An updated Ethnicity Facts and Figures website

What is the Ethnicity facts and figures website?

The Ethnicity facts and figures website (EFF) was launched in October 2018. In a world first, it brought together ethnicity data from across government, often disaggregated by other factors such as age and sex, to help shed light on disparities in outcomes for people from ethnic minority groups.

The website launched with 100 ‘measures’; web pages where an indicator such as Students getting 3 A grades or better at A level is presented with commentary, important information such as caveats and methodology, as well as further disaggregations if available.

Notwithstanding a pause during the Covid-19 pandemic, the website has continued to grow and now has 185 individual measures published. The website is regularly used by central and local government, NHS service providers, academics, charities and think tanks, as well as the general public.

Why we’re making changes to Ethnicity facts and figures

The analytical demands of the Covid-19 pandemic showed that users wanted more than just descriptive statistics; they wanted insights into the drivers of the disparities. In addition, the need for data on specific ethnic groups was much higher, with users and people from different ethnic minority groups finding the ‘umbrella’ groupings (eg white compared with ‘‘All other ethnic minorities’) to be unhelpful and objectionable. Furthermore, we identified that the structure of the website did not allow easy analysis of related statistics (eg, Key Stage measures), meaning that patterns across them might not be evident.

Each measure is updated annually as government departments publish their statistical bulletins. This is a particularly resource intensive process, as the website’s original design did not incorporate an automated updating process. Data provided by government departments must be quality assured manually, and the commentary and data updates copied and pasted into the publisher.

Furthermore, Google analytics identified that around 80% of users’ visits to the website were to around 20% of the pages available; further research supported this finding. We recognised the need to rationalise the website and build new, more efficient processes, to ensure that we are focussed on the areas most important to users and investing our resources efficiently.

The programme of work we developed and put to user consultation was designed to provide useful and high quality data and analysis for the highest priority and most used measures on Ethnicity facts and figures, while making considerable efficiency savings. Some details of the programme were adjusted in light of users’ feedback, but the overarching result was that most users were content with our proposals and reiterated their support for the continuation of the website.

What has changed?

We have provided summaries and links to further analyses for the highest priority measures. Around 40 measures were identified as higher priority by assessing against a number of criteria and confirming with users in the consultation. For these measures we will provide a ‘call out box’ summarising research and analysis that provides additional insights to help users understand the data better.

We have brought related measures together on one page to facilitate easier analysis. We have identified 67 pages where data can be grouped into 23 related themes; for example, instead of having 5 separate measure pages for each KS1 subject, all 5 KS1 subjects will be available on a single measure page. There will be no reduction in the amount of data provided as downloads, and the main headlines will still be published. This will provide a more holistic picture of each subject and allow users to identify patterns and similarities across these measures more efficiently.

We have reduced the amount of descriptive commentary on the lower priority measure pages. All pages will have main headlines provided to aid interpretation and highlight interesting and useful information. Caveats and other metadata will also be available, to mitigate the risk of misinterpretation.

We will work with data providers — government departments — to improve the ethnicity disaggregation available for those measures that are currently published as 2 groups (White and All other ethnic minorities). Improving the quality of data provided by ethnic groups is a priority for us in the Equality Hub, so we will advise and support departments to increase sample sizes through pooled data and other techniques, so that more useful and appropriate data is made available.

We will receive and publish data files in a standardised machine-readable format. This will ease processing when received from departments and ensure they are coherent and comparable across different measures. This will also make data processing more efficient for users.

We have developed a Reproducible Analytical Pipeline (RAP) to quality assure and prepare the data files received from departments. This will result in a more efficient, standardised process, creating outputs quickly and accurately. In addition, we have developed an automated commentary RAP, to identify more easily the main headlines from multiple large data sets. As a result, analytical resources are now available to deploy across additional projects and workstreams, enhancing the capability of the Equality Hub.

How will success be measured?

The primary outcome of these changes should be a more efficient process for Equality Hub analysts. This will be measured by the time spent preparing data files, resolving queries, creating draft measure pages and publishing pages. The improvements may not be immediately noticeable, but over a number of months, as the process becomes embedded, they will become more apparent.

Additional outcomes may be less quantifiable but we expect to see:

• The enhancement of higher priority measures, which will provide users with deeper insights into disparities. This will provide policy colleagues with a better understanding of where and how to direct interventions and support more informed debate.
• Upskilling of the analytical team
• Reduced burden on data suppliers
• Better accessibility to machine-readable data for users
• Continued emphasis on a trustworthy, high quality and valuable resource

For more information, please see our logic model diagram, showing how the changes we’ve made will lead to better outcomes.