measures of overall and gender‐specific predicted employment growth rates using a shift‐share approach that exploits exogenous variation in national industry‐specific growth rates and baseline industry employment shares across municipalities. Using detailed household‐level panel data and individual fixed effects, we find that increases in overall formal labour demand improve physical health of men but have no effect on the health of women. However, increases in gender‐specific formal labour demand improve the physical health of both men and women, with larger effects among men. We also find significant but small effects of male labour demand on the mental health of men. All effects are more pronounced for less educated people. Finally, we explore a range of potential mechanisms, finding that the effects might operate through changes in labour market outcomes, but we do not find evidence that the effects operate through changes in health behaviours.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.107
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.121
GPT teacher head0.272
Teacher spread0.151 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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