Multivariate understanding of income and expenditure in United States households with statistical learning

Mingzhao Hu ()
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Mingzhao Hu: University of California

Computational Statistics, 2022, vol. 37, issue 5, No 3, 2129-2160

Abstract: Abstract In recent decades, data-driven approaches have been developed to analyze demographic and economic surveys on a large scale. Despite advances in multivariate techniques and learning methods, in practice the analysis and interpretations are often focused on a small portion of available data and limited to a single perspective. This paper aims to utilize a selected array of multivariate statistical learning methods in the analysis of income and expenditure patterns of households in the United States using the Public-Use Microdata from the Bureau of Labor Statistics Consumer Expenditure Survey (CE). The objective is to propose an effective data pipeline that provides visualizations and comprehensive interpretations for applications in governmental regulations and economic research, using thirty-five original survey variables covering the categories of demographics, income and expenditure. Details on feature extraction not only showcase CE as a unique publicly-shared big data resource with high potential for in-depth analysis, but also assist interested researchers with pre-processing. Challenges from missing values and categorical variables are treated in the exploratory analysis, while statistical learning methods are comprehensively employed to address multiple economic perspectives. Principal component analysis suggests that after-tax income, wage/salary income, and the quarterly expenditure in food, housing and overall as the five most important of the selected variables, while cluster analysis identifies and visualizes the implicit structure between variables. Based on this, canonical correlation analysis reveals high correlation between two selected groups of variables, one of income and the other of expenditure.

Keywords: Principal component analysis; Cluster analysis; Canonical correlation analysis; Interpretive modeling; Data visualizations (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00180-022-01251-2

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