The Potential, Structure, and Effectiveness of the Local Tax System in Strengthening Local Own-Source Revenue (PAD) in Kotabaru Regency
Bibliographic record
Abstract
Local own-source revenue (PAD) represents a core component of regional fiscal capacity and an essential indicator of subnational fiscal sustainability. This study examines the potential, revenue structure, and performance of local taxes in strengthening the local fiscal capacity of Kotabaru Regency. The analysis is based on secondary data on local tax revenues for the 2020–2024 period obtained from the Regional Revenue Agency of Kotabaru Regency. The empirical approach combines an assessment of tax composition and sectoral contributions, an evaluation of revenue target achievement, and trend and medium-term projection analysis using a logarithmic specification. The findings reveal that the local tax revenue structure in Kotabaru Regency is highly concentrated in Street Lighting Tax, Non-Metal and Rock Mineral Tax, and the Land and Building Rights Acquisition Duty (BPHTB), indicating a strong sectoral dependence on energy and extractive activities. While aggregate tax performance appears relatively strong, with average realizations exceeding annual targets, considerable heterogeneity is observed across tax instruments. Service-based and locally embedded taxes exhibit comparatively weak performance, pointing to untapped local tax capacity. The logarithmic trend estimates suggest a deceleration in revenue growth, consistent with diminishing returns in mature tax bases. These results imply that future fiscal strengthening should prioritize potential-based revenue planning, administrative efficiency and compliance enhancement, and diversification of the local tax base to improve regional fiscal resilience.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".