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George R. Parsons - One of the best experts on this subject based on the ideXlab platform.
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Willingness to pay for electric vehicles and their attributes
Resource and Energy Economics, 2011Co-Authors: Michael K. Hidrue, Willett Kempton, George R. Parsons, Meryl P. GardnerAbstract:This article presents a stated preference study of electric vehicle choice using data from a national survey. We used a choice experiment wherein 3029 respondents were asked to choose between their preferred gasoline vehicle and two electric versions of that preferred vehicle. We estimated a latent class random utility model and used the results to estimate the willingness to pay for five electric vehicle attributes: driving range, charging time, fuel cost saving, pollution reduction, and performance. Driving range, fuel cost savings, and charging time led in importance to respondents. Individuals were willing to pay (wtp) from $35 to $75 for a mile of added driving range, with incremental wtp per mile decreasing at higher distances. They were willing to pay from $425 to $3250 per hour reduction in charging time (for a 50 mile charge). Respondents capitalized about 5 years of fuel saving into the purchase price of an electric vehicle. We simulated our model over a range of electric vehicle configurations and found that people with the highest values for electric vehicles were willing to pay a premium above their wtp for a gasoline vehicle that ranged from $6000 to $16,000 for electric vehicles with the most desirable attributes. At the same time, our results suggest that battery cost must drop significantly before electric vehicles will find a mass market without subsidy. © 2011 Elsevier B.V.
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compensatory restoration in a random utility model of recreation demand
Contemporary Economic Policy, 2010Co-Authors: George R. Parsons, Ami K KangAbstract:"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) Copyright (c) 2010 Western Economic Association International.
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state dependence and long term site capital in a random utility model of recreation demand
2008Co-Authors: Matthew D Massey, George R. ParsonsAbstract:Conventional discrete choice random utility Maximization (RUM) models of recreation demand ignore the influence of knowledge, or site capital, gained over past trips on current site choice, despite its obvious impact. We develop a partially dynamic RUM model that incorporates a measure of site capital as an explanatory variable in an effort to address this shortcoming. To avoid the endogeneity of past and current trip choices, we estimate an auxiliary instrumental variable regression to purge site capital of its correlation with the error terms in current site utility. Our instrumental variable regression gives a fitted value ranging between 0 and 1 for each alternative for each person – a prediction of whether or not a person visited a site. Results suggest that the presence of accumulated site capital is an important predictor of current trips, and that failure to account for site capital will likely lead to underestimates of potential welfare effects.
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the effect of nesting structure specification on welfare estimation in a random utility model of recreation demand an application to the demand for recreational fishing
American Journal of Agricultural Economics, 2000Co-Authors: Brett A Hauber, George R. ParsonsAbstract:Nested logit has become common in estimating random utility models of recreation demand. Because welfare analysis is often the objective of estimating these models, it seems natural to ask, what effect does the choice of nesting structure have on the welfare estimates generated by these models? Therefore, we compare the results of nine nesting structures andfindthat the variation in welfare estimates across the models is not large. Our results are contrary to those of Kling and Thomson and Shaw and Ozog. The difference appears to originate with differences in the estimated dissimilarity coefficients in the nested models. Copyright 2000, Oxford University Press.
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narrow choice sets in a random utility model of recreation demand
Land Economics, 2000Co-Authors: George R. Parsons, Andrew J Plantinga, Kevin J BoyleAbstract:We consider the implications of narrow choice sets on welfare estimation in a random utility model of recreation demand. We hypothesize that careful formulation of the choice set focusing on the sites of policy interest and their closest substitutes will give reasonably accurate welfare estimates. We use nearby sites as close substitutes and treat more distant sites as aggregate alternatives in our application to fishing in Maine. We find that the welfare estimates are rather sensitive to narrowing choice sets in this manner. The sensitivity largely tracts variation in the estimated travel cost coefficient across the different models considered.
Jan J V Busschbach - One of the best experts on this subject based on the ideXlab platform.
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the episodic random utility model unifies time trade off and discrete choice approaches in health state valuation
Population Health Metrics, 2009Co-Authors: Benjamin M Craig, Jan J V BusschbachAbstract:Background To present an episodic random utility model that unifies time trade-off and discrete choice approaches in health state valuation.
