Tax expenditures in OECD countries
Bibliographic record
Abstract
In all OECD countries, governments collect revenues through taxes and redistribute this public money, often by obligatory spending on social programs such as education or health care. Their tax systems usually include "tax expenditures" - provisions that allow certain groups of people, such as small businessmen, retired people or working mothers, or those who have undertaken certain activities, such as charitable donations, to pay less in taxes. The use of tax expenditures by governments is pervasive and growing. At a time when many government budgets are threatened by population aging and adverse cyclical developments, there is a pressing need to avoid inefficient government programs, some of which may utilize tax expenditures. This book sheds light on the use of tax expenditures, mainly through a study of ten OECD countries: Canada, France, Germany, Japan, Korea, Netherlands, Spain, Sweden, the United Kingdom and the United States. This book will help government officials and the public better understand some of the technical and policy issues behind the use of tax expenditures. It highlights key trends and successful practices, and addresses a broad range of government finance issues, including tax policy making, tax and budget efficiency, fiscal responsibility and rule making.--Publisher's description.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".