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Abstract
Congestion levels in protected areas can be predicted by site selection probability models estimated from choice data. There is growing evidence of subjects inattention to attributes in choice experiments. We estimate a Latent Class-Random Parameters model (LC-RPL) that jointly handles inattention and preference heterogeneity. We use data from a choice experiment designed to elicit visitors preferences towards sustainable management of a protected area in the Italian Alps. Results show that the LC-RPL model produces improvements in model fit and reductions in the implied rate of inattention, as compared to traditional approaches. Implications of results for Park management authorities are discussed.
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