Using financial data to enhance labour market statistics in Scotland

Conclusion

The feasibility study demonstrated that pseudonymised financial transaction data can be used to derive stable proxy estimates of labour market status at both national and sub-national levels. The findings show that behavioural financial data can provide meaningful labour market intelligence and have the potential to complement traditional survey-based approaches. The large-scale financial transaction data could support detailed geographic and demographic analysis that is increasingly difficult to obtain from surveys alone. Weekly transaction summaries may also provide insight into changes in labour market status between conventional survey time periods.

The study found that transaction-based classifications cannot replace survey measures. Surveys directly ask about work, job search and availability, while the feasibility method inferred labour market status from financial behaviour. The resulting concepts overlap, but they are not the same. In particular, receipt of a benefit is not sufficient to determine unemployment under the International Labour Organization definition.

Financial data instead reflects account use, customer composition and evolving banking behaviour. Their use in producing statistics would therefore require robust arrangements for data continuity, quality assurance and governance, alongside clear communication with users.

The significance of these findings extends beyond Scotland. As statistical systems across the UK face challenges from declining survey response rates and increasing demand for timely, granular insights, financial transaction data offers a promising new source of evidence. The methods developed through this feasibility study may therefore have wider relevance for a range of statistics across the UK and other devolved administrations.

While this study focused on labour market measurement, the richness, scale and frequency of consumer transaction data suggest broader applications across economic and social policy. Future work could explore areas such as the cost of living, household financial resilience, debt and borrowing, spending behaviour and the impacts of economic shocks, helping to unlock additional public value from this type of data.