Recreational demand for the Swedish
Mountains: A case from Storulvån and
Helags during winter
An Environmental Valuation using the Travel Cost model
Amanda Tomasdotter
Business and Economics, master's level 2017
Luleå University of Technology
Recreational demand for
the Swedish Mountains: A case from
Storulvån and Helags during winter
An Environmental Valuation using
the Travel Cost model
Amanda Tomasdotter
Luleå University of Technology Master of Science in Business and Economics
ABSTRACT
SAMMANFATTNING
ACKNOWLEDGEMENTS
TABLE OF CONTENTS
CHAPTER 1 INTRODUCTION ... 1
1.1 Background ...1
1.2 Aims of the study ...2
1.3 Delimitations ...2
1.4 Method ...2
CHAPTER 2 STORULVÅN AND HELAGS MOUNTAIN AREA ... 4
2.1 The study area: Storulvån and Helags mountain areas ...5
CHAPTER 3 LITERATURE REVIEW ... 6
3.1 Previous literature ...6
3.1.1 Recreational demand in mountain areas ...6
3.1.2 Individual travel cost model ...7
3.1.3 Zonal travel cost model ...8
3.1.4 Choice experiment model ...9
3.1.5 Multiple site model ...10
3.2 Literature discussion ... 10
CHAPTER 4 TRAVEL COST METHOD ...12
4.1 The Travel cost method ... 12
4.2 Visitation rate depending on individuals optimizing behavior ... 13
4.2.1 An individual’s choice ...14
4.2.2 Non-market goods and free riding...14
4.3 The Travel Cost model as a measurement for recreational demand ... 15
4.3.1 The Zonal travel cost model for a mountain area ...15
4.3.2 Econometric specification ...16
4.3.3 Econometrics for calculating the recreational demand ...16
4.3.4 Calculating the consumer surplus ...17
4.3.5 Measurement issues with the traveller’s costs and solutions ...18
4.4 Data collection and constructing an on-site survey ... 18
4.4.1 Data collection ...19
4.5 Defining zones ... 21
CHAPTER 5 ...24
RESULTS AND ANALYSIS ...24
5.1 Visitors socioeconomics ... 24
5.1.1 Storulvåns mountain area; wage rate ...24
5.1.2 Storulvåns and Helags mountain areas; age ...25
5.1.3 Storulvåns and Helags mountain areas; preferences for attributes ...26
5.2 Summing up the total travel cost with respect to visitation rate ... 27
5.2.1 Included costs ...28
5.2.2 Multipurpose trip ...28
5.2.3 Time value...28
5.2.4 Total travel cost ...30
5.3 Recreational demand and consumer surplus ... 32
5.3.1 Recreational demand for Storulvån and Helags Mountain Area ...32
5.3.2 Consumer surplus for Storulvåns Mountain Area ...35
5.3.3 Consumer surplus for Helags Mountain Area ...36
CHAPTER 6 CONCLUSIONS ...37
REFERENCES ...40
APPENDICES ...42
Appendix no. 1 Survey: Storulvån ... 42
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CHAPTER 1 INTRODUCTION
1.1 Background
The Swedish government invests approximately 18 - 19,5 million SEK each year in maintenance of the trail systems in the mountains, according to a report from the Swedish Environmental Protection Agency in 2014. The investments are used, for example, to make the trails visible for hikers and snowmobile users and to keep the bridges in good condition. The level of maintenance on the trails varies depending on the area, with most of the work done in areas that have the most visitors or where the problems are greatest. In 2014 there were some trails that had major issues with bad maintenance; for example, that trail markings were bad or missing, and that some bridges were not safe for snowmobilers. This can result in people getting lost or snowmobilers getting stuck far from help. The Swedish Environmental Protection Agency made an investigation and wrote a report in 2014 about the maintenance of the trail system in the Swedish mountains, where they made an estimate of how much money was needed to keep the trails in good conditions and safe for visitors to use. They estimated that an initial investment of 97 – 117 million SEK was necessary, and then an increase of money invested annually in the trail system from 18 – 19,5 million SEK to approximately 25 – 30 million SEK was needed to keep the trails in good condition. So how to evaluate whether if this is a welfare gain or loss for society; how much do people value having trails or bridges in remote areas?
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making the equation complete. In 1985 (cited by Lindhjem in 2007) Bojö made a contingent valuation study in Vålådalen, a Swedish mountain area in Jämtland, in the summer. Bojö found that an average person was willing to pay 58 USD (approx. 510 SEK) per year for Vålådalens mountain area, summing over all the visitors at the time, will give the level of recreation demand. To determine the value today in the mountain areas it is necessary to get a more appropriate benefit estimate; it has gone 32 years since the study was made.
1.2 Aims of the study
This study mainly aims to use environmental valuation as a tool to calculate the recreational value for visitors for two Swedish mountain areas during the winter months, Storulvån and Helags. More precisely it aims to derive the recreation demand for the two mountain areas and then also calculate the consumer surplus, hence the welfare gain the consumers experience from visiting the area. This study also aims to value the importance of different attributes in the mountain areas.
1.3 Delimitations
In this study, the consumers are all the visitors to the area in the winter months, since all data collection was conducted during the winter months. Some foreign visitors are excluded in the calculations for Storulvån due to lack of data collected when conducting the survey. At the present time, there are no visitors from USA and Africa, making it only natural to exclude these regions from the study. The time factor restricted the analyses of the daily guests’ recreational demand, where a different method would have had to be used, and therefore the daily guests were also excluded from this study.
1.4 Method
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CHAPTER 2
STORULVÅN AND HELAGS MOUNTAIN AREA
Storulvån and Helags mountain areas are both located in the county of Jämtland, in the center of Sweden with borders to Norway. The areas are big open landscapes with large mountains surrounding them, peaks with 1700 meters above sea level. A visitor in the winter can meet both reindeers, golden eagles and rock ptarmigan if they have a bit of luck. However, as this study shows, it is not the animals, but the mountains and the snow that attract people to visit the area. The maps below show the location of the study area, marked with a circle on the left map.
