Economic Convergence Across Regions in the Era of Technological Disruption: A Dynamic Panel and Spatial Econometrics Approach

Authors

  • Firayani Firayani Universitas Islam Negeri Sulthan Thaha Saifuddin Jambi Author

DOI:

https://doi.org/10.62872/ewjrgt95

Keywords:

Digital economy, Dynamic panel data, Regional convergence, Spatial econometrics, Technological disruption

Abstract

Regional economic inequality remains a major challenge in many countries, particularly in the context of rapid technological disruption and digital transformation. The concept of regional economic convergence suggests that less developed regions may grow faster than advanced regions, thereby reducing disparities in income and productivity. However, recent evidence indicates that convergence processes are increasingly influenced by technological innovation, spatial spillovers, and structural regional differences. This study aims to analyze regional economic convergence in the era of technological disruption by applying dynamic panel and spatial econometric approaches to capture both temporal dynamics and spatial interactions among regions. This research employs a quantitative approach using secondary panel data on regional economic indicators, including gross regional domestic product per capita, digital economy development, infrastructure, and human capital. The analysis applies dynamic panel estimation using the Generalized Method of Moments (GMM) to identify β-convergence, followed by spatial econometric modeling to examine spatial spillover effects between regions. The results indicate that regional convergence occurs conditionally rather than absolutely, with technological innovation and digital economy development playing important roles in shaping regional growth dynamics. Spatial econometric results reveal significant spillover effects, indicating that technological development in one region can positively influence economic growth in neighboring regions. In conclusion, regional convergence in the era of technological disruption is strongly influenced by innovation spillovers and spatial interactions, highlighting the importance of dynamic panel and spatial econometric models in analyzing regional economic development patterns.

Downloads

Download data is not yet available.

References

Ataeva, A., & Klimenteva, A. (2025). Process approach to assessing technological convergence of Russian regions: Dynamics of the innovation gap. Voprosy Ekonomiki. https://doi.org/10.32609/0042-8736-2025-5-130-152

Billé, A., Tomelleri, A., & Ravazzolo, F. (2023). Forecasting regional GDPs: A comparison with spatial dynamic panel data models. Spatial Economic Analysis, 18, 530–551. https://doi.org/10.1080/17421772.2023.2199034

Capello, R., & Cerisola, S. (2024). Towards a double bell theory of regional disparities. The Annals of Regional Science, 73, 1701–1728. https://doi.org/10.1007/s00168-024-01316-8

Chen, J., Cui, G., Sarafidis, V., & Yamagata, T. (2025). IV estimation of heterogeneous spatial dynamic panel models with interactive effects.

Ding, C., Liu, C., Zheng, C., & Li, F. (2021). Digital economy, technological innovation and high-quality economic development: Based on spatial effect and mediation effect. Sustainability. https://doi.org/10.3390/su14010216

Ding, R., Shi, F., & Hao, S. (2022). Digital inclusive finance, environmental regulation, and regional economic growth: An empirical study based on spatial spillover effect and panel threshold effect. Sustainability. https://doi.org/10.3390/su14074340

Dubovik, M., Dmitriev, S., & Aitkazina, M. (2025). The impact of gross regional product per capita on the processes of convergence between different regions of the country. Qubahan Academic Journal. https://doi.org/10.48161/qaj.v4n4a931

El-Doakly, W. (2025). Spatial spillover effects of foreign direct investment flows to Arab countries based on static and dynamic spatial panel data models: A spatial panel modelling study. المجلة العلمية للإقتصاد و التجارة. https://doi.org/10.21608/jsec.2025.442483

Elhorst, J., & Emili, S. (2021). A spatial econometric multivariate model of Okun's law. Regional Science and Urban Economics. https://doi.org/10.1016/j.regsciurbeco.2021.103756

Emili, S., & Galli, F. (2025). Capturing inter and intra sectoral productivity spillovers in industrial filière: A system of dynamic spatial panel data models. Journal of Regional Science. https://doi.org/10.1111/jors.12757

Feng, P., Yasar, M., & Rejesus, R. (2023). Innovation and regional economic convergence: Evidence from China. The Annals of Regional Science. https://doi.org/10.1007/s00168-023-01210-9

García-Vidal, G., Loredo-Carballo, N., Pérez-Campdesuñer, R., & García-Vidal, G. (2025). Economic convergence analyses in perspective: A bibliometric mapping and its strategic implications (1982–2025). Economies. https://doi.org/10.3390/economies13100289

Isla-Castillo, F., Garashchuk, A., & Podadera-Rivera, P. (2024). Cross-sectional and spatial panel data analysis of territorial economic cohesion in the European Union regions based on convergence approach: From 2 to 8 per cent. Socio-Economic Planning Sciences. https://doi.org/10.1016/j.seps.2024.102012

