Investigating high-speed rail construction's support to county level regional development in China: An eigenvector based spatial filtering panel data analysis

Danlin Yu, Daisuke Murakami, Yaojun Zhang, Xiwei Wu, Ding Li, Xiaoxi Wang and Guangdong Li

Transportation Research Part B: Methodological, 2020, vol. 133, issue C, 21-37

Abstract: The construction of high-speed rail in China was initially a direct response to the increasing demand of up-to-date infrastructure. It is commonly understood that the construction of HSR has significant wider economic impact on local development. The benefits of HSR are represented by the accessibility to the HSR stations. Our study defines accessibility to HSR with a simple distance measure and a transportation network measure that considers travel from the center of the county through different grades of roads to the nearest HSR stations. For better understanding, we estimate both global and local (i.e., location-specific) impacts from HSR, using per capital GDP as a representation of the wider economic impact. With access to a panel dataset from 2008 to 2015 of regional socioeconomic indicators at the county-level units in China, the current study employs an eigenvector based spatial filtering (ESF) approach with and without spatially varying coefficients in an attempt to establish potential global and local relationships between HSR accessibility and county-level regional development. The analysis result suggests that it is likely that HSR accessibility might significantly contribute to regional development. A 10% decrease of the travel time to the nearest HSR station could bring about 0.44% (locally ranging from 0.28% to 3.1%) increase in local GDP per capita at the county level, ceteris paribus. The panel analysis suggests that the continued development of HSR construction in China will have long-term and sustainable support to local economic development. This is especially important to the relatively underdeveloped regions in the North and West China.

Keywords: High-speed rail; Regional development in China; County level analysis; Eigenvector based spatial filtering analysis; Panel data analysis (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (9)

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DOI: 10.1016/j.trb.2019.12.006

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