Maps 1: Maps of the study area
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2.1 The study area: Storulvån and Helags mountain areas
Storulvån is located at the foot of the mountains surrounded by mountain birches 735 meters above sea level. It is just a few hundred metres to the tree-line by foot or (in the winter) by skiing. Visitors travel to the area by car or bus to the main station. From the main station, there is a famous trail called “Jämtlandstriangeln”; for many skiers (and hikers during summer), this is a bucket list trail with two nights in the wilderness. A trend with skiing from the peak of the mountains started a couple of years ago and many people visit Storulvån during weekends, since there are many tracks for this purpose. Storulvån is a part of a restricted area for snowmobiles (visitors are forbidden to use them), making it attractive to people who want to experience the quiet outdoors. During the winter months (February to April) of 2016 there were approximately 5400 visitors to Storulvåns mountain area according to the Swedish Tourist Association (2017), making it one of the most visited mountain area during winter and thus an interesting site to study.
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CHAPTER 3 LITERATURE REVIEW
This chapter discusses previous studies of how to estimate a recreational demand curve in various ways, using different methods in different areas. There is also previous literature that has conducted research on multiple sites. The first study to be discussed is a study conducted previously in the mountain area of Sweden (and a small part of Norway), in the year 2001.
3.1 Previous literature
3.1.1 Recreational demand in mountain areas
Fredman and Emmelin (2001) conducted a study in the mountain area of Sweden and Norway called Femundsmarka-Rogen-Långfjället, which is part of a national park. The study was conducted during four months in summer using the contingent valuation method with an open-ended mail questionnaire. The study mainly concentrated on the important aspects when managing a mountain area: therefore, respondents were presented with a series of statements and asked, on a scale ranging from one to five, how they felt each statement applied to them. Also, questions about the amount they would be willing to pay to visit the mountain area and how high travelling expenses they had, were included in the questionnaire.
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urbanists, the researchers found that the purists had a total willingness to pay at 444 700 SEK per year and the urbanists 628 600 SEK per year. For Sweden, it is the Swedish Environmental Protection Agency that manages the National Parks in the mountain areas, and this is an important result for their management decisions.
3.1.2 Individual travel cost model
Previous studies that have successfully calculated the demand curve, and could then calculate the consumer surplus, by using the individual travel cost method is a study by Menkhaus and Lober (1996). The study was conducted in Costa Rica to emphasis the value of the non-market good of ecotourism, specifically international ecotourism for tropical rainforest. The international tourism in Costa Rica is one major visitation group for the reserves and therefore interesting to value. In year 1990 there were 435 000 international tourists; 35 percent of these were from the U.S, and 39 percent (59 400) of the U.S visitors stated that the main reason for visiting Costa Rica was the country’s nature. Since no correlation could be seen between distance and travel expenses, hence the zonal travel cost model could not be used. Menkhaus and Lober chose to model the recreational demand with the individual travel cost method.
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3.1.3 Zonal travel cost model
Another way of modelling the travel cost method is by using the zonal travel cost model. Tourkolias et al (2015): Application of the travel cost method for the valuation of the Poseidon temple in Sounio, Greece, applied this method of modelling the recreation demand. The aim of their study was to calculate the consumer surplus of the recreational value for one of the most important archaeological sites in Greece; Poseidon’s temple, a temple for the Greek god of the sea. It was built sometime around 440 BC and now has over 150 000 visitors each year, of which 65 percent is international tourists.
They used on-site personal interviews and collected 150 answers during two months. Before they conducted the collection of data they defined different geographical zones where visitors came from. They divided the visitors in to six different zones: the first zone was in the range up to 19.8 km from the temple and the last, zone six, was all countries outside Europe. Then they used these different zones and data on how many visitors from each zone is visiting the Poseidon’s temple to calculate the recreation demand. The researchers tried to model some of the known problems with the travel cost method, for example how to deal with leisure; if leisure has a positive recreation value, this should be reflected in the calculations. They used three different scenarios, where the first modelled the travel costs, the second took in consideration of the multipurpose trip problem added to the travel costs, and scenario three modelled both multipurpose trip, travel costs and leisure time. Based on the different scenarios the estimated consumer surplus for Poseidon’s temple ranged between €2.3 and 19.3 million (approx. 22 – 183 million SEK). However, the researchers thought this was not a complete model, and modelled the consumer surplus taking socioeconomic factors into account. For the full model the value was ranging between € 1.5 and 24.5 million (14 – 232 million SEK) for Poseidon’s temple.
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conducting a travel cost study; the problems with multipurpose and travelling time do not (per the researchers) occur in their case since most of the travellers are assumed to be there to make a summit attempt. The site was listed on WHS in the year of 2009 and the data collection started in the summer of 2008 and continued until year 2013. They divided all the visitors into 21 different zones and collected approximately 500 completed questionnaires each year. Jones et al. could then model a recreation demand for each year and then also a consumer surplus. After calculating the consumer demand for each year, they tested their hypothesis if the site’s value increased after being listed on the UNESCO world heritage list. The result was that the listing did not have a significant effect on the recreational demand for Mount Fuji.
3.1.4 Choice experiment model
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3.1.5 Multiple site model
To conduct a study in multiple sites, Sidique et al. (2013) estimated the demand for drop-off recycling sites using a random utility travel cost approach. They collected data from eight different drop-off sites in one area in the U.S (Lansing, Michigan), with on-site surveys. They collected full data from 356 recyclers in four weeks using a random pattern in where they went each week. An interesting result was that it was approximately 19 times more likely a recycler would visit site 3 than site 4. The researchers could also, with the help of hypothetical combinations of different attributes, find out what happened to the visitation ratio when a site changed its attributes. From the results of the research, they suggested that policy makers could use the results for finding out what influences visitation rates to the drop-off recycling sites.
These presented studies have, as noted before, managed to model the recreational demand for a non-market good. The values could each one separately be used in a welfare cost benefit analysis of the presented areas.
