Discrete Choice Experiments: A Guide to Model Specification, Estimation and Software

We provide a user guide on the analysis of data (including best–worst and best–best data) generated from discrete-choice experiments (DCEs), comprising a theoretical review of the main choice models followed by practical advice on estimation and post-estimation. We also provide a review of standard software. In providing this guide, we endeavour to not only provide guidance on choice modelling but to do so in a way that provides a ‘way in’ for researchers to the practicalities of data analysis. We argue that choice of modelling approach depends on the research questions, study design and constraints in terms of quality/quantity of data and that decisions made in relation to analysis of choice data are often interdependent rather than sequential. Given the core theory and estimation of choice models is common across settings, we expect the theoretical and practical content of this paper to be useful to researchers not only within but also beyond health economics

The Incidence of Sin Taxes: Evidence from Italy

The term \u201csin goods\u201d is used to identify goods or services whose consumption can be considered self-destructive for individuals and source of negative externalities for the entire society. The potentially harmful nature of sin goods makes these categories of products subject to special fiscal rules. The regulation of sin goods markets in Italy deeply changed over the last 15 years, and it has now reached a particularly complex and potential unfair taxation system. In this paper, we analyse the fairness of the Italian taxation system applied to sin goods in order to measure their economic incidence. After investigating the Italian taxation on sin goods, we analyze and compare their degree of regressivity using data of the Italian Household Budget Survey (HBS). Results show that, among \u201csin taxes\u201d, gambling taxes can be considered the most regressive form of taxation, followed by cigarettes and beer