Effect of Tourism Building Investments on Tourist Revenues in China: A Spatial Panel Econometric Analysis

ABSTRACT

The development of tourism greatly promotes China’s economic growth, and tourism buildings are a core component of tourism industry. From the perspective of spatial panel econometrics, this study verifies the spatial relevance between tourism building investments and tourism economic growth in China. It is verified that the development of China’s tourism industry possesses a significant spatial clustering effect. Since the coefficient of capital is greater than that of labor, the tourism industry is primarily a labor-intensive industry in China. Tourism building investments positively and significantly affect tourism revenue which is affected by the levels of manufacturing and human capital. Robust tests are provided by separating the regions and policy implications are offered.

KEY WORDS:

• growth of tourist economy
• spatial econometric model
• tourist buildings

Funding

This research is supported by the Natural Science Foundation of China (Nos. 71371195, 71633006, 71403017, 71203224, 71673277, and 71431008).

Notes

^1. The total tourist revenue was the sum of the domestic tourist revenue and the international tourist revenue. The international tourist revenue was denominated in USD; therefore, multiplying the international tourist revenue by the average exchange rate of the year led to the RMB-denominated international tourist revenue.

^2. Certain years of the “China Tourism Statistics Yearbook (supplement)” for tourism have no total revenue; rather, the reference lists the domestic tourism income and international tourism income alone. Because the international tourism income is denominated in dollars, we used the average exchange rate conversion to renminbi in this article.

^3. In Figures 1–4, BJ, TJ, HB, SAX, NMG, LN, JL, HLJ, SH, ZJ, AH, FJ, JX, SD, HL, HB, HN, GD, GX, HAIN, CQ, SC, GZ, YN, XZ, SX, GS, QH, NX, and XJ represent Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong, Henan, Hubei, Hunan, Guangdong, Guangxi, Hainan, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang, respectively.

Additional information

Funding

This research is supported by the Natural Science Foundation of China (Nos. 71371195, 71633006, 71403017, 71203224, 71673277, and 71431008)