Model based approach for household clustering with mixed scale variables

TL;DR

This paper introduces a Bayesian nonparametric mixture model designed to cluster households based on mixed scale demographic and poverty data, accounting for complex survey sampling, to improve social program targeting.

Contribution

It presents a novel Bayesian nonparametric approach that jointly models mixed scale variables and adjusts for complex survey sampling in household clustering.

Findings

Model performs well on simulated data

Provides detailed socio-economic household analysis in Mexico

Enhances targeting accuracy for social programs

Abstract

The Ministry of Social Development in Mexico is in charge of creating and assigning social programmes targeting specific needs in the population for the improvement of quality of life. To better target the social programmes, the Ministry is aimed to find clusters of households with the same needs based on demographic characteristics as well as poverty conditions of the household. Available data consists of continuous, ordinal, and nominal variables and the observations are not iid but come from a survey sample based on a complex design. We propose a Bayesian nonparametric mixture model that jointly models this mixed scale data and accommodates for the different sampling probabilities. The performance of the model is assessed via simulated data. A full analysis of socio-economic conditions in households in the State of Mexico is presented.