Comparative analysis of the economic effects of special economic zones in China and Korea: evidence from a PSM difference-in-difference method

ABSTRACT

This paper examines the economic effects of the establishment of Special Economic Zones (SEZs) on the export of industrial complexes in China and Korea, focusing on the synergistic economic effects between different types of SEZs, using the Propensity Score Matching difference-in-difference method. We use panel data of 284 Chinese cities and 162 Korean cities between 2000 and 2019, focusing on 93 industrial complexes in China, and 569 industrial complexes in Korea. We compare the effects of Free Trade Zones (FTZs) with other special economic zones, including Bonded Areas (BA) in China and Free Economic Zones (FEZ) and Foreign Investment Regions (FIZ) in Korea. The establishment of SEZs has a significant positive impact on the export growth of industrial complexes in both China and Korea. In Korea, a significant synergy effect on export is found between the FTZ and the FIZs. There is no synergy effect found between FEZs and other economic zones. After the implementation of the FTZ policy in China, the export of BA’s industrial complexes with ports and airports significantly increased, indicating that the FTZs have a positive synergy effect on the BAs with convenient transportation.

KEYWORDS:

• Special economic zones
• difference-in-difference
• export
• industrial complex

JEL CLASSIFICATION:

• F14
• F29
• F15
• N90

Acknowledgments

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2021S1A5A2A03063693),The National Social Science Fund of China (23BJY207), The Social Science Fund of Fujian Province (FJ2024C023).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ The propensity score represents the probability of an area being designated as ‘treatment’ (having an SEZ established) based on observable characteristics. Initially, the propensity score distribution is skewed, indicating systematic differences between areas with and without SEZs. Following matching, the propensity score distribution becomes more balanced between the treatment and control groups, suggesting that the matching process mitigated bias resulting from observable disparities between the groups.