1 AN APPLICATION OF THE INDIVIDUAL TRAVEL COST METHOD TO NYANGA NATIONAL PARK, ZIMBABWE Manzote Emiriya Lecturers at Midlands State University, Faculty of Commerce, Department of Economics, Private Bag 9055, Gweru, Zimbabwe Mandishekwa Robson Lecturers at Midlands State University, Faculty of Commerce, Department of Economics, Private Bag 9055, Gweru, Zimbabwe Esnath N Tavengwa Gombarago Lecturers at Midlands State University, Faculty of Commerce, Department of Economics, Private Bag 9055, Gweru, Zimbabwe Reviewed by Jaison Chireshe Research, Monitoring and Evaluation Officer GOAL Ireland, Zimbabwe
2 Abstract This study aimed at finding the monetary value that people attach on Nyanga National Park (NNP) using the individual travel cost method (ITCM). It also intended to find factors that influence individuals to visit NNP. From the findings we discovered that NNP is highly valued as indicated by a consumer surplus of US$ 9426.0576 per year or US$134.678 per average visitor. We also found out that substitute sites, travel costs and income influence an individual to visit NNP. Substitute sites and travel costs have a negative influence on an individual s visit to NNP and this conforms to what theory says. An individual s income has a negative impact on visits to NNP, we concluded that maybe people use their income mostly for consumption purposes and treat visits to NNP as inferior. From these findings, we recommended the NNP management committee to take great care of NNP since sustainable management of wildlife resources could provide very significant and much needed revenue for the country. We also recommended the NNP management committee to to take into consideration the quality of the site if its goal of being a superior site in the local, regional and international arena is to be attained. Keywords: Value, Individual Travel Cost Method, Nyanga National Park, Zimbabwe 1. Background of the study The study uses the individual travel cost method (ITCM) to find the monetary value that people attach on Nyanga National Park (NNP). The knowledge that will be obtained among tourists is of great value in planning investments and pricing in the tourism sector. The protection of biodiversity and national heritage is of great importance since it yields meaningful benefits to people (Dixon and Sherman, 1990). Natural resources such as ecological and environmental services are difficult to market yet they are beneficial to society. Among the approaches used in the evaluation of non-market environmental goods are the travel cost method (TCM) and the contingent valuation method (CVM) which were both pioneered in the USA (Chase, Lee, Schulze, and Anderson, 1998). We chose the TCM to find the monetary value that people place on NNP. NNP was established under the Parks and Wildlife Management Authority Amendment Act (1976) (www.zimparks.org). The act confers the park the mandate to conserve, manage and control the flora and fauna within the park. 1.1 Statement to the problem The study was motivated by the difficulties in ascribing economic values to public goods such as national parks yet rational and informed decision making requires among other things, a consideration of economic costs and benefits of environmental improvements. The policy makers ascribe a value using price as a
3 standard measure of value which determines whether it is worthwhile to continue its provision or divert its resources to some alternative uses. Zimbabwe has eleven National Parks and few parks have been valued by economic means. This study seeks to establish a monetary value for NNP thereby filling a gap in the field of economic valuation in Zimbabwe by applying the individual travel cost method (ITCM). Furthermore, the assignment of monetary value to NNP will demonstrate the intuitive appreciation for the environment based on the visitors preferences. 1.2 Study objectives The major objective of this study is to measure the value of NNP by assigning a monetary value using the ITCM. Other sub-objectives are: To find the determinants of visitation rates to NNP To provide recommendations to policymakers basing on the results obtained in the study. 1.3 Hypothesis to be tested In this study we hypothesize that there is no relationship between total visits and travel costs. 1.4 Organisation of the rest of the paper The rest of the study is organized as follows: section two reviews literature on the economic valuation of nonmarketed environmental goods, the third section covers the methodology; the fourth part presents the regression results, their explanation as well as the value placed on NNP. The last section presents concluding remarks. 2. Literature Review 2.1 Theoretical Literature Review Prices are the basis for resource allocation among competing uses and are the standard measure of value for some privately traded goods. Mitchel and Carsons (1989) argue that public goods fail to reveal the values of the commodities in question. Values for public goods are necessary for comparing alternative government programmes that provide goods to improve efficiency and have a balance in the production of other goods. Value refers to the price individuals are willing to pay in order to obtain a good. The basic economic concepts of demand and supply are employed to estimate the willingness to pay. Willig (1995) defines economic value as a measure of the maximum amount an individual is willing to forgo in other goods and services in order to obtain some good. He adds that economic valuation is established by applying welfare
