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Abstract
Using estimation of demand for the George Washington/Jefferson National
Forest as a case study, it is shown that in a stratified/clustered on-site
sample, latent heterogeneity needs to be accounted for twice: first to account
for dispersion in the data caused by unobservability of the process that results
in low and high frequency visitors in the population, and second to
capture unobservable heterogeneity among individuals surveyed at different
sites according to a stratified random sample (site specific effects). It is shown
that both of the parameters capturing latent heterogeneity are statistically significant.
It is therefore claimed in this paper, that the model accounting for
site-specific effects is superior to the model without such effects. Goodness
of fit statistics show that our empirical model is superior to models that do
not account for latent heterogeneity for the second time. The price coefficient
for the travel cost variable changes across model resulting in differences in
consumer surplus measures. The expected mean also changes across different
models. This information is of importance to the USDA Forest Service
for the purpose of consumer surplus calculations and projections for budget
allocation and resource utilization.