3.2 Literature discussion
To model recreational demand like Christie et al. (2007) did in Great Britain, with a choice experiment method and contingent valuation, is a way to get more explicit demand for the different features in the environment. In the case of their study, the value of the mountain areas is included and it is more appealing to use a more direct method as Menkhaus and Lober (1996) did in the study of Costa Rica’s Rainforest and in Japan where Jones et al. (2017) studied the Mount Fuji, using the travel cost model. So, depending on what to value, an area or an attribute, different methods should be used. Thus, with the aim of this study, the valuation of the mountain areas of Storulvån and Helags in winter, the more appropriate method is the travel cost model.
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travel cost model has been looked at in previous literature. The differences between the two is that the studies that have used the individual travel cost model, such as the one that was conducted by Menkhaus and Lober (1996), had visitors that came more than once to the area (the visitation ratio was higher per individual), than in the two studies that used the zonal travel cost model. In this study conducted in Storulvån and Helags, the visitors have similar visitation patterns as in the earlier studies when the zonal travel cost model is used, with only a few visits per person; this makes the visits are easy to aggregate, to get a value for visitors from a zone.
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CHAPTER 4
TRAVEL COST METHOD
Non-market goods without a market price, such as the recreational value of a mountain area, can be valued with the help of different environmental valuation methods. There are different types of methods, which value different types of benefits, and should be chosen from what the aim of the study is. As the examples in the literature chapter show, some derive the values for different attributes and some derive the value for a specific spot or event. Ward and Beal (2000) describe the difference between when a study use stated or revealed preference methods to value a non-market good. In stated preference methods, a customer is given different hypothetical choices and from that a value can be derived for the good (or attributes of the good) in question. Revealed preference methods are based on a customer’s actual behavior, such as the travel cost method where a visitor is asked what they actual paid to travel.
4.1 The Travel cost method
13 VR1 VR2
An increased travelling time and travelling cost had a negative relationship to the visitation rate, hence this could be used to derive the demand for the site from the travelers’ behavior. When travel cost increases demand decreases. The relationship between the number of visits, also called visitation rate (VR), and the price, or costs for travel (TC) is illustrated in Figure 1. The cost for travel includes both travel expenses and the cost of time spent travelling, hence the total travel costs.
Figure 1: Recreational demand and consumer surplus for a mountain area
Source: Ward and Beal (2000)
Figure 1 illustrates how the demand curve can be derived for the recreational value of a mountain area, by using the number of trips and visitors’ travel cost to the area from different zones, here zone 1 and 2 (TC1, VR1 and TC2,VR2). The area under the recreation
demand curve but above the actual price, or here costs for travel from each zone, is the net benefit value for all visitors to the area, also called consumer surplus. For visitors from zone 1 the consumer surplus is area called A, for visitors from zone 2 the consumer surplus equals A + B + C.
4.2 Visitation rate depending on individuals optimizing behavior
Demand theory is the theory behind individuals’ economic behavior. To know the main assumptions behind recreational demand theory makes it easier to understand how a
C A
B
Visitation Rate Recreational demand curve TC / Price
TC1
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specific value can be attached to a good that is not included on the market, such as the non-market good, mountain environment.
4.2.1 An individual’s choice
When modelling the optimizing behavior of an individual’s choice among different bundle of goods, an understanding about the individual’s choice is important. Daniel Bernoulli (1738, cited in 1954) were researching a letter from Gabriel Cramer in year 1728 that explains shortly how an individual optimizes its utility:
From Gabriel Cramer to Nicolas Bernoulli 1728,
“…in practice, people with common sense evaluate money in proportion to the utility they can obtain from it.”
Hence people will evaluate their utility for mountain areas with respect to the costs surrounding the actual travel to the site, keeping in mind that individuals have a monetary budget constraint and their restricted time.
4.2.2 Non-market goods and free riding
The travel cost is a cost that individuals pay for themselves, and it is included in the decision on whether they travel or not. However, with public and semi-public goods such as the costs of maintenance and investments in the mountain area, for example of the trails, it is not clear who will pay, especially not if payments are voluntary. In the initial stage before paying for the cost of the mountain areas different features, such as bridges or trail markings, with voluntary payments all individuals would need to reveal their preferences for each other so that they can share the costs. All individuals would then have incentives to not reveal their values. If for example individual A says that their preference to buy trail markings is equal to 0 SEK, then individual B will pay all costs and individual A will still benefit because in Sweden nature is not a restricted good, everybody can use it.
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value, we need to first derive the demand for mountain areas using environmental valuation. In this study, the travel cost model is used.
4.3 The Travel Cost model as a measurement for recreational demand
4.3.1 The Zonal travel cost model for a mountain area
Since Harald Hotelling’s first applications, the travel cost model has been developed further. Perman et al (2011) describe four different ways of modelling of the travel cost model; the individual, the random utility, the pooled and the zonal travel cost model. The differences are what the object of study is. All the models derive the value for a specific site, or interactions between different sites.
- The Individual travel cost model is used for studying individual use of the site over a specific period, and from that derive a consumer surplus. An average consumer surplus derived for visitors can then be aggregated to determine the sites recreational value. This method can most easily be applied when the same visitors revisit the same site frequently, so that individual visitation rates can be used in the travel cost model.
- The random utility model derives an expected utility for one or several sites, for example the model can be used to derive the changes in the welfare for consumers, when the site’s accessibility changes. This method can most easily be applied when comparisons between several sites or one site at different time periods, are evaluated since the random utility model is using a different type of regression than the other models.
- The pooled travel cost model can be used to derive changes in the recreational value when the quality of a site changes, such as air quality, or anglers’ benefits when water quality changes. This method can most easily be applied when more data is collected and analyzed together.
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The zonal travel cost model is chosen for this specific study, since the object of the study is to value the recreational demand for the mountain areas of Storulvån and Helags, since there are the different visitation patterns depending on the visitors geographical traveling origin. Another reason for choosing the zonal travel cost model instead of the individual travel cost model is because the visitation rate per person is often a few or only one visit, for these two mountain areas.