4 economics concepts of producer and consumer surplus to issues involving natural resources and state of the environment. The basic idea of economic valuation was discovered by Dupoit (1844) as cited in Dorfman and Nancy (1972) who estimated the economical amount spend on proposed bridges. Dupoit (1844) reasoned that the maximum economical amount for any bridge is just the greatest amount that users of the bridge would be willing to pay to have it in place, and this is equal graphically to the area under the demand curve. He stated that this area is usually called the consumer surplus. Pearce and Turner (1990) argue that the TCM uses the complementary relationship between quality of a natural resource and its recreational value. The recreational demand function is modeled using visitation rates and travel costs. Perman, Ma, MacGilvray and Common (1996) add that a common method of imputing the values associated with non-marketed recreational facilities has been through the use of observed travel costs. This technique is finding application in developing countries in the estimation of the value of game reserves and tourism developments. The idea of the TCM is attributed to Hotelling (1947), who noted that TCM is one of the frequently used approaches to estimating user benefits of recreational sites. He wrote a letter to the director of US national park services in which he suggested that the costs incurred by visitors could be used to develop a measure of a recreational value of the site visited (www.answers.org). This was subsequently developed by Clawson and Knetsch (1966) basing on the idea that visitors to outdoor recreational sites usually incur costs in terms of time and money in traveling to such sites. Knowledge of this expenditure can be used to infer values placed by visitors on an environmental amenity from costs incurred traveling to a particular site. The method imputes the price-quantity reactions of consumers by examining their actual current spending behavior. Since its original inception by Hotelling (1947), uses of the TCM is now the most widely applied revealed preference approach to valuing recreational sites. TCM falls into the general category of neo-classical welfare economics which assumes that individuals maximize their utility subject to certain constraints. The technique relates the total costs to the number of visits made by each visitor to the site. By extending this across a sample survey of a number of visitors, we come up with the visitor s demand curve. According to Perman et al (1996), the underlying assumption of the TCM is that, the costs incurred visiting a site in someway reflect the recreational value of that site. Costs of visit comprise travel costs and admission price. Visitors treat travel costs and admission fee as equivalent elements of the total visit. Questionnaires are used to ask visitors their places of origin. From visitors response, we can estimate their travel costs and relate this to the number of visits per year. This relationship shows a typical downward sloping demand curve. Statistical techniques can estimate the demand curve for the site, that is, the relationship between the price of
5 a visit to the site (travel costs) and the number of visits made. The demand curve is then used to obtain the total number of visits made to the site per year to get an estimate of the annual recreation value for the site. The basic principle of the TCM is that although the actual value of recreational experience does not have a price tag, the costs incurred traveling to the site can be used as a surrogate price (www.ecosystemvaluation.org). According to Perman et al (1996) total visits to the park are determined by a trip generation function of the form V f c, x ) where V i are the visits from individual i, c i are the costs incurred by individual i and x i i ( i i are other explanatory variables. Using this trip generation function, TCM can be used to estimate the economic benefits resulting from changes in access costs for a recreational site, elimination of an existing recreational site, addition of a new recreational site and changes in the environmental quality. Perman et al (1996) argue that the area surrounding a site is divided into concentric zones of increasing distance. A survey of users conducted at the site determines the zone of origin, visitation rates and various socio-economic characteristics. Users close to the site would be expected to make more trips because the implicit price as measured by travel costs, is lower than for more distance users. Analysis of questionnaires enables a demand curve to be constructed basing on the willingness to pay for entry into sites, costs of getting to the site and forgone earning or opportunity cost of time spent and the associated consumer surplus can be determined. The estimates represent the value of the environment resource in question. As cited in Perman et al (1996), there is the Zonal Travel Cost Method (ZTCM) and the Individual Travel Cost Method (ITCM). The ZTCM uses mostly secondary data with some simple data collected from visitors. It makes use of the whole area from which visitors originate into a set of visitor zones and then defines the dependent variable as the visitor rate which is the number of visits made from a particular zone. The ZTCM uses zonal averages for costs assuming that the cost per individual per trip is the same for all individuals from a given zone. The ITCM uses a more detailed survey of visitors. It uses survey data from individual visitors in the statistical analysis, rather than data from each zone. The method differs from the zonal in that the zones assume that the cost per individual per trip is the same for all individuals from a given zone while the individual travel cost allows the costs to vary across individuals. Using the survey data, the researcher estimates the relationship between number of visits and travel costs and other relevant variables in a way similar to the zonal model. The regression model gives the demand function for the average visitor to the site and the area below this demand curve gives the consumer surplus. We thus use this approach in this study.
