Assessment of the local economic impacts of national park tourism: the case of Pallas-Ounastunturi National Park
Maija Huhtala
Kokkakuja 7, FI-21120 Raisio, Finland. maija.huhtala@helsinki.fi Abstract
The local economic impacts of national park tourism remain largely unknown, for no generally accepted, standardised method for measuring them yet exists. In a Finnish case study, national park visitor spending was examined using two methods: a standard visitor survey and an expendi- ture diary. Together with the park’s annual attendance information, the spending figures from the latter were used for assessing the local economic impacts applying a U.S. visitor survey/input-out- put method. To construct a local input-output table, a location quotient method was utilised. This study shows that visitor spending may impact the local economy considerably, and that the diary method is well suited for measuring national park visitor spending. It appears that the visitor sur- vey/input-output method can also be applied in Finland, with a few modifications. However, a standardised assessment of recreation tourism’s local economic impacts will require more research.
Keywords: tourist expenditure, expenditure diary, recreation, input-output method, Nordic model
1 Introduction
Tourism is one of the fastest growing industries in the world, and in Finland, growth is occur- ring largely in nature-based tourism. Nature tourism may provide a new beginning, or at least a helping hand, for the economically depressive countryside by increasing employment and public activities in the area. For this reason, many organisations are interested in acquir- ing information on the regional economic impacts of nature tourism. On a practical level, many actors, including Metsähallitus (the Finnish Forest and Park Service), local entrepre- neurs and the public administration, require concrete figures representing the number of jobs and flow of funds in order to justify the maintenance of and investments in the recre- ation facilities of national parks and other recreation areas (BERGSTRÖM et al. 1990b;
CORDELLet al. 1992). Because many nature-tourism destinations, such as national parks, are public services, economic impact information should be comparable between the regions and be available at a moderate cost.
When discussing economic impacts, one must bear in mind that although the economic impacts of tourism are typically positive, tourism may also negatively impact society, culture or the environment. In addition, measuring economic impacts concerns only the flow of funds between regions and has little to do with the total value of the area.
Despite the growing need for information, it remains unclear how nature tourism affects the local economy, for no generally accepted and standardised method for measuring the employment and income effects yet exists. In the beginning of 1980s, the Nordic countries developed “the Nordic model” for estimating the regional economic impacts of tourism (PAAJANEN1993, 20). The model compares tourist spending to the tourist income of enter- prises, and the multiplier effects are counted based on enterprise surveys and interviews.
This model has been applied to a few nature tourism areas in Finland, including the Southwestern Archipelago National Park (BERGHÄLL 2006), the Ruunaa Hiking Area (EISTO 2003) and the municipality of Kuhmo (RINNE1999). This method has also been
applied in part; for example, ALAKIUTTUand JUNTHEIKKI(1999) studied only the income of entrepreneurs in the municipality of Inari, and KANGASet al. (1998) studied only visitor spending in the Teijo Hiking Area. In other Nordic countries, the method has more or less been forgotten. Because the method is not standardised, its implementation varies between studies and consequently the results are incomparable. According to PAAJANEN(1993, 46) the Nordic model may also produce inaccurate results, as the resulting spending and income figures tend to differ significantly from each other. The method requires not only a visitor survey but also several enterprise surveys or interviews, and is therefore very laborious.
Internationally the Nordic model has generated less interest than have the more popular spending-related methods based on the input-output or pure multiplier analysis.
Researchers have studied the regional economic impacts of recreation, particularly in the United States, where they have investigated the impacts of national park tourism by combin- ing visitor survey data with regional input-output tables (STYNESet al. 2000). BERGSTRÖMet al. (1990a) applied this method in their study of the regional economic impacts of recreation on the countryside, while CORDELLet al. (1992) used it to study the effect of State Park visi- tors on the local and state economies. DOUGLASand HARPMAN(1995) used the method to estimate the effect of the Glen Canyon Dam region’s recreation use on employment. Others have also used this method to estimate the impacts of tourism in general (ARCHER1995;
ARCHERand FLETCHER1996), as well as to estimate the impacts of sporting events (e.g.
GELAN2003; DANIELSet al. 2004). However, these spending-related methods are not appli- cable in Finland as such, because, unlike in the United States, the input-output tables for the local level are not produced, and also because the visitor numbers and geographical size of Finnish national parks are only a fraction of those in United States.
