Bibliographic record
Abstract
This book examines key aspects of one of the more controversial questions in tax policy: what is the appropriate way to tax capital gains? The focus of the book is the capital gains tax (CGT) rate and rate preferences. The research is by way of a review of the relevant tax literature on capital gains, followed by qualitative and quantitative studies and recommendations and conclusions. The literature review identifies an absence of empirical evidence on how Australian personal taxpayers respond to CGT rate changes. This significant gap in the knowledge is the primary motivation for this book. The qualitative study presents findings from in-depth interviews conducted with 24 CGT experts in Australia, Canada and the United States on issues related to the taxation of capital gains. In particular, it explores the role of CGT preferences. The interview data are compared with relevant tax literature as well as current practices in the three jurisdictions in taxing capital gains. The study establishes that most interviewees do not support CGT rate preferences. Armed with this background, the quantitative study uses regression analysis to estimate the capital gains realisations response in Australia, with the tax rate change of interest being the enactment of the 50% CGT discount in 1999 for personal taxpayers. The elasticity point estimates from the quantitative study support the primary hypothesis that the 50% CGT discount has caused a decrease in CGT revenue. This raises questions about the central rationale for the introduction of the CGT discount-the forecast increase in CGT revenue. The qualitative and quantitative results are drawn on to recommend a tax policy reform to improve the operation of the CGT regime in Australia: specifically, reinstating taxation of personal capital gains at marginal rates and introducing an annual exempt amount for net capital gains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".