Willingness to Pay for Rural Landscape Improvements: Combining Mixed Logit and Random-Effects Models

This paper reports the findings from a discrete-choice experiment designed to estimate the economic benefits associated with rural landscape improvements in Ireland. Using a mixed logit model, the panel nature of the dataset is exploited to retrieve willingness-to-pay values for every individual in the sample. This departs from customary approaches in which the willingness-to-pay estimates are normally expressed as measures of central tendency of an a priori distribution. Random-effects models for panel data are subsequently used to identify the determinants of the individual-specific willingness-to-pay estimates. In comparison with the standard methods used to incorporate individual-specific variables into the analysis of discrete-choice experiments, the analytical approach outlined in this paper is shown to add considerable explanatory power to the welfare estimates.

Using choice experiments to explore the spatial distribution of willingness to pay for rural landscape improvements

We report findings from a choice experiment survey designed to estimate the economic benefits of policy measures to improve the rural landscape in the Republic of Ireland. Using a panel mixed logit specification to account for unobserved taste heterogeneity we derived individual-specific willingness-to-pay (WTP) estimates for each respondent in the sample. We subsequently investigated the spatial dependence of these estimates. Results suggest the existence of positive spatial autocorrelation for all rural landscape attributes. As a means of benefit transfer, kriging methods were employed to interpolate WTP estimates across the whole of the Republic of Ireland. The kriged WTP surfaces confirm the existence of spatial dependence and illustrate the implied spatial variation and regional disparities in WTP for all the rural landscape improvements investigated.

Parametric and non-parametric estimates of willingness to pay for forest recreation in Northern Ireland: A discrete choice contingent valuation study with follow-ups

In this study we show that forest areas contribute significantly to the estimated benefits from om outdoor recreation in Northern Ireland. Secondly we provide empirical evidence of the gains in the statistical efficiency of both benefit and parameter estimates obtained by analysing follow-up responses with Double Bounded interval data analysis. As these gains are considerable, it is clearly worth considering this method in CVM survey design even when moderately large sample sizes are used. Finally we demonstrate that estimates of means and medians of WTP distributions for access to forest recreation show plausible magnitude, are consistent with previous UK studies, and converge across parametric and non-parametic methods of estimation.

Utility in Willingness to Pay Space: A Tool to Address Confounding Random Scale Effects in Destination Choice to the Alps

We compare two approaches for estimating the distribution of consumers' willingness to pay (WTP) in discrete choice models. The usual procedure is to estimate the distribution of the utility coefficients and then derive the distribution of WTP, which is the ratio of coefficients. The alternative is to estimate the distribution of WTP directly. We apply both approaches to data on site choice in the Alps. We find that the alternative approach fits the data better, reduces the incidence of exceedingly large estimated WTP values, and provides the analyst with greater control in specifying and testing the distribution of WTP. © 2008 Agricultural and Applied Economics Association.

The marginal willingness-to-pay for attributes of a hypothetical HIV vaccine

This paper estimates the marginal willingness-to-pay for attributes of a hypothetical HIV vaccine using discrete choice modeling. We use primary data from 326 respondents from Bangkok and Chiang Mai, Thailand, in 2008–2009, selected using purposive, venue-based sampling across two strata. Participants completed a structured questionnaire and full rank discrete choice modeling task administered using computer-assisted personal interviewing. The choice experiment was used to rank eight hypothetical HIV vaccine scenarios, with each scenario comprising seven attributes (including cost) each of which had two levels. The data were analyzed in two alternative specifications: (1) best-worst; and (2) full-rank, using logit likelihood functions estimated with custom routines in Gauss matrix programming language. In the full-rank specification, all vaccine attributes are significant predictors of probability of vaccine choice. The biomedical attributes of the hypothetical HIV vaccine (efficacy, absence of VISP, absence of side effects, and duration of effect) are the most important attributes for HIV vaccine choice. On average respondents are more than twice as likely to accept a vaccine with 99% efficacy, than a vaccine with 50% efficacy. This translates to a willingness to pay US$383 more for a high efficacy vaccine compared with the low efficacy vaccine. Knowledge of the relative importance of determinants of HIV vaccine acceptability is important to ensure the success of future vaccination programs. Future acceptability studies of hypothetical HIV vaccines should use more finely grained biomedical attributes, and could also improve the external validity of results by including more levels of the cost attribute.