the growth and changing complexion of luton’s population · o luton’s population live in...

The growth and changing

complexion of Luton’s population

A structural analysis and

decomposition

Dr L. Mayhew Sam Waples Mayhew Harper Associates Ltd. January 2011 [email protected]

Luton population growth and change

2

Executive summary

In recent years, Luton has experienced significant in-migration from Eastern Europe

(both EU and non-EU countries), West Africa and elsewhere. This has significantly

changed the demographic composition and ethnic complexion of the town. This in

turn has impacted upon public service delivery and processes of engagement between

the Council (and other public bodies) and local communities. The Council recognises

the importance of understanding the demographics of the town when planning and

delivering services and in engaging with its diverse communities.

The Council is aware of the limitations of official statistics in providing this evidence.

In particular, the limitations of the 2001 Census, where Luton experienced one of the

lowest response rates in the country. The Council does not accept that the ONS Mid-

Year Population Estimates are an accurate measure of the population of the town. In

turn, this means that the Council does not accept the ONS experimental ethnicity

estimates as being accurate.

The Migration Impact Fund provided an opportunity to plug this gap in knowledge.

The Luton Local Strategic Partnership accepted the business case for research into the

changing population of Luton and released MIF monies for this purpose. The Council

commissioned Mayhew Harper Associates Ltd. to undertake this work with the

following terms of reference:

These were to:

o identify the ‘new communities’ within Luton

o understand the demographic profiles of these new communities

o understand the drivers of migration and how it will change the Luton

population in the future

o understand the drivers of migration and how it will impact upon these new

communities in Luton

o develop a proactive approach to monitoring and assessing the Luton

population.

Mayhew Harper Associates Ltd. used administrative data provided by the Borough

Council and NHS Luton to measure and profile Luton’s population. These data were

supplemented by analysis of a ‘names’ database to help with the identification of

different ethnicities. The analysis is a snapshot of Luton in 2010.

The key findings of the research are:

o Luton’s population is a confirmed minimum of 202,748. This is comparable

with the Council’s own estimate of 204, 700 and significantly above the ONS

Mid-Year Estimate of 194,400.

o Luton’s population live in approximately 77,000 households.

o Average household size in Luton is 2.6 – which is above national averages and

has not decreased since the 2001 Census.


3

o There is wide variation in household size amongst different ethnic groups –

with Asian households being larger than average.

o There have been significant shifts in the ethnic composition of Luton since the

last Census including:

generally increasing ethnic diversity among the population

growth in the Asian population from 33,600 to 50,200;

the Black population increasing from 11,700 to 19,800;

a decline in the White and ‘other’ population from 139,000 to 132,000.

concentrations of different groups across the town, for example

Turkish people in Farley

high turnover of population with estimates that between 50% and 75%

of the population would not have lived in Luton or not have been born

at the time of the 2001 Census.

The key recommendations of the research are to:

o undertake periodic snapshots to understand and monitor demographic changes

over time

o use the evidence in the study to ensure that different ethnic groups are familiar

with the 2011 Census – its importance, the legal obligations, and the form

itself

o take a snapshot that is synchronised with the 2011 Census to provide evidence

to challenge the ONS in event that the Census has a low level of enumeration

and the resultant population figures are significantly lower than anticipated

o develop a single database of all residents (linked to LLPG) that contains key

demographic information and is at the hub of all Council systems

o link all administrative data with the LLPG with appropriate data management

arrangements to provide the basis for demographic intelligence and local level

population profiling

o work with NHS Luton to ensure continued access to key datasets ‘owned’ by

the NHS such as GP Registration Data

o link partnership data to the LLPG to provide high quality intelligence to

support demographic profiling, service planning and monitoring and reduce

reliance on external datasets

Acknowledgements

The authors are most grateful to Paul Barton and Eddie Holmes from the Council’s

Research and Intelligence Team, Caroline Thickens from NHS Luton and to all who

supplied administrative data without which this analysis would not be possible.


4

Contents

1. Introduction

2. Data on migration

3. Counting Luton’s population using administrative data

4. Households by type, benefit and tenure status and occupancy

5. Ethnicity by broad groupings

6. Population by household and ethnic grouping

7. Income deprivation by ethnicity and age

8. Conclusions

Annex A: Analysis of administrative data using Polish surnames

Annex B: Map of households with 6 or more people

Annex C: Ward level tables by age and ethnicity

Dr Les Mayhew

Mayhew Harper Associates Ltd.

[email protected]

February 2011


5

The growth and changing complexion of Luton’s population

~ A structural analysis

1. Introduction

In recent years, Luton has experienced significant in-migration from EU and non-EU

countries and from West Africa, as well as major growth in the number of people

from Pakistan, Bangladesh and India. This has changed the demographic composition

of the town with a resultant impact on the delivery of public services and levels of

engagement with different communities.

A number of key demographic data sources such as the 2001 Census are now

outdated. Population data on ethnicity is now largely invalidated by subsequent

inflows and the underlying demographic composition of the town has undergone a

radical shift, as the population has grown.

Previously Luton had a traditional demographic profile with a dominantly white

population complemented by two or three large ethnic groups (Pakistani, Afro-

Caribbean) and a larger number of relatively small groups.

This has now changed with the addition of new communities from eastern and

southern Europe and also people from various African countries. When added to the

more established populations, this reinforces the perception that Luton is now

becoming more diverse both culturally and ethnically.

Luton Council’s Research & Intelligence Team is investigating this aspect of Luton’s

population and commissioned Mayhew Harper Associates to examine the current

position in more detail by quantifying as far as possible the major ethnic groups and

analysing them demographically.

This is an important undertaking. The Council is committed to ensuring that its

service delivery meets the needs of its community and is also aware that it does not

necessarily fully understand the needs of its new communities. Some of these new

communities comprise young adults and do not necessarily engage with public sector

bodies.

Without a full understanding of the demographics of the new communities the

Council cannot be certain that its attempts to engage with these communities will be

effective. It is of further significance since Luton experienced one of the lowest

response rates to the 2001 Census of all local authorities. With the next Census due in

March 2011, the Council is committed to working with ONS to try and increase the

response rate.

Luton Council recognises that its systems for monitoring demographic changes

arising from international migration are limited and is therefore looking for ways of

improving this capability within existing resources. The aims of this study include the

following: they are to:

o identify and quantify the demographic profiles of these communities


6

o understand the drivers of migration and how they will change the Luton

population in the future

o make recommendations on how the Council can implement a proactive

approach to monitoring and assessing the Luton population on an ongoing

basis.

The Mayhew Harper approach differs from similar studies in that it is based entirely

on administrative data rather than official sources. The argument for this radically

new approach is that, as official sources essentially derive from the 2001 Census, they

no longer reflect accurately the current position. Although there is a new Census in

March 2011, the results will not be available for some time and there are concerns that

because of the ethnic complexion of Luton’s population response rates will be low, in

doing so jeopardising accuracy.

Our approach uses various sources of administrative data including the GP register,

annual school pupil census, electoral roll, and official data on births and deaths and

other sources including tax and benefit records. It combines these data with the Local

Land and Property Gazetteer (LLPG) to derive a demographic profile of households

in Luton, by cross referencing the data according to a set of rules to produce what is

termed a ‘confirmed minimum’ population.

On this basis, it finds that Luton has 202,748 residents living in around 77,000

households as of the 31st March 2010. Our population figure compares with the

ONS’s latest estimate of 194,400, which is 8,348 lower than ours. Our figure is higher

by around 18,000 on the population at the last Census in 2001, which in turn was

about a 12,000 increase on 1991.

Other published data show that births are consistently higher than deaths in Luton

adding weight to the evidence that Luton is continuing to grow through natural

increase as well as by migration over the long term. This growth is in turn putting

pressure on housing and other services; for example we find that average household

size is particularly high among the Asian community in which there are also

significant problems of income deprivation.

Of the total confirmed population, we found that as many as 73% may not have been

living in Luton at the time of the last Census, although this is an upper estimate.

During this time, we find that Asian and Black groups now make up a much larger

percentage of the population than they did in 2001.

Other groups originating from Europe are harder to identify and may not be as great

as was thought based on the evidence of administrative sources such as new National

Insurance (NI) registrations. For example, we could only partly corroborate high

figures quoted for the Polish community that are evident from this and other related

sources (this is discussed further and at Annex A).

It must be noted that NI registration does not necessarily mean that the individuals are

actually working (or living) in a given authority area. For example Luton Airport is a

major point of entry into the UK and may simply act as a staging post for some.


7

However, it is also possible that many migrants stay for short periods only and do not

necessarily appear on any administrative data bases such as the GP register or

Electoral Roll, which are used in this study, especially if they do not bother to

register. This argument applies particularly to some European migrants. Hence, these

populations are more difficult to verify with exactitude.

The report is divided into sections as follows:

o Section 2 briefly reviews administrative data on international migration and

concludes that such data are unable to shed much light on the changes

occurring

o Section 3 describes the methodology and results for counting the population of

Luton and compares it with ONS population estimates

o Section 4 considers household types by ethnicity, tenure and occupancy

o Section 5 breaks down the population by ethnicity and analyses different

groupings insofar as the data allow

o Section 6 considers household structures by ethnicity and occupancy and finds

significant differences in household size and type

o Section 7 considers income deprivation in different communities and age

groups and finds wide variations in deprivation by age and ethnicity

o Section 8 concludes and makes some further recommendations

2. Data on migration

Often the starting point for analyses of changing demography is levels of international

migration, especially if it is perceived that this is the primary reason for population

change. There are two main sources of information on migration at local authority

level: one based on the International Passenger Survey (IPS), and the other on

administrative sources.

2.1 The International Passenger Survey (IPS)

The International Passenger Survey (IPS) is a survey of a random sample of over

250,000 passengers entering and leaving the UK by air, sea or the Channel Tunnel.

The interviewer asks for a passenger’s country of residence (for overseas residents) or

country of visit (for UK residents), and the reason for their visit. It collects

information on intended destinations or areas of departure to and from the UK.

For Luton, the IPS suggests that there have been net inflows averaging 3,000 from

2004 onwards. Prior to 2004 net inflows were more modest (see Table 1). However,

IPS figures may be criticised on several grounds. We have concerns, for example, that


8

they are based on a small sample of people that state Luton as their destination (who

might later move to somewhere else in the UK) or point of departure. This means that

IPS data on the origins and destinations of migrants is likely to be spuriously accurate.

year In Out Net

2001-2 2136 1513 623

2002-3 1995 1169 826

2003-4 2169 1572 597

2004-5 3132 756 2376

2005-6 3268 1386 1882

2006-7 4853 1186 3667

2007-8 4972 1266 3706

2008-9 5140 1870 3270

total 27665 10718 16947

Table 1: Inflows and outflow to and from Luton based on the International Passenger

Survey

2.2 Administrative sources

There is currently no fully functional administrative source set up expressly for the

purpose of international migration measurement. As a result, what administrative

sources collect and who they cover may not match the definitions needed for and used

in the ONS mid-year population statistics, for example.

