scholarly journals What Do Global Metrics Tell Us About The World?

2018 ◽  
Author(s):  
John Rennie Short ◽  
Justin Vélez-Hagan ◽  
Leah Dubots
Keyword(s):  
Economic Development ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Group Classification ◽  
Social Progress ◽  
The World ◽  
Components Analysis ◽  
Underlying Dimension ◽  
Degree Of Overlap

There are now a wide variety of global metrics. To find the degree of overlap between these different measures, we employ a principal components analysis (PCA) to 15 indices across 145 countries. Our results demonstrate that the most important underlying dimension highlights that economic development and social progress go hand in hand with state stability. The results are used to produce categorical divisions of the world. The threefold division identifies a world composed of what we describe and map as Rich, Poor and Middle countries. A five-group classification provided a more nuanced categorization described as; The Very Rich, Free and Stable, Affluent and Free, Upper Middle, Lower Middle, and Poor and Not Free.

scholarly journals What Do Global Metrics Tell Us about the World?

2019 ◽  
Vol 8 (5) ◽  
pp. 136
Author(s):  
John Rennie Short ◽  
Justin Vélez-Hagan ◽  
Leah Dubots
Keyword(s):  
Principal Component Analysis ◽  
Economic Development ◽  
Principal Component ◽  
Component Analysis ◽  
Group Classification ◽  
Social Progress ◽  
The World ◽  
Global Indicators ◽  
Underlying Dimension ◽  
Degree Of Overlap

There are now a wide variety of global indicators that measure different economic, political and social attributes of countries in the world. This paper seeks to answer two questions. First, what is the degree of overlap between these different measures? Are they, in fact, measuring the same underlying dimension? To answer this question, we employ a principal component analysis (PCA) to 15 indices across 145 countries. The results demonstrate that there is one underlying dimension that combines economic development and social progress with state stability. Second, how do countries score on this dimension? The results of the PCA allow us to produce categorical divisions of the world. The threefold division identifies a world composed of what we describe and map as rich, poor and middle countries. A five-group classification provided a more nuanced categorization described as: The very rich, free and stable; affluent and free; upper middle; lower middle; poor and not free.

scholarly journals Estimationof a Tax Compliance Index with Specific Application to Pay-As-You-Earn

2021 ◽  
Vol 04 (02) ◽  
Author(s):  
Cathrine T Koloane ◽  
Keyword(s):  
Time Series ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Composite Index ◽  
Tax Compliance ◽  
Upward Trend ◽  
Monthly Data ◽  
The World ◽  
The Future ◽  
Components Analysis

This article provides a composite index for Pay-As-You-Earn (PAYE)tax using Principal Components Analysis (PCA). The study uses time series from April 2012 to March 2020 (using monthly data) for the ratios derived from the four compliancemeasures namely, payments on time, registration on time, filing on time and accurate declarations. The index is computed using the weights of the four derived principal components. According to the model results, the PAYE tax compliance index averages around 75.0% for the period, with the lowest value of 72.3% in 2013/14 and the highestvalue of 77.1% achieved in 2018/19. There is a clear upward trend, indicating improving levels of compliance in PAYE. Similarly, setting the baseline index of 100 i.e. assuming 100% compliance for 2012/13, results in PAYE tax compliance index averaging around 101.6% for the period, with the lowest value of 97.72% in 2013/14 and the highest value of 104.26% achieved in 2018/19. The study recommends this methodology to be applied to all the tax products and that the overall tax compliance index be computed. This will assist tax authorities all over the world to actively monitor tax compliance levels and institute timeous corrective measures in order to address non-compliance and ultimately maximise PAYE revenue collections. Moreover, this study also serve as a base for many of the future tax compliance indices studies.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

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