Socioeconomic Covariate-Dependent Bayesian Nonparametric Mixture Model for Household Spending Patterns to Identify Multidimensional Vulnerability

Household vulnerability assessment in Malaysia has traditionally relied on income-based indicators, which do not adequately capture multidimensional deprivation. To address this limitation, this study employs Random Tree–Dirichlet Process Mixture Model (RT-DPMM) to identify latent heterogeneity in spending patterns and their associated socioeconomic characteristics. Using microdata from Household Expenditure Survey (HES), this study performs clustering on 5130 stable household head samples with nine spending proportional features to model their joint distribution as mixtures of Dirichlet distributions, while five socioeconomic covariates inform cluster allocation through Random Tree embeddings. The proposed RT-DPMM identifies four distinct spending clusters: Balanced Budget Households (Cluster 1, N = 2883), Mobility and Home-Support Households (Cluster 2, N = 642), Basic Essentials-Focused Households (Cluster 3, N = 977), and Luxury Households (Cluster 4, N = 628). Cluster 1 and 3 are characterized as relatively vulnerable groups. These clusters have lower income levels and allocate a larger budget share in Food and Beverages, consistent with the Engel Law’s interpretation of higher food percentage in lower income households. Cluster 1 households primarily allocate their budget evenly across essential and non-essential spending. Cluster 3 are mostly elderly household heads with the highest budget shares in essential spending. In contrast, Cluster 2 and 4 appear relatively better off financially, given their higher income and larger spending share to non-essential categories. These findings suggest that social assistance policies should target expenditure patterns, rather than relying solely on income-based targeting.