Why the Indices of Deprivation are still important in the open data era

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The Index of Multiple Deprivation 2010 (IMD 2010) is used very widely to target programmes and resources to tackle inequality and deprivation – below I set out my thoughts on why the Indices of Deprivation are still important, despite the wealth of data now being released by government (but of course has its limitations which I have also covered).

What is the IMD 2010?

The Indices of Deprivation attempt to measure a broad concept of ‘multiple deprivation’, made up of several distinct dimensions, or domains, of deprivation. The data is based on 38 separate indicators across seven domains: Income, Employment, Health and Disability, Education Skills and Training, Barriers to Housing and Other Services, Crime and Living Environment. As well as the overall IMD 2010 and domains, data for the underlying indicators has also been published.

Commissioned by CLG from the Social Disadvantage Research Centre at Oxford University, the Indices of Deprivation are an update of previous datasets in 2007, 2004 and 2000. The full datasets and technical report are available, see links at the end of this article.

There is lots of data out there. Why should we be interested in the IMD 2010?

It is true that the data landscape has changed massively since the last IMD was published in 2007, with thousands of new datasets available through data.gov.uk, the London Data Store  and many others – including our own Data4nr, which is no longer running.

However, along with the Census, the IMD is a key dataset for targeting services to help tackle deprivation. The IMD provides a single overview indicator of how all English areas compare on levels of deprivation (separate measures are available for Wales, Scotland and Northern Ireland. Also, the same methodology has been used in South and Southern Africa by the Oxford University team responsible for the IMD).

Government programmes – both national and local – aiming to tackle deprivation often use the IMD to target funding to the most deprived areas – put simply, there is no other robust indicator that captures the wealth of issues covered by the IMD. The Oxford University team has identified that as much 1% of all government spending was allocated using the IMD. Although this analysis is now a few years old (I’d be very interested if anyone has looked at this more recently), there are some striking examples of where the IMD has been used to target government resources:

  • Targeting regeneration programmes: Including the Neighbourhood Renewal Fund (NRF), Single Regeneration Budget (SRB), Neighbourhood Management, and programmes to increase enterprise in disadvantaged areas. In addition, the 2000 Spending Review funding for all domestic regeneration programmes used the IMD (eg, in 2002-03 £430 million of the £2.55 billion single pot budget to RDAs was allocated based upon the IMD)
  • Sure Start and Children’s Centres: The location of the initial waves of Sure Start centres were based on the most deprived areas according to the IMD, as was funding for the Neighbourhood Nurseries Initiative and other programmes aimed at supporting vulnerable children and families
  • Many of the National Lottery grants are explicitly targeted at the most deprived areas based on the IMD, as are other funds eg Bill and Melissa Gates Foundation gifts for the provision of information technology learning centres.
  • Stamp duty on property and land transactions were reduced in deprived areas , based on the IMD.

The bottom-line is that the IMD is an important measure of how local areas compare with others on a comprehensive basket of deprivation indicators – so provides a key input to understanding “need” for service commissioning.

What are the limitations of the Indices?

Although the IMD 2010 and other datasets published today are an important piece of the data puzzle, there are limitations:

  • Not a direct measure of deprivation: The IMD score is used to compare (rank) areas, but is not a direct or meaningful measure of deprivation, in the way that eg the ‘unemployment rate’ is a real measure of the proportion of people out-of-work. An area with a score of 50 is not twice as deprived as an area with a score of 25.
  • Change over time is relative to other areas: The IMD 2010 can be compared with older datasets to identify how areas are changing over time relative to other areas across England. However, it is not a direct measure of whether areas are “improving” or “closing the gap” against the average – so we need to be a bit cautious about using to identify trajectories over time. The difference in 2007 and 2010 measures is down to how an area has fared compared to areas across England with similar levels of deprivation. A local neighbourhood could well have improved in real-terms (eg lower levels of unemployment, higher incomes, higher skill levels, lower crime rates, better environment and so on),  and may have improved faster than the average. However if other areas with similar levels of deprivation have done slightly better, the local neighbourhood will score as more deprived in 2010 than 2007. The message is you need to use direct measures alongside the IMD to understand how local areas are changing.
  • Older data: The IMD2010 is based on data from 2008 (with one indicator still based on Census data). In many cases this is the most recent data available at neighbourhood level – timeliness of data is still (and always was, always will be?) an issue.
  • Area-based measures are only one part of the story: Many groups argue that the IMD2010 – and other area-based measures which essentially measure concentration – are biased against rural areas, where deprivation is more scattered. Our own analysis has highlighted that although only 2% of the most deprived areas in England are rural, 17% of all households in poverty (earning less than 60% of the median income) in England are in rural areas.

Links to data, comments and analysis:

We’ve been hard at work providing the data and visualisations to our Data Packs users and identifying the headline national and local messages. Create your own visualisations using the Local Insight tool, created by OCSI. For further analysis of the Indices and other key social and economic data, please get in touch!


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