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A Discrete-Count Model of Recreation Demand
Abstract
Discrete choice models generate welfare measures resulting from environmental quality changes on a per trip basis instead of over a longer, more policy-relevant time period. Seasonal or annual welfare measures resulting from a quality change are often computed by multiplying each individual′s per trip welfare measure by the number of trips the individual is forecasted to take at the new quality level. These models are typically formulated so that increases in the quality of any site causes every individual to increase his or her participation. This paper presents an alternative method of linking the discrete choice model to a demand model, enabling welfare changes to be estimated in a conventional manner.
... We begin with four studies: two that report ESVs by HUC (Hansen and Hellerstein 2007;Feather, Hellerstein, and Tomasi 1995). The other two ESVs are reported by USDA, ERS Farm Production Region (FPR) but were originally generated on smaller geographic scales (Huszar and Piper 1986;Ribaudo 1989). ...
... Consider the first study (Feather, Hellerstein, and Tomasi 1995), which estimated ESVs using a Willingness to Pay (WTP) function for water-based recreation. The recreational behavioral and household characteristics variables were from the 1992 National Survey of Fishing, Hunting, and Wildlife Associated Recreation. ...
... The available ESVs provide a perspective of the importance of spatial resolution of ESVs and value functions. Three studies generating HUC-level ESVs (Hansen and Hellerstein 2007;Hansen et al. 2002;and Feather, Hellerstein, and Tomasi 1995). Summing by HUC, ESVs are shown to range from zero to over $16 per ton and vary widely across the country (Figure 2). ...
Much of the research on ecosystem service values (ESVs) has limited applicability to USDA program benefit analyses, largely because the models/data/results (1) lack spatial breadth and hence cannot be applied in national analyses of USDA programs, and (2) do not link land use changes to the changes in ESs. This article provides an overview of a set of 15 ESVs related to agriculture's impacts on erosion in order to identify (1) weaknesses in methods, data, and assumptions that limit the quality of the ESVs and means of avoiding such weaknesses in future ESV development, and (2) approaches that might improve the reliability and spatial resolution of future ESV estimates.
... Seasonal benefits estimates are calculated as the product of choice occasions (which is assumed to be fixed and exogenous) and the per trip consumer surplus estimate. Proposals by Parsons and Kealy (1995) and Feather, Hellerstein and Tomasi (1995) also begin with the RUM and use it to estimate the predicted probabilities of trips to different choice alternatives. In Parsons and Kealy (1995) the predicted probabilities weight alternative specific prices and site attributes. ...
... These price and attribute indexes are then used as explanatory variables in an aggregate trip "demand" equation. Feather, Hellerstein and Tomasi (1995) expected value of the index of site attributes with the expected value of each index for the seasonal demand. Both approaches acknowledge the link to the trip demands to be ad hoc. ...
... When the aggregate quantity index is not derived from the price index, this condition will not hold. Consider the price index for Parsons and Kealy (1995) and Feather, Hellerstein and Tomasi (1995) and divide it into the total expenditure of recreation trips during the season. The process does not yield the aggregate quantity measure they propose -trips during a season. ...
... Water quality may impact anglers' fishing experiences through effects on aesthetics and/or on the health of fish. Different measures have been employed for water and environmental quality measures including: perceptual ratings (Watson et al., 1994;Peters et al., 1995); fish advisories (Jakus et al., 1997;Montgomery & Needleman, 1997;Jones & Lupi, 1999;Parsons, Jakus, & Tomasi, 1999a); EPA standards (Parsons & Hauber, 1998;Hauber & Parsons, 2000); areas of concerns and impacts (Hausman, Leonard, & McFadden, 1995;Chen & Coslett, 1998;Jones & Lupi, 1999); and secci depth (Feather, 1994;Feather, Hellerstein, & Tomasi, 1995;Lupi & Feather, 1998). Some researchers have also used specific measures of dissolved oxygen, suspended solids, fecal coliform bacteria, acidity, phosphorous, lead, copper, pcb's, toxins and oil (see Kealy [1992, 1994], Parsons and Needleman [1992], Tay and McCarthy [1994], Kaoru [1995], Tay et al. [1996], Montgomery and Needleman [1997], and Phaneuf, Kling, and Herriges [1998] for details). ...
... Through this assumption, a researcher may simply estimate parameters that measure the change in preferences for an attribute induced by the characteristics of the individual (e.g., older anglers may have less preference for catching fish than do other anglers). Many researchers who used choice models to examine fishing site choice have employed this approach (Milon, 1988a(Milon, , 1988bCreel & Loomis, 1992;Morey et al., 1993;Parsons & Kealy, 1994;Tay & McCarthy, 1994;Feather et al., 1995;Tay et al., 1996;Greene et al., 1997;Jakus et al., 1998;Morey & Waldman, 1998;Shaw & Ozog, 1999;Morey et al., 2002;Jakus & Shaw, 2003). These past studies have all used inductive techniques to identify characteristics of individuals that explain varying preferences among anglers. ...
... As changes to the resource or management of the resource occur, the forecasts of total fishing trips may be affected by the changing utilities of fishing sites. A few researchers have employed a variant of this approach (see Parsons et al. (1999a) for a discussion of these model variants) to study fishing site choices and participation decisions (Creel & Loomis, 1992;Feather et al., 1995;Hausman et al., 1995;Lin et al., 1996;Parsons et al., 1999a). Figure 2 shows that the participation decision for fishing should depend on characteristics of the angler in addition to the expected utility from the fishing sites. ...
This article reviews published research that has focused on the recreational fishing site choices made by anglers. A conceptual model for predicting fishing site choice is developed by synthesizing the findings from past studies that employed fishing site choice models. From these and other relevant studies, six general attributes (i.e., costs, fishing quality, environmental quality, facility development, encounters with other anglers, and regulations) are identified that may influence an angler’s selection of a fishing site. The article also enhances the conceptual model by accounting for differing preferences among anglers, for complex substitution patterns among fishing sites, for fishing participation decisions, for habit forming behaviors and for differences in anglers’ awareness levels for fishing sites. Finally, the article discusses several avenues that researchers could exploit to improve on past fishing site choice model studies.The author is grateful to two anonymous reviewers for their very insightful comments and suggestions that were used to greatly improve an earlier version of this article. The author also thanks Barry Boots and Peter Boxall for their constructive comments and discussions about recreational fishing site choice model studies. Finally, the author acknowledges the support provided by the Ontario Ministry of Natural Resources and the Living Legacy Trust that provided the opportunity to write this article.
