A method to improve consumer surplus estimates from truncated recreation demand data Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/np193f580

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  • A method is presented in this study for improving empirical travel cost analyses when truncated data are available. The analysis expands upon a Monte Carlo study conducted by Riyadi (1993) which compared the coefficient and consumer surplus estimates for five travel cost estimators using both truncated and censored data. Riyadi showed that if travel costs within origin zones are equal and the sampling rate is the same for all zones, the estimated coefficients for the Bowes-Loomis zonal average travel cost (ZATC) method are identical to estimates from ordinary least squares applied to the censored sample. A method related to Riyadi's equality was developed in this study to expand a truncated data set to include nonuser observations and permit estimation by a censored estimator, such as the standard negative binomial. In the Monte Carlo study presented here, the data expansion method provided substantial efficiency gains for predicted consumer surplus estimates relative to estimates obtained from Redacted for Privacy truncated data. The average percent deviation of predicted consumer (CS) from the standard negative binomial fitted to the expanded data were only 31 to 80 percent of the average deviation of the truncated negative binomial CS estimates. The method was also applied to empirical data. The standard negative binomial fitted to expanded data appeared to give much better empirical estimates of the travel cost coefficient and related consumer surplus estimates than did the truncated negative binomial.
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