4.3.2 Econometric specification
The methodology of the zonal travel cost model is described by Perman et al (2011); the main steps are as follows:
Collect information on visitors’ values on their travel costs and then divide them into geographical zones. The most important issue is that, within each zone, the visitors should have approximately the same costs. From each zone the numbers of visitors need to be collected, and from that the visitation rate from each zone with respect to the size of the population in that specific zone is calculated. The last step is to carry out a regression using travel cost data and other data, such as for example the zonal populations’ average wages; in this study the values were collected from Statistics Sweden, Eurostat and Worldometers. There are different ways to estimate the consumer surplus; the most common way is to derive a curve with a hypothetical admission fee.
4.3.3 Econometrics for calculating the recreational demand
Ward and Beal describe how one can use a least square regression for calculating the relation between the dependent variable; visitation rate and the independent variables, hourly wage and age for the population in each zone. The regression in this specific study is specified as follows:
VR𝑖 = 1+ 2TC𝑖+ 3WAGE𝑖+ 4AGE𝑖 Where:
VRi= Visitation rate for zone i
17 TCi = Mean travel cost from population in zone i
WAGEi = Hourly wage rate from population in zone i
AGEi = Mean age from population in zone i
After calculations are made, the calculated result is tested for the statistical significance. In this study, the F – values, the coefficient of determination (R2), the test of slope
coefficients by using t-test and standard errors, and last the standard error with respect to the model, are used.
4.3.4 Calculating the consumer surplus
To determine the consumer surplus the estimated recreational demand function is used. Then a hypothetical admission fee to zone i is added to the equation to derive what happens to the visitation rate for zone i. The variables wage and age will be constant. The theory (explained by Perman et al.) is that visitors would change their behavior the same way in response to an admission fee as when they get raised travel costs. The equation will then be as follows:
VR𝑖 = 1+ 2(TC𝑖+ hypotetical admission fee) + 3WAGE𝑖 + 4AGE𝑖
Computing this for each zone, adding a hypothetical admission fee in steps of 50 SEK each time, then creates a relationship with increased costs and visitation rate. The new curve can then be plotted in a figure, and the area under the plotted curve is calculated. The area under the curve is the consumer surplus.
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4.3.5 Measurement issues with the traveller’s costs and solutions
One of the main assumptions used for the travel cost model is that the cost of travel to the site reveals the traveller’s preference for the site. When making the choice of whether to travel or not the traveller is weighing how valuable the visit to the site is to them and then comparing this to the cost of travel, hence they will only go if they feel that the benefits outweigh the costs.
To make a correct valuation of the site, the value needs to reflect only the value of the site. Hence if the travel has multiple purposes, such as if the visitor makes a visit on the way to some other site or if the visitor is travelling to several places, the calculation needs to reflect that. Another issue is the value of travelling time. The value of the site might be overestimated if the travelling time was a positive experience and had a recreational value; this should be extracted from the travelling cost, and not be used to infer a positive recreational value for the site (Perman et al, 2011). To avoid these problems, several questions need to be asked about how the traveler enjoyed their trip in the car or train, and if they went to another destination on their way there, such as to another mountain area. Modelling the equation correctly with adjustments for these difficulties, the demand for the environmental good, here the mountain areas of Storulvån and Helags during the winter months, can be derived.
An issue that is somewhat hard to deal with is that if a defined zone has no respondents the value for these visitors will not be captured, even if the researcher is defining the zones after looking at the responses. It is important to include this issue in the analysis to make correct conclusions about the value.
4.4 Data collection and constructing an on-site survey
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road, accessibility was a factor in how often data could be collected, hence the difference in number of collected answers.
4.4.1 Data collection
To collect a visitor’s travel costs Perman et al (2011) have studied different tools that can be used. On-site survey, posting or emailing a survey are some alternatives. Email surveys is less time consuming but response rates are found to be low and often the responses sent in are not completed correctly. Posting a survey gives higher response rates than using emails, but by far the highest response rate is achieved by conducting on-site surveys; this is, however, time consuming. On-site survey is therefore the recommended method for achieving the best response rates. The data were gathered on visitors’ travel costs with the help of an on-site survey in mountain stations in the study areas. By using this approach, the difficult questions could be explained directly to the respondent. Clarifying the questionnaire was also easy when knowing exactly what the respondent was wondering about.
A computer was used as answering tool in Storulvån and paper surveys were used in Helags. There was a concern about whether using a computer would give less useful responses, as people might not feel that they are totally anonymous when answering questions. Evaluating these concerns after the first few days, the computer survey was in general fully completed when using a computer, since respondents were not allowed to click to the next page without giving complete and logical answerers. Logical answers were set as rules in the survey; for example, when entering the travel costs the respondents had to answer a number above or equal to zero, so letters could not be used to complete the questionnaire. Thus, this made it possible to avoid getting “no comment” answers. A discussion with the respondents could then get them to understand the importance of answering the correct number. Helags is located further up in the mountains and a paper survey was distributed there. The advantage with paper is that more people can answer at the same time.
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make continuous changes to the language and to making the examples clearer. When processing the data, the computer survey responses were automatically inserted as soon as a respondent answered, and hence quicker to analyze. For Helags paper survey a data conversion needed to be conducted first. An English translation of the final questionnaire is given in appendix number 1, Survey. I give the example of Storulvån. The questionnaire was distributed in Sweden and mostly targeted at Swedish visitors, hence the questionnaire was in Swedish.
4.4.2 The relevant questions
Ward and Beal (2000) write in their book about, quote:
“There is a difference between `we would like to have´ and information we must have to complete the study”
A `must have´ question that respondents were asked was question 13; How much costs did you have for this trip (round trip)? Without this question, there is no reason to use a survey in a travel cost study. A question that was asked, but not necessary to complete a travel cost study was question 8; This question is about how you value different types of attributes in the environment as a part of your choice of site. Scale from 1 to 5 how you value following, where one is that the attribute did not have any relevance to my choice of site and 5 where it was completely vital to my choice of site. The mountain scenery, the amount of snow, wildlife, marked trails and prepared trails.
