The real time geography of the UK cost of living crisis for low-income public sector workers.

Following years of austerity measures and associated stagnant public sector wages, many UK low-income households continue to experience the negative consequences of an ongoing cost of living crisis and limited financial resilience to unforeseen circumstances. Financial Lives Survey data (Financial Conduct Authority 2020) held by the ESRC Consumer Data Research Centre indicate that 20% of UK adults had low financial resilience in 2020, defined as being over-indebted or having little capacity to withstand even a small financial shock (such as a £50 reduction in income or losing the main source of household income for even a week (Harrison et al. 2021).

Recent developments in the availability of Open Banking data make possible a detailed understanding of financial resilience. CDRC data partner Salad Money - a not-for-profit provider of loans to low-income individuals drawn principally from the public sector - have provided anonymised Open Banking data detailing 2+ years of transactions for its loan applicants since June 2023.

By the start date of this studentship, it is anticipated that these data will pertain to more than 150,000 individuals and will include applicant details and neighbourhood of residence. The size of the dataset will be multiple terabytes, and will be supported and summarised by a CDRC data scientist in UCL's ISO 27001 accredited secure research environments. Crucially for this research, the raw data are supplied in something approaching real time.

This research will address the following research questions using real time feeds of loan application data: 1. What are the circumstances of loan applicants, and how do they change over time, in the

context of changes to the cost of living? How may applicants be characterised by (a) annual earnings and other sources of income (including benefits and remittances); (b) individual demographic characteristics (age, gender, etc); neighbourhood characteristics (ESRC CDRC Output Area Classification Types (Wyszomierski et al. 2023) and Index of Multiple Deprivation scores); (d) employment type and duration; and (e) tenure and residential status.

2. How can the financial resilience of applicants be best characterised - based on the detailed temporal structure and character of inflows and outflows and their sources? To what extent are financial circumstances governed by changes in the cost of living or by individual circumstances? Is there a geography to observed real time changes in applicant circumstances?

3. How is the share of income attributable to earnings, benefits and pensions changing over time? How is 'other income' recorded in Open Banking transactions best characterised in terms of sources, reliability and durability?

4. What is the portfolio of loan, overdraft and credit card use that characterises loan applicants? Is there a geography to this? The studentship will have strong methodological and technical components, developed

around spatial and time series analysis. In addition to consolidated research ready data (RRD) outputs, it is envisaged that the student will develop real time feeds for incorporation into ESRC Smart Data Research UK web services (see Van Dijk and Longley 2020). The student will be required to develop advanced database management skills, to manage 200 million unique Open Banking records using advanced quantitative methods. The work will both utilise, and contribute towards, ESRC Smart Data Research UK data assets.

The work will develop and report on a near real time data pipeline of prevailing cost of living challenges to low paid public sector workers. Reporting will be geographically disaggregate and will provide breakdowns using ESRC CDRC Output Area Classifications and harmonised neighbourhood deprivation scores. This will be an important contribution to geo-temporal analysis of the cost of living crisis and understanding of spatial inequalities in general (see Longley et al. 2023). The research will inform Salad Mone