Non-Market Valuation: Foster Joseph Sayers Lake

Non-Market Valuation: Foster Joseph Sayers Lake

April 27, 2003

Angela Fonzi

Jamie Smith

Jim Neyhard

Introduction

Our non-market valuation project was focused around fish catch rates on Foster Joseph Sayers Lake in the Bald Eagle Watershed. The lake was dammed as a part of the Comprehensive Flood Control Program and was approved to be constructed in 1954 with an initial cost of $30,890,000. The lake is within Bald Eagle State Park which is comprised of 5,900 acres with an annual attendance (in hours of recreation) of 1,917,900 (U. S. Army Corps of Engineers., 2002). The non-market good we are valuing is an improvement in fish catch rates of both largemouth bass (Micropterus salmoides) and small mouth bass (Micropterus dolomieui) on the lake (Fish Species Sorted by Scientific Name, 1996). We used the travel-cost method to determine both willingness to pay and consumer surplus for the improvement. The specific scenario we constructed involved a doubling in the current bass catch rates on the lake with no impact on current crappie fishing quality by implementing a bass habitat improvement program. This program would include both new breeding and feeding areas for the fish, and would theoretically stimulate the bass fishing on Sayers Lake.

Literature Review/Previous Studies

There has been previous research conducted in the area of fish catch rates. The U.S. Army Corp. of Engineers conducted a similar survey process in its Walla Walla District. During the 1997-1998 fishing season, the survey was conducted to gauge the value of an improvement in the fishing quality if the dam in the lower section of the Snake River were removed. Data for possible fish quality after the removed dam was gathered from the upper section of the river that flows past Asotin, Washington which is unaffected by the dam. Our process was similar in some ways but differs in others. As in our study, the USACE used the travel cost method to gauge willingness to pay for the improvement in fishing quality. However, most of the surveys were sent through the mail for the Snake River Study, where as all of our surveys were conducted onsite. The Snake River Study also focused on all populations of fish, whereas our study looked only at bass fishing. Overall, the purpose of the two studies is very similar, but the actual implementation for the Snake River Study was slightly more complex and detailed. (U. S. Army Corps of Engineers,1999).

A second similar study was conducted in the Michigan Great Lakes district by an unnamed MSU affiliated individual. Again, in this study the travel cost method was used to gain knowledge of willingness to pay and consumer surplus. This study focuses around salmon and trout catch rates and how an increase benefits fisherman. They study provided a few useful visual models which show how improvements in environmental quality lead to and improvement of fishing quality and economic benefit.

http://www.msu.edu/user/lupi/aec829/MichiganRUMsummary.pdf

Here is a model that helps define angler’s choice.

http://www.msu.edu/user/lupi/aec829/MichiganRUMsummary.pdf

The travel cost method used by the Michigan study was a Random Utility Model (RUM) in which “anglers pick the site they consider to be best” and the model uses statistical based tools to determine why the angler made these choices. Our model was a single site travel cost method which makes it a different valuation however; it still serves to value the same improvement in fishing quality only on a smaller scale (Case Study of Travel-Cost Analysis: The Michigan Angling Demand Model, 2003).

Theoretical Model/Expectations

Obviously, before we gathered any data our group had some expectations for the results from an improvement in fishing quality on Sayers Lake. The expected result of the doubling of bass catch rates on the lake was that the number of trips any individual would make to the lake would increase. This would mean that the catch rate improvement caused an increase in that individual’s consumer surplus thus making that person more likely to come to Sayers Lake to fish. We calculated consumer surplus by using the travel cost method. Other expected data results include a person with a farther driving distance would make fewer visits than one that lived closer.

This report will go through all the ins and outs of our non-market valuation project. We will discuss the methods of survey development, implementation of the survey, the actual results we received, and will discuss what these result mean in the context of valuation of the actual fishing quality improvement.

Materials and Methods

Major components of this study included a unique survey and a trip demand function. These were used to estimate the change in consumer surplus in reaction to improved quality bass habitats.

A unique survey created in conjunction with this study for research at Foster Joseph Sayers Lake focused on travel cost, fishing preferences, and demographics in order to develop a trip demand function. This trip demand function is developed using data regarding fishing experience, boat ownership, targeted fish species, distance from the home of the respondent to Foster Joseph Sayers Lake, number of trips made during 2002, number of planned trips during 2003, travel time to the lake, typical number of travelers in the vehicle going to the lake, number of trips planned if bass habitat program was implemented, and finally five socio-demographics regarding zip code, year of birth, gender, income level, and typical number of hours worked in a week. Please see attached survey materials for further reference.

In-person interviews were conducted at Foster Joseph Sayers Lake. Individuals fishing along the shore or preparing to fish on the lake were informed that the survey was being conducted for coursework at The Pennsylvania State University and that all results would be kept confidential. Individuals, the driver of the vehicle that traveled to the lake, were then asked if they would be willing complete the survey. Surveys were then completed by the reader stating the questions in an open-ended manner and waiting for a response from the individual. Categories or ranges of responses were given to the respondent only when individuals appeared to struggle with formulating a unique response. Income levels were specifically categorized into five different ranges with increments of $20,000. These categories were presented to respondents on a code card with one letter corresponding to each income range. Respondents then gave the letter which most accurately corresponded to their income level. Please see appendix A for all survey materials.

Data was then entered into a spreadsheet. Data units corresponded to each survey question. Responses asking for distance, travel time, number of trips during 2002, number of planned 2003 trips, years spent fishing, total number of travelers, and number of planned 2003 trips after program implementation, zip code, annual household income, year of birth, and typical hours in one work week were giving numerical values corresponding directly to the answers provided by the respondent. Values for annual household income correspond to the mean of the income range identified by the respondent as corresponding to their annual household income. Survey items related to survey location, boat access, targeting of bass as species fished for, and gender were given binomial codes. A “1” was typically used to represent positive responses while a “0” was used for negative responses.