6 Derivation of the demand curve and calculation of the consumer surplus can be done basing on the step by step summary of the Clawson-Knetsch (1966) method of estimating recreational demand (Myrik and Freeman111) as cited in Bateman and Turner (1993). The first step defines the set of zones surrounding the site. These may be defined by concentric circles around the site, or by geographical divisions surrounding the site. The second step is to form a sample of visitors at the recreational site from each zone. Then calculate the visitation rates per thousand populations for each zone. This is simply the total visits made per year from the zone, divided by the number of zones population in thousands. The fourth step is to calculate the average round trip travel distance and travel time to the site for each zone, that is, construct a travel cost measure that reflects the round trip cost of travel from the zone of origin to the recreational site. Then collect relevant socio-economic data such as income and educational data from each distance zone. The next step is to estimate, using the regression analysis, the equation that relates visits per capita to travel costs and other important variables, that is, use regression analysis to determine the relationship between visitation rates and per unit travel cost (costs per unit).then locate one point on the demand curve by using observed total number of visits to the site from all travel cost zones as the quantity variable and the total costs for the price variable. The other points on the demand curve are found by using the estimated equation that relates visitation rates to per unit travel costs, assuming that individuals respond to a $1 increase in the entry fee in exactly the same way as they would to a $1 increase in per unit travel costs. That is find the various visitation rates and total number of visits that corresponds to a series of hypothetical increases in the entry fee added to existing travel costs. The series for entry fee visitation rate pairs plots out the demand for recreational use for the resource. This produces a demand curve which shows the overall trend between the total costs and the visits rates for all the visitors interviewed. Using this information, a demand curve can be estimated on the average visitor s total recreational value for the site. Multiplying this by the total number of visitors per annum allows the estimation of the total annual recreational value of the site itself. 2.2 Empirical Literature Review Several studies have been carried out using the TCM so as to find the value attached to the environmental goods. Most of them used variables such as travel costs, income, travel time, age, substitute sites, distance traveled and country of origin. Kaliampakos and Damigos (1999) carried out a study in Greece. Their aim was to put a monetary value on an abandoned quarry site located in Athens Greece. Variables adopted were travel cost, income, substitute sites, occupation and age. They found out that travel cost, income, substitute sites and occupation are negatively related to the number of visitation rates. They found their consumer surplus as to be between Grid (GRD) 60 000 000 and 440 000 000 using the Monte Carlo Percentiles.
