Lab

Quantitative Regional and Computational Science Lab (QuaRCS-lab)

About the lab

At the Quantitative Regional and Computational Science (QuaRCS) Lab, we exploit the integration of econometrics, data science, and machine learning to understand and inform the process of economic development of countries, regions, industries, and firms.

https://quarcs-lab.org/

Featured projects (1)

Project
Study economic growth and inequality across regions within countries

Featured research (5)

We study sectoral productivity convergence through the input-output structure of the economy and its network representation. In particular, we study 106 production sectors in Japan over the 2003-2012 period and identify highly interconnected sectors using community detection algorithms. We next characterize the dynamics of these communities by evaluating the evolution of productivity dispersion using parametric and nonparametric frameworks of sectoral productivity convergence. We find two dominant communities: The central members of community 1 are mostly service-related industries, while the central members of community 2 are mostly high-tech manufacturing industries. The convergence analyses indicate that the two communities have contrasting convergence-divergence patterns. Robust convergence is only found for community 1. In contrast, community 2 appears to be the source of the weak divergence pattern that is observed across all industries in Japan.
The slides can be accessed from this link: https://haginta.github.io/IRSA-slides/IRSA_slides.html#1
This article aims to re-examine the regional convergence hypothesis on income in Indonesia over the 2000–2017 period. By applying a non-linear dynamic factor model, this article tests the club convergence hypothesis using a novel dataset of income at the district level. The results show significant five convergence clubs in Indonesian districts’ income dynamics, implying the persistence of income disparity problems across districts even after implementing the decentralization policy. The subsequent analysis reveals two appealing features regarding the convergence clubs. First, districts belonging to the same province tend to be in the same club, and second, districts with specific characteristics (i.e. big cities or natural resources-rich regions) dominate the highest income club. Overall, our findings suggest some insightful policy implications, including the importance of differentiated development policies across convergence clubs and inter-provincial development strategies.
This paper aims to re-examine the regional convergence hypothesis on income in Indonesia over the 2000-2017 period. By applying a non-linear dynamic factor model, this paper tests the club convergence hypothesis using a novel dataset of income at the district level. The results show significant five convergence clubs in Indonesian districts' income dynamics, implying the persistence of income disparity problems across districts even after implementing the decentralization policy. The subsequent analysis reveals two appealing features regarding the convergence clubs. First, districts belonging to the same province tend to be in the same club, and second, districts with specific characteristics (i.e., big cities or natural resources-rich regions) dominate the highest income club. Overall, our findings suggest some insightful policy implications, including the importance of differentiated development policies across convergence clubs and inter-provincial development strategies.
Reducing regional income disparities is a central challenge for promoting sustainable development in Indonesia. In particular, the prospect for these disparities to be reduced in the post-decentralization period has become a major concern for policymakers. Motivated by this background, this paper aims to re-examine the regional convergence hypothesis at the district level in Indonesia over the 2000-2017 period. By using non-linear dynamic factor model, this study analyzes novel data set to investigate the formation of multiple convergence clubs. The results indicate that Indonesian districts form five convergence clubs, implying that the growth of income per capita in Indonesian 514 districts can be clustered into five common trends. From the lens of spatial distribution, two common occasions can be observed. First, districts belonging to the same province tend be in the same club and second, the highest club is dominated by districts with specific characteristic (i.e., big cities or natural resources rich regions). From a policy standpoint, this findings of multiple convergence clubs at significantly different levels of income allows regional policy makers to identify districts facing similar challenges. It may have meaningful implications for regional development policies, including the call of inter-provincial development policy.

Lab head

Carlos Mendez
Department
  • Department of International Development(DID)
About Carlos Mendez
  • My research interests focus on the integration of econometrics, spatial data science, and machine learning methods to understand and inform the process of economic development. In particular, my current research deals with (1) the quantitative geography of development; (2) regional economic growth and convergence; (3) regional labor markets outcomes and macroeconomic shocks; and (4) structural change and firm productivity dynamics. For further details, see https://carlos-mendez.rbind.io

Members (14)

Harry Aginta
  • Nagoya University
Ugur Ursavas
  • Zonguldak Bülent Ecevit University
Felipe Santos-Marquez
  • Technische Universität Dresden
Dadang Jainal Mutaqin
  • Nagoya University
Ragdad Cani Miranti
  • Statistics Indonesia
Anang Budi Gunawan
  • Badan Perencanaan Pembangunan Nasional (Bappenas)
Sook Yan Siew
  • Nagoya University