Population Trends 139 Spring 2010
Contents
In brief
1
Population Trends: readers’ views invited; Quick online access to older Population Trends Articles;
Publication of revised population estimates and subnational population projections; Reference Data
Tables; Population projections for Scottish areas (2008-based); Data visualisation and demography
– popularising population statistics
Features
The ONS Longitudinal Study – a prestigious past and a bright future
Shayla Goldring and Jim Newman
4
Self-rated health and mortality in the UK: results from the first comparative analysis
of the England and Wales, Scotland and Northern Ireland Longitudinal Studies
Harriet Young, Emily Grundy, Dermot O’Reilly, Paul Boyle
11
Do partnerships last? Comparing marriage and cohabitation using longitudinal
census data
Ben Wilson, Rachel Stuchbury
37
Households and families: Implications of changing census definitions
for analyses using the ONS Longitudinal Study
Emily Grundy, Rachel Stuchbury, Harriet Young
64
Ten year transitions in children’s experience of living in a workless household:
variations by ethnic group
Lucinda Platt
70
2008-based national population projections for the United Kingdom and
constituent countries
Emma Wright
91
This issue is available from 25 March 2010 at: www.statistics.gov.uk/populationtrends
Office for National Statistics
Population Trends
Spring 2010
In brief
Population Trends: readers’ views invited
At ONS we continually strive to maintain the quality of Population Trends as an important
demographic journal. The views of our readership are important to us and we would welcome any
comments and suggestions you have about the future scope and direction of the journal to ensure
it remains fresh and pertinent while maintaining the high standards expected by our readership.
Please email your comments and suggestions to: population.trends@ons.gov.uk
Readers are also reminded that we always welcome submission of papers from external
colleagues that are appropriate to the scope of the journal.
Quick online access to older Population Trends articles
Readers interested in locating previously published articles may like to know that a searchable
database is available. This online article search facility covers all ONS journals. To find an article it
is possible to do a text search for keywords, journal title, article title, author’s name, issue number,
and publication year. All articles published in Population Trends since Winter 1997 (issue no. 90)
are available online. Using this free search facility, pdf files can be downloaded for each article. To
use the service, go to: www.statistics.gov.uk/cci/articlesearch.asp
Publication of revised population estimates and subnational population
projections
On 30 March 2010 the ONS Centre for Demography (ONSCD) will be publishing a report
summarising the outcome of methodological work undertaken since the release of indicative impacts
to changes to the mid-2002 to mid-2008 local authority population estimates on 30 November 2009
and the responses received from users on the impact of the package of improvements that will be
implemented in May 2010 when revised estimates are published.
More detailed information, including data by age and sex, together with detailed papers on the
revisions, will be published on 27 May 2010. Also on 27 May 2010, ONSCD will be publishing
2008-based Subnational Population Projections for local authorities in England, and the Welsh
Assembly Government will be publishing 2008-based Subnational Population Projections for local
authorities in Wales.
Reference Data Tables
Population Trends and Health Statistics Quarterly have been developed as online publications. As
part of the ‘web-only’ publication approach the content and format of all the reference data tables
within these publications is being reviewed.
To help ONS determine user requirements, proposed changes are outlined in a consultation
document, which is available at: www.statistics.gov.uk/STATBASE/Product.asp?vlnk=15354
Comments from users are welcomed. Please email your responses and suggestions to:
vsob@ons.gov.uk
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Population projections for Scottish areas (2008-based)
On 3 February 2010 the General Register Office for Scotland published its 2008-Based Population
Projections for Scottish Areas. The report covers the period from 2008 to 2033, and the key points
are:
• Scotland’s population is expected to increase over the next 25 years, although this rise is
projected to be unevenly spread across the country.
• The population of 19 of the 32 council areas in Scotland is projected to increase, while the
population in the other 13 is projected to decrease. The council areas with the greatest
projected increase in population are East Lothian (+33 per cent) and Perth & Kinross
(+27 per cent). Inverclyde (–18 per cent) and East Dunbartonshire (–13 per cent) have the
largest projected decreases.
• Every council area is projected to have more elderly people than today, though the scale of the
increase will vary.
