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.

Site choices in recreational demand: a matter of utility maximization or regret minimization?

This paper compares the Random Regret Minimization and the Random Utility Maximization models for determining recreational choice. The Random Regret approach is based on the idea that, when choosing, individuals aim to minimize their regret – regret being defined as what one experiences when a non-chosen alternative in a choice set performs better than a chosen one in relation to one or more attributes. The Random Regret paradigm, recently developed in transport economics, presents a tractable, regret-based alternative to the dominant choice paradigm based on Random Utility. Using data from a travel cost study exploring factors that influence kayakers’ site-choice decisions in the Republic of Ireland, we estimate both the traditional Random Utility multinomial logit model (RU-MNL) and the Random Regret multinomial logit model (RR-MNL) to gain more insights into site choice decisions. We further explore whether choices are driven by a utility maximization or a regret minimization paradigm by running a binary logit model to examine the likelihood of the two decision choice paradigms using site visits and respondents characteristics as explanatory variables. In addition to being one of the first studies to apply the RR-MNL to an environmental good, this paper also represents the first application of the RR-MNL to compute the Logsum to test and strengthen conclusions on welfare impacts of potential alternative policy scenarios.

Preferences for a third-trimester ultrasound scan in a low-risk obstetric population: a discrete choice experiment

Objective: Establish maternal preferences for a third-trimester ultrasound scan in a healthy, low-risk pregnant population.

Design: Cross-sectional study incorporating a discrete choice experiment.

Setting: A large, urban maternity hospital in Northern Ireland.

Participants: One hundred and forty-six women in their second trimester of pregnancy.

Methods: A discrete choice experiment was designed to elicit preferences for four attributes of a third-trimester ultrasound scan: health-care professional conducting the scan, detection rate for abnormal foetal growth, provision of non-medical information, cost. Additional data collected included age, marital status, socio-economic status, obstetric history, pregnancy-specific stress levels, perceived health and whether pregnancy was planned. Analysis was undertaken using a mixed logit model with interaction effects.

Main outcome measures: Women's preferences for, and trade-offs between, the attributes of a hypothetical scan and indirect willingness-to-pay estimates.

Results: Women had significant positive preference for higher rate of detection, lower cost and provision of non-medical information, with no significant value placed on scan operator. Interaction effects revealed subgroups that valued the scan most: women experiencing their first pregnancy, women reporting higher levels of stress, an adverse obstetric history and older women.

Conclusions: Women were able to trade on aspects of care and place relative importance on clinical, non-clinical outcomes and processes of service delivery, thus highlighting the potential of using health utilities in the development of services from a clinical, economic and social perspective. Specifically, maternal preferences exhibited provide valuable information for designing a randomized trial of effectiveness and insight for clinical and policy decision makers to inform woman-centred care.

It's not that I don't care, I just don't care very much: confounding between attribute non-attendance and taste heterogeneity

With the growing interest in the topic of attribute non-attendance, there is now widespread use of latent class (LC) structures aimed at capturing such behaviour, across a number of different fields. Specifically, these studies rely on a confirmatory LC model, using two separate values for each coefficient, one of which is fixed to zero while the other is estimated, and then use the obtained class probabilities as an indication of the degree of attribute non-attendance. In the present paper, we argue that this approach is in fact misguided, and that the results are likely to be affected by confounding with regular taste heterogeneity. We contrast the confirmatory model with an exploratory LC structure in which the values in both classes are estimated. We also put forward a combined latent class mixed logit model (LC-MMNL) which allows jointly for attribute non-attendance and for continuous taste heterogeneity. Across three separate case studies, the exploratory LC model clearly rejects the confirmatory LC approach and suggests that rates of non-attendance may be much lower than what is suggested by the standard model, or even zero. The combined LC-MMNL model similarly produces significant improvements in model fit, along with substantial reductions in the implied rate of attribute non-attendance, in some cases even eliminating the phenomena across the sample population. Our results thus call for a reappraisal of the large body of recent work that has implied high rates of attribute non-attendance for some attributes. Finally, we also highlight a number of general issues with attribute non-attendance, in particular relating to the computation of willingness to pay measures.

