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Math. Comput. Appl. 2017, 22(1), 13; doi:10.3390/mca22010013

New Unidimensional Indexes for China

Warrington College of Business, University of Florida, P.O. Box 117150, Gainesville, FL 32611‐7150, USA
Academic Editor: Fazal M. Mahomed
Received: 18 December 2016 / Revised: 4 January 2017 / Accepted: 4 January 2017 / Published: 24 January 2017
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Abstract

A first principal component combines several indicators so as to maximize their internal consistency for measuring a construct. First principal components are extracted here from Swiss Economic Institute and World Bank datasets containing yearly societal indicators for China. These indicators are input to population-weighted regressions without recourse to survey sampling or probabilistic inference. The results demonstrate Chomskyan globalization and domestic credit as strong exogenous and endogenous predictors of Chinese per capita GDP. These encouraging findings, easily extendable to other nations, are brought by two new societal indexes with assured unidimensionality. View Full-Text
Keywords: Chomskyan globalization; domestic credit; latent principal‐components and regression; per capita GDP; population-weighted indexes and regression slopes; quality assurance; ɷ‐homogeneity Chomskyan globalization; domestic credit; latent principal‐components and regression; per capita GDP; population-weighted indexes and regression slopes; quality assurance; ɷ‐homogeneity
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Bechtel , G. New Unidimensional Indexes for China. Math. Comput. Appl. 2017, 22, 13.

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