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Record W3124418781

Les déterminants du travail autonome au Québec et au Canada (1993-2010)

2015· preprint· fr· W3124418781 on OpenAlexaboutno aff
Raquel Fonseca, Simon Lord

Bibliographic record

VenueRePEc: Research Papers in Economics · 2015
Typepreprint
Languagefr
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsUnemploymentMarket liquidityEconomicsPaid workWelfare economicsHumanitiesLabour economicsWorking hoursPhilosophyMonetary economicsEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

This paper studies the determinants of self-employment in Québec and in the rest of Canada by focusing on liquidity constraints, age and aggregate unemployment. We use a random effects probit model to analyse panel data from the Survey of Labour and Income Dynamics (SLID) for the period 1993-2010. The main results are as follows. The positive effect of investment income on the probability of being self-employed confirms the existence of liquidity constraints. Being older increases the probability of choosing self-employment, rather than salaried work. This suggests that working for oneself may be a stepping stone towards retirement. High unemployment decreases the probability of being self-employed, suggesting that pull factors dominate push factors. Résumé – Cet article analyse les déterminants du travail autonome au Québec et dans le reste du Canada en se concentrant sur les contraintes de liquidité, l’âge et le chômage agrégé. Nous utilisons les données en panel de l’Enquête sur la dynamique du travail et du revenu (EDTR) couvrant la période 1993-2010. Les résultats principaux tirés du modèle avec effets aléatoires sont les suivants : l’effet positif des revenus de placement sur la probabilité d’être travailleur autonome confirme la présence de contraintes financières; être plus âgé accroît la probabilité de choisir le travail autonome, suggérant que ce type d’emploi peut être un tremplin vers la retraite; un chômage élevé diminue la probabilité d’être à son compte ce qui suggère que les facteurs de pull dominent sur les facteurs de push.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.901
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.316
Teacher spread0.269 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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