Attribute Non Attendance in a Revealed Preference Study
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This dissertation investigates attribute non-attendance in an urban random utility model (RUM). Using the RUM, this dissertation also investigates the local residents' willingness to pay (WTP) to improve the conditions of the riparian vegetation in southern Sydney, Australia. To elicit self-reported ANA, in the survey we ask respondents either how important they think of the attributes in a public green space or how frequently they use the attributes when they visit a public green space and use this information to estimate stated ANA and inferred ANA models. Stated ANA model results show that ANA does impact the WTP estimates for most of the site attributes but people in the non-attendance group do not necessarily have zero or lower WTP for these attributes. However, stated model results do show that self-reported ANA statements from 'importance questions' for some attributes such as riparian vegetation are more consistent with the estimated ANA. This finding suggests that elicitation method affects the accuracy of self-reported ANA. We also find that the consistency between respondents' self-reported ANA and the estimated ANA from inferred ANA models largely depends on the particular attribute but it can be concluded that when respondents say they 'never' used the site attributes or see the attribute as 'unimportant' or 'somewhat unimportant', it is very likely that they truly ignored the attributes in their decision making process. Our study also finds that respondents are willing to pay a compensation to improve the conditions of the riparian vegetation and the WTP increases if the channel is less modified and there is more vegetation. For example, an average respondent with 19 trips annually is willing to pay $58 to improve the riparian vegetation condition from the lowest level to the highest level. Another interesting finding of the study is that those who considered riparian vegetation in decision making processes differentiated between different riparian vegetation conditions more than those who did not. The importance sample results also show that ignoring the ANA in model estimation will under-estimate the annual value of the vegetation improvement for a person with 20 trips by $2.85.
- Doctoral Dissertations