7 Lamtakul and Fischer (2004) used the TCM to estimate the economic value of three different parks in Saga City, Japan. They wanted to assess how visitors value Saga Castle Park, Kono Park and Shrin Park. The study used travel costs, time cost, income and age as their important variables. The results showed that on all the three sites, travel cost, income, travel time and age were positively related to the number of visits made to the parks. The estimated consumer surplus for Castle Park was $11, 35; Kono Park was $230,972 and $242,107 for Shrin Park. Navrud and Mungatana (1994) carried out a study in Kenya. They wanted to find the recreational value of wildlife viewing in Lake Nakuru National Park (LNNP). They used both the TCM and CVM. They distinguished between residents and non-residents to account for the fact that resident visitors were on a single destination trip to LNNP. For non-residents they used variables such as travel costs, income, age, sex and education. They found a negative relationship between visits and travel costs. For all the other variables they found a positive relationship with visits to LNNP. For Kenyan residents they found a negative relationship on travel costs, age and sex with visits and a positive relationship on the other variables with visits. The authors found out that the annual recreational value of wildlife viewing in LNNP was between 7.5 and 15 million United States Dollars. Garrod and Willis (1992) wanted to establish the monetary value of a visit to public forest managed for the nation in UK. Variables used in this study are visits made to the area per year, travel cost from area i to forest j, time cost from area i to forest j, substitute sites and income. Travel time, travel cost and substitute sites were negatively related to the number of visits. Income had a positive coefficient. The study produced the average net benefit per person of 2.00 pounds ranging across the fifteen clusters of forests from 1.34 pounds to 3.31 pounds. This gave $53 million as total consumer surplus. Smith, (1993) carried out a research in Water Quality Benefits for 21 water based sites in USA. They used travel cost and income as their explanatory variables. A regression analysis was carried out using the Ordinary Least Square (OLS) method. They found out that twelve of the sites had a negative coefficient for income and nine had a positive coefficient. Of all the twenty one sites, they produced a negative relationship between travel cost and visitation rate. Using the Marshallian Consumer Surplus, the study found out that $5.87 to $54.20 per trip were for Corps sites. The estimate for Monongahela sites had a consumer surplus of between $0.98 and 42.03 per trip.
8 3. Methodology 3.1 Model Specification The model developed by the researchers was adopted from the empirical works of Kaliampakos et al (1999), used in the valuation of abandoned quarry sites in Athens, Greece as stated in the empirical literature. The model adopted is of the following form: V ij 0 1TCij 2Yi 3 Where: i = individual j = the park V ij TC ij Y i S i S i i = number of visits made per year by individual (i) to site (j) = travel costs incurred by individual (i) to site (j) = income of individual (i) = substitute site to individual (i) and is a dummy variable taking values of 1 if individual visits another site and zero if he visits NNP only μ i = the error term β 0, β 1.., β 3 are regression coefficients which measure the changes in the number of visits as a result of a unit change in the explanatory variable, other things remaining constant. The econometric modeling makes use of the Ordinary Least Squares (OLS). The model in this case allows for plotting the travel costs against the number of visits (V) made within a year to the site. We adopted the Clawson-Knetsch (1966) Method of Estimating Recreational Demand Curve. 3.2 Data sources, instruments and collection methods The data for this study is from primary sources. Primary data is up to date. We used questionnaires as the main instrument for sourcing data from the targeted respondents. The respondents were asked to complete the questionnaires and return them to the researcher. The questionnaire was the preferred method because it permitted a wider coverage. Appointments were made to other individual visitors and an interview guide was sent in advance to the respondents to give them ample time to prepare for the interview. We used in-person interviews. This technique has the advantage that it explains fully parts of the survey to respondents and also verifies results (Shultz, Pinazzo and Cifuentesl, 1998) Personal surveys also enable one to get a greater response rate than other techniques such as emails and telephone interviews. Although the method is time consuming and therefore labour intensive, we were able to elicit responses to the questions.