Typically administrative sources will include some visitors and short term migrants

who stay for less than twelve months as well as those who move for more than 12

months (long-term migrants). The ONS has usefully described the strengths and

weakness of the various different administrative sources available1.

There are three main sources at a local authority level that have been used to inform

estimates of international immigration at this level. These are (a) the Worker

Registration Scheme (WRS), (b) National Insurance Number (NINo) allocations, and

(c) the Patient Register Data System (PRDS), recording new registrations with

General Practitioners (GPs). Two of these, NINos and the PRDS, are covered in more

depth below2.

1 http://www.lga.gov.uk/lga/aio/1098388

2 The Luton Research and Intelligence team has already produced a thorough examination of these

sources in a report entitled: ‘Statistical Issues Relating to the ONS Population Estimates of Luton’,

which may be found at

http://www.luton.gov.uk/media%20library/pdf/chief%20executives/communications/ons/populationstat

isticsreportfinal.pdf


9

NINo data

Each source has its strengths and weaknesses. If we take the example of NI

registrations to illustrate the issues involved, the population coverage includes

o All non-UK born nationals aged 16 or over working, planning to work or

claiming benefits legally in the UK

o All registrations are included, regardless of how long individuals intend to stay

However, it excludes:

o Dependants of NINo applicants, unless they work or claim benefits

o Individuals from overseas not working, planning to work, or claiming benefits

– for example, this will include many students

o Those with an existing national insurance number, for example returning UK

nationals

o Migrants who are not of working age if they are not claiming benefits.

By excluding key groups and not counting returners, NINo data can only ever provide

a partial account of migration activity, but picture it creates may also be misleading.

With these caveats in mind, Figure 1 shows new NINo registrants for Luton by

selected countries of origin from 2002, during which time there were over 34k new

registrants. Table 2 shows the underlying data. The data shows that Polish registrants

have been a particularly active group alongside various Asian groups; however, if the

new registrants make only short stays their numerical impact on Luton’s population

will be smaller than those that make Luton their long term home.


10

0

500

1000

1500

2000

2500

3000

2002 2003 2004 2005 2006 2007 2008 2009 2010

(1st qtr)

year

nu

mb

er

of

ne

w r

eg

istr

atio

ns

Poland

Pakistan

India

Bangladesh

Africa

other

Figure 1: National Insurance registrations by country of origin in Luton from 2002 to

first quarter of 2010

Country of origin

total 2002 to 1st qtr 2010

Poland 11570

Pakistan 4370

India 3270

Bangladesh 1970

Africa 2550

Other 10820

Total 34550

Table 2: NINo data underlying the chart in Figure 1

GP registration data

GP registration data has the advantage of covering all age groups as long as they are

registered with a GP, which in practice is almost the whole population. It covers all

people requiring access to NHS services through a GP, regardless of age or reason for

visit. So, for example, many children and students will be covered. In addition,

individuals staying in the UK for longer than 3 months can register with a GP, so it

includes people that intend to stay for longer periods and possibly make Luton their

home.

GP registration data gives the date at which a person registers with their current GP.

Normally first registration takes place at birth but a person may decide to change GP

on change of address or for other reasons (e.g. if a practice closes). Registrant activity


11

therefore reflects a composite of new births, movements within an area as people

switch GPs, or new arrivals into the area including people from overseas.

Normally one would wish to base registrant analysis on data from at least two

snapshots in time in order to analyse both leavers and joiners by practice geographical

neighbourhood; however, important insights are possible though an examination of

registrant activity at a single snapshot in time, the hypotheses being that high

registrant activity by ethnic group is likely to be correlated with population influxes.

In section 5 we analyse registrant activity and compare our findings with population

breakdowns by ethnic group.

3. Counting Luton’s population using administrative data

In order to understand the relative significance of different communities within Luton,

we need to be able to count them as well as measure their shifts through time. In this

section, we describe how we use administrative data sources to count the population

of Luton. The techniques used are collectively known as ‘neighbourhood knowledge

management’ or nkm and involve data matching techniques, in which administrative

data sources are linked to the Local Land and Property Gazetteer (LLPG). The

resultant geo-referenced data are checked and cleaned to eliminate duplicates and

many other tests are applied to ensure results are robust. The population figure

obtained from this process is called the ‘confirmed minimum’ population which

means that it conforms to the nkm counting rules.

In our approach we adopt several tests before a person is deemed to be confirmed:

o a person is ‘confirmed’ if they are on the GP register3 and on another database

o if they are on the GP register, but not on any other database, they should be

related to someone else at that address by name e.g. a young child

o if they are not on more than one database the person should be the latest

person at that address according to the GP register

o a person may also be included if an address would otherwise be vacant; this is

ascertained after checking for people on other datasets with that address and

removing any records with the same names/dates of birth so as to avoid the

possibility of double counting

o all persons included in the database should have a UPRN and therefore an

address

3 Everyone living in the UK has a right to register with a GP. This right is based on residency and not

nationality or payment of taxes. However, patients must only be registered with one practice at any one

time and generally need to reside in the UK for more than 3 months. If a person moves away and

changes GP the new practice contacts the previous GP for their medical records to be forwarded. Since

well over 95% of the population is typically registered with a GP this is the most reliable source of

information about people living in an area.


12

The word ‘minimum’ is used to signify that there will be people living in Luton that

do not appear on any datasets and people that do not have valid or therefore linkable

addresses. These could include short term economic migrants who work or just visit

for short periods only. Anecdotally these are likely to come from countries in Europe,

especially eastern Europe but also southern Europe and Turkey.

The main finding is that Luton had a confirmed minimum population of 202,748

persons as of the 31st March 2010 living in over 77,500 households. This figure is in

accordance with the nkm methodology which only includes people that have an

address, are confirmed on more than one database, are the latest person at an address,

or are related to someone at that address or can be allocated to an address if the

address would otherwise be unoccupied.

Table 3 provides a breakdown of the population into standard 5-year age groups. In

the nkm methodology there are some gaps where age is unrecorded in the

administrative data and these appear in the table as ‘age n/a’; of which there are 9,109

in our count. In the second column, we include an adjusted version in which the age

unknowns are distributed pro-rata across the age groups4. Figure 2 shows these data in

the form of a population pyramid and shows strong distributional similarities in age

structure between nkm and ONS figures.

age groups

unadjusted nkm

nkm adjusted

ONS MYE

0 3695 3695 3,500

1-4 14404 14404 12,900

5-9 14448 14448 12,700

10-14 13428 13428 11,900

15-19 13057 13107 13,100

20-24 13898 18101 17,500

25-29 15343 17510 17,200

30-34 14403 14403 13,600

35-39 13648 13648 13,300

40-44 13714 14164 14,100

45-49 12929 12929 12,400

50-54 10891 10891 10,700

55-59 8933 9128 9,100

60-64 8236 8894 8,800

65-69 6531 6728 6,700

70-74 6150 6325 6,300

75-79 4685 4936 4,900

80-84 2997 3351 3,300

85-89 1620 1830 1,800

90+ 629 829 800

age n/a 9109 - -

Total 202748 202748 194600

Table 3: Comparison of the population of Luton by age based on nkm (basic), and

nkm (adjusted), and the ONS 2009 mid-year estimates.

4 Prorating is based on differences with the ONS age distribution. As is seen young adults aged

between 20 and 34 tend to be smaller in size than those in the same ONS age bands. Note that the ONS

figures themselves are estimates.


13

20,000 15,000 10,000 5,000 0 5,000 10,000 15,000 20,000

0

1-4

5-9

10-14

15-19

20-24

25-29

30-34

35-39

40-44

45-49

50-54

55-59

60-64

65-69

70-74

75-79

80-84

85-89

90+

age

nkm population ONS MYE

ONS MYE

nkm adjusted

Figure 2: Population pyramid based on information in Table 3 above showing the

number of people living in Luton by age (based on nkm adjusted column).

4. Households by type, benefit and tenure status and occupancy

Using the nkm population database each person is classified according to the

demographic characteristics of the households in which they live. There are 8

categories defined altogether. These are distilled from 81 different sub-types, the

definitions which are shown in Table 4 below. These categories are mutually

exclusive meaning that a household can only fall into one category at a time.

They range from single person dwellings (type G), family households with dependent

children (type A), single parent households (type B), cohabiting adult households with

no children (type F), and then older and three generational households (types C, D,

and E). There is also a small category (type H) called ‘other’ households that do not

fall into any of A to G.

In more detail, households with at least one adult aged 65 or over would be classified

as C, an older cohabiting household, if there was another adult at the address; or they

would be classified as D if that person lived alone. In some cases it would be

categorised as E, a three generational household, if there are young people at the

address aged 19 or under and also at least one adult aged 65+.

Type H households are a residual category for households that do not fit into another

group. They could comprise for example cases where there was an older person(s) 65

or over living with a young person(s) age 19 or under. It could also comprise

examples of households with teenagers who are also young parents.


14

category Description

A family households with dependent children

B single adult households with dependent children

C older cohabiting5 households

D older person living alone

E three generational households

F cohabiting adult households no children

G single adult households

H other households

Table 4: Household classification by type

Table 5 shows the household structure of Luton based on this classification scheme.

There are 77,477 identifiable households comprising 202,748 people of 2.62 per

household on average. The most numerous households are type A family households

with dependent children totalling nearly 19k; they have the second highest average

size at 4.63 members per household.

These are followed by type F households comprising cohabiting adults with no

children totalling 14k. Types B, C and G households are similar in number at around

20k of each and there are about 6.6k households with an older person living alone.

The most densely occupied households are type E, 3-generational households of

which there are about 1.7k cases. This is a relatively larger number than we tend to

find in other studies and is probably reflective of the ethnic composition of Luton.

Average household size in this category is 66.

