The statistical efficiency of conventional dichotomous choice contingent valuation surveys can be improved by asking each respondent a second dichotomous choice question which depends on the response to the first question—if the first response is “yes,” the second bid is some amount greater than the first bid; while, if the first response is “no,” the second bid is some amount smaller. This “double‐bounded” approach is shown to be asymptotically more efficient than the conventional, “singlebounded” approach. Using data from a survey of Californians regarding their willingness to pay for wetlands in the San Joaquin Valley, we show that, in a finite sample, the gain in efficiency can be very substantial.
This paper presents a simple computational method for measuring the difference of independent empirical distributions estimated by bootstrapping or other resampling approaches. Using data from a field test of external scope in contingent valuation, this complete combinatorial method is compared with other methods (empirical convolutions, repeated sampling, normality, nonoverlapping confidence intervals) that have been suggested in the literature. Tradeoffs between methods are discussed in terms of programming complexity, time and computer resources required, bias, and the precision of the estimate. Copyright 2005, Oxford University Press.
Truncated Poisson and truncated negative binomial count data models, as well as standard count data models, OLS, nonlinear normal, and truncated nonlinear normal MLE were used to estimate demand for deer hunting in California. The truncated count data estimators and their properties are reviewed. A large sample (N = 2223) allowed random segmenting of the data into specification, estimation, and out‐of‐sample prediction portions. Statistics of interest are therefore unbiased by the specification search, and the prediction results allow comparison of the statistical models' robustness. The new estimators are found to be more appropriate for estimating and predicting demand and social benefits than the alternative estimators based on a variety of criteria.
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