England&Wales MSOA maps

Page last modified 17/9/2021

 

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Great Britain History

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 updatedcensus data becomes available on Nomis. Source of data,

https://www.nomisweb.co.uk/query/select/getdatasetbygeog.asp?cat=8&geogtype=297

 

Allmaps 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

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Here

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Mean Age

2011

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Obesity

Child, 2017

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Household

Deprivation

2011

See notes (1)

below

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% White

Population

2011

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Ethnicity map

2011

See notes (2)

below

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Here

 

 

 

 

Crowded

H�holds

1+ to room

2011

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Here

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People in

bad health

2011

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People with

Degree or

Above 2011

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Households

No car

2011

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Benefit

Claimants

5/2021

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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 fewerpeople 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 reducethe 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 noteablylower. 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 inhigher %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/latestdownloaded 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.htmlIndependent,, 2017

https://policyexchange.org.uk/wp-content/uploads/2017/03/The-two-sides-of-diversity-2.pdfPolicy Exchange, 2017

 

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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.

 

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