The table also gives breakdowns by tenancy and benefits status. For example nearly

10k households are designated as social housing or 12.8% of the total. Of all

households about 25% receive means tested benefits; the households types with the

largest percentages receiving benefits are types B (single parent households), type D

older persons living alone, and type E 3-generational households. These types of

households therefore represent the most income deprived households in Luton.

household type frequency population

persons/ household

no. of households on benefits

% households

benefits

no. households

social housing

% households

social housing

A 18921 87537 4.63 4365 23.1 2197 11.6

B 7545 21385 2.83 3112 41.2 1522 20.2

C 8500 19780 2.33 2063 24.3 785 9.2

D 6622 6622 1.00 2789 42.1 1585 23.9

E 1669 10011 6.00 711 42.6 164 9.8

F 14083 35830 2.54 2186 15.5 1252 8.9

G 19114 19114 1.00 4366 22.8 2339 12.2

H 1023 2469 2.41 326 31.9 107 10.5

total 77477 202748 2.62 19918 25.7 9951 12.8

Table 5: Breakdown of Luton population by household type, tenancy, benefit status

and average household size.

5 Cohabiting simply means two or more adults: it does not imply anything about the relationships or its

legal status 6 Similar but slightly more extreme figures were obtain for example in the London Borough of Tower

Hamlets


15

5. Ethnicity by broad groupings

Definitions of ethnicity are operationally difficult to apply and vary according data

source and purpose. Ethnic status is not the same as nationality or skin colour.

Available data tend to mix all three definitional concepts; in addition, it is extremely

unhelpful that the most comprehensive source of ethnicity data is based on the 2001

Census.

Although partial in their coverage and incomplete in the picture they generate,

administrative data point to different influxes of people of varying nationality and by

extension ethnic status. The aim is therefore to bring together the various sources of

information on ethnicity in to something more comprehensive, up to date and

therefore useful.

A methodology to quantify the ethnic composition of the local population is thus

essential for assessing recent migration and for identifying populations that are likely

to have special needs or requirements (e.g. in terms of employment, local health and

council services). In this section we describe such a methodology to identify and

quantify different ethnic groups in the current population.

There is no routine or complete record of a person’s ethnic origin on any

administrative dataset. One of the few consistent albeit partial sources of information

is the School Census (formerly know as PLASC), the register of pupils attending state

schools which contains both names and ethnicity. It identifies up to 100 different sub-

groups but many of these are small or non-existent in Luton.

It also identifies the first language of each pupil but we have not used these data

because language and ethnicity are not necessarily aligned. The basis for ethnicity

recording is self assessment and some groups overlap, so for example, a person of

African heritage may choose to identify themselves as ‘Other Black African’ or as

Nigerian.

In addition, the list of country codes for ethnicity is not exhaustive. For example,

there is a separate category for White Eastern European but not necessarily for

individual Eastern European countries. These classifications are nevertheless valuable

and can usually be mapped accurately onto broader classifications (e.g. as sometimes

used by the NHS), for example, White, Black, Asian, other and mixed.

Within the classification system used, some countries are easier to identify than

others; for example, it is possible to identify several culturally different Pakistani sub-

groups quite accurately. However, not every parent specifies the ethnicity of a child

and in a small number of cases some children are not assigned to any group e.g. where

a child’s parents have refused to provide information, so some uncertainties remain.

It is known that personal and family names are frequently associated with particular

ethnic groups but also that some are associated with more than one ethnic group.

There are also many surnames in the adult population, examples of which are not

represented in the school population (e.g. in households that do not have children or

are not attending a state school).


16

In our approach, we supplement reported ethnicities in the School Pupil Census with a

large database of unique surnames based on an accumulation of studies. Therefore

each person in the nkm database we can assign a probability of belonging to one of a

small core of high level ethnic groups and a specific selection of groups that are

relevant to Luton.

The justification is as follows. In a majority of cases only one ethnic group is

indicated by any given surname and so it is easy to assign to a group but in other cases

the same name appears in two or more ethnic groups. In these cases a probable ethnic

origin is assigned to the name, based on the frequency of occurrences of the name

within the data base. Extreme cases of representation across multiple ethnic groups

include names like Ahmed, Khan and Brown which appear in all of the basic ethnic

groups (including refused/unknown).

The range and diversity of surnames is very large and in most local authorities there

will be names that appear on local databases which have no comparator on the wider

database nor have any ethnicity assigned. In the methodology, it is thus necessary to

allocate these to a group which comprises mixed, other and not known.

Testing indicates that the method adopted using available data is able to assign an

ethnicity in between 80% to 90% of all cases with an accuracy of over 90%

depending on how many ethnic groups are defined at the outset. The method works

according to the following procedure:

1. Children on the Luton School Census are assigned to their stated ethnic group

based on their self reported classification

2. Adults living at the address of children on the School Census are assigned the

same ethnicity as the child

3. Adults at addresses with no children are assigned the most probable ethnic

group based on their surname using the wider database

For the higher level analysis reported in this section, we used three groupings that

made most sense for Luton: White and other, Black and Asian. The results are shown

in Table 6 in which it is seen that White and other account for 132,770 of the 202,748

previously reported as the confirmed minimum population of Luton (i.e. 65%). Of the

remainder Black people account for 10% of the population and Asian for 25%.

Our definition of Asian for these purposes is restricted to Pakistan, Bangladesh, India

and Sri Lanka and other ‘sub-continent’. Other groups from the Asian continent e.g.

from the Far East are relatively few in number and are included under ‘White and

other’.

The table breaks down the population by age groups and shows for example that

whereas the white population is relatively ‘old’, the Asian population is somewhat

younger, with 40% in the 0-19 age group as compared with 34% in the Black

population and 24% in the White and other population.


17

Our measure of income deprivation used in this report is whether a person lives in a

household receiving means tested benefits. This is a useful proxy for a range of

applications; it denotes for example to what extent a person is likely to use or access

other public services or benefits (e.g. such as free school meals, social care, advice

services).

The results show that around 36% of all those classified as Asian rely on benefits as

compared with 27% for the community as a whole. This is a clear sign of higher

levels of income deprivation in this particular community.

category 0-19 20-64 65+ age NA total % of total

% living in households on benefits

White and other 32243 75208 18550 6769 132770 65 23.2

Black 6737 10394 1698 954 19783 10 27.2

Asian 20053 26393 2363 1386 50196 25 36.3

total 59032 111995 22612 9109 202748 100 27

Table 6: Population breakdown by age and broad ethnic grouping including

percentages living in households on means tested benefits.

In comparison with the 2001 Census, we estimate that the Asian population has grown

from 33.6k to 50.2k today, the Black population from 11.7k to 19.8k; meanwhile, the

White and other population has fallen from 139k to 132k.

However, we also find that within the White and other mix there is a larger European

component than previously seen, although it may not be as large as has been

suspected on the basis of Administrative sources such as NI registrations (see below).

Overall therefore, the data suggest that Luton’s population has grown from 184k in

2001 to 202k today or by around 10%.

5.1 Analysis by broad sub-group

(a) Asian

We broke down each of these high level groups into smaller, more meaningful groups

as allowed by the data. Tables 7 show our population breakdown for the Asian sub-

groups. In the Asian community Pakistanis are the largest group with nearly 25,000

members; this is followed closely by the Bangladeshi community with over 13,000

members. The Indian and rest of sub-continent categories are smallest among the

Asian groups, although still more sizeable than many other non-Asian groups.

Table 7 shows the far greater proportionate dependency on means tested benefits in

the Bangladeshi and Pakistani communities than in the other two Asian categories.

The higher proportion in the Bangladeshi group is substantiated for example by

evidence from another area which indicate strong cultural factors in terms of

marriage, child rearing and the fact that households tend to be larger and have more

young children (e.g. see later sections).


18

Asian 0-19 20-64 65+ age NA total % of total


Bangladeshi 5699 7080 648 317 13744 27.4 49.8

Indian 2308 5754 782 368 9212 18.4 18.0

Pakistan 10904 11904 779 592 24179 48.2 36.7

other Asian 1142 1655 155 109 3061 6.1 27.2

total 20053 26393 2363 1386 50196 100 36.3

Table 7: Population breakdown by age and sub-group in the Asian community,

including percentages living in households on means tested benefits. (Note to table:

other Asian = sub-continent)

Figure 3 is a map of the Asian population based on the number in each Super Output

Area (Lower level). Overlaid on the map is a 0.5 x 0.5 km grid for ease of reference

which works like a spreadsheet with letters in the columns and numbers in the rows.

The map shows an overwhelming concentration of Asian people in one part of the

town covered by rows 6 and 11 on the map and columns F and L.

Figure 3: Asian population density map of Luton based on Super Output Areas

(SOAs) (units: persons per SOA)

(b) Black

The corresponding table for Black sub-groups (Table 8) shows that a majority are

split between Black African and Black Caribbean groups with an estimated 47%

consisting of Black Caribbean. Care is needed with quantifying the Black Caribbean

category as many share surnames with White British groups and so the total may not

be as accurate as for other Black sub-groups. There is also a large but more difficult to

quantify group of mixed Black and White heritage which we have not counted

separately.


19

The population structure of the Black community tends to be intermediate between

the White and other communities and the Asian community in terms of age.

Unfortunately the Black African community, whilst easier to identify than people of

Caribbean origin, is not as specific as we would like it to be in terms of country of

origin, although Somalis are a relatively easily identifiable group with over 1,300

members followed by the Nigerian community.

It is noteworthy that the percentage of Black Africans that live in households on

benefits is relatively small compared with the Asian community and comparable with

the White and other group. An exception is the Somali group in which an estimated

42.6% live in households on means tested benefits.

Black 0-19 20-64 65+ age NA total

% of

total


Congolese 14 30 1 2 46 0.2 31.1

Ghanaian 143 272 24 26 464 2.3 20.1

Nigerian 322 538 41 54 955 4.8 17.4

Sierra Leone 21 41 6 4 73 0.4 22.8

Somali 579 691 48 42 1360 6.9 42.6

Black African (general) 2524 2750 174 255 5703 28.8 28.8

Black Caribbean 2484 4964 1203 459 9110 46.0 25.7

Any other Black 650 1109 202 112 2072 10.5 25.4

total 6737 10394 1698 954 19783 100 27.2

Table 8: Population breakdown by age and sub-group in the Black community,

including percentages living in households on means tested benefits.

Figure 4: Black population density map of Luton based on Super Output Areas

(SOAs) (units: persons per SOA)


20

Figure 4 is a population map of the Black community based on the number of Black

people living in each Super Output Area. The map shows concentrations of the Black

population in the northwest of the town in rows 2 to 8 and columns A to H and in

south central Luton e.g. see cells M and N 10 and cells below.

(c) Other European

Ignoring for these purposes those of White British origin, the hardest group to

breakdown into sub-groups by country of origin are those of European descent. Table

9 shows sub-groups in four categories; one of these, Eastern European, is not as

precise as we would have liked and some will have been included in the next category

which is designated ‘Other European’.

