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Record W3115678579 · doi:10.3386/w20778

Reservation Prices: An Economic Analysis of Cigarette Purchases on Indian Reservations

2014· preprint· en· W3115678579 on OpenAlexaff
Philip DeCicca, Donald Kenkel, Feng Liu

Bibliographic record

VenueNational Bureau of Economic Research · 2014
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsMcMaster University
FundersNational Institutes of Health
KeywordsReservationBusinessReservation systemEconomicsAgricultural economicsComputer scienceComputer network

Abstract

fetched live from OpenAlex

The special legal status of Indian tribes in the U.S. means that state excise taxes are not necessarily collected on cigarette purchases on Indian reservations.We focus on two under-studied but basic empirical economic questions this raises.Using novel data from New York surveys that asked directly about cigarette prices and purchases from reservations, we first ask: What is the economic incidence of the tax break?In data from New York over a period when the state did not attempt to collect taxes on reservation purchases, our estimates suggest that the tax break is usually fully shifted to the consumer.The notable exception is on one reservation where a tribal monopoly captures almost half of the tax break.Second, we ask: Has the tax break increased consumer demand for low-quality cigarettes relative to high-quality cigarettes?New York's cigarette tax is a fixed amount per pack, providing an opportunity to test the Alchian and Allen substitution theorem.We find some support for the prediction that the tax break increases consumer demand for lower-quality cigarettes.

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)0.003
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 score0.119
Threshold uncertainty score0.237

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.002
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 head0.399
GPT teacher head0.460
Teacher spread0.060 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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