Global Cybersecurity Index (GCI) and the Role of its 5 Pillars

Computer crime is a matter of increasing concern, and worldwide action is required if the proper responses to it are to be found. One of the tools that can be deployed here is the Global cybersecurity index (GCI), a control and feedback mechanism based on a composite indicator. The GCI is based on a hierarchy of sub-indicators. The indicators used for the final aggregation of the CGI are called pillars. Five pillars are applied to rank the eleven countries that are top of the rankings in a worldwide study. In this paper, our ranking is based on these pillars, and their role is investigated using partial order methodology. It turns out that the pillars “Technical (aspects)”, “Capacity building”, and “Cooperation” are of particular importance. In conclusion, a strategy is suggested for an “individualized ranking” that may be helpful for small and medium-sized enterprises (SMEs) or other institutions. Here, we apply the procedure for the project “Awareness Laboratory SME (ALARM) information security” and put our ideas up for discussion. In particular, the mathematical method will be transferred to SMEs as a means to support the effectiveness of awareness-raising measures and to improve the security behaviour of company employees.

     

Application of the “Benefit-Of-the-Doubt” Approach for the Construction of a Digital Access Indicator: A Revaluation of the “Digital Access Index”

The use of composite indicators as a tool for ranking and making decisions is ever increasing in a world marked by the inequalities and competition in all domains. However, the dependence of countries ranking on the weighing scheme used to aggregate individual indices or sub-indicators, most of the time set by experts/stakeholders may weaken the credibility of composite indicators. One method which is able to overcome these limits is the “Data Envelopment Analysis” approach, particularly named “Benefit-Of-the-Doubt” in the context of composite indicators’ construction. We propose a revaluation of the Digital Access Index given that Information technology is the most important factor driving improvement in a wide array of areas critical for the quality of life for individuals as well as societies. We have shown that this method is more suitable for identifying trends and drawing attention to particular issues and also for setting policy priorities. In fact, the weights of the individual indices, used to compute the composite indicators are based on the data itself and are proper for the country under consideration. These weights contain adequate information in order to help policy-makers to better understand the nature of the new innovation economy and the types of public policies needed to drive innovation, productivity and broad-based prosperity for its citizens.     

A Multidimensional Approach to Measuring Quality of Employment (QoE) Deprivation in Six Central American Countries

This paper proposes a methodology for measuring Quality of Employment (QoE) deprivation from a multidimensional perspective in six Central American countries (Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama) using a dataset specifically designed to measure employment conditions. Building on previous work on multidimensional poverty and employment indicators, the paper uses the Alkire/Foster (AF) method to construct a synthetic indicator of the QoE at an individual level. It selects four dimensions that must be considered as essential to QoE deprivation: income, job stability, job security and employment conditions. These dimensions then subdivide into several indicators, a threshold for each indicator and dimension is established before defining an overall cut-off line that allows for the calculation of composite levels of deprivation. The results generated by this indicator show that Central American countries can be divided into three distinct and robust performance groups in terms of their QoE deprivation. Overall, approximately 60% of the deprivation levels are attributable to non-income variables, such as occupational status and job tenure. The methodology used can allow policymakers to identify and focus on the most vulnerable workers in a labour market and highlights the fact that having a formal written contract is no guarantee of good job quality, particularly in the case of women.

     

Measuring World Better Life Frontier: A Composite Indicator for OECD Better Life Index

