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Why is the rate of single‐parenthood lower in Canada than in the U.S.? A dynamic equilibrium analysis of welfare policies

2009· article· en· W1899996688 on OpenAlexvenueaboutno aff
Nezih Guner, John Knowles

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsGenerosityWelfareSingle mothersEconomicsHuman capitalInvestment (military)Overlapping generations modelFertilitySingle parentLabour economicsGeneral equilibrium theoryDemographic economicsWelfare systemMicroeconomicsPopulationEconomic growthPsychologyDemographyPolitical scienceSociologyMarket economy

Abstract

fetched live from OpenAlex

Abstract A critical question in the design of welfare policies is whether to target aid according to household composition, as was done in the U.S. under the Aid to Families with Dependent Children (AFDC) program, or to rely exclusively on means‐testing, as in Canada. Restricting aid to single mothers, for instance, has the potential to distort behaviour along three demographic margins: marriage, fertility, and divorce. We contrast the Canadian and the U.S. policies within an equilibrium model of household formation and human capital investment on children. Policy differences we consider are eligibility, dependence of transfers on the number of children, and generosity of transfers. Our simulations indicate that the policy differences can account for the higher rate of single‐parenthood in the U.S. They also show that Canadian welfare policy is more effective for fostering human capital accumulation among children from poor families. Interestingly, a majority of agents in our benchmark economy prefers a welfare system that targets single mothers (as the U.S. system does), yet (unlike the U.S. system) does not make transfers dependent on the number of children.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.258
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.058
GPT teacher head0.207
Teacher spread0.149 · 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

Citations22
Published2009
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Economics/Revue canadienne d économiqueSame topicGender, Labor, and Family DynamicsFrench-language works237,207