The numbers in the European category are much smaller than the previous Asian and

Black categories and probably reflect the fact that these groups are based on less

established or permanent influxes. The stock figures that they indicate are less than

the cumulative flows based on NI registration data which may indicate that people of

these backgrounds do not stay for as long in Luton.

However, another explanatory factor is that some registering for work may not go on

to register with a GP and so that the true population is potentially higher than

indicated; it is simply that they do not appear on any of the data sets available. It is

also seen that the percentage of the Eastern European population living in households

on means tested benefits is smallest among the three major groups, suggesting that a

majority are likely to be economic migrants. More refined methods including surveys

may be needed to split and quantify these sub-groups better than has been possible

here.

European origin 0-19 20-64 65+ age NA total

% of total


Irish 601 1366 351 105 2422 39.4 24.8

Former Yugoslavia and Albania 69 110 4 4 187 3.0 40.8

Eastern European 346 840 42 124 1353 22.0 23.4

Other European (not specified) 601 1258 198 134 2191 35.6 24.7

total 1618 3575 595 367 6154 100.0 24.9

Table 9: Population breakdown by age and sub-group in the Other European

community, including percentages living in households on means tested benefits.


21

Figure 5: Map showing some common European nationalities by place of residence

Beyond the groupings analysed above, it is possible to break down some of the

figures into much smaller groups, usually by country of origin. We found that

individually they were very small in number and some in cases were it was necessary

to aggregate them into broader groups. Some of the small but significant sub-groups,

because of their distinctive cultures, included Irish travellers, Gypsy Roma, Greek and

Turkish communities7.

Figure 5 is a population dot map of selected European groups by country of origin and

household. The map clearly shows a large Turkish community in columns I to J and

rows 11 to 13. ‘Other’ East Europeans tend to be more concentrated in south central

Luton.

On the evidence of the new National Insurance registrations, a large number in the

Eastern European categories are from Poland; however, the ethnicity data base does

not distinguish Polish surnames as separate group. Using a different data set of over

20k common Polish surnames, we matched these against the names on the confirmed

minimum population data base. Our results are set out in detail in Annex A.

In undertaking this analysis, it is important to realise that Polish surnames have been a

feature of the UK for at least three generations and it is a matter of sorting the more

recent arrivals from those with established roots or who were born here. On this basis,

7 We estimated for example that there were around 220 Irish Travellers and 200 Gypsy Roma and 750

in the mainly Turkish and Greek communities.


22

we estimated around 2,700 likely recent arrivals, although this is clearly only an

estimate.

5.2 Population by ethnicity and date of registration with GP

Section 2 described some of the difficulties involved in estimating population influxes

into Luton. Yet the differences in ethnic structure and population size since 2001

identified in the previous section are indicative that significant changes are occurring,

especially in the Asian community but also among certain smaller groups.

Populations can only grow by people living longer, or by more being born, or through

net immigration into an area. One of the main issues is to try to unpick why the Asian

population has grown to its current size since 2001.

The GP register is still the best and most comprehensive source of information about

population movement, but ideally one would require two full snap shots to be able to

separate these three components of change8. The GP register is not designed to

measure immigration. For example, there will be a delay between arrival into an area

and the registration process.

However, it is possible to analyse general movement activity based on the date of

registration with a person’s current GP using just a single snapshot. It can be safely

assumed for example that a person registered at birth would be likely to have been

born in Luton; those registered at older ages could be the result of internal GP

switches, inward flows from outside Luton or flows into Luton from abroad.

Figure 6 shows the pattern based on registrants aged below 1in which we track five

groups from different countries or areas: Pakistan, Bangladesh, India, other Asian

sub-continent and Europe over a 15 year period. This shows significantly higher

levels of registration activity in the last four years with the most activity occurring in

the Pakistani, Bangladeshi and Indian groups, in that order.

It is noteworthy that the patterns peak and trough together perhaps suggesting a

common underlying factor or factors. These could include housing, the state of the

local labour market or other factors such as changes to primary care practices.

Figure 7 shows a similar pattern of registrants at birth for these groups over the same

period. It shows a steady increase in registrants of children still living in Luton in

2010 that were in their first year of life when registered. Analysis shows that the ratio

of these registrants in the selected ethnic groups to all registrants has remained steady

at about 40% regardless of year of registration.

8 Two snaps shots would allow one to add new arrivals and births, to subtract people who leave Luton

or die and in addition quantify the amount of movement within Luton itself.


23

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

<1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

years since registration

nu

mb

er

of re

gis

tra

nts

pakistani

bangladeshi

indian

other sub-continent

all europe

Figure 6: Pattern of registration among confirmed population aged 1or over based on

years since registration with current GP by broad ethnic grouping

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

<1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15


nu

mb

er

of re

gis

tra

nts

pakistani

bangladeshi

indian

other sub-continent

other Europe

Figure 7: Pattern of registration among confirmed population who were under 1 year

at the time of registration by broad ethnic grouping

Further analysis suggests significant GP registration churn in the wider population

and not just in the selected ethnic groups. In addition, we find only minimal

differences in registration activity between males and females regardless of ethnic

grouping.

These findings may suggest that the population as a whole is in flux. For example, the

data show that within the confirmed minimum population of 202,748, 73% had


24

registered with their current GP after 2000; but among the Pakistani and Bangladeshi

communities this percentage rises to 85% and 83% respectively.

Figure 8 shows the ratio of registrants in the selected groups to the number of

registrants in the whole population aged greater than one at the time of registration.

This shows an approximate doubling in the proportion of registrants that are from the

selected ethnic groups over a 15 year period, thus indicating far greater churn

In conclusion, although it is impossible to be precise, the implication of GP

registration data is that as many as 73% of the current population were not living in

Luton at the time of the 2001 Census. Clearly, this is an upper bound because some

registrations will have been internal to Luton and thus were not first time registrants

in the area.

However, put a different way, of the 202,748 currently confirmed population, we

estimate that around 32,000 were not alive in 2001, 54,000 were registered with their

current GP, but that 116,000 were registered with their present GP after 2001.

Even if 50% of these were internal GP switchers that would still leave 58,000 arrivals

from outside Luton over the period (including both national and international

migrants) - although clearly this suggestion must necessarily be speculative.

To summarise, it is impossible to escape the conclusion that the population has

changed radically over the last 10 years in terms of people and ethnic mix. All of

these changes have contributed to the growth in population observed today.

0

5

10

15

20

25

30

35

40

<1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15


Sp

ecifie

d e

thn

ic g

rou

ps a

s p

erc

en

tag

e o

f a

ll

reg

istr

an

ts

Figure 8: Percentage of registrations in the specified ethnic groupings as a

percentage of all registrants (base: the confirmed minimum population)


25

6. Population by household and ethnic grouping

6.1 Household size by age and sex

In this section, we consider the level of occupation by UPRN9 and tenure based on the

confirmed minimum population as of March 31st 2010. We are interested in the

number and frequency of persons by household in each UPRN in different ethnic

groups and tenancy type. The resultant distributions offer an approach to quantifying

issues such as relative levels of overcrowding in different ethnic communities.

The differences in occupancy that arise could represent variations in family size and

formation between ethnic groups but also other factors. For example, the white

population tends to be older and it is well known that age and occupancy are strongly

linked. As populations age average household size tends to decline.

For Luton this effect is shown clearly in Figure 9 which is a population pyramid with

males on the left and females on the right and age on the vertical axis. Each bar is

scaled to the size of the population in each age group and then colour coded according

to size of household. As age increases, the number of households with two or more

people shrinks and far greater proportions tend to live alone or as couples.

This effect varies slightly between genders with more female single households at the

oldest ages. This is because females tend to be older than their male partners and have

longer life expectancy. Cohabitation is strongest at younger ages with family

formation and child rearing. Given that the Asian community tends to be younger we

would expect larger average household sizes in this age range.

10,000 8,000 6,000 4,000 2,000 0 2,000 4,000 6,000 8,000 10,000

Under 1

1 - 4

5 - 9

10-14

15-19

20-24

25 - 29

30 - 34

35-39

40-44

45-49

50-54

55-59

60-64

65-69

70-74

75-79

80-84

85-89

90+

age

males population females

living alone

2 person household

3 person household

4 person household

5 person household

6+ person household

Figure 9: Population by household size, age and gender

9 In the Luton property gazetteer, each address is assigned a Unique Property Reference number of

UPRN which we use as our fundamental counting unit and definition of a ‘household’.


26

6.2 Occupancy by tenure and ethnicity

Figure 10 (a) to (c) is a frequency distribution of households based on the number of

people per UPRN by broad ethnic grouping. Figure 10 (a) shows clear differences

between the frequency distributions for Asian ethnicities compared to the Black and

White (and other) ethnicities shown in (b) and (c). Whereas 9.5% of Asians live in

social housing this figure rises to 13.6% in the Black population and 14.6% in the

White and other population.

In Asian households, most people live in households with between 3 to 5 people and

30% live in households with 6 or more people. This compares with only 11% in the

population as a whole living in households with 6 or more people. The Black

population is intermediate between the Asian population and the White and other

grouping.

10 (b) illustrates that the pattern of occupancy in the Black population differs

substantially from Asian occupancy with proportionally more people living in one

person households. Figure 10 (c) for the White and other group shows proportionately

fewer households with more than two people, establishing three distinctive patterns

among the three broad groupings.

As indications of potential overcrowding, we found around 500 UPRNs with more

than 10 people representing 0.6% of all UPRNs. Some of these will be registered

nursing or residential care homes, but others will be normal residential housing stock.

We found that 4.1% of the Asian population lived in households with 10 or more

people, 1.2% of the Black community and 0.3% of the White and other community.

However, these figures are likely to be an underestimate since it is likely that there

will be some people living in such addresses that are not registered with a GP and do

not appear on any of the other administrative data bases. These will arguably consist

of short stay workers (workers that have been here for less than 3- months) or visitors.

However, it has not been possible to analyse these.

0

500

1000

1500

2000

2500

1 2 3 4 5 6 7 8 9 10 11 12 >12

persons per UPRN

nu

mb

er

of o

ccu

pie

d U

PR

Ns

private tenure

social housing

(a)


27

0

200

400

600

800

1000

1200

1400

1600

1 2 3 4 5 6 7 8 9 10 11 12 >12

persons per UPRN

num

ber

of U

PR

Ns

social housing

private tenure

(b)

0

5000

10000

15000

20000

25000

1 2 3 4 5 6 7 8 9 10 11 12 >12

number of persons per UPRN

nu

mb

er

of U

PR

Ns

social housing

private tenure

(c)

Figure 10 (a)-(c): Frequency of UPRN by household size, tenancy and ethnicity:

Asian households; (b) Black households; (c) White and other households

7. Income deprivation by ethnicity and age

Income deprivation is an important indicator of dependency on, and use of a wide

range of council services, especially among young people (e.g. Childrens Centres,

schools, free school means, special educational needs, social services), and for the

population in general (housing, access to benefits, Council Tax, planning applications,

environmental services and so on).

