Relative Importance of Environmental Attributes Using Logistic Regression
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We investigate the problem of determining the relative importance of attributes in the discrete choice setting. Four alternative methods of extracting the relative importance of attributes are considered. The empirical application involves the development of a risk index system for individual herbicides combining the information on the herbicides' different human and environmental risks. The values of the pesticide risk indices are found to be consistent across the different methods.