Does Human Development Index Provide Rational Development Rankings? Evidence from Efficiency Rankings in Super Efficiency Model

The human development index (HDI) rankings have provided a referenced measure for people to choose a country in which to travel or live. This paper employs a superefficiency model to evaluate the rationality of the HDI rankings of 19 evaluated OECD countries in 2009. Compared to the HDI rankings, the efficiency rankings measured by the super-efficiency model have the following two advantages: (1) they consider the inputs that are used to generate the indicators for constructing the HDI, and decide the weights of inputs and outputs endogenously; (2) the input slacks measured by the super-efficiency model can evaluate whether the inputs are over-used and provide the improvement path of each country’s input variables. Empirical result shows that approximately 75 % of the evaluated countries had rather different results in the efficiency rankings and the HDI rankings. Additionally, the input slack shows that roughly 70 % of sample countries over-used their capital per labor relative to their existing outputs (or the HDI).     

Neighborhoods in Development: Human Development Index and Self-organizing Maps

The Human Development Index (HDI) has been instrumental in broadening the discussion of economic development beyond money-metric progress, in particular, by ranking a country against other countries in terms of the well being of their citizens. We propose self-organizing maps to explore similarities among countries using the components of the HDI rather than rankings. The similarities approach using the HDI components reveals information which is not available from ranking or bilateral comparisons. By illustrating clusters of countries, which we call “neighborhoods in development”, self-organizing maps draw out the potential for mutual policy learning among countries and shift the focus to discovering what kind of policies might have led countries change their position in the rankings.     

A Human Development Index for the Dalit Child in India

There have been many attempts to measure the quality of life of society in general (such as the Human Development Index of UNDP), or of children in particular (Jordan 1993; Corrie 1994). This article constructs a Human Development Index (HDI) for the Dalit Child in India following the methodology used by UNDP (1990) to construct a human development index for the countries of the world. Dalits (also known as Untouchables, Harijans, Scheduled Castes) have and continue to be a marginalised group in India. Section 1 presents the indicators used to construct the HDI for the Dalit child in India. Section 2 presents the rationale for the choice of the indicators chosen. Section 3 presents the methodology used to construct the HDI for the Dalit child in India. Finally, Section 4 presents the relative ranking of 15 states in India based on the level of human development as reflected in the HDI constructed for the Dalit child. It also compares the HDI rankings from perspective of the Dalit Child in India with a recent HDI constructed for 17 states in India using similar indicators as UNDP (1990). The policy usefulness of this human development index for the Dalit child in India is that it could serve as an indicator of the social progress achieved in India as the country attempts to fulfill its constitutional vision of equality for all citizens.     

“Healthy” Human Development Indices

In the Human Development Index (HDI), life expectancy is the only indicator used in modeling the dimension ‘a long and healthy life’. Whereas life expectancy is a direct measure of quantity of life, it is only an indirect measure of healthy years lived. In this paper we attempt to remedy this omission by introducing into the HDI the morbidity indicator, “expected lost healthy years” (LHE), used in the World Health Report Though LHE is only weakly correlated with life expectancy and displays considerable variation across countries, the ranking of nations using the adjusted HDI is very similar to that from the HDI. Nevertheless, there are some outlier countries (including large countries like China and the United States) that experience notable changes in rank. Given the considerable variation in the morbidity data across gender, we also adjust the Gender-related Development Index (GDI) in a similar fashion. The ranking using the adjusted GDI is very similar to that from the GDI, but it has a lower rank correlation with the HDI.     

Values Reflected in the Human Development Index

The Human Development Index (HDI) implicitly defines ``human development' and ranks countries accordingly. To elucidate the HDI's meaning of ``human development,' the paper examines the sensitivity of the HDI to changes in its components, namely social indicators of education, longevity and standard of living. The HDI is next compared with two alternatives, the Life Quality Index (LQI) and a Time Allocation Index (TAI) developed in this paper from the HDI's components. Also considered is the likely uncertainly in the HDI and what it means for HDI rankings.It is concluded that the HDI's weighting of the gross domestic product is in good agreement with peoples' preferences as revealed in the LQI and the TAI; further, that the HDI places many times greater weight on education than is indicated by peoples' allocation of time in developed countries. Literacy is accorded very high weight in the HDI, but its measure is unreliable. The HDI ranking of highly developed nations is so close and so uncertain that it is meaningless.     