Thus, the need to develop a method suitable for estimating the local economic impacts of nature tourism on any individual resort in Finland remained in the background. Optimally, the method would be based on visitor survey data and spending information collected regu- larly by Metsähallitus in the recreation areas under its administration. The method should also permit the estimation of the economic impacts on all types of recreation areas as a part of general planning. Based on these preconditions, the aforementioned U.S. visitor survey/input-output model (STYNESet al. 2000) was chosen for testing in a case study on a popular Finnish national park, the Pallas-Ounastunturi National Park, in 2004. The Nordic model was rejected because it is far more laborious, requiring enterprise surveys, and there- fore unsuitable considering the aims of the new method presented above.
In this case study most attention was focused on measuring visitor spending because it is, together with the number of visitors, a critical factor in terms of the magnitude of the eco- nomic impacts. Visitor spending was measured with an expenditure diary in order to deter- mine whether the standardised visitor survey conducted in the area one year earlier pro- vides valid estimates of visitor spending. The results of the two spending measurement meth- ods were compared and the results of the diary study were used to calculate the local economic impacts on the Pallas-Ounastunturi area.
The objectives of this study were to determine how well the standard visitor survey meas- ures visitor spending and to test whether the U.S. visitor-survey/input-output method is applicable in Finland, when the ultimate aim is to create a standardised method for estimat- ing the local economic impact of nature tourism. First, this paper presents the methods and results of a case study, beginning with a spending study and moving on to a study on the magnitude of the regional economic impacts of nature tourism, as well as the distribution of these impacts among various industries. This paper also shortly describes what kinds of tourists produce the greatest economic impacts. Finally, this paper discusses the applicability of this method in Finland and defines its future research needs.
2 Site, material and methods
2.1 The Pallas-Ounastunturi National Park
The site of investigation, The Pallas-Ounastunturi National Park1, is located in northern Finland and is one of the oldest and largest national parks in Finland. The annual number of visitors is around 100 000, of which 60 % are summertime and 40 % wintertime visitors. The most popular activities in the park include hiking and cross-country skiing.
2.2 Visitor expenditure data and methods
Metsähallitus collected the first visitor expenditure data set by means of the standard visitor survey in Pallas-Ounastunturi in 2003. A year later the researcher organised and Metsähallitus staff members implemented the other data set collection by means of an expenditure diary. Comparison of the results (i.e., average total trip-related spending and spending divided into categories) from these two studies shed light on how accurately the standard visitor survey estimates visitor expenditures.
The expenditure diary was chosen because previous studies have shown it to be the most reliable method for measuring visitor spending, as it is less prone to recall bias than the other spending measurement methods (BREENet al. 2001; FAULKNERand RAYBOULD1995;
MAKet al. 1977). However, due to the high amount of input required from the respondent, it may suffer from a low response rate or non-response bias.
Metsähallitus’s standard visitor survey is an on-site survey conducted in all recreation areas at regular intervals. In the survey, visitors are requested, among other things, to record all their trip-related expenditures in certain categories. Previous experience has shown that respondents have difficulty answering the spending questions as they seldom remember all their expenses or, if they remain in the area after the survey, poorly predict their future spending. Thus, respondents often leave the questions blank and, consequently, spending may be underestimated.
In both data sets, the population consisted of Pallas-Ounastunturi National Park visitors from which random samples were selected. In the visitor survey, local visitors were included in the sample frame but removed from the data prior to analysis. In the expenditure survey, locals were already excluded from the sample because their spending does not increase the total amount of money in the area, and therefore has no external effect on the local econo- my (BERGSTRÖMet al. 1990a).
The same sampling method was applied to both data collection sets. First, the question- naire and diary delivery points were set, taking into account the entrance and exit points as well as the most typical routes inside the park. Then, based on the number of visitors and the staff’s knowledge of the popularity of the places, a preliminary target number of responses was set at each point. Finally, the number of dates needed to attain the target at each deliv- ery point was estimated, and the mix of dates and delivery points were drawn out of a hat. A Metsähallitus staff member delivered the questionnaires (diaries) to responding visitors, moving on to the next visitor immediately after finishing with the previous one. From the groups, the person whose birthday was the next to come was chosen as the respondent.
1 The park was enlargened in 1.2.2005 and renamed as Pallas-Ylläs National Park. All figures present- ed here apply to former Pallas-Ounastunturi National Park.
