Fiscal Decentralization and Economic Growth in Thailand: A Cross-Region Analysis
Bibliographic record
Abstract
Prior to the 1997 decentralization, over 90% of national revenue in Thailand were held at the central government and less than 10% of public expenditure were allocated to local governments across country. Lack of adequate revenue and access to sufficient expenditure budget has caused disparity and ineffectiveness of public services and economic development at the local level. This study examines the effects of the fiscal decentralization on the economic growth in Thailand from 2004 to 2017. The research methodology uses a cross panel data analysis across five provincial regions and considers revenue decentralization, expenditure decentralization, transfer dependency, and vertical fiscal imbalance as influential factors of growth. By applying Panel Fully Modified Least Squares (FMOLS) and Panel Dynamic Least Squares (DOLS) regression approaches, the study finds empirical evidence of positive effects of revenue decentralization, transfer dependency, and vertical fiscal imbalance on regional economic growth across five regions. However, this study also finds that expenditure decentralization has a negative impact on regional economic growth, but level of significance is weak. These findings suggest that the rapid increase in metropolis government expenditure budget following the years of political transition in 2006 and 2014 has caused stagnation in public investment at local level across country, thereby resulted in a lagged behind industrial output and gross provincial product. Lack of budget expenditures also weakens demand and stagnates growth in manufacturing, construction, and real estate activities, thereby rendering fiscal imbalances and development gaps in Thai economy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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".