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To make the respondents comfortable with answering questions about costs, there was first one section about the respondent’s socioeconomic and demographic factors such as; age, gender, where they lived and what type of activity they were performing. After getting to know some factors about the respondents, they were also asked how they felt the following five factors contributed to the choice of site: The shape of the mountains, the amount of snow, wildlife, marked trails and prepare trails such as a ski track and so on. The socioeconomic and demographic factors are used to see if the chosen sample of visitors can be assumed to represent the full population. It is also interesting to investigate if those factors can play a role in how much they spend on their travelling, for example, does an older person have higher willingness to pay comparing with a younger person? Different attributes in the mountain environment are interesting to look at since this can have an importance for the value attached to the site. It can give important information about how to allocate resources in the mountain environments.
4.5 Defining zones
After the data was collected, two different zonings shown in map 2 and 3, and specified in table 4.1 for Storulvån and in table 4.2 for Helags were made. The zone borders follow the political borders, as recommended in the literature, since population data can then easily be found at Sweden Statistics. The decision on which county should be included in what zone was made after looking at what counties had similar travel cost patterns. The study area’s origin, named zone 1, is the zone closest to the site. Storulvån lies in Åre municipality and is therefore the first zone. The other zones consist of one or several counties each. Counties that have approximately the same costs are then put in the same zones. Helags lies in Berg municipality. Most visitors from Berg stay for some nights, making the costs increase, and these are approximately the same as for the rest of Jämtland County; they are therefore included in the same zone. Visitors further away from the origin, and where data could be collected was, apart from Swedish counties, also from Norway and the European Union’s 28 countries, creating a total of five zones for Helags. For Storulvåns mountain area data was collected from the rest of Europe as well, but then excluded since the data from Europe was inconclusive; only Trondelag in Norway was included together with the zones in Sweden, creating five zones for Storulvåns mountain area.
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Map 2: Defined zones for Storulvån
Sources: Modified maps from Worldatlas (2016)
Table 4.1 Defined Zones for Storulvån
Zone Geographical area
1 Åre Municipality
2 Jämtland County except for Åre municipality, and Trondelag, Norway.
3 Dalarna, Gävleborg and Västernorrland Counties
4 Uppsala, Stockholm, Södermanland, Västmanland, Örebro and Värmland Counties
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Map 3: Defined zones for Helags
Sources: Modified maps from Worldatlas (2016)
T able 4.2 Defined zones for Helags
Zone Geographical area
1 Jämtland County
2 Dalarna, Gävleborg and Västernorrland Counties
3 Uppsala, Stockholm, Södermanland, Västmanland, Örebro and Värmland Counties
4 Östergötland, Jönköping, Kronoberg, Kalmar, Gotland, Blekinge, Skåne, Halland, Västra Götaland, Västerbotten and Norrbotten Counties
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CHAPTER 5
RESULTS AND ANALYSIS
5.1 Visitors socioeconomics
The socioeconomic values such as wage rate and age, were asked and in Storulvåns mountain area both variables were answered. In Helags only the age question was answered by most respondents. To be able to make a comparison of the visitors’ socioeconomics and the socioeconomic values for the entire population from each zone, the mean values were calculated. The hourly wage rate and mean age for each zone’s general population was collected from Statistics Sweden, Eurostat and Worldometers.
5.1.1 Storulvåns mountain area; wage rate
The mean values show that in Storulvån, the visitors from zone 3- 5 have a higher wage rate compared to the general population from the same zone (see figure 2).
126 190 135 143 137 138 183 175 254 204 0 50 100 150 200 250 300 1 2 3 4 5
Hourly wage rate (SEK)
Zone
Storulvån wage
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Figure 2. Comparison between Storulvåns visitors wage and population wage
The implication from the data collection is that visitors from zone one and two has similar wage rates as the general population from these zones, hence the difference is small. For zone 3 – 5 the wage rate the difference is larger comparing with the general population. The collected data show a higher wage rate for the visitors from zone 3 – 5 comparing to the general population in that same zones.
5.1.2 Storulvåns and Helags mountain areas; age
When calculating the visitors’ age, visitors to Storulvåns mountain area have an increasing mean age with an increased distance from the area with zone 5 as an exception, as shown in figure 3.
Figure 3. Comparison between Storulvåns visitors age and population age
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Figure 4. Comparison between Helags visitors age and population age
For Helags mountain area there is no pattern for distance to the area, but a pattern where the mean age of visitors is higher than the population’s mean age, except for the European visitors and visitors from Jämtland, where the mean age of a visitor is lower.
5.1.3 Storulvåns and Helags mountain areas; preferences for attributes
A question was asked in the questionnaire as follows: This question is about how you value different types of attributes in the environment as a part of your choice of site. Rate on a scale from 1 to 5 how you value the following, where one was that “the attribute did not have any relevance for my choice of site”, and five was “it was completely vital to the choice of site”. The respondents were then given five different attributes: The mountain view in the area, the amount of snow, wildlife, marked trails and prepared trails (such as a skiing tracks). The mean value from each zone, shown in figure 5 and 6, shows that the most important was the mountain view, the amount of snow and the marked trails, and that this was true for both Storulvån and Helags mountain area.
27
Figure 5. Attributes for Storulvåns visitors
Figure 6. Attributes for Helags visitors
The pattern for “marked trails” shows that the attribute is more important, the further away from the sites origin the visitor lives in, this is true for both mountain areas. The results can be used as indicators since, for example, it shows that the mountain view is an important attribute for visitors when choosing a mountain area when travelling while, on the contrary, wildlife and ski tracks are less important.
5.2 Summing up the total travel cost with respect to visitation rate
The respondents were asked to calculate their total cost for the trip to the specific mountain environment. The measurement issues discussed in the previous chapter with
4 3.5 2.1 3 2.1 4 4 3.3 3.3 2.8 4.2 3.6 1.9 3.1 1.8 4.2 4.1 2.3 3.6 2.3 4.1 4.2 2.4 3.8 2.6 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
Mountain view Amount of snow Wildlife Marked trails Ski tracks
Value
Zone
Mean value of importance of the attributes for Storulvån
1 2 3 4 5 4.3 3.9 2.3 3.2 2.5 4.3 3.4 2.7 3.7 2.2 4.2 3.7 3.4 3.7 1.5 4.1 3.3 2.6 4.3 2.8 4 4 1.5 3.8 2.3 0 1 2 3 4 5
Mountain view Amount of snow Wildlife Marked trails Ski tracks
Value
Zone
Mean value of importance of the attributes for Helags
28
multipurpose trip, the value of the journey and the time value was captured in the questionnaire to be able to correct the issues.
