Bibliographic record
Abstract
Property taxes are generally considered by economists to be good taxes, and many countries are being advised to increase and improve their property taxes. In practice, however, property tax reforms have often proved to be difficult to carry out successfully. This paper discusses why property taxes are particularly challenging to reform and suggests several ways in which efforts to reform this tax may become more successful in the future. After a brief introductory section on the ‘disconnect’ between the economics and the politics of property tax reform, Section 2 summarizes recent experiences in five OECD countries with property tax reform. Against this background, Section 3 sets out the key elements of a good property tax reform and Section 4 discusses several aspects of property tax reform that seem to have derailed or distorted reforms in practice. Unfortunately, some of the solutions countries have adopted to deal with such problems are themselves problematic, either because they do not really solve the problem or because they hamper rather than work towards the establishment of a good property tax. Fortunately, as Section 5 outlines, it is possible to devise strategies for property tax reform that incorporate more acceptable solutions to most problems. As Section 6 concludes, good property tax reform is not easy. But it can definitely be achieved if an appropriately designed reform package is properly introduced and implemented.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".