Kacou, K. (2022). Interregional inequality in Africa, convergence, and multiple equilibria: Evidence from nighttime light data. Review of Development Economics. https://doi.org/10.1111/rode.12851

Kadochnikova, E., Varlamova, Y., & Kolesnikova, J. (2022). Spatial analysis of regional productivity based on β-convergence models. Montenegrin Journal of Economics. https://doi.org/10.14254/1800-5845/2022.18-3.11

Kijek, T., Kijek, A., & Matras-Bolibok, A. (2023). Innovation and regional technological convergence: Theory and evidence. https://doi.org/10.1007/978-3-031-24531-2

Li, J. (2023). A fuzzy comprehensive evaluation method of regional economic development quality based on a convolutional neural network. Journal of Circuits, Systems and Computers, 32. https://doi.org/10.1142/s0218126623502687

Li, J., & Zhang, B. (2024). Construction strategy of regional investment and financial management system under spatial effect modeling. Applied Mathematics and Nonlinear Sciences, 9. https://doi.org/10.2478/amns-2024-1108

Lu, P., Liu, J., Wang, Y., & Ruan, L. (2021). Can industrial agglomeration improve regional green total factor productivity in China? An empirical analysis based on spatial econometrics. Growth and Change. https://doi.org/10.1111/grow.12488

Maket, I., Kanó, I., & Vas, Z. (2023). Estimations of pooled dynamic panel data model with time-space dependence of selected Sub-Saharan African urban agglomerations, 2000–2020. Regional Statistics. https://doi.org/10.15196/rs130404

Navarro-Chávez, C. (2025). Economic and sectoral convergence in Latin America and the Caribbean: An analysis of beta, sigma, and gamma convergence. Journal of Risk and Financial Management. https://doi.org/10.3390/jrfm18020061

Pagaduan, J. (2023). Spatial income inequality, convergence, and regional development in a lower middle-income country: Satellite evidence from the Philippines. The Developing Economies. https://doi.org/10.1111/deve.12354

Prasetyia, F., Pangestuty, F., Ariestiningtyas, D., & Hans, M. (2025). The role of ICT development in promoting ASEAN regional economic convergence. Jurnal Ilmu Ekonomi dan Pembangunan. https://doi.org/10.20961/jiep.v25i1.95514

Rosés, J., & Wolf, N. (2020). Regional growth and inequality in the long run: Europe, 1900–2015. Oxford Review of Economic Policy, 37, 17–48. https://doi.org/10.1093/oxrep/graa062

Sayifullah, S., & Arifin, S. (2024). Relative risk of COVID-19 pandemic and regional inflation convergence in Indonesia: Spatial panel data approach. Optimum: Jurnal Ekonomi dan Pembangunan. https://doi.org/10.12928/optimum.v14i1.8935

Tian, B. (2023). Estimation of the non-parametric spatial dynamic panel data model with fixed effects. Mathematics. https://doi.org/10.3390/math11132865

Tian, S., & He, Y. (2025). The impact of digital industry development on regional economic resilience: Evidence from China. PLOS ONE, 20. https://doi.org/10.1371/journal.pone.0315203

Xun, J., Li, W., & Xu, Y. (2022). A random matrix model of regional economic disparity based on spatial panel data analysis. Mathematical Problems in Engineering. https://doi.org/10.1155/2022/7993818

Yang, X., Zhang, H., Lin, S., Zhang, J., & Zeng, J. (2021). Does high-speed railway promote regional innovation growth or innovation convergence? Technology in Society, 64, 101472. https://doi.org/10.1016/j.techsoc.2020.101472

Yi, G., Gao, J., Yuan, W., Zeng, Y., & Liu, X. (2025). Digital economy, R&D resource allocation, and convergence of regional green economy efficiency. Sustainability. https://doi.org/10.3390/su17020384

Zhang, W., Zhao, S., Wan, X., & Yao, Y. (2021). Study on the effect of digital economy on high-quality economic development in China. PLoS ONE, 16. https://doi.org/10.1371/journal.pone.0257365

Zhong, Y., Yang, Y., & Lin, Y. (2025). Empirical study on the impact of digital inclusive finance on urban–rural consumption quality gap based on dynamic panel and spatial econometric models. Scientific Journal of Economics and Management Research. https://doi.org/10.54691/q8hgkd47

Zhou, X., Cai, Z., Tan, K., Zhang, L., Du, J., & Song, M. (2021). Technological innovation and structural change for economic development in China as an emerging market. Technological Forecasting and Social Change, 167, 120671. https://doi.org/10.1016/j.techfore.2021.120671

Downloads

Published

2026-02-25

How to Cite

Economic Convergence Across Regions in the Era of Technological Disruption: A Dynamic Panel and Spatial Econometrics Approach. (2026). Nomico, 3(1), 23-35. https://doi.org/10.62872/ewjrgt95

Similar Articles

51-60 of 190

You may also start an advanced similarity search for this article.