Measurement Assessment in Cross-Country Comparative Analysis: Rasch Modelling on a Measure of Institutional Quality

This paper uses recent multidimensional well-being measurements to examine multidimensional well-being and inequality across the European regions in 2000 and 2014 with the use of eleven well-being indicators from the OECD Better Life Index. We use generalized mean aggregation method with alternative parameters to allow different substitutability and complementarity levels between well-being dimensions, which range between perfect substitutability and some degree of complementarity between the dimensions, to examine well-being and inequality across the European regions. Accounting for the interactions between the well-being dimensions matters for the multidimensional well-being and inequality across the European regions. The results show that the multidimensional well-being across the European regions are relatively lower when the dimensions are more seen as complements compared to the case when they are considered to be perfect substitutes. Furthermore, there is also a higher degree of multidimensional inequality across the European regions when the dimensions are considered to have some complementarity. Changes in well-being dimensions between 2000 and 2014 indicates that multidimensional well-being improved and inequality decreased in the personal and community well-being categories, but remained unchanged in material well-being category across the European regions irrespective of interaction levels between well-being dimensions. Policy implications of these multidimensional well-being indices are also evaluated by using these indices to determine the eligible regions for the European Union structural funds where the number eligible regions shows some variation depending on whether the dimensions are perfect substitutes or more of complements.

     

Exploring Partial Order of European Countries

Partial Order Theory has been recently more and more employed in applied science to overcome the intrinsic disadvantage hidden in aggregation, if a multiple attribute system is available. Despite its numerous positive features, there are many practical cases where the interpretation of the partial order can be rather troublesome. In these cases the analysis of underlying dimensions could be useful to uncover particular data structures. The paper shows a way of addressing the problem with the help of an actual case study, which deals with European opinions on services of general interest. In particular, a partial order of countries is firstly provided and then a method to detect dimensions is discussed and applied. The analysis stems directly from the Partially Order Set (poset) and Lattice theory with particular references to dimension theory and Formal Concept Analysis. The study is eventually able to pinpoint role and relevance of different attributes characterizing EU countries which are used to define the partial order.

A multidimensional view on poverty in the European Union by partial order theory

Poverty can be seen as a multidimensional phenomenon described by a set of indicators, the poverty components. A one-dimensional measure of poverty serving as a ranking index can be obtained by combining the component indicators via aggregation techniques. Ranking indices are thought of as supporting political decisions. This paper proposes an alternative to aggregation based on simple concepts of partial order theory and illustrates the pros and cons of this approach taking as case study a multidimensional measure of poverty comprising three components – absolute poverty, relative poverty and income – computed for the European Union regions. The analysis enables one to highlight conflicts across the components with some regions detected as controversial, with, for example, low levels of relative poverty and high levels of monetary poverty. The partial order approach enables one to point to the regions with the most severe data conflicts and to the component indicators that cause these conflicts.     

The Unbalance Penalisation Method for Metrics of Social Progress

This study contributes to the debate on the development of aggregate metrics of societal progress. Summarising societal progress into a single number poses various methodological challenges, including the choice of indicators, normalisation, weighting and aggregation. This paper addresses the issue of aggregation in the case of metrics of well-being and uses as a case study the European Union regional Social Progress Index—EU-SPI—published by the European Commission. The index is an aggregate measure of 55 social and environmental indicators observed for all the European regions grouped into 12 components. In metrics of this type, while complete substitutability among components is rarely acceptable, controlling their level of substitutability is highly desirable. To this aim, we adopt a modified version of the unbalance penalisation approach originally proposed by Casadio Tarabusi and Guarini (Soc Indic Res 112:9–45, 2013). A penalisation is applied to the regions whose performance across the index components is unbalanced, that is when they perform well on some components but worse on others. The penalisation applied by this approach depends on two parameters that, in its original formulation, are generally arbitrarily chosen. We design a data-driven approach allowing for an informed choice of the penalisation parameters. The comparison between the EU-SPI original and penalised scores shows that the penalisation effect is particularly evident for regions with a strongly unbalanced profile across the components. The proposed method allows for adjusting the level of substitutability between components when constructing an aggregate metric, an important functionality especially when measuring societal progress for policy-making.

     

What is the Influence of News Media on People’s Perception of Corruption? Parametric and Non-Parametric Approaches

Does the source of one’s news media have a systematic effect on one’s perception of political corruption? While numerous studies have investigated the extent to which media affects trust in institutions, or the polarization of political values, this study shifts the focus on to how one’s media source conceived here as social media versus traditional media affects the perception of corruption in 2 ways. First, we hypothesize that citizens who consume their news predominately from social media will have higher perceptions of political corruption than consumers of more traditional media sources. Second, we hypothesize that perceptions among social media consumers will be more polarized. Specifically, we argue that the gap in corruption perception between supporters of government and opposition political parties will be larger among social media consumers compared to traditional news consumers. We test our hypotheses using newly collected survey data from the European Quality of Government Index survey from 2017, which contains nearly 78,000 respondents in 21 countries in the European Union. Estimating our model with both parametric and non-parametric approaches, we find robust empirical support for two of our 3 hypotheses.

     

Measuring the impact of the Web: Rasch modelling for survey evaluation

In 2012, the World Wide Web Foundation launched for the first time the Web Index (WI), which combines the existing secondary data with new primary data to rank countries according to their progress and use of the Web. Primary data are gathered via a multi-country specifically designed questionnaire. The aim of our analysis is (1) to evaluate the measurement properties of the expert assessment survey and to provide survey designers with some insights into possible problematic questions and/or unexpectedly behaving countries and (2) to assess the experts’ perception of the state and the value of the Web. To do so the Rating Scale Rasch model is employed. Results show that about 10% of survey questions are detected as misfitting and need to be reworded. Possible reasons are: counter-orientation with respect to the WI polarity, difficulty in understanding the question's words or binary instead of the multiple response scale. Country analysis shows that no country can be considered as an outlier due to notable unexpected pattern of answers. Since the survey is to be expanded in future editions of the WI, the results of our analysis are very important in pin-pointing the questions most in need of refinement for the next edition of the Index.