England&Wales MSOA maps
Page last
modified 1/4/2022
Old Ordnance Survey maps Great Britain
Contents of this page
1) Choropleth maps of England & Wales (links from
this section) covering ten essential socio-econbomic/demographic variables. Future map updates will be added as later year�s data becomes
available.
Choropleth maps for later data will use the same colour
bands as earlier maps, facilitating comparison of changes within individual
MSOAs.
2) Basic statistical data on these variables.
3) Raw correlation data between all 10 of these
variables.
4) Selected Partial Correlation transects between
these vriables
5) Partial Correlation Data Narratives.
6) Explanation of how the Partial Correlation
Transects work.
1)
Choropleth maps of England & Wales.
These choropleth maps show key socio-demographic
and economic indicators for England and Wales, mapped at the MSOA level (7,201
census areas). MSOAs are indicated by number and Local Authority area.
The colour shading is the same for all maps, as indicated
below
|
Most Deprived decile decile |
9th |
8th |
7th |
6th |
5th |
4th |
3rd |
2nd |
Wealthiest decile |
|
|
|
|
|
|
|
|
|
|
|
The choropleth scale is indicated on each map. All indicators
are adjusted by total population of the MSOAs.
Further maps will be added as updated� census data becomes available on Nomis. Source of data,
https://www.nomisweb.co.uk/query/select/getdatasetbygeog.asp?cat=8&geogtype=297
All� maps are split into 7 regions;
Southern England � includes Cornwall,
Gloucestershire, Oxfordshire, Buckinghamshire, Surrey, Kent
London � Greater London
Wales
Eastern England � includes Essex,
Hertfordshire, Bedfordshire, Northamptonshire, Leicestershire, Nottinghamshire,
Lincolnshire
West Midlands � includes Herefordshire,
Shropshire, Staffordshire, Warwickshire
North West England � includes
Liverpool, Manchester, Lancashire, Cumbria
North East England � includes
North Lincolnshire, Yorkshire, Durham, Northumberland
The Covid-19
deaths maps have two extra choropleths,
|
MSOAs with just one death |
MSOAs with no deaths |
|
|
|
Links to
the maps. See below
table for basic data on variable values, mean, median etc.
|
|
S England |
London |
Wales |
E England |
W
Midlands |
NW
England |
NE
England |
All E+W (small) |
|
Covid Deaths to 30/4/2021 |
||||||||
|
Mean Age 2011 |
|
|||||||
|
Obesity Child, 2017 |
|
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|
Household Deprivation 2011 See notes (1) below |
|
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|
% White Population 2011 |
|
|||||||
|
Ethnicity map 2011 See notes (2) below |
|
|
||||||
|
Crowded H�holds 1+ to room 2011 |
|
|||||||
|
People in bad health 2011 |
|
|||||||
|
People with Degree or Above 2011 |
|
|||||||
|
Households No car 2011 |
|
|||||||
|
Benefit Claimants 5/2021 |
|
|||||||
|
|
S England |
London |
Wales |
E England |
W
Midlands |
NW
England |
NE
England |
|
1) Household Deprivation. This measures households �not deprived� as
a proportion of all households. A �household not deprived; contains nobody
who 1) is unemployed and not a
student or long term ill, 2) has not
attained educational qualifications of at least GCSE / O Levels A*-C), 3) Has a long term illness or poor
health, and 4) The household is not
�overcrowded�, that is, has 1 or more persons per �room�. For more details see Nomis QS119EW,
https://www.nomisweb.co.uk/query/construct/components/simpleapicomponent.aspx?menuopt=5190&subcomp=
2) BAME�
/Ethnicity map. This map
indicates variation from the median in the % of the three main minority ethnic
groups in the UK; Asian, Black+Caribbean, and White non-British (includes
European mainland, Hispanic, and also White+Mixed ethnicity). By default, paler
colours indicate more White British; darker shaded indicate more %BAME. Pink/purple shades indicate higher%Black.
Green colours indicate higher%Asian, and blue shades indicate more
%White-non-British. Other colours are indicated on the 5x5x5 diagram shown
with the map itself.
