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Record W3124067355 · doi:10.1504/gber.2016.078669

Parametric and non-parametric analysis of tax changes

2016· article· en· W3124067355 on OpenAlex
James Bugden, Robert Waschik, Iain Fraser, Jeffrey S. Racine

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

VenueGlobal Business and Economics Review · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsMcMaster University
FundersAustralian Government
KeywordsParametric statisticsEconomicsProperty taxPublic economicsEconometricsGovernment (linguistics)Parametric modelEstimationSample (material)Residential propertySemiparametric modelGoods and servicesTax policyProperty valueTax reformNonparametric statisticsEconomyMathematicsFinanceStatisticsEconomic geography

Abstract

fetched live from OpenAlex

In this paper, we examine the net effect of several major tax changes in Australia on residential property prices. Specifically, we consider the announcement and introduction effects that resulted from several policy changes including the introduction of the Goods and Services Tax (GST) and the accompanying First Home Owner Grant (FHOG). Using a large dataset of residential property sales in Melbourne, Australia, between 1992 and 2002 we estimate various models using parametric and non-parametric methods. While our parametric models suggest that the tax policy changes appear to have a statistically significant impact on house prices, no economically significant impact is detected by our non-parametric models, nor (upon closer inspection) by the parametric models themselves. Given the enormity of the sample size, this provides a telling example of the fundamental difference between statistical and economic significance and its implications for detecting government policy effectiveness.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.025
GPT teacher head0.228
Teacher spread0.202 · 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