Land Tax, Justice, and the Unaffordability of Housing: Australian Experience
Bibliographic record
Abstract
Taxation and tax ‘reform’ particularly, appears to be a perennial topic, in the major economies of the western world at least. Recently, in Australia there was the “Henry Review” of 2010 – a major review of Australia’s tax system including substantial recommendations for tax reform; and observation shows that both sides of politics in Australia spent most of 2016 and part of 2015 talking about tax ‘reform’. A key aspect of the Henry Review (2010) is the strong recommendation for a land tax.Advocacy for land tax has a long and powerful history. Prominent economists lauding the land tax include David Ricardo, Adam Smith, Henry George, Milton Friedman, and Mason Gaffney. The Henry George land tax has been recommended for a very long time, the latest mainstream recommendation for its implementation coming via the above-mentioned Henry Review of Taxation in Australia (2010).The purpose of this paper is to address the question: is there something special about the natural resource, land, that makes it the subject of so many recommendations for a tax? That is to say, is there anything special about the tax base in the case of a land tax?This paper argues that the land tax is not just another tax – for the reason that the nature of the base of the tax – land – is special. Further, because a land tax would lower the price of land, implementation of a land tax would help solve the housing crisis (the unaffordability of housing). The research findings are different from previous studies because previous studies all focus on the efficiency aspect of taxes, not on any special nature of the tax base.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".