0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
2) Basic statistical
data on variables Purple figures
are NOMIS references
|
Variable 30/4/21` |
Mean |
Median |
1st decile |
2nd decile |
3rd decile |
4th decile |
5th decile |
6th decile |
7th decile |
8th decile |
9th decile |
10th decile |
|
Covid-19 population per death |
784.16 |
489 |
102- 267 |
267- 326 |
326- 376 |
377- 492 |
492- 489 |
489- 558 |
558- 660 |
660- 815 |
815- 1,133 |
1,133- No deaths |
Highest death rate = 1 death
per 102 population (Tendring 106 MSOA = Frinton, Walton on the Naze)
Lowest death
rate� = 0 deaths (several
MSOSAs, mainly in SW England and W Wales)
KS102UK
|
Variable 3/2011 |
Mean |
Median |
1st decile |
2nd decile |
3rd decile |
4th decile |
5th decile |
6th decile |
7th decile |
8th decile |
9th decile |
10th decile |
|
Mean Age yrs. |
39.9 |
40.19 |
24.1 � 33.7 |
33.7 � 35.9 |
35.9 � 37.5 |
37.5 � 38.8 |
38.8 � 39.9 |
39.9 � 41.0 |
41.0 � 42.1 |
42.1 � 43.3 |
43.3 � �44.9 |
44.9 � 57.5 |
Lowest Age = 24.1
(Birmingham 096 MSOA = Selly Oak)
Highest Age = 57.5
(Arun 010 MSOA = west Angmering)
|
Variable 2016 |
Mean |
Median |
1st decile |
2nd decile |
3rd decile |
4th decile |
5th decile |
6th decile |
7th decile |
8th decile |
9th decile |
10th decile |
|
% Child Obesity |
20.45 |
19.43 |
4.1 � 11.8 |
11.8 � 14.0 |
14.0 � 15.9 |
15.9 � 17.6 |
17.6 � 19.3 |
19.4 � 21.1 |
21.1 � 22.7 |
22.7 � 24.7 |
24.9 � 27.3 |
27.3 � 39.0 |
Lowest % Child
Obesity = 4.1% (North Somerset 002 MSOA = west Portishead, Stockport
017 MSOA = Marple Bridge)
Highest % Child
Obesity = 39.0% (Swansea 021 MSOA = Port Tennant, Carmarthen 023
MSOA = west Llanelli)
QS119EW
|
Variable 3/2011 |
Mean |
Median |
1st decile |
2nd decile |
3rd decile |
4th decile |
5th decile |
6th decile |
7th decile |
8th decile |
9th decile |
10th decile |
|
H�hold Not deprived |
2.56 |
2.32 |
3.58 � �7.33 |
3.02 � 3.58 |
2.72 � 3.02 |
2.50 � 2.72 |
2.32 � 2.50 |
2.18 � 2,32 |
2.05 � 2.18 |
1.92 � 2.05 |
1.80 � 1.92 |
1.41 � 1.80 |
Ratio of all
households to households not deprived.
Most households
not deprived = 1.41 ratio (Stockton on Tees 023 MSOA = west Ingleby
Barwick)
Most households
deprived = 7.33 ratio (Leicester 018 MSOA = St Matthews to Spinney
Hills)
Most deprived
5% = ratios 4.04 � 7.33
Most deprived
2.5% = ratios 4.52 � 7.33
DC2301EW /
QS211EW
|
Variable 3/2011 |
Mean |
Median |
1st decile |
2nd decile |
3rd decile |
4th decile |
5th decile |
6th decile |
7th decile |
8th decile |
9th decile |
10th decile |
|
% White population |
86.77 |
94.93 |
5.46 � 60.33 |
60.33 � 79.10 |
79.10 � 87.80 |
87.80 � 92.20 |
92.21 � 94.93 |
94.93 � 96.35 |
96.35 � 97.25 |
97.25 � 97.89 |
97.89 � 98.45 |
98.45 � 99.57 |
Lowest % White = 5.46%
(Birmingham 082 MSOA= Sparkhill)
Highest %
White = 99.57% (South Lakeland 008 MSOA = SW of Ambleside)
Lowest 5%
White = 5.46% -44.50%
Lowest 2.5%
White = 5.46% - 32.82%
DC4209EW
|
Variable 3/2011 |
Mean |
Median |
1st decile |
2nd decile |
3rd decile |
4th decile |
5th decile |
6th decile |
7th decile |
8th decile |
9th decile |
10th decile |
|
H�holds 1+ person Per room |
21.59 |
92.19 |
3.87 � 20.17 |
20.22 �
39.11 |
39.12 �
56.30 |
56.30 �
72.98 |
73.00 �
92.16 |
92.21 �
114.36 |
114.36
� 139.82 |
139.84
� 175.35 |
175.44
� 237.00 |
237.08
� 2,767.00 |
Ratio of all
households to households Overcrowded = 1+ person per room.