In this section, we identify and profile the population that is at risk of income

deprivation based on whether they live in households receiving means tested benefits,

which is a common proxy for low income families. Figure 11 splits the population in

three broad ethnic groupings, Asian, Black and White and other. On the horizontal

axis is age and on the vertical axis the percentage of the population that is living in a

household on means tested benefits.


28

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80 90

age

% o

f a

ge

gro

up

Other

Black

Asian

Figure 11: The percentage of the population living in households on means tested

benefits by age and broad ethnic grouping

The chart shows clear patterns: in the White and Other and Black populations the

chances of living a household on benefits is around 30% at birth, gradually falling to a

low at around age 55 when it is between 17% and 20%. It then rises again in older

age to around 40% of all those living. The range of variation at older ages is greater as

there are fewer people in the oldest age groups but also incomes vary more.

In the Asian, population income deprivation is higher throughout the age range even

among older working ages when it might be expected to be lower. At birth it is around

30% but rises to 40% at the age of five and stays at that level until aged 20. After that

it falls back to 30% by around age 50, before rising again to 50% or more.

We conclude from the evidence that Asian households are therefore not only likely to

be newer to Luton, but also relatively income deprived and more likely to live in

overcrowded accommodation and private tenure. As indicated by the map in Figure 3

they are also highly geographically concentrated.

7.1 Deprivation Risk Ladders

In this sub-section, we analyse and segment income deprivation by broad age group.

The aim is to disaggregate income poverty by key risk factors to measure the depth

and range of income deprivation in different sub-groups. We concentrate on three age

groups: 0-19, 20-64, and 65+ and use risk factors that have been shown in over 20

studies10

to be highly significant predictors of income deprivation.

The methodology uses a technique called ‘risk ladders’ which have been developed to

identify and quantify particular groups and their associated levels of exposure to risk.

In this case the risk outcome is income deprivation. Since there are no data at a local

level on income by household we use take up of means tested benefits (Council Tax

benefit) as a proxy. Households are eligible for means tested benefits if they have an

10

See: http://www.nkm.org.uk/case_studies.html for examples of links to studies using risk ladders

http://www.nkm.org.uk/case_studies.html


29

income that would put them below the Government poverty line based on their

circumstances.

(i) Children 0-19

Table 10, an example of a risk ladder, covers the whole of the age group 0-19 years.

The risk factors used to estimate the risk of income deprivation are influenced by

what we have found to be the case elsewhere, namely housing tenure (whether private

or social housing), whether the child lives in a single adult household (i.e. there is

only one adult aged 20 or over at an address), and if there are 3 or more children

living at the address.

Each row shows the numbers of children and young people in each of 8 mutually

exclusive categories ranked from most to least income deprived. The totals at the foot

of the columns show the number of people to whom a particular risk factor applies.

For example 28,082 out of 59,032 children and young people children live in social

housing (see foot of col. 5).

The table shows 23.5% of children and young people in this age group live at

addresses receiving means tested benefits. The categories least at risk of income

deprivation are located in row 8 of the table, to whom none of the risk factors apply.

There are 20,283 children and young people based on these criteria of which only

17.1% live at addresses that receive benefits as compared with 80.0% in the highest

risk group (row 1).

(a) 0-19

category frequency social

housing

single adult

household

3+ children

at address

% in households on benefits

lower CI%

upper CI%

1 1567 Y Y Y 80.0 78.0 82.0

2 1628 Y Y 70.7 68.4 72.9

3 3426 Y Y 68.3 66.7 69.9

4 2240 Y 56.3 54.2 58.4

5 3948 Y Y 47.0 45.4 48.6

6 6799 Y 31.3 30.2 32.4

7 19141 Y 30.0 29.4 30.7

8 20283 17.1 16.5 17.6

total 59032 8861 13942 28082 32.5 32.1 32.9

Table 10: Risk ladder showing the number and percentage of children and young

people living in households receiving means tested benefits by risk group (CI = 95%

confidence interval)

The risk factors can be translated into odds of an event happening. In this case, in

Luton, a child or young person aged 0-19 is:

o 5.3 times more likely to be on benefits if living in social housing

o 2.1 times more likely if it is a single adult household

o 2.0 times more likely if there are 3+ children at the same address


30

Note that the risks or odds are multiplicative so that a young person in social housing

and in a single adult household and living with 3 or more children is 5.3 x 2.1 x 2.0

times more likely to be on benefits i.e. 22.2 times more likely. This group is located in

row 1 of the table and comprises 1,567 individual children of whom 80% live at an

address receiving benefits within a 95% confidence interval ranging from 78% to

82%. The chart in Figure 12 shows the predicted risk based on the given risk factors

versus the observed risk and shows clearly their predictive ability, explaining over

99% of the observed variation in benefit entitlement.

y = 1.019x - 0.9831

R2 = 0.9957

0

10

20

30

40

50

60

70

80

90

0 10 20 30 40 50 60 70 80 90

observed %

pre

dic

ted

%

Figure 12: Predicted risk of income poverty based on the given risk factors versus

observed risk for population aged 0-19

(b) 20-64


housing

single adult

household

children or

young person

at address


lower CI%

upper CI%

1 1522 Y Y Y 72.5 70.2 74.7

2 2339 Y Y 71.1 69.2 72.9

3 5729 Y Y 60.5 59.2 61.8

4 3859 Y 60.2 58.6 61.7

5 6023 Y Y 33.4 32.2 34.6

6 45728 Y 21.1 20.8 21.5

7 16775 Y 16.1 15.6 16.7

8 39129 14.0 13.7 14.4

total 121104 13449 26659 59002 23.5 23.2 23.7

Table 11: Risk ladder showing the number and percentage of adults aged 20-64 living

in households receiving means tested benefits by risk group (CI = 95% confidence

interval)


31

Table 11 shows the equivalent table for the 20-64 age group, i.e. persons of working

age. There are 121,104 people in this group of whom 23.5% live in a household on

means tested benefits, a much lower percentage than is the case for children and

young people. The risk factors used to estimate the risk of income deprivation are

housing tenure (whether private or social housing), whether the person lives in a

single adult household (i.e. there is only one adult aged 20 or over at an address), and

if there is at least one child or young person under 20 living at the address.

The highest risk category is located in row 1. This comprises of 1522 persons, of

whom 72.5% receive benefits. They are single adult households with at least one child

at the address living in social housing. The least income deprived group in this age

range has 39,129 persons of whom only 14.0% are on benefits (row 8).

There are differences in terms of the relative impact of housing tenure which has a

greater association with this age group than with the 0-19 age group. This is shown in

the odds of income deprivation increasing:

o 7.5 times more likely to be on benefits if living in social housing

o 1.5 time more likely to be on benefits if it is a single adult household

o 1.7 times more likely to be on benefits if there is at least one child at the

address

The risks or odds are multiplicative so that a person in social housing and in a single

adult household and living with at least one child is 7.5 x 1.5 x 1.7 times more likely

to be on benefits i.e. 18.2 times more likely. The graph in Figure 13 shows the

predicted risk based on the given risk factors versus the observed risk and shows that

it explains over 96% of the observed variation.

y = 0.9675x + 0.4603

R2 = 0.9643

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80

observed %

pre

dic

ted

%


observed risk for population aged 20-64


32

(c) 65+


housing living alone 75+


lower CI%

upper CI%

1 668 Y Y 83.7 80.7 86.4

2 917 Y Y Y 80.3 77.5 82.8

3 519 Y Y 76.7 72.8 80.3

4 857 Y 74.6 71.5 77.4

5 2991 Y Y 32.3 30.6 34.0

6 2046 Y 25.8 23.9 27.8

7 5504 Y 23.1 22.0 24.3

8 9110 18.7 17.9 19.6

total 22612 2961 6622 9931 30.1 29.5 30.7

Table 12: Risk ladder showing the number and percentage of adults aged 65+ living

in households receiving means tested benefits by risk group

Table 12 is the equivalent risk ladder for older adults aged 65 or over. There are

22,612 people aged 65+ living in Luton based on our analysis. Of the total 2,961 live

in social housing, 6,622 live alone and 9,831 are aged 75 or over; 30.1% live in

households on benefits. Based on the given risk factors we find that the groups at

highest risk live alone and are also in social housing (rows 1 – 4). They account for

1,531 people (13% of the total). More than 70%-84% of this population sub-group

receives means tested benefits as compared with 32.3% and under in the remaining

risk groups.

Living in social housing is a key distinguishing indicator for income deprivation in

this age range. We find that the odds of being on benefits increases:

o 11.4 times if they live in social housing

o 1.5 times if an older person is living alone

o 1.3 times if they are aged 75 or over

Again, the risks or odds are multiplicative so that a person in social housing, living

alone and being aged 75+ is 11.4 x 1.5 x 1.3 times more likely to be on benefits i.e.

22.3 times more likely. The graph in Figure 14 shows the predicted risk based on the

given risk factors versus the observed risk and shows that it explains over 99% of the

observed variation.

The cluster of points on this chart clearly show a split in levels of income poverty

between those in rows 1 to 4 of Table 12 who receive benefits and are therefore the

most needy among this age group and the rest. In the first group, 3.0k people or 13%

of the 65+ population over 74% are on benefits, but in the second group comprising

19.7k older people, it is less than 32%.


33

y = 0.9954x + 0.1321

R2 = 0.9951

0

10

20

30

40

50

60

70

80

90

0 10 20 30 40 50 60 70 80 90

observed %

pre

dic

ted

%


observed risk for population aged 65 or over

8. Conclusions

This report finds that 202,748 people lived in Luton in around 77,000 households as

of 31st March 2010. Our population figure compares with the ONS’s latest estimate

for Luton of 194,400, which is 8,348 lower than ours. Our figure is higher by around

18,000 compared with the population at the last Census in 2001, which in turn was

about a 12,000 increase on 1991.

We also find that there have been substantial changes in the ethnic composition of the

population over the last 9 years. For example, in comparison with the 2001 Census,

we estimate that the Asian population has grown from 33.6k to 50.2k by 2010, the

Black population from 11.7k to 19.8k; meanwhile, the White and other population has

fallen from 139k to 132k.

