The Analysis of Sub-National Fiscal Risk in Indonesia
Bibliographic record
Abstract
A Fiscal Risk Management will increase fiscal sustainability in the nation development. The International Monetary Fund revealed that one source of fiscal risk for the government is Sub-national Risk or a fiscal pressure which came from the local governments. Until 2021, the Government of Indonesia has not assessed the Sub-national fiscal risk as a source of fiscal risk in the preparation of the State Revenue and Expenditure Budget (APBN). This study aims to propose a sub-national fiscal risk assessment model and carried out an assessment of the risk level in the provincial governments, and analyzed it for the year 2023. The data used was The Budget Realization Report and Balance Sheet of the Provincial Government for the year 2015 - 2020. The method used is literature study, quantitative descriptive analysis, and forecasting time series analysis. The results of this study are the definition of subnational fiscal risk, a measurement formula, and a map of Indonesia's subnational fiscal risk. The Local Government which lowest fiscal risk in the year 2023 is DKI Jakarta and the highest fiscal risk is DI Aceh. A deeper analysis shows the results that regions with the Trade development sector have better fiscal management and lower levels of fiscal risk. This study provides new insights for the Government of Indonesia in develop a strategy to improve a fiscal capacity of local government and the formulation of policies regarding the financial balance between central government and local government.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".