The standard visitor survey
The standard visitor survey, carried out by Metsähallitus in 2003, included questions con- cerning the respondent’s visit and opinions about the national park, their socio-economic background and trip-related spending. The visitors were also asked to record their personal spending and to indicate a share of their group’s costs in the national park and its immediate surroundings. Because the expression “immediate surroundings” was not clearly defined in the instructions, the visitor had to decide which expenses to declare. The visitors could declare their expenditures either as a lump sum or divided into the following categories: eat- ing, accommodation, travel costs, programme services, and other expenses.
Since the visitors to the Pallas-Ounastunturi National Park, as well as their activities and motives in the winter differ from those in the summer, the questionnaires were distributed in two seasons: in the winter season (from January to May) and in the summer season (from June to October). Staff members delivered the questionnaires to the responding visitors on the dates and places defined in the sample frame. Answering the survey took about fifteen minutes and a staff member was available the whole time to help visitors to complete the questionnaire, if needed. The visitors were mainly approached as they were leaving the area, but because it is quite common to leave the park and return later, some visitors were approached in the middle of their trip. Consequently the ‘fortune teller’s’ bias was unavoid- able, as some visitors had to predict their spending for the rest of their trip.
Altogether 1040 visitors answered the survey and either returned the questionnaire right away or mailed it back afterwards. The number of refusals was about 5 % of the approached visitors, who refused mainly because they were in a hurry. After removing the local residents and unanswered spending questions from the data, 870 observations remained: 391 were col- lected during the winter and 479 during the summer season (Sulkava et al. 2006, Metsähallitus, an unpublished report).
The expenditure diary
When this study began, the visitor survey had already been conducted, and thus the researcher could not affect its implementation, but received only the data resulting from it.
Consequently, this set a few limitations on another data set collected with an expenditure diary in 2004. To achieve comparable data with the visitor survey, the diaries were also dis- tributed in two seasons. To increase the number of diary-keeping days, the visitors were approached in the first part of their trip, whenever possible. They were asked to complete the diary during their whole trip or in the first seven days of it beginning from the date they received the booklet, and to return the diary afterwards in a pre-paid envelope. The visitors were asked to record their expenses in the national park and its immediate surroundings, which were defined on a map as the area around the park within a radius of approximately 20 km. The diary also included a few questions concerning the respondent’s socio-economic background, possible pre-paid costs and other trip details. The visitors’ contact information was collected only if they wanted to participate in a drawing for the motivation prizes (two accommodation packages in a local hotel or a book about Finnish national parks). No fol- low-up contacts were made, as collecting spending information after the trip would have vitiated the idea of diary-keeping. The number of visitors who refused to participate in the survey was marginal. The main reason for refusal was that they or someone from their group had already received a booklet or that they were just about to leave the area.
The diary did not include set expenditure categories; rather the respondents recorded their expenses in free form. Expenses were categorised only when the data were entered into statistical software. Thus, the respondent’s burden was minimised, but the disadvantage was that the researcher now had to classify the expenses industry by industry. Eight cate- gories were used to classify the expenses: accommodation costs, cafés and restaurants, gro-
ceries, other retail spending, programme services, travel costs in the area (taxis, bus fares) and petrol. Spending that did not fit into these categories was classified as “other”.
Altogether 1142 visitors received the diary booklet, 728 of which were returned. The response rate, 63.7 %, was excellent for a survey that applied the diary method, since previ- ous studies have typically achieved response rates of less than 40 % (BREEN et al. 2001;
FAULKNERand RAYBOULD1995). The data were also of good quality, as 725 questionnaires were usable (441 were from the winter and 284 from the summer season).
Statistical methods
To estimate the average amount spent per trip, the mean daily expense recorded in the diary was multiplied by the number of days the visitor stayed in the area. Arithmetic means were used to describe the data. Although the surveys were conducted in different years, the change in the consumer price index was not taken into account, since it was only 0.2 % between 2003 and 2004 (Statistical Yearbook of Finland 2005). The data in the spending questions were not normally distributed, but were right-tailed since a few respondents spent considerably more than the median. Thus, a non-parametric Mann-Whitney U-test was used to assess whether the average spending estimates from the two surveys originated from the same distribution. The test’s null hypothesis predicts that the samples come from the same population, which in this study meant that average spending was equal between methods.
The null hypothesis was rejected at p > 0.05. The Student’s T-test was applied for testing whether the continuous background variables of respondents in the expenditure diary study differed from those of standard visitor survey respondents, assumed to represent the visitor population. The null hypothesis of differing background variables was rejected at p > 0.05.