The OECD Better Life initiative recently released a comprehensive set of 11 indicators of well-being covering a group of countries. Each individual indicator corresponds to a key topic that is essential to well-being. However, the problem of aggregating them is left to users of this dataset. Using these as individual indicators, we propose a composite indicator of overall well-being, which is intended to measure the performance of each country in terms of providing well-being to its people. The ‘benefit of the doubt’ approach (BOD), a well-known aggregation tool based on a weighed sum, assigns the most favourable weights for each entity under investigation. BOD may also be considered to evaluate the performance of each entity in terms of its efficiency. Regarding individual indicators as outputs, it constructs the benchmark production frontier from observed individual indicators. A composite indicator based on BOD equals the distance between each entity’s individual indicator and the production frontier, indicating its efficiency. It is widely considered that the well-being of a country’s people stems from its productive base, which is characterized by capital assets and social infrastructures. Thus, the productive base can be considered the input used to produce well-being, which is reflected by individual indicators. Therefore, when we apply BOD to aggregate individual well-being indicators across countries, we implicitly assume that all countries have the same productive base, as BOD addresses only the output and neglects the input. This inaccurate assumption leads to a distorted performance measure. Data envelopment analysis (DEA), in which BOD has its roots, is a tool to measure the efficiency of each entity by allowing for differences in inputs as well as outputs across entities. DEA also measures efficiency by using the distance to the production frontier; however, unlike BOD, DEA constructs the production frontier more accurately by utilizing the information of inputs as well as outputs, leading to a better performance measure. We apply DEA to aggregate 11 individual well-being indicators into a composite indicator using the World Bank’s estimates of each country’s productive base. The composite indicator based on BOD is distributed similarly to and is highly correlated with the existing Human Development Indicator (HDI). It is also positively correlated with GDP per capita. On the other hand, we show that the composite indicator based on DEA is negatively correlated with HDI as well as GDP per capita.     

Drivers of Youth Labour Market Integration Across European Regions

Territorial disparities and youth labour markets have been often considered as separated themes, due to challenges in data availability. Comparative regional or sub-regional research on youth labour market integration (YLMI) have been therefore scarce. In this article, we address this gap by presenting a composite measure of YLMI that covers a wide range of indicators and sheds light on the EU territorial divide of young peoples’ opportunities at regional level. In order to build the YLMI index, we use benefit-of-the-doubt-weighting, a seminal methodology on composite indicators (CI) that combines sequence with conditional weights based on the range of each sub-indicator. To proof the usefulness of YLMI, we analyze the evolution of regional YLMI in the EU before and after the economic crisis; and the trends of homogenization or differentiation across EU territories. Furthermore, we investigate to what extent employment conditions, skills supply and technological resources explain cross-regional variations in YLMI.

     

The Stability of Rankings Derived From Composite Indicators: Analysis of the “IL Sole 24 Ore” Quality of Life Report

The calculation of composite indicators and the derivation of respective rankings is a common method used to benchmark countries or regions. However, although the statistical robustness of these rankings is often criticised, they often still spark off heated political debate. Here, we assess the sensitivity of the province ranking published by the Italian economics newspaper “Il Sole 24 Ore”. For this purpose, various weighting, normalising and aggregation schemes were compared. Furthermore, to assess the accuracy of the underlying model, confidence intervals were calculated for the indicator values. The dynamic properties of the rankings were also analysed by evaluating the short- and long-term forecast properties of the rankings.     

Rank-Optimal Weighting or “How to be Best in the OECD Better Life Index?”

We present a method of rank-optimal weighting which can be used to explore the best possible position of a subject in a ranking based on a composite indicator by means of a mathematical optimization problem. As an example, we explore the dataset of the OECD Better Life Index and compute for each country a weight vector which brings it as far up in the ranking as possible with the greatest advance of the immediate rivals. The method is able to answer the question “What is the best possible rank a country can achieve with a given set of weighted indicators?” Typically, weights in composite indicators are justified normatively and not empirically. Our approach helps to give bounds on what is achievable by such normative judgments from a purely output-oriented and strongly competitive perspective. The method can serve as a basis for exact bounds in sensitivity analysis focused on ranking positions. In the OECD Better Life Index data we find that 19 out the 36 countries in the OECD Better Life Index 2014 can be brought to the top of the ranking by specific weights. We give a table of weights for each country which brings it to its highest possible position. Many countries achieve their best rank by focusing on their strong dimensions and setting the weights of many others to zero. Although setting dimensions to zero is possible in the OECD’s online tool, this contradicts the idea of better life being multidimensional in essence. We discuss modifications of the optimization problem which could take this into account, e.g. by allowing only a minimal weight of one. Methods to find rank-optimal weights can be useful for various multidimensional datasets like the ones used to rank universities or employers.     

