​1. Study Objectives and Method

▮ Research background and purpose

￮ Jeollabuk-do experienced the economic crisis as a slump centered on Gunsan City in the mid-2010s, with large companies’ withdrawal from the region and a decline in key industries. This situation created a need for a system to cope with future economic difficulties proactively. We expect this system to help Jeollabuk-do’s provincial government (hereinafter the Government) analyze the region’s economic structure and estimate the impact of economic and industrial shocks, facilitating the formulation and implementation of policies to navigate economic uncertainties effectively. Nevertheless, given the region’s relatively small economy and the difficulty in compiling regional statistics, the Government has been unable to develop regional-level analytical models, while instead applying national-level models to the region to obtain at least indirect results.

￮ This study therefore seeks to develop a dedicated, regional economic model that diagnoses the industrial structure of Jeollabuk-do’s cities and counties and analyzes its economic implications. 

▮ Scope and Methods of Research

￮ Many model types were considered as candidates to analyze the region’s economic structure. As time series data are more difficult to obtain at regional level, this study aimed to create an inter-industry relations table (the input-output table or IOT) and enhance its applicability. 

￮ After reviewing various table generation techniques, the chosen basis was a multi-regional IOT covering 14 cities and counties in Jeollabuk-do and five neighboring extra-regional economic zones, with the year 2020 as a benchmark.

￮ Two methods were proposed to derive regional technical coefficients for this multi-regional IOT; in the end, both were combined in a two-stage table-generation process.

2. Conclusion and Policy Suggestion

▮ Details of the multi-regional input-output table

￮ The IOT enables us to ascertain industrial relationships both among Jeollabuk-do’s cities and counties and between Jeollabuk-do and other extra-regional economic zones. In terms of intermediate goods trading, Jeollabuk-do has a high proportion of procurement of intermediate goods from other extra-regional economic zones, but the proportion is not that high compared to other regions. Considering trading relationships between Jeollabuk-do’s cities/counties and extra-regional zones, Gunsan, Iksan and Wanju (in more industrialized northwestern Jeollabuk-do), have a high trading volume with Chungcheong Province, while Sunchang, Jeongeup and Gochang (in southern Jeollabuk-do) trade more with Jeolla Province. Considered as a whole, Jeollabuk-do plays an intermediary role between Chungcheong and Jeolla. The trading pattern of intermediate goods between Jeollabuk-do’s cities and counties shows that the industrial production structures of Gunsan, Gimje and Wanju are all inter-related, with Iksan serving as a center. Buan also trades actively with Jeongeup, while procuring goods disproportionately from remote Gunsan and Iksan. 

▮ Leveraging the multi-regional input-output table

￮ The multi-regional IOT facilitates analysis of regional industrial structures and, ultimately, cost-benefit analyses that allow better tailored policy implementation. Additionally, when developing multi-regional collaborative economic projects, the IOT can be used to incorporate the likely effects of key industries inside and outside of Jeollabuk-do and ultimately to prioritize those industrial sectors that can most positively affect Jeollabuk-do’s economy. Finally, the IOT will enable more profound and sophisticated analysis and evaluation of existing industrial policy, informing future restructuring measures through which all cities and counties in Jeollabuk-do can thrive.

▮ Need for further studies

￮ This study is the first experimental research ever conducted by the Jeollabuk-do provincial government to create a multi-regional IOT for its cities and counties. To improve the table’s effectiveness and real-world applicability, relevant statistical data must be continually compiled and used to fine-tune the model.