Background information
Before comparing the results from the two survey methods, the similarity between the respondents of both surveys had to be tested. The researcher assumed that the visitor survey respondents would represent the total visitor population, but that the more laborious expen- diture survey might suffer from response bias. Therefore, variables reflecting the visitor’s socio-economic and demographic background in two data sets were compared. Because vis- itors and their activities in the winter and in the summer differ, the comparison was made seasonally.
To study whether the expenditure survey respondents were approached in the first part of their trip, the gap between the date of arrival and the date of receiving a diary booklet was examined. In the summer, over half of the respondents had received a booklet on the first or second day of the average five-day trip in the area. In the winter season, half of the visitors received their diary booklet during the first four days, or in the middle of their trip, because wintertime visitors stayed an average of seven days in the area. In the visitor survey, the respondents were only asked when their current visit to the national park began and how long it lasted. The total time spent in the park and its surroundings never emerged.
Thus, the questionnaire delivery date in relation to the arrival date could not be examined.
2.3 Regional economic impacts – methods and data
The methodology used in this study is based on a U.S. visitor survey/input-output-model, in which the annual number of visitors, the visitor’s average spending, and regional multipliers derived from local input-output tables are multiplied by each other (STYNESet al. 2000). To improve the accuracy of the results, STYNESet al. also recommend dividing the visitors into groups depending on their type of accommodation or on other factors affecting their spend-
ing. The model cannot be applied to Finland as such, though few modifications are needed, as the input-output tables are not produced on a local level but only on a national or provin- cial level. Metsähallitus has estimated the annual number of visitors at around 100 000, which served as the accepted figure in this study. The rationale for measuring visitors’ aver- age spending by means of a diary was described in previous subchapters of this article, leav- ing only the question of how the regional multipliers were derived.
Deriving the multipliers
In addition to the annual number of visitors and their average spending, the third compo- nent in the U.S. economic impact model is the regional multipliers, which reflect how extra income related to tourism is multiplied in a regional economy. Multipliers are typically derived from the local input-output table. Statistics Finland produces national input-output tables annually and province-level tables approximately every decade. The province-level table, however, is too large to be used in the national park economic impact analysis, and thus the tables must be constructed for each specific region either by surveying the region and constructing the table from scratch, or by converting the regional table from a higher level. Because the multiplicative effects of national park visitor spending were assumed to be small, and because constructing the input-output table is expensive and laborious, the table for this study was converted from the province-level table by applying a non-survey technique called the location quotient (LQ) method.
In the LQ method, the proportionate share of one industry in the region is divided by its share at the national or provincial level, thus producing a technical coefficient (MCCANN
2001, 144). To describe the industry’s share of total output, any factor representing economic activity can be used. The most typical measures of activity include employment and output.
A coefficient for a certain industry larger than one implies that the industry’s share of the total regional output is larger than the industry’s average share in the nation/province, and that the regional economy is specialised in that industry. This specialisation means that the industry can most probably meet local demand and even export goods. However, an LQ of less than one for a particular industry indicates that the region’s output is too small to respond to local demand, thus requiring imports. When applied to the construction of a regional input-output table, an LQ of less than one is used to multiply the input coefficient table, as the inputs required in the region are smaller than the national average. For indus- tries where LQ is greater than one, national input figures are used.
In the Pallas-Ounastunturi impact study, the table was converted from the province level table. As information about regional employment or output was unavailable for the specific Pallas-Ounastunturi region, using the number of companies in each industry group as a loca- tion quotient was tested. In the original province-level table, 38 industries existed, which, for the Pallas-Ounastunturi region, were combined into 8 industry groups according to Statistics Finland’s classification. The Leontief inverse matrix derived from the input-output table for the Pallas region is presented in Table 1. According to WALL(1997) the multipliers in the research area should be smaller than the provincial and national multipliers defined for the geographically larger areas, which turned out to be the case (Table 2). In addition, previous studies, which applied the Nordic Model and were conducted in other, smaller regions, produced smaller multipliers than those received in the Pallas region (BERGHÄLL 2006;
RINNE1999; KANGASet al. 1998).
Primary production and mining Manu- facturingElectricity, water and gas supply ConstructionTradeLodging and restaurantsTransport, storage and communication
Services Primary production and mining1.04809650.05432710.01837220.01665260.00142620.01122670.00123680.0018084 Manufacturing0.01210101.14843850.08286620.05772880.02290990.07816310.00948690.0220228 Electricity, water and gas supply0.00000000.00000001.00000000.00000000.00000000.00000000.00000000.0000000 Construction0.00341910.00235590.00157841.00858680.00435110.01229900.04613070.0169532 Trade0.01191420.00330450.00216950.01209541.02005460.00829100.01462640.0115633 Lodging and restaurants0.00330420.00621250.00410180.00186250.00688591.00321580.01240370.0054083 Transport, storage and communication0.01379060.02965140.01015450.02548220.04576490.00825411.04424520.0166056 Services0.01496070.02035800.01489620.00719230.10098560.18458250.02770201.0999866 1.1081.2651.1341.1301.2021.3061.1561.174
Table 1. Leontief inverse matrix (input-output model) for the Pallas region.
