Taste indicators and heterogeneous revealed preferences for congestion in recreation demand
Researchers using revealed preference data have mostly relied on the Mixed Logit (ML) framework to model unobserved heterogeneity. In this paper, we suggest an extension of this model where we integrate direct measures of taste and revealed preferences, under a unified econometric setting, to describe heterogeneous preferences for congestion in recreation demand. ML is a random parameter discrete choice model, which decomposes the coefficients of the regression equation into a mean effect shared by all individuals in the sample, and a deviation with respect to this mean, specific to each individual. Within this structure, heterogeneity is summarized using a parametric density function for the coefficients of the model. From this distribution one can identify the portion of people who like or dislike an attribute of the good. On the other hand, taste indicators, represented in a like-dislike scale, constitute complementary information about the distribution of tastes in the population. We combine both sources of information to characterize preferences in our model. The traditional ML suggests almost 60% of people in the sample like crowded places while our integrated model implies almost 100% of the people dislike congestion. These results show the beneâ€¦ts of using taste indicators to describe heterogeneous preferences for attributes describing alternatives of a choice set.
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