Welsh Index of Multiple Deprivation (WIMD) 2019

This morning saw the publication of the WIMD 2019 with the related data. The Index analyses a range of factors in order to measure the relative levels of multiple deprivation by small areas right across Wales. The 2019 Index updates on work previously reported in 2014.

To quote the report:

“The Welsh Index of Multiple Deprivation (WIMD) is the official measure of relative deprivation for small areas in Wales. It is a National Statistic produced by statisticians at the Welsh Government. WIMD identifies areas with the highest concentrations of several different types of deprivation. The prime purpose of the Index is to provide the evidence needed about the most deprived areas of Wales to inform a variety of decisions, such as funding or targeting of programmes and services for local areas. WIMD ranks all small areas in Wales from 1 (most deprived) to 1,909 (least deprived)”. 

 

For me the big picture message is that the communities that were measured as suffering from relative deprivation in 2019 are broadly the same as in 2014. Given that we are living through a period of austerity this perhaps is no surprise, but is clearly of concern and must continue to inform future political decisions.

Communities across the South Wales Valleys continue to feature in the list of areas with the highest concentrations of relative deprivation in Wales.

The data is compiled from various sources:

“WIMD is currently made up of eight separate domains (or types) of deprivation. Each domain is compiled from a range of different indicators. The domains included in WIMD 2019 are:
 Income
 Employment
 Health
 Education
 Access to Services
 Housing
 Community Safety
 Physical Environment”

As the report states:

It does not provide a measure of the level of deprivation in an area, but rather whether an area is more or less deprived relative to all other areas in Wales”.

The report also states that “WIMD cannot be used to compare deprivation for one area with its deprivation in a previous iteration of the Index”.

 

There is a lot of data analysis behind the construction of such an index and those with an interest in the details can read more here.

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