Table 2. Multipliers for the national, provincial and Pallas level.
1 Primary production and mining; 2 Manufacturing; 3 Electricity, water and gas supply; 4 Construction; 5 Trade; 6 Lodging and restaurants; 7 Transport, storage and communication; 8 Services.
1 2 3 4 5 6 7 8
National multipliers 1.516 1.832 1.751 1.783 1.557 1.888 1.512 1.558 Provincial multipliers (Lapland) 1.157 1.472 1.477 1.207 1.268 1.449 1.235 1.263 Pallas-region multipliers 1.108 1.265 1.134 1.130 1.202 1.306 1.156 1.174
Calculating direct, indirect and induced effects
Direct effects were calculated for each expenditure category by multiplying the average spending per visitor in each visitor group by the number of visitors in that group. This pro- duced a figure for the regional gross income, and to obtain a figure for the increase in turnover, the value-added tax was subtracted. This was performed separately for each indus- try, as the value-added tax in Finland varies between industries from 8 to 22 %. In addition to the income for the area and the increase in turnover of local enterprises, this study also focused on the effects of nature tourist spending on employment (man-years), wages and taxes. To calculate the number of jobs created by recreation tourism, an average ratio of annual turnover to the number of employees in each industry was applied based on the information obtained from Statistics Finland. In addition, gross wages and salaries were esti- mated by applying the average ratio of annual turnover to wages and salaries paid by enter- prises in each industry. Municipal taxes due to tourism were calculated by multiplying gross wages by the municipal tax rate (the average of the three municipalities).
Indirect effects emerge when enterprises that primarily serve tourists purchase products and services from other enterprises employing local people. When intermediate products or services are bought from outside the region, part of the tourism-related income leaks outside the region. These leakages must be subtracted from the increase in turnover to calculate the indirect effects. Such leakages differ from industry to industry, and to avoid excessively posi- tive results, enterprises in the Pallas region were assumed to conduct all of their material and supply purchases outside the region; thus, these purchases were treated as leakages. The aver- age share of turnover represented by material and supply purchases in each industry was obtained from Statistics Finland. After subtracting these leakages, the remaining sums were fed into the local input-output model, which resulted in the sum of direct and indirect impacts.
Tourist spending generates not only direct and indirect impacts but induced impacts as well, when local people employed either directly or indirectly by tourism buy products and services in the region. Although the input-output table used in this study did not permit cal- culation of the induced income effects, the induced effects of wage income were estimated with the concept of marginal propensity to consume (MPC). The MPC describes the share of extra income used for spending in the region, and in Finnish studies its value has typically been 0.10 (SAARINENet al. 1996). Induced effects on wages are estimated using the follow- ing formula [1/(1-MPC)]* (the sum of direct and indirect wages), where the value of the first term equals 0.11 when MPC = 0.1.
3 Results
3.1 Visitor background
When testing the similarity between the respondents of both surveys it occurred that none of the observed variables (age, gender, education and means of transport) differed signifi- cantly between the studies in the winter data sets. In the summer data sets, the visitor survey respondents were on average almost four years younger than the respondents in the expen- diture survey (t= 3.526, p= 0.000). The difference was also significant if the categorised age- variable was compared (Z= –2.992, p= 0.003). Other variables did not differ significantly between the methods. The type of accommodation, which considerably affects spending, was impossible to compare because the Metsähallitus visitor survey did not enquire about it. The visitors were, however, divided into groups depending on whether they were travelling on a package trip or independently. The reason for this division was the difference in spending patterns between the groups.
3.2 Average spending
Visitor expenditures were measured using two methods and the results were compared. The spending of package tour and independent travellers were examined separately, since the package tour travellers mostly provided only the total cost of the trip (Table 3). More atten- tion focused on the independent travellers, who had declared their expenses more elabo- rately. As Tables 4a and 4b indicate, total expenditures per independent trip and per visitor were equal or nearly equal between the survey methods in summer and in winter. However, if total spending was divided into travel costs and expenses in the research area, visitor spending measured in the visitor survey was smaller in both winter and in summer. In con- trast, travel costs were significantly higher in the visitor survey data in both seasons.