Most households
overcrowded = ratio to all households 3.87 (Newham 014 MSOA = Plashet
west)
Least
households overcrowded� =ratio
2,767.00 to all households (Tonbridge 002 MSOA = Snodland)
LC4304EW
|
Variable 3/2011 |
Mean |
Median |
1st decile |
2nd decile |
3rd decile |
4th decile |
5th decile |
6th decile |
7th decile |
8th decile |
9th decile |
10th decile |
|
Population. in bad health |
21.58 |
19.99 |
6.29 � 12.11 |
12.11 � 14.31 |
14.31 � 16.24 |
16.24 � 18.16 |
18.17 � 19.99 |
20.00 � 22.05 |
22.05 � 24.55 |
24.55 � 27.85 |
27.86 � 32.89 |
32.89 � 105.48 |
Ratio of total
population to population self-reporting health as bad / very bad
Highest level of
Bad Health = ratio to total population 6.29 (Liverpool 024 MSOA = east
Everton)
Lowest ;level
of Bad Health = ratio to total population 105.48 (Manchester 060 =
Deangate)
KS501EW
|
Variable 3/2011 |
Mean |
Median |
1st decile |
2nd decile |
3rd decile |
4th decile |
5th decile |
6th decile |
7th decile |
8th decile |
9th decile |
10th decile |
|
People with
level 4
Qualification or above |
5.56 |
4.80 |
9.28 � 28.51 |
7.37 � 9.28 |
6.28 � 7.37 |
5.43 � 6.28 |
4.80 � 5.43 |
4.29 � 4.80 |
3.85 � 4.29 |
3.41 � 3.85 |
2.90 � 3.41 |
1/52 � 2.90 |
Level 4
Qualification� = University Degree or
equivalent.
Ratio of total
population to population with Level 4 Qualifications or above
Highest level
of degree level qualifications�
= ratio to total population 1.52 (Manchester 060 MSOA = Deansgate)
Lowest level of
degree level qualifications = ratio to total population 28.51 (Hull 003 MSOA =
Orchard Park)
KS404EW /
QS416EW
|
Variable 3/2011 |
Mean |
Median |
1st decile |
2nd decile |
3rd decile |
4th decile |
5th decile |
6th decile |
7th decile |
8th decile |
9th decile |
10th decile |
|
H�holds No car or van |
5.77 |
4.58 |
1.21 � 2.15 |
2.15 � 2.71 |
2.71 � 3.27 |
3.27 � 3.86 |
3.86 � 4.58 |
4.58 � 5.52 |
5.52 � 6.81 |
6.81 � 8.60 |
8.61 � 11.17 |
11.17 � 34.23 |
Ratio of all
households to households without access to private car or van.
Lowest level
of households without private car/van = ratio to all households 34.23
(Stockton on Tees 023MSOA = west Ingleby Barwick).
