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

Area of Science:

• Socioeconomics
• Statistical analysis
• Household surveys

Background:

• Household wealth is theoretically measurable via income, consumption, or expenditure.
• Accurate income and consumption data collection is resource-intensive for household surveys.
• Principal component analysis (PCA) is increasingly used with asset data for socio-economic status (SES) indices.

Purpose of the Study:

• To review the construction, application, validity, and limitations of PCA-based SES indices.
• To address issues in variable selection, data preparation, and data clustering for PCA.
• To discuss the interpretation of results and household classification into SES groups.

Main Methods:

• Review of principal component analysis (PCA) methodology for SES index creation.
• Analysis of data preparation techniques and potential data clustering issues.
• Examination of methods for interpreting PCA results and classifying households.

Main Results:

• PCA is a validated method for describing socio-economic status differentiation within populations.
• The choice of variables and data preparation significantly impact PCA outcomes.
• Understanding data limitations is essential for generating and interpreting PCA-based SES indices.

Conclusions:

• PCA offers a robust approach to constructing SES indices from asset data.
• Methodological choices in data handling directly influence the reliability of SES differentiation.
• Effective application of PCA requires careful attention to data quality and interpretation.