When independent traveller spending in the area was examined more closely and the expenses were divided equally between all visitors, the most money was spent on accommo- dation. Results also showed (Table 5a and 5b) that lodging expenses did not differ signifi- cantly either in winter or in summer. On the contrary, spending in the ‘other expenses’ cate- gory differed significantly between the survey methods in both seasons. In the rest of the cat- egories, the results were contradictory because spending on eating differed in winter, but not in summer, and programme service expenses differed significantly in summer, but not in winter. However, only a small minority (< 5 %) of standard visitor survey respondents declared expenses in programme services. When only their spending was surveyed, the aver- age expenses for programme services were 44 euros in winter and 63 euros in summer.
According to the diary study almost 10 % of wintertime and 12 % of summertime visitors had used programme services. Their programme service expenses were almost equal to the visitor survey results, being 48 euros in the winter and 58 euros in the summer.
The categories in Tables 5a and 5b are based on the categories used in the visitor survey.
As Table 6 shows, more detailed information was obtained from the expenditure diaries, which was one of the reasons for applying these data in estimating the economic impacts.
The amount of spending and its distribution among industries differed depending on the type of accommodation visitors used (Fig. 1). Visitors staying in hotels spent most of their budget on accommodation and less on other things, despite the high total expenditures.
Visitors using two other main accommodation types, rental cottages or private accommoda- tion (their own cottages, caravans, etc.), used the local services more widely.
Table 3. The average total cost of a package holiday by season and survey method.
Winter Summer
Visitor survey 492 € (n = 36) 335 € (n = 7)
Expenditure survey 447 € (n = 131) 348 € (n = 46)
Mann-Whitney statistics Z = –0.370 Z = –0.566
(p = 0.712) (p = 0.572)
Table 4a. Independent mean trip expenses in the winter.
Total expenses Travel expenses Expenses in the research area
Visitor survey (n = 256) 423 € 166 € 257 €
Expenditure survey (n = 310) 423 € 117 € 306 €
Mann-Whitney statistics Z = –0.826 Z = –2.895 Z = –3.575
(p = 0.409) (p = 0.004) (p = 0.000)
Table 4b. Independent mean trip expenses in the summer.
Total expenses Travel expenses Expenses in the research area
Visitor survey (n = 378) 270 € 143 € 128 €
Expenditure survey (n = 237) 276 € 120 € 156 €
Mann-Whitney statistics Z = –0.655 Z = –3.236 Z = –1.729
(p = 0.512) (p = 0.001) (p = 0.084)
Table 5a. Independent visitors’ mean expenses in the research area in winter.
Lodging Eating Programme Other
services expenses
Visitor survey (n = 256) 168 € 78 € 2 € 9 €
Expenditure survey (n = 310) 102 € 118 € 4 € 82 €
Mann-Whitney statistics Z = –1.894 Z = –5.065 Z = –1.524 Z = –4.238 (p = 0.058) (p = 0.000) (p = 0.127) (p = 0.000)
Table 5b. Independent visitors’ mean expenses in the research area in summer.
Lodging Eating Programme Other
services expenses
Visitor survey (n = 378) 67 € 48 € 2 € 10 €
Expenditure survey (n = 237) 47 € 58 € 7 € 45 €
Mann-Whitney statistics Z = –1.179 Z = –0.716 Z = –4.543 Z = –11.368 (p = 0.238) (p = 0.474) (p = 0.000) (p = 0.000)
Table 6. Independent travellers’ average spending per visit according to the diary study.
Winter Summer
Independent visit Independent visit
(n = 310) (n = 237)
€ €
Lodging 102 47
Restaurants and cafes 49 23
Groceries 70 35
Other retail purchases 37 22
Programme services 4 7
Public transportation, taxis 3 5
Gas station purchases 21 11
Other expenses 11 6
Spending in the area, Σ 306 156
300
250
200
150
100
50
0
120
100
80
60
40
20
0
Hotel Rental cottage Private cottage/
caravan
Hotel Rental cottage Private cottage/
caravan
Accommodation Restaurants and cafes Groceries
Other retail shopping Fuel and car accessories Programme services Other expenses (public transport etc.) a) Winter
b) Summer
Fig. 1. Spending per trip (€) in the a) winter and b) summer by main type of accommoda- tion.
