2017
DOI: 10.3390/mca22010013
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New Unidimensional Indexes for China

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 predictor… Show more

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Cited by 4 publications
(5 citation statements)
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“…We have also benefited from Dr. Bechtel's insistence on monitoring a population's economic indicators over time, coupled with a gradual approach to resolving societal conflicts and loosening entrenched beliefs. This paper generalizes previous work reported in [5,6] and [1,7,8]. The author thanks the reviewers of all five of these open access articles for their stringent reviews, which have strengthened this article's content and clarity.…”
Section: Acknowledgementssupporting
confidence: 80%
“…We have also benefited from Dr. Bechtel's insistence on monitoring a population's economic indicators over time, coupled with a gradual approach to resolving societal conflicts and loosening entrenched beliefs. This paper generalizes previous work reported in [5,6] and [1,7,8]. The author thanks the reviewers of all five of these open access articles for their stringent reviews, which have strengthened this article's content and clarity.…”
Section: Acknowledgementssupporting
confidence: 80%
“…The author thanks Dr. Bethany Bechtel for her gift [4] that alerted him to the importance of GDP to us all, and for her insistence on monitoring a nation's economic indicators over time. This adaptation of [12] [35] improves upon [35] by parsimoniously linking GDP to the United Nation's Human Development Index.…”
Section: Acknowledgementsmentioning
confidence: 99%
“…unique up to multiplication by a positive scalar.The second weighting of G j derives from a 2-level principal components analysis, with populations nested within successive years for a given nation: Lemma 1. M = a 1 G 1 + a 2 G 2 + a 3 G 3 is the first principal component of G 1 , G 2 , and G 3 where(a 1 a 2 a 3 )is the first eigenvector of the covariance matrix of G 1 , G 2 , and G 3[12] [13] (pp. 536-544).…”
mentioning
confidence: 99%
“…This section treats latent individual-time points on the real line, with individuals nested within successive years for a given nation (Bechtel 2017;Johnston 1984, pp. 536-44).…”
Section: Latent 2-level Principal Components Analysismentioning
confidence: 99%
“…It differs from standard lation, which equally weights the indicators in Section 2 by an arithmetic summation. It from other indexes, which weight their indicators to maximize the prediction of external -Level Principal Components Analysis ection treats latent individual-time points on the real line, with individuals nested within years for a given nation (Bechtel 2017;Johnston 1984, pp. 536-44 onents analysis implied by axioms 1 and 2 in Section 4.2.…”
Section: Latent 2-level Principal Components Analysismentioning
confidence: 99%