Using Data Envelopment Analysis to Construct Human Development Index

This chapter addresses problems related to methodological issues, such as data normalization, weighting schemes, and aggregation methods, encountered in the construction of composite indicators to measure socio-economic development and quality of life. It also addresses the use of several Data Envelopment Analysis (DEA) models to solve these problems. The models are discussed and applied in constructing a Human Development Index (HDI), derived from the most recent raw and normalized data, using arithmetic and geometric means to aggregate the indices. Issues related to data normalization and weighting schemes are emphasized. Kendall Correlation was applied to analyze the relationship between ranks obtained by DEA models and HDI. Recommendations regarding the advantages and disadvantages of using DEA models to construct HDI are offered.

Composite Indicators of Development

Development is a complex and multidimensional phenomenon. The quantification of such a phenomenon requires indicators that may capture its most important components. In this chapter we present an extensive list of composite indicators of development, identifying their main possible common dimensions: income, income distribution, education, health, employment, infrastructures, values, and environment. We also discuss in detail five recent indices characterized by their comprehensiveness: 1) Regional Quality of Development Index (QUARS) of Sbilanciamoci!; 2) Wellbeing Index (WI) and Wellbeing/Stress Index (WSI) for measuring sustainable development; 3) Gross National Happiness (GNH) from the Center for Bhutan Studies; 4) Bertelsmann Transformation Index (BTI) of Bertelsmann Stiftung; and 5) World competitiveness scoreboard from the Institute for Management Development (IMD).

Composite Indicators of a Knowledge Society

The concept of Knowledge Society (KS) began due to recognition of the importance of knowledge and information in the development of a society. This chapter proposes a holistic view of knowledge society based on the development of composite indicators in nine different dimensions. The objective of the study is to propose a multi-dimensional approach comprising human capital, ICT, spirituality, economy, social, institutional and sustainability as determinants towards achieving a KS. These dimensions are discussed in-depth by the experts in semi-structured interviews and also validated by using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). The semi-structured interview data are presented in a verbatim manner so as to provide readers with in-depth feedback from the experts, while the EFA and CFA results of composite indicators are presented in graphics. Thus, this chapter contributes to the understanding of composite indicators of a knowledge society which can then be used by policy makers for future policy-making decision.

Constructing a Multidimensional Socioeconomic Index and the Validation of It with Early Child Developmental Outcomes

The chapter focuses on the development of a socioeconomic index (SEI) using a Principal Components Analysis (PCA) of 26 variables at the Dissemination Area (DA) level for Alberta. First, the importance of socioeconomic factors in understanding child development outcomes is discussed, addressing the micro-macro level influences. Second, a description of the framework is provided along with the statistical procedures. Third, the results are presented, followed by a discussion of the benefits of having a summary measure in understanding kindergartners' developmental outcomes. The five components of SEI explained 56 per cent of the total variation in the overall index. The SEI patterns across Alberta were examined and the index was validated for its associations to the five domains of early child developmental outcomes, physical, social, emotional, language and cognitive skills, and communication and general knowledge. The index emerged as a strong correlate of all five domains with the strength of relationships varying across developmental domains and geography. A major strength of the procedure presented in the study is that it can be applied to different levels of geography and provides meaningful information to developmental research.

Developing a Sustainable Urban Mobility Index

Sustainability is a multi-dimensional concept that can be assessed by means of constructing a composite indicator or index. In doing so, a scientifically sound and appropriate index methodology is required. The research proposed in this chapter aims to provide a guideline for developing a sustainability index that is able to assess the impact of mobility on the urban quality of life. By studying the index development process in other domains critically and taking the specific sustainable urban mobility context into account, this study investigates the different methodological steps that are essential in the construction of a sustainable urban mobility index. The main challenges and potential options when developing such an index are discussed.

The Delphi Method Application in the Analysis of Postgraduate Students' Attitudes on the Environmental Performance Index

In the context of growing concern over the global environment and related sustainability issues, the purpose of this research is to stimulate discussion about the Environmental Performance Index (EPI) and its necessary role not only in measuring postgraduate students' attitudes about EPI, and its nine categories underpinned, but also, their important role in the development of appropriate curriculum of programs that are about environmental management and sustainable development as well. Further on, the obtained results of the presented research in the paper broaden the understanding of the opportunities for not only the Republic of Serbia but every country as well, in using the EPI methodology as it has a wide applicability in improving environmental pillars of their future sustainability.

Methodological Challenges in Building Composite Indexes

Composite indicators are emerging in several fields and disciplines as appealing method to synthesize a multitude of information, in a compact, single, and unique way. The process of aggregating heterogeneous information is itself very challenging and exposed to numerous threats. The chapter deepens on the methodological challenges that scientists, analysts, and final users must be aware of for a correct interpretation of the composite indexes. By mean of a worked example on the construction of composite indicators for food security, the chapter concludes that while different normalization and weighting approaches do not alter composite indicators, data imputation and aggregation methods are the most crucial steps: different methods convey very different results. For instance, the adoption of different aggregation procedures may largely alter the rankings based on composite indicators. In sum, the analysis shows that the index construction decisions matter and comment on policy and practical implications for the construction of composite indicators.

Composite Indices in Technology Management

This chapter focuses on composite indices used in Technology Management (TM). It provides a critical comparative analysis of 5 indices, summarizes their structure, weighting process and emphasizes technology related components of indices as well as their advantages and disadvantages. The second part of the chapter examines the ranks of OECD and BRICS countries for Global Competitiveness Index (GCI), Global Innovativeness Index (GII) and Global Entrepreneurship Index (GEI), and further, we analyzed the correlations of GCI and GII pillars and clusters with the final ranks of countries. The research proved the presence and the importance of TM in the construction of the selected indices, but also identified that there is a lack of composite indicators used exclusively for TM performance which are measured globally by official institutions.

Comparison of Input and Output Indicators in Measuring Human Capital

Despite the intensive research on human capital, the debate regarding its measurement is ongoing. In this context, the objective of the present study is to underline the distinction between input and output indicators in human capital measurement, which has not attracted sufficient attention, and to present the importance of indicator selection by explaining the findings obtained. To that end, separate indexes will be developed for input and output indicators to measure the level of human capital for Turkey, and it will be analyzed whether the two index groups developed exhibit significant differences between provinces. In accordance with the purpose of this study, index estimations are made using the PCA method with the 2013 data of 81 provinces in Turkey. Province-based estimations demonstrate that the index values estimated by the input and output indicators produce significantly different conclusions. Therefore, selecting appropriate indicators according to the purpose of the study will enable the analyses to produce more accurate policy implications.

The Rule of Law Index

The indicator-based scientific research has recently become a valuable source of information for policymakers, scholars and eventually, civil society. Many socioeconomic spheres are evaluated using composite indicators, whereas governance has not been left behind. Among several assessment measurements oriented on the rule of law and law enforcement, the Rule of Law Index devised by the World Justice Program stands out. Namely, it is a comprehensive and methodologically consistent index which measures the extent to which the countries follow the principles of the rule of law. However, the controversial question of the justification of its equal weighting scheme emerges. The presented study addresses this issue by applying the twofold I-distance approach to propose unbiased weights and an in-depth analysis of the index dynamics. Consequently, the aim of this paper is to scrutinize the Rule of Law Index and to shed light on its methodology. Furthermore, the proposed approach can serve as a foundation for future research on weighting schemes, which are enveloped with subjectivity.