Has Fiscal Rules changed the Fiscal Behaviour of Union Government in India? Anatomy of Budgetary Forecast Errors in India
Bibliographic record
Abstract
We analyse the fiscal marksmanship of the macro-fiscal variables of Union Government ex-ante and ex-post to the formulation of fiscal rules in India. The fiscal marksmanship is the accuracy of budgetary forecasting. The fiscal rules have been legally mandated in India in the form of fiscal responsibility and budget management Act (FRBM Act) in 2003, with a criteria of fiscal-deficit to GDP threshold ratio of 3 per cent and gradual phasing out of revenue deficit. Using Theil’s inequality coefficient (U) based on the mean square prediction error, the paper estimates the magnitude of errors in the budgetary forecasts in India during the period ex-ante and ex-post to fiscal rules, and also decomposed the errors into biasedness, unequal variation and random components. The decomposition of errors is to analyze the source of error in both the regimes. Our results found that in both regimes, the proportion of error due to random variation has been significantly higher, which is beyond the control of the forecaster. In other words, the error due to bias of the policy maker in preparing the Union Budget has been negligible in the period ex-ante and ex-post to fiscal responsibility and budget management (FRBM) Act in India. The estimates also showed that the errors due to policy maker’s bias has comparatively reduced in the regime ex-post to fiscal rules. The analysis related to efficiency of forecasts showed that no significant improvement in forecasts over time. This result has significant policy implications especially in the context of repeal of 2003 FRBM Act in India and the Union Government has announced clauses for a ‘New FRBM Act’ in India in the Finance Bill 2018.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".