Nonparametric and Semi-Nonparametric Recreational Demand Analysis (original) (raw)
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A TRAVEL COST ESTIMATION OF CONSUMER SURPLUS IN RECREATIONAL VISITS: A COUNT MODEL APPROACH
Assam Economic Journal , 2022
Recreational services are demanded because they generate benefits. The recreational benefits connected with a destination can be valued based on visitor preferences, which can aid in the formulation of an appropriate Natural Resource Management policy. Environmental and natural resource management studies often attempt to quantify the welfare shift caused by a policy change. In general, welfare is defined as the area under the demand curve; and accordingly, by estimating the demand curve, consumer surplus is obtained, which illustrates the welfare changes connected with an environmental policy change. But unfortunately, conventional markets fail to determine the recreational demand preferences for lack of a proper price mechanism. So the only choice is a non-market method. The Travel Cost Method (TCM) is a well-documented demand based method in environmental literature for measuring such non-marketed recreational benefits. This study attempts to estimate the consumer surplus, in the recreational demand for the Dibru Saikhowa National Park (DSNP), Assam with the help of TCM. In the process, it also seeks to answer the question of whether the existing user charges of DSNP reflect the true recreational demand assigned to the park by its visitors. The findings of the study are expected to be useful for different stakeholders associated with the park's conservation in general and its recreational services in particular, including policy makers.
Demand and Welfare Effects in Recreational Travel Models: A Bivariate Count Data Approach
2005
In this paper we present a non-linear demand system for households' joint choice of number of trips and days to spend at a destination. The approach, which facilitates welfare analysis of exogenous policy and price changes, is used empirically to study the effects of an increased CO2 tax. In the empirical study, a bivariate zero-inflated Poisson lognormal regression model is
Environmental Management, 2009
One of the basic assumptions of the travel cost method for recreational demand analysis is that the travel cost is always incurred for a single purpose recreational trip. Several studies have skirted around the issue with simplifying assumptions and dropping observations considered as non-conventional holiday-makers or as non-traditional visitors from the sample. The effect of such simplifications on the benefit estimates remains conjectural. Given the remoteness of notable recreational parks, multi-destination or multi-purpose trips are not uncommon. This paper examines the consequences of allocating travel costs to a recreational site when some trips were taken for purposes other than recreation and/or included visits to other recreational sites. Using a multi-purpose weighting approach on data from Gros Morne National Park, Canada, we conclude that a proper correction for multi-destination or multi-purpose trip is more of what is needed to avoid potential biases in the estimated effects of the price (travel-cost) variable and of the income variable in the trip generation equation.
A cross-model comparison of travel time inclusion techniques in recreational fishing demand analysis
Incorporating travel time into recreational demand analysis is a much debated topic. The aim of this paper is to contribute to discourse surrounding the travel cost method by analysing data from a bass angling tournament, the Amatola Bass Classic, held annually in the Eastern Cape Province of South Africa. Firstly, data collected from 64 respondents were used to test the goodness-of-fit of four models: the Standard Poisson, the Zero-Truncated Poisson, the Negative Binomial and the Zero-Truncated Negative Binomial. These models were then utilised to gauge the difference between two standard ways of treating time, by including it both as a separate parameter and as an intrinsic part of travel costs. The data were found to be overdispersed, pointing to a greater level of reliability in the Negative Binomial Models. Using all four models it was shown that the incorporation of time into the overall cost of the trip produced results which were better able to explain the amount of trips taken by anglers than those model specifications where time was included as a separate parameter.
Estimating Demand for Recreational Fishing in Alabama Using Travel Cost Model
2009
Individuals and households reveal their willingness to pay to enjoy environmental and natural resource services by engaging in outdoor recreation activities. The state of Alabama and the Black-Belt region possess significant recreational fishing resources whose qualities could be improved through public and private management innovations. To measure the value of such interventions, a baseline estimate of recreational fishing demand and potential for increasing the demand by on-site improvements needs to established. Using direct mail survey, count data obtained on individual angler characteristics, expenditures on fishing equipment, and destinations and expenditures on time and travel for each trip taken. In addition, the kinds and quantities of fish that anglers sought on each trip were obtained. This paper employs a full a full economic analysis based on recreation demand models—a.k.a. Travel Cost models (TCM). The travel costs’ Negative Binomial regression reveals that the averag...
