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The Causality Analysis of Airports and Regional Economy: Empirical Evidence from Jiangsu Province in China

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  • Yang Bai

    (School of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Cheng-Lung Wu

    (School of Aviation, University of New South Wales, Sydney, NSW 2052, Australia)

Abstract

China is the second largest aviation country in the world. The Chinese aviation industry and economy both developed quickly in the last two decades. However, the interaction mechanisms of aviation and the regional economy were different in each province. Jiangsu was the most important province in the Yangtze River delta region. The GDP of Jiangsu ranked second in China, but air transportation didn’t have the same leading position in the last decade. Taking Jiangsu province as for analysis, this paper provided empirical evidence of the causality between airport development and regional economic growth. The results showed that: (1) From 2008 to 2018, the overall volume of airport passenger throughput and GDP in Jiangsu had a strong upward trend with strong seasonal fluctuations; (2) There was a bi-directional Granger causality relationship between the airport passenger throughput and GDP in Jiangsu based on the vector autoregression (VAR) model and the Granger causality test; (3) From the impulse response and variance decomposition, the inter-contribution of GDP and airport development was sustainable and increasing over time. However, the impact of economic growth on airports was more significant than the impact of airports on economic growth.

Suggested Citation

  • Yang Bai & Cheng-Lung Wu, 2022. "The Causality Analysis of Airports and Regional Economy: Empirical Evidence from Jiangsu Province in China," Sustainability, MDPI, vol. 14(7), pages 1-11, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:4295-:d:786976
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    References listed on IDEAS

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    Cited by:

    1. Kaya, Gizem & Aydın, Umut, 2024. "The nexus between air transport and economic growth geographically: An evidence based on heterogeneous panel data models," Journal of Air Transport Management, Elsevier, vol. 115(C).

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