9 We chose our sample randomly and individual visitors were regarded as respondents for the interview. We defined our respondents as those who use NNP for recreational purposes and therefore, concentrated on people between the ages of twenty and sixty. Interviews were conducted between 10am and 6pm on weekdays and weekends. Tourists were approached at random and after a brief introduction and once participation has been secured, respondents were asked to answer the questionnaire. We ended up with a sample of 70 respondents. Visitors were asked their place of origin, the frequency with which they visit the site and other socio-economic factors such as income, age and level of education. These were interviewed in the park enjoying recreational benefits as they refused to be interrupted at the gate. The survey was undertaken from 20 to 28 June 2011 with the aid of some colleagues. Some of the visitors had more than one visit in the last twelve months and it was easy to find a way of coming up with a dependent variable which is not a dummy, so that a relationship could be produced. We calculated the travel time cost using 75% of the visitors income. The zonal population was difficult to collect and this was one of our major constraints. We therefore estimated the population basing on the 1992 population census Provincial Profile which we got from (www.zimbabwepopulationcensus.org ). All the monetary values are in US$. 4. Results And Interpretation 4.1 Presentation of Results To estimate the parameters of the demand function, a regression of the dependent variable V ij on the linear function of the other explanatory variables was run using the Ordinary Least Squares (OLS).The regression results are summarized in the table below. Variable Coefficient Std. Error t- statistic Probability Constant 6.02799 0.466241 12.928249 0.0000 Travel Cost -0.004611 0.000671-6.866755 0.0000 Income -0.063216 0.013115-4.820208 0.0000 Substitutes -1.667034 0.0210865-7.905676 0.0000 Table 4.1: Regression Results R- Squared = 0.801511 Adjusted R- squared = 0.7924 Durban Watson = 1.61 F- Statistic = 88.84 Probability (F- statistic) = 0.0000
10 The estimated equation of the linear functional form is: V 6.0278 0.004611TC 0.06322Y 1. 6670S ij ij i i The R 2 of the model is 0.80 indicating a good fit of the model. It explains that about 80 percent of variations in the dependent variable of visits to the site are explained by the variations of the explanatory variables in the model. The adjusted R 2 of 0.79 shows that, about 79 % of variations in visitation rate, is explained by the explanatory variables, taking into account the degrees of freedom. The remaining 21 % is explained by other variables captured by the error term. The F- statistic of 88.84 shows that the whole model is significant since it is greater than five. The model shows a probability F-statistic of 0.00000 which shows that there is a zero probability of rejecting the model. The constant of 6.0278 has a positive sign. It is statistically significant reinforcing the fact that the demand for visits to the site is just a normal good. The three parameters of the model are statistically significant by simply inferring to the rule of thumb that the t-statistic should be greater than two. The travel cost has a negative co-efficient of 0.004611. This illustrates the negative relationship between trip costs and the number of visits. This is consistent with theory. This negative relationship is responsible for the negative slope of the demand curve implying that the demand for visits to the site is a normal good. This finding is consistent with the empirical works of most researchers discussed in section two such as Garrod et al (1992) in the valuation of a forest carried out in the UK. According to the law of demand, the price of a good influences the demand for a commodity, thus travel costs act as a proxy of price which influences the number of visits to the site. A 1% increase in the travel costs reduces the visitation rate by about 0.46 percent. We would expect a positive relationship between the financial ability to pay and the demand for a product. In most cases, there is a positive relationship between income and product demand, that is, as income rises, the demand for products tends to rise. We obtained a negative relationship between income and the number of visits to the site. Any one percent increase in income reduces visitation rates by about 6.3 percent. This could be because visitors might now prefer better quality products. Products that are characterized by such a relationship are referred to as giffen goods. Thus, the negative coefficient of income implies that a visit to the site is inferior because as the income rises, a smaller proportion is spent to make visits to the site. This might also be due to the fact that an increase in income results in visitors saving more of their income on consumption rather than on leisure. Thus, visitors trips can be explained by other additional income and not the net income. This negative relationship is consistent with the works of Kaliampakos and Damigos (1999)
