Does Fiscal Decentralization Encourage Corruption in Local Governments? Evidence from Indonesia
Bibliographic record
Abstract
This study examines the effects of fiscal decentralization on corruption by analyzing whether the degree of fiscal decentralization facilitates or mitigates the number of corruption cases in Indonesia’s local governments. The research utilizes a panel data model and a system Generalized Method of Moments (GMM) estimator to assess the degree of fiscal decentralization on corruption in 19 provinces for the period between 2004 and 2014. The estimation results reveal that the degree of fiscal decentralization, both expenditure and tax revenue sides, drives a growing number of corruption cases in local governments. A lack of human capital capacity, low transparency and accountability, and a higher dependency on intergovernmental grants from the central government may worsen the adverse effects of corruption. Our results suggest that a more heterogeneous population and higher political stability mitigate the adverse effects of corruption. Furthermore, this is the first corruption study in Indonesia to create corruption measures from the number of corruption cases investigated by the Indonesia Corruption Eradication Commission as well as extensive, provincial-level government financial data. As a result of using these different datasets, this research advances existing empirical studies and makes policy recommendations for the local governments in Indonesia.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".