Abstract:
Socio-economic status measurement is an ongoing problem where different suggested
measurements are given by researchers. This work investigates a socio-economic
status measurement derived from natural correlations of variables which can
better and meaningfully cluster African countries for the level of status. The researcher
used 48 African countries socio-economic yearly time series data from 1993 to 2013 of
IMF 2013 data set for data management (i.e, 2737 variables for 21 years), however, the
analysis is reasonably done based on recent 14 years time series data. In data management,
missing values are treated (imputed) by using regression estimates, Lagrange
interpolation, linear interpolation and linear spline interpolation based on the appropriate
method which best fits for the trend of data with minimum error at each time level.
From principal component and factor analysis of average time series data, 7 principal
factors contributed by 84 variables which explain 70% of the variation in the data set
are suggested as a socio-economic status measuring components and as a result the
considered clustering methods (K-mean Method, Average linkage method, Ward’s
method and Bootstrap Ward’s method) are agreed on six clusters of countries, those
are statistically significant at 95%, where as three countries each where suggested as
outlier-countries made an individual cluster.