• The number of children aged 0–15 is projected to decrease in 20 of the 32 council areas, with
the largest percentage decreases in Shetland (–33 per cent) and Inverclyde (–29 per cent).
The biggest increases are projected in East Lothian (+38 per cent) and Perth & Kinross
(+24 per cent).
• The population of working age (accounting for future changes to the state pension age) is
projected to increase in 15 council areas and decrease in 17 – increasing the most in East
Lothian (+29 per cent) and decreasing the most in Inverclyde (–26 per cent).
• The population of pensionable age (accounting for future changes to the state pension age) is
projected to increase in all council areas, the largest increases being projected in Aberdeenshire
(+65 per cent) and West Lothian (+59 per cent), with the smallest increase in Dundee City
(+8 per cent) and Glasgow City (+11 per cent).
• It is hard to predict how many people might migrate to Scotland. The high migration projection
shows what would happen if Scotland were to gain larger numbers through migration than
expected. The populations in 26 councils would rise under this variant. The greatest increase
is again projected in East Lothian (+38 per cent) and Perth & Kinross (+37 per cent) and the
largest decrease again in Inverclyde (–14 per cent) and East Dunbartonshire (–11 per cent).
• The low migration projection shows the population if Scotland were to gain smaller numbers
through migration than expected. Populations are expected to rise in 15 councils under this
variant. The greatest increase is again projected in East Lothian (+29 per cent) and Perth &
Kinross (+25 per cent) and the largest decrease again in Inverclyde (–20 per cent) and East
Dunbartonshire (–17 per cent).
Further details can be found at:
www.gro-scotland.gov.uk/statistics/publications-and-data/popproj/2008-based-pop-proj-scottishareas/index.html
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Data visualisation and demography – popularising population statistics
Population trends have long benefited from graphical presentation techniques – for example
William Playfair, the father of quantitative data presentation, produced early population diagrams
in the late eighteenth century. However, traditional data graphics usually reserve one whole
dimension (typically the x axis) of a 2-d static graph to reveal change over time. Modern web
technologies allow us to challenge convention, using richer means of graphical presentation to
produce more engaging representations of change over time.
The Data Visualisation Centre at ONS works closely with the ONS Centre for Demography to
develop a range of animated and interactive data graphics aimed at revealing structural changes
in the UK population over extended periods. These include an animated local authority map
representing changes in subnational age structure in the UK from 1992 to 2031. This is a visual
representation of work first reported on in Population Trends in June 20091.
The classic demographic display –the population pyramid– has also received fresh treatment.
For example, a new animated edition allows users to explore combined population estimates and
projections from 1961 (England and Wales) or 1971 (UK) through to 2083. Users can click and
drag across age bands on the graphic to define their own summary statistics on the fly.
A further innovation is the twin-pyramid display, initially published for National Population
Projections. This display allows users to visualise ONS’ variant population projections in parallel,
visualising change over time, and dynamically overlaying the two images to provide easy
comparisons of structural differences between the projections.
The new graphics are all published in Adobe Flash format and allow the user to control not only
the animation but interact with the graphical content to query the underlying data (which can be
downloaded separately). They have ‘full screen’ functionality, making them ideal for lectures and
presentations.
The visualisations have all been designed as templates which can be reused and extended where
appropriate. Further work is planned this year, refining and adding functionality to the existing
visualisations, and reusing them, where appropriate, with other datasets. This approach reflects
and reinforces the move of ONS publications away from print to online formats. It is anticipated
that these new graphics will be the first of a new generation of data graphics optimised for web
presentation.
Links
Animated map of ageing, available at: www. statistics.gov.uk/ageingintheuk/default.htm
Animated national population pyramid, available at: www.statistics.gov.uk/populationestimates/
flash_pyramid/default.htm
Animated twin population projections, available at: www.statistics.gov.uk/ national projections/
flash_pyramid/projections.html
Reference
1 Blake, S. (2009) ‘Subnational patterns of population ageing’. Population Trends 136.
Available at: www.statistics.gov.uk/articles/ population_trends/PT136SubnationalAgeing.pdf
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The ONS Longitudinal Study – a
prestigious past and a bright future
Shayla Goldring and Jim Newman
Office for National Statistics
This issue of Population Trends includes a number of articles and reports resulting from
research based on the ONS Longitudinal Study (ONS LS). They have been drawn together in
one issue to highlight the value of this type of study for demographic research.
2009 marked the 35th anniversary of the establishment of the ONS LS. The study now
contains data from the last four censuses (1971 to 2001), linked to vital events data since
1971, for a sample of one per cent of the population of England and Wales.
More recently, sister studies have been established in Scotland and Northern Ireland. The
Scottish Longitudinal Study (SLS) started with 1991 Census data and the Northern Ireland
Longitudinal Study (NILS) started with 2001 Census data.
The lead article in this issue comes from an exemplar project that was established to
explore how to utilise the three studies to carry out UK-wide longitudinal analysis. Two
different methods were used to analyse socio-economic and country level differences in
health and mortality across the studies. The article summarises the results of this analysis,
reports on the relative strengths of the different methods used, and draws attention to a
number of new resources that have been developed by the project researchers as aids to
using all three studies.
This is an excellent example of collaborative working across the UK, involving researchers
from the Centre for Longitudinal Study Information and User Support (CeLSIUS) at the
London School of Hygiene and Tropical Medicine, the Longitudinal Studies Centre –
Scotland (LSCS) at the University of St. Andrews and the Northern Ireland Longitudinal
Study – Research Support Unit (NILS-RSU) at Queen’s University Belfast. The project also
involved collaboration between ONS, the General Register Office for Scotland (GROS) and
the Northern Ireland Statistics and Research Agency (NISRA) to ensure the secure transfer
and handling of data from the three studies so that it could be brought together in one place
for analysis.
The other ONS LS-based articles and reports in this edition largely focus on research into
issues related to families and households, as summarised below:
• A collaborative project involving Ben Wilson (ONS) and Rachel Stuchbury (CeLSIUS)
comparing the stability of partnerships involving marriage and cohabitation.
• A project looking at transitions in children’s experience of living in a workless household
and how this varies by ethnic group, submitted by Lucinda Platt (Institute for Social &
Economic Research, University of Essex).
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• An article on the effect of a change in the census definition of a child between 1991 and
2001 submitted by Emily Grundy, Rachel Stuchbury and Harriet Young (CeLSIUS).
The remainder of this introductory article will focus on the ONS LS, its history and some
examples of its use, and gives a summary of planned developments over the coming years.
Please refer to the contact details at the end of the article if you require further information
on any of the three longitudinal studies.
Contents
Introduction to the ONS LS................................................................................................................ 6
Research using the ONS LS.............................................................................................................. 6
Future plans for the ONS LS.............................................................................................................. 8
References....................................................................................................................................... 10
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Introduction to the ONS LS
Longitudinal data sets are based on repeated measurements of a sample population. They allow
us to answer questions about how a particular cohort of people changes over time, and to explore
reasons for change. In addition, the ONS LS allows users to look at and compare the experiences
of different cohorts at different points in time. This enables users to separate age, period and
cohort effects in their analysis.
Cohort analysis was identified as a priority in developing the analysis of mortality by William Farr,
a noted epidemiologist who was appointed the first ‘Compiler of Abstracts’ at the newly established
General Register Office for England and Wales in 1839. At this time the analysis of information
collected by statistical offices was mainly cross-sectional as a result of the limited data available.
Farr was the first to combine information from a national census (1861) and the death registers to
look at the occupation of men, their age at and cause of death.
The ONS LS was established in 1974 by taking a sample of records from the 1971 Census for
England and Wales of all those born on one of four dates of birth. This original sample has been
continuously augmented since 1971 with new members. New members enter the study through
one of the following three routes if born on one of the four dates of birth:
• completion of a census form
• birth registration through the civil registration service; or
• registration as a patient with a doctor.
Information from the 1971, 1981, 1991 and 2001 Censuses has been linked, along with information
on events such as births, deaths, immigration, emigration and cancer registrations for study
members. More than half a million study members have been identified at each of the four
censuses, and the study now includes information on more than one million different individuals.