Highest level of
households without private car/van = ratio to all households 1.21
(Camden 025 MSOA = Judd Street, south of Kings Cross)
|
Variable 6/2021 |
Mean |
Median |
1st decile |
2nd decile |
3rd decile |
4th decile |
5th decile |
6th decile |
7th decile |
8th decile |
9th decile |
10th decile |
|
Benefit Claimants |
33.90 |
29.99 |
5.29 � 13.23 |
13.25 � 17.00 |
17.01 � 21.02 |
21.03 � 25.31 |
25.31 � 29.99 |
29.99� - 35.46 |
35.46 � 41.53 |
41.54 � 48.74 |
48.74 � 59.48 |
59.48 � 147.17 |
Ratio of total
population to people claiming Universal Benefit
Lowest levels
of Universal Benefit Claimants = ratio to total population 174.17
(Warrington 025 MSOA = Appleton)
Highest levels of
Universal Benefit Claimants = ratio to total population 5.29 (Leeds 053 MSOA =
east Harehills)
0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
3) Raw
correlation figures for all variables, all England and Wales
|
XXX |
1/CovDth |
Mean Age |
Obese |
HHDeprived |
%White |
1/Crowded |
1/BadHlth |
1/Degree |
1/NoCar |
1/BenClaim |
|
1/CovDth |
XXX |
|
|
|
|
|
|
|
|
|
|
Mean Age |
-0.04 |
XXX |
|
|
|
|
|
|
|
|
|
Obese |
-0.03 |
-0.437 |
XXX |
|
|
|
|
|
|
|
|
HHDeprived |
-0.03 |
-0.432 |
0.675869 |
XXX |
|
|
|
|
|
|
|
%White |
0.024 |
0.658 |
-0.388971 |
-0.4642 |
XXX |
|
|
|
|
|
|
1/Crowded |
--.009 |
0.569 |
-0.436525 |
-0.4448 |
0.4458 |
XXX |
|
|
|
|
|
1/BadHlth |
0.126 |
0.001 |
-0.619588 |
-0.6664 |
0.0611 |
0.236366 |
XXX |
|
|
|
|
1/Degree |
-0.03 |
-0.218 |
0.497078 |
0.72888 |
0.0439 |
-0.27788 |
-0.596839 |
XXX |
|
|
|
1/NoCar |
0.019 |
0.481 |
-0.56334 |
-0.6083 |
0.3916 |
0.540726 |
0.5704349 |
-0.387 |
XXX |
|
|
1/BenClaim |
0.035 |
0.593 |
-0.625781 |
-0.6591 |
0.47 |
0.675274 |
0.5437331 |
-0.4594 |
0.741 |
XXX |
0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
4) Partial
correlation transects between these variables (for
explanation see below)
Ref Number (RHS of table), see below for indeces
transected on each line.
Minus most affluent ie. These
are more deprived MSOAs���Minus least affluent (less
deprived here)���� Ref.No.
These are higher
%BAME MSOAs������������..These are
higher %White MSOAs
|
-90% |
-80% |
-70% |
-60% |
-50% |
-40% |
-30% |
-20% |
-10% |
All |
-10% |
-20% |
-30% |
-40% |
-50% |
-60% |
-70% |
-80% |
-90% |
|
|
-.283 |
-.119 |
-.123 |
-.125 |
-.126 |
-.106 |
-.099 |
-.085 |
-.071 |
-.041 |
-.061 |
-.031 |
-.024 |
-.021 |
-.017 |
-.005 |
-.049 |
-.048 |
-.110 |
1-2x5 |
|
-.027 |
-.053 |
-.059 |
-.057 |
-.059 |
-.035 |
-.027 |
-.027 |
-.022 |
-.030 |
-.021 |
-.019 |
-.012 |
-.008 |
-.004 |
-.018 |
-.030 |
-.033 |
-.078 |
1-3x5 |
|
-.089 |
-.053 |
-.054 |
-.053 |
-.055 |
-.029 |
-.027 |
-.027 |
-.027 |
-.029 |
-.018 |
-.019 |
-.011 |
-.005 |
-.004 |
-.024 |
-.030 |
-.030 |
-.038 |
1-4x5 |
|
+.210 |
+.072 |
+.045 |
+.039 |
+.032 |
+.031 |
+.033 |
+.028 |
+.027 |
+.024 |
-.000 |
-.004 |
+.014 |
+.027 |
+.032 |
+.027 |
+.023 |
+.020 |
+.010 |
1-5x6 |