Human Development Index (HDI) Rank-Order Variability

In accordance with the increasing demand for information, indices are created and national and global rankings made to represent and through which to understand and build policy related to complex situations, processes and trajectories. Different indices for a single concept are also created that have advantages or disadvantages over one another or to overcome certain calculation problems. As one such, the Human Development Index (HDI) presently lists countries according to four different criteria, and remains at the heart of democratic and humanitarian recovery efforts. This type of indicator is taken as a function of past performances, with high performances being the extreme values at positively skewed distributions. Thus, the variability of each unit’s repeated measures is regarded as the result of efforts made between the measurement time points (in the HDI case, of a country to promote development). However, it is assumed that the variability of the units is not homogenous. Here, it is shown that in the HDI case, high performance units show relatively low variability, whereas the middle and middle-low performance units show a high variability. Cluster analysis and Friedman test have been used to determine the characteristics of ordered country rankings. The variability of rank-order should also be taken into account besides the location on the list by clustering the countries according to HDI.     

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.     

Dynamics of HDI Index: Temporal Dependence Based on D-vine Copulas Model for Three-Way Data

The HDI (Human Development Index) is a widely used index based on the average of measures of health, education, and income. It assesses the progress of countries worldwide. The publicly available data set associated with the HDI can be seen as a table with 3 dimensions (three-way table): countries, indexes regarding progress, and years (from 2010 to 2018). Thus, modeling the serial dependence structure of this type of intricate three-way tables is a challenge. D-vine copulas are a special class of multivariate copulas that are particularly suited for modeling serial dependence. This work aims to assess the evolution of the dependence relationship between the indexes of the HDI data set over time through D-vine copulas, which has not been fully used before in the area, as far as we are concerned. We tested our approach to European and African countries and compare their results.

     

Measurement of social development: an international comparison

PQLI and HDI are the two most popular measures of development, besides per capita income. Over the years, PQLI appears to be not much in use for regional comparisons, especially after the introduction of HDI. While PQLI considers only the physical variables—adult literacy, life expectancy at birth and infant survival rate, HDI has life expectancy at birth, educational attainment and real GDP per capita (PPP$). PQLI and HDI are similar, the main difference between the two being the inclusion of income in HDI and exclusion of the same from PQLI. In a sense, HDI represents both physical and financial attributes of development and PQLI has only the physical aspects of life. The present author took the lines of PQLI to express development in terms of physical variables and considering development as a multidimensional phenomenon, Ray (1989) [Ray, A. K. (1989). On the measurement of certain aspects of social development, Social Indicators Research (Vol. 21, pp. 35–92). The Netherlands: Kluwer Academic Publishers.] included as many as 13 physical variables to represent social development across 40 countries; no financial variable was included in the construction of composite index, termed as the Social Development Index, SDI. Incidentally, like PQLI, SDI was introduced before HDI. Unlike PQLI and HDI, SDI considers (i) a large number of indicators representing various concern areas and (ii) a set of objective methods for combining the development indicators as a composite index. Ray (1989) has been restated and updated in this article with newer cross-country information. In the present study, SDI has been constructed for over 102 countries, including 21 OECD countries, using 10 development indicators, instead of 13 indicators in the past. Apart from presenting objective methods for combining indicators into SDI, the present study asserts that SDI works better than HDI as a measure of development for an international comparison. The views expressed in the article are those of the author and not of the institution he serves.     

Measuring Youth Development: A Nonparametric Cross-Country ‘Youth Welfare Index’

This paper develops an empirical methodology for the construction of a synthetic multi-dimensional cross-country comparison of the performance of governments around the world in improving the livelihood of their younger population. The devised ‘Youth Welfare Index’ is based on the nonparametric Data Envelopment Analysis (DEA) methodology and allows for cross-country benchmarking and comparison over time. The value added of the youth index is to produce country-specific rankings and trace performance evolution with respect to indicators solely centered on youth, unlike other development indicators like the Human Development Index (HDI) which bundles many social and development indicators.

Inequality in Human Development: An Empirical Assessment of 32 Countries

One of the most frequent critiques of the HDI is that is does not take into account inequality within countries in its three dimensions. In this paper, we apply a simply approach to compute the three components and the overall HDI for quintiles of the income distribution. This allows a comparison of the level in human development of the poor with the level of the non-poor within countries, but also across countries. This is an application of the method presented in Grimm et al. (World Development 36(12):2527–2546, 2008) to a sample of 21 low and middle income countries and 11 industrialized countries. In particular the inclusion of the industrialized countries, which were not included in the previous work, implies to deal with a number of additional challenges, which we outline in this paper. Our results show that inequality in human development within countries is high, both in developed and industrialized countries. In fact, the HDI of the lowest quintiles in industrialized countries is often below the HDI of the richest quintile in many middle income countries. We also find, however, a strong overall negative correlation between the level of human development and inequality in human development.     

