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The chapter examines the relationship between social inclusion patterns and economic development in selected EU countries. This research has two objectives: (i) to reveal whether there are similarities among the EU countries in nation’s social inclusiveness of three demographic groups, namely the entire population, immigrants, and Roma communities; and (ii) to analyze the influence of social inclusion indicators on sustainable economic development and prosperity for the EU countries by accounting for the ethnicity impact. The chapter presents the results of a cluster analysis approach, which indicates best-positioned countries and emphasizes vulnerabilities in terms of social inclusiveness both in a broad demographic sense (the entire population) and a narrow sense (immigrants and respective Roma population). The chapter then employs a panel data regression approach to investigate which social inclusion indicators might have the potentially influencing role on economic development. Seven alternative proxies for sustainable economic development and prosperity measures were used. The social inclusion indicators, as explanatory variables, are represented by the education-related indicators and labor market-related indicators. The robustness and stability of the estimates are validated by including several interaction terms in the baseline regression model to account for the occurrence of the financial crisis. Overall, improving both population as well as immigrants’ inclusion is shown to have an important impact on sustainable economic development in the EU countries.

Freeways in Australia play a significant role in connecting distant communities, shifting freight and strengthening the country’s economy. To meet the growing needs of present and future generations, delivering a socially sustainable road infrastructure that creates generational benefits is essential. However, the existing literature reveals the lack of comprehensive indicators to assess the social sustainability performance of freeway projects. Therefore, this paper aims to identify a critical set of system-specific indicators to evaluate the life cycle social footprint of Australian freeways.

This study conducted 31 interview questionnaire surveys with actively engaged stakeholders involved in various freeway projects around Australia. The data collected was analysed using fuzzy set theory and other statistical approaches.

The study identified 42 critical indicators for assessing the social sustainability performance throughout the life cycle of freeways in the Australian context. For example, stakeholder involvement, reduction of casualty rate due to road accidents, fair remuneration to project workforce and improved accessibility to required services.

The context-specific opinions extracted from the industry experts and the comprehensive set of critical indicators identified would ensure that all the vital aspects of social sustainability are considered throughout the life cycle of Australian freeways in the future, assisting the decision-makers in enhancing the project’s social sustainability performance.

The linguistic explanations associated with the ratings given by the industry experts provide greater insight into the context of the life cycle social sustainability assessment of Australian freeways exclusively.

The term “social indicator” has become familiar in recent years in reference to the quantitative measurement of social phenomena. International organisations within the United Nations family and OECD have devoted special programmes to their development, and the term is frequently used by planners, politicians and the press. It has received the accolade of scientific respectability by having a special journal to its name, research programmes of the US National Science Foundation and the United Nations University, annual volumes under its title published by statistical offices of many countries and bibliographies devoted to the literature on the subject.

Revitalising the debate about how to operationalise and measure the extent of welfare states – the so-called dependent variable problem – recent research claims a close theoretical interaction between three different indicators: aggregated data on social expenditure, social rights and social benefit receipt. It is suggested that they all serve as an approximation of welfare state generosity as a dependent variable and help understand variation between as well as change of welfare states. The purpose of this paper is to investigate how these three indicators statistically relate to each other, using data on unemployment cash benefits.

To this end, a time series cross-sectional analysis is carried out, covering 16 European countries for the period 2003–2011.

Results confirm theoretical reflections on the link between the different indicators, whereby higher levels of social expenditure are positively associated with more generous social rights as well as higher levels of benefit receipt. Additionally, the study points to an ambivalent relation between benefit access and benefit levels within indicators as well as across them. This suggests competing policy choices in European welfare states, whereby more generous benefit access implies lower benefit levels and vice versa.

The study contributes to the existing dependent variable literature in a twofold way. First, the conceptual link between the three different indicators and the assumptions they are associated with are critically reviewed. Second, by providing a statistical analysis of the relation between the different indicators across 16 countries, the study goes beyond theoretical elaborations about their association as well as existing small-N or medium-N case time trend studies.

With this reappraisal, the purpose of this paper is to present a reflexion on and discussion of the concept of quality of life (QL) with the intention of delimiting its meaning and application within the scope of the research project entitled “Costs and benefits of urban dispersion on a local scale”.

The concept of QL contains a significant degree of complexity and multidimensional variables, in addition to the dynamic nature inherent in all social phenomena. The application of this concept at a local level and within the context of the Portuguese socio‐territorial reality requires rethinking the concept through the different authors and approaches, in order to delineate the research process, and guarantee its operationalisation, selecting the social indicators than can serve this purpose, with the intent of gaining a clearer understanding of QL as perceived and evaluated by the people and groups living in various dispersed urban areas.

