Accounting for Skill Levels in Recreational Demand Modelling using a Clustered RUM approach

Abstract

We adapt the standard random utility model to take account of the heterogeneity of recreational preferences by using what we call a 'clustered conditional logit framework'. By separating out our sample of whitewater kayakers into two exogenously identifiable groups (based on their skill level) and running separate conditional logits for each group we are able to take account of the fact that kayakers of different skill levels are looking for different characteristics from the whitewater site they choose to visit. We find that not taking into account the differences in the skill of the kayakers and the grade of the river will result in an overestimation of the welfare estimates associated with improvements to lower grade whitewater sites (which are frequented by basic/intermediated proficiency level kayakers) and underestimating welfare estimates associated with changes in the attributes of higher grade whitewater sites (which are frequented by advanced proficiency level kayakers).