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Record W2088036692 · doi:10.3386/w8845

Subsidizing the Stork: New Evidence on Tax Incentives and Fertility

2002· report· en· W2088036692 on OpenAlexaffabout
Kevin Milligan

Bibliographic record

VenueNational Bureau of Economic Research · 2002
Typereport
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFertilitySubsidyIncentiveStorkEconomicsPopulationJurisdictionEarned income tax creditDemographic economicsPublic economicsLabour economicsTax creditMicroeconomicsDemographyPolitical scienceBiologySociology

Abstract

fetched live from OpenAlex

Variation in tax policy presents an opportunity to estimate the responsiveness of fertility to prices.This paper exploits the introduction of a pro-natalist transfer policy in the Canadian province of Quebec that paid up to C$8,000 to families having a child.I implement a quasi-experimental strategy by forming treatment and control groups defined by time, jurisdiction, and family type.This permits a tripledifference estimator to be implemented -both on the program's introduction and cancellation.Furthermore, the incentive was available broadly, rather than to a narrow subset of the population as studied in the literature on AFDC and fertility.This provides a unique opportunity to investigate heterogeneous responses.I find a strong effect of the policy on fertility, and some evidence of a heterogeneous response that may help reconcile these results with the AFDC literature.

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)0.007
metaresearch head score (Gemma)0.039
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 score0.702
Threshold uncertainty score0.600

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.528
GPT teacher head0.532
Teacher spread0.004 · 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

Citations90
Published2002
Admission routes2
Has abstractyes

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