Budget Issues: Accrual Budgeting Useful in Certain Areas but Does Not Provide Sufficient Information for Reporting on Our Nation's Longer-Term Fiscal Challenge
Bibliographic record
Abstract
Accrual budgeting continues to be used to some extent in all six countries reviewed in 2000. The six countries have taken different approaches in the design of their accrual-based budgets, and all continue to use cash information particularly for evaluating the overall fiscal condition. Since 2000, more OECD countries have expanded the use of accrual measurement in the budget, including Denmark and Switzerland. However, two countries in our study-Canada and the Netherlands which had considered broader expansions of accrual budgeting, have thus far made only limited changes. Two other countries-Norway and Sweden-also considered adopting accrual budgeting in recent years but decided against it, primarily because they believed the cash budget provides for better control, particularly over capital investment. When significantly expanding the use of accrual budgeting, there are several common transitional challenges countries initially faced including developing accounting standards for the budget and deciding what assets to value and how to value them. Countries tended to work through these issues over time. However, a number of implementation challenges cited in our 2000 report still exist. These challenges illustrate the inherent complexity of using accrual-based measures for managing a nation's resources. For example, accrual-based measures experience volatility due to changes in the value of assets and liabilities or changes in assumptions (e.g., interest rates, inflation, and productivity) used to estimate future payments whether or not there has been a change in the underlying fiscal stance. Management and oversight of noncash expenses were also cited as challenges. These challenges have led some countries to modify their approaches to accrual budgeting and to continue a reliance on cash-based measures for broad fiscal policy making.
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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 | 0.050 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.011 |
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".