Caspar G. Chorus

Efficiently allocating scarce healthcare resources requires nuanced understanding of individual and collective interests as well as relative concerns, which may overlap or conflict. This paper is the first to empirically investigate whether and to what extent self-interest (SI), positional concerns (PC) and distributional considerations (DC) simultaneously explain individual decision making related to access to healthcare services. Our investigation is based on a stated choice experiment conducted in two countries with different healthcare systems, the United States (US) and the United Kingdom (UK). The choice experiment is on allocation of medical treatment waiting times for a hypothetical disease. We carry out the investigation under two different perspectives: (i) in a socially inclusive personal perspective decision makers were asked to choose between waiting time distributions for themselves and (ii) in a social perspective decision makers were asked to make similar choices for a close relative or friend of opposite gender. The results obtained by estimating a variety of advanced choice models indicate that DC, SI and PC, in this order of importance, are significant drivers of choice behaviour in our empirical context. These findings are consistent regardless of the choice perspective and the country where decision makers live. Comparing the results from different choice perspectives, we find that US respondents who chose for their close relative or friend attach significantly larger weight to their close relative’s or friend’s waiting times as well as to the overall distribution of waiting times than US respondents who chose for themselves. Looking at differences between countries, our results show that UK respondents who made choices for themselves placed significantly larger weight on SI and DC than US respondents, while US respondents, in turn, displayed relatively stronger but not significantly different positional concerns than UK respondents. In addition, we observe that UK respondents who chose for their close relative or friend put a larger weight on DC than their US counterparts. We conclude that the methodological (data collection and analysis) approach allows for disentangling the relative importance of the three motivations and discusses the potential implications of these findings for healthcare decision making.

This paper proposes a new method to combine choice- and text data to infer moral motivations from people’s actions. To do this, we rely on moral rhetoric, in other words, extracting moral values from verbal expressions with Natural Language Processing techniques. We use moral rhetoric based on a well-established moral, psychological theory called Moral Foundations Theory. We use moral rhetoric as input in Discrete Choice Models to gain insights into moral behaviour based on people’s words and actions. We test our method in a case study of voting and party defection in the European Parliament. Our results indicate that moral rhetoric have significant explanatory power in modelling voting behaviour. We interpret the results in the light of political science literature and propose ways for future investigations.

As the application of machine learning (ML) algorithms becomes more widespread, governmental organisations try to benefit from this technology. While ML has the potential to support public services, its application also introduces challenges. Several scholars have described the possible opportunities and challenges of ML applications in the public sector conceptually. However, it is not yet investigated if and how these concepts materialise and are perceived by end-users in the public sector when ML is applied. Therefore, it is neither clear whether these concepts are valid, nor what regulation could be introduced to address them effectively. This empirical study’s objective is to shed light on how challenges and opportunities of governmental use of ML algorithms are perceived by Dutch professionals in the public sector. We attain our objective by conducting interviews with twelve professionals from Dutch executive and supervisory organisations in the public sector that respectively use ML and supervise the use of ML. Results show that ML is used primarily for improvements in the accuracy and speed of public task execution. Furthermore, interviewed professionals experience several barriers for ML implementation as well as risks following from the use of ML. The implications of these findings for practice are discussed, as well as opportunities for further research.

This paper investigates the potential of association rules learning and random forests to analyse data from complex choice experiments, and to obtain relevant insights for choice modellers and practitioners. We apply these data-driven methods on data from a so-called Participatory Value Evaluation (PVE) choice experiment. In a PVE choice experiment, respondents choose a combination of alternatives, subject to a resource constraint. More specifically, we show that both methods can successfully assist choice modellers to specify their (theory-driven) choice models. We clarify how association rules learning can be used to identify interactions that would otherwise potentially go unnoticed; and how random forests can be used to determine the importance of each attribute, and as a mechanism to test the validity of the behavioural assumptions of a theory-driven choice model. By applying these methods, we are able to obtain considerable model fit improvements, as well as to reduce the time invested on the model specification phase. As such, our results support the notion that data-driven and theory-driven methods are complementary tools for choice modellers.

The growing polarization of our societies and economies has been extensively studied in various disciplines and is subject to public controversy. Yet, measuring polarization is hampered by the discrepancy between how polarization is conceptualized and measured. For instance, the notion of group, especially groups that are identified based on similarities between individuals, is key to conceptualizing polarization but is usually neglected when measuring polarization. To address the issue, this paper presents a new polarization measurement based on a grouping method called “Equal Size Binary Grouping” (ESBG) for both uni- and multi-dimensional discrete data, which satisfies a range of desired properties. Inspired by techniques of clustering, ESBG divides the population into two groups of equal sizes based on similarities between individuals, while overcoming certain theoretical and practical problems afflicting other grouping methods, such as discontinuity and contradiction of reasoning. Our new polarization measurement and the grouping method are illustrated by applying them to a two-dimensional synthetic data set. By means of a so-called “squeezing-and-moving” framework, we show that our measurement is closely related to bipolarization and could help stimulate further empirical research.