Measuring longevity achievements under welfare interdependencies: a case for joint life expectancy indicators

Whereas period life expectancy constitutes an intuitive indicator of the survival conditions prevailing at a particular period, this paper argues that, given the existence of welfare interdependencies, that widespread indicator is nonetheless an incomplete measure of the longevity achievements relevant for human well-being. The central importance of coexistence for human-beings implies that usual life expectancy measures should be complemented by joint life expectancy indicators, which measure the average coexistence time under particular survival conditions. After a study of the theoretical foundations of ‘single’ and ‘joint’ life expectancy indicators, it is shown that joint life expectancy measures tend to enrich significantly the comparison of longevity achievements across countries and periods. Moreover, the introduction of joint life expectancy indicators—as a complement to conventional life expectancy measures—into multi-variable indexes such as the United Nations’ HDI is also shown to affect international rankings of standards of living to a non negligible extent.

Keeping things simple: why the Human Development Index should not diverge from its equal weights assumption

Using a range of statistical criteria rooted in Information Theory we show that there is little justification for relaxing the equal weights assumption underlying the United Nation’s Human Development Index (HDI) even if the true HDI diverges significantly from this assumption. Put differently, the additional model complexity that unequal weights add to the HDI more than counteracts the improvement in goodness-of-fit. This suggests that, in some cases, there may be limited validity in increasing the complexity of a range of other composite sustainability indices.     

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.     

Focussing on the Extremes of Good and Bad: Media Reporting of Countries Ranked Via Index-Based League Tables

The paper provides the first published evidence for a ‘U’ shaped relationship between country ‘league-table’ ranking based on the Human Development Index and Corruption Perception Index and media reporting. The results suggest that the Extremity Hypothesis proposed by Heath (Glob Environ Change 21(1): 198–208, 1996) applies to such data rather than the alternative of the Centrality Hypothesis. In the Extremity Hypothesis people are more likely to transmit information regarding extremes, perhaps because people value ‘surprisingness’ or think that others do so, and the inevitable polarity of league-tables would appear to invite greater attention on those countries that rank high and low. This is an important finding as it suggests that countries at these extremes could act as exemplars. However, this is not to say that at more regional scales the media may pick-up on differences between ‘peer group’ countries ranked towards the middle of the league-table. Much more attention needs to be given by researchers to the use of indicators and indices and what helps to influence this, especially as it would help inform further development of existing indicators/indices and the creation of new ones.     

Exploring Wider Well-Being in the EU-15 Countries: An Empirical Application of the Stiglitz Report

We draw on the recommendations of the Stiglitz Report to select a set of economic and social variables that can be used to make cross-country comparisons of wider well-being. Using data for the EU-15 countries for 1999 and 2005, we show how three-way analysis can be used to extract synthetic information from a large data set to determine the main latent explanatory factors. In our case, we identify one dominant factor that we term the development profile, which is positively associated with the level of education outputs, technological progress and female labour market participation and negatively associated with the level of pollution. We rank the countries according to this factor and compare these rankings with simpler GDP comparisons and find that the two rankings are only weakly correlated.     

Another Approach to Measuring Human Development: The Composite Dynamic Human Development Index

This paper seeks mainly to contribute to the debate on how the relative degree of development of a country should be measured by proposing an indicator to build on the valuable starting point provided by the Human Development Index (HDI). The indicator proposed is called the “Composite, Dynamic Human Development Index”. It incorporates in a simple way additional points which are significant for the current concept of human development and provides a dynamic factor that distinguishes between countries on the basis of achievements attained. It helps ensure that the static average data on which the HDI is based does not conceal wide-ranging economic, social and political differences within countries, lack of sustainability in current levels of development or effective development strategies drawn up by governments.     

Are Major Events Capable of Affecting Country Rankings? Validating Composite Indexes of Human Progress and Environmental Performance

A desired characteristic of composite indicators is sensitivity to major adverse events. This paper explores how major civil wars and the 2004 tsunami have influenced Human Development Index (HDI) and Environmental Performance Index (EPI) index values of the affected countries, respectively. The analysis shows that HDI and EPI scores have barely changed, being almost exclusively due to variations in GNI/capita for HDI and air quality for EPI. This casts doubt on the composite indexes’ usefulness and their ability to reflect major environmental and societal changes in the affected countries, or shows which dimensions are truly resilient to these events and can constitute a sustainable base for postwar/post-disaster recovery. Human progress and ecological indicators may need an overhaul, in order to account for the changes that actually happen at a point in time, in order to capture substantial changes in the socio-economic and ecological fabric of a country.     

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.

     

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.