The growing diversity within Luton’s population is partly demographic and partly

economic in origin. Population growth is partly through natural increase and partly by

inflows from elsewhere in the UK and by immigration. It is difficult to separate

inflows from immigration and official data sources are fragmentary or controversial.

Our own work has used different sources albeit based on one snapshot in time. Using

the GP registration as a proxy for population turnover we found that substantial

numbers of new registrants since the last Census and that an increasing proportion of

registrants were of Asian but also European origin.

It is calculated for example that over 50% and possibly as much as 73% of the current

population were not alive or did not live in Luton at the time of the last Census. This


34

finding therefore has obvious implications for the conduct of the forthcoming Census

in March 2011. For example, Luton experienced low response rates at the time of the

last Census and there appears the likelihood that this may happen again in 2011.

Maps of these broad ethnic groups show that the Asian population is the most heavily

concentrated geographically, in the south central area of town. The Asian population

is both poorer and more concentrated than other groups that we have analysed. They

are more likely to live in households on benefits and household size is numerically

larger. In addition, a greater proportion of the Asian population live in private tenure

rather than social housing.

Unpicking the many other nationalities and ethnic groups in Luton is complicated due

to definitional issues. However, it is clear that there is an emerging and growing

African community. The hardest groups to analyse are people from various European

nations although we found distinctive but small concentrations of Turkish

communities, as well as some other nationalities and grouping (e.g. ‘other’ east

Europeans).

The most problematic group to count were Polish migrants, in part because people

with Polish names have been long established in the UK so it is a matter of

distinguishing between established families and new arrivals. National Insurance data

suggest large inflows of Polish economic migrants in recent years but it is less clear

how many of these stayed on in Luton or went elsewhere.

A separate analysis of Polish names showed that the population could be as high as

8.7k. However, closer analysis suggests that in the most mobile age group (16-35) the

numbers registering with a GP in the last six years and living in cohabiting adult or

single person households falls to around 2,700 individuals.

Recommendations

The analysis set out in this report is based on a single snapshot of administrative data.

These datasets have been linked together by each individual in the confirmed data

base meeting a set of conditions before being included. Clearly, these and other

findings have implications for the way the next Census is conducted to ensure that

response rates are maximised and sufficient forms are dispatched to each address. It is

highly recommended therefore that they are taken into account as Census day

approaches.

To understand how this population changes between two points in time it is necessary

to take a second snapshot to show who is new, and who has left or died. Two

snapshots would enable one to look at both leavers and joiners and changes in

mortality and fertility by ethnicity. This in turn would show how long people were

staying in Luton on average, turnover in different areas of town, whether income

deprivation was changing, whether the population was getting younger, older and

more or less cosmopolitan and so on.

The next Census is imminent. It is important that Luton’s population is measured

fairly and accurately. It is therefore recommended that a new snapshot if undertaken

synchronously with the Census would provide independent evidence with which to


35

challenge the ONS in the event of disagreement; a baseline for measuring turnover

and change and enable detailed analyses at any level of geographical resolution.

By routinely combining the data sets with other administrative data such as education

services, children and families and social services and linking them to the Local Land

and Property Gazetteer (LLPG), it would be possible to profile needs and measure

progress across a range of council and other public services. Specific topics such as

voter registration among BME communities; unmet need in social care; youth

offending and crime; and child poverty could be routinely analysed. Thus, it will be

important to establish links across all the data owners to ensure this happens and this

is recommended accordingly.

Such an arrangement may be further facilitated by the transfer of responsibilities in

the area of health and well being from NHS Luton. This project was fortunate to be

able to share data with the PCT for statistical purposes and research. Other data sets

owned by the PCT such as hospital episode data and health visitor data would enable

Luton to expand its understanding of both ethnicity and health needs. With the

abolition of PCTs and Public Health moving to the council, it is therefore vitally

important that access to key NHS data and systems is maintained. This is a priority

for Public Health to enable work to continue at the current level and aid data sharing

with partners and the new GP consortia and so this also recommended.

The greater use of administrative data linked to the Local Land and Property

Gazetteer would help to improve efficiency by reducing reliance on external data sets

and those that require licences; improve quality by using the latest data rather than

data that is up to ten years out of date; and improve the information intelligence

provided to the council and local population in a range of different applications. It is

therefore recommended that consideration should be given to how this could be

achieved within current resources within the Council and among Luton’s public sector

partners.

From a strategic standpoint, the Council and its partners need to make greater use of

their administrative data in order to understand the dynamics of the Luton population.

This is increasingly important as population mobility increases. At present there is no

single comprehensive database of residents, although the GP Register is probably the

most comprehensive. Therefore consideration should be given to the establishment of

a comprehensive residents’ database to which all administrative systems can be

linked. This would provide significant efficiencies in data management terms but also

provide the underlying information infrastructure for demographic analysis and

customer profiling. It is therefore recommended that the Council assesses the business

case for such a database.


36

Annex A: Analysis of administrative data using Polish surnames

The following analysis matches names based on the confirmed minimum population

for Luton against the top 20k common Polish family names. It is important to

recognise that just because a person’s family name is of Polish extraction it does not

mean that they are recent arrivals to the UK.

Polish names and families date back at least three generations or more and it is

probable that intermarriage will have increased their occurrence still further. We

looked at the frequency of Polish names by type of household and by date of

registration with their current GP.

Our hypothesis was firstly that more recent arrivals to the UK were more likely to live

in certain types of households than others, for example cohabitating adult or single

adult households (types F and G) rather than family households with dependent

children or older households (types A, C, D and E). Our second hypothesis was that

that they were more likely to have been recent rather than long standing registrants

with a General Practice.

Table A1 shows the frequency of occurrence of Polish names by household type and

number of households (UPRNs). The analysis finds 8,696 occurrences of Polish

names overall in the Luton population and of these 2,702 live in type F and G

households. It shows that there are 975 type F cohabiting households with at least one

person with a Polish surname and 1,042 single adult (type G) households with a

person that has a Polish surname. This compares with the group of those with Polish

surnames who live in type A family households with dependent children in which

there are 3,899 people.

Household type population

population with

Polish surnames

polish surnames as % of

population total all UPRNs

UPRN where person with a Polish

surname resides

UPRNs where Polish names >=50%

% UPRN

where a Polish name

resides

% UPRNs where Polish names >=50%

A 87537 3899 4.5 18921 1384 909 7.3 4.8

B 21385 1088 5.1 7545 617 526 8.2 7.0

C 19780 506 2.6 8500 273 243 3.2 2.9

D 6622 176 2.7 6622 176 176 2.7 2.7

E 10011 232 2.3 1669 86 39 5.2 2.3

F 35830 1660 4.6 14083 975 848 6.9 6.0

G 19114 1042 5.5 19114 1042 1042 5.5 5.5

H 2469 93 3.8 1023 64 56 6.3 5.5

Total 202748 8696 4.3 77477 4617 3839 6.0 5.0

Table A1: Polish surnames by household type and UPRN

If we consider Polish surnames according to the time elapsed since registering with

their current GP, we observe the pattern shown in Figure A1. This is based on all

those aged greater than 1 at the time of registration, those aged 16 to 35 at the time of


37

registration (this age group tends to be more mobile), and those registered at birth.

Table A2 provides the figures on which this chart is based and includes a breakdown

by gender. The results show over 6,217 registrants of people with polish surnames in

the last 6 years; of the total 693 were registered from birth and 2,788 were aged 16 to

35. Around half of these were males.

0

200

400

600

800

1000

1200

1400

1600

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15


num

ber

of

regis

trants

Registrants aged >1 with Polish surname

Registrants age 16 to 35 with Polish surname

Regiistrants <1 with Polish surname

Figure A1: Numbers of people with Polish surnames versus the years elapsed since

registering with their current GP

Years Since

Registration polish

surnames <1 >1 16-35 16-35 female

16-35 male

0 1056 91 965 543 247 296

1 1588 219 1369 737 361 376

2 1536 176 1360 693 335 358

3 1124 115 1009 451 236 215

4 589 59 530 236 120 116

5 324 33 291 128 66 62

total 6217 693 5524 2788 1365 1423

Table A2: Numbers of people with Polish surnames registering with their current GP

versus years since registration


38

Annex B: Map of households with 6 or more people

Figure B1: Map of households with 6 or more people


39

Annex C: Ward level tables by age and ethnicity

Key:

Table no: Ward

C1 All Wards (Luton)

C2 Barnfield Ward

C3 Biscot Ward

C4 Bramingham Ward

C5 Challney Ward

C6 Crawley Ward

C7 Dallow Ward

C8 Farley Ward

C9 High Town Ward

C10 Icknield Ward

C11 Leagrave Ward

C12 Lewsey Ward

C13 Limbury Ward

C14 Northwell Ward

C15 Round Green Ward

C16 Saints Ward

C17 South Ward

C18 Stopsley Ward

C19 Sundon Park Ward

C20 Wigmore Ward


40

C1: Luton

age group Pakistan

i Bangladesh

i

Indian and

other Black

White and

other total

of which Asian sub-

continent

Under 1 731 328 215 375 2047 3695 1273

1-4 2834 1412 865 1564 7729 14404 5111

5-9 3084 1641 867 1763 7094 14448 5592

10-14 2363 1314 754 1634 7363 13428 4431

15-19 1892 1005 749 1401 8011 13057 3646

20-24 2021 1042 935 1153 8745 13898 3999

25-29 2404 1276 1170 1182 9311 15343 4850

30-34 2215 1344 1091 1288 8465 14403 4650

35-39 1723 1131 910 1458 8426 13648 3764

40-44 1224 724 798 1578 9389 13714 2746

45-49 722 455 685 1423 9644 12929 1862

50-54 698 468 740 1002 7982 10891 1907

55-59 577 398 634 693 6631 8933 1609

60-64 320 241 444 616 6615 8236 1005

65-69 235 194 340 491 5270 6531 770

70-74 268 233 272 503 4874 6150 773

75-79 166 136 175 346 3862 4685 477

80-84 74 55 99 210 2559 2997 229

85-89 26 21 40 107 1425 1620 88

90+ 10 7 10 42 560 629 27 Age

Unknown 592 317 477 954 6769 9109 1386

Total 24179 13744 12273 19783 132770 202748 50196


41

C2:

