Beyond unidimensional poverty analysis using distributional copula models for mixed ordered-continuous outcomes

TL;DR

This paper introduces a novel multivariate copula GAMLSS model for analyzing the dependence between mixed ordinal and continuous poverty dimensions, allowing for covariate-driven dependence and comprehensive risk assessment.

Contribution

It develops a distributional copula model for mixed outcomes, extending existing methods to jointly analyze income and education dependence with covariate effects.

Findings

Dependence between income and education varies spatially in Indonesia.

The model identifies groups at high risk of multidimensional poverty.

Covariates influence the dependence structure significantly.

Abstract

Poverty is a multidimensional concept often comprising a monetary outcome and other welfare dimensions such as education, subjective well-being or health, that are measured on an ordinal scale. In applied research, multidimensional poverty is ubiquitously assessed by studying each poverty dimension independently in univariate regression models or by combining several poverty dimensions into a scalar index. This inhibits a thorough analysis of the potentially varying interdependence between the poverty dimensions. We propose a multivariate copula generalized additive model for location, scale and shape (copula GAMLSS or distributional copula model) to tackle this challenge. By relating the copula parameter to covariates, we specifically examine if certain factors determine the dependence between poverty dimensions. Furthermore, specifying the full conditional bivariate distribution, allows us to derive several features such as poverty risks and dependence measures coherently from one model for different individuals. We demonstrate the approach by studying two important poverty dimensions: income and education. Since the level of education is measured on an ordinal scale while income is continuous, we extend the bivariate copula GAMLSS to the case of mixed ordered-continuous outcomes. The new model is integrated into the GJRM package in R and applied to data from Indonesia. Particular emphasis is given to the spatial variation of the income-education dependence and groups of individuals at risk of being simultaneously poor in both education and income dimensions.