... However, there is still a debate regarding the manner in which the two component models are linked (Parsons et al. 1999, Haab and McConnell 2002, Bockstael and McConnell 2006. Applications of the model have appeared in Creel and Loomis (1992), Feather et al. (1995), Hausman et al. (1995), and Englin et al. (1996). Parsons et al. (2009) provide an elaborate description and application. ...
... The opportunity cost of travel time has been the subject of persistent debate given its implications for marginal valuations of attributes and overall economic welfare debate (Bockstael 1995). In the literature, there are two approaches to valuing the opportunity cost of travel time: empirically estimating each individual's implicit value of leisure time or shadow wage from a model of labor-leisure supply decision (Feather et al. 1995) or from a model of transportation mode choice (Hausman et al. 1995); and using an arbitrarily chosen fraction of recreationist hourly wage rate varying from 25 to 50%, with the most common being 33%. In recreation demand analysis, the later approach is used more frequently because of the ease of implementation although it has been criticized because of its arbitrary nature. ...
Wildlife management areas (WMAs) provide a significant portion of the publicly available hunting sites in Mississippi. Using discrete-count data modeling, this research investigated factors that influence a hunter’s choice of a WMA on each trip, and trips frequency during hunting season. The parameter estimates were used to determine changes in compensating variation associated with marginal changes in WMAs attributes and the closing of most visited WMAs in each physiographic region of the state. The results should promote resource management in WMAs that is consistent with the welfare of hunters.
... Despite high demand, estimates of the value of clean water are often not available at the relevant scale for proposed interventions, are time-and resource-intensive to obtain, and are difficult to link to empirical measurements of water quality (Iovanna and Griffiths 2006;Keeler et al. 2012). The most common non-market methods for estimating the value of water-quality improvements typically require time-consuming and costly surveys, either to assess respondents' stated willingness-to-pay for improved water quality (Carson and Mitchell 1993) or to gather information on past recreational behavior (sites visited and distances traveled to sites; see Feather et al. 1995;Phaneuf and Smith 2005;Egan et al. 2009). In the first case, a survey is administered to ask respondents how much they would be willing to pay for a given change in water quality (stated preference approach). ...
... This conclusion is consistent with stated preference studies using contingent valuation surveys or choice experiments, which have found evidence for a positive relationship between water quality and willingness-to-pay (Carson and Mitchell 1993;Phaneuf 2002;Johnston et al. 2003;Banzhaf et al. 2006;Viscusi et al. 2008;Van Houtven et al. 2014). Far fewer studies have evaluated the benefits of improved lake water quality using revealed preference approaches based on the surveyed behavior of recreationists (eg Feather et al. 1995;Egan et al. 2009). ...
More than 41 000 water bodies are listed as impaired by the US Environmental Protection Agency under the Clean Water Act. Implementation and enforcement of regulations designed to address these impairments can be costly, raising questions about the value of the public benefits derived from improved surface water quality. Here, we assess the recreational value of changes in water quality using freely available geotagged photographs, taken by members of the public, as a proxy for recreational visits to lakes. We found that improved water clarity is associated with increased numbers of visits to lakes and that lake users were willing to incur greater costs to visit clearer lakes. Lake users were willing to travel 56 minutes farther (equivalent to US$22 in travel costs) for every one-meter increase in water clarity in Minnesota and Iowa lakes, when controlling for other lake attributes. Our approach demonstrates the potential for social-media data to inform social–ecological research, including assessment of the recreational benefits of improvements in water quality.
... [3] Best practice appears to have converged on the use of count models to characterize total trips. 3 Recent applications include Feather, Hellerstein and Tomasi [6]; Yen and Adamowicz [26]; and Hausman, Leonard and McFadden [7]. This form of TCM only requires information on the detailed characteristics of trip choice on one choice occasion, plus the total number of trips. ...
... To date, the treatment of censored duration data in the applied literature has been limited for the most part to contexts in which the censoring occurs for only part of the sample. 6 Our ongoing research is directed at developing censoring treatments suitable for estimating the model with more general distributions. We also develop the procedures for calculating total trips from the between-trip duration models. ...
In this paper we characterize seasonal recreational participation with a model of between-trip durations, a variation on seasonal participation models that provides certain advantages relative to modeling total trip counts directly. Employing a competing risk framework, we are able to model anglers? demands for trips of different lengths. The duration framework also allows us to model the effect on total trips of intra-seasonal variations in explanatory variables such as fish catch rates. Further we develop the model for a context where the available data are limited to detailed information on the most recent trip, supplemented by the return date of the survey. The proposed resolution to the challenges of left- and right-censored duration data employs the ?memoryless? exponential distribution for the duration model, which implies a Poisson count model. With another dataset, we provide an external validation test of the predicted number of angler trips from the model.
... Typically, such studies use survey data containing information on the origin and destination of an individual recreational trip. However, such data sets are time-consuming to develop and are usually only available for relatively small areas (Pouta and Ovaskainen 2006;Hausman et al. 1995;Loomis 1995;Feather et al. 1995;Parsons and Hauber 1998;Shaw and Ozog 1999;. The purpose of the present study is to investigate the determinants of recreational use of NPs at a European scale and therefore we use data from visitor monitoring studies for NPs across Europe. ...
Spatial assessments and mapping of ecosystem service values has become a widely addressed research issue. It is more and more used to consult land-use and conservation policies as well as landscape planning. Nevertheless, a variety of methods is used for ecosystem service value mapping, which lead to maps with a broad range of quality and accuracy. Still, little consensus has been reached on what method to use best for specific applications.
This thesis provides guidelines and recommendations for future applications and research in monetary ecosystem service value mapping. Therefore, a broad literature review is presented that identifies current practices and developments in the mapping of EES values. Based on the findings, a best-practice approach for ESS value mapping is proposed that separates between the mapping of ESS supply in biophysical units and the mapping of the economic value per biophysical unit of ESS supply.
