Friday, 27th September 2024
In both academia and policy making, understanding the causes and consequences of over-indebtedness is a key topic of both debate and concern. The circumstances that can lead to inequalities in financial security are complex and varied, creating a significant obstacle to understanding these issues on the wider scales needed for informed policy decision-making. In order to tackle this challenge, and as part of their mission to use “public data for public good”, Registry Trust has sponsored my PhD as an industry partner, allowing me to utilise the CCJ register, alongside modern data science techniques, to improve our understanding of both the where and the why of regional inequalities and over-indebtedness.
I work within the Geographic Data Science Research Group at the university of Liverpool, which uses modern statistical and modelling techniques in combination with a range of novel datasets to try and understand the world around us through a data driven lens. Covering topics from coastal erosion to criminal sentencing, the research group focuses on the spatial aspects of issues; exploring how the location of an event can provide insights about why it has occurred there. In my case this means looking at how different places across England and Wales have experienced great variation in case rates over the past 5/6 years, and how that relates to a range of underlying geodemographic characteristics.
Figure 1. CCJ cases per capita (2016-2022)
Registry Trust regularly releases dashboards and bulletins that provide immediate insights into the regional patterns of CCJs in the UK (For example, the Q2 2024 dashboard can be found here). These summaries show clear patterns, and that CCJ cases are rarely spread evenly across the nation. The goal of my research therefore has been to build on this and dig even deeper. As the map I’ve included above shows, when looking at case rates at an even more local level, you can see great variations within local authorities. Understanding what differentiates these concentrated pockets of CCJs is critical to both our knowledge of over-indebtedness and for promoting targeted and efficient policy decision making.
The links between CCJs and deprivation is well known and have been discussed before in previous blog pieces by the Trust. However, ‘deprivation’, much like ‘indebtedness’ can mean many things, so I focused on breaking this down to try and understand the specific traits of the neighbourhoods experiencing the highest rates of judgments. Drawing from both academic literature and current industry/government practise, I compiled a list of characteristics commonly associated with increased risks of indebtedness. From this I then shortlisted a set of “debt drivers” that can be used to accurately predict over 80% of the case distributions shown in the above map. Key examples of these traits (perhaps unsurprisingly) are the tenure breakdowns of an area’s housing stock (i.e. are people mainly renting or homeowners), and the types of careers represented in the neighbourhood’s population.
Understanding these neighbourhood patterns doesn’t give us all the answers; every individual person’s experience of debt is different after all, and the fallout of the cost-of-living crisis which has fuelled much of the current debt landscape is anything but neat and tidy. However, it does allow us to see which areas home those facing the worst of over indebtedness, gain insights into part of the reason behind this, and begin to predict how these patterns could potentially change looking ahead. With increasing strain on debt support networks due to rising demand all across the UK, being able to acknowledge and understand these trends could prove critical to effective policy making, as the nation looks to weather a troubling financial landscape and support those most in need.
None of this research would be possible without the support of Registry Trust, who through its analyst team’s assistance and the invaluable provision of CCJ register data have opened the door to a wealth of analysis possibilities. As I noted earlier in this blog piece debt is a key issue that can mean many things, but there is still a general lack of data available to allow for widespread quantitative analysis at local levels. The CCJ dataset therefore provides a unique and invaluable opportunity to begin to tackle these critical questions head on.
Matthew Howard is a PhD candidate working in partnership with Registry Trust, based within the University of Liverpool’s geographic data science lab.