Understanding financial distress by using Markov random fields on linked administrative data

Abstract

Household financial distress is a complicated problem. Several social problems have been identified as potential risk factors. Conversely, financial distress has also been identified as a risk factor for some of those social problems. Graphical models can be used to better understand the co-dependencies between these problems. In this approach, problem variables are network nodes and the relations between them are represented by weighted edges. Linked administrative data on social service usage by 6, 848 households from neighbourhoods with a high proportion of social housing were used to estimate a pairwise Markov random field with binary variables. The main challenges in graph estimation from data are (a) determining which nodes are directly connected by edges and (b) assigning weights to those edges. The eLasso method used in psychological networks addresses both these challenges. In the resulting graph financial distress occupies a central position that connects to both youth related problems as well as adult social problems. The graph approach contributes to a better theoretical understanding of financial distress and it offers valuable insights to social policy makers.