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Record W3123321870 · doi:10.1257/aer.20210937

Multigenerational Impacts of Childhood Access to the Safety Net: Early Life Exposure to Medicaid and the Next Generation’s Health

2022· preprint· en· W3123321870 on OpenAlexfundno aff
Chloe N. East, Sarah Miller, Marianne Page, Laura Wherry

Bibliographic record

VenueAmerican Economic Review · 2022
Typepreprint
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentCalifornia Center for Population Research, University of California, Los AngelesNational Institute of Child Health and Human DevelopmentUniversity of California, DavisUniversity of PennsylvaniaCenters for Disease Control and PreventionUniversität MannheimUniversity of Notre DameUniversity of ConnecticutUniversity of Southern CaliforniaSan Diego State UniversityYork UniversityPurdue UniversityUppsala UniversitetNational Science Foundation
KeywordsMedicaidOffspringPsychological interventionDemographyIn uteroSafety netEnvironmental healthMedicineLow birth weightGerontologyPregnancyHealth careEconomicsNursingEconomic growthBiology

Abstract

fetched live from OpenAlex

We examine multi-generational impacts of positive in utero health interventions using a new research design that exploits sharp increases in prenatal Medicaid eligibility that occurred in some states. Our analyses are based on U.S. Vital Statistics Natality files, which enables linkages between individuals' early life Medicaid exposure and the next generation's health at birth. We find evidence that the health benefits associated with treated generations' early life program exposure extend to later offspring. Our results suggest that the returns on early life health investments may be substantively underestimated.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.227
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.111
GPT teacher head0.453
Teacher spread0.341 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2022
Admission routes1
Has abstractyes

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