Bibliographic record
Abstract
The marginal cost of public funds (MCF) measures the loss incurred by society in raising additional revenues to finance government spending. The MCF has emerged as one of the most important concepts in public economics; it is a key component in evaluations of tax reforms, public expenditure programs, and other public policies. The Marginal Cost of Public Funds provides a unified treatment of the MCF, carefully developing its theoretical foundations in a variety of contexts and describing its application to a wide range of policies--from excise taxes in Thailand to public sector borrowing in Canada and the United States. The Marginal Cost of Public Funds develops the basic theory of the MCF within the framework of public economics and shows how it is related to the traditional measures of the efficiency loss from distortionary taxation. The MCF concept is then applied to the major sources of revenues for governments--sales and excise taxes, taxes on labor income, taxes on the return to capital, public sector borrowing, and intergovernmental grants. This book will be an essential reference for economists and public policy analysts both in and out of government. Exercises and recommendations for further reading at the end of each main chapter highlight its usefulness as a supplementary text in advanced undergraduate or graduate courses in public economics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".