Constructing socio-economic status indices: How to use principal components analysis

  • Vyas S
  • Kumaranayake L
  • 1.0k

    Readers

    Mendeley users who have this article in their library.
  • 892

    Citations

    Citations of this article.

Abstract

Theoretically, measures of household wealth can be reflected by income, consumption or expenditure information. However, the collection of accurate income and consumption data requires extensive resources for household surveys. Given the increasingly routine application of principal components analysis (PCA) using asset data in creating socio-economic status (SES) indices, we review how PCA-based indices are constructed, how they can be used, and their validity and limitations. Specifically, issues related to choice of variables, data preparation and problems such as data clustering are addressed. Interpretation of results and methods of classifying households into SES groups are also discussed. PCA has been validated as a method to describe SES differentiation within a population. Issues related to the underlying data will affect PCA and this should be considered when generating and interpreting results.

Author-supplied keywords

  • Cluster analysis
  • Methodology
  • Principal components analysis
  • Socio-economic status

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Get full text

Authors

  • Seema Vyas

  • Lilani Kumaranayake

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free