Research team
Expertise
My research is situated within statistical methodology. An important area of expertise is the design and analysis of discrete choice experiments (DCEs) to quantify people’s preferences. Besides that, I work on the development and application of econometric and data science methods in health. Results of DCEs that I have conducted in collaboration with practitioners have not only appeared in international publications, but have also easily found their way into the popular and professional press, with policy-relevant articles in Economisch Statistische Berichten, De Standaard and Le Soir, among others.
Managing the statistical aspects of the project 'Establishing a decision method for vaccination policy in Flanders'.
Abstract
In the context of the government contract 'Establishing a decision method for vaccination policy in Flanders' issued by the Flemish Agency for Care and Health, all statistical aspects will be performed of the discrete choice experiment that will be conducted among the Flemish population and among vaccination experts. This includes (1) the design of the experiment, (2) the cleaning of the data sets that are retrieved, (3) the analysis of the resulting data sets, (4) the formulation of conclusions, (5) summarizing and writing down the studies in one or more reports and (6) developing these reports into international publications in renowned journals. For the interpretation of the discrete choice experiment, reference is made to the technical file with specification N° AP/IZ-VAC/2018/2.Researcher(s)
- Promoter: Kessels Roselinde
Research team(s)
Project type(s)
- Research Project
Empirical and methodological challenges in choice experiments
Abstract
Economic values are usually revealed in the market place. However, no such mechanism exists to reveal people's relative values for goods and services that are currently not being bought and sold in the marketplace. Still, scientists would like to know the monetary value people attribute to them. We want to be able to carry out cost-benefit analysis to determine the welfare effects of technological innovations or public policy, to forecast new product success, and to understand the degree to which behavior is consistent with preferences and beliefs. Choice experiments (CEs) are arguably the most popular method currently used in preference and willingness to pay (WTP) elicitation studies, both in hypothetical and non-hypothetical settings. Originally, the method was developed for marketing and transport studies, but in the last two decennia, it has spread to environmental and resource economics, agricultural and food economics, and health economics. The ever growing body of literature on CEs emphasizes the increasing role they are playing. In this elicitation method, respondents are generally asked to make choices between multiple alternatives, also called profiles, which are described by a number of attributes with different levels. Consequently, through nonlinear regression models, generally based on random utility theory (RUT), the utility each attribute (level) contributes to the good or service under study can be quantified and translated into (marginal) willingness to pay. To a large extent, the design of the CE drives the precision and the validity of the conclusions and it is therefore considered to be a key aspect of the planning of a CE. Designing a CE involves selecting the profiles to be used in the experiment The current state of the art is the Bayesian optimal design method. However, the design and analysis methods for CEs are constantly improving, which goes along with the improvement of the discrete choice models and the increasing number of applications in different fields. Research on empirical and methodological advances in CEs faces the following challenges. First, RUT assumes the respondent to act in a fully compensatory manner based on stable preferences. This has been found to be a demanding assumption. Hence, it is up to empirical research to determine what causes these assumptions to be violated and how sensitive the obtained estimates are to them. Second, the debate concerning what drives (out) hypothetical bias, being the difference between what people say they are willing to pay in a hypothetical survey question and what they will actually pay in a non-hypothetical experiment when money is really on the line or in real-life situations, has not been closed. Third, most CEs are hitherto single-site and/or single-case studies. By consequence, spatial and socio-cultural effects are often ignored, which impedes generalization. Despite the vast amount of studies, findings often remain context-specific and cross-case comparisons are limited. Researchers from various applied economic disciplines continuously keep improving the way of designing, collecting and analyzing choice data in search of behavioral insights as well as efficient policy development. While some informal connections between several of the participating groups are already in place, a more formal setup would provide a driving force for more rapid knowledge dissemination and state of the art development of expertise. Therefore, it is important for Flanders to create a united and multi-disciplinary platform to keep up to date with the latest developments on CEs and to gather sufficient critical mass to be able to compete with other consortia for publications and project funding. Moreover, with this scientific research network, we aim to provide a platform for postdoctoral researchers to exchange knowledge and to more easily and intensively collaborate intra- and internationally.Researcher(s)
- Promoter: Goos Peter
- Co-promoter: Kessels Roselinde
Research team(s)
Project type(s)
- Research Project
Task complexity, framing effects and post-hoc individual-level model analysis in discrete choice experiments.
Abstract
This project deals with three important issues in discrete choice experiments (DCEs) which are widely used to study preferences for attributes of competing products or services in various areas of economics. To maximize the information content of the data from DCEs, it is crucial to design the experiments optimally. In our search so far, we have focused on improving the statistical quality of DCEs. However, the statistical quality is not the only aspect to consider. The response quality of a DCE is at least as important and depends on whether respondents can answer the choice questions well, that is, whether the choice questions are not too complex. Also, the framing or the labelling of the attributes and attribute levels plays a key role. Positive frames generally stimulate risk-averse responding as opposed to negative frames. Accounting for each of these two difficulties in the design and analysis of DCEs each makes up a part of this project. The designs we aim to construct will score well on overall quality, which includes both statistical quality and response quality. A final part of the project is devoted to post-hoc individual-level discrete choice modelling in which we show how to use individual preferences for market segmentation and the construction of indifference maps.Researcher(s)
- Promoter: Goos Peter
- Co-promoter: Erreygers Guido
- Fellow: Kessels Roselinde
Research team(s)
Project type(s)
- Research Project
Discovering housing preferences using discrete choice experiments
Abstract
The aim of this project is to run an online discrete choice experiment to quantify housing preferences of young people in Antwerp. We will ask a panel of respondents to choose between different housing accommodations ranging from collective housing to individual studios. The study will allow us to validate our latest Bayesian design methodology and to define a policy plan in the housing market.Researcher(s)
- Promoter: Goos Peter
- Promoter: Kessels Roselinde
Research team(s)
Project type(s)
- Research Project
Design of discrete choice experiments adapted to the respondent's cognitive process.
Abstract
Discrete choice experiments (DCEs), which involve respondents choosing among alternatives presented in choice sets, are widely used to study preferences for attributes of products or services in various economic fields. To maximize the power of the statistical inference from data from DCEs, it is crucial to design the experiments optimally. Most research in this area focuses on optimizing the design of DCEs under the simplifying assumption that respondents make compensatory decisions. This means that unattractive levels of an attribute can be compensated for by attractive levels of another attribute. However, the assumption of compensatory decision-making often proves to be unrealistic. This research project studies three scenarios in which respondents depart from the compensatory decision rule when making choices: (i) the scenario where respondents ignore attributes in the decision making because there are too many, (ii) the scenario where respondents favor certain attributes because of their position in the description of the alternatives and (iii) the scenario where respondents favor certain alternatives because of their position in the choice set. Pro-actively accounting for respondents' cognitive processes when constructing optimal DCEs in these scenarios will result in more practical designs for DCEs, with applications in marketing, transportation, environmental and health economics.Researcher(s)
- Promoter: Goos Peter
- Co-promoter: Erreygers Guido
- Fellow: Kessels Roselinde
Research team(s)
Project type(s)
- Research Project