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 make use of contingent behavior (CB) data; i.e., asking survey respondents to forecast their trip patterns under hypothetical quality conditions. However, relatively little is known 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 recreational lake usage in Iowa using observed and CB trip data collected from the 2004 Iowa Lake 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.     

Modeling Multiple-Objective Recreation Trips with Choices Over Trip Duration and Alternative Sites

Traditionally, recreation demand studies have focused on single-day, single-activity trips, despite anecdotal and empirical evidence that many recreational trips involve overnight stays and multiple purposes. This paper develops a random utility model that explores how visitors choose alternative sites and trip durations for multiple-objective trips. We focus on a recreational activity, beach visits, that appear to have significant proportions of the population taking single and multiple-day trips, and many of the multiple day trips involve multiple objectives. Multiple-duration and multiple-objective issues are incorporated in pricing trip costs. The results of the research suggest that the accepted method for incorporating travel costs into random utility models can lead to biased estimates of the structural utility parameters and, consequently, biased measures of welfare in a multiple-objective trip setting for single- and multiple-day users.     

COMPENSATORY RESTORATION IN A RANDOM UTILITY MODEL OF RECREATION DEMAND

Natural Resource Damage Assessment cases often call for compensation in non‐monetary or restoration equivalent terms. In this article, we present an approach that uses a conventional economic model, a travel cost random utility model of site choice, to determine compensatory restoration equivalents for hypothetical beach closures on the Gulf Coast of Texas. Our focus is on closures of beaches on the Padre Island National Seashore and compensation for day‐trip users. We identify restoration projects that compensate for beach closures and that have good alignment in terms of compensating those who actually suffer from the closures. (JEL Q26)     

Public acceptance of and heterogeneity in behavioral beach trip responses to offshore wind farm development in Catalonia (Spain)

A combined travel cost – contingent behaviour survey of residents and tourists in Catalonia is conducted on-site to examine the effects on beach recreational demand of developing an offshore wind farm (OWF) project. The survey considers four potential OWF scenarios with different degrees of visual impact. We allow for heterogeneity in trip preferences among individuals and control for on-site sampling through the use of a random parameters negative binomial (RPNB) model and a Multivariate Poisson log-normal (MPLN) model, respectively. The welfare measures derived from the RPNB model relate to the current beach users only, whereas those from the MPLN model refer to the general population of residents and tourists in Catalonia. The results show the importance of the specific place of location of the OWF project and how the installation of wind turbines would significantly decrease the demand for trips, depending on their degree of visual impacts, leading to a substantial welfare loss. However, the results also show that the project mainly would cause a displacement of trips to other beaches within Catalonia rather than outside Catalonia and that the welfare per trip measures generated by the RPNB and MPLN models substantially differ. Policy implications of these findings are discussed.     

A Random Parameter Model with Onsite Sampling for Recreation Site Choice: An Application to Southern California Shoreline Sportfishing

Estimation of consistent parameter estimates for recreational demand models faces challenges arising from the choice-based nature of the data collected primarily for resource management purposes. As an alternative to randomized respondent-based sampling, choice-based onsite sampling can provide information on actual choices made by a subset of the population where participation has a low incidence. While the literature has shown that under specific restrictions the estimation of choice models from onsite sampling data yields unbiased fixed parameter estimates for the conditional logit model, this result does not carry over to estimation of the random parameter logit model. We propose an estimator for the unbiased estimation of the random parameter model using choice-based data; our estimator uses weights based on information about the level of sampling effort. An empirical application of the standard and weighted discrete choice RUM models to onsite sample data on recreational fishing illustrates the advantages of the proposed estimator. The estimation results indicate the compensating variation associated with an decrease, or increase, of 50 % in expected catch rates for a recreational shoreline sportfishing trip to a man-made structure in southern California is $$-{\$}2.80$$ or $${\$}3.54$$ per trip, respectively.     

Combining stated and revealed choice research to simulate the neighbor effect: The case of hybrid-electric vehicles

According to intuition and theories of diffusion, consumer preferences develop along with technological change. However, most economic models designed for policy simulation unrealistically assume static preferences. To improve the behavioral realism of an energy–economy policy model, this study investigates the “neighbor effect,” where a new technology becomes more desirable as its adoption becomes more widespread in the market. We measure this effect as a change in aggregated willingness to pay under different levels of technology penetration. Focusing on hybrid-electric vehicles (HEVs), an online survey experiment collected stated preference (SP) data from 535 Canadian and 408 Californian vehicle owners under different hypothetical market conditions.Revealed preference (RP) data was collected from the same respondents by eliciting the year, make and model of recent vehicle purchases from regions with different degrees of HEV popularity: Canada with 0.17% new market share, and California with 3.0% new market share. We compare choice models estimated from RP data only with three joint SP–RP estimation techniques, each assigning a different weight to the influence of SP and RP data in coefficient estimates. Statistically, models allowing more RP influence outperform SP influenced models. However, results suggest that because the RP data in this study is afflicted by multicollinearity, techniques that allow more SP influence in the beta estimates while maintaining RP data for calibrating vehicle class constraints produce more realistic estimates of willingness to pay. Furthermore, SP influenced coefficient estimates also translate to more realistic behavioral parameters for CIMS, allowing more sensitivity to policy simulations.     