3.3 Direct, indirect and induced impacts
Although 60 % of all visitors visited the park in summertime, the impacts on the local econ- omy were, by all measures, greater in winter, because wintertime visitors stayed longer and spent more money in the area. The indirect income impacts were less than one third of the direct impacts, thus indicating that every euro spent by tourists corresponded to 1.27 euros in the local economy (Table 7). As Table 7 indicates, the share of indirect jobs was a bit more than 10 % of the total employment effect. In the Pallas-Ounastunturi National Park, the induced impacts, calculated by applying the concept of marginal propensity to consume, were 342 000 euro. When the impacts were reviewed industry by industry, the most direct impacts occurred in traditional tourism businesses, such as accommodation and restaurant enterprises. Moreover, retail shops benefited from nature tourism (Table 8).
Because the indirect effects arise when tourism-serving industries purchase intermediate products and services from other industries, much of the impacts were generated in the serv- ice sector, including, for example, real estate activities, financial intermediation and other community, social and personal service activities. Manufacturing businesses also seemed to benefit indirectly from tourism.
Table 7. Local direct and indirect economic impacts.
Winter Summer Winter and Summer Total
Direct Direct Indirect impact
Net income, 1000 € 5450 4118 2559 12127
Number of jobs 81 64 18 163
Salaries, 1000 € 1556 1184 367 3107
Municipal taxes, 1000 € 297 225 70 591
Table 8. Direct impacts on income, employment, salaries and municipal taxes in different industries (winter and summer added together).
Net income, Number of jobs Salaries, Municipal taxes,
1000 € 1000 € 1000 €
Accommodation and restaurants 6460 100 1870 360
Retail 1680 25 525 100
Programme services 335 6 55 10
Public transport 320 6 90 17
Service stations (fuel, etc.) 190 3 80 14
Others 585 5 120 23
4 Discussion
The method tested in the Pallas-Ounastunturi National Park consisted of three components:
the number of visitors, their average spending, and the multipliers derived from the local input-output table. In this study, the number of visitors recorded was considered accurate, and attention focused on visitor spending and on calculating the regional economic impacts.
Experiences from this study proved that an expenditure diary is a useful method when seek- ing information on national park visitor spending. The response rate, which could have been the major source of failure, was excellent compared to previous studies (BREENet al. 2001;
FAULKNERand RAYBOULD1995). The diary method could also have suffered from non- response bias, as it is more laborious than the standard visitor survey, which, in this study, was assumed to be representative of the visitor population. However, when the visitor sur- vey and the diary background variables of the respondents were compared, the only differ- ence occurred in the age variable in summer. Because no age difference existed in the larger winter data, the researcher assumed that it occurred due to chance variation, and did not sig- nificantly affect the results. Thus, according to the observations of BREENet al. (2001) and FAULKNER and RAYBOULD (1995), the diary method did not seem to suffer from non- response bias and was well suited to provide a basis for economic impact estimation.
BREENet al. (2001), FAULKNERand RAYBOULD(1995) and MAKet al. (1977) have also suggested that expenditure diaries suffer less from recall bias than the other spending meas- urement methods, which indicates that the results are more accurate and that the spending estimates are typically higher. The same pattern applied to spending in the research area in the Pallas-Ounastunturi case study. However, when comparing total trip-related spending, including travel costs, the methods produced astonishingly similar results. Thus, the standard visitor survey can apparently provide accurate results on total spending but may not work if one seeks to measure spending in the area. Compared to the diary results, considered here to generally represent reality, the visitor survey respondents underestimated their spending in the area and respectively overestimated their travel costs. One could assume that the trav- el costs were easy to approximate, so these differences were unexpected, but because most of the visitors arrived by car, estimating the travel costs is not so straightforward, and miscal- culations are possible. The question concerning travel costs in the visitor survey was also unclear, as the survey failed to specify whether the costs should be recorded for only one way or for the return. However, as the declared travel costs in general were quite high, they were considered as return costs.
When expenses in the research area were examined more closely, the visitor survey respondents declared higher accommodation expenses than the expenditure survey respon- dents. In theory, accommodation expenses should be even easier to estimate than travel costs, but they nevertheless differed between methods. One possible explanation is that the respondents in the visitor survey recorded the lodging costs of their entire party rather than just their share of the costs. The visitors were instructed to declare their personal costs and their share of their party’s total expenses, but apparently if a visitor had paid for their entire party’s accommodation, such as for their family, they recorded the entire sum as their own personal cost. In the expenditure survey the instructions were more specific, instructing the respondent to provide return travel costs and to divide the accommodation costs by the size of the party even if the respondent alone had paid the costs.
