Budgeting and implementing fiscal policy in Italy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Forecast errors in budgetary variables are frequent. When systematic, they are a source of concern, as they signal misconduct in fiscal policymaking, undermine the government’s credibility and compromise long-term fiscal sustainability. This paper analyses the characteristics of fiscal forecasting and implementation errors in Italy using real-time data over the period 1998–2009. Several empirical methods are applied in order to identify the features of policymakers’ behaviour in preparing and implementing annual fiscal policy and to discover potential determinants in the formation of the implementation errors. Our results show that implemented budgetary plans systematically fall short one year ahead of ambitious planned adjustments for the main public finance aggregates. Fiscal illusion dominates revenue and GDP forecasting, and preliminary data releases are severely biased estimators of the final data, especially for expenditures. The role of the parliamentary session in driving a severe expenditure drift is confirmed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it