Abstract
As an important growth point of Qinghai’s economic development, tourism has become increasingly prominent in its contribution to the development of the national economy and has become one of the most dynamic industries in the province. Based on the relevant data from 2001 to 2018, this paper explores the influencing factors of tourism revenue in Qinghai Province by constructing an error correction model and combining Granger causality test. The research results show that there is a significant correlation between the total number of tourist visits, per capita GDP and traffic conditions, and the growth of tourism income in Qinghai Province. Among them, every 1% increase in the total number of tourist arrivals drives an average increase of 1.566% in tourism revenue; and the short-term elasticity of tourism revenue to the total number of tourist arrivals is slightly greater than the long-term elasticity.
Highlights
Tourism as an industry associated with high, features a wide range of integrated industry, for many regions, it can become a new economic growth point for the promotion of regional economic development is of great significance
(4.346) (6.237) (5.693) R2=0.971 DW=1.550 F=21.904 The estimation results show that the change of tourism income in Qinghai Province depends on the total number of tourist visits and on the deviation of the previous period’s tourism income from the equilibrium level
Through the Granger causality test, it can be seen that in the long run, the total number of tourist visits has a strong stimulus effect on the tourism income of Qinghai Province, but the increase in tourism income will not increase the total number of tourist visits
Summary
Tourism as an industry associated with high, features a wide range of integrated industry, for many regions, it can become a new economic growth point for the promotion of regional economic development is of great significance. Li Guobing (2019) [3] used the grey correlation method and expert scoring method to rank the income influencing factors of the Pearl River Delta cities, and found that their tourism income is affected by three factors (tourists, tourism income, and tourism services) He Zhen (2009) [4] used the grey comprehensive correlation analysis method to analyze 9 factors that have an impact on tourism income. To solve the above problems and focus on Qinghai Province as a world-class tourism province with plateau characteristics, and the tourism characteristics of the provinces are very different, and the influencing factors are not the same, this article collects relevant data on the tourism industry of Qinghai Province from 2001 to 2018, Combined with the error correction model and Granger causality test, to explore the influencing factors of tourism income. When there are only two variables, they should be single integers of the same order; 3 EMPIRICAL ANALYSIS BASED ON ERROR if there are more than two variables, the single integer order of the explained variable is less than or equal to the single integer order of the explanatory variable
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