A variance equality test of the ICAPM on Philippine stocks: post-Asian financial crisis period

Analysis of the data generated from various residential time-of-day electricity pricing projects often involves estimation of a system of demand equations. Both ad hoc demand models and neoclassical demand models have been estimated, often resulting in considerably different estimates of price elasticities. In this paper, elasticity estimates are presented from each type of demand model using data from the Arizona and North Carolina rate demonstration projects. The relationship between the elasticities generated from ad hoc models and separable neoclassical models is explored and the divergent price elasticity estimates are reconciled.     

Meta-Analysis,Benefit Transfer,and Methodological Covariates: Implications for Transfer Error

Meta-regression models in the valuation literature demonstrate that willingness to pay estimates vary according to methodological factors. Neither theory nor characteristics of policy sites dictate the treatment of associated covariates within benefit transfer, however, and the literature provides few insights into potential impacts of common empirical treatments. This paper introduces a method to systematically characterize the impact of methodological variables on transfer error. Using a repeated leave-one-out convergent validity framework, the analysis contrasts errors for a hypothetical ideal case in which correct methodological covariate treatments are known to the realistic case in which the correct treatment is unknown. Results indicate that the common assumption of mean values for methodological covariates leads to only a modest increase in mean transfer error relative to that found in the hypothetical ideal case.     

Functional forms and habit effects in the US demand for coffee

Coffee remains the leading hot beverage consumed in the United States. The present study specifies for estimation an unrestrictive Box-Cox demand model (1957–87 data) and its nested forms, to provide more recent estimates and assess potential functional misspecification in past studies fitting a priori restrictive models; incorporates the (health trend) effects of sugar and orange juice and tests the compatibility of coffee demand data with the habit

formation hypothesis; and assesses implications of demand elasticity estimates for projected producer revenues and demand for imported coffee. Maximum-likelihood estimates of the Box-Cox model: automatically satisfy theoretical demand properties; support strongly incorporating habits and related beverage and sugar prices in coffee demand model; predict inelastic US coffee consumption to reduce per capita 24% and increase producer revenues 6.3% in 2000.     

A demand and supply analysis of productivity growth

The paper rejects growth accounting as failing to reveal the economic forces that drive growth. Instead, it seeks to explain changing productivity growth in terms of economic phenomena such as the changing structure of output, the rate of adoption of new technology, and the strength of aggregrate demand. We introduce such a model and test it using pooled cross section and time series data for 16 OECD economies over a 30 year period. The parameter estimates allow us to decompose each economy's productivity growth into the part caused by its changing structure and the part explained by demand conditions. The estimates are used to account for the productivity slowdown that occurred in these economies after 1973, and to examine the recent productivity increase in the US. The model fully explains this growth surge in terms of the changed demand factors and structure of the US economy. We conclude by arguing that a prime benefit of strong aggregate demand is its stimulation of investment and technological change, leading to the adoption of new technology on a broad front.     

The effect of temporal closures and individual quotas on fishing trip duration: a hazard function analysis

In this article, we assess the effect that two different fishery management regimes have on the duration of the fishing trip. A basic theoretical model predicts that trip duration should increase with temporal closures and decrease with an individual quota system. Therefore, we propose and apply an empirical trip duration model. Estimations are based on data for the pelagic fleet in central-southern Chile. Conforming to the theoretical predictions, the results indicate that temporal closures tend to increase trip duration, whereas individual quotas reduce it. Moreover, the regulatory regimes also affect the magnitude of the impact that other determinants have on trip duration. The results are consistent with increased efficiency in fleet operation under an individual quota system.     

SIZE MATTERS: THE ECONOMIC VALUE OF BEACH EROSION AND NOURISHMENT IN SOUTHERN CALIFORNIA

Despite the widespread use of nourishment in California, few studies estimate the welfare benefits of increased beach width. This paper relies on panel data funded by National Oceanic and Atmospheric Administration and other agencies. Beach choices of respondents were combined with beach attribute data to reveal how changes in width affect choice and the economic value of beach visits. We use a random‐utility approach to show that the value of beach width varies for different types of beach uses: water contact, sand‐, and pavement‐based activities. We also find that the marginal value of beach width depends on initial beach width. (JEL Q50)     

Latent Consideration Sets and Continuous Demand Systems

This paper develops a theoretically consistent continuous demand system model that incorporates latent, probabilistic consideration sets. In contrast to existing discrete choice consideration models, the proposed model is econometrically tractable with consumption data for many goods. The model’s empirical properties are illustrated with an 89-site recreation data set from the 1994 National Survey of Recreation and the Environment (NSRE). Parameter and welfare estimates suggest that the latent consideration set models fit the data better and may imply a bias-variance tradeoff relative to traditional models.      

