The Economic Value of Water and Ecosystem Preservation

Boating 46%
Nature 65%
Camping 43%
Photo 41%
Hiking 48%
Birding 62%
4.3.2. Travel Cost Method – Analysis and Results
Initial analysis was conducted for the aggregate data set. The approach used
here follows a two step procedure to estimate individual demand and consumer
surplus (Upneja, Shafer et al., 2001). Ordinary least squares regression was
applied to survey data to estimate a function relating travel cost to number of
visits. This individual demand equation was then used to estimate individual
consumer surplus.
The regression model was set up to estimate a demand function for ecotourism.
The dependent variable was the reported number of day trips. The
independent variable included (log of) travel cost (LnCost), state residency status
(TX_home), duration of trip in hours (Duration), estimated travel time from local
base (Trip_time), and whether the respondent participated in bird watching,
fishing or beach going. The model yielded an adjusted r-squared value of 0.20.
Results showed that the natural log of travel cost was the most significant
variable. This was followed by fishing, beach-going, and birding activities.
Duration of trip in hours was also shown to be a significant variable. Other
variable were not reported as significant. Results of the regression analysis are
presented in Table 1.
Table 4-2. Results of regression analysis for aggregate data set
Coefficients Standard Error t Stat
Intercept 87.65 11.91 7.36
LnCost -9.56 1.56 -6.11
TX_home 5.92 5.48 1.08
Duration -5.54 1.96 -2.82
Trip_time -0.72 2.11 -0.34
Birding 13.20 5.35 2.47
Fishing 23.89 5.33 4.48
Beach -21.98 5.876 -3.74
Holding all variables constant except LnCost the demand function was
estimated as:
Equation 1. q
= −9
. 55(ln
p)
+ 73.
164
36