Policy Strategies to Increase Motor Vehicle Tax Revenue to Support Regional Development in Lampung Province
Bibliographic record
Abstract
The Provincial Government of Lampung faces significant challenges in increasing its Regional Own-Source Revenue, one of which is through the Motor Vehicle Tax sector. Despite the continuous growth in the number of vehicles, taxpayer compliance remains very low, ranging only between 30–38%. This situation directly affects the region's fiscal capacity to fund development and public services. This policy paper examines the underlying causes of low PKB revenue and formulates data-driven policy recommendations based on field surveys and public policy theories. The data analysis employs a qualitative descriptive approach. Survey results reveal that 86 percent of taxpayers encounter obstacles when paying taxes, such as long queues at Samsat offices or disruptions in the online payment system. Additionally, 44 percent of taxpayers perceive the tax payment procedures to be overly complicated. The analysis indicates that the core issue is the low compliance rate among motor vehicle taxpayers, primarily due to limited access to payment services caused by uneven digital infrastructure and restricted service hours. These factors have significantly contributed to the low PKB revenue in Lampung Province. This policy paper adopts the New Public Management (NPM) approach and the principles of Good Governance by promoting the integrated digitalization of PKB payment services through mobile and web-based applications. Furthermore, extending service hours into the evenings and weekends is recommended to provide greater time flexibility for the public. As a concrete step, it is recommended that the Provincial Government of Lampung issue a new Governor Regulation outlining the technical aspects of PKB services, including coordination among the Regional Revenue Agency, Samsat, the police department, and financial institutions. This initiative is expected to significantly enhance tax compliance, strengthen PAD, and support more autonomous and sustainable regional development.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".