Abstract
This paper constructs four structural indices by using 42 socioeconomic variables for 129 countries and the 10 years period from 2003 to 2012. Each structural index can be considered as a measure of a certain dimension of development. The first two indices are the most useful in explaining gaps in development across countries. The first captures the role of technology and institutional quality while the second provides a measure of the basic level of development. The contrast between them signifies that the notion of development is not only multidimensional, but also changing with the stage of development. These two indices are combined to form a development index (DI). A comparison of DI to income per capita and the Human Development Index highlights the importance of institutions in the transition of countries from merely having high income to full development. A methodological contribution of the paper is to use a Jackknife approach within the factor analysis routine to test for the significance of the extracted factors/indices.
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Notes
Internationally comparable, PPP adjusted.
A more detailed explanation of the estimation of factors can be found in Ganegodage (2008).
This is a visual aid to determine the appropriate number of factors. In a Scree Plot (Cattell 1966) eigenvalues from largest to smallest are plotted. Using this plot, the decision on the number of factors is taken to be the point at which the remaining eigenvalue are relatively small and all about the same size. This is determined through discontinuity or breaks in the plot.
For \(\alpha =0.05\), and an orthogonal rotation \(d_u = pm-m(m-1)/2\) where p is the number of variables, and m is the number of factors; for oblique rotation \(d_u = pm-m(m-1)\).
The rotated loading matrix, \(\hat{L}\) is obtained from \(\hat{L} = (\hat{L}{^o}' \hat{L}{^o})^{-1} \hat{L}{^o} P\), where \(\hat{L}^o\) is the orthogonal factor loading matrix, P is known as the pattern matrix and it has elements \(| l^{\kappa +1}_{ij} | / l_{ij}\); \(\kappa > 1\), and its value determines the degree of correlation amongst the factors.
PPP adjusted GDP is typically referred to as ‘real’ in the international comparisons literature, and the series can be in current prices or in constant prices.
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Ganegodage, K.R., Rambaldi, A.N., Rao, D.S.P. et al. A New Multidimensional Measure of Development: The Role of Technology and Institutions. Soc Indic Res 131, 65–92 (2017). https://doi.org/10.1007/s11205-015-1139-7
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DOI: https://doi.org/10.1007/s11205-015-1139-7