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Record W7030272073

More or Less Unmarried. The Impact of Legal Settings of Cohabitation on Labour Market Outcomes

2021· preprint· en· W7030272073 on OpenAlexaboutno aff

Bibliographic record

VenueRePEc: Research Papers in Economics · 2021
Typepreprint
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsnot available
Fundersnot available
KeywordsCohabitationAlimonyEarningsAffect (linguistics)Selection bias
DOInot available

Abstract

fetched live from OpenAlex

We study how different levels of protection upon separation affect the labour market behaviour of unmarried cohabiting partners. In Canada, unmarried cohabitation becomes a legal status after one year of relationship. Most provinces automatically expand couples’ rights and responsibilities after several years of cohabitation: some provinces allow cohabiting partners to claim for alimony upon separation, while others consider cohabiting couples to be equal to married couples. Using cross-province variations in legal settings and minimum eligibility duration, we show that eligibility for a more protective regime increases men’s labour supply and earnings and decreases those of women’s. The impact of the marriage-like regime is stronger, especially for women. We find that the effect is significantly stronger for couples directly eligible at the time of the reform than for couples who are eligible after the reform and may have anticipated changes in the legal settings. Our results show that eligibility affects within-household allocation of earnings and hours of work, and reinforces existing inequality. We present some evidence that enhancing protection upon separation has an effect on the selection of couples into cohabitation. Our results contribute to the ongoing public debate regarding the legal recognition and level of protection that should be given to unmarried cohabiting partners. Our results show that behavioural response may offset additional protection upon separation by increasing women’s dependence on their partner.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.389
Teacher spread0.342 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

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