|
+.067 |
+.0341 |
+.021 |
+.023 |
+.023 |
+.019 |
+.018 |
+.021 |
+.020 |
+.024 |
+.039 |
+.043 |
+.043 |
+.044 |
+.043 |
+.041 |
+.039 |
+.055 |
+.050 |
1-5x9 |
|
+.011 |
+.037 |
+.009 |
+.004 |
-.001 |
-.006 |
-.009 |
-.012 |
-/013 |
-.009 |
-.018 |
-.015 |
-.017 |
-.023 |
-.028 |
-.028 |
-.027 |
-.031 |
-.029 |
1-6x5 |
|
+.053 |
+.063 |
+.278 |
+.193 |
+.200 |
+.176 |
+.197 |
+.169 |
+.144 |
+.126 |
+.129 |
+.126 |
+.097 |
+.139 |
+.154 |
+.177 |
+.109 |
+.121 |
+.287 |
1-7x4 |
|
+.415 |
+.352 |
+.296 |
+.266 |
+.252 |
+.200 |
+.165 |
+.155 |
+.138 |
+.126 |
+.122 |
+.055 |
+.041 |
+.033 |
+.022 |
+.035 |
+.039 |
+.044 |
+.077 |
1-7x5 |
|
+.266 |
+.027 |
+.045 |
+.040 |
+.072 |
+.156 |
+.158 |
+.140 |
+.129 |
+.126 |
+.123 |
+.129 |
+.126 |
+.137 |
+.139 |
+.103 |
+.095 |
+.092 |
+.133 |
1-7x10 |
|
-.199 |
-.083 |
-.085 |
-.084 |
-.083 |
-.033 |
-.023 |
-.025 |
-.025 |
-.031 |
-.030 |
-.014 |
-.006 |
-.000 |
-.007 |
/032 |
-.052 |
-.054 |
-.081 |
1-8x5 |
|
+.006 |
+.029 |
+.022 |
+.017 |
+.031` |
+.026 |
+.015 |
+.017 |
+.021 |
+.019 |
+.029 |
+.025 |
+.018 |
+.015 |
+/015 |
+.016 |
+.069 |
+/067 |
+/070 |
1-9x3 |
|
-.170 |
-.044 |
-.037 |
-.019 |
-.000 |
-.003 |
+.002 |
+.003+ |
+.004 |
+.019 |
+.013 |
+.032 |
+.034 |
+.029 |
+.020 |
+.030 |
+.039 |
+.040 |
+.075 |
1-9x5 |
|
+.110 |
+.096 |
+.086 |
+.073 |
+.069 |
+.044 |
+.031 |
+.030 |
+.027 |
+.035 |
+.026 |
+.030 |
+.020 |
+.012 |
+.002 |
+.012 |
+.029 |
+.024 |
+.047 |
1-10 x 5 |
|
-.334 |
-.242 |
-.180 |
-.174 |
-.168 |
-.182 |
-.197 |
-.198 |
-.206 |
-.218 |
-.256 |
-.342 |
-.411 |
-.470 |
-.507 |
-.524 |
-.552 |
-.567 |
-.559 |
2-8x5 |
|
-.152 |
-.250 |
-.280 |
-.310 |
-.334 |
-.350 |
-.371 |
-.384 |
-.390 |
-.389 |
-.324 |
-.283 |
-.249 |
-.209 |
-.184 |
-.180 |
-.159 |
-.112 |
-.101 |
3-5x4 |
|
-.311 |
-.502 |
-.592 |
-.625 |
-.634 |
-.641 |
-.643 |
-.637 |
-.827 |
-.620 |
-.635 |
-.660 |
-.675 |
-.676 |
-.683 |
-.673--.655 |
-.655 |
-.643 |
-.623 |
3-7x5 |
|
-.394 |
-.440 |
-.475 |
-.526 |
-.552 |
-.575 |
-.586 |
-.584 |
-.580 |
-.563 |
-.525 |
-.511 |
-.522 |
-.536 |
-.536 |
-.530 |
-.516 |
-.503 |
-.489 |
3-9x5 |
|
+.199 |
+.287 |
+.318 |
+.358 |
+.367 |
+.346 |
+.320 |
+.296 |
+.275 |
+.236 |
+.217 |
+.243 |
+.286 |
+.316 |
+.332 |
+.346 |
+.364 |
+.368 |
+.321 |
6-7x5 |
|
+.318 |
+.364 |
+.438 |
+.521 |
+.589 |
+.610 |
+.614 |
+.610 |
+.598 |
+.570 |
+.573 |
+.628 |
+.699 |
+.742 |
+.752 |
+.763 |
+.772 |
+.7788 |
+.760 |
7-9x5 |
|
+.735 |
+.639 |
+.640 |
+.690 |
+.718 |
+.736 |
+.736 |
+.736 |
+.737 |
+.741 |
+.715 |
+.698 |
+.698 |
+.696 |
+.689 |
+.680 |
+.685 |
+.677 |
+.673 |
9-10x5 |
Colours of cells indicate +/- strength of
correlation, in decile steps. Red =
0-.099, Pink = .1-.199, Orange = .2-.299, Yellow = .3-.399, Light Green =
.4-.499, Dark Green = .5-.599, Light Blue = .6-.699, Dark Blue = .7-.799
0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
5) Partial
Correlation Data Narratives.
