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Record W2028604435 · doi:10.1111/0008-4085.00025

Outlet types and the Canadian Consumer Price Index

2000· article· en· W2028604435 on OpenAlexvenueaboutno aff
Alan G. White

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Market Behavior and Pricing
Canadian institutionsnot available
Fundersnot available
KeywordsPrice indexWelfare economicsEconomicsConsumer price index (South Africa)Index (typography)HumanitiesEconometricsArtComputer scienceMonetary policy

Abstract

fetched live from OpenAlex

Recent phenomenal growth in popularity of large warehouse/discount stores has important implications for price measurement. Consumer substitution to such outlets could produce a bias in consumer price indexes (CPIs), which may be exacerbated by unrepresentative sampling and discount outlets’ apparent slower rates of price increases. It is shown that in 1990‐96 unit value indexes rose at a lower rate than the corresponding Canadian CPI subaggregate indexes for other household equipment, non‐prescribed medicines, and audio equipment, and biases arising from unrepresentative sampling and differential rates of price increases across outlets have resulted in an additional overstatement for these subaggregates. La croissance dans la popularité des magasins/entrepôts à forts escomptes au cours des der;chnières années a été fort importante et a eu des effets importants sur la mesure des prix. Le comportement du consommateur qui choisit ces magasins pourrait entraîner un biais dans la mesure des indices de prix à la consommation. Un échantillonage non représentatif et le fait que ces magasins semblent avoir des augmentations de prix plus faibles dans le temps peuvent biaiser encore plus les indices. Cette recherche montre que entre 1990 et 1996 les indices de la valeur unitaire ont cru à un taux plus faible que les indices correspondant de prix à la consommation pour des sous‐agrégats comme autre ´equipement des ménages, médicaments non‐prescrit, et ´equipement audio. Il semble que les biais engendrés par les différentiels de croissance des prix entre magasins et un échantillonnage non représentatif se soient traduits par un biais à la hausse dans ces sous‐agrégats

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.064
GPT teacher head0.173
Teacher spread0.110 · 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
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

Citations14
Published2000
Admission routes2
Has abstractyes

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