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Record W2936090486 · doi:10.1017/dem.2019.1

Female labor force participation and fertility differentials

2019· article· en· W2936090486 on OpenAlex
Irakli Japaridze

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Demographic Economics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsFertilityGirlEconomicsTotal fertility rateDemographic economicsDistribution (mathematics)Labour economicsDemographyPopulationFamily planningPsychologySociologyResearch methodologyMathematicsDevelopmental psychology

Abstract

fetched live from OpenAlex

Abstract US women, on average, had approximately two children in both the 1930s and in the 1970s, yet the fertility distribution in the 1930s was less concentrated. This implies change in reproductive behavior, which cannot be captured by models focusing on average fertility. To explain these changes, I have developed a model that makes a distinction between sons and daughters. In this model, the female labor force participation rate is the probability of each girl becoming an employed woman. This endogenizes the empirically observed difference in the propensity for an all-girl household to have another child compared to an all-boy household, generating large fertility differentials at low participation rates. Higher participation rates raise the expected return from an additional child, as well as the expected return from existing daughters. The first effect tends to increase fertility, while the second effect, for relatively concave utility functions, tends to decrease it, so that the distribution of completed fertilities becomes more concentrated.

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.

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 score0.004
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.021
GPT teacher head0.274
Teacher spread0.253 · 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