5.2.1 Included costs
The stated cost that is included in the calculated sum is what the respondent had calculated as the costs for transportation and their stay, excluding the price for e.g. extra wine at the station in the evenings, purchases of new skiing gear, or costs of guided tours in the area, since they get utility from these expenditure items and therefore this should not be reflected in the travel costs. Some people had experienced extra costs at the stations due to, for example, lack of snow or harsh weather at the original route, so that then they had to spend an extra night at the station and thus increasing their costs. The cost for an extra night was then excluded since it had not been expected before traveling, and hence not included the decision on whether to travel or not. The mean values for the stated costs per zone are shown in figure 7 and 8 on the next pages.
5.2.2 Multipurpose trip
The problem with multipurpose trip was solved by asking the respondents if the trip was a part of another purpose, such as visiting a relative or another area when travelling to or from the mountain station. If they answered yes, they had to calculate the specific costs they had for the mountain station. They were asked to include the price for the stay at the mountain station and transportation costs from the other area visited to the mountain environment. This is then seen as their total travel costs to the site, solving the issues with overestimating the travel costs regarding a multipurpose trip with different areas visited.
5.2.3 Time value
29
the time spent travelling so that the value of their travel time could be included in the calculations. In Storulvån mountain station respondents answered the questions about their monthly income, which were converted into a hourly wage rate using a mean value for hours worked per month, 167 hours more precisely. For Helags the questionnaires were not fully completed, hence an average salary was adapted from Statistics Sweden for Helags. In the study of travel cost for Mount Fuji climbers in Japan, they modelled the value of time when travelling as 30 per cent of the wage rate per hour spent travelling. This approach for valuing the time is confirmed by Freeman et al (2014), therefore also possible to use in this study, although Freeman et al. discuss that further research on valuing time should be made. The time value spent in the area is not commonly accepted as part of the travel cost and is therefore not included in this study. Another way would be to exclude the value of time in the equation altogether; however, that would imply that the individual opportunity cost for the time spent travelling is equal to zero, hence underestimating the recreational demand for the mountain area. Therefore the value of the time spent travelling was included, in the manner described above. The mean value for the time value per zone is shown in figure 7 and 8.
Time Value = Hourly wage rate × hours spent travelling (roundtrip) × 0,3
Figure 7. Stated cost and time value for Storulvån
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 1 2 3 4 5 Cost (SEK) Zone
Stated cost and time value for Storulvån
30
Figure 8. Stated cost and time value for Helags
5.2.4 Total travel cost
The mean value of the travel cost was derived for each zone with respect to the different mountain areas. The results of the study have shown zonal travel costs, including both stated costs and time value, ranging between 641 and 6138 SEK per visitor for Storulvåns mountain area. For Helags zonal travel costs were estimated to range from 1251 to 2588 SEK per visitor. These results can be compared to those in the earlier study made by Fredman and Emmelin in the mountain area Femundsmarka-Rogen-Långfjället, where visitors’ average spending was approximately 4000 SEK, similar to the observations from Storulvån. In Helags the travel costs are lower which can be explained by the fact that a night at Helags costs around 300 - 400 SEK, while a night at Storulvån costs around 500 - 700 SEK. This does not in itself necessarily mean that visitors to Helags value the mountain site less than visitors to Storulvån value the site, only that their total spending is lower. The total results for each zone and mountain area is shown in tables 5.3 and 5.4 on the next page.
0 500 1000 1500 2000 2500 1 2 3 4 5 Cost (SEK) Zone
Stated Cost and Time value for Helags
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Table 5.3 Storulvåns Mountain Area, zonal data
Zone Geographical area TC Wage Age
1 Åre Municipality 641 126 40,5
2 Jämtland County except for Åre municipality, and Trondelag, Norway.
1420 190 40,1
3 Dalarna, Gävleborg and Västernorrland Counties 3093 135 43,5
4 Uppsala, Stockholm, Södermanland, Västmanland, Örebro and Värmland Counties
5990 143 41,5
5 Östergötland, Jönköping, Kronoberg, Kalmar, Gotland, Blekinge, Skåne, Halland, Västra Götaland,
Västerbotten and Norrbotten Counties
6138 137 42,3
Table 5.4 Helags Mountain Area, zonal data
Zone Geographical area TC Wage Age
1 Jämtland County 1251 130 43.2
2 Dalarna, Gävleborg and Västernorrland Counties 2123 135 43.5
3 Uppsala, Stockholm, Södermanland, Västmanland, Örebro and Värmland Counties
2588 143 41.5
4 Östergötland, Jönköping, Kronoberg, Kalmar, Gotland, Blekinge, Skåne, Halland, Västra Götaland, Västerbotten and Norrbotten Counties
2388 137 42.3
5 The rest of the European Union as well as Norway (28 countries)
2151 81 42.6
5.2.5 Visitation rate for a zone
The visitation rate for a zone depends on the travel costs from that zone to the site, as the main assumption when using the travel cost method. To estimate the visitation rate (VR), the visits from each zone (zone i) is divided by the zonal population:
VRi = Visitor zone i/ population zone i
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population in the rest of Europe, secondary data from Eurostat are collected. The visitation rate to both mountain stations is given by the Swedish Tourist Association. All numbers used are from the years 2016 and 2017. When calculating the visitation rate, the number of visits from one zone is divided by the population in than zone. For example, the visitation rate from Åre municipality, hence the probability that a person from Åre municipality will make a visit where they stay at least the night at Storulvåns mountain area during the winter months, is 223 visitors / 11088 zone population = 0.02 or 2 per cent. Observe that day guests are excluded from the calculations since not enough information is found. The visitation rate for each zone and mountain area is shown in table 5.5 and in table 5.6.
