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Many times governments and policy makers have to choose among different projects or policies to implement. In principle, the best choice is the one which maximizes the social welfare that, in turn, depends on individual preferences. But very often preferences are unknown and even not observable. In practice, a common procedure is to directly ask a sample of individuals about their preferences, which are therefore stated by agents rather than revealed by their behaviour. Methods for preference revelation can be classified into two broad families. The first one involves the case in which respondents are asked to simulate their market behaviour in a fictitious context designed by the researcher. The final goal of these studies is the estimation of willingness to pay (WTP), or willingness to accept (WTA), for changes in provision of non-market goods. A large literature investigates both theoretical issues connected with these procedures (Bates, 1988) and empirical results from country experiences (Mackie at al., 2003). The second family of surveys are commonly employed in public opinion analysis. In this case respondents are asked to reveal their current attitudes, whilst in some circumstances they are required to state their satisfaction with a certain policy or service. In the last decades the interest towards such analysis largely increased and a broad amount of surveys have been systematically collected (Rabin, 2002). Whatever the kind of analysis, when individuals correctly report the behaviour they would keep in a real context, or honestly admit their attitudes and perceptions, the target of the policy maker is reached. Hence, the issue of reliability of stated preferences becomes crucial in order to understand what we can learn from surveys and how SP analysis can be exploited by policy makers. Our research question is simply the following one: can we trust in SP methods? In order to answer this question the work is organised in three sections. The first one is devoted to the definition of the concept of "reliability". In the first place, the latter depends on the family of SP methods we are dealing with. When individuals are required to replicate their market behaviour in a fictitious scenario, two perspectives can be applied: the first one based on mainstream economic theory (Hicks and Allen, 1934) and the other one in accordance to the so called behavioural programme (Sunstein and Thaler, 2008). Both approaches are discussed, pointing out the problematic issues which characterise each methodology and trying to propose a definition for the concept of reliability. The second family of surveys can be classified into two sub-groups, based on the object of the analysis. The first group includes all situations where agents are required to reveal their actual behaviour (Bertrand and Mullainathan, 2001) while the second one is composed by those studies in which agents are asked to express their feelings or perceptions about a certain aspect of their life (McFadden et al. 2005). Again, the concept of reliability has been investigated for each group of surveys. The second and the third sections are devoted to empirical works which try, recalling the definition of reliability suggested in the first chapter, to apply this concept to empirical studies.

Dissertação de mestrado em Health Economics and Policy ; Introduction: In Portugal, the pharmaceutical consumption is subsidized by public funds. The rising NHS expenditures and the recent need of cost containment policies emphasize the discussion on priority setting in health care and raise questions of which criteria are appropriate to support funding decisions. Decision-makers base the pharmaceutical funding grant on clinical and economical evidence. Vulnerable sub groups, such as chronically ill and elderly with low income, benefit of higher financing rates than the general population. Little is known about the preferences of the public for pharmaceutical funding criteria in Portugal. Discrete Choice Experiments (DCEs) are suitable for the estimation of stated preferences as they measure of benefit that describes the good through a bundle of attributes and levels and it is based on the assumption that an individual's valuation depends upon the levels of these attributes. DCE have the potential to contribute to outcome measurement for use in economic evaluation, uniquely allowing the investigation of diverse questions, such as clinical, economic and ethical. Aim: This work seeks to investigate criteria considered important by the Portuguese public for allocating resources for pharmaceuticals. In particular, we estimate the importance of the severity of the disease for which the treatment is indicated, the prevalence of the disease in Portugal, the efficacy of the pharmaceutical and the government costs per person treated. Method: A self-completion DCE survey, with 18 binary choice sets, was administered to two samples of the general population. Choice data are used to consider the relative importance of changes across attribute levels, and to model utility scores and relative probabilities. Results: A total of 90 individual completed the DCE. For the levels and units presented in the DCE, all attributes were statistically significant, in both samples. The attributes "severity of the disease for which the ...

The Relative Utility Pricing model is used to explain the fact that when faced with two "safety packs", the second giving three times the safety benefit of the first, discriminating respondents will place a value on the second pack that is, on average, twice the amount they say they will be prepared to pay for the first. When the safety packs reduce fatal accident frequencies, the "value of a prevented fatality" (VPF) figures deduced from the valuations of the two safety packs must then be significantly different. Such response patterns on the part of respondents were found in a high-profile study carried out on behalf of a number of UK Government Departments. However, the authors of that study considered the responses "aberrant", and dismissed their survey in favour of their later one, which they based on a novel elicitation technique and which led to a VPF that was lower by a factor of between 5 and 10. That method has been shown elsewhere to be invalid, which returns the focus to the original study rejected by its authors. This paper shows that the VPFs produced by the first study are fully explicable and cannot be dismissed if the stated preference approach is to be accepted. However, in view of the difficulties experienced with stated preference techniques in the valuation of life, it is clear that an urgent reappraisal is needed of revealed preference techniques if people's safety is to be safeguarded adequately.

BACKGROUND AND OBJECTIVE: In health preference research, studies commonly hypothesize differences in parameters (i.e., differential or joint effects on attribute importance) and/or in choice predictions (marginal effects) by observable factors. Discrete choice experiments may be designed and conducted to test and estimate these observable differences. This guide covers how to explore and corroborate various observable differences in health preference evidence. METHODS: The analytical process has three steps: analyze the exploratory data, analyze the confirmatory data, and interpret and disseminate the evidence. In this guide, we demonstrate the process using dual samples (where exploratory and confirmatory samples were collected from different sources) on 2020 US COVID-19 vaccination preferences; however, investigators may apply the same approach using split samples (i.e., single source). RESULTS: The confirmatory analysis failed to reject ten of the 17 null hypotheses generated by the exploratory analysis (p < 0.05). Apart from demographic, socioeconomic, and geographic differences, political independents and persons who have never been vaccinated against influenza are among those least likely to be vaccinated (0.838 and 0.872, respectively). CONCLUSIONS: For all researchers in health preference research, it is essential to know how to identify and corroborate observable differences. Once mastered, this skill may lead to more complex analyses of latent differences (e.g., latent classes, random parameters). This guide concludes with six questions that researchers may ask themselves when conducting such analyses or reviewing published findings of observable differences.

AbstractWhen making choices over jobs with different characteristics, what trade‐offs are decision‐makers willing to make? Such a question is difficult to address using typical household surveys that provide a limited amount of information on the attributes of the jobs. To address this question, a small but growing number of studies have turned to the use of stated preference experiments; but the extent to which stated choices by respondents reflect systematic trade‐offs across job characteristics remains an open question. We use two popular types of experiments (profile case best–worst scaling and multi‐profile case best–worst scaling) to elicit job preferences of nursing students and junior nurses in Australia. Each person participated in both types of experiments twice, within a span of about 15 months. Using a novel joint likelihood approach that links a decision‐maker's preferences across the two types of experiments and over time, we find that the decision‐makers make similar trade‐offs across job characteristics in both types of experiments and in both time periods, except for the trade‐off between salary and other attributes. The valuation of salary falls significantly over time relative to other job attributes for both types of experiments. Also, within each period, salary is less valued in the profile case compared to the more traditional multi‐profile case.