A trichotomic segmentation approach for estimating composite indicators

Composite indicators (CIs) are commonly used for benchmarking of countries over the years, summarizing in a single measurement, complex social, economic, environmental etc. concepts by involving several thematically related sub-indicators. When estimating  CIs and for a few specific countries, it is possible to have a strong indication and belief about their performance, prior to obtaining their scores. Based on that, a number of countries are likely to occupy the top-ranking positions; some will remain at the bottom list, while others may range in intermediate places. This initial preference information imposes a trichotomic segmentation that divides the countries under assessment into categories of superior, inferior and of ambiguous future performance. In this paper, we introduce the trichotomic segmentation as initial preference information to estimate the values of the CIs. We build on the popular Benefit of the Doubt (BoD) method with common weights and develop a two-goal linear programming model, that next to the evaluation of the common weights for the sub-indicators, estimates cut-off points for the CI values that distinguish the superior and inferior countries. The proposed model maintains the advantages of the common variable weighting and produces scores that induce better discrimination of the countries, having also a significant correlation with the original CI scores. The proposed methodology is applied to reassess the Digital Economy and the Society Index.

     

Fertility Behaviour and Wealth Situation in Nigeria: Evidence from 2013 Demographic and Health Survey

With the growing interest in evaluation of quality of life, emerging number of methods are presented. Each contribution varies depending on the matter of interest, and all of them address the issue of subjective weighting factors. The objective of this paper is to explore possibilities to enhance Better Life ranking methodology, available from the Better Life initiative website, using I-distance method. The result was twofold: firstly, we pointed out potential shortcomings of subjectively chosen weights of Better Life ranking methodology by employing our I-distance approach. Secondly, we provided detailed information on how each Better Life indicator contributes to the final position and emphasize the essential indicators in the process of ranking. We have collected the latest available data for 2014, including all 24 indicators of the Better Life composite index. After that we have compared the two ways of rankings, i.e. the I-distance ranking and the Better Life ranking, emphasizing the improvement offered by the I-distance methodology. Further, through iterative exclusion of indicators based on the level of their significance, we have reached the highest quality of the model. That model includes the following six indicators: personal earnings, water quality, life satisfaction, household net adjusted disposable income, employment rate, rooms per person. Hereby, we have compared and presented ranking changes at each iteration for the top 10 countries, which offer a level of consistency in their rank. In addition, one of the objectives is to help policymakers focus on the key indicators in order to improve the ranking of the country, showing governments and administrations which indicators are the most important to invest into. Moreover, our approach could be the foundation for impartial framework of the quality of life’s assessment, independent of subjectively formed weighting factors.     

An Introduction to ‘Benefit of the Doubt’ Composite Indicators

Despite their increasing use, composite indicators remain controversial. The undesirable dependence of countries’ rankings on the preliminary normalization stage, and the disagreement among experts/stakeholders on the specific weighting scheme used to aggregate sub-indicators, are often invoked to undermine the credibility of composite indicators. Data envelopment analysis may be instrumental in overcoming these limitations. One part of its appeal in the composite indicator context stems from its invariance to measurement units, which entails that a normalization stage can be skipped. Secondly, it fills the informational gap in the ‘right’ set of weights by generating flexible ‘benefit of the doubt’-weights for each evaluated country. The ease of interpretation is a third advantage of the specific model that is the main focus of this paper. In sum, the method may help to neutralize some recurring sources of criticism on composite indicators, allowing one to shift the focus to other, and perhaps more essential stages of their construction. An abridged version of this paper was presented at the Workshop on European Indicators and Scoreboards, organised by DG Education and the Joint Research Centre within the auspices of CRELL, in Brussels, October 24–25, 2005.     

