Resolving Vertical and Horizontal Imbalances in India
Bibliographic record
Abstract
Abstract This chapter brings together the ideas developed based on theoretical considerations, and the Canadian and Australian examples, to comprehensively examine the design of fiscal transfers in India in resolving vertical and horizontal imbalances. With respect to the vertical dimension, we note that in India, there has been long-term stability, at least up to the Twelfth Finance Commission, in the shares of the centre and the states in the combined tax revenues of the system after tax devolution. This chapter also shows that, subject to certain assumptions, tax revenue sharing under an axiomatic framework will result in transfers that will be consistent with the concept of revenue side equalization as used in Canada. The difference is that in the Indian case, a macro base rather than a tax-by-tax approach is followed on the revenue side. A major issue in following a criteria-based tax devolution approach relates to objectively determining weights attached to different criterion. We have suggested an axiomatic framework for this. It is also argued that for determining grants, the revenue gap approach leads to significant adverse incentives. This should be replaced by a suitable norm-based approach. The volume of fiscal transfers and the extent of borrowing are interdependent. With respect to borrowing, it is important to develop a sustainability framework. A healthy and stable system of fiscal transfers requires that borrowing by the central and state governments is kept at sustainable levels.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".