Structural shocks and dynamic elasticities in a long memory model of the US gasoline retail market

A structural multivariate long memory model of the US gasoline market is employed to disentangle structural shocks and to estimate the own-price elasticity of gasoline demand. Our main empirical findings are: (1) there is strong evidence of nonstationarity and mean reversion in the real price of gasoline and in gasoline consumption; (2) accounting for the degree of persistence present in the data is essential to assess the responses of these two variables to structural shocks; (3) the contributions of the different supply and demand shocks to fluctuations in the gasoline market vary across frequency ranges; and (4) long memory makes available an interesting range of convergent possibilities for gasoline demand elasticities. Our estimates suggest that after a change in prices, consumers undertake a few measures to reduce consumption in the short- and medium-run but are reluctant to implement major changes in their consumption habits.     

Exploring the effects of aggregation error in the estimation of consumer demand elasticities

Errors introduced by using aggregate data in estimating a consumer demand model have long been a concern. We study the effects of such errors on elasticity estimates derived from AIDS and QUAIDS models. Based on a survey of published articles, a generic parameterization of the income distribution, and the range of Gini coefficients reported for 28 OECD countries, we generate and analyze a large number of “observations” on the differences between elasticities calculated at the aggregate level and those calculated at the micro level. We suggest a procedure for evaluating the likely range of aggregation error when a model is estimated with aggregate data.     

Estimating elasticities of residential energy demand from panel county data using dynamic random variables models with heteroskedastic and correlated error terms

A dynamic random variables model correcting for heteroskedastic and correlated error terms over time and space and dynamic demand and using panel county data offers consistent and efficient elasticity estimates of residential electricity and natural gas demands. The model developed by Swamy [Swamy, P.A.V.B., 1974. Linear models with random coefficients. In: P. Zarembka (Eds.), Frontiers in Econometrics, Academic Press, London, pp. 143–168.] with a modification suggested by Maddala et al. [Maddala, G.S., Trost, R.P., Li, H., Joutz, F., 1997. Estimation of short-run and long-run elasticities of energy demand from panel data using shrinkage estimators. Journal of Business and Economic Statistics 15, 90–100.] uses a panel of selected California counties for the years 1983–1997 to yield elasticity estimates that differ from those obtained from more standard panel data procedures.     

Beach ‘lovers’ and ‘greens’: A worldwide empirical analysis of coastal tourism

This paper examines worldwide tourist coastal destination choice using a comprehensive global dataset at the country level, for both domestic and international tourists. This data includes a systematic profile of the countries' coastline with respect to economic and natural environments, such as marine biodiversity related indicators. Tourist demand is modelled using a system of simultaneous structural equations estimated by a 3SLS routine. We identify two tourist demand segments, denoting different preferences for the worldwide coastal destinations. International tourists choose their coastal destination because they have a strong preference for the cultural and natural environments. This, in turn, depends on the destination of country's coastal habitat abundance and marine biodiversity. We label this segment of coastal tourism, as “greens”. Alternatively, domestic tourists have a preference for beach characteristics, in particular beach length. This in turn depends on anthropogenic pressure, the built environment and climatic variables. For this reason we interpret this tourism segment as “beach lovers”. This information is, in turn, of high significance for stimulating coastal tourism demand as well as for identifying market based policy instruments with the objective to finance the conservation of environmental and cultural capital hosted at the coastal communities.     

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.     

Reassessing forest products demand functions in Europe using a panel cointegration approach

In a panel of European countries, we analyse paper products, sawnwood and wood panels consumption data. With this object, we use a classical demand model where national consumption depends on real GDP and real prices. In contrast to previous panel estimations in the literature, we highlight non-stationarity time series which can lead to spurious regressions. We explicitly take into account the issue by using recent panel cointegration techniques. Cointegration is present for printing paper and fibreboard, though less clear cut for other products. Then we estimate demand elasticities and find that GDP elasticities are significantly lower than estimates from the literature. Finally, we simulate the implications of modified demand elasticities by using a partial equilibrium model of the forest sector. For most products, changes in elasticities would lead to lower projected demand and lower prices over a 20-year time horizon. Lower demand for solid wood and wood fibre would lead to less tensions with fuel wood- and wood-based chemical markets. In a context of rising interest for renewable bio-based products, updated long-term demand models contribute to the analysis of the forest sector’s sustainability.