The ONS LS is a study – not a survey. Its strength lies in the re-use of data that have already been
collected for other purposes, significantly reducing the effects typically associated with respondent
burden. As a result, both retention and response rates are relatively high.
Research using the ONS LS
The study was originally set up primarily to meet the need for better data on occupational mortality
and fertility patterns.
Data at the time were inadequate for the study of occupational mortality rates. In order to provide
evidence for a causal relationship between occupation and mortality, information on occupation
is needed for a period well before the onset of illness and death. In addition, information on an
individual’s characteristics such as employment status, area of residence, qualifications and
general health would be needed for some years before death to use as control variables, as these
may also have an influence on mortality.
It was also accepted that there was a need for more detailed information on fertility patterns, in
particular changes in the spacing of births, and the part that social and economic characteristics
play in family formation.
The ONS LS addresses these needs, and many more, by linking existing census and vital event
data. The strengths of the study include:
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• the robustness of the sample size, around 500,000 at any one census
• the relatively high rates of retention and response
• the range and stability of the information available for analysis over time, from censuses and
vital events
• the inclusion of census information on co-residents of study members
• the availability of sister studies in Scotland and Northern Ireland for those interested in a UKwide perspective; and
• the services of dedicated user support teams (contact details for these teams are provided at
the end of this article).
The ONS LS enables analysis of a wide range of key sub-groups and topics of policy interest.
Since its inception, the study has been used to address research questions including studies of
social mobility, ageing and migration. Studies that make the fullest use of the data link social,
occupational and demographic information at successive censuses to data about fertility, mortality,
and cancer incidence and survival. Some examples of recent research that used the ONS LS
follow below.
1
In the field of fertility, the study has recently been used to explore lifelong childlessness , a topic
which has received little attention given the decline in fertility experienced since the baby boom.
This research investigated the degree to which socio-economic characteristics of women and,
where present, their partners were related to female lifelong childlessness. The study measured
the extent to which women who remained childless throughout their life course were distinctive
from those who became mothers, and therefore improved our understanding of childlessness
among women in England and Wales.
The researchers on this project, Martina Portanti and Simon Whitworth from ONS, won the
inaugural Neville Butler Memorial Prize in 2009, awarded by the Economic and Social Research
Council for excellence in the analysis of longitudinal data. It received a great deal of media
attention, as demonstrated by the following newspaper headlines:
“One in five women stays childless because of modern lifestyle”, Daily Telegraph
“Fifth of women childless as careers take precedence, study shows”, The Times
Recent work in the field of mortality includes a project led by Dr David Pevalin from the School of
Health and Human Sciences at Essex University. Dr Pevalin’s research analysed social inequalities
in avoidable mortality, looking to test empirically the theory of social conditions as fundamental
causes of disease. Findings from this project were presented at the 2009 conference of the British
Society for Population Studies.
The information available on co-residents from the census, and also vital events such as the
registration of the death of a spouse, are very important for analyses of partnership formation and
dissolution. This information means that it is possible to look at the characteristics of both partners
in a relationship and use them or the differences between them to study the factors relating to
the stability of different partnership types over time. This is the approach used by Ben Wilson and
Rachel Stuchbury in their paper ‘Do partnerships last? Comparing marriage and cohabitation using
the ONS Longitudinal Study’, included in this issue of Population Trends.
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The study also has internal uses within ONS, such as quality assurance of other data sources.
It played a key role in the assessment and adjustment of population estimates based on the 2001
Census. Analysis using the study highlighted a shortfall in men aged 25 to 34 in the Census.
The 2001 mid-year population estimates for 68 local authorities were adjusted as a result of this
2
analysis. For more information on this analysis, refer to Section 7 of Series LS no.10 and the
3
Census 2001 Quality Report for England and Wales .
Planning for the 2011 Census reflects the importance of the part the study played after 2001. This
time the ONS LS will be a key source of data used in carrying out quality assurance of census
data. This will allow information from the study to be considered alongside other quality assurance
material before any population estimates are published.
