The choice of principle variables for computing the human development index

Ogwang, Tomson

doi:10.1016/0305-750x(94)90189-9

Cited by 45 publications

(26 citation statements)

References 7 publications

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“…F k are therefore obtained by principal components analysis, with W i being the first principal component extracted from the x t k,i and F k being an (m ¥ 1) eigenvector. The corresponding eigenvalue is l k and the normalization condition is that Ram (1982), Ogwang (1994) and Lai (2000) also use the principal components technique to derive well-being measures.…”

Section: Non-economic Well-being Achievementmentioning

confidence: 99%

Measuring Non-Economic Well-Being Achievement

McGillivray

2005

Rev Income Wealth

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Income per capita and most widely reported, non-or non-exclusively income based human well-being indicators are highly correlated among countries. Yet many countries exhibit higher achievement in the latter than predicted by the former. The reverse is true for many other countries. This paper commences by extracting the inter-country variation in a composite of various widely-reported, non-income-based well-being indices not accounted for by variations in income pre capita. This extraction is interpreted inter alia as a measure of non-economic well-being. The paper then looks at correlations between this extraction and a number of new or less widely-used well-being measures, in an attempt to find the measure that best captures these achievements. A number of indicators are examined, including measures of poverty, inequality, health status, education status, gender bias, empowerment, governance and subjective well-being.

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Section: Non-economic Well-being Achievementmentioning

confidence: 99%

Measuring Non-Economic Well-Being Achievement

McGillivray

2005

Rev Income Wealth

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“…Ogwang (1994) and Ogwang and Abdou (2003) justify the HDR's equally weight scheme by PCA (based on the covariance matrix) with 1997 data. Noorbackhsh (1998) similarly considers PCA using data for 1992 as one option for deriving weights for the components in the HDI.…”

mentioning

confidence: 99%

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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“…The first principal component is that linear combination of the original attributes which explains the highest fraction of the variance in the original variables. Ogwang and Abdou (2003) used principal components analysis to find that the first principal component weights attached to the three HDI components are approximately equal, a result consistent with the equal weighting scheme adopted by the United Nations Development Program (UNDP) in the computation of the HDI. 7 Moreover, in the 1993 UNDP report, it is noted that the almost equally-weighted combination of components explains 88% of the variation in the original set.…”

Section: Literaturementioning

confidence: 54%

“…Ogwang (1994) points out that many countries fail to have uninterrupted collections of census data from which information on life expectancy and literacy could be obtained. Srinivasan (1994) highlights the weaknesses of the data as for example GNP data of many developing countries suffer from problems of incomplete coverage, measurement errors and biases.…”

Section: Literaturementioning

confidence: 99%