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

09/08/2020
by   Maike Hohberg, et al.
0

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.

READ FULL TEXT

page 36

page 37

page 38

page 39

page 40

research
06/05/2023

Truly Multivariate Structured Additive Distributional Regression

Generalized additive models for location, scale and shape (GAMLSS) are a...
research
03/23/2022

Bivariate Distribution Regression with Application to Insurance Data

This article introduces an estimation method for the conditional joint d...
research
12/04/2020

Ordinal pattern dependence as a multivariate dependence measure

In this article, we show that the recently introduced ordinal pattern de...
research
01/30/2021

Bayesian Cumulative Probability Models for Continuous and Mixed Outcomes

Ordinal cumulative probability models (CPMs) – also known as cumulative ...
research
09/10/2018

DistdichoR a R Package for the distributional dichotomisation of continuous outcomes

We introduce the functions included in the R Packet distdichoR for the i...
research
09/17/2018

Rank-based approach for estimating correlations in mixed ordinal data

High-dimensional mixed data as a combination of both continuous and ordi...
research
04/18/2020

Heritability curves: a local measure of heritability

This paper introduces a new measure of heritability which relaxes the cl...

Please sign up or login with your details

Forgot password? Click here to reset