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.
Economic Impacts | Sources |
---|---|
Direct Impact | The direct construction and operation of airports. |
Indirect Impact | The chain of suppliers of goods and services. |
Induced Impact | The spending of income by employees created by the direct and indirect effects. |
Catalytic Impact | The role of airports as a driver of productivity growth and then as an attractor of new firms. |
The nine airports of Jiangsu were selected as research objects, and included Nanjing,Lianyungang, Yancheng, Xuzhou, Nantong, Changzhou, Wuxi, Huaian and Yangzhou. Nanjing, the capital city of Jiangsu, is the metropolitan region, with a population of 8.4 million compared with the total population of 80.3 million for Jiangsu province. Wuxi, Changzhou, and Nantong are the southern city clusters, which are very close to the most developed city: Shanghai. Xuzhou, Lianyungang, Huaian and Yancheng are the northern city clusters, which are less developed compared to Nanjing and the southern city clusters. Yangzhou City is located in the center of Jiangsu province, which is a well-known tourist destination.
An empirical analysis was conducted by using quarterly data of airport passenger throughput (PAX), and the local Gross Domestic Product (GDP) of the Jiangsu province. To test the causal relationship between airports and regional economic development, PAX was employed as an index to measure the development of airports, and GDP was used as a measurement of the regional economic development. Quarterly data of the total PAX of the nine selected airports and GDP of Jiangsu province from 2008 to 2018 was collected from the Statistical Yearbook by the Civil Aviation Administration of China and the China Statistical Yearbook by the National Bureau of Statistics. (all of the data were downloaded from www.caac.gov.cn (accessed on 15 July 2019) and www.stats.gov.cn (accessed on 15 July 2019)).
The test results showed that there was bi-directional short-run Granger causality in lag order 1, 3 and 4 at the 10% significance level. These results indicated that there was a bi-directional Granger causality relationship between GDPSA and PAXSA in Jiangsu province. It also concluded that the airports and regional economy in Jiangsu province were mutually promoting growth.
Impulse responses are the most commonly applied tools for describing these dynamic reactions in vector autoregressive analysis. The impulse response function is used to reflect the response of an endogenous variable on the impact of innovation (shock) in an exogenous viable. Specifically, if a standard deviation impact is given to the random error term, it will affect the current and future values of the endogenous variables.
Figure 4 showed the impulse response of PAXSA and GDPSA to Cholesky one S.D. innovation. The blue line was the impulse response estimate for the horizon period, and the two red lines were the one-standard error confidence bands. From Figure 4a, when there was an innovation on GDPSA for horizon periods H = 30, the response of PAXSA fluctuated greatly, which reached a peak in the fourth period and stabilized after the 20th period. Therefore, the impact of GDPSA on PAXSA was persistent and stably increasing in the long-term development. From Figure 4b, when there was an innovation on PAXSA, the response of GDPSA also fluctuated significantly in the first six periods, which reached the peak in the second period, then dropped quickly to the lowest in the fourth, and gradually stabilized. But after the 20th period, the response was unstable. These results indicated that the impact of PAXSA on GDPSA existed for a long period and was uncertain. The mutual impacts of passenger throughput and economic growth were positive in Jiangsu province, and there was a more substantial impact in the short period. The impact of economic growth on passenger throughput was more significant than the impact of passenger throughput to economic growth. This result showed that airport growth was more sensitive to the change of economy in Jiangsu province.
Figure 4. (a) Impulse Response of PAXSA to GDPSA; (b) Impulse Response of GDPSA to PAXSA. The blue line was the impulse response estimate for the horizon period, and the two red lines were the one-standard error confidence bands.
The mutual impacts of passenger throughput and economic growth were positive in Jiangsu province, and there was a more substantial impact in the short period. The impact of economic growth on passenger throughput was more significant than the impact of passenger throughput to economic growth. This result showed that airport growth was more sensitive to the change of economy in Jiangsu province.
Impulse response functions trace the effects of a shock on one endogenous variable on the other variables in the VAR. Variance decomposition separates the variation in an endogenous variable into the component shocks to the VAR. Thus, the variance decomposition provides information about the relative importance of each random innovation in affecting the variables in the VAR. From Figure 5 a,b, it could find that the interaction between airport passenger throughput and the local economy was lagging. Figure 5a showed the percentage of GDPSA variance explained by self-change that was decreasing gradually, however, it became stable at about 60% after the 35th period. The proportion of variance explained by PAXSA increased gradually, and stabilized at 40% after 35 periods. Taken together, the proportion of passenger throughput contribution to the change of GDP was 40%. Figure 5b showed the change variance of PAXSA, which was mostly caused by its fluctuation in the beginning. The proportion of self-disturbance decreased from 92% to 50% in the 50th period. On the contrary, the proportion of variance explained by the change of GDPSA increased from 0% to about 30%. In the long term, the proportion of GDP contribution to passenger throughput was about 45%. Therefore, the inter-contribution of GDP and aviation development was sustainable and increasing over time.
This entry aimed to establish empirical evidence for determining the causal relationships between aviation/airports and economic growth in a typical Chinese provincial area—Jiangsu province. Using a VAR model and a Granger causality test, it found that economic growth and the development of airports were interdependent and mutually promoting. The results of Impulse Response and Variance Decomposition showed that the impact of GDP on airport passenger throughput was persistent and stably increasing in the long-term development; the impact of airport passenger throughput on GDP also existed for a long period but was unstable. Jiangsu is located in the core of the Yangtze River delta region, which is the polycentric urban region. Jiangsu faces regional economic competition from Shanghai and the Zhejiang province. Jiangsu airports are less competitive than the Shanghai and Zhejiang airports. From the research, the GDP had a long-term impact on airport development, but the impact of airports on the GDP was less. It is therefore important to leverage airport development to improve the regional economy. The contribution of aviation is not only about the airport itself but also about the induced and catalytic impacts, such as the funding of infrastructure and the attraction of new firms. Governments could support some kind of long-term strategic spatial framework to inform key investment decisions of airport-based sub-centers in regional polycentric growth and promote closer collaboration in the Jiangsu provincial airports, including the airline network, flight schedules and air service quality.
This entry is adapted from the peer-reviewed paper 10.3390/su14074295