Rounding in Recreation Demand Models: A Latent Class Count Model

A commonly observed feature of visitation data, elicited via a survey instrument, is a greater propensity for individuals to report trip numbers that are multiples of 5's, relative to other possible integers (such as 3 or 6). One explanation of this phenomenon is that some survey respondents have difficulty recalling the exact number of trips taken and instead choose to round their responses. This paper examines the impact that rounding can have on the estimated demand for recreation and the bias that it may induce on subsequent welfare estimates. We propose the use of a latent class structure in which respondents are assumed to be members of either a nonrounding or a rounding class. A series of generated data experiments are provided to illustrate the range of possible impacts that ignoring rounding can have on the estimated parameters of the model and on the welfare implications from site closure. The results suggest that biases can be substantial, particularly when then unconditional mean number of trips is in the range from two to four. An illustrative application is provided using visitation data to Saylorville Lake in central Iowa.recreation demand; count data; rounding

Convergent Validity of Contingent Behavior Responses in Models of Recreation Demand

Recreation demand modeling efforts are often limited by the range of variation in observed environmental quality. To address this limitation, the practitioners increasingly makes use of contingent behavior (CB) data; i.e., asking survey respondents to forecast their trip patterns under hypothetical quality conditions. However, relatively little is know as to whether these stated responses are consistent with how households response to actual quality variation. The purpose of this paper is to investigate the convergent validity of CB data with observed trip patterns. Toward this end, we jointly model the recreation lake usage for in Iowa using observed and CB trip data collected from the 2004 Iowa Lakes Survey. The Iowa lakes survey collected three sets of trip data for 131 lakes in the state: (a) actual trips in 2004, (b) anticipated trips in 2005 to the same lakes given current lake conditions and (c) anticipated trips in 2005 given hypothetical improvements to a subset of the lakes. The three types of recreation demand data provide a unique opportunity to investigate the convergent validity of individual responses to actual versus hypothetical environmental conditions.

Model Uncertainty in Characterizing Recreation Demand

A Bayesian variable selection procedure is used to control for uncertainty in the specification of a recreational demand model. Specifically, we propose a model that draws on the Bayesian paradigm to integrate the variable selection process into the model and reflect the accompanying uncertainty about which is the “best†specification used for counterfactual predictions. The advantage of this procedure over previous non-Bayesian approaches is that overcomes the problem of pre-testing in specification searches.

CHOICE SET DEFINITION ISSUES IN A KUHN-TUCKER MODEL OF RECREATION DEMAND

Much of the literature on choice sets has focused on how alternative specifications of market scope and site definition impact site selection models and the resulting welfare estimates per choice occasion. In this paper, choice set definition issues are investigated using the Kuhn-Tucker model, which integrates the site selection and participation decisions in a unified and utility theoretic framework. This allows us to consider the impact that alternative site set definitions may have on both where individuals recreate and the numbers of trips they take. Using data from the 1997 Iowa Wetlands Survey we examine the effects on estimates and welfare measures of choice sets representing various levels of site aggregation and market scope. We find that significant differences in welfare measures arise from changing choice set definitions.Resource /Energy Economics and Policy,

Controlling for Observed and Unobserved Site Characteristics in Rum Models of Recreation Demand

�Random Utility Maximization (RUM) models of recreation demand are typically plagued by limited information on environmental and other attributes characterizing the available sites in the choice set. To the extent that these unobserved site attributes are correlated with the observed characteristics and/or the key travel cost variable, the resulting parameter estimates and subsequent welfare calculations are likely to be biased. In this paper we develop a Bayesian approach to estimating a RUM model that incorporates a full set of alternative specific constants, insulating the key travel cost parameter from the influence of the unobserved site attributes. In contrast to estimation procedures recently outlined in Murdock (2006), the posterior simulator we propose (combining data augmentation and Gibbs sampling techniques) can be used in the more general mixed logit framework in which some parameters of the conditional utility function are random. Following a series of generated data experiments to illustrate the performance of the simulator, we apply the estimation procedures to data from the Iowa Lakes Project. In contrast to an earlier study using the same data (Egan \textit{et al.} \cite{eganetal}), we find that, with the addition of a full set of alternative specific constants, water quality attributes no longer appear to influence the choice of where to recreate.nonmarket valuation; water quality; discrete choice

Capturing Preferences Under Incomplete Scenarios Using Elicited Choice Probabilities.

Manski (1999) proposed an approach for dealing with a particular form respondent uncertainty in discrete choice settings, particularly relevant in survey based research when the uncertainty stems from the incomplete description of the choice scenarios. Specifically, he suggests eliciting choice probabilities from respondents rather than their single choice of an alternative. A recent paper in IER by Blass et al. (2010) further develops the approach and presents the first empirical application. This paper extends the literature in a number of directions, examining the linkage between elicited choice probabilities and the more common discrete choice elicitation format. We also provide the first convergent validity test of the elicited choice probability format vis-\`a-vis the standard discrete choice format in a split sample experiment. Finally, we discuss the differences between welfare measures that can be derived from elicited choice probabilities versus those that can obtained from discrete choice responses.discrete choice; Elicited Choice Probabilities

Kuhn-Tucker Estimation of Recreation Demand – A Study of Temporal Stability

The paper examines the Kuhn Tucker model in the context of estimating recreation demand when the choice set it very large. It examines the temporal stability of parameter estimates using multiple years of data on trips to 127 lakes in Iowa made by households in Iowa. The study finds that for the given dataset, the estimates derived from a Kuhn-Tucker model are largely stable over time.Recreation demand, Kuhn-Tucker, Temporal Stability, Environmental Economics and Policy, C2, Q2,

Estimation and Welfare Calculations in a Generalized Corner Solution Model with an Application to Recreation Demand

The Kuhn-Tucker model of Wales and Woodland (1983) provides a utility theoretic framework for estimating preferences over commodities for which individuals choose not to consume one or more of the goods. Due to the complexity of the model, however, there have been few applications in the literature and little attention has been paid to the problems of welfare analysis within the Kuhn-Tucker framework. This paper provides an application of the model to the problem of recreation demand. In addition, we develop and apply a methodology for estimating compensating variation, relying on Monte Carlo integration to derive expected welfare changes.

What's the Use? Welfare Estimates from Revealed Preference Models when Weak Complementarity Does Not Hold

In this paper we consider the theoretical and empirical ramifications of welfare measurement in revealed preference models when weak complementarity does not hold. In the context of a Kuhn-Tucker model of recreation demand we show that, while it is possible to estimate preferences that do not appear to exhibit weak complementarity, the calculation of welfare measurements from these models requires a cardinal interpretation of preferences that cannot be tested. Furthermore, we reiterate the under-appreciated fact that even traditional use value estimates require a cardinal restriction on preferences that, while often intuitive, also cannot be tested. We demonstrate empirically that the choice of restrictions can have significant ramifications, as use value estimates can vary widely based on the assumed preference structure.