AbstractThe influence of the treatment of Multiple Destination Trips (MDT) to the Consumer Surplus (CS) estimates obtained by the Travel Cost Method (TCM) is investigated. Three distinct routes will be pursued: First, the MDT effect is decomposed into two measurable components: the direct effect of the price change, and the indirect effect of the shift of the empirical demand function. Second, ordinal preference rankings of the alternative MDT sites are utilized for recovering upper- and lower-bounds for cardinal cost-shares required by the TCM. Third, the influence of the MDTs on the TCM consumer surplus estimates is studied empirically using the data of the TCM study for the Bellenden Kerr National Park in Australia. We estimate the theoretical minimum and maximum bounds for the TCM both with and without a MDT-correction, using a parametric weighted-ordinary-least-squares and a non-parametric trapezoid-rule approach for estimating the demand functions.

TheoryStart from the ideal case where incremental travel cost (pMP) to the site is correctly measured. CS is represented by the turquoise triangle in Figure A.

Figure A: Direct effect (DE) and Indirect effect (IE)

Using total costs (pTC : pTC > pMP) without MPT correction has the direct effect of increasing the prevailing price, which decreases the Consumer surplus by area DE. Using total costs also has the indirect effect of shifting the empirical demand function to the right, represented by area IE. The total MDT effect is the difference of these two offsetting components. In theory, ignoring MDT need not lead to any systematic bias in either direction.

Allocating total cost of MDT to specific sitesThe respondents are asked to rank the MDT sites according to the marginal utility from the site. Duality of utility and expenditure functions implies the preference rankings correspond to equivalent ordinal cost rankings. The upper- and lower bounds of the cost shares are obtained by the extreme value approach of Kmietowicz and Pearman (1981).

Timo Kuosmanen, Eleonora Nillesen, and Justus Wesseler 1

Zonal TCM of Bellenden Kerr National Park, Australia

DataCamping permits records for visitors who stay overnight. Postal survey to permit holders to study MDT. Response rate 36.8%. 48% of respondents had made MDT. Data set includes 3 travel cost estimates (min, max, and total cost), visitation rate, and socio-economic variables (income, age, education).

OLS regression analysisSix different function forms were tested, including and excluding socio-economic variables. Model specification where visitation rate was explained by the reciprocal of the travel cost proved the most appropriate specification

Non-parametric demand analysisThe non-parametric trapezoid-rule approach (Cooper, 2000). Assumptions: 1) Every visitor is willing to pay any price less than or equal to the observed price. 2) No visitor is willing to pay a higher price.

Figure B: Non-parametric Demand Functions

Results

(all figures in AUD per visit)

ReferencesCooper, J. C. (2000): Nonparametric and Semi-nonparametric Recreational Demand Analysis, American Journal of Agricultural Economics 85: 451-62.

Kmietowicz Z. W. and A. D. Pearman (1981): “Decision Theory and Incomplete Knowledge,” Gower, Aldershot.

Does Ignoring Multi-Destination Trips in the Travel Cost Method Cause a Systematic Downward Bias?

1 Environmental Economics and Natural Resources Group Department Social Sciences Hollandseweg 1, NL - 6706 KN Wageningen The Netherlands Phone: +31 317 484 738 Fax: +31 317 484 933 E-mail: Timo.Kuosmanen@wur.nl

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OLS regression Non-parametricmin max min max

CSMP 137 773 100 343DE -113.40 -713.10 -93.30 -116.30

IE +622.10 +585.00 +572.30 +352.60

= CSTC 645 645 579 579