�Grey shading indicates no significant
correlation trend (still informative).
1-2x5 = 1/Covid deaths vs. Mean Age transected by %White
(more BAME = LHS, more deprived)
Narrative. Age
slightly raises the Covid death rate at all levels of ethnicity. However there is a markedly higher death rate with age for areas with
higher %BAME populations; in higher %White areas the risk of death with age is
less, except for the very Whitest areas (perhaps more very old persons here?).
1-3x5 = 1/Covid deaths vs. %Child Obesity,
transected by %White (more BAME = LHS, more deprived)
Narrative. Very low +ve correlation between level of Covid
deaths and %Child Obesity at all levels of ethnicity.
See 1-7x5.
1-4 x 5 = 1/Covid deaths vs. 1/Hhold Not Deprived transected by %White (more BAME = LHS)
Narrative. Very small +correlation between increased household
deprivation and Covid deaths, with little variation across different
levels of %White.
1-5x6 = 1/Covid deaths vs. % White transected by Overcrowding
(more Overcrowded = LHS, more deprived)
Narrative. At almost all levels of Overcrowding, the mortality
rate from Covid is slightly smaller for White than for BAME people. However at the very highest levels of overcrowding, this differential
widens markedly. Perhaps because Overcrowded BAME households contain many
people in more rooms, of multiple generations, raising the chances or Covid
transmission; whereas Overcrowded White households may consist of fewer� people in fewer rooms.
1-5x9 = 1/Covid deaths vs. % White transected by Hhold No
Car (less Cars = LHS, more deprived)
Narrative.
1-6x5 = 1/Covid deaths vs. Overcrowding transected by %
White (more BAME = LHS, more deprived)
Narrative. Small trend ; in more BAME areas, overcrowding is
+vely associated with higher Covid rates, but the reverse in Whiter areas. Overcrowded BAME areas are likely to be poorer, but overcrowded White
areas may be where younger degree-educated professionals live e.g. London.
1-7x4 = 1/Covid deaths vs. 1/Bad health, transected by 1/Households not Deprived (LHS = more
households deprived)
Narrative. General
positive association between Covid deaths and bad health, but little variation
by levels of household deprivation, see 1-7x5.
1-7x5 = 1/Covid deaths vs. 1/Bad
Health, transected by %White (less %White = LHS, more deprived)
Narrative. Self-reported Bad/Very Bad health has a major +ve
impact on the Covid death rate in more BAME areas, but the impact is much
smaller in more White areas. Unlike levels of Benefit Claims, Child Obesity, or
Deprived Households levels, there is
a variation across % White, with Covid deaths vs. Bad Health rising with rising
BAME populations. For some reason, poor health seems to impact Covid deaths more in the
more BAME areas.
1-7x10 = 1/Covid deaths vs. 1/Bad Health, transected by 1/Universal
Benefits Claimants (more Benefits Claimants = LHS, more deprived). Narrative. Positive
correlation between Covid deaths and Bad Health but little variation across all
levels of Benefits Claimants, see 1-7x5.