Future plans for the ONS LS
Linking data from the 2011 Census
With the 2011 Census only a year away, plans for incorporating the next set of census data into
the ONS LS are well advanced. As a result of this work, the study will contain linked data from five
successive censuses. The new census data will be available from the study in autumn 2013.
The addition of 2011 Census data will enable users to study transitions in people’s caring
responsibilities for the first time. This isn’t currently possible as the census question on caring was
first asked in 2001. Many users will also want to update previous analyses such as
• socio-economic and/or ethnic differences in mortality, life expectancy, cancer incidence, fertility
and migration behaviour; and
• transitions over time in topic areas such as occupational and social mobility, household
composition and partnerships.
UK-wide longitudinal study research
As noted earlier, the establishment of longitudinal studies in Scotland and Northern Ireland means
that a longitudinal study infrastructure now exists that enables researchers to take a UK-wide view,
or to draw comparisons between different regions and countries across the whole of the UK.
While there are a number of differences in the structure, content and operation of the three studies,
the basic principle behind each of them is essentially the same. That is, to link the wide range
of information collected at each census with data from subsequent events, most notably those
relating to fertility and mortality.
The exemplar project reported in this issue of Population Trends has involved both researchers
and the statistical offices working through a number of issues that required resolution to allow
this research to take place. These are reported on in a technical working paper on the CeLSIUS
4
website .
One significant outcome of this work is that a provisional working model has been established for
anyone wishing to conduct research across the three studies. As part of a wider review of user
needs of the ONS LS, ONS will be gauging the demand for a more permanent solution that allows
UK-wide research to take place. Further work on this will be led by ONS and will, of course, involve
very close collaboration with colleagues at GROS and NISRA. In the meantime, any researchers
interested in exploring this as an option should contact their nearest user support team (contact
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details for these teams are provided at the end of this article). Any proposed projects will be
considered by all three statistical offices on a case-by-case basis.
Linking additional administrative data
The richness of information in the ONS LS comes from matching census data with administrative
data over time. These administrative data are currently limited to birth and death related data from
the civil registration service, cancer data from the cancer registries and demographic data from the
patient registration system.
The last Longitudinal Study review in 1998 recommended that the linkage of administrative data
held by other government departments should be considered. The passing of the Statistics and
Registration Service Act 2007, which came into effect in April 2008, provides the legal framework
through which ONS can seek access to any data held by other government departments.
ONS and the Department for Work and Pensions (DWP) are currently working closely to make
a case for linking unemployment related benefit data to the study. This is the first attempt to use
the new legislation to extend the content of the ONS LS. It is intended that this will be the first of a
number of new linkages that will enrich the data available through the study.
The aim of linking additional data is to extend the range of research topics that can be explored
through the study. This will enhance the value of the study for existing users, as well as reach out
to new users carrying out research in areas that the study cannot currently address. Users will be
consulted to identify which additional data are most in demand. This will form part of a wider user
review that will allow ONS to prioritise this alongside other development activity.
Using the ONS LS
ONS actively promotes use of the ONS LS while maintaining the confidentiality of the individuals
in the sample. ONS LS records available for analysis are anonymised but the database contains
individual-level data that have not been aggregated or disguised.
To ensure confidentiality, these microdata can only be accessed at ONS sites and can only be
accessed from a secure area known as the Virtual Microdata Laboratory (VML). Support officers
are available to help users extract and use the data.
For further information, or for an informal discussion about using the ONS LS, government and
other non-academic users should contact the Microdata Analysis and User Support team at ONS.
Tel: 01633 455844
email: maus@ons.gsi.gov.uk
Website: www.ons.gov.uk/about/who-we-are/our-services/longitudinal-study
Academic users should contact the CeLSIUS team at the London School of Hygiene and Tropical
Medicine.
Tel: 020 7299 4634
email: celsius@lshtm.ac.uk
Website: www.celsius.lshtm.ac.uk
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For further information about the SLS, users should contact the Longitudinal Studies Centre –
Scotland at the University of St. Andrews.
Tel: 01334 463992
email: lscs@st-andrews.ac.uk
Website: www.lscs.ac.uk/sls/
For further information about the NILS, users should contact the Northern Ireland Longitudinal
Study – Research Support Unit (NILS-RSU) at Queen’s University Belfast.
