Measuring Non-Economic Well-Being Achievement

McGillivray, Mark

doi:10.1111/j.1475-4991.2005.00157.x

Cited by 77 publications

(46 citation statements)

References 17 publications

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“…According to Cracolici et al [18], CO2 emissions and the literacy rate are variables capable of capturing the differences among countries in regard to social and environmental dimensions, and a similar result regarding literacy rate was obtained by McGillivary [33].…”

Section: Educationmentioning

confidence: 58%

Environmental Performance in Countries Worldwide: Determinant Factors and Multivariate Analysis

et al. 2014

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Abstract:The aim of this study is to analyze the environmental performance of countries and the variables that can influence it. At the same time, we performed a multivariate analysis using the HJ-biplot, an exploratory method that looks for hidden patterns in the data, obtained from the usual singular value decomposition (SVD) of the data matrix, to contextualize the countries grouped by geographical areas and the variables relating to environmental indicators included in the environmental performance index. The sample used comprises 149 countries of different geographic areas. The findings obtained from the empirical analysis emphasize that socioeconomic factors, such as economic wealth and education, as well as institutional factors represented by the style of public administration, in particular control of corruption, are determinant factors of environmental performance in the countries analyzed. In contrast, no effect on environmental performance was found for factors relating to the internal characteristics of a country or political factors.

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Section: Educationmentioning

confidence: 58%

Environmental Performance in Countries Worldwide: Determinant Factors and Multivariate Analysis

et al. 2014

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“…We use data for the years 1975-2005. Data from 1975-1995 were obtained from Human Development Report Office (HDRO) and are calculated using a consistent method that has been used since 1999.…”

Section: -Methodology and Resultsmentioning

confidence: 99%

“…236-43. United Nations Development Program (1999, 2000, 2002, 2003, 2004, 2005, 2007, Human Development Policy Report , Oxford University Press, Oxford. …”

Section: -Discussion and Summarymentioning

confidence: 99%

“…Principal component scores, and the resulting country ranks were computed for each year shown in Tables 1a and 1b. Altogether this suggests that dimension reduction using PCA, and relying on just the first principal component to capture the latent concept of 'human development' is justified (see Tables 1a, 1b; see also McGillivray, 2005).…”

Section: As Is Shown Inmentioning

confidence: 97%

“…Noorbackhsh (1998) similarly considers PCA using data for 1992 as one option for deriving weights for the components in the HDI. The use of PCA and associated techniques to derive an index is not new; Ram (1982) suggested its use to derive weights for the components of the Physical Quality of Life Index (which can, in part, be seen as a precursor to the HDI), augmented by GDP per capita; Del Valle Irala and Puerta Gil (1999), for example, proposed a weighted principal components analysis (WPCA) for measuring human development; McGillivray (2005) used PCA to generate a non-income well-being index based on a range of well-being indicators to study which countries perform better in the first principal component than predicted by per-capita incomes. Maasoumi and Nickelsburg (1988) applied multivariate techniques for measuring inequality for Michigan data.…”

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confidence: 99%

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On Weighting the Components of the Human Development Index: A Statistical Justification

Nguefack‐Tsague

Klasen

Zucchini

2011

Journal of Human Development and Capabilities

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe Human Development Index (HDI) published in the Human Development Report (HDR) of the United Nations Development Program is calculated as a simple average of the Life Expectancy Index (LEI), the Education Index (EI) and the Gross Domestic Product Index (GDPI). This paper provides statistical support for the use of this seemingly arbitrary equal weighting of the three components by treating human development as a latent concept imperfectly captured by its three component indices. We show that a principal component analysis (PCA) based on the correlation matrix of the components leads to practically the same weights. Specifically we show that, for the period 1975 to 2005, the first principal component accounts for between 78% and 90% of the total variability in the data, and that its coefficients are positive and nearly equal. By normalizing the coefficients, the simple average weighting (1/3, 1/3, 1/3) scheme is obtained. The ranks of countries obtained using the PCA weightings are very similar to those based on the HDI. An advantage of the simple equal weighting is that one can define a simple index to measure the balance of a country's development, given its HDI which we show below.

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Is Human Development Multidimensional?

French

Moore

Canning

2012

J of Intl Development

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Stiglitz's Commission on the Measurement of Economic Performance and Social Progress (CMEPSP) argued that well-being is multidimensional and identified eight distinct dimensions. Conventional linear techniques confirm that a large number of dimensions are needed to describe development. In contrast, a new non-linear technique that we introduce from chaos theory shows that a smaller number of dimensions are needed to span the development space. From the analysis, variables representing the Health, Education, Inequality and Individual Rights areas of life quality would provide a broad picture of development, whereas income per capita adds little extra information. ). These techniques are non-parametric and allow for non-linear relationships in the data. They were originally developed for the study of dynamic systems in high dimensions where the outcome paths may be cycles, or strange attractors, that may appear random at first sight but have a structure that means they occupy only a low dimensional subspace. We consider a set of 23 development indicators. We find these have dimension five. Our method identifies the dimension of the development space but does not provide a coordinate system. We investigate which variables span the five dimensions of the larger development space. We find that variables representing Health, Education, Inequality and Individual Rights are consistently represented in those subsets with the highest dimension and so should be included in any summary picture of development.Our results suggest that that the process of development is not arbitrary; there are 'laws' that seem to limit possible outcomes. For example, Preston (1975) argues that at each point in time, there is a (non-linear) relationship between a country's level of income per capita and its life expectancy, a relationship that continues to hold (Bloom and Canning, 2007). Kuznets (1955) suggested an inverted-U relationship between income and inequality with the distribution of income initially worsening with economic growth and improving only at 446 D. French et al. (2) and (4) are formed by dividing the number of appearances in column (1) and (3) by the number of indicators used for each area of life quality (e.g. 4 for health: life expectancy at birth, daily calorie intake, daily protein intake (grammes), mortality-infant). Note: Columns

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Measuring Non-Economic Well-Being Achievement

Cited by 77 publications

References 17 publications

Environmental Performance in Countries Worldwide: Determinant Factors and Multivariate Analysis

Environmental Performance in Countries Worldwide: Determinant Factors and Multivariate Analysis

On Weighting the Components of the Human Development Index: A Statistical Justification

Is Human Development Multidimensional?

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