Mobile phone effect on activity space and GHGs: Finally, the parameter phone “mobile access that individuals with a mobile phone available are likely to indicator” clearly highlights have a larger GHGs than individuals who do not, with a statistically significant effect at the 0.05 level. The same result was obtained for the activity space model - individuals with a mobile phone available were likely to have larger activity spaces compared to individuals who did not. However, in this model, the effect is not significant. Note also that the copula parameters in both models are statistically significant, clearly supporting the influence of common unobserved factors on both mobile phone access and the two travel-related outcomes, activity space and GHGs. Further, the non-significance of the variable in the activity space model “mobile phone” implies only that there is no mean observed effect of mobile phone on activity space. However, the significance of one copula parameter (0.04) and marginal significance (0.11) of the other copula parameter implies that the unobserved factors affecting mobile phone ownership also influence the dimension of activity spaces. This confirms the existence of endogeneity in the decision process. 5.4 Model results for Internet access Table 5 uses the same layout as Table 4 to show the results for access to the Internet at home. Thus, top left is the estimation of Internet access choice within the activity space model, and top right within the GHG model. Similarly, bottom left shows results for a choice model of activity space, and bottom right a choice model of total GHGs, where an indicator of access to the Internet is included as an independent variable. Internet access (0/1): The list of variables that affect access at home to the Internet is similar to that identified in the phone model. For the activity space model, variables “mobile access” positively associated with Internet access were residential ownership (0.81), presence of
Mobile phone effect on activity space and GHGs: Finally, the parameter “mobile phone access indicator” clearly highlights that individuals with a mobile phone available are likely to have a larger GHGs than individuals who do not, with a statistically significant effect at the 0.05 level. The same result was obtained for the activity space model - individuals with a mobile phone available were likely to have larger activity spaces compared to individuals who did not. However, in this model, the effect is not significant. Note also that the copula parameters in both models are statistically significant, clearly supporting the influence of common unobserved factors on both mobile phone access and the two travel-related outcomes, activity space and GHGs. Further, the non-significance of the “mobile phone” variable in the activity space model implies only that there is no mean observed effect of mobile phone on activity space. However, the significance of one copula parameter (0.04) and marginal significance (0.11) of the other copula parameter implies that the unobserved factors affecting mobile phone ownership also influence the dimension of activity spaces. This confirms the existence of endogeneity in the decision process. 5.4 Model results for Internet access Table 5 uses the same layout as Table 4 to show the results for access to the Internet at home. Thus, top left is the estimation of Internet access choice within the activity space model, and top right within the GHG model. Similarly, bottom left shows results for a choice model of activity space, and bottom right a choice model of total GHGs, where an indicator of access to the Internet is included as an independent variable. Internet access (0/1): The list of variables that affect access at home to the Internet is similar to that identified in the “mobile phone access” model. For the activity space model, variables positively associated with Internet access were residential ownership (0.81), presence of
ICT_Miranda-Moreno_Eluru_Lee-Gosselin_Kreider.docx