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Distributional effects of ‘general population’ prescription drug programs in Canada

2005· article· en· W2000847166 on OpenAlexaffvenueabout
Şule Alan, Thomas F. Crossley, Paul Grootendorst, Michael R. Veall

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of TorontoMcMaster UniversityYork University
Fundersnot available
KeywordsMedical prescriptionSubsidyQuantile regressionDemographic economicsPopulationPrescription drugEquity (law)EconomicsActuarial scienceEconometricsMedicineEnvironmental healthPolitical science

Abstract

fetched live from OpenAlex

Abstract. Canadian household prescription drug expenditures are studied using the Statistics Canada Family Expenditure Survey masterfiles for periods that include the introduction of provincial ‘general population’ prescription drug programs. Budget shares for non‐senior households are examined over time using non‐parametric regression, parametric ‘difference‐in‐difference’ techniques, and quantile regression methods. The evidence suggests that while program effects are muted when there are high deductibles, a non‐senior prescription drug subsidy is more redistributive than an equal‐cost proportional income transfer, in part because of differential private health insurance coverage by income. This is in contrast to previous evidence on Canadian senior prescription drug subsidies. JEL classification: I18, J42

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)0.007
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 score0.955
Threshold uncertainty score0.327

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.097
GPT teacher head0.186
Teacher spread0.089 · 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

Citations19
Published2005
Admission routes3
Has abstractyes

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