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.