5.1.2 Comparisons between different regions In Section 2.2.3, the uneven geographical distribution of international tourism in China has been identified. In short, three hotspot cities attracted more tourists than the others, and the east regions attracted more tourists than the west. Therefore, it is also meaningful to check if air pollution has produced different effects in different geographical locations. The fully specified model has been applied to the investigation for this s section. Note that the correlation problem of variables is not mitigated yet, which might still defect the confidence of these results. Three hotspots vs. other regions It is clear that three hotspots (Beijing, Shanghai and Guangzhou) received more tourists than others. However it is difficult to formulate a regression with only three cities because of lack of observations (only 24). Instead, a regression excluded these three cities is built with 210 observations. The result of regression is showed in following Table 13, the original result of Model 4 is also showed for comparison. Table 13: Results of in/excluding three hotspot cities Model Model 4 Model 4 Variable With all observations Without hotspots .067037*** .069611*** FDI (.000) (.000) -.001498 -.037387 Hotel (0.996) (.888) .026640** ME (.027) Omitted .008021*** -.006740*** PP (.000) (.000) -.000125*** -.000060 SD (.005) (.171) -.001089*** -.000560 MP (.001) (.441) Source: own elaboration Note: * Significant at 10% level, ** Significant at 5% level, *** Significant at 1% level
5.1.2 Comparisons between different regions In Section 2.2.3, the uneven geographical distribution of international tourism in China has been identified. In short, three hotspot cities attracted more tourists than the others, and the east regions attracted more tourists than the west. Therefore, it is also meaningful to check if air pollution has produced different effects in different geographical locations. The fully specified model has been applied to the investigation for this s section. Note that the correlation problem of variables is not mitigated yet, which might still defect the confidence of these results. Three hotspots vs. other regions It is clear that three hotspots (Beijing, Shanghai and Guangzhou) received more tourists than others. However it is difficult to formulate a regression with only three cities because of lack of observations (only 24). Instead, a regression excluded these three cities is built with 210 observations. The result of regression is showed in following Table 13, the original result of Model 4 is also showed for comparison. Table 13: Results of in/excluding three hotspot cities Model Variable Model 4 With all observations Model 4 Without hotspots FDI .067037*** (.000) .069611*** (.000) Hotel -.001498 (0.996) -.037387 (.888) ME .026640** (.027) Omitted PP .008021*** (.000) -.006740*** (.000) SD -.000125*** (.005) -.000060 (.171) MP -.001089*** (.001) -.000560 (.441) Source: own elaboration Note: * Significant at 10% level, ** Significant at 5% level, *** Significant at 1% level