  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.