From the readings of literature in the field, one can understand the importance of choosing the relevant domains when analysing and measuring QL. As with the choice of indicators, in order to be able to measure the QL, simultaneously, at a local level, the choice of indicators and the delimitation of units of analysis are also fundamental in order to be able to obtain the comparison and real measure of quality of life and not the contingencies of specific contextual characteristics.

The study aims to open a new research perspective in the field of social sciences, more specifically in the areas related to QL and urban dispersion.

Indicators of economic and social phenomena can be useful descriptive and analytical inputs for public policy. The “social indicators movement” has emerged in the last decade and is devoted to the measurement of widely‐ranging dimensions of human welfare. For the most part, questions of systematic measurement for public policy are explored here. Drawing initially on some traditions of measurement in economics, the principal aim is to provide a broad theoretical frame of reference for policy indicator design. Questions of indicator development necessarily involve ideas of suitability or validity of indicators designed for a purpose. Approaches to indicator design for the purpose of enhancing collective decision‐making—including formal model building approaches—are subsumed as special cases once a more general theory is espoused in sections II and III.

In 1971 Land argued that a social indicator should be a component, that is a parameter or a variable, in a sociological model of a social system or some segment of a social system. This was the first strong suggestion that social indicators needed to be more than some sort of statistical series. Lineberry et al, writing on the use of indicators by municipalities, warned that the first conceptual limitation which should be identified when promoting social indicator use must be the poor record of indicators in detecting causal relationships among various factors contributing to a specific social problem. They attribute this inability to the general lack of social theory. Bunge points out that the very definition of a social indicator of some life quality contains a causal notion relating that indicator to well‐being. This would be acceptable if there were a science of well‐being or at least some reasonable model. He goes on “since no such thing has been constructed so far, we are forced to use our treacherous common‐sense to an extent that is uncommon in science. Which is a polite way of saying that, so far, the study of the quality of life has not been thoroughly scientific.”

This article begins with a brief reading of the state of the practice of empirical social science research on measurement before proceeding to the discussion of an exemplary instance of this researcher's ethnographic effort to improve indicators of social capital formation. Given the central role measurement plays in social science research, it is appropriate, that a volume on methodological innovations in ethnography would contain a chapter about the relationship of ethnography to measure development. However, it is worth acknowledging that the line of argumentation advanced in this chapter is unconventional. The central tenant of this chapter – that ethnography has much to offer to the field of measurement and that ethnographers ought to take the contribution that they have the potential to make to the field of measurement seriously – at present might be thought to have little agreement either among those researchers whose primary focus is measurement or among ethnographers. This chapter contends that the features and strengths of ethnography specifically, and qualitative research more generally, makes it uniquely suited to contribute to the development of new indicators and the improvement of existing indicators. This chapter modestly hopes to encourage discussion of this contention and illustrate how this author sees his own ethnographic research into indicators of social capital formation as an attempt to address a pressing methodological dilemma within the field, more general of social scientific measure development.

The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies.

This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible.

The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method.

This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.

The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development.

To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators.

The purpose of this paper is to present a framework for evaluation and ranking of potential surrogates to select the optimum surrogates and test it for five selected social resilience indicators in a disaster context. Innovative resilience assessment approaches are required to capture key facets of resilience indicators to deepen the understanding of social resilience. Surrogates can adequately represent the target indicator that is difficult to measure, as surrogates are defined as key facets of a target indicator.

To optimize the selection of surrogates, five key evaluation criteria were used. Disaster management experts completed an online survey questionnaire and evaluated three potential surrogate options. Surrogates were then ranked using PROMETHEE, a multi-experts multi-criteria group decision analysis technique.

A framework was devised to evaluate and rank potential surrogates to assess social resilience in a disaster context. The findings revealed that the first ranked surrogate can be the most critical facet of a resilience indicator of measure. In most instances, highly experienced cohort of practitioners and policy makers have aligned their preferences of surrogates with the overall ranking of surrogates obtained in this study.

The surrogate approach can also be tested in different disaster and geographic contexts. The resilience indicators used in this study to explore surrogates are largely applicable in all contexts. However, the preference of surrogates may also vary in different contexts.

Once the surrogate is selected through an evaluation process proposed in this paper, the resilience status can be updated regularly with the help of the selected surrogate. The first ranked surrogate for each of the social resilience indicator can be applied, since the findings revealed that the first ranked surrogate can be the most critical facet in the context of the social resilience indicator being measured.

The framework and the selection of optimal surrogates will assist to overcome the conceptual and methodical challenges of social resilience assessment. The applicability of selected surrogates by practitioners and policymakers in disaster management will play a vital role in resilience investment decision-making at the community level.

The surrogate approach has been used in the fields of ecology and clinical medicine to overcome the challenges in measuring difficult to measure indicators. The use of surrogates in this study to measure social resilience indicators in a disaster context is innovative, which was not yet explored in resilience measurement in disaster management.