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The episodic random utility model unifies time trade-off and discrete choice approaches in health state valuation
Population Health Metrics, 2009Co-Authors: Benjamin M Craig, Jan J V BusschbachAbstract:Background To present an episodic random utility model that unifies time trade-off and discrete choice approaches in health state valuation. Methods First, we introduce two alternative random utility models (RUMs) for health preferences: the episodic RUM and the more common instant RUM. For the interpretation of time trade-off (TTO) responses, we show that the episodic model implies a coefficient estimator, and the instant model implies a mean slope estimator. Secondly, we demonstrate these estimators and the differences between the estimates for 42 health states using TTO responses from the seminal Measurement and Valuation in Health (MVH) study conducted in the United Kingdom. Mean slopes are estimates with and without Dolan's transformation of worse-than-death (WTD) responses. Finally, we demonstrate an exploded probit estimator, an extension of the coefficient estimator for discrete choice data that accommodates both TTO and rank responses. Results By construction, mean slopes are less than or equal to coefficients, because slopes are fractions and, therefore, magnify downward errors in WTD responses. The Dolan transformation of WTD responses causes mean slopes to increase in similarity to coefficient estimates, yet they are not equivalent (i.e., absolute mean difference = 0.179). Unlike mean slopes, coefficient estimates demonstrate strong concordance with rank-based predictions (Lin's rho = 0.91). Combining TTO and rank responses under the exploded probit model improves the identification of health state values, decreasing the average width of confidence intervals from 0.057 to 0.041 compared to TTO only results. Conclusion The episodic RUM expands upon the theoretical framework underlying health state valuation and contributes to health econometrics by motivating the selection of coefficient and exploded probit estimators for the analysis of TTO and rank responses. In future MVH surveys, sample size requirements may be reduced through the incorporation of multiple responses under a single estimator.
Benjamin M Craig - One of the best experts on this subject based on the ideXlab platform.
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the episodic random utility model unifies time trade off and discrete choice approaches in health state valuation
Population Health Metrics, 2009Co-Authors: Benjamin M Craig, Jan J V BusschbachAbstract:Background To present an episodic random utility model that unifies time trade-off and discrete choice approaches in health state valuation.
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The episodic random utility model unifies time trade-off and discrete choice approaches in health state valuation
Population Health Metrics, 2009Co-Authors: Benjamin M Craig, Jan J V BusschbachAbstract:Background To present an episodic random utility model that unifies time trade-off and discrete choice approaches in health state valuation. Methods First, we introduce two alternative random utility models (RUMs) for health preferences: the episodic RUM and the more common instant RUM. For the interpretation of time trade-off (TTO) responses, we show that the episodic model implies a coefficient estimator, and the instant model implies a mean slope estimator. Secondly, we demonstrate these estimators and the differences between the estimates for 42 health states using TTO responses from the seminal Measurement and Valuation in Health (MVH) study conducted in the United Kingdom. Mean slopes are estimates with and without Dolan's transformation of worse-than-death (WTD) responses. Finally, we demonstrate an exploded probit estimator, an extension of the coefficient estimator for discrete choice data that accommodates both TTO and rank responses. Results By construction, mean slopes are less than or equal to coefficients, because slopes are fractions and, therefore, magnify downward errors in WTD responses. The Dolan transformation of WTD responses causes mean slopes to increase in similarity to coefficient estimates, yet they are not equivalent (i.e., absolute mean difference = 0.179). Unlike mean slopes, coefficient estimates demonstrate strong concordance with rank-based predictions (Lin's rho = 0.91). Combining TTO and rank responses under the exploded probit model improves the identification of health state values, decreasing the average width of confidence intervals from 0.057 to 0.041 compared to TTO only results. Conclusion The episodic RUM expands upon the theoretical framework underlying health state valuation and contributes to health econometrics by motivating the selection of coefficient and exploded probit estimators for the analysis of TTO and rank responses. In future MVH surveys, sample size requirements may be reduced through the incorporation of multiple responses under a single estimator.
Mary Jo Kealy - One of the best experts on this subject based on the ideXlab platform.
