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Record W4393244988 · doi:10.1257/app.20210816

The Impact of Cash Transfers to Poor Mothers on Family Structure and Maternal Well-Being

2024· article· en· W4393244988 on OpenAlexafffund
Anna Aizer, Sung‐Woo Cho, Shari Eli, Adriana Lleras‐Muney

Bibliographic record

VenueAmerican Economic Journal Applied Economics · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentUniversity of TorontoQueen's UniversityCalifornia Center for Population Research, University of California, Los AngelesUniversity of MinnesotaPrinceton UniversityCarnegie Mellon UniversityNational Institutes of HealthYale University
KeywordsCash transfersDemographic economicsPovertyEconomicsEconomic growth

Abstract

fetched live from OpenAlex

We use newly collected data for 16,000 women who applied for Mothers' Pensions, America's first welfare program, to investigate the effect of means-tested cash transfers on lifetime family structure and maternal well-being. In the short term, cash transfers delayed marriage and lowered geographic mobility. In the long run, transfers had no impact on the probability of remarriage, spouse quality, or fertility. Cash transfers did not affect women's well-being, measured by longevity and family income in 1940. Given the lack of significant negative behavioral impacts, the benefits of transfers appear to exceed costs if they have-even modest-positive impacts on 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 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)0.009
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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.249
Teacher spread0.243 · 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

Citations12
Published2024
Admission routes2
Has abstractyes

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