The natural log was then calculated for both the planned 2003 trips and the planned 2003 trips following program implementation. This was done in order to reduce the weight placed on those responses corresponding to very high number of trips taken.

A round trip variable was then calculated by doubling the one way distance to Foster Joseph Sayers Lake. An opportunity cost variable was calculated by determining an hourly wage rate for each respondent and then multiplying this result by the number of minutes it takes each respondent to complete a round trip to Foster Joseph Sayers Lake. The hourly wage rate for each respondent was calculated by dividing the household income variable by the average number of hours worked in a year, which were determined by multiplying fifty by their stated number of typical hours in a work week. Fifty was assumed to be the average number of work weeks in a year. This quotient was then divided by four in order to better reflect the actual opportunity cost of the traveler. Finally this result was multiplied by the round trip minutes needed to reach and return from Foster Joseph Sayers Lake.

The total travel cost was calculated by summing the travel cost and the opportunity cost. The travel cost was determined by multiplying the round trip number of miles times an operating cost of $0.13 per mile. This operating cost is the standard 2002 operating cost given by the Automobile Association of America for a car relatively close in size to a Mercury Grand Marquis (AAA 2002). This product was then divided by the number of passengers typically present in the car in order to divide the costs among all trip takers. This quotient was then added to the opportunity cost and thus summing to the total travel cost.

All of the data was doubled in order to associate each respondent data set with the program not being implemented as well as the program being implemented. This step was used to allow the recognition of a baseline scenario when running our regression analysis.

Finally, the program and bass targeting responses were multiplied in order to create a new random variable representing the possible relationship between boat access and bass targeting. Because each of the responses used to determine this new random variable were binomially coded, the new random variable “bass X program” was also binomially coded.

In running a regression analysis for trip demand this study used as its dependent variable the natural log of the number of trips taken. The independent variables used were total travel costs, boat access response, and bass targeting response. The regression compared the effects of the independent variables on trip demand in response to the implementation of the bass habitat improvement program. The regression for this study was performed using Microsoft Excel software.

Results

The survey was implemented on-site at Foster Joseph Sayers Lake, with all participants being fishermen. Travel time to the lake, one-way for the participant’s ranged from 10 minutes to 90 minutes. The opportunity cost of the time of travel, calculated using wages and travel time, ranged from $1.60 to $25.00. Travel costs for before and after the scenario was indicated had the same range of $2.00 to $32.00. Before the scenario was proposed, number of trips to Sayers Lake for fishing ranged from 3 to 200, and after trips ranged from 7 to 200.

The regression results are shown in Chart 1 below:

The t-statistic showed a negative relationship for having a boat and targeting bass, therefore they were not included in the trip demand function. The log-linear equation for the regression of the number of trips (trip demand function) is:

ln (N) = 3.674 - (-0.005 * TC) + 0.214 + (0.5 * 1.005).

The 3.674 is the intercept, -0.005 is the travel cost coefficient, 0.214 is the program coefficient (shifter), and 1.005 is the residual (value of sigma-squared). The t-statistic for the program is 0.7539 and for the travel cost is -0.2396. The R squared value is 0.1548.

The equation was then inverted in order to make the log of trips the dependant variable. After creating a data set the Trip Demand Model was graphed as the following (Graph 1):

Discussion

Our results show an increase in the number of visits with the implementation of the bass habitat improvement program. For a visitor with total travel cost of $80, the bass habitat improvement program will increase their number of trips by 11. The coefficient for the program is positive to a greater extent than travel cost is negative; therefore the second demand curve (with program) is shifted upward.

To determine compensating surplus, the semi-log formula from the class text in Chapter 9, “The Travel Cost Model,” written by George R. Parsons was used. The formula is the following:

Nnew - Nold

∆CS = -βTC

At travel cost of $0, trips without the program are 65 and trips with the program are 81. The natural log of trips needs to be taken for this formula to give the following results:

4.394 – 4.174

∆CS = -(-0.0049) = 44.91

The change in compensating surplus is the area between the two demand curves from the total travel cost up. This value, $44.91, represents how much visitors are willing to pay to get the improvement in bass habitat.

We are confident with the trends that our model depicts. Due to the low number of surveys taken (25 completed surveys), the T statistic is not very significant for either the program or travel cost. The R squared value depicts the closeness of the relationship between the independent variable and the dependent variable. Ideally, it should be above 0.50 but in our case it is 0.1548. Completing more surveys would help to increase the R squared value.

Our survey results are not without imperfections. There were people surveyed that do not fish for bass and do not care of a program being implemented. Being our proposed improvement plan directly targeted bass fishing, those that do not care would not show a benefit. However, since it was stated in the survey that other fishing would not be harmed due to this project, those that do not target bass would have a zero non-use value.

The impact that our study may have is to increase the quality of bass fishing at Foster Joseph Sayers Lake. With a better environment for bass to live in the lake, they will attract fishermen if the catch rate increases. In order for this effect to take place, knowledge of the program needs to be advertised, such as on the news or in a newspaper or magazine. If no one knows of the changes being made, there will be less of an effect. Therefore, based on this analysis, the number of trips per year to Foster Joseph Sayers Lake would increase with an improvement in bass habitat.

Conclusion

The results of our research were concurrent with our expectations and followed the trends of previous studies. With the improvement of fishing quality at Sayers Lake, the trip demand function shifted outward. In order to gain a more accurate analysis of this environmental good, more surveys would need to be completed. Also, with additional funding for the project we could expand our sampling frame by using multiple survey methods including phone, and mail techniques.
References