People who provide services often need to know about local variations so that they can focus efforts in the right places. We are all witnessing this first hand at the moment in how the country is responding to COVID-19, for example, with a need for detailed geographical data to help NHS planning.

The Race Disparity Unit (RDU) is a team within the Cabinet Office. It is primarily a data and statistical unit which collates, publishes and analyses UK ethnicity data, works across Government on issues where ethnicity is an important factor, and engages with external stakeholders to understand different perspectives.

When RDU talks to users of its Ethnicity facts and figures website, they tend to say two things. First, it’s a great resource. It includes a wide range of data on different topics for different ethnic groups. And it presents the data in an accessible way. This makes us feel very happy.

But they also ask for data at the local authority (LA) level. Users find that regional or national figures mask local variations. They need to know about these variations so that they can deliver the right services – which makes perfect sense across the piece: detailed geographical data about where those aged over 70 live at a local level to help provide support during COVID-19  is just one example, albeit extreme and traumatic, of this wider pattern.

This need is also true for small area ethnicity data. And the user demand for small area ethnicity data makes us feel a bit anxious, because our website doesn’t have much data for individual LAs. It does include a dashboard which shows the data we have for different geographies. But this doesn’t address the user need.

So RDU has linked together the datasets we have that include local authority data. This includes data on school performance, employment rates, and so on. It also includes data about local circumstances – for example, how deprived the area is. So far, we’ve made great progress with the prototype. But getting a range of datasets to talk to one another can be difficult. Many of them don’t follow statistical geography standards/best practice. We’ve talked about the various hurdles faced in a previous blog.

Our work on geography has made us think about how we can improve the value of the data on the website. “Value” is one of the three pillars of good statistical practice promoted by the Office for Statistics Regulation (OSR). It is hard-wired into its Code of Practice for Statistics (along with trustworthiness and quality).

First, context is everything. Statistics need to be relevant and reflect the lived experience to be most useful to a wider audience. The power of statistics is in providing insight through the aggregation of many individual data points to form a big picture. Context provides the colour for what would otherwise be a grey-scale image.

Many official statistics are not presented at ‘local’ levels. There can be good reasons for this but without this information insight is narrowed. The Code of Practice encourages statisticians to provide data at the greatest level of detail that is practicable. Anything produced at the national level is usually required at the local level. And so, it’s worth all producers thinking about what information their users need and what the data tells them.

Be curious – see what patterns are in the data, by place.

Second, the little things matter. Putting the dot in St. Albans, or not, matters. A single full stop can be the difference between two datasets automatically linking together and the need for a manual correction. And while that single full stop will never be complex to resolve, it is rarely just a single full stop. Instead it is a series of manual corrections that are a barrier to the insight gained by linking data. Metadata on the year of the geographic classification used is also valuable to those of us wanting to join datasets. Local government structural boundaries can change every few years. We would rather know in advance that some of the records won’t match, than have to play trouble-shooter later.

While ‘place’ is flexible in its degree of specificity, it is best standardised. We can link key geographic information if variables are coded consistently. Bespoke coding frames get in the way of data linkage and reduce the value of the data.

Be consistent – enable the greater value of your data to be achieved by using harmonised codes.

Third, innovation is vital. Arguably the geography prototype is ‘only’ an Excel spreadsheet. What is innovative about it is the way that it draws data together. Over time this will support a mapping function. This will help bring the data to life and to allow users to overlay different data sets at the LA level. We are already using the dataset to identify areas of policy interest and to target our engagement.

Another potential innovation – at present no more than a twinkle in RDU’s eye – is an Index of Multiple Ethnic Disparity (IMED). The IMED is analogous to MHCLG’s Index of Multiple Deprivation. It would allow users to identify those parts of the country where ethnic disparities are most pronounced, across a range of topics. If we were able to add in historical data, we would be able to look at the interplay between geography and time. There are some presentational, methodological and conceptual challenges in producing such an Index. RDU will begin to address these as we think about the use we want to make of data from the 2021 Census (see below).

Fourth, we can add value by working together, sharing perspectives and expertise. The RDU is keen to work with local authorities on the ‘geography prototype’. We are already working with Bristol City Council, which is using data to address ethnic disparities.

OSR have said that they will review the use of harmonised geographic codes and standards as part of their regulatory work. They will also provide guidance on meeting the standards of the Code of Practice. ONS’s Open Geography Portal makes it easier for data owners to use the correct classifications. Various groups can help unlock the potential of ethnicity data. These include:

All it requires is shared commitment!

The ONS has a team that supports everyone in the GSS to improve official statistics. This is the Best Practice and Impact (BPI) division. BPI encourages everyone in the GSS to share best practice. One of the ways we do this is by running champion networks, this includes a geography network. If you would like to represent your department or share a piece of work you have done please get in touch.

Fifth, more (and better) data will allow us to deliver much more value. RDU is starting to consider how to use data from the 2021 Census of Population. It will enable us to paint a far richer picture about the different ethnic groups than we can by using surveys or administrative sources. We are exploring the scope to link datasets to provide more geographical insights. And we are continuing to work with the ONS to improve the way that ethnicity is classified across government. Our goal is that in future users can compare data from different data sources directly.

This is a guest blog from Richard Laux (Cabinet Office) and Claire Pini (GSS Harmonisation Team in ONS).

 

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