Parametric and non-parametric analysis of tax changes
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.
Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it