The proposed approach is implemented and illustrated for recreational ecosystem services of non-urban ecosystems at European scale. Therefore, geostatistical models are developed and applied to predict recreational visitor numbers. In a second step, a spatial meta-analytic-value transfer function is developed and applied to map the monetary value per recreational visit. For both steps, primary data is combined with GIS explanatory variable layers in geostatistical regression analysis (general linear models, mixed models, spatial auto-correlation) to identify the spatial drivers of recreational use and its values. The collection of primary data, the development of spatial GIS predictor layers and the geo-statistical models are elaborated. Recommendations for future applications are given. For primary data of recreational visitor counts the collected data is presented in a geo-database and the relevant literature on visitor counting is reviewed to give recommendations in this domain. The results exemplify how to best map ESS values by incorporating the spatial dimensions of ESS supply and demand.
... However, other authors found a mistake in the way the Hausman et al. model was derived (Smith 1997;. Some studies, including Bockstael et al. 1987, Parsons and Kealy 1995, Feather et al. 1995, and Parsons et al. 2009, have offered intuitively appealing justifications for repeated-RUM choices linked to a demand function for total trips, but have stopped short of proposing any fully developed utility theory. ...
Logit models are widely used in recreation studies, but connecting the model to a theory of utility-maximizing behavior has proved difficult. Random utility maximization is widely cited as a supporting theory, but it correctly applies only to a single discrete choice, not to the series of choices that characterizes recreation behavior. I show that a logit model with repeated choices can instead be viewed as a system of demand equations satisfying integrability conditions for consistency with utility maximization. The proposed approach provides a theoretical foundation for repeated logit and permits the calculation of theoretically valid, income-adjusted welfare measures.
... Often however, decision-makers are challenged to know how and where to develop or promote recreational opportunities, because they lack good information on which mix of landscapes, activities, and experiences are demanded by local visitors and tourists. Traditionally, methods for measuring the demand for recreation opportunities involve counting the number of visitors to a place [6], measuring the cost or distance traveled to get there [7], or surveying visitors to learn about their preferences and the benefits they experience [7][8][9]. Unfortunately, the cost of collecting these data limits the number of sites that can be studied. ...
Recreation and tourism are important ways that people interact with and derive benefits from natural environments. Understanding how and where nature provides recreational opportunities and benefits is necessary for management decisions that impact the environment. This study develops and tests an approach for mapping tourism patterns, and assessing people’s preferences for cultural and natural landscapes, using user-generated geographic content. The volume of geotagged images and tweets shared publicly on Flickr and Twitter and proprietary mobile phone traffic provided by a telecommunications company, are used to map visitation rates to potential tourist destinations across Jeju Island, South Korea. We find that densities of social media posts and mobile phone traffic are all correlated with ticket sales and counts of gate entries at tourist sites. Using multivariate linear regression, we measure the degree to which attributes of the natural and built environment explain variation in visitation rates, and find that tourists to Jeju Island prefer to recreate near beaches, sea cliffs, golf courses and hiking trails. We conclude that high-resolution and spatially-explicit visitation data provided by user-generated content open the door for statistical models that can quantify recreation demand. Managers and practitioners could combine these flexible and relatively inexpensive user-generated data with more traditional survey data to inform sustainable tourism development plans and policy decisions. These methods are especially useful in the context of landscape or regional-scale ecosystem service assessments, where there is a need to map the multiple ecological, economic, and cultural benefits of the environment.
... Resource quality can relate to the overall environmental conditions or more specifically, the water quality at a waterbody . Measures of water quality may include assessments of dissolved oxygen, phosphorus (Montgomery and Needleman 1997;Phaneuf et al. 1998), and secchi depth (Feather et al. 1995;Lupi and Feather 1998). Resource quality is often related to the size of the waterbody (i.e., surface area). ...
Angler populations and the waterbodies they use are patchily distributed, creating putatively complex user-resource dynamics on the landscape. Spatially and temporally dynamic relationships between anglers and waterbodies can be difficult to track, understand, and manage. We often focus our efforts on the angler (directly or indirectly) with far less attention devoted to understanding the spatial structure and dynamics of fisheries on the landscape. Waterbodies serve as dynamic attractors on the landscape, shaping landscape patterns in angler participation. We surmise that by understanding the spatial structure and dynamics of recreational fisheries we can gain tremendous insight to cross-scale patterns that shape angler behavior. We constructed waterbody-specific “anglersheds” that reveal critical links between anglers (i.e., users) and waterbodies (i.e., resources) on the landscape. Anglersheds represent the area of influence or spatiotemporal draw of anglers to a waterbody. Anglersheds were constructed from frequencies of anglers’ zip codes that were collected during on-site interviews (April-October 2014-2017) at eight prominent Nebraska waterbodies. We used these anglersheds to visually depict the spatiotemporal structure and dynamics of these recreational fisheries at multiple spatial scales and temporal levels. We then quantified these spatiotemporal dynamics by extracting multiple anglershed metrics. Anglersheds were dynamic in both space and time; anglershed features such as anglershed area (i.e., size), the degree of fragmentation (i.e., number of patches), and compactness (i.e., angler density) also differed among waterbodies. We then selected 11 independent variables that encompassed variation in the spatial socioeconomic structure, on-site attributes, and angler heterogeneity to explain changes in anglershed area for seven prominent Nebraska waterbodies. Anglershed area exhibited a positive relationship with air temperature, wind speed, and population density, but was unrelated to angler effort, catch rate, fuel price, household income, party size, precipitation, trip days, and waterbody size. Anglersheds have the potential to “unlock” a wealth of information concerning the underlying spatial structure and dynamics of recreational fisheries. This approach has the ability to expose and capture cross-scale interactions within coupled social-ecological systems. Advisors: Mark A. Kaemingk and Kevin L. Pope
... If the resources are allocated to one activity, they cannot be directed to other activities (e.g. Feather, Hellerstein, & Tomasi, 1995). ...
