Analyzing Factors Affecting GRDP in Indonesia Using Spatial Panel Data Model
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it