age group Pakistan

i Bangladesh

i

Indian and

other Black

White and

other total

of which Asian sub-

continent

Under 1 18 10 11 6 75 119 39

1-4 79 38 42 43 275 477 159

5-9 95 44 54 56 363 612 193

10-14 68 41 52 51 341 553 160

15-19 61 25 54 44 326 510 140

20-24 38 22 49 25 243 377 109

25-29 58 21 62 26 271 439 142

30-34 65 34 62 35 326 522 161

35-39 65 37 62 47 356 567 164

40-44 41 17 58 45 436 597 116

45-49 28 17 49 55 408 557 94

50-54 31 12 48 34 352 477 91

55-59 14 6 38 36 319 413 58

60-64 9 5 37 21 288 359 50

65-69 8 3 19 14 216 260 30

70-74 7 2 19 15 158 201 28

75-79 4 2 10 13 131 160 17

80-84 2 1 7 8 123 141 10

85-89 0 0 2 5 76 83 2

90+ 0 0 0 2 33 35 0 Age

Unknown 8 6 18 15 166 213 32

Total 699 342 754 596 5282 7672 1795


42

C3

age group Pakistan

i Bangladesh

i

Indian and

other Black

White and

other total

of which Asian sub-

continent

Under 1 142 74 20 36 122 394 236

1-4 555 347 88 133 441 1564 990

5-9 585 421 79 102 270 1457 1085

10-14 438 353 62 80 214 1146 853

15-19 370 293 57 74 234 1028 720

20-24 445 301 95 75 375 1290 841

25-29 571 347 118 99 467 1602 1036

30-34 439 348 97 89 414 1388 884

35-39 319 260 74 83 338 1074 653

40-44 223 192 53 82 259 809 468

45-49 130 126 34 54 290 633 289

50-54 142 127 45 51 262 628 315

55-59 138 113 50 31 205 536 301

60-64 80 62 35 20 165 362 177

65-69 57 52 27 21 129 286 136

70-74 68 77 24 29 134 333 169

75-79 36 37 15 17 109 214 88

80-84 16 15 7 12 91 142 39

85-89 9 4 4 3 51 71 16

90+ 2 3 1 1 36 43 6 Age

Unknown 115 78 62 74 439 768 255

Total 4880 3630 1046 1167 5045 15768 9556


43

C4

age group Pakistan

i Bangladesh

i

Indian and

other Black

White and

other total

of which Asian sub-

continent

Under 1 7 3 7 7 70 93 16

1-4 16 6 33 39 284 379 55

5-9 13 3 38 37 301 392 54

10-14 12 5 34 37 288 376 51

15-19 11 5 32 42 340 430 48

20-24 10 4 36 34 299 383 50

25-29 15 9 51 32 383 490 75

30-34 16 8 46 41 341 452 70

35-39 16 6 38 39 373 473 61

40-44 15 6 42 52 453 567 63

45-49 6 3 40 53 534 636 49

50-54 9 4 59 46 428 545 71

55-59 5 3 37 38 395 479 45

60-64 4 5 25 38 399 470 34

65-69 4 2 15 16 266 302 20

70-74 5 1 10 16 222 254 16

75-79 4 1 11 15 197 227 15

80-84 1 0 3 6 113 123 4

85-89 0 0 5 4 58 68 5

90+ 0 0 2 1 23 27 2 Age

Unknown 8 3 11 16 182 220 22

Total 177 75 576 611 5947 7386 827


44

C5

age group Pakistani Bangladesh

i

Indian and

other Black

White and

other total

of which Asian sub-

continent

Under 1 82 20 19 21 102 243 120

1-4 321 84 113 90 442 1050 518

5-9 361 97 97 121 380 1056 555

10-14 302 83 93 121 373 973 479

15-19 204 65 81 88 386 824 350

20-24 209 62 83 72 382 809 355

25-29 241 73 107 68 466 955 421

30-34 247 85 117 84 470 1003 449

35-39 196 75 123 96 485 975 395

40-44 128 45 83 98 481 834 255

45-49 92 39 73 87 457 748 204

50-54 70 34 79 65 400 648 183

55-59 59 22 70 36 361 547 150

60-64 31 16 65 40 403 555 112

65-69 15 8 39 32 337 431 62

70-74 21 15 30 37 344 447 66

75-79 19 12 22 28 296 376 52

80-84 6 3 10 20 213 252 19

85-89 2 2 5 8 115 131 8

90+ 0 1 1 3 34 39 2 Age

Unknown 57 18 45 51 300 472 121

Total 2664 858 1353 1264 7228 13368 4876


45

C6

age group Pakistan

i Bangladesh

i

Indian and

other Black

White and

other total

of which Asian sub-

continent

Under 1 11 4 5 10 74 103 19

1-4 36 19 19 46 337 457 74

5-9 28 22 25 55 302 432 75

10-14 24 23 14 53 340 453 60

15-19 22 18 29 63 334 466 69

20-24 22 20 28 43 385 497 69

25-29 28 18 35 44 375 500 81

30-34 32 21 35 50 375 513 87

35-39 17 14 25 53 373 482 56

40-44 14 7 24 64 413 522 46

45-49 10 8 27 56 441 542 45

50-54 15 11 21 38 389 474 47

55-59 9 7 23 28 313 379 38

60-64 4 4 10 29 264 311 18

65-69 6 2 19 23 186 237 27

70-74 5 2 6 19 195 226 13

75-79 5 3 6 14 169 196 13

80-84 0 0 3 7 98 109 3

85-89 0 0 2 4 62 68 2

90+ 0 0 0 2 25 27 0 Age

Unknown 10 3 15 30 252 311 28

Total 297 206 370 731 5701 7305 873


46

C7

age group Pakistan

i Bangladesh

i

Indian and

other Black

White and

other total

of which Asian sub-

continent

Under 1 165 67 25 41 126 424 257

1-4 637 299 112 120 507 1674 1047

5-9 689 339 86 145 299 1558 1114

10-14 497 261 61 107 224 1150 819

15-19 420 184 78 83 261 1026 682

20-24 494 231 121 68 429 1343 846

25-29 565 294 145 93 546 1642 1003

30-34 480 283 114 84 425 1387 877

35-39 355 218 79 97 338 1087 652

40-44 269 156 72 89 300 887 498

45-49 142 85 52 69 279 627 279

50-54 130 102 63 41 267 604 296

55-59 136 102 44 32 188 502 282

60-64 55 62 31 20 174 342 148

65-69 47 48 32 21 148 295 126

70-74 58 60 37 28 159 342 155

75-79 35 32 21 17 129 234 88

80-84 18 13 13 8 79 131 44

85-89 4 7 3 4 33 50 13

90+ 4 2 2 2 25 35 8 Age

Unknown 120 61 41 71 394 688 222

Total 5322 2906 1230 1240 5331 16028 9458


47

C8

age group Pakistan

i Bangladesh

i

Indian and

other Black

White and

other total

of which Asian sub-

continent

Under 1 31 15 13 22 139 221 60

1-4 108 54 35 98 522 817 197

5-9 128 62 38 119 495 843 229

10-14 94 53 27 100 523 797 174

15-19 81 32 17 86 589 805 130

20-24 76 41 35 85 656 892 151

25-29 86 45 47 68 654 900 178

30-34 87 49 40 82 551 808 176

35-39 63 45 29 78 528 743 137

40-44 44 23 30 87 602 785 96

45-49 28 20 17 83 618 766 64

50-54 22 12 25 60 515 635 59

55-59 17 15 18 43 441 534 49

60-64 11 9 15 30 395 460 35

65-69 7 6 10 24 287 334 23

70-74 9 9 13 19 251 302 32

75-79 7 5 7 19 249 286 19

80-84 4 2 5 17 185 213 11

85-89 2 2 2 7 119 132 6

90+ 0 0 0 5 43 48 0 Age

Unknown 28 10 22 48 443 551 60

Total 932 509 445 1181 8805 11872 1886


48

C9

age group Pakistan

i Bangladesh

i

Indian and

other Black

White and

other total

of which Asian sub-

continent

Under 1 14 10 11 20 126 180 35

1-4 32 31 32 79 352 526 95

5-9 31 22 14 59 273 399 68

10-14 28 24 12 51 227 341 64

15-19 25 15 17 42 247 347 58

20-24 31 22 45 48 429 575 98

25-29 35 33 62 74 613 817 130

30-34 31 27 37 90 526 712 95

35-39 19 23 30 73 396 541 72

40-44 24 13 20 71 405 532 57

45-49 12 13 17 56 408 505 41

50-54 10 12 20 40 302 384 42

55-59 5 7 13 29 262 316 25

60-64 5 5 6 24 232 273 17

65-69 4 7 9 15 159 195 20

70-74 3 4 9 13 138 166 15

75-79 3 4 7 11 107 132 14

80-84 1 1 5 7 78 93 8

85-89 1 0 1 6 63 71 2

90+ 1 0 1 2 31 34 1 Age

Unknown 34 20 36 116 820 1027 90

Total 352 293 403 924 6194 8166 1048


49

C10

age group Pakistan

i Bangladesh

i

Indian and

other Black

White and

other total

of which Asian sub-

continent

Under 1 6 5 12 7 44 74 23

1-4 42 34 34 36 224 370 110

5-9 55 33 38 41 265 432 126

10-14 48 23 51 51 353 526 123

15-19 46 24 51 41 394 555 120

20-24 32 13 37 26 268 376 82

25-29 27 21 53 31 266 398 101

30-34 39 30 54 31 241 395 123

35-39 41 26 40 40 289 437 108

40-44 24 17 57 40 424 562 98

45-49 18 8 58 53 443 580 84

50-54 22 11 48 35 378 495 81

55-59 9 6 44 28 322 411 60

60-64 8 4 18 32 404 466 30

65-69 9 4 23 30 379 446 36

70-74 3 2 16 32 383 437 21

75-79 3 1 13 25 285 328 18

80-84 2 0 9 14 193 218 10

85-89 1 0 1 8 109 119 2

90+ 0 0 1 3 29 33 1 Age

Unknown 14 5 12 18 154 202 30

Total 450 267 671 623 5848 7860 1389


50

C11

age group Pakistan

i Bangladesh

i

Indian and

other Black

White and

other total

of which Asian sub-

continent

Under 1 29 20 17 36 133 235 66

1-4 140 69 42 135 466 853 252

5-9 149 81 44 202 471 947 274

10-14 107 52 40 186 487 873 200

15-19 91 46 34 123 519 813 171

20-24 57 39 41 89 534 760 137

25-29 97 46 62 76 505 786 205

30-34 99 66 70 104 506 846 235