11 who had a negative relationship between income and the number of visits to the site as discussed in section two. A negative coefficient of 1.667 exists between the number of visits and the existence of substitute sites. This confirms theory that the existence of substitute sites depresses the demand for the good in question. The coefficient of this dummy variable shows the extent to which the behavior of the category taking values of 1 deviate from the base value of zero. According to Perman et al (1996), another way in which goods might be related is for them to have similar uses, that is, they can be substituted for one another. Substitutes sites are related in a way that an increase in consumption of one site, holding consumer satisfaction level and quantity consumed constant, decreases the marginal rate of substitution of the other site. If the entry fee of a substitute site increases or there is deterioration in quality of the site, visitors will turn away from the now relatively higher priced and poor quality site. The negative coefficient of substitute sites indicates that the existence of substitute sites is associated with less frequent visits to the particular site. The existence of substitute sites reduces the visitation rates by 1, 67 percent. The negative coefficient of substitute site is consistent with the empirical works of Kaliampakos and Damigos(1999) as discussed earlier on in section two on the valuation of the abandoned quarry site in Athens, Greece. 4.3 Derivation of the Demand Curve Using data collected at the park, we derived the demand curve and estimated the value of the site. The demand curve produced in this study used the Clawson and Knetsch (1996) Method of Estimating Recreational Demand Curve. Plot of the total costs against the number of visits will give the demand curve for the site and the consumer surplus is calculated. we followed is a mixture of steps by Knetsch et al (1966) as cited in the Dorfman et al (1972) and www.ecosystemvaluation.org as discussed in section two. The visitation rate is calculated per thousand populations in each zone. This is simply the total visits made from the zone divided by the zone s population. The table below shows the calculations of visits per capita. AREA Distance traveled to the Approximate Travel visits to Visits per capita recreational site in Population the site per year kilometres in (1000) A 17 100 25 250 B 100 200 19 95 C 280 700 14 20 D 460 500 9 18 E 800 700 3 4.286 Table 4.2 Computation of visits per thousand
12 The costs incurred by visitors are aggregated, this is the summation of transport costs, entrance fee and time costs (1/4 of income).the table below illustrates the computation of the total costs for the first two visitors captured at the park and was extended to all the 70 visitors. Visitor Proportion of time cost Trip Time Trip Cost Entrance Fee Transport cost Total trip cost 1 ¼ * 200 2 100 20 130 250 2 ¼ *300 4 300 20 80 400 Table 4.3 Total costs for the visitors In an attempt to make the data and the analysis manageable, average trip costs for each zone from which the identified visitor came from was calculated. The total cost of all visitors from the same zone are summed up together and then divided by the number of visitors in the zone in question. Table 4.4 below shows the computations of the average total cost for Zone A. Visitor Trip Costs 1 220 2 150 3 180 4 150 5 220 6 160.. 25 165 Table 4.4 Computation of the Average Total Cost for Zone A Total Trip Costs 4250 25 Average Total Costs 169.6 The computation of the average total cost is extended to all zones from which the visitors were received. Thus, the table below shows a computation of total costs for all the zones. Area Travel Costs A 169.6 B 252 C 318 D 443,33 E 481,67 Table 4.5 Travel costs for all zones
TOTAL TRAVEL COST Researchjournali s Journal of Economics 13 As cited in the www.ecosystemvaluation.org, using regression analysis, the number of visits is regressed against the total cost in order to create the visitation rate curve. Plugging the estimated regression equation V 6.028 0.004611*TC Computation of Total Visit for zone A V 6.028 0.004611*TC V 6.028 0.004611*169.6 V 5.246 Area Travel Costs Total Visits Population A 169.6 5.246 100 B 252 4.866 200 C 318 4.562 700 D 443,33 3.984 500 E 481,67 3.807 700 Table 4.6 Total visits and total costs Using the information in table 4.6 a demand curve can be plotted using total costs as the proxy for price. A plot of total costs against the total visits gives the demand curve for the site as figure 1 below. NYANGA NATIONAL PARK DEMAND CURVE demand curve 600 500 400 300 200 100 0 3.8 3.9 4.562 4.866 5.246 TOTAL TRAVEL VISITS Figure 1: Derived demand curve for Nyanga National Park The demand curve produced by this model relates to individual annual visits to the cost of these visits and is negatively sloped. This downward sloping demand curve concurred with theoretical literature as argued by
14 Pearce et al (1990), thus the researchers have rejected the null hypothesis outlined in the introductory section that there is no relationship between total travel cost and the total visits made. According to Knetsch et al (1966) as cited in Pearce et al (1990), the estimated total economic benefit of the site is derived by calculating the consumer surplus, or the area under the demand curve. It is calculated as the area under the demand curve minus the cost of visit (which is given at each point) and multiplying by the number of visits from that area. The estimate for economic benefits from recreational uses of NNP is US$ 9426.0576 per year or US$134.678 per average visitor. 5. Conclusion And Policy Recommendations 5.1 Conclusion The central objective of the study was to obtain a monetary value of NNP using the ITCM. The estimated consumer surplus is $9426, 0576 per year. We also aimed to find what influences individual visitors to come to NNP. We found that travel costs to the site, income of an individual visitor and substitute sites are statistically significant. The researchers accepted the null hypothesis outlined in the introductory section that there is a negative relationship between travel costs and the number of visits made per year. This is consistent with theory discussed in section two. Thus this relationship is responsible for the downward slopping demand curve derived in in the previous section. 5.2 Policy Recommendations The annual value of the park is $9426.0576 per year. Realizing that this is one of the many parks in Zimbabwe, and that wildlife viewing is becoming an important part of the global trend of increasing ecotourism, this shows that sustainable management of wildlife resources could provide a very significant and much needed revenue source for the country in future. Basing on travel costs and travel visits, visitors are inelastic demand meaning they respond more proportionately to the changes in price, taking price as a proxy for travel costs. Thus, the value of the site is shown by an individual devoting the costs in traveling to the site, so it is wiser for the park to keep the site at its best use in order to attract more visitors. We also recommend the NNP management committee to take into consideration the negative coefficients on income and substitute sites without relying on the visitors willingness to pay. They should take into consideration the quality of the site if its goal of being a superior site in the local, regional and international arena is to be attained. Basing on the research findings, it is evident that the sites quality has deteriorated as shown by the negative relationship between income and substitute sites and the number of visits. This implies that visitors may regard the site as an inferior good and is thus associated with less frequent visits. The
15 deterioration in the quality of the park and the existence of alternative sites work hand in hand. We thus recommend the stakeholders to take upgrade the site because they may lose more visitors to other alternative sites. We also recommend that stakeholders such as the Zimbabwe Tourism Authority (ZTA), the Environment 2000, World Wide Fund for Nature (WWF) and African Wildlife Foundations (AWF) whose responsibility is to market programmes locally, regionally and internationally should work together as a team for the marketing of NNP and facilitate good management so as to attract more foreigners. If the park is well marketed it can lure a lot of visitors due to its superior activities. 6. References Bateman, I., & Turner, R. (1993). 'Valuation of Environmental goods',. London: Belhaven Press. Chase, L., Lee, D., Schulze, W., & Anderson, D. (1998). 'Ecotourism Demand and Differential Pricing of Nationa Park Access in Costa Rica',. Land Economics, 466-482. Clawson, M., & Knetsch, J. L. (1966). 'Economics of Outdoor Recreation',. Washington D C: John Hopkins University Press. Dixon, J., & Sherman, p. b. (1990). 'Economics of protected areas: A new look at benefits and costs'. Washington, DC: Island Press. Dorfman, R., & Nancy, C. (1972). 'Economic of the Environment',. W.W Norton and Company, Inc. Garrod, G., & Willis, K. (1992). 'The amenity value of woodland in Great Britain: A comparison of Economic Estimates',. Environmental and Resource Economics, 415-434. Hotelling, H. (1947). 'The Economics of Public Recreation, An Economic Survey of the Monetary Valuation of Recreation in the National Park',. Washington D C. Kaliampakos, D., & Damigos, D. (1999). 'Using Environmental Economics to Evaluate Quarry Rehabilitation Alternatives'. 60 Conference of Environmental Science and Technology. Samos. Lamtakul, J., & Fischer, A. C. (2004). 'Accessibility and attractiveness for Public Park Utilisation, Case study of Saga, Japan',. International Symposium of Lowland Technology. Thailand. Mitchel, R., & Carsons, R. T. (1989). 'Using Surveys to Value Public Goods: The Contingent Valuation Method',. New York: Resources for the Future. Navrud, S., & Mungatana, E. D. (1994). 'Environmental Valuation in Developing Countries: The recreational value of wildlife viewing',. Ecological Economics, 135-151. Pearce, D., & Turner, R. K. (1990). 'Economics of Natural Resources and the Environment'. Harvestor Wheatsheaf.
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