The Nexus Among Human Capital, Monetary Policy, and Regional Economic Growth: Comparison of the West and East Region Indonesia
Bibliographic record
Abstract
Regional economic growth is one of the key indicators of national economic development.This investigation is conducted to assess the nexus among human capital, monetary policy, population, investment, and regional economic growth in Indonesia with the comparison of the West and East regions.To assess the dynamic nexus among regional economic growth, human capital, monetary policy, population size, and investment, Indonesia's 34 provinces were grouped into two regions: the western and eastern parts.The research period is 2016 to 2023 using panel data.The model applied is a dynamic panel model, and the estimation method used is the generalized method of moments (GMM).The results indicate that previous-period regional economic growth, human capital, investment, and population size influence economic growth across all provinces, as well as in the western and eastern sub-regions.However, the magnitude of change varies across sub-regions.In the eastern region (KTI), monetary policy and population size have no meaningful effects on economic growth.These findings suggest that, in the long run, Human resource development should be the focus of government through education, research, and development to enhance regional economic growth.Additionally, investment should take environmental sustainability into account to support sustainable development.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".