Unveiling Regional Growth Patterns Spatial Heterogeneity and Infrastructure Quality under a Bayesian Framework in Central Java
Abstract
: This study investigates the impact of infrastructure quality, encompassing roads, clean water distribution, and electricity consumption, on regional economic growth in Central Java from 2018 to 2024. A Bayesian Panel Data Regression model with a hierarchical structure was estimated using the Markov Chain Monte Carlo (MCMC) method, assisted by Python programming, to address spatial heterogeneity, lag effects, and parameter uncertainty. Model validation employed Posterior Predictive Checks (PPC), Bayesian R², R-hat statistics, and Effective Sample Size (ESS). The findings reveal that past GRDP significantly influences current regional economic growth, while the direct effects of infrastructure variables are statistically insignificant. This outcome highlights that infrastructure quality is more important than quantity in promoting development. The study advances empirical methodologies by integrating full posterior inference with predictive validation, representing a state-of-the-art approach in regional economic analysis. The results provide strong evidence in support of formulating infrastructure policies that focus on long-term, sustainable Growth.
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Jiang, W., & Wang, Y. (2023). Asymmetric Effects of Human Health Capital on Economic Growth in China: An Empirical Investigation Based on the NARDL Model. Sustainability, 15(6), 5537. https://doi.org/10.3390/su15065537
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Published
2025-12-08
How to Cite
SURIPTO, Suripto et al.
Unveiling Regional Growth Patterns Spatial Heterogeneity and Infrastructure Quality under a Bayesian Framework in Central Java.
Eko-Regional: Jurnal Pembangunan Ekonomi Wilayah, [S.l.], v. 20, n. 2, p. 214-234, dec. 2025.
ISSN 2620-8849.
Available at: <https://jos.unsoed.ac.id/index.php/er/article/view/17670>. Date accessed: 14 dec. 2025.
doi: https://doi.org/10.32424/er.v20i2.17670.
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Articles
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