Table 5.5 Visitation Rate for Storulvån
Zone Visitors Population Visitation Rate
1 223 11 088 0,020111833
2 747 517 585 0,001443241
3 312 814 689 0,000382968
4 1226 3 760 434 0,000326026
5 803 5 291 357 0,000151757
Table 5.6 Visitation Rate for Helags
Zone Visitors Population Visitation Rate
1 296 128 673 0,002300405
2 356 814 689 0,000436977
3 257 3 760 434 0,000068343
4 179 5 291 357 0,000033829
5 79 500 289 277 0,000000158
5.3 Recreational demand and consumer surplus
5.3.1 Recreational demand for Storulvån and Helags Mountain Area
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Table 5.7 Variables for Storulvån
Zone VR TC WAGE AGE
1 0,020111833 641 126 40,5 2 0,001443241 1420 190 40,1 3 0,000382968 3093 135 43,5 4 0,000326026 5990 143 41,5 5 0,000151757 6138 137 42,3
Table 5.8 Variables for Helags
Zone VR TC WAGE AGE
1 0,002300405 1251 130 43,2 2 0,000436977 2123 135 43,5 3 0,000068343 2588 143 41,5 4 0,000033829 2388 137 42,3 5 0,000000158 2151 81 42,6
Table 5.9 Coefficient values
Storulvån Helags
Variables Coefficient Standard error Coefficient Standard error Intercept 0,223823139* 0,024640634 0,014951673 0,005934296 TC -1,8417E-06* 2,64616E-07 -2,15845E-06* 2,01E-07 WAGE -0,000295143* 2,67088E-05 1,06164E-05 2,97841E-06 AGE -0,004084283* 0,0005520515 -0,000262312 0,000131235 F-value 75,74980814 58,5180189
R2 0,995618828 0,994336012
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For Storulvåns mountain area the equation shown in table 5.9, is as follows:
VR𝑖 = 0,223823139– 1,841E06TC𝑖 − 0,000295143WAGE𝑖− 0,004084283AGE𝑖
The equation implies that increases in travel cost, wage or age will result in a decrease in visitation rate. The F-value is 76. This value is statistically significant, implying that at least one of the independent variables, TC, WAGE and/or AGE affects the dependent variable, VR. The significance of the coefficient for the travel cost variable follows the theoretical model where an increase in cost decreases the probability for visitation from that zone. The values for the wage should, in theory, be positive since an increase in wage should increase the number of visitors. The negative value can be explained by the fact that mean wage in different counties in Sweden is almost the same, so that there is not enough variation in this variable to permit identification of the relationship to the visitation rate. The R2 value is 99.6 per cent, it is a high value that implies that 99.6 per
cent of the variation in the dependent variable can be explained by the independent variables
For Helags mountain area the equation shown in table 5.9, is as follows:
VR𝑖 = 0,014651673– 2,15845E06TC𝑖 + 1,06164E05WAGE𝑖 − 0,000262312AGE𝑖 The implications of the Helags coefficient values are as follows; when the costs or the age increase the visitation rate decreases. A higher wage rate will increase the probability to visit. The F-value for Helags is 58; this is lower than for Storulvån, but still a statistically significant number that implies that at least one of the independent variables affects the depended variable. The coefficient variable for TC is statistically significant on a 10 % level. The explanatory variables, WAGE and AGE, are not statistically significant, but both are commonly used in theory as well as in practical modelling and are therefore included in the calculations. The R2 value is 99.4 per cent; as with the results
for Storulvån, it is a high value that implies that 99.4 per cent of the variation in the dependent variable can be explained by the independent variables. For both equations, the result when testing the statistical significance of the full model, the measures R2 and
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5.3.2 Consumer surplus for Storulvåns Mountain Area
To derive a consumer surplus the equation for Storulvån is used and a hypothetical admission fee to zone i is added to the equation to derive what happens to the visitation rate for zone i. The equation will then be as follows:
VR𝑖 = 0,223823139– 1,841E06(TC𝑖+ hypotetical admission fee) − 0,000295143WAGE𝑖− 0,004084283AGE𝑖
Computing this for each zone, adding a hypothetical admission fee in steps of 50 SEK each time, then creates a relationship with increased costs and visitation rate. The relationship derived for Storulvån is shown in figure 9, showing that visits from all zones decrease when the hypothetical admission fee is added.
Figure 9. Consumer surplus for Storulvån derived from a hypothetical admission fee
Deriving the consumer surplus from the graph in figure 9, the total area under the curve is calculated. See in appendix 2 all values at all points calculated. Deriving the consumer surplus for the recreational demand at Storulvåns mountain area during winter months gives a value of 2 762 650 SEK annually. With a total of 3311 visitors in year 2016 the estimated value per visitors equals 834 SEK.
0 2000 4000 6000 8000 10000 12000 0 1000 2000 3000 4000 5000 6000
Added fee (SEK)
Number of visits
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5.3.3 Consumer surplus for Helags Mountain Area
The consumer surplus is calculated for Helags and the new equation for Helags including the hypothetical admission fee, is shown below:
VR𝑖 = 0,014651673– 2,15845E06(TC𝑖+ hypotetical admission fee) + 1,06164E05WAGE𝑖− 0,000262312AGE𝑖
The added hypothetical cost to Helags mountain area is also done in steps of 50 SEK. The values for Helags are shown in appendix no 2. The relationship derived for Helags is shown below:
Figure 10. Consumer surplus for Helags derived from a hypothetical admission fee
Deriving the consumer surplus for the recreational demand at Helags mountain area during winter months gives a value of 220 500 SEK annually. With a total number of 1167 visitors spending at least one night in 2016, the consumer surplus per visitor is 189 SEK. 0 200 400 600 800 1000 1200 0 200 400 600 800 1000 1200 1400 1600
Added fee (SEK)
Number of visits
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CHAPTER 6 CONCLUSIONS
This study aimed to make an environmental valuation for the non-market good, Storulvån and Helags mountain areas during the winter months. These areas are growing to be more popular each year, and are therefore interesting to study for policymakers. A travel cost study was used to derive the relationship between the number of visitors to the mountain areas and the costs for visiting. The costs used in this study were the costs for traveling to the site, costs for the stay and the time value. Questions about what attributes the visitors to found important were also asked. In these mountain areas, a visitor in winter can meet some different types of animals such as the bird rock ptarmigan. However, the results from asking people to rate the importance for different attributes indicate that it is not wildlife but, rather, the mountain scenery and the snow, that people find most important. The implications that it is not wildlife but marked trails that is more important for the visitors shows that the investment strategy is correct in allocating the money in reparations for trails. (It is, however, important to note that the wildlife has a value in itself, even if visitors do not find it important when choosing the mountain area they will travel to.)
