UNIVERSAL CREDIT STATISTICS
As you are aware, we recently completed a compliance check of the DWP Universal Credit (UC) statistics. We considered the trustworthiness, quality and value of these statistics in relation to the Code of Practice for Statistics. We have made a number of recommendations to support your continued development of these important experimental statistics.
We appreciate the positive way that the team has engaged with us during the compliance check, and it is clearly committed to delivering improvements. Our Labour Market and Welfare Domain Lead, Catherine Bremner, will continue to engage with the team on progress over the coming months.
We found a range of positive features that demonstrate the value and quality of the statistics. The statistics are easily accessible, and the gov.uk landing page is well laid out, with useful links to previous publications, publication release strategy and interactive maps and data tools. It also highlights related publications, research and analysis papers, evaluation frameworks, and ad hoc statistical publications, all of which add insight and value. The bulletin is easy to follow, the ‘What you need to know’ and ‘About these statistics’ sections explain the key features of the statistics, and informative subheadings explain key trends. We welcome the comprehensive report on the limitations of the data sources and the clear definitions in the Background and Methodology document.
We identified several areas for improvement which would enhance the clarity, value and quality of these statistics. We consider that the commentary in the bulletin could generate more insight for instance by explaining what the different comparisons mean, such as those between male and female claimants. We also encourage you to consider more-informative ways of presenting the data, for example a trend analysis of the four measures (claims made, starts, people on Universal Credit, households on Universal Credit).
We encourage the team to explain technical terms to help all users understand key concepts and methods, such as ‘pathfinder’ and ‘conditionality regime’. In addition, it may be helpful for you to review whether some of the visualisations could be improved to aid interpretation of the statistics; for instance, the pie charts or the map with the breakdown on UC claimants by local authority, which lacks context. The team may find the Good Practice Team’s guidance on effective charts and maps helpful.
We welcome the section on uses and users of the statistics and would look to DWP to proactively engage with external users and stakeholders to encourage wider use of the statistics, and to ensure that users’ needs are understood, and can feed into further developments of the statistics.
To help users understand the limitations of the statistics and the methods, further quality information could be included on the impact of the limitations, and information on methods changes and why the four measures (claims, starts, people, households) were chosen. Further information on comparability with other statistics would help users generate additional insight, by explaining why these comparisons are relevant and important. It would also be useful to signpost users to related statistics and explain why they are relevant, for example, the Northern Ireland Universal Credit statistics produced by the Department for Communities.
An essential part of assuring yourselves and users about quality, and enhancing the trustworthiness of the statistics, is to provide information about quality assurance. Producers of statistics should explain clearly how the statistics and data are accurate, reliable, coherent and timely. As these statistics are based on administrative data, we encourage the team to apply our Quality Assurance of Administrative Data (QAAD) framework to assure users about the quality assurance arrangements and to help them understand how the Universal Credit data are collected and processed.
These statistics have been published as experimental statistics since they were first introduced in December 2013. We encourage the team to continue to develop these important statistics, to enhance public confidence in their trustworthiness, quality and value. In general, we feel that you could be more ambitious about improvements to the Universal Credit statistics, focusing on how they can enhance value through, for example, linkage with other DWP benefit data and survey data.
Please do not hesitate to contact us if you would like to discuss this further.
Assessment Programme Lead