Transportation Research Part A: Policy and Practice, 2012
In this paper we present a non-linear demand system for households' joint choice of number of trips and days to spend at a destination. The approach, which facilitates welfare analysis of exogenous policy and price changes, is used empirically to study the e¤ects of an increased CO 2 tax. In the empirical study, a bivariate zero-in ‡ated Poisson lognormal regression model is introduced in order to accommodate the large number of zeroes in the sample. The welfare analysis reveals that the equivalent variation (EV) measure, for the count data demand system, can be seen as an upper bound for the households welfare loss. Approximating the welfare loss by the change in consumer surplus, accounting for the positive e¤ect from longer stays, imposes a lower bound on the households welfare loss. From a distributional point of view, the results reveal that the CO 2 tax reform is regressive, in the sense that low income households carry a larger part of the tax burden.
Estimating Forest Recreation Demand Using Count Data Models
Forestry Sciences, 2003
Forests, along with related natural areas such as mountains, lakes, and rivers, provide opportunities for a wide variety of recreational activities. Although the recreational services supplied by forested areas produce value for the consumers of those services, the measurement of recreational value is complicated by the fact that access to most natural areas is non-priced. Because outdoor recreation often competes with commodity uses of forests, such as timber harvesting or mineral extraction, failure to account for the recreational use of forest land makes it impossible to determine the efficient use of forest resources. A key insight attributed to Harold Hotelling is that the price of recreational access can be inferred from information on travel costs. Subsequent development of this idea was undertaken by Marion Clawson (1959) and, a few years later, articulated in a general work on the economics of outdoor recreation (Clawson and Knetsch 1966). The basic Hotelling-Clawson-Knetsch (HCK) approach to estimating recreation demand is to statistically regress the number of trips taken to a recreational site on the round-trip cost of travel between trip origins and the site. A set of demand shift variables are also typically included in the specification to control for socioeconomic characteristics of visitors, indicators of site quality, and costs associated with visiting substitute sites. Once a travel cost demand curve is estimated, the value of a recreational site can be computed by integrating the area under the demand curve. Two types of data can be used to estimate travel cost models (see, for example, Bockstael et al. 1991 and Freeman 1993). The early studies Sills and Abt (eds.
Methodological Approach in Estimating the Demand for Recreational Sites
2018
Basically, there are two main problems faced in the recreation location especially in outdoor. One side, the rate of visiting some tourism objects is still low until now. It raises an assumption that recreation location doesn’t create any opportunity in regional and domestic economy. On the other hand, undervalue of recreation services, based on the willingness to pay caused a very low attractiveness of investment in tourism objects. To solve these problems, one should be able to predict the number of recreation location demand, so that a good planning and development could be implemented in this estimated area. One of the very common methods to calculate this demand is to use the travel cost methods. Many independent variable could be implemented in a multiple linear regression model, depends on the objective of the research. Somehow, a valid data is necessary in the application of statistical and quantitative analysis. Experiences showed a significant result of analysis using this...
A Contingent Trip Model for Estimating Rail-trail Demand
Journal of Environmental Planning and Management, 2003
The authors develop a contingent trip model to estimate the recreation demand for and value of a potential rail-trail site in north-east Georgia. The contingent trip model is an alternative to travel cost modelling useful for ex ante evaluation of proposed recreation resources or management alternatives. The authors estimate the empirical demand for trips using a negative binomial regression specification. Their findings indicate a per-trip consumer surplus rangingfrom US$l8.46 to US$29.23 and a price elasticity of -0.68. In aggregate, they estimate that the rail-trail would receive approximately 416 213 recreation visits per year by area households and account for a total consumer surplus in excess of US$7.5 million.
Augmenting travel cost models with contingent behavior data
Environmental and Resource Economics, 1996
This paper proposes contingent behavior survey questions as a valuable supplement to observed data in travel cost models of non-market demand for recreational resources. A set of observed and contingent behavior results for each survey respondent allows the researcher to control for individual heterogeneity by taking advantage of panel data methods when exploring the nature of respondent demands. The contingent scenarios also provide opportunities to (a) test for differences between observed and contingent preferences and/or (b) assess likely demands under conditions beyond the domain of observed variation in costs or resource attributes. Most importantly, contingent scenarios allow the researcher to impose exogenously varying travel costs. Exogenous imposition of travel costs together with panel methods reduces the omitted variables bias that plagues observed-data travel cost models of recreational demand. Using a convemence sample of data for illustrative purposes, we show how to estimate the demand for recreational angling by combining observed and contingent behavior data. We begin with simple naive pooled Poisson models and progress to more theoretically appropriate fixed effects panel Poisson specifications.