35-39 77 52 52 136 503 821 181

40-44 53 33 28 159 678 952 114

45-49 31 12 40 125 588 796 84

50-54 29 12 50 85 479 655 91

55-59 19 10 40 54 389 512 69

60-64 10 6 21 50 489 576 37

65-69 8 6 21 48 390 473 35

70-74 8 7 16 42 316 388 30

75-79 6 2 4 34 263 309 12

80-84 1 2 5 16 170 194 8

85-89 1 1 2 8 95 107 5

90+ 0 0 0 5 51 57 0 Age

Unknown 19 14 18 58 334 443 51

Total 1031 577 649 1772 8367 12396 2257


51

C12

age group Pakistan

i Bangladesh

i

Indian and

other Black

White and

other total

of which Asian sub-

continent

Under 1 16 9 15 24 143 207 40

1-4 79 60 65 114 647 966 204

5-9 99 76 72 159 573 979 246

10-14 58 48 47 148 604 905 153

15-19 39 33 43 110 671 896 115

20-24 32 23 43 91 586 775 98

25-29 57 39 50 71 559 776 146

30-34 47 34 66 84 581 813 147

35-39 48 47 67 109 630 902 163

40-44 37 18 51 142 728 976 106

45-49 17 14 39 127 826 1023 70

50-54 18 16 42 80 600 755 75

55-59 10 6 33 54 408 511 50

60-64 7 5 27 41 379 459 39

65-69 6 6 14 41 389 455 25

70-74 11 7 15 47 450 531 34

75-79 4 4 12 27 334 381 19

80-84 1 1 4 19 223 248 6

85-89 0 0 0 7 92 99 0

90+ 0 0 0 2 33 36 0 Age

Unknown 17 9 20 43 268 357 46

Total 603 455 725 1542 9724 13050 1784


52

C13

age group Pakistan

i Bangladesh

i

Indian and

other Black

White and

other total

of which Asian sub-

continent

Under 1 19 8 5 11 82 125 32

1-4 65 37 21 57 246 426 123

5-9 77 50 28 75 311 541 155

10-14 57 42 38 84 376 597 137

15-19 43 22 29 59 414 567 94

20-24 29 15 26 41 328 438 69

25-29 49 22 35 39 323 469 107

30-34 50 39 28 43 310 469 117

35-39 60 36 44 55 338 534 141

40-44 37 21 30 61 427 576 88

45-49 19 8 34 66 467 593 60

50-54 15 7 24 50 392 488 46

55-59 9 5 19 31 308 372 33

60-64 5 2 21 30 348 407 28

65-69 6 3 14 30 344 397 23

70-74 5 2 8 36 352 402 15

75-79 2 0 3 16 245 265 5

80-84 2 0 3 10 129 144 6

85-89 1 0 1 5 73 79 1

90+ 1 0 0 2 19 22 1 Age

Unknown 19 7 8 30 172 236 34

Total 568 327 421 830 6002 8147 1316


53

C14

age group Pakistan

i Bangladesh

i

Indian and

other Black

White and

other total

of which Asian sub-

continent

Under 1 11 12 4 22 101 150 27

1-4 55 49 16 127 381 628 121

5-9 51 59 19 128 407 664 129

10-14 38 52 24 121 457 692 114

15-19 32 31 30 98 450 641 93

20-24 33 44 29 74 393 573 106

25-29 41 49 38 68 428 624 128

30-34 36 48 39 81 438 642 123

35-39 36 35 19 105 443 639 91

40-44 17 22 25 120 469 654 64

45-49 11 8 28 94 395 536 47

50-54 13 13 34 66 346 473 61

55-59 16 12 24 37 349 437 51

60-64 7 10 24 41 331 412 40

65-69 4 7 16 19 229 275 27

70-74 4 3 6 20 195 229 13

75-79 2 3 8 15 105 134 13

80-84 2 2 1 4 45 55 6

85-89 0 0 0 3 31 34 0

90+ 0 0 0 0 9 9 0 Age

Unknown 8 6 6 36 192 248 20

Total 416 468 391 1281 6193 8749 1275


54

C15

age group Pakistan

i Bangladesh

i

Indian and

other Black

White and

other total

of which Asian sub-

continent

Under 1 10 4 7 16 129 165 21

1-4 44 20 32 90 525 712 97

5-9 51 36 36 91 553 767 123

10-14 49 41 32 94 625 841 122

15-19 38 30 35 96 620 819 103

20-24 33 27 26 60 591 737 86

25-29 30 20 34 66 561 711 84

30-34 36 29 40 67 547 719 105

35-39 37 22 40 81 628 808 99

40-44 25 20 32 104 725 906 78

45-49 21 13 33 84 744 895 67

50-54 21 11 23 57 575 687 55

55-59 7 9 37 42 442 538 54

60-64 5 3 15 42 471 536 23

65-69 3 2 8 25 341 380 13

70-74 6 4 7 25 325 367 17

75-79 2 1 4 22 293 322 8

80-84 3 0 5 13 162 183 8

85-89 1 1 1 8 97 108 3

90+ 0 0 0 2 40 42 0 Age

Unknown 10 7 20 44 383 465 38

Total 433 301 468 1129 9377 11708 1202


55

C16

age group Pakistan

i Bangladesh

i

Indian and

other Black

White and

other total

of which Asian sub-

continent

Under 1 144 51 25 27 102 349 220

1-4 528 184 89 84 350 1235 801

5-9 580 222 111 108 317 1337 913

10-14 490 170 101 98 310 1169 761

15-19 360 146 88 104 338 1035 593

20-24 409 136 82 61 339 1027 627

25-29 423 172 114 69 409 1187 709

30-34 434 174 98 70 368 1143 705

35-39 315 175 102 81 347 1020 592

40-44 232 106 91 73 388 891 430

45-49 135 66 76 75 359 711 277

50-54 131 70 78 61 319 659 279

55-59 102 62 61 34 236 495 225

60-64 63 33 47 28 239 410 143

65-69 37 34 37 23 222 353 108

70-74 43 30 28 28 193 323 102

75-79 24 22 17 19 167 248 63

80-84 13 9 9 9 101 142 32

85-89 4 4 6 7 70 91 14

90+ 1 2 0 2 23 28 3 Age

Unknown 57 27 31 46 299 460 115

Total 4523 1896 1291 1106 5496 14313 7711


56

C17

age group Pakistan

i Bangladesh

i

Indian and

other Black

White and

other total

of which Asian sub-

continent

Under 1 18 8 10 30 181 247 36

1-4 42 37 33 93 557 762 112

5-9 38 24 29 76 355 523 92

10-14 25 13 15 58 329 440 53

15-19 17 15 13 62 414 520 44

20-24 42 20 103 120 1119 1405 166

25-29 46 34 85 134 1043 1342 165

30-34 40 31 72 108 760 1011 143

35-39 20 25 25 95 585 751 70

40-44 17 14 29 84 521 666 61

45-49 5 6 11 75 529 626 21

50-54 5 8 18 49 403 483 31

55-59 8 7 14 37 360 427 29

60-64 4 3 8 25 322 362 15

65-69 4 2 8 22 272 308 14

70-74 6 4 4 23 224 261 14

75-79 5 4 3 11 162 185 12

80-84 1 1 2 12 142 159 5

85-89 0 0 3 8 72 83 4

90+ 1 0 0 3 40 44 1 Age

Unknown 53 31 77 172 1264 1596 160

Total 397 288 562 1298 9657 12201 1246


57

C18

age group Pakistan

i Bangladesh

i

Indian and

other Black

White and

other total

of which Asian sub-

continent

Under 1 1 2 0 4 57 64 3

1-4 5 5 8 27 257 303 19

5-9 6 3 12 31 302 354 21

10-14 10 3 15 39 371 438 28

15-19 12 3 19 34 365 433 34

20-24 6 3 10 25 344 388 19

25-29 3 5 16 21 297 343 25

30-34 5 3 10 25 290 333 18

35-39 6 2 14 37 374 434 23

40-44 6 3 15 46 460 530 24

45-49 6 2 15 45 507 574 22

50-54 3 1 13 32 414 464 18

55-59 2 1 16 25 376 420 19

60-64 2 1 4 30 446 483 7

65-69 4 1 5 27 355 392 10

70-74 1 2 4 21 300 329 8

75-79 1 0 4 16 262 283 5

80-84 1 0 2 9 180 192 3

85-89 0 0 1 6 92 99 1

90+ 0 0 1 3 32 36 1 Age

Unknown 2 1 16 17 180 216 19

Total 83 42 200 521 6262 7108 325


58

C19

age group Pakistan

i Bangladesh

i

Indian and

other Black

White and

other total

of which Asian sub-

continent

Under 1 5 4 4 20 111 144 13

1-4 32 26 16 84 377 535 74

5-9 29 32 15 85 359 520 76

10-14 10 17 15 82 347 470 41

15-19 12 8 15 78 432 544 35

20-24 17 14 16 56 436 539 47

25-29 22 18 24 52 455 571 65

30-34 23 25 22 60 347 477 70

35-39 17 19 15 75 431 558 52

40-44 6 5 25 81 483 600 36

45-49 7 5 16 75 474 578 29

50-54 7 4 20 53 421 505 31

55-59 7 3 23 35 365 433 33

60-64 5 5 13 35 336 394 23

65-69 2 1 11 33 327 375 14

70-74 3 1 11 30 292 337 15

75-79 3 2 5 15 199 225 10

80-84 0 1 3 10 129 143 4

85-89 0 0 0 3 43 46 0

90+ 0 0 0 0 12 12 0 Age

Unknown 9 7 6 34 188 244 22

Total 217 197 276 996 6564 8250 690


59

C20

age group Pakistani Bangladeshi

Indian and

other Black

White and

other total

of which Asian sub-continent

Under 1 3 2 6 15 132 158 11

1-4 18 14 33 66 540 670 64

5-9 17 15 32 73 498 635 64

10-14 10 11 19 74 574 688 40

15-19 10 9 27 72 679 798 47

20-24 7 6 29 62 609 714 43

25-29 10 9 32 52 688 791 51

30-34 10 11 44 59 646 770 65

35-39 13 11 32 74 672 802 56

40-44 12 6 32 81 737 868 51

45-49 4 3 28 93 875 1003 34

50-54 3 0 30 60 739 832 34

55-59 4 2 29 43 593 671 35

60-64 4 1 23 40 532 599 27

65-69 3 2 13 27 292 337 18

70-74 1 1 7 24 242 275 9

75-79 1 0 6 12 161 180 7

80-84 1 1 3 7 103 115 5

85-89 0 0 1 4 76 81 1

90+ 0 0 0 1 21 22 0 Age

Unknown 4 3 14 32 340 392 20

Total 135 106 442 970 9748 11401 683

the growth and changing complexion of luton’s population · o luton’s population live in...

Documents