Evidence type: Insight i
Qualitative research is more exploratory, and uses a range of methods like interviews, focus groups and observation to gain a deeper understanding about specific issues - such as people’s experiences, behaviours and attitudes.
Quantitative research uses statistical or numerical analysis of survey data to answer questions about how much, how many, how often or to what extent particular characteristics are seen in a population. It is often used to look at changes over time and can identify relationships between characteristics like people’s attitudes and behaviours.
In recent years there have been increased concerns about individual borrowers falling into financial distress, against a backdrop of rising household leverage during a period of falling interest rates. The Financial Conduct Authority’s (FCA’s) 2017 Financial Lives Survey showed that 15% of UK adults had recently missed or were struggling to make domestic bill and credit repayments. While numerous studies have investigated both how the composition of consumer debt varies across individuals and the experiences of those in financial distress, limitations associated with the survey datasets used have made it hard to fully explore the granularity of these patterns.
This 2020 report from the Financial Conduct Authority uses a rich and detailed administrative dataset which contains the credit files of a large and representative sample of UK borrowers to fully investigate the composition of consumer debt.
The panel dataset that is used is based on 1% of individuals with a credit file at a UK Credit Reference Agency (CRA) between January 2015 - February 2018. CRA data covers the vast majority of credit users, as well as most consumers that have a current account or a contract with a utilities or telecommunications provider. The data included detailed monthly information about peoples’ credit portfolios, including types of debt, outstanding balances and details of any arrears, as well as balances and turnover. After excluding certain individuals from the dataset (for example, those who were debt-free or who were not responsible for the bills), a final sample of 428,097 individuals is included in the analysis.
A statistical technique known as cluster analysis is used to divide credit users into distinct groups based on observed characteristics and debt usage. The following variables are used to define the clusters:
The cluster analysis identifies four data-driven subgroups of borrowers:
For the purpose of the analysis, financial distress is defined as at least one of the following happening in a given month: