The benefits of collaboration: working together to improve the evidence base on deprivation

How statisticians in the four nations are working together to improve the evidence base on deprivation

Deprivation is a complex concept. The term is often used interchangeably with poverty (which relates to a lack of income to meet basic needs) when in fact deprivation refers to a serious lack of something which is considered to be a basic necessity in society. From healthcare to housing, there are multiple factors which determine how deprived an area is. In recent weeks, deprivation has hit the headlines in relation to the COVID-19 pandemic. Analysis published by the Office for National Statistics found that people living in more deprived areas have continued to experience COVID-19 mortality rates more than double those living in less deprived areas.

It therefore remains crucial that data are made available to identify the most disadvantaged areas and to build the evidence base on the different facets of deprivation. The indices of multiple deprivation are an important tool for achieving this and for supporting decisions about addressing local needs. They are a relative measure which look at how deprived different areas are compared to one another. This means an area may see improvements in absolute terms (such as increased job prospects) but still fall in the overall rankings if other areas have also experienced improvements. The indices are widely used by central and local government and community organisations to target their services.

Today we released a series of letters about our review of the indices of multiple deprivation produced by the Ministry of Housing, Communities and Local Government (MHCLG) , the Welsh Government and the Scottish Government . We didn’t review the statistics produced by the Northern Ireland Statistics and Research Agency (NISRA ) as these are produced to a different time scale.

Our review identified some real strengths and opportunities in the way the statistics teams have worked together to improve the public value of the statistics. They all spoke positively about being part of a ‘four nations group’ which works collaboratively to make guidance and presentation across the deprivation statistics more consistent. There were two areas in particular that we feel have benefited from this joined up working.


Putting users first

One thing that became clear in our conversations with each of the teams is that they have a good understanding of the uses and users of their respective statistics. The statistics are relied upon for local decision making and interventions, which is something the statisticians are keen to prioritise in the development of the indices.

As part of its regular meetings, the four nations keep each other updated on emerging areas of user interest and reoccurring queries from the public to remain alert to developments in this field. Even the frequency of the statistics is determined by user need. We discovered that it can be a burden on local authorities and third sector organisations who use the statistics in their own analyses if the indices are updated too regularly – particularly where changes between years are slow moving. Similarly, users are at the heart of any methodological changes to the construction of the indices between iterations – these are carefully considered and reviewed by domain experts and key user groups.


Bringing the data to life

There has been a collective effort by the teams to demonstrate the relevance of the statistics to users and help them understand the complexity of deprivation. From interactive maps, to pen pictures, to case studies, the producers have tailored their outputs to bring out the key messages whilst also offering the flexibility to delve deeper into the data. For example, MHCLG has recently published a new mapping tool which allows users to visualise the statistics at new geographical levels including Westminster Parliamentary Constituencies and Travel to Work areas.

The Welsh Government and Scottish Government also publish their own interactive tools. Alongside this, we found the Scottish Government’s analysis of deep-rooted deprivation (areas that have remained the most deprived in previous iterations of the index) is an innovative way of bringing out insight from the statistics whilst addressing the limitations of the statistics in a way which can be understood by all. We were pleased to see that the Welsh Government took inspiration from this and has also carried out analysis of deep-rooted deprivation in Wales. The team in MHCLG has welcomed our recommendation to agree and adopt a common definition of deep-rooted deprivation with the Welsh and Scottish Governments, to further improve harmonisation and consistency across the indices of multiple deprivation.

To summarise, the indices of multiple deprivation are a fascinating set of statistics which have benefited from collaboration between the statistics teams in the four nations. The statistics continue to be relevant to a wide range of users and the teams’ collective approach to putting users at the centre of the statistics presents further opportunities for developing the public value of the statistics going forward. We look forward to seeing these opportunities realised in the future.

Piecing things together

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).