Fiscal decentralization, institutional quality, and government size: an asymmetry analysis for Asian economies
Bibliographic record
Abstract
Abstract The aim of this study is to examine the asymmetric effects of fiscal decentralization and institutional quality on government size by employing asymmetric autoregressive-distributed lag (ARDL) methodology by using the time series data of Asain economies from 1984 to 2017. The results show that positive shocks in expenditures decentralization (ED) enhance government size in Japan, Kazakhstan, Thailand, Turkey, and reduces it in Korea, Rep. in long run. While negative shock in ED reduces government size in Pakistan, Thailand, Turkey and increases it in Kazakhstan and Mongolia in long run. Whereas asymmetric results in the long show that a positive shock in revenue decentralization (RD) increase government size in Pakistan, Japan, Kazakhstan, Thailand, and Turkey, and a negative shock in RD is also decreased government size in Pakistan, Mongolia, Thailand, and Turkey. The results also disclosed that positive shock in institutional quality (IQ) increases government size in Azerbaijan, Japan, Thailand and negative shock in IQ also increases government size in Pakistan, Azerbaijan, Japan, Kazakhstan, Thailand, in the long run. While short-run asymmetric results of fiscal decentralization and institutional quality on government size have robust in the public sector. Based on the empirical outcomes, some economic policy implications are proposed for the provincial and federal governments of Asain economies.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".