Labour market integration of immigrants: estimating local authority effects

We propose a method for computing indicators of the relative success of local authorities at integrating new immigrants in the labour market, taking account of differences in characteristics of immigrants and local labour markets. The indicator for integration success is based on mean duration from date of residence permit to start of an employment spell. We apply this method to a Danish administrative dataset. Correcting for differences in immigrant characteristics and local labour market conditions has an important impact on the estimated ranking of local authorities. We investigate how robust the ranking is with respect to changes in the specification of criteria for integration success and controls.

Highlighting Methodological Limitations in the Steps of Composite Indicators Construction

The paper opens the debate on the need to find a stable methodological framework in the construction of composite indicators (CIs) in order to address the methodological challenges including those of sensitivity and uncertainties related to methods used. As CIs are well-known to be essential in public debate, their methodological construction must be known by a large public. Illustrating CIs’ construction steps by a simple indicator, the paper aims to “democratize” this disciplinary field which is still a black box for some researchers but also to show how composite scores are sensitive to methods used and then, its impacts on policies. For example, in the Sustainable Development Indicator case, the geometric aggregation system is favorable to emerging countries which lead the ranking table whereas high income countries (which are leaders in the linear and equal weight system) except Australia, are misclassified. Uncertainty and sensitivity analysis confirm these results showing that the indexes’ scores seem to be influenced by the orientation (implied theoretical framework) given by its sponsors including policy makers. Regarding the validity of the index, correlation tests with some lights and well known indicators, reveal very consistent results.     

Measuring Active Citizenship through the Development of a Composite Indicator

This article defines Active Citizenship within a European context as a broad range of value based participation. It develops a framework for measuring this phenomenon which combines the four dimensions of Protest and Social Change, Community Life, Representative Democracy and Democratic values. The European Social Survey 2002 is used to populate the framework as this survey provided the best data coverage available and covered 19 European Countries. In total 61 indicators were selected. A composite indicator (CI), The Active Citizenship Composite Indicator (ACCI), is built using the framework provided and using experts’ weights. In addition, the robustness of the results is tested using sensitivity analysis. The limitations to the ACCI are explained in terms of the limitation of the data availability in particular concerning the new forms of participation and less organised forms of participation. Nevertheless, ACCI proved to be statistically robust and reliable and proved to be a useful tool for monitoring levels of citizenship in Europe. The results of ACCI exhibit interesting and quite distinct regional patterns. The results showed that the Nordic countries, and in particular Sweden, have the highest rate of Active Citizenship, followed by Central Europe and Anglo-Saxon countries. Mediterranean countries are next followed by Eastern European countries that close the ranking.

Does Culture Make a Better Citizen? Exploring the Relationship Between Cultural and Civic Participation in Italy

In the last decades, policy discussions have increasingly considered participation in arts and cultural activities as a vehicle to reach broader social policy goals, such as social inclusion or active citizenship. However, convincing empirical evidence on the social impacts of arts and culture is still scarce. In particular, little attention has been given to the impact that cultural participation may have on individuals’ engagement in civil society, especially in Europe. In order to address this lack of evidence, this paper explores the connection between cultural participation and civic participation in Italy using Likert-scale data collected in the ISTAT “Aspetti della Vita Quotidiana” Survey (2014). To do so, two composite indicators—the participation in cultural life indicator and the participation in civic life indicator—have been built using an approach based on partial order theory and the concept of Average Rank. The effect of cultural participation on civic engagement has been estimated using a quantile regression model which controls for potentially cofounding factors such as education, income, age and gender. Results do confirm that participation in arts and cultural activities is highly correlated with participation in civic life. It is particularly worth noticing that at higher levels of civic participation, cultural participation has a positive and strong effect on civic engagement of people having a low educational level.

     

Issues in the Conceptualisation and Measurement of Socioeconomic Background: Do Different Measures Generate Different Conclusions?