1-8x5 = 1/Covid deaths vs. Degree Level Qualifications, transected by %White (less %White = LHS,
more deprived)
Narrative. More
degree-level qualifications is slightly associated with lower Covid deaths at
all levels of ethnicity, but there is a small tendency
for this effect to operate more strongly in more BAME areas; perhaps BAME
degree-holders are also younger than White ones. This corresponds to
the narrative for 2-8x5, Degree Qualifications vs. Age transected by %White,
below.
1-9x3 = 1/Covid
deaths vs. Hhold no car,
transected by Child Obesity (LHS = higher Obesity)
Narrative, Very low correlation between
Covid deaths and Households no car at all levels pf Child Obesity.
1-9x5 = 1/Covid deaths vs. 1/Hhold No car, transected by %White. LHS = More Hholds without cars.
Narrative. Slight tendency for higher levels
of car ownership to go with lower car deaths at most levels of %White/ However
this reverses, a little, in the highest BAME areas with more car ownership going
with higher Covid death rates. Maybe because these higher BAME
areas contain more taxi and delivery drivers; workers from abroad doing
�trnasferable skills� jobs, that involve more people-contact? �See 7-9x5.
1-10x5 = 1/Covid deaths vs. 1.Benefit Claimants transected by % White (more BAME = LHS, more deprived)
Narrative. Low +ve
correlation between more benefits Claimants and higher Covid deaths at all
levels of %White but slight tendency for this correlation to ruse in higher
%BAME areas. This deprivation marker seems to raise the Covid
mortality rate of BAME claimants a little more than it does for White claiamnts.
2-8x5 = Mean Age vs. Degree Level Qualifications, transected by %White (less %White = LHS,
more deprived)
Narrative. In higher
%White areas there is a strong negative correlation between Mean age and % of
population with Degree Level Qualifications, whereas in higher %BAME areas this
is not so much the case (see 1-8x5 above). Younger age plus more degree-level
education seems to be operating to reduce�
the excess Covid death rate in the higher %BAME areas.
3-5x4 = Child Obesity vs. %White transected by Households
not Deprived. (less households not
deprived = LHS).
Narrative. In the
areas of lower household deprivation, the �White� advantage of lower obesity
rates reduces whereas in more deprived areas there is a noticeable Obesity advantage
to being White (apart form the areas of very highest deprivation where this
advantage begins to disappear again. Obesity is a marker of poverty;
in the moderately-deprived areas, the BAME population may be worse off
economically.
3-7x5 = Child Obesity vs. 1/Bad Health, transected by %White.
(less %White = LHS).
Narrative. Strong
correrlation between (child) obesity and bad health at most levels of %White,
except in the highest %BAME areas where the correlation is noteably� lower. Is something else than obesity causing
bad health in these areas?
3-9x5 = Child
Obesity vs. 1/Hhold No Car,
transected by %White. (less %White = LHS).
Narrative. +ve Correlation at all levels of
%White betweenb Chgild Obesity and Hhold without car, but slight reduction in
the correlation in the highest %BAME areas.
6-7x5 = 1/Hhold Overcrowded vs. 1/Bad Health., transected by %White. (less %White = LHS).
Narrative. Some +ve correlation betweem Bsad
Health and Hhold Overcrowded at all levels of %White. No major trend across
ethnicity.
7-9x5 = 1/BadHealth
vs. Hhold No Car, transected by %White. (less %White = LHS).
Narrative. Strong correlation, especially in
higher^White areas, between having no car and being in bad health. However, as
with 1-9x5, in higher%BAME areas, this correlation is considerably weaker. Here, having a car is much less �protective� against bad health.
9-10x5 = H�hold No Car vs. Benefit
Claimants transected by %White.
(less %White = LHS).
Narrative. In contrast
with 7-9x5 (Bad Health) and 1-9x5, (Covid deaths) there is little change across
ethnicity ;levels with Benefit Claimants and No Car. In this instance there is little or no work effect (Benefits) so
suggesting that the lack of health benefits associated with a car in� higher %BAME areas was to do with the type of
work done by BAME people at the lower-wages end where private transport is
utilised, see 1-9x5.
