The Principal Components Analysis and Cluster Analysis as Tools for the Estimation of Poverty, an Albanian Case Study

The measurement and analysis of poverty have traditionally relied on reported income or consumption expenditure as the preferred indicators of poverty and living standards. Income is generally the measure of choice in developed countries but a number of methods have been used to assess poverty levels and trends which rely not on consumption or income data but rather on non-monetary dimensions of living conditions. The purpose of this study is to make an estimation of the poverty level by using a multivariate statistical technique called Principal Components Analysis (PCA). The purpose of this technique is the reduction of the variables in a data set into a smaller number of dimensions. The data used for the analysis in this paper come from Living Standards Measurements Surveys (LSMS) in 2012. The principal components analysis was used to create an asset index which gave the social economic status (SES) of each household. The cluster analysis is used to give us a full background of the partition of households according to the social-economic groups low, medium and high.