Tel: 028 9082 8210 or 028 9034 8199
email: nils-rsu@qub.ac.uk
Website: www.qub.ac.uk/research-centres/NILSResearchSupportUnit/
References
1 Portanti, M and Whitworth, S (2009) A comparison of the characteristics of childless women
and mothers in the ONS Longitudinal Study, Population Trends 136, Summer 2009, pp 10–20.
Available at: www.statistics.gov.uk/downloads/theme_population/Popular-Trends136.pdf
2 Blackwell, L, Lynch, K, Smith, J and Goldblatt, P (2003) Longitudinal Study 1971–2001:
Completeness of Census Linkage, Series LS no. 10, September 2003. Available at:
www.statistics.gov.uk/downloads/theme_population/LS_no10.pdf
3 Census 2001: Quality Report for England and Wales, 2005.
Available at: www.statistics.gov.uk/StatBase/Product.asp?vlnk=14212
4 Young, H (2009) Technical Working Paper: Guide to parallel and combined analysis of the
ONS LS, SLS and NILS, July 2009. Available at: www.celsius.lshtm.ac.uk/UKLS/Guide%20
to%20parallel%20and%20combined%20LS%20analysis.doc
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Self-rated health and mortality
in the UK: results from the first
comparative analysis of the England
and Wales, Scotland, and Northern
Ireland Longitudinal Studies
Harriet Young, Emily Grundy
London School of Hygiene & Tropical Medicine
Dermot O’Reilly
Queen’s University Belfast
Paul Boyle
University of St Andrews
Previous studies have shown that self-reported health indicators are predictive of
subsequent mortaity, but that this association varies between populations and population
sub-groups. For example, self-reported health is less predictive of mortality at older ages,
has a stronger association with mortality for men than for women and is more predictive of
mortality for those of lower than those of higher socio-economic status, particularly among
middle aged working adults
This article explores this association using individual level, rather than ecological, data
to see whether there are differences between the constituent countries of the UK in the
relationship between self-reported health and subsequent mortality, and to investigate
socio-economic inequalities in mortality more generally. Data are used from the three
Census based longitudinal studies now available for England and Wales, Scotland and
Northern Ireland.
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Contents
Introduction....................................................................................................................................... 14
Previous research on associations between self-reported health and mortality.............................. 14
Methods............................................................................................................................................ 15
Descriptive results............................................................................................................................ 19
Multivariate results........................................................................................................................... 20
Mortality............................................................................................................................................ 22
Summary of results.......................................................................................................................... 23
Discussion........................................................................................................................................ 24
Strengths and weaknesses of each analysis strategy...................................................................... 24
Acknowledgements.......................................................................................................................... 28
References....................................................................................................................................... 36
List of figures
Figure 1Percentage of the population aged 35–74 with fair or poor self-rated health by
age group, gender and country, ONS LS, SLS, NILS, 2001....................................... 18
Figure 2Mortality rate by gender and country for those aged 35–49, ONS LS, SLS,
NILS, 2001.................................................................................................................. 20
Figure 3Mortality rate by gender and country for those aged 50–64, ONS LS, SLS,
NILS, 2001.................................................................................................................. 20
Figure 4Mortality rate by gender and country for those aged 65–74, ONS LS, SLS,
NILS, 2001.................................................................................................................. 21
Figure 5Mortality rate by gender and country for those aged 35–74, ONS LS, SLS,
NILS, 2001.................................................................................................................. 21
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List of tables
Table 1Variables and categories used in individual level and aggregated datasets, ONS
LS, SLS, NILS 2001................................................................................................... 16
Table 2Socio-demographic and socio-economic characteristics of the population
aged 35–74 in England and Wales, Scotland and Northern Ireland, ONS LS, SLS,
NILS 2001................................................................................................................... 17
Table 3Odds Ratios from logistic regression analysis of variations in the proportion
of the population aged 35–74 with poor or fair self-rated health in 2001 by
socio-demographic and socio-economic characteristics in England and Wales,
Scotland and Northern Ireland. ONS LS, SLS, NILS 2001, using parallel datasets... 25
Table 4Odds Ratios from logistic regression analysis of variations in the proportion
of the population aged 35–74 with poor or fair self-rated health in 2001 by
socio‑demographic and socio-economic characteristics in England and Wales,
Scotland and Northern Ireland and for all countries combined. ONS LS, SLS,
NILS 2001 using combined aggregated datasets....................................................... 26
Table 5Rate ratios of mortality for the population aged 35–74 by socio-demographic
and socio‑economic characteristics and health status in England and Wales,
Scotland and Northern Ireland. ONS LS, SLS, NILS 2001 using parallel datasets.... 29
Table 6Rate ratios of mortality for the population aged 35–74 by socio-demographic
and socio-economic characteristics and health status in England and Wales,
Scotland and Northern Ireland, and for all countries combined. ONS LS, SLS,
NILS 2001 using combined aggregated datasets....................................................... 32
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Introduction
There are now three census based record linkage studies covering all constituent parts of the UK.
