Is It Time to Recast India's Fiscal and Monetary Policy Frameworks?
Bibliographic record
Abstract
Abstract With the onset of the Coronavirus disease (COVID-19) pandemic, a number of macro parameters of the Indian economy have been thrown out of gear. The fiscal deficit on the combined account of centre and state governments in 2020-21 may increase to 11-12% of estimated GDP. Consequently, the combined debt-GDP ratio of the central and state government may reach close to 81% of GDP at the end of 2020-21, more than 20% points above the targeted threshold of 60% as per centre’s 2018 amendment to the Fiscal Responsibility and Budget Management Act (FRBMA). The CPI inflation rate breached the upper tolerance limit of the monetary policy framework (MPF) in the last quarter of 2019-20 and the first quarter of 2020-21. In fact, India’s economic crisis predates the pandemic. The infirmities of the FRBMA and the MPF had already started becoming visible with 2019-20 real and nominal GDP growth rates plummeting to 4.2% and 7.2% respectively. It is high time that we consider recasting India’s fiscal and monetary policy frameworks. In this article, we review these frameworks, identify their inconsistencies, and consider remedial changes so as to serve India’ future needs and compulsions.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".