Examining Transparency and Efficiency in Local Government Unit Disbursement Using Publicly Available Data from Cebu Province, Philippines
Bibliographic record
Abstract
This study examines the relationship between fiscal transparency and disbursement efficiency in a local government unit (LGU) in Cebu, Philippines, using publicly available 2024 fiscal data. Transparency is defined as the percentage of actual quarterly disbursement relative to the expected amount—25% of the annual budget per quarter—while efficiency measures the proportion of funds utilized. Both variables are derived from disbursement percentages, contributing to the strong positive correlation and unusually high regression coefficients observed. Linear regression analyses across four quarters reveal a consistent, statistically significant positive relationship between transparency and efficiency. Unstandardized coefficients (B) rise from 0.25 in Quarter 1 to 0.57 in Quarter 4, with Quarter 4 showing the highest standardized beta (β = 10.19, R² = 0.985). These results indicate that transparency increasingly influences efficiency as fiscal pressures mount toward year-end. The findings affirm transparency as a critical governance tool that aligns planned and actual disbursements to improve budget execution. Practically, enhanced transparency fosters accountability, strengthens fiscal discipline, and supports evidence-based resource allocation, which are vital for effective LGU policy and responsive public service delivery. However, the close operational linkage between transparency and efficiency warrants cautious interpretation of coefficient magnitudes. The study recommends performance-informed budgeting, improved monitoring, and inter-agency coordination to balance transparency with execution flexibility, optimizing resource use and governance outcomes in decentralized fiscal systems.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".