Worse health - Occupation, deprivation,
Covid-19 is
exacerbated by Inequality, Joseph
Stiglitz, 2020
https://www.imf.org/external/pubs/ft/fandd/2020/09/COVID19-and-global-inequality-joseph-stiglitz.htm
Ethnicity
and occupation in the UK
From https://www.ethnicity-facts-figures.service.gov.uk/work-pay-and-benefits/employment/employment-by-occupation/latest� downloaded 20/8/2021
https://www.independent.co.uk/news/uk/home-news/most-ethnically-diverse-jobs-britain-policy-exchange-forum-employment-workforce-farmers-taxi-drivers-a7625456.html� Independent,, 2017
https://policyexchange.org.uk/wp-content/uploads/2017/03/The-two-sides-of-diversity-2.pdf� Policy Exchange, 2017
0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
6) Explanation
of how the Partial Correlation Transects work.
Correlations as �social
transects�
A form of partial
correlation, where the extent to which the relationship between two cariables
is controlled by a third, can be achieved by converting the �overall� (all-7200-MSOA) correlations into �social transects�. These are analogous to
geographical transects, for example, taking a metro line trip across a city
from north to south and exiting at every alternate stop tonote the
socio-economic conditions prevailing in that area.
By taking e.g. the
correlation between Covid-19 deaths and % White population, and shaving off the
10% MSOAs with the highest % over 65 year olds, recalculating, shaving off the
next 10% highest % over 65s, and so on down to the last 10% (which will by
definition be the cohort of MSOAs with the youngest population) one can obtain
a series of correlations suggesting how the relationship between Covid-19
mortality and ethnicity changes as one transects from older-age areas to more
youthful areas. The analysis can of course then be done the other way, shaving
off progressively 10% MSOAs with the lowest % over 65, creating a trabsect
towards ever-older areas.
This methodology can be
used to investigate the relationship/correlation between any two variables A
and B as one transects the entire sample by a 3rd factor C. In fact
C can equally be one of the initial two factors A or B. One could investigate
the �narrative� of the correlation between e.g. Covid-19 deaths and % White
population, produced by a transect of MSOA selections from higher % White areas
to lower % White areas. Or, as above, by a 3rd factor such as age.
This �narrative� (akin to
the socio-geographical �narrative� an observer might tell after she had
transected a city by alternate Metro stops, from outer affluent areas through
to inner city areas) now comprises 19 correlation figures; the 10% lowest
factor C census areas, the 20% lowest factor C areas, the lowest
30%......(overall 100% figure)�..highest 30%, highest 20%, highest 10%.
This social transect can
reveal much more than a single figure can about possible relationships between
socio-economic factors. For example in a study of obesity in Birmingham, UK (Food access diet and health in the
UK: an empirical study of Birmingham�,
British Food Journal, Vol. 114, Issue 4, (2012), pp. 598-616, https://www.emerald.com/insight/content/doi/10.1108/00070701211219577/full/html?skipTracking=true
)
it was found that the expected positive correlation between % population obese
and several indeces of deprivation was slanted from more positive in wealthier
areas (as indicated by e.g. less unempoloyment, less overcrowding) to actually
negative in more deprived areas. In other
words, 1) deprivation did, overall as expected, imply more obesity 2) In
wealthier areas, this relationship was more pronounced, likely because the poor
in wealthier areas faced a more expsnive slelection of foodstores, Sainsbury
and Waitrose rather than Asda and Iceland, and their living costs
(accommodation rentals) may be higher, further reducing the funds available for
purchasing healthier (often more expensive � see e.g. https://www.hsph.harvard.edu/news/press-releases/healthy-vs-unhealthy-diet-costs-1-50-more/� 3) In less
affluent areas the positive correlation between obesity and deprivation was
lower and as one transected to the poorest areas the correlation sign actually
reversed and became negative � being more affluent, or being employed, in a
poorer area could make you MORE prone to obesity. This may have been due
to i) the unemployed having more time to access cheaper street markets and more
time and energy to cook fresh produce, whilst ii) the employed in these
less-affluent areas would still have been on lowish wages, and their work hours
may have precluded them from accessing street markets and pushed them more
towards easier microwave or takeaway meals at the end of a hard working day.