The results from looking at different attributes of visitors from each zone, such as difference in wage and age, shows two different visitor patterns. Visitors to Storulvån have higher income than the averages in their respective zone of origin. In the zones near Storulvån, where the population is close to the mountain area, the wage difference between the population as a whole and the visitors is not as large as for those further away. In Helags people visitors tend to be older than the average population in their respective zone.
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mountain area, with a total of 3311 visitors per year spending at least one night, showed a total consumer surplus from the recreational demand of 2 762 650 SEK annually, and 834 SEK per visitor. The consumer surplus derived for Helags mountain area, with a total of 1167 visitors per year spending at least one night, showed a total consumer surplus from the recreational demand of 220 500 SEK, and 189 SEK per visitor. One reason for Storulvåns’ higher estimated consumer surplus is the easy access to the mountain area in contrast to Helags, making the number of visits larger than the number of visits to Helags mountain area.
The results from the study can be used by policymakers to determine where the resources should be allocated, for example when evaluating where the resources should be allocated from the invested budget, the relationship between the areas should be reflected since more people, that is also the taxpayers can gain from these investments. In total, it is a good investment for taxpayers to invest in these two mountain areas since both consumer surpluses have a positive benefit value. What can also be concluded is that an investment in accessibility to Helags mountain area, similar to the accessibility to Storulvåns mountain area, will make the Helags more attractive to consumers and will increase the consumer surplus.
The results can also be applied as an estimator to similar mountain areas in Sweden, making it possible for policymakers when making benefit (or loss of benefit) analysis for mountain areas. For example, valuing changes in access to a mountain area during winter, or when the quality of the mountain area changes, such as the effect on visitation ratio with a decreasing amount of snow to the area.
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APPENDICES
Appendix no. 1 Survey: Storulvån
A Visitor´s Value on
the Mountain Environment
Luleå University of Technology
Master of Science in Business and Economics
By Amanda Tomasdotter
1. Age
_______ Years
2. Gender
Female Male Other
3. City
Answer: _______________________
4. How often do you visit the mountains in the winter?
At least once a week during winter At least once a month during winter At least once a year
Less than once a year
5. How many did you travel with here?
____ people
5b. How many of those you travelled with are under 18 years old??
Answer: _______________
43
6. How many days are you going to stay in Storulvåns mountains?
________Days
7. What is your main activity during your trip to Storulvåns mountains?
Exercise (example, skiing or skate training)
Recreation (example, birdwatching or cross-country skiing) Scooter mobile
Hunting
Other: _____________
8. This question is about how you value different types of attributes in the environment as a part of your choice of site.
Scale from 1 to 5 how you value the following, where 1 is that the attribute did not have any relevance to your choice of site and 5 where it was completely vital to your choice of site.
a. The mountain view in the area 1 2 3 4 5 b. The amount of snow 1 2 3 4 5 c. Wildlife 1 2 3 4 5 d. Marked trails 1 2 3 4 5 e. Prepared trails (such as e.g. skiing tracks) 1 2 3 4 5
9. How many times did you visit Storulvån the last five years?
___________ times
10. How far did you travel to visit Storulvåns mountains (round trip)?
____________ kilometres
11. How many hours did it take you to travel to Storulvån (ROUND TRIP in hours)?
____________hours
12. 0 is that the trip was only a necessary evil, and 10 is that the trip here was a part of the vacation, at least as big value as the actual visit to Storulvåns mountains?
0 1 2 3 4 5 6 7 8 9 10
13. How high costs did you have for this trip (round trip)?
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14. Was this trip part of a package?
If no you can skip to question 15
Yes No
14b. If the answer is yes, how much was the costs for Storulvån only?
________________SEK
15. How important for this trip was it to visit Storulvåns mountain area?
Scale from 0 to 10, where 0 is that it was not important at all and 10 is that it was the only reason to travel.
0 1 2 3 4 5 6 7 8 9 10
16. How much higher than you answered in question 13, would the total cost for the travel have had to be for you to travel somewhere else?
______________________ SEK
17. Where would you then have traveled?
Answer: _____________________
18. Your income in thousands per month, before taxes?
___________ SEK/ month
The following questions are for you who live in a household with more than one person. If you do not you can skip questions 19 – 21, to the next section.
19. The household total income in thousands, before taxes?
________ SEK/month
20. How many persons are included in the household?
__________persons
20b. How many persons under 18 years old are included in the household?
__________ persons
21. How many persons from the household are traveling with you on this trip?
__________persons
21b. How many persons under 18 years old from the household are traveling with you on this trip?
_________persons
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Appendix no. 2 Consumer surplus for Storulvån and Helags
STORULVÅN
Added fee Total
46 6650 86 6700 85 6750 84 6800 83 6850 82 6900 81 6950 80 7000 79 7050 78 7100 77 7150 76 7200 75 7250 74 7300 73 7350 72 7400 71 7450 70 7500 69 7550 68 7600 67 7650 66 7700 65 7750 64 7800 63 7850 62 7900 61 7950 60 8000 59 8050 58 8100 57 8150 56 8200 55 8250 54 8300 53 8350 52 8400 51 8450 50 8500 49 8550 48 8600 47 8650 46 8700 45 8750 44 8800 43 8850 41 8900 40 8950 39 9000 38 9050 37 9100 36 9150 35 9200 34 9250 33 9300 32 9350 31 9400 30 9450 29 9500 28 9550 27 9600 26 9650 25 9700 24 9750 23 9800 22 9850 21 9900 20 9950 19 10000 18 10050 17 10100 16 10150 15 10200 14 10250 13 10300 12 10350 11 10400 10 10450 9 10500 8 10550 7 10600 6 10650 5 10700 4 10750 3 10800 2 10850 1 10900 0 HELAGS
