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Record W4392906867 · doi:10.1162/rest_a_01430

The Health Care Cost of Air Pollution: Evidence from the World’s Largest Payment Network

2024· article· en· W4392906867 on OpenAlex
Panle Jia Barwick, Shanjun Li, D. C. Rao, Nahim Bin Zahur

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

VenueThe Review of Economics and Statistics · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsQueen's University
Fundersnot available
KeywordsPaymentBusinessHealth careActuarial scienceNatural resource economicsEconomicsFinanceEconomic growth

Abstract

fetched live from OpenAlex

Abstract This paper exploits the universe of credit- and debit-card transactions in China during 2013–2015 and provides the first nationwide analysis of the health care cost of PM2.5 for a developing country. We leverage spatial spillovers of PM2.5 from long-range transport to generate exogenous variation in local pollution, and we employ a flexible distributed lag model to capture semiparametrically the dynamic response of pollution exposure. We find significant impacts of PM2.5 on health care spending in both the short and medium terms. A 10 μg/m3 decrease in PM2.5 would reduce annual health care spending by over $9.2 billion, about 1.5% of China’s annual health care expenditure.

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)0.002
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.403
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.066
GPT teacher head0.437
Teacher spread0.371 · 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