This study examined involvement in outdoor recreation activities, focusing on whether individuals who are involved in one recreation activity at a high level are less or more likely to participate intensively in other outdoor activities. Participation in fishing in Finland was taken as a starting point. The data were obtained from a large national population survey that included 3592 respondents who participated in fishing. Three groups were distinguished based on behavioral involvement in fishing. The high behavioral involvement (HBI) group reported the highest annual frequencies for most outdoor activities. The moderate behavioral involvement (MBI) group were modestly active in fishing, and moderately active in outdoor recreation in general. Compared to the other groups, the low behavioral involvement (LBI) group participated least frequently in fishing and were also less active in many other recreational activities. The results imply that behavioral involvement in outdoor recreation is cumulative in nature.
Management implications
The study provides interesting findings for rural tourism and land use management:
● A high level involvement in one activity is not necessarily reducing the participation intensity in others.
● In order to understand activity clusters and key activities in a region, it is strongly recommended to investigate the whole palette of outdoor recreation activities.
● Within the group of fishermen various subgroups exist, which are characterized by different motivation and behavioral patterns.
● These insights may help in offering substitutes for fishing activities or new service packages that include a broader array of activities for people to participate, and to extend their stay and enhance their contribution to the regional economy.
... The recreational use of nature areas, such as forests can be evaluated by considering the travel cost of individuals. The travel cost models usually have two componentsa site preference model and a count model which in combination can be used to analyse welfare changes associated with quality changes (Feather et al. 1995, Hausman et al. 1995, Herriges et al. 1999, Parsons et al. 1999, Bujosa and Riera 2010, Raguragavan et al. 2013). This approach is useful to estimate demand over a given period of time or a season (Feather et al. 1995, Bujosa andRiera 2010). ...
In this paper, we investigate the factors that can influence the site choice of forest recreation. Relevant attributes are identified by using spatial data analysis from a questionnaire asking people to indicate their most recent forest visits by pinpointing on a map. The main objectives of the study are (1) to examine the preferences of visitors for different forest attributes using data from actual visits and (2) to illustrate how many alternative sites need to be considered for estimation in case of a large number of potential recreational sites. Estimation is performed using a conditional logit as well as a random parameter logit model. The variables that are found to affect the choice of forest site to a visit for recreation include: forest area, tree species composition, forest density, availability of historical sites, terrain difference, state ownership, and distance. Regarding the second research objective, we empirically show the possibility of getting consistent parameter estimates through random selection of alternatives. We find that increasing the number of alternatives increases consistency of parameter estimates.
... Typically, such studies use survey data containing information on the origin and destination of an individual recreational trip. However, such datasets are time-consuming to develop and are usually only available for relatively small areas (Pouta & Ovaskainen, 2006;Bateman et al., 2011;Hausman, Leonard, & McFadden, 1995;Jones, Wright, Bateman, & Schaafsma, 2010;Loomis, 1995;Feather et al., 1995;Parsons & Hauber, 1998;Sen et al., 2013;Shaw & Ozog, 1999;Termansen, Zandersen, & McClean, 2008). The purpose of the present study is to investigate the determinants of recreational use of NPs at a European scale and therefore we use data from visitor monitoring studies for NPs across Europe. ...
Recreation is a major ecosystem service and an important co-benefit of nature conservation. The recreational value of National Parks (NPs) can be a strong argument in favour of allocating resources for preserving and creating NPs worldwide. Managing NPs to optimize recreational services can therefore indirectly contribute to nature conservation and biodiversity protection. Understanding the drivers of recreational use of national parks is crucial.In this study we use a combination of primary data on annual visitor counts for 205 European NPs, GIS and statistical regression techniques to analyse how characteristics of NPs and their surroundings influence total annual recreational visitor numbers. The statistical model can be used for land-use planning by assessing the impact of alternative conservation scenarios on recreational use in NPs. The recreational use of new NPs can be estimated ex-ante, thereby aiding the optimisation of their location and design.We apply the model to: (1) map recreational visits to potential new NPs across Europe in order to identify best NP location; (2) map recreational visits to a proposed new NP in the west of Germany in order estimate monetary values and to show how visits are distributed across the site; and (3) predict annual visits to all NPs of 26 European countries. Total annual visits amount to more than 2 billion annually. Assuming a mean value per visit derived from 244 primary value estimates indicates that these visits result in a consumer surplus of approximately € 14.5 billion annually.
... The linked model segments the consumer's decision into two components, namely site selection and the participation decision. Studies that have employed the linked model include those of Bockstael et al. (1986Bockstael et al. ( , 1987; Yen and Adamowicz (1994); Feather et al. (1995); Hausman et al. (1995); Parsons and Kealy (1995). The linked model can be used to deal with site selection and the participation decision; however, these are not derived from a unified utility theory 1 . ...
... The travel cost and quality to the site are not included as independent variables because the inclusive value measure summarizes the influence of these variables on site choice and therefore represents an index of sorts for these variables (Creel and Loomis, 1992). Bockstael, Hanemann, and Kling (1987), Creel and Loomis (1992), and Feather, Hellerstein, and Tomasi (1995) are a few examples of studies that have utilized this approach to account for trip frequency and estimate per-season welfare values in RUM travel cost models. A drawback of this approach is that the two separate models that are used to model behavior, the site choice model and the trip frequency model, are not typically consistent with a well-behaved set of individual preferences. ...
This report documents the development of the Beneficial Use Values Database (BUVD). The purpose of the BUVD is to provide an educational and informational tool to the general public and interested specialists, documenting the economic values for beneficial uses of water identified by the State Water Resources Control Board (SWRCB). It is envisioned that the BUVD be a companion to the Water Quality Standards Inventory Database (WQSID), which currently provides information to the public on water quality standards for, and beneficial uses of, water bodies throughout California, but no information on the value of those beneficial uses. In preparing this alpha version of the database, the literature on economic values of water was consulted widely but not exhaustively, so in its current form the BUVD should be considered as (1) a representation of many though not necessarily all economic values for beneficial uses of water available in the current literature; and (2) a template which can be added to as more studies are identified as suitable for inclusion in the database over time. Section I explains the background and purpose of the BUVD as an educational and informational tool, and its potential role in benefit-cost (B-C) analysis of stormwater
... Count models, such as poisson and negative binomial models, can be used to explain changes in the trip numbers due to changes in destination quality, but they cannot verify the changes in site substitution across recreational sites. Due to the weakness of these models in terms of inability to explain changes in trip numbers and site substitution, some researchers have developed linked site selection models to explain the site selection and number of trips taken by recreationists (Feather et al., 1995;Hausman et al., 1995;Parsons and Kealy, 1995;Parsons et al., 1999). The linked site selection model is based on a two stage estimation. ...