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a demand theory for number of trips in a random utility model of recreation
Journal of Environmental Economics and Management, 1995Co-Authors: George R. Parsons, Mary Jo KealyAbstract:We present a simple random utility model of recreation site choice that incorporates an aggregate demand function for number of trips during a season. We derive the trip demand function using conventional demand theory and use it to calculate seasonal welfare changes due to improvements in site characteristics or addition of new sites. The model is based on Bockstael, et al.′s participation function.
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benefits transfer in a random utility model of recreation
Water Resources Research, 1994Co-Authors: George R. Parsons, Mary Jo KealyAbstract:We divide a data set on lake recreation in Wisconsin into two nonoverlappmg samples, Milwaukee residents and non-Milwaukee residents. We then consider several hypothetical benefit transfers from a non-Milwaukee-based random utility model to Milwaukee residents. All transfers are for measuring water quality improvements. We consider transfers in which we assume no information on Milwaukee residents, limited (no behavioral) information, and some behavioral information. We consider simple transfers, model transfers, and updated transfers. In all cases we test the viability of the transfer by comparing it with benefits estimated from a random utility model estimated over the Milwaukee sample, which in effect is our “true” model. The values from the model and updated transfers typically deviated less than 10% from the true values. Confidence intervals are estimated by using the Krinsky-Robb procedure for all of the benefit measures computed from the random utility model.
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randomly drawn opportunity sets in a random utility model of lake recreation
Land Economics, 1992Co-Authors: George R. Parsons, Mary Jo KealyAbstract: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.
Stefano De Luca - One of the best experts on this subject based on the ideXlab platform.
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a random utility model for park carsharing services and the pure preference for electric vehicles
Transport Policy, 2016Co-Authors: Armando Cartenì, Ennio Cascetta, Stefano De LucaAbstract:Most of the existing Carsharing business models mainly rely on gasoline vehicles and diesel vehicles, but in recent years there has been a significant increase in hybrid electric vehicles (HEVs) and a resurgence in electric vehicles (EVs). Within this framework, this paper investigates and models the choice to switch from a private car trip to a carsharing service available in peripheral parks as well as the propensity to choose an electric vehicle for such a service. In particular, three issues are addressed: (i) investigating and modelling the propensity to choose carsharing as a transport alternative within a neighbourhood residential carsharing business model; (ii) estimating the effect of also having an EV option available; (iii) measuring the “pure preference”, if any, in using electric vehicles over traditional ones, in a context excluding factors that may bias such users preference (e.g. purchase price, energy costs, recharging facilities etc). The analyses are based on a stated preferences survey undertaken on 600 car drivers entering the city centre of Salerno (Southern Italy), and on the estimation of a binomial Logit model with serial correlation. Results allow an interpretation of the main determinants of the short-term choice of carsharing services (i.e. without any car-ownership changes), give general behavioural insights, make it possible to quantify the “pure preference” for EV and the demand elasticity with regard to different pricing strategies of the carsharing services.
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A random utility model for park & carsharing services and the pure preference for electric vehicles
Transport Policy, 2016Co-Authors: Armando Cartenì, Ennio Cascetta, Stefano De LucaAbstract:Most of the existing Carsharing business models mainly rely on gasoline vehicles and diesel vehicles, but in recent years there has been a significant increase in hybrid electric vehicles (HEVs) and a resurgence in electric vehicles (EVs). Within this framework, this paper investigates and models the choice to switch from a private car trip to a carsharing service available in peripheral parks as well as the propensity to choose an electric vehicle for such a service. In particular, three issues are addressed: (i) investigating and modelling the propensity to choose carsharing as a transport alternative within a neighbourhood residential carsharing business model; (ii) estimating the effect of also having an EV option available; (iii) measuring the "pure preference", if any, in using electric vehicles over traditional ones, in a context excluding factors that may bias such users preference (e.g. purchase price, energy costs, recharging facilities etc). The analyses are based on a stated preferences survey undertaken on 600 car drivers entering the city centre of Salerno (Southern Italy), and on the estimation of a binomial Logit model with serial correlation. Results allow an interpretation of the main determinants of the short-term choice of carsharing services (i.e. without any car-ownership changes), give general behavioural insights, make it possible to quantify the "pure preference" for EV and the demand elasticity with regard to different pricing strategies of the carsharing services.
