Tax Expenditures--Shedding Light on Government Spending through the Tax System : Lessons from Developed and Transition Economies
Bibliographic record
Abstract
Recently developing countries have focused attention on the usefulness of tax expenditures' in shaping prudent and transparent fiscal policy. In adopting a market economy, developing countries commonly use tax expenditures as major fiscal policy instruments. However, with limited theoretical understanding of, and ad hoc experience with, applying tax expenditures, developing countries now confront not only revenue losses higher than they had anticipated but also the erosion of their tax bases in systems that generally have been in existence fewer than 10 years. Fortunately, the experience and practice of developed countries offer insights into understanding and applying tax expenditures. Most developed countries have established tax reporting systems, which provide empirical information on their tax expenditures. Such tax reporting systems tend to be part of a country's overall fiscal system for strengthening government finance and contribute significantly to fiscal transparency. Using the information available, several governments attempt to analyze the cost and economic effects of individual tax expenditures. Some governments even bring tax expenditures into the budgetary process and subject them to a level of scrutiny similar to that for direct expenditures. This book contains several papers on how both developed and transition economies define and apply tax expenditure systems. The developed countries-Australia, Belgium, Canada, the Netherlands, and the United States-have established tax expenditure accounting and, in varying degress, brought tax expenditures into budgetary process. The experience of China and Poland shed light on why it is important for developing and transition economies to ensure fiscal transparency and to perform systematic fiscal analysis when implementing tax expenditures, as well as how to address these issues in relatively new tax systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".