... It is expected that larger water areas may hold fishes of wider variety and larger size and may provide anglers with more fishing opportunities (Hunt, 2005). Previous recreational freshwater fishing demand and destination choice studies have emphasized a positive relationship between the size of the water bodies and destination choices made by anglers (see, for example, Feather, Hellerstein and Tomasi 1995;MacNair and Cox 1999;Parsons and Kealy 1992;Tay, McCarthy, and Fletcher 1996). Train (1998) used the logarithm of size of each site as an explanatory variable to capture the fact that each angler has the option of many locations within a site, and the number of locations is higher at larger sites. ...
... Besides Parsons, Massey and Tomasi (1999), other articles, namely, Feather, Hellerstein and Tomasi (1995), Parsons (2002), Parsons and Massey (2003), and also Parsons (2003), seem to have misrepresented the expression for the expected utility. While some other authors, Ben-Akiva and Lerman (1985), Morey (1999), Louviere, Hensher and Swait (2000), Haab and McConnell (2003) have used the correct formula, they did not present the complete direct derivation of the expected value. ...
Misspecification of expected utility for random utility models could result in an undervaluation of environmental amenities. The expression for the expected value of visiting sites for econometrically estimating welfare losses of recreational amenities, presented by some authors, appears to be incorrect. In this article, we present a direct and correct derivation and approximation of the expected utility for the random utility models.
... Seasonal benefits estimates are calculated as the product of choice occasions (which is assumed to be fixed and exogenous) and the per trip consumer surplus estimate. Proposals by Parsons and Kealy [1995] and Feather et al. [1995] also begin with the RUM and use it to estimate the predicted probabilities of trips to different choice alternatives. In Parsons and Kealy the predicted probabilities weight alternative specific prices and site attributes. ...
Travel cost recreation demand models stem from a simple, but penetrating, insight. Consumption of an outdoor recreation site's services requires the user to incur the costs of a trip to that site. Travel costs serve as implicit prices. These costs reflect both people's distances from recreation sites visited and their specific opportunity costs of time. Today, economic analyses of recreation choices are among the most advanced examples of microeconometric modeling of consumer behavior in economics.The primary focus of this chapter is on the methods used to describe individuals' recreation choices. We are interested in the economic assumptions made in descriptions of behavior and measures of the economic value of amenities. Before developing this summary, in Section 2 we discuss how outdoor recreation fits within consumers' overall expenditures. Section 3 describes how we might ideally like to estimate consumers' preferences for recreation resources and the compromises implied by the models currently being used. Econometric details are deferred until Section 5, after a discussion of the features of recreation data in Section 4. In Section 6 we turn to conceptual issues in welfare measurement. We close in Section 7 with a discussion of a few research opportunities that seem especially important for the future.
... We therefore explore both components by modelling the choice of destination and the volume of demand. To estimate total demand we use a national household data set that gives the information to model demand by using a count data approach as previously done by others (Hutchinson et al., 2003; Romano et al., 2000) who followed a number of seminal papers (Bockstael et al., 1987a; Feather et al., 1995; Hausman et al., 1995; Parsons and Kealy, 1995). The destination choice behaviour model is based on a national data set including information about individual trips undertaken by the Danish public. ...
... Over the last 30 years, Random Utility Models (RUM) have become the predominant approach of the Travel Cost Method (TCM) (Feather et al., 1995;Greene et al., 1997;Phaneuf and Smith, 2005). RUMs specification provides a convenient way to explain the choice among mutually exclusive alternatives incorporating relevant substitution and site quality effects and, consequently, overcoming the incomplete representation of recreational demand of previous TCM approaches such as the varying parameter model (Vaughan and Russell, 1982;Smith et al., 1983), the hedonic travel cost method (Brown and Mendelsohn, 1984;Mendelsohn, 1984) and Morey's model (Morey, 1981(Morey, , 1984. ...
The use of Geographical Information Systems (GIS) in Travel Cost Method (TCM) applications has transformed many aspects of valua- tion practice. However, a review of the recreational demand literature gives evidence of the presence of major gaps in the set of attributes needed to evaluate environmental policies related to the heterogeneity of landscape and the spatial conguration of land use in and around recreational areas. In this context, this paper presents an empirical application to forest recreation in Mallorca (Spain), implementing a mixed logit model and evaluating the inuence of landscape hetero- geneity, viewed as the forests property of being diverse, in the recre- ational site-choice process.
... Other variables that could be used are quality variables associated with the choices the individuals make. For example, Feather and Hellerstein (1995) use the expected destination qualities in a model for recreational demand. Berkhout and Plug (2004) investigate the number of trips the Dutch population makes to cultural events and tourist attractions. ...
In this article a trip frequency model for recreational and shopping trips is suggested. The data used comes from a Swedish travel habit survey where the observations of trip frequencies of both types on each individual are made on the same day. This is likely to introduce a cor- relation structure, which is modeled with a conditional approach where the number of recreational trips are assumed to be made conditional on the number of shopping trips. A special interest is devoted to the eect of travel cost on trip frequencies. As a measure of the sensitivity of cost changes, elasticity of demand is calculated. The precision of the elasticities are evaluated with simulated confidence intervals.
... Demand models can be estimated and consumer surplus evaluated. More information can be found in Mendelsohn et al. (1992) ;Feather, Hellerstein and Tomasi (1995), and Feather and Hellerstein (1997). ...
