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Record W4401507799 · doi:10.1016/j.jpubeco.2024.105194

Estimating intergenerational health transmission in Taiwan with administrative health records

2024· article· en· W4401507799 on OpenAlexaff
Harrison Chang, Timothy J. Halliday, Ming‐Jen Lin, Bhashkar Mazumder

Bibliographic record

VenueJournal of Public Economics · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Toronto
FundersTinbergen InstituteNational Taiwan University of Science and TechnologyNational Science and Technology CouncilAdelphi UniversityUniversität HohenheimAgricultural and Applied Economics AssociationResearch Institute, Georgia Institute of Technology
KeywordsEconomicsTransmission (telecommunications)Public economicsTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

We use population-wide administrative health records from Taiwan to estimate intergenerational persistence in health, providing the first estimates for a middle-income country. We measure latent health by applying principal components analysis to a set of indicators for 13 broad ICD categories and quintiles of visits to a general practitioner. We find that the rank–rank slope in health between adult children and their parents is 0.22 which is broadly in line with results from other countries. Maternal transmission is stronger than paternal transmission and sons have higher persistence than daughters. Persistence is also higher at the upper tail of the parent health distribution. Persistence is lower when complete data on outpatient care is unavailable. Health transmission is almost entirely unrelated to household income levels in Taiwan. We also find that there are small geographic differences in absolute health mobility across townships and that these are modestly correlated with area-level income and doctor availability.

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)0.004
metaresearch head score (Gemma)0.014
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 score0.074
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.090
GPT teacher head0.398
Teacher spread0.308 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

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