The oldest of these, the Office for National Statistics Longitudinal Study (ONS LS) which covers
England and Wales, was established in the mid 1970s and includes individual level information
from the 1971, 1981, 1991 and 2001 Censuses. The Northern Ireland Longitudinal Study (NILS)
and the Scottish Longitudinal Study (SLS) were launched in 2006 and 2007 respectively. The SLS
includes information from the 1991 and 2001 Censuses and NILS data from the 2001 Census. All
three studies include linked data from vital registration systems, including mortality. This means
that for the first time there is the potential to analyse differentials between the constituent elements
of the UK, using information from large representative longitudinal studies including individual
level information from both census and vital registration sources. All three sources are subject to
stringent disclosure control safeguards and it is currently not possible to combine individual level
data from them to create a UK dataset. However, comparative analysis may be carried out in two
ways: firstly, by conducting separate parallel individual level analyses of the three studies and
comparing results; and secondly, by appending datasets of aggregated counts of individual level
data from each study and then analysing this combined dataset. In this paper we show results
from using both methods to analyse socio-economic and country level differences in health and
mortality. This is an important topic because of research and policy interest in health inequalities
in the UK, and indications from previous research using ecological data that patterns of reporting
health may differ between the constituent countries of the UK.1 We examine the strengths and
weaknesses of each method for addressing this question and discuss the issues involved in
working with the three datasets together.
Previous research on associations between self-reported health and
mortality
Previous studies have shown that self-reported health indicators are predictive of subsequent
mortality,2,3 but that this association varies between populations and population sub-groups.
For example, self-reported health is less predictive of mortality at older ages;4 has a stronger
association with mortality for men than for women;2 and is more predictive of mortality for those of
lower than those of higher socio-economic status, particularly among middle aged working adults.4
Variations in reporting of self-rated health over time,5 and by geographic region,6,7,8 including by
constituent country of the UK, have also been reported. Analysis of ecological associations using
area level data has shown that for a given level of health, mortality rates are higher in Scotland
than in Northern Ireland or Wales, an association that persists after control for socio-economic
status.1 Thus the Scottish population has the highest mortality rates of the constituent countries
of the UK, England the lowest, with Northern Ireland and Wales in between. However, on the
evidence of self-reported health data, the population of Northern Ireland is less healthy than that of
Scotland.1,9 In this study, we are able to explore this association using individual level, rather than
ecological, data to see whether there are differences between the constituent countries of the UK
in the relationship between self-reported health and subsequent mortality, and to investigate socioeconomic inequalities in mortality more generally.
Office for National Statistics
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Spring 2010
Methods
Data
We use data from the three census based longitudinal studies now available for England and
Wales, Scotland, and Northern Ireland. The ONS LS is a record linkage study of approximately
one per cent of the population of England and Wales enumerated at the 1971 Census (some
500,000 people); sample members were selected on the basis of four birthdays in the year. Record
linkage has been used to add information from subsequent censuses (1981, 1991, 2001) and data
from vital registration sources including births, to sample mothers and deaths of sample members
and their spouses.10 While losing emigrants and the deceased, the sample has been maintained