... In questo modo, infatti, si assume implicitamente che la variabile dipendente (il numero di gite) abbia natura continua e non discreta come avviene in realtà e la stima della funzione di domanda ottenuta non può perciò essere considerata del tutto adeguata (Hellerstein, 1991). Per ovviare a tale problema è possibile ricorrere all'utilizzo di modelli di conta le cui applicazioni, a livello nazionale, sono ancora piuttosto limitate (Romano et al., 2000; Scarpa et al., 2003a; Marangon et al., 2002; Signorello, 1998), mentre sono oramai abbastanza diffuse all'estero (Creel, Loomis, 1990; Englin, Mendelsohn, 1991; Feather et al., 1995; Fleisher, Tsur, 2000; Gurmu, Trivedi, 1996; Haab, McConnell, 1996; Hanley et al., 2001; Hanley et al., 2002; Hellerstein, Mendelsohn, 1993; Shaw, 1988; Smith, 1988; Cameron, Trivedi, 1998). ...
Strong brand image is an invaluable asset and organizations are giving continuous endeavor to build strong image for their goods and services. Likewise, marketing researchers are focusing on researches to find out ways to enhance brand image. In this study, Aaker’s Customer-Based Brand Equity Framework-1996, Keller’s Customer-Based Brand Equity Pyramid-2003 and Baker’s Corporate Social Responsibility Model-2001 have been taken into consideration for identifying the pertinent factors relating to brand image of hotel brands. Subsequently a number of variables have been extracted through extensive literature review to investigate their influence on building brand image for branded hotels in the context of Bangladesh. The variables include brand awareness, brand association, brand superiority, brand affection, brand resonance and corporate social responsibility which are deemed to have a direct influence on brand image while tourism management and advertisement have been used as the moderators in the relationship between these six IVs and brand image. Data were collected from the customers of some selected 4 star and 5 star hotels in Bangladesh using multi-stage sampling procedure. The Partial Least Square Structural Equation Modeling (PLS-SEM) was used to test the hypotheses of this study. It was found that brand awareness, brand association, brand superiority, brand resonance and corporate social responsibility were significant factors influencing brand image of branded hotels. Among these five determinants, corporate social responsibility was found to be the most significant followed by brand superiority, brand association, brand awareness and brand resonance. In the moderating effect test of tourism management, it was found that the relationship between brand superiority and brand image was moderated by tourism management; the relationship between brand resonance and brand image was also found to be moderated by tourism management. On the other hand, advertisement was found to be a moderator in the relationship between brand awareness and brand image; brand association and brand image, and Corporate Social Responsibility (CSR) and brand image. Based on the findings, this study proposes a new model named “SAARCAT” to measure brand image of branded hotels. The current study gathers opinions from the diverse group of participants on the factors influencing the hotel brand image. Thus it supplements empirical evidence to address the research gaps and responds to the calls for examining the brand image of branded hotels within hotel industry. In this way it adds some new insights into the customer based brand equity models as proposed by Aker’s (1996), Keller’s (2003) and Baker’s (2001). The results derived from this research provide several practical implications for hotel managers and policy makers specifically, in terms of how to evaluate, build and improve the hotel brand image.
Multi-site linked travel cost models answer the need for aggregate demand and economic value estimations that consider the attributes of all forest recreation sites in a region and the preferences of visitors at the regional level. This study aimed to predict the aggregate value of forest recreation services in Ankara Province, Turkey, with a discrete choice-count data-linked model in the context of current and changing levels of forest attributes. In the first stage, the site choice model was predicted with the random parameter logit model; in the second stage, the trip demand model was estimated with the negative binomial model. The expected consumer surplus per trip derived from the first model was used as the link variable in the second model. The expected consumer surplus per trip of 14 forest recreation sites was estimated at 137.34 TL, and the annual aggregate consumer surplus per capita was estimated at 156.59 TL—a total of 13.49 million TL in the study area. This study predicts the total benefit will increase by 65% if the development stage of stands at recreation sites increases by two stages, degraded stands are improved, stands have a normal canopy, and the camping facilities are improved.
The author proposes a novel application for estimating demand for a single recreation site when data are limited. An aggregated zonal travel cost model is outlined and estimated in a panel setting using a negative binomial estimator with time-specific constants to capture unobserved time-varying quality attributes. The data are based on catch records for the Mörrum river salmon fishery, one of the most popular sport fishing destinations in Northern Europe. These are aggregated to the municipal level for the zonal estimation and give number of visits per municipality per week for the period 1999-2011. The results confirm the expected negative influence of travel cost on visits. Further, the inclusion of time-specific constants does not render all time-varying quality attributes insignificant.
One of the key strengths of SMILE is its facility to combine spatial data from many diverse sources in order to examine economic, social and environmental issues of importance to the rural economy in Ireland. Indeed, spatial context lies at the heart of many aspects of the rural economy and SMILE, as a modelling and data infrastructure, can be usefully applied in conjunction with techniques such as geographic information systems (GIS) analysis and microeconometrics to examine such issues. One such area of interest is rural tourism. Rural tourism is now an important contributor to rural development in Ireland given the long term decline of agriculture, particularly in its potential for stimulating employment and providing a viable option for off-farm diversification.
In Finland, cross-country skiing is part of the cultural heritage and an important leisure activity. However, its future is uncertain as, according to climate scenarios, southern Finland will be on the boundary of the winter snow cover. Behavioral responses to change raise the question of who will give up skiing and who will be able to continue the activity in a changed climate. In analyzing skiing participation and frequency, this study extends the multiple hierarchy stratification perspective (MHSP) by showing that the socio-demographic variables gender, age, socioeconomic status and living environment have an interaction effect, The results also indicate that female gender, a lower socioeconomic status and an urban living environment are associated with a higher sensitivity to climate change.
Leisure researchers' conceptions of how individuals make decisions involve an understanding of the personal tastes, motives, and private decisions that directly affect an individual's utility (satisfaction) of substitute site choices. Following recent theoretical advances in recreation demand, the participant is first viewed as deciding on the number of site trips to take per season and next, as deciding on how to allocate the trips across substitute sites. Decisions are analyzed by linking the results from a nested logit model to separate count-data models using consumer demand theory and trip-price indexes. Our purposes are two-fold. The first is to determine if the inclusion of the respondents' perceived importance of lake attributes in a nested logit model of lake boating improves the allocation of trips to the various lakes in a geographic region. The second is to demonstrate an empirical application of discrete-count modeling and to compare annual trip-counts of lake boating from a discrete-count model with a traditional pooled lakes model.
