Analyzing Factors Affecting GRDP in Indonesia Using Spatial Panel Data Model
Bibliographic record
Abstract
Each region in Indonesia has diverse economic growth. Various empirical studies focus on this problem and attempt to identify the factors that affecting Gross Regional Domestic Product (GRDP) at constant prices or economic growth. However, the research on GRDP at constant prices or economic growth is not solely enough on observation units in a certain time (cross-section); these units also need to be observed in several periods of time. Moreover, the existence of spatial dependencies, which usually occur on the objects observed in form of regions or locations, causes estimation with OLS generating biased and inconsistent results. This study aims to analyze the factors that affecting GRDP at constant prices, namely population, original local government revenue, government expenditure, domestic investment, foreign investment, and the total manpower using the spatial panel data model with the quasi-maximum likelihood estimation method. This study is a quantitative study with panel data of 33 provinces in Indonesia during 2010-2016 periods. The best model obtained from these data was the Spatial Lag Fixed Effect Model with five independent variables. The referred variables are the number of populations, original local government revenue, government expenditure, domestic investment, and foreign investment which have a positive and also significant influence on GRDP at constant prices of provinces in Indonesia, while the total manpower do not have significant influence.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".