Trade Spillover-Induced Backwash Effect and Regional Growth Disparities: A System GMM Approach in Central Java
Bibliographic record
Abstract
This study investigates the determinants of regional economic growth in Central Java Province, focusing on trade spillovers, total factor productivity (TFP), and labour composition. Using panel data from 35 regencies and cities over 2011–2024, the analysis applies the System Generalized Method of Moments (SYS-GMM). Results reveal persistent growth, as past output strongly influences current performance. Specifically, the coefficient of the lagged GRDP variable remains above 0.96 across specifications, indicating strong path dependence. Investment, measured by Gross Fixed Capital Formation (GFCF), significantly drives short- and long-term growth. Human capital also matters, with educated and less-educated labour contributing, though the latter remains dominant. Elasticity estimates show that the long-run effect of GFCF reaches approximately 0.45, reinforcing its central role in capital-driven expansion. In contrast, TFP shows weak and insignificant effects, reflecting technological adoption, infrastructure, and workforce quality constraints. Notably, trade spillovers exert a negative influence. Having a commercial link or geographical proximity to a main growth centre does not always guarantee positive outcomes and may even lead to a backwash effect. Indicating a conditional spillover pattern whereby only regions with sufficient absorptive capacity benefit, while others experience backwash tendencies. These findings highlight the need for spatial policies that focus on enhanced connectivity, the creation of additional growth hubs, and upgrading workforce skills to ensure a more balanced spread of development benefits.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".