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

TLDR

Household wealth can be gauged by income, consumption, or expenditure, yet gathering accurate data is resource‑intensive. The study reviews how PCA‑based SES indices are constructed, applied, and evaluated for validity and limitations. The authors examine variable selection, data preparation, clustering challenges, result interpretation, and household classification techniques for PCA‑based indices. PCA reliably distinguishes SES groups, but its accuracy depends on underlying data quality, which must be considered during generation and interpretation.

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.