Ocean and coastal management regimes are increasingly subject to competing demands from stakeholders. Regulations must not only address fishing, recreation, and shipping, but also sand and gravel mining, gas pipelines, harbor/port development, offshore wind and tidal energy facilities, liquefied natural gas terminals, offshore aquaculture, and desalinization plants. The growing variety and intensity of ocean and coastal uses increases the call for a more holistic, comprehensive, and coordinated management approach that recognizes the often complex relationships between natural and human systems. For both economist and non-economist audiences, this book describes ways in which economic analysis can be an important tool to inform and improve ecosystem-based management (EBM). Topics include modeling economic impacts, benefit-cost analysis, spatial considerations in EBM, incentives and human behaviors, and accounting for uncertainty in policy analysis. Throughout the book the authors elucidate the different kinds of insights which can be gained from the use of different economic tools. In this rigorous and accessible work, the authors defy the conventional stereotype that economic perspectives necessarily favor the greatest commercial development. Instead, they demonstrate how comprehensive economic analyses consider the full range of potential services offered by marine and coastal ecosystems, including the conservation of biodiversity and creation of recreational opportunities.
Conceptual studies of place bonding have often stopped short of verifying the relationship of scales/models to other relevant recreation behavior variables. The current paper addresses this concern by comparing three models (full, parsimonious, and partial models) of place bonding and their scale measurement to a dependent variable, experience use history (EUH), for degree of predictive validity. Results reveal that the full and parsimonious place bonding models predicted more EUH variance than the partial model. Because the parsimonious model's fit and psychometric properties were as good as the full model, the parsimonious model is recommended over the other models. The parsimonious scale provides researchers with a robust and nuanced measure of people's attachment to place.
The aim of this paper is to analyze the factors of decision to stay in rural areas. The standard logit model has been often used for an analysis of rural recreation behavior. But I can not use this model, if the errors exhibit heteroscedasticity. Thus, I use the random parameter logit (RPL) model, which can be coped with heteroscedasticity.
To confirm whether the errors exhibit heteroscedasticity, I compare the RPL model with the logit model by likelihood ratio test. The result is that the RPL model is superior to logit model. Then, I hypothesize that the estimated parameter of the travel cost including staying cost is random. When the staying cost in Miyama Town is discounted by half, the probability of choice of staying rises to 9.1% and the social surplus is 0.10 billion yen.
This study is a methodological evaluation of studies on importance and performance measurement, and importance–performance analysis (IPA) which has gained widespread acceptance in the hospitality and tourism research. A synthesis of IPA literature on conceptual and measurement issues is presented with a view to identifying and mitigating potential validity concerns.
This work illustrates the use of a non-timber valuation model linked to a number-of-trips prediction model to examine the significance of various forestland attributes and hunter characteristics on hunting site choice and participation in the Foothills Model Forest in west-central Alberta. Data were obtained through a survey that featured determination of hunting trip locations over a three-year period. Clearcut density, fire density, two access variables and, in particular, habitat suitability for ungulate game species were found to be significant variables that influenced hunter site choice. Welfare estimates determined by the number-of-trips prediction model highlight the applicability of the linked model in presenting a more complete picture of the effects of two landscape change scenarios. These scenarios show how the models could be used by land managers to balance economic and social benefits in a bid to move towards sustainable development of forest resources.
Most new recreation demand (travel cost) models either do an adequate job of estimating allocation choice
among competing sites or total season's trips, but a poorer job of doing both simultaneously. Recently,
efforts have been made to link these two models. Two issues that arise are whether aggregation of demands provides a logically consistent price index for total season's trips and whether the linked models provide general welfare.measures for environmental or site quality changes. We develop and estimate an incomplete demand system which yields the true welfare measures for price and environmental or site quality changes. The system is developed by specifying a particular quasi-indirect utility function for closely related goods which is conditional on the total quantity of trips taken in a year. The resulting price index is a linear homogeneous argument of the aggregate demand function. The application is to rock climbing destinations in the northeastern United States.
The role of on-site trips in recreation demand modeling is examined by means of the travel cost method. An understanding of on-site choices of site attractions and costs incurred by visitors is important if analysts are to estimate the popularity of the various attractions and, ultimately, to derive site benefits. We investigate two primary questions. First, can on-site trips to attractions be adequately modeled? Next, how important is onsite travel in valuing site benefits? Visitor survey data for the North Carolina Zoological Park were used to determine that economic site benefits are underestimated by 24% if expected on-site travel costs from recreation demand analysis are excluded.
This paper investigates the role of congestion in influencing the site choice of recreation participants and derived values for a trip. Estimation is performed in a revealed preference framework by constructing an instrument for congestion within the estimation using the predicted choices of participants. The instrument is allowed to affect choices in two ways, directly influencing the choice of site and indirectly influencing the total number of trips in a season. The model is also linked to a model of behavior onsite to allow participants to mitigate the effects of greater congestion by spreading out over choices at the site.
El capital natural compuesto por la base de recursos naturales y ambientales como el agua, suelos, biodiversidad y paisaje, aire, entre otros se caracterizan por no contar con un precio que refleje su verdadero valor. En este escrito se presenta un manual de la teoría desarrollada por la economía del bienestar aplicado para estimar el valor de estos bienes y servicios caracterizados por no contar con precios de mercados. Se describen metodologías y se presentan estudios de caso con datos empíricos en donde se muestra como estimar el valor de estos recursos caracterizados por no contar con precios de mercado.
A study of 571 business and leisure travelers examined the environmentally friendly attributes that guests seek in hotel rooms. Using conjoint analysis of bundles of seven hotel room attributes, the study found that the most influential single attribute on hotel room preference for this sample was green hotel certification (such as LEED). These respondents were also supportive of rooms with a refillable shampoo dispenser, energy-efficient light bulbs, and towel and linen reuse policies. Guests also appreciated the idea of a key card that would control all energy use in the room. Relatively few respondents agreed that they would pay more to stay in a “green” room. The study included only respondents who indicated a willingness to stay in green rooms and as such cannot be generalized. However, the study found few differences between the preferences of the 284 business travelers and the 287 leisure travelers. Three findings differed from other research outcomes in the weight ascribed to green certification, the preference for a shampoo dispenser, and lobby-based recycling. The idea of an energy-controlling key card is also uncommon, but would represent a considerable retrofitting expense for most hotels. While these findings are of interest, one limitation is that only seven attributes were studied, and the inclusion of additional attributes—or more levels of the seven attributes—might alter the results. Nevertheless, these findings give hoteliers information about preferences of a substantial number of guests who would consider staying in a “green” hotel room.
