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In antitrust class-action litigation, courts are increasingly unlikely to accept the presumption that all class members were harmed by price-fixing among a group of firms or by exclusionary behavior by a single firm. Econometric methods typically applied in antitrust and other settings estimate the average effect of the challenged conduct, but do not inform impact for individual class members. We present classwide econometric methods and statistical tests for detecting the existence (or lack thereof) of common impact and determining what proportion (if any) of the proposed class suffered injury in many class actions. We conclude that econometric tools can meaningfully inform the legal process, even when courts demand proof of common impact.

Purpose – Information collected from police crash reports has long been the primary source of data for the analysis of factors that determine the likelihood of a crash (crash frequency) and its resulting severity (measured in terms of the extent of injuries to vehicle occupants). Proper cross-sectional analyses techniques, covered in this chapter, are important for guiding safety policy and countermeasures.

Methodology – This chapter provides an overview of some of the more commonly used cross-sectional statistical and econometric methods, and discusses the nuances and their limitations with regard to how they are applied to typical crash-report data.

Findings – The wide variety of analytic methods available to safety researchers makes the selection of appropriate methods critical. This chapter provides important guidance for safety researchers in their choice of methodological approach.

Implications – Understanding the importance of proper model specification, unobserved heterogeneity, endogeneity and other factors covered in this chapter is extremely important in analysing safety data and must be given full consideration before any results are finalised.

This chapter reviews the recent developments in the estimation of panel data models in which some variables are only partially observed. Specifically we consider the issues of censoring, sample selection, attrition, missing data, and measurement error in panel data models. Although most of these issues, except attrition, occur in cross-sectional or time series data as well, panel data models introduce some particular challenges due to the presence of persistent individual effects. The past two decades have seen many stimulating developments in the econometric and statistical methods dealing with these problems. This review focuses on two strands of research of the rapidly growing literature on semiparametric and nonparametric methods for panel data models: (i) estimation of panel models with discrete or limited dependent variables and (ii) estimation of panel models based on nonparametric deconvolution methods.

Sustainable development calls for concerted efforts towards building an inclusive, sustainable and resilient future for people and planet. The approach that considers sustainable development as the fight against poverty, through the promotion of a sustainable and equitable economy, as the attempt to reduce polluting emissions to promote environmental protection and as the satisfaction of social goals to increase the well-being of populations is adopted. Sustainability development is therefore a complex and subjective concept, considering the three dimensions that define the phenomenon: economic, environmental and social.

The authors have chosen subjective variables, which provide information on the perception of the ‘sustainable development’ in the European countries. Data come from the database of ‘Eurofound’, the European Foundation for the Improvement of Living and Working Conditions. The authors applied a formative measurement model, according to which indicators are considered as the cause of the phenomenon analysed, unlike with the reflective model. To conduct the quantitative analysis, the authors have adopted a non-compensatory approach: Mazziotta and Pareto index which summarising a set of individual indicators that are assumed to be not fully substitutable. The authors place at the centre of the analysis, variables deriving from the perceptive state of the different European populations, offering new hints to measure sustainable development on the basis of subjective assumptions.