A guide for constructing and using composite indicators for policy makers, academics, the media and other interested parties. In particular, this handbook is concerned with indicators which compare and rank country performance.

Perceiving complex multidimensional problems has proven to be a difficult task for people to overcome. However, introducing composite indicators into such problems allows the opportunity to reduce the problem's complexity. Emerging Trends in the Development and Application of Composite Indicators is an authoritative reference source for the latest scholarly research on the benefits and challenges presented by building composite indicators, and how these techniques promote optimized critical thinking. Highlighting various indicator types and quantitative methods, this book is ideally designed for developers, researchers, public officials, and upper-level students.

The improvement of the quality of life of all South Africans is high on the agenda at national (The National Planning Commission, 2012) and regional levels of government (The Gauteng Planning Commission, 2012) and it is therefore important to develop an instrument that can measure this multi-dimensional concept. The need therefore exists for a composite index of quality of life with the ability to both track the quality of life of people over time and compare it across different demographic and socio-economic groups. Such a measure could identify those demographic and socio-economic groups with low levels of quality of life and also highlight dimensions that need to be prioritised in order to improve the wellbeing of people. In South Africa there are a limited number of quality of life indices and measures of wellbeing. Indices that measure wellbeing nationally include: the Quality of Life Index of Moller and Schlemmer (1983), the Living Standard Measure (LSM) Index produced by the South African Audience Research Foundation (SAARF) (2013), the South African Development Index of the South African Institute of Race Relations (2011), and the Everyday Quality of Life Index (Higgs, 2007). The following indices measure wellbeing at a regional level: the Quality of Metropolitan City Life in South Africa Index (Naude, et al., 2009), the Non-Economic Quality of Life Index at Sub-National Levels (Rossouw & Naude, 2008) and the Quality of Life Index of the Gauteng City-Region Observatory (GCRO, 2011). Although these quality of life indices make distinctive contributions to the study field, the focus of these studies is often to measure only objective or subjective quality of life or only economic or non-economic quality of life, rather than all of the above. Furthermore, many of the indices use equal weighting, which does not necessarily reflect the priorities of the communities.

This paper leverages the IMF’s Financial Access Survey (FAS) database to construct a new composite index of financial inclusion. The topic of financial inclusion has gathered significant attention in recent years. Various initiatives have been undertaken by central banks both in advanced and developing countries to promote financial inclusion. The issue has also attracted increasing interest from the international community with the G-20, IMF, and World Bank Group assuming an active role in developing and collecting financial inclusion data and promoting best practices to improve financial inclusion. There is general recognition among policy makers that financial inclusion plays a significant role in sustaining employment, economic growth, and financial stability. Nonetheless, the issue of its robust measurement is still outstanding. The new composite index uses factor analysis to derive a weighting methodology whose absence has been the most persistent of the criticisms of previous indices. Countries are then ranked based on the new composite index, providing an additional analytical tool which could be used for surveillance and policy purposes on a regular basis.

This paper evaluates the robustness of rankings obtained from composite indices that combine information from two or more components via a weighted sum. It examines the empirical prevalence of robust comparisons using the method proposed by Foster, McGillivray and Seth (2010). Indices examined are the Human Development Index, the Index of Economic Freedom and the Environmental Performance Index. Key theoretical results demonstrate links between the prevalence of robust comparisons, Kendall's correlation coefficient, and statistical association across components. Implications for redundancy among index components are also examined.

Il libro descrive e affronta alcune questioni tutt’ora aperte nel campo della costruzione di indicatori compositi. Si propone come complemento a riferimenti consolidati quali OECD handbook of composite indicators, e approfondisce temi che saranno certo oggetto di ampio sviluppo nel prossimo futuro, soprattutto nell’ambito degli indicatori di benessere e progresso umano. La prima parte del libro illustra lo stato dell’arte delle metodologie per la costruzione di indicatori compositi mentre la seconda parte affronta specifici nodi critici piu’ recenti. Il libro fornisce strumenti utili sia a ricercatori con una conoscenza limitata degli indicatori compositi, che a studiosi interessati ad un aggiornamento su temi più avanzati.

This book presents powerful tools for integrating interrelated composites--such as capabilities, policies, treatments, indices, and systems--into structural equation modeling (SEM). Jörg Henseler introduces the types of research questions that can be addressed with composite-based SEM and explores the differences between composite- and factor-based SEM, variance- and covariance-based SEM, and emergent and latent variables. Using rich illustrations and walked-through data sets, the book covers how to specify, identify, estimate, and assess composite models using partial least squares path modeling, maximum likelihood, and other estimators, as well as how to interpret findings and report the results. Advanced topics include confirmatory composite analysis, mediation analysis, second-order constructs, interaction effects, and importance–performance analysis. Most chapters conclude with software tutorials for ADANCO and the R package cSEM. The companion website includes data files and syntax for the book's examples, along with presentation slides.

Measuring innovation in education and understanding how it works is essential to improve the quality of the education sector. Monitoring systematically how pedagogical practices evolve would considerably increase the international education knowledge base. We need to examine whether, and how ...

This book highlights the core elements of a possible performance measurement framework to assess health systems at the international and national levels. It also addresses further challenges which remain.

The topic of Uncertainty Quantification (UQ) has witnessed massive developments in response to the promise of achieving risk mitigation through scientific prediction. It has led to the integration of ideas from mathematics, statistics and engineering being used to lend credence to predictive assessments of risk but also to design actions (by engineers, scientists and investors) that are consistent with risk aversion. The objective of this Handbook is to facilitate the dissemination of the forefront of UQ ideas to their audiences. We recognize that these audiences are varied, with interests ranging from theory to application, and from research to development and even execution.