Fiscal Decentralisation and Economic Growth across Provinces: New Evidence from Vietnam Using a Novel Measurement and Approach
Bibliographic record
Abstract
Fiscal decentralisation has attracted great attention from governments, practitioners, and international institutions with the aims of enhancing economic growth in the last 5 decades. However, satisfactorily measuring the degree of fiscal decentralisation across countries has appeared to be problematic. In addition, the link between fiscal decentralisation and economic growth across provinces has largely been ignored, in particular for emerging markets such as Vietnam. As such, this study is conducted to determine the extent of fiscal decentralisation and to assess its impact on economic growth based on data from all 63 provinces of Vietnam in the period after the 2008 financial crisis. Instead of using traditional measures of fiscal decentralisation, the study uses the Fiscal Decentralisation Index (FDI) together with the two most important and inseparable components of the index, those being (i) the Fiscal Importance (FI) and (ii) the Fiscal Autonomy (FA). The Difference Generalised Method of Moments (DGMM) is utilised to correct for the potential problem of endogeneity between fiscal decentralisation and economic growth. Results show that the two indicators (FI and FDI) have a negative impact while FA has a positive impact on economic growth across provinces. On the ground of these empirical findings, implications for specific policies have emerged for Vietnam and other emerging markets on the extent of fiscal decentralisation, and its major determinants, which positively support economic growth in the future.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".