Expenditures on programme services and eating were higher in the expenditure survey in both seasons, which supports the notion that the diary method suffers less from recall bias than does the regular visitor survey. The same pattern was extremely clear in the “other expenses” category, in which the visitor survey respondents had declared sums 78 to 87 % smaller than those of the expenditure survey respondents. Daily shopping and café visits are
easy to forget even when recorded daily, not to mention if they are recorded a few days later.
In conclusion, it seems that the recall error in the visitor survey applies more to the distribu- tion of the spending than to the total sums.
The distribution of the responses to spending questions is typically right-tailed, as a small number of visitors spend significantly more than the average visitor. This may result in an overestimation of economic impacts when the means are used to calculate total visitor spending. One possible solution to this problem could be to remove the outermost 5 % of values from both ends of the range, or to use the medians instead of the means.
When comparing the spending results of the Pallas-Ounastunturi study to other visitor spending studies conducted in Finnish nature resorts, it turned out that in the Pallas area vis- itor expenditures were greater than in any other area. For example, in the Teijo Hiking Area (KANGASet al. 1998) total expenses per trip were 26 euros (converted from marks to euros), in the Ruunaa Hiking Area (EISTO2003, 45) 86 euros and in the Archipelago National Park (BERGHÄLL2006, 32) 53 euros, while in Pallas they varied – depending on the measurement method and season – from 128 to 306 euros. The reason for the difference lies in the charac- teristic of the areas, Pallas being geographically larger and offering more spending possi - bilities and also longer stays than other areas.
The least important component in the formula used to calculate the economic impacts are the multipliers. As the method used here to derive the multipliers involved many assumptions and technical conversions, the resulting multipliers are only indicative.
According to WALL(1997) the size of the multipliers depends on the size of the area, so that the larger the area, the larger the multipliers are. When the Pallas-Ounastunturi case study multipliers were compared to those of other nature tourism studies conducted using the Nordic model (RINNE1999; BERGHÄLL2006) in Finland, the multipliers were well in line with WALL’s observations. General tourism studies conducted in Finland have achieved multipliers from 1.15 to 1.46 (KAUPPILA1999) and thus Pallas, with a multiplier of 1.27, fits well on this scale. Multipliers in U.S. studies are not worth comparing, since the research areas and visitor numbers are much greater than in Finland.
The magnitude of the economic impacts on the Pallas-Ounastunturi area is notable on a local scale. National park visitor spending creates employment especially in the accommo- dation and restaurant businesses. The employment effects of programme services seem rather small, but if the programme services used by package tour travellers could be includ- ed in the assessment, the results might look more positive. Since only a small number of independent travellers used programme services, the potential may exist for new services, or at least for more effective marketing. Although wintertime visitors staying in hotels spent more money per person than did visitors in any other group, the local economy benefited more from cottage renters, who spent money on a wider range of services or products.
Cottage owners, in particular, tended to stay in the area for longer periods and were more interested in using local services. According to this study, however, dividing the visitors into groups based on their spending profile has no significant effect on the total economic impacts, although STYNESet al. (2000) recommend this procedure. Thus, if interest focuses only on the total economic impacts, dividing the visitors into groups and calculating the impacts for each group separately is unnecessary.
Comparing the results to previous economic impact studies is challenging, because there are differences not only in the research areas, but also in the methods and reported key fig- ures. It seems, however, that due to the Pallas region’s large size and visitor attendance, the impacts there are notable compared to other Finnish regions where nature tourism’s eco- nomic impacts have been assessed using the Nordic model (RINNE 1999; EISTO 2003;
BERGHÄLL2006).
All in all, the visitor survey/input-output method applied in this study seems applicable to Finland as well, as long as the inaccuracy resulting from the technical conversion of the input-output table is accepted. Improving the method, however, requires more research. In addition to visitor spending, another critical factor is the number of visitors, which also requires some careful investigation. Defining the research area commensurately in different types of recreation areas is also a challenging task: for example, deciding over how large an area visitor spending should be measured such that it remains the impact of a specific area, or alternatively, deciding how widely one area’s impacts can spread, or how to take into account the different-sized areas located in totally different economic regions. Taking into account WALL’s (1997) observations of multipliers being smaller in geographically smaller areas, it may also be worth considering whether deriving the multipliers is worth the trouble in small areas where the economy is underdeveloped.
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Revised version accepted July 17, 2007
