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

• Maike Hohberg ^[2] ; Francesco Donat ^[3] ; Giampiero Marra ; Thomas Kneib ^[1]
  1. [1] University College London

     University College London

     Reino Unido

     
  2. [2] University of Goettingen
  3. [3] Single Resolution Board

  Mostrar afiliaciones +
• Localización: Proceedings of the 35th International Workshop on Statistical Modelling : July 20-24, 2020 Bilbao, Basque Country, Spain / Itziar Irigoien Garbizu (ed. lit.), Dae-Jin Lee (ed. lit.), Joaquín Martínez Minaya (ed. lit.), María Xosé Rodríguez Álvarez (ed. lit.), 2020, ISBN 978-84-1319-267-3, págs. 114-119
• Idioma: inglés
• Enlaces
  • Texto Completo Libro
• Resumen

  • 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 and we demonstrate its power by studying two important poverty dimensions: income and education. Since the level of education is often measured on an ordinal scale and 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.