Parental occupation and education are used extensively in the analysis of socioeconomic inequalities in education and subsequent social and economic outcomes. The purpose of this paper is to examine if different ways of measuring socioeconomic background substantially alter substantive conclusions on cross-national differences in socioeconomic inequalities in student achievement. The effects of father’s occupational group are largely consistent across countries, with students from teaching backgrounds scoring very highly in many countries. Student performance by mother’s educational group is quite similar across countries although the relative performance of students whose mothers completed vocational education differs between countries. Notwithstanding these differences, continuous measures of father’s and mother’s occupation and education, and composite measures comprising combinations of these four indicators and additional indicators produce similar, but not identical, orderings of countries in terms of socioeconomic inequalities in student performance. However common single indicator measures, mother’s education and father’s occupation do not show a particularly high correspondence, cross-nationally. On theoretical and empirical grounds, the preferred measure is a composite of both parents’ occupation and education.     

A Gender Wellbeing Composite Indicator: The Best-Worst Global Evaluation Approach

In this paper we present a new global evaluation methodology for constructing composite indicators. Given the limitations in the construction of wellbeing indicators that include a gender perspective, this paper proposes separate measures for men and women, using new linear programming models to improve the traditional Data Envelopment Analysis models. We obtain composite indicators using common weights (which are most and least favourable) so that the indicators’ values are able to discriminate between all units and provide a global vision of each one. This approach does not require prior knowledge of the weights for initial indicators, nor does it require a normalization method. Thus, the new composite indicator is less subjective than others. For the practical implementation of this proposal, we use the countries of the European Union as an example.     

Assessment of Composite Indicators Using the ANOVA Model Combined with Multivariate Methods

The synthesis of a number of single observed indicators into a unique composite indicator involves various subjective choices related, for instance, to the type of combination (linear, non-linear) and to the aggregation method (simple average, geometric average) used in its construction. Thus, it is clearly important to analyse the variability of a composite indicator according to all possible alternatives before employing it in any decision-making process. Within such a framework, in this paper, we present a new approach based on a combination of explorative and confirmative analyses aiming to investigate the impact of different subjective choices on the variability of composite indicators. This new approach also allows the analysis of the related individual differences among the statistical units and the use of external information on the same units to enhance the interpretation of the final results.     

The Measurement of Multidimensional Gender Inequality: Continuing the Debate

The measurement of multidimensional gender inequality is an increasingly important topic that has very relevant policy applications and implications but which has not received much attention from the academic literature. In this paper I make a comprehensive and critical review of the indices proposed in recent years in order to systematise the different underlying ideas. I also present new gender inequality indices that overcome some limitations of the preceding ones. Using United Nations data, empirical results for the new indicators are provided, suggesting that the choice of one indicator or another can make an important difference for the ranking of those countries that have achieved high levels of gender equality.     

Erratum to: The Measurement of Multidimensional Gender Inequality: Continuing the Debate

The measurement of multidimensional gender inequality is an increasingly important topic that has very relevant policy applications and implications but which has not received much attention from the academic literature. In this paper I make a comprehensive and critical review of the indices proposed in recent years in order to systematise the different underlying ideas. I also present new gender inequality indices that overcome some limitations of the preceding ones. Using United Nations data, empirical results for the new indicators are provided, suggesting that the choice of one indicator or another can make an important difference for the ranking of those countries that have achieved high levels of gender equality.     

Comparing MCDA Aggregation Methods in Constructing Composite Indicators Using the Shannon-Spearman Measure

Composite indicators have been increasingly recognized as a useful tool for performance monitoring, benchmarking comparisons and public communication in a wide range of fields. The usefulness of a composite indicator depends heavily on the underlying data aggregation scheme where multiple criteria decision analysis (MCDA) is commonly used. A problem in this application is the determination of an appropriate MCDA aggregation method. Of the many criteria for comparing MCDA methods, the Shannon-Spearman measure (SSM) is one that compares alternative MCDA aggregation methods in constructing composite indicators based on the information loss concept. This paper assesses the effectiveness of the SSM using Monte Carlo approach-based uncertain analysis and variance-based sensitivity analysis techniques. It is found that most of the variation in the SSM arises from the uncertainty in choosing an aggregation method. Therefore, the SSM can be considered as an effective measure for comparing MCDA aggregation methods in constructing composite indicators. We also use the SSM to evaluate five MCDA aggregation methods in constructing composite indicators and present the findings.