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Record W2087015943 · doi:10.1257/pol.20130101

The Impact of Including, Adding, and Subtracting a Tax on Demand

2015· article· en· W2087015943 on OpenAlex

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

VenueAmerican Economic Journal Economic Policy · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsMonetary economicsTax creditEconomicsCashAd valorem taxTax reformDeferred taxValue-added taxIndirect taxEquivalence (formal languages)MicroeconomicsPublic economicsState income taxMacroeconomicsGross income

Abstract

fetched live from OpenAlex

We test the equivalence of tax-inclusive, tax-exclusive and tax-rebate prices through a series of experiments differing only in their handling of the tax. Subjects receive a cash budget and decide how much to keep and how much to spend on various attractively priced goods. Subjects spend significantly more under tax-exclusive prices whereas total purchases under tax-inclusive and tax-rebate prices are similar. These results persist throughout most of the ten rounds despite feedback and the ability to revise purchases. The asymmetric response to tax liabilities and rebates highlights consumers' ability both to internalize and to willfully ignore hidden price components. (JEL D12, H25, H31)

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.060
GPT teacher head0.328
Teacher spread0.268 · 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