This paper describes efforts to estimate economic benefits of improved sea lamprey (Petromyzon marinus) suppression on the St. Marys River. By linking an economic model of recreational fishing in Michigan to fish populations in the Great Lakes, a method is illustrated for estimating economic benefits that accrue to recreational anglers when fish populations increase. Previous economic efforts to evaluate sea lamprey control have taken a basin-wide view to determine optimal steady-state control levels based on economic injury levels, or have focused on whether or not the entire sea lamprey treatment program is justified. While capable of being adapted to either of these types of evaluations, the method presented here was used to estimate benefits to Michigan anglers of several sea lamprey treatments options for the St. Marys River. When estimated benefits are compared to treatment costs, all treatment options examined are shown to have different, but positive, net present value. Thus, the results suggest that sea lamprey suppression efforts on the St. Marys River yield economic benefits that exceed costs, and this holds even though only part of the economic benefits have been measured.
We propose a “partial aggregation” strategy for defining the recreation sites that enter choice sets in random utility models. Under the proposal, the most popular sites and sites that will be the subject of policy analysis enter choice sets as individual sites while remaining sites are aggregated into groups of similar sites. The scheme balances the desire to include all potential substitute sites in the choice sets with practical data and modeling constraints. Unlike fully aggregate models, our analysis and empirical applications suggest that the partial aggregation approach reasonably approximates the results of a disaggregate model. The partial aggregation approach offers all of the data and computational advantages of models with aggregate sites but does not suffer from the same degree of bias as fully aggregate models.
Participation and site choice for Atlantic salmon fishing are modeled in the context of a repeated three-level nested-logit model. Consumer's surplus measures are derived for different levels of species availability in the Penobscot River, the most important salmon river in New England. For comparison, six other travel-cost models are estimated. These include restrictive cases of the nested-logit model, a partial demand model, and two single-site demand models. Comparisons across these models indicate the importance of modeling the participation decision, including income effects, and of adopting a nested-logit structure rather than a single-level logit structure.
Truncated Poisson and truncated negative binomial count data models, as well as standard count data models, OLS, nonlinear normal, and truncated nonlinear normal MLE were used to estimate demand for deer hunting in California. The truncated count data estimators and their properties are reviewed. A large sample (N = 2223) allowed random segmenting of the data into specification, estimation, and out-of-sample prediction portions. Statistics of interest are therefore unbiased by the specification search, and the prediction results allow comparison of the statistical models' robustness. The new estimators are found to be more appropriate for estimating and predicting demand and social benefits than the alternative estimators based on a variety of criteria. -Authors
In order to control for censoring and the integer nature of trip demand, the use of count data models in travel cost analysis is attractive. Two such models, the Poisson and negative binomial, are discussed. Robust estimation techniques that loosen potentially stringent distributional assumptions are also reviewed. For illustrative purposes, several count data models are used to estimate a county-level travel cost model using permit data from the Boundary Waters Canoe Area.
Random Utility Models are widely applied in studies of recreation demand. The model is particularly useful when the number of recreation sites from which individuals may choose is large. Yet, when the number gets too large, say in the hundreds, estimation becomes burdensome. We present an analysis suggested by McFadden (1978) for dealing with large numbers of sites. We estimate a model using randomly drawn opportunity sets. We use each person's chosen site plus a random draw of as few as eleven other sites (when hundreds are available) to estimate a plausible behavioral model.
This paper deals with specification, estimation and tests of single equation reduced form type equations in which the dependent variable takes only non-negative integer values. Beginning with Poisson and compound Poisson models, which involve strong assumptions, a variety of possible stochastic models and their implications are discussed. A number of estimators and their properties are considered in the light of uncertainty about the data generation process. The paper also considers the role of tests in sequential revision of the model specification beginr ing with the Poisson case and provides a detailed application of the estimators and tests to a model of the number of doctor consultations.
In this article the author studies the properties of the two-step estimation method proposed by Domencich and McFadden (Urban Travel Demand, North-Holland, 1975) for a multivariate logit model and shows that it is consistent but asymptotically less efficient than the maximum likelihood estimator. Its computation, however, can be considerably simpler than that of the maximum likelihood estimator, especially in models involving several dependent variables.
Corruption in the public sector erodes tax compliance and leads to higher tax evasion. Moreover, corrupt public officials abuse their public power to extort bribes from the private agents. In both types of interaction with the public sector, the private agents are bound to face uncertainty with respect to their disposable incomes. To analyse effects of this uncertainty, a stochastic dynamic growth model with the public sector is examined. It is shown that deterministic excessive red tape and corruption deteriorate the growth potential through income redistribution and public sector inefficiencies. Most importantly, it is demonstrated that the increase in corruption via higher uncertainty exerts adverse effects on capital accumulation, thus leading to lower growth rates.
Recent micro-simulation studies of the demand for electricity by residences have attempted to model jointly the demand for appliance and the demand for electricity by appliance. Within this context it becomes important to test the statistical exogeneity of appliance dummy variables typically included in demand for electricity equations. If, as the theory would suggest, the demand for durables and their use are related decisions by the consumer, specifications which ignore this fact will lead to biased and inconsistent estimates of price and income elasticities. The present paper attempts to test this bias using a subsample of the 1975 survey of 3249 households carried out by the Washington Center for Metropolitan Studies for the Federal Energy Administration. We discuss and derive a unified model of the demand for consumer durables and the derived demand for electricity. To determine the magnitude of the bias resulting from estimating a unit electricity consumption equation by ordinary least squares when unobserved and estimate a joint water-heat space-heat choice model, and conclude with the consistent estimation and specification of demand for electricity equations.