Jorge E. Araña

Emotions and decision rules in discrete choice experiments for valuing health care programmes for the elderly

The evaluation of health care programmes is commonly approached with stated preference methods such as contingent valuation or discrete choice experiments. These methods provide useful information for policy decisions involving health regulations and infrastructures for health care. However, econometric modelling of these data usually relies on a number of maintained assumptions, such as the use of the compensatory or random utility maximization rule. On the other hand, health policy issues can raise emotional concerns among individuals, which might induce other types of choice behaviour. In this paper we consider potential deviations from the general compensatory rule, and how these deviations might be explained by the emotional state of the subject. We utilized a mixture econometric model which allows for various potential decisions rules within the sample, such as the complete ignorance, conjunctive rule and satisfactory rules. The results show that deviations from the full linear compensatory decision rule are predominant, but they are significantly less observed for those subjects with a medium emotional state about the issue of caring for the health state of the elderly. The implication is that the emotional impact of health policy issues should be taken into account when making assumptions of individual choice behaviour in health valuation methods.

A Comparison of Tourists’ Valuation of Climate Change Policy Using Different Pricing Frames

Tourism destinations are increasingly concerned about global climate change and considering to become involved in the adoption of mitigation policies that reduce global emissions. On the other hand, behavioral sciences have shown that consumers’ choices may be significantly influenced by the way that they are framed. In this article, we test the impact of alternative ways to frame the pricing of climate change policies on the values and preferences of tourists. The evidence comes from three different experiments on a proposal to mitigate climate change through a carbon offset program using both an opt-out pricing frame and in an opt-in pricing frame. The results show that the opt-out frame led to a larger probability of accepting to pay the price for a carbon offset policy proposal. The main implication is that destinations’ climate change policies should take into account the designs of the frame in which policies are posed.

Correcting for Scale Perception Bias in Tourist Satisfaction Surveys

Tourist satisfaction analysis provides important information that contributes to design public and private policies in tourist services and destinations. However, the responses to tourist satisfaction scales can lead to biased results when comparing tourists from different nationalities and backgrounds, since they might use different internal scale interpretations to assess their satisfaction level in a survey. In this article, the anchoring vignette methodology is used to test and correct for scale response bias in tourist satisfaction surveys. The results of the empirical application find out that scale response is not homogeneous across tourists from different nationalities and that the correction for scale differences can lead to an evaluation of tourist satisfaction that more accurately informs and facilitates improvements in policy decisions at tourist destinations and operators.

CO2 Emissions and tourism in developed and less developed countries

Tourism is a major contributor to CO2 emissions and therefore to climate change. In this article, we look at the relationships between CO2 emissions and tourism in the context of both developed and less developed countries. We utilize a STIRPAT approach applied to a balanced panel data of developed and less developed countries for the period from 1998 to 2006. The results show that tourism contributes significantly to CO2 emissions in both less developed and developed countries. However, the impact in developed countries is larger than in less developed countries. Tourism should find sustainable development paths involving less CO2 emissions in the production and consumption of tourist services.

The Dynamics of Preference Elicitation after an Environmental Disaster: Stability and Emotional Load

Nonmarket valuation is commonly applied to infer the preferences of individuals for restoration policies after an environmental disaster. A crucial issue in this task is to determine the appropriate lapse of time after which the valuation techniques should be applied. This study investigates the role of the emotional load in explaining the dynamic patterns of elicited preferences. The results show that preferences tend to stabilize when the emotional load is also stable. The main implication is that attitudinal investigation of emotions could provide satisfactory information for determining the time frame for implementing more costly nonmarket valuation studies. (JEL Q51, Q54)

Economic Valuation of Tourism Destination Image

This paper develops a novel methodology for estimating the value of destination image, which incorporates two principal advantages over the methods used to date. First, it allows tourism destination image to be assessed in economic terms, so a formal cost–benefit analysis can be executed to ascertain whether or not a specific marketing action should be implemented. Second, it enables a disentangling of the economic assessment of tourist destination image in terms of destination attributes. This can be used to design marketing actions aimed at optimizing marketing efforts to enhance a destinations image.

Modelling unobserved heterogeneity in contingent valuation of health risks

Human preferences for alternative levels of health risks can be heterogeneous. In this paper a flexible distribution approach to model health values elicited with the dichotomous choice contingent valuation method is considered. Rigid parametric structures cannot model sample heterogeneity while imposing strong assumptions on the error distribution. A mixture of normal distributions is considered which can approximate arbitrary well any empirical distributions as the number of mixtures increases. The model is applied to data on willingness to pay for reducing the individual risk of an episode of respiratory illness. The mixture distribution model is compared with the rigid probit model using a Bayes factor test. The results show that the mixture modelling approach improves performance while allowing for the consideration of alternative groups of individuals with different preferences for health risks.

Designing Tourist Information Offices The Role of the Human Factor

Rapid technological change is leading to the introduction of new ways of providing services in the tourism industry. However, the human factor is also key in providing satisfaction to visitors. This article evaluates the preferences of visitors for different service designs at tourist information offices (TIOs). The methodology used is discrete choice experiments. Results show that visitors place higher values on information services received through personal interaction than through automated processes based on new technology. The implication is that the personal interaction continues to be an important element in the design of TIOs, but that new technology may increase the quality of the provision of services and visitor satisfaction. The methodology employed also allows us to identify different visitors’ segments based on their preferences for TIO services.

Tourists' Evaluation of Climate Change Risks in the Canary Islands: A Heterogeneous Response Modelling Approach:

This paper compares the differences between the monetary measures of willingness to accept and willingness to pay (WTP) for changes in climate change risk. The empirical evidence comes from tourists in the Canary Islands. The modelling approach utilizes a Bayesian mixture of normal distribution model that allows the authors to consider heterogeneity across both WTP and willingness to accept (WTA) question formats. The results show that the WTP for lower risks is lower than the WTA higher risks, and that the disparity between these measures depends on the market segments, the specific climate change risk and the characteristics of the individuals.

Bayesian Estimation of Dichotomous Choice Contingent Valuation with Follow-Up

Dichotomous choice contingent valuation involves a binary yes/no question that can be followed by a subsequent question in order to obtain more information from the respondent, and leading to more efficient welfare estimates. Estimation methods for these data have mainly focused on classical maximum likelihood. In this paper we study possible improvements utilising a Bayesian MCMC approach to model this type of data. The classical and Bayesian approaches are compared with a Monte Carlo simulation experiment. The results show that the Bayesian approach improves the performance of the model, particularly with relatively small samples.