Efficiency and Effectiveness of Government Expenditure on Education at Districts/Cities Level in East Java Indonesia
Bibliographic record
Abstract
The purpose of this study is to investigate and analyze the efficiency and effectiveness of local government expenditure on education sector in districts and cities level of East Java, during the periods 2007-2014. Furthermore, this study will evaluate the impacts of local government expenditure, household expenditure for education, and regional product domestic bruto or (PDRB) on the educational outcomes, namely education index.Data Envelopment Analysis (DEA) is selected as the methodology for analyzing the efficiency of local government expenditure on educational outcome. The model assumes constant return to scale (CRS) and variable return to scale (VRS). Measurement of the effectiveness of government spending is done by using panel data regression. Data for supporting the analyses is panel data from 38 districts and cities in East Java for the periods of 2007 – 2014. The results show that government expenditure in educational sector is relatively inefficient. Government Expenditure for Education (PPP) has no significant impact on educational index, while Household expenditure for education (PPRT) and GRDP per Capita positive has significant impact on the Education Index (IP). This imply that government expenditure for educational sector is not effective improving educational index.
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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 | 0.002 |
| 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".