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Fiscal Externalities of Becoming a Parent

2011· article· en· W2108722879 on OpenAlexaff
Douglas A. Wolf, Ronald Lee, Timothy Miller, Gretchen Donehower, Alexandre Genest

Bibliographic record

VenuePopulation and Development Review · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsEmployment and Social Development Canada
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on Aging
KeywordsExternalityEconomicsPopulationHomogeneousArgument (complex analysis)Demographic economicsPublic economicsEconometricsMicroeconomicsDemography

Abstract

fetched live from OpenAlex

Theoretical and empirical results suggest that there are externalities to childbearing, but those results usually assume that these externalities accrue uniformly within a homogeneous population. We advance this argument by developing separate estimates of the fiscal externalities associated with parents—those who devote time or material resources to minor children—and nonparents. Our analysis uses data from the US Panel Study of income Dynamics on the age profiles of taxes paid and publicly funded benefits consumed by parents and nonparents, together with a previously developed intertemporal economic-demographic accounting model. The accounting framework takes into account the net fiscal impacts of future generations as well as the present population. Our findings indicate that, with a 3 percent discount rate, parents produce a substantial net fiscal externality, about $217,000 in 2009 dollars. This is equivalent to a lifetime annuity of nearly $8,100 per year beginning at age 18. The results are sensitive to both the discount rate used and the proportion of parents within the cohort.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.138
GPT teacher head0.341
Teacher spread0.203 · 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

Citations33
Published2011
Admission routes1
Has abstractyes

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