Klaus Moeltner

Specification of Driving Costs in Models of Recreation Demand

Existing recreation demand models have paid much attention to the heterogeneous nature of the opportunity cost of time, but generally stipulate constant per mile costs in access price specifications. This study proposes two alternative approaches to introduce user-specific driving costs into recreation demand models. The first approach is based on a refined measurement of driving costs based on engineering considerations. The second strategy estimates perceived per mile cost as a function of vehicle attributes in an empirical framework. We find strong evidence that driving costs are a visitor-specific concept, and that prescribed and perceived costs differ substantially. However, welfare measures generated by these alternative specifications are not statistically different from those produced by the standard model in our application of jet skiing in the Lake Tahoe region. (JEL Q26)

The Value of Snowfall to Skiers and Boarders

An interesting winter sport phenomenon inrecent years has been the growth ofsnowboarding. Snowboarding has outpaced skiingat many resorts and has become the snow ridingactivity of choice for many young people. Thisstudy develops an empirical demand model forwinter sport trips amongst college studentsfrom both camps and estimates economic welfareassociated with the two different activities. The results show that both trip demand andsurplus values are strongly affected by snowconditions. These effects are distinctlydifferent for the two consumer groups.

Correcting for On-Site Sampling in Random Utility Models

This study demonstrates how the joint distribution of a set of conditional trip counts to a system of recreation-sites can be adjusted for on-site sampling. An econometric approach is proposed that addresses both the size-biased distribution of the sampled visits and the weighted distribution of reported visits to ancillary destinations in a multivariate random utility framework. Estimation results indicate that uncorrected models produce biased estimates of trip counts and welfare measures. The empirical application examines jet skiing in the Lake Tahoe region.

Choice Behavior Under Time-Variant Quality: State Dependence Versus

In past studies of consumer loyalty changes in brand attributes over time were generally unobservable and treated as additional model parameters. In this study we consider ski resorts, for which observable quality attributes change frequently. Using a repeated-purchase model with observed time-variant brand attributes, indicators for state dependence, and individual heterogeneity, we show that purchase history and time-variant site characteristics have a significant and offsetting effect on repurchase decisions. This suggests a third category of consumer along with habit formers and variety seekers, the “play-it-by-ear” type, who, unaffected by purchase history, moves across brands in pursuit of high quality.

Addressing aggregation bias in zonal recreation models

Abstract Models of recreation demand are often based on zonal data. Results from such models are susceptible to aggregation bias. We propose a zonal model of recreation that captures some of the underlying heterogeneity of individual visitors by incorporating distributional information on per-capita income from census sources into the aggregate demand function. This adjustment eliminates the unrealistic constraint of constant income across zonal residents, and thus reduces the risk of aggregation bias in estimated parameters. In addition, the corrected aggregate specification reinstates the applicability of generalized maximum-likelihood methods, and increases model efficiency.

The Cost of Power Outages to Heterogeneous Households

We use a repeated dichotomous choice contingent valuation survey to elicit households’ willingness to pay to a void unannounced interruptions in electricity service. The data pose multiple econometric challenges including: correlated responses for a given household, heteroskedastic errors, and a willingness to pay distribution with large mass near zero. We address these issues by combining a gamma distribution for outage costs with a lognormally distributed scale parameter defined as a function of household characteristics, outage attributes, outage history, and random coefficients. The model is estimated through simulated maximum likelihood. We demonstrate that cost estimates are sensitive to the interaction of attributes of previously experienced and hypothetical interruptions.

Cross-Context Benefit Transfer: A Bayesian Search for Information Pools

Commodity equivalence and population similarity are two widely accepted paradigms for the valid transfer of welfare estimates across resource valuation contexts. We argue that strict adherence to these rules may leave relevant information untapped, and propose a Bayesian model search algorithm that examines the probabilities with which two or more sub-sets of meta-data, each corresponding to a different combination of commodity and population, share common value distributions. Using a large meta-data set of willingness-to-pay for diverse outdoor activities across various regions of the United States as an example, we find strong potential for contexts that would not traditionally be considered as transfer candidates to form information pools. Exploiting these commonalities leads to substantial efficiency gains for benefit estimates.

Latent Thresholds Analysis of Choice Data under Value Uncertainty

In many non-market valuation settings stakeholders will be uncertain as to their exact willingness-to-pay for a proposed environmental amenity. It then makes sense for the analyst to treat this value as a random variable with distribution only known to the respondent. In stated preference settings, researchers have used elicitation formats with multiple bids and uncertain-response options to learn about individual value distributions. Past efforts have focused on inference involving the expectation of individual densities. This requires stringent and likely unrealistic assumptions regarding the shape or moments of individual value distributions. We propose a Latent Thresholds Estimator that focuses instead on the range, i.e. minimum and maximum willingness-to-pay of individual respondents. The estimator efficiently exploits correlation patterns in individual responses and does not require any restrictive assumptions on underlying values. It also nests some of the existing approaches, which are not statistically supported for our empirical application. Copyright 2012, Oxford University Press.

Valuing the Prevention of an Infestation: The Threat of the New Zealand Mud Snail in Northern Nevada

The Truckee/Carson/Walker River watershed in northern Nevada is under an imminent threat of infestation by the New Zealand mud snail, an aquatic nuisance species with the potential to harm recreational fisheries. We combine a utility-theoretic system-demand model of recreational angling with a Bayesian econometric framework to provide estimates of trip and welfare losses under different types of regulatory control policies. We find that such losses can be substantial, warranting immediate investments in preemptive strategies via public outreach and awareness campaigns.

Cloud-Sourcing: Using an Online Labor Force to Detect Clouds and Cloud Shadows in Landsat Images

We recruit an online labor force through Amazon.com’s Mechanical Turk platform to identify clouds and cloud shadows in Landsat satellite images. We find that a large group of workers can be mobilized quickly and relatively inexpensively. Our results indicate that workers’ accuracy is insensitive to wage, but deteriorates with the complexity of images and with time-on-task. In most instances, human interpretation of cloud impacted area using a majority rule was more accurate than an automated algorithm (Fmask) commonly used to identify clouds and cloud shadows. However, cirrus-impacted pixels were better identified by Fmask than by human interpreters. Crowd-sourced interpretation of cloud impacted pixels appears to be a promising means by which to augment or potentially validate fully automated algorithms.