Fiscal Decentralization, its Political Economy Determinants and Issues in Effective Measurement
Bibliographic record
Abstract
The recent global financial crisis has highlighted the limitations of monetary policy as a macroeconomic stabilization tool in open economies exposed to external trade and financial shocks. Fiscal policy is now again at the forefront of macroeconomic stabilization and to address the fact that there exist large regional disparities within countries, fiscal decentralization is a major policy issue. According to the fiscal decentralization theorem, lower levels of government can better address the unique needs of their region, but this needs to be balanced against the higher taxable capacity of central government. However, fiscal decentralization remains a rather elusive concept to define in practice. Crude measures of fiscal decentralization based on the shares of sub-national government taxation and expenditure in total national tax revenue and spending often mask a large variation in terms of the actual degree of autonomy over fiscal decisions. Therefore, one of the objectives of this paper is to identify the key legal and institutional characteristics for the effective measurement of the degree of sub-national fiscal autonomy. By going beyond the mainstream normative perspective on the optimal degree of fiscal decentralization, the paper also aims to contribute to the literature by incorporating political economy considerations into the analysis of the determinants of fiscal decentralization. The positive political economy perspective thus sheds light on the feasibility of fiscal decentralization in different political and legal systems.
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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 | 0.024 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".