To date, no attempt has been made to design efficient choice experiments by means of the G-and V-optimality criteria. These criteria are known to make precise response predictions, which is exactly what choice experiments aim to do. In this article, the authors elaborate on the G-and V-optimality criteria for the multinomial logit model and compare their prediction performances with those of the D-and A-optimality criteria. They make use of Bayesian design methods that integrate the optimality criteria over a prior distribution of likely parameter values. They employ a modified Fedorov algorithm to generate the optimal choice designs. They also discuss other aspects of the designs, such as level overlap, utility balance, estimation performance, and computational effectiveness.

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Recently, the use of Bayesian optimal designs for discrete choice experiments, also called stated choice experiments or conjoint choice experiments, has gained much attention, stimulating the development of Bayesian choice design algorithms. Characteristic for the Bayesian design strategy is that it incorporates the available information about people's preferences for various product attributes in the choice design. This is in contrast with the linear design methodology, which is also used in discrete choice design and which depends for any claims of optimality on the unrealistic assumption that people have no preference for any of the attribute levels. Although linear design principles have often been used to construct discrete choice experiments, we show using an extensive case study that the resulting utility-neutral optimal designs are not competitive with Bayesian optimal designs for estimation purposes.

Due to the globalization and the fragmentation of industrial production processes, the logistics sector, which organizes the linkages between different production plants and the market, is growing fast. This results in an increasing demand for suitable new business locations. Previous research has indicated that accessibility is a key factor in the location decision making process. Though the literature on this subject is extensive, little research has been done to quantify the impact of the different dimensions of accessibility on the location decision process of logistics companies. This paper aims to fill this void in the literature by means of a revealed preference study (using a Geographic Information System (GIS) analysis) and a stated preference study (using a designed discrete choice experiment) in Flanders (Belgium). The results of the revealed preference study served as input to the design of the choice situations in the stated preference study. In this study, the respondents were confronted with a series of choice situations described by means of accessibility variables as well as land rent information. An analysis of the resulting data revealed that land rent is the most important factor in the location choice of logistics companies in Flanders. Access to a port is the one but most important factor, followed by access to a motorway and an inland navigation terminal, and the location in a business park. Finally access to a rail terminal plays no significant role in the location choice of logistics companies in Flanders.

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In this paper we explore different ways to obtain decompositions of rank-dependent indices of socioeconomic inequality of health, such as the Concentration Index. Our focus is on the regression-based type of decomposition. Depending on whether the regression explains the health variable, or the socioeconomic variable, or both, a different decomposition formula is generated. We illustrate the differences using data from the Ethiopia 2011 Demographic and Health Survey (DHS).Acknowledgements: We thank Tom Van Ourti and Philip Clarke for discussions related to decomposition analysis, and Ellen Van de Poel for assistance with regard to the Ethiopian data. We are also grateful to seminar participants at the

Our study suggests that according to the Belgian public, contextual factors of health gains such as patient's age and health-related lifestyle should be considered in priority setting decisions. The studies, however, revealed substantial disagreement in opinion between different population subgroups.

Background High uptake of Covid-19 vaccination is required to reach herd immunity. Methods A representative sample of 2,060 Belgians were surveyed in October 2020. Regression analyses identified the predictors associated with willingness to get vaccinated against Covid-19, and attitudes toward vaccination in general. Results 34% of the participants reported that they will definitely get vaccinated against Covid-19 and 39% that they would “probably”. Intended uptake was strongly associated with age, opinion on the government’s dealing with the Covid-19 pandemic, medical risk, spoken language, gender, and to a lesser extent with having known someone who was hospitalised because of Covid-19. Similar predictors were identified for attitudes to vaccination in general. Covid-19 vaccine hesitancy was more marked in age groups below 54 years old. We further analysed a sample of 17% (N=349) found favourable to vaccination in general but not willing to vaccinate against Covid-19. They were mainly female, young, French speaking, slightly less educated, working, and did not belong to a Covid-19 risk group. They were very dissatisfied with the government’s dealing with the pandemic, and did not know someone who was hospitalised because of Covid-19. Conclusions Vaccine hesitancy is higher for Covid-19 vaccines than for other vaccines. The part of the population being convinced of the utility of vaccination in general but hesitant about the Covid-19 vaccine is a primary interest group for tailored communication campaigns in order to reach the vaccine coverage needed for herd immunity.