Addressing inequalities in physical activity participation: Implications for public health policy and practice

OBJECTIVE: To investigate the characteristics of those doing no moderate-vigorous physical activity (MVPA) (0days/week), some MVPA (1-4days/week) and sufficient MVPA (≥5days/week) to meet the guidelines in order to effectively develop and target PA interventions to address inequalities in participation.

METHOD: A population survey (2010/2011) of 4653 UK adults provided data on PA and socio-demographic characteristics. An ordered logit model investigated the covariates of 1) participating in no PA, 2) participating in some PA, and 3) meeting the PA guidelines. Model predictions were derived for stereotypical subgroups to highlight important policy and practice implications.

RESULTS: Mean age of participants was 45years old (95% CI 44.51, 45.58) and 42% were male. Probability forecasting showed that males older than 55years of age (probability=0.20; 95% CI 0.11, 0.28), and both males (probability=0.31; 95% CI 0.17, 0.45) and females (probability=0.38; 95% CI 0.27, 0.50) who report poor health are significantly more likely to do no PA.

CONCLUSIONS: Understanding the characteristics of those doing no MVPA and some MVPA could help develop population-level interventions targeting those most in need. Findings suggest that interventions are needed to target older adults, particularly males, and those who report poor health.

Content Validity in Landscape Valuation: A transdisciplinary approach to valuing the landscape of the peninsula of Sorrento, Italy.

This paper addresses the representation of landscape complexity in stated preferences research. It integrates landscape ecology and landscape economics and conducts the landscape analysis in a three-dimensional space to provide ecologically meaningful quantitative landscape indicators that are used as variables for the monetary valuation of landscape in a stated preferences study. Expected heterogeneity in taste intensity across respondents is addressed with a mixed logit model in Willingness to Pay space. The results suggest that the integration of landscape ecology metrics in a stated preferences model provides useful insights for valuing landscape and landscape changes

Stated preference methods and landscape ecology indicators: an example of transdisciplinarity in landscape economic valuation

This paper addresses the representation of landscape complexity in stated preferences research. It integrates landscape ecology and landscape economics and conducts the landscape analysis in a three-dimensional space to provide ecologically meaningful quantitative landscape indicators that are used as variables for the monetary valuation of landscape in a stated preferences study. Expected heterogeneity in taste intensity across respondents is addressed with a mixed logit model in Willingness to Pay space. Our methodology is applied to value, in monetary terms, the landscape of the Sorrento Peninsula in Italy, an area that has faced increasing pressure from urbanization affecting its traditional horticultural, herbaceous, and arboreal structure, with loss of biodiversity, and an increasing risk of landslides. We find that residents of the Sorrento Peninsula would prefer landscapes characterized by large open views and natural features. Residents also appear to dislike heterogeneous landscapes and the presence of lemon orchards and farmers' stewardship, which are associated with the current failure of protecting the traditional landscape. The outcomes suggest that the use of landscape ecology metrics in a stated preferences model may be an effective way to move forward integrated methodologies to better understand and represent landscape and its complexity.

Random Regret Minimization: Exploration of a New Choice Model for Environmental and Resource Economics

This paper introduces the discrete choice model-paradigm of Random Regret Minimization (RRM) to the field of environmental and resource economics. The RRM-approach has been very recently developed in the context of travel demand modelling and presents a tractable, regret-based alternative to the dominant choice-modelling paradigm based on Random Utility Maximization-theory (RUM-theory). We highlight how RRM-based models provide closed form, logit-type formulations for choice probabilities that allow for capturing semi-compensatory behaviour and choice set-composition effects while being equally parsimonious as their utilitarian counterparts. Using data from a Stated Choice-experiment aimed at identifying valuations of characteristics of nature parks, we compare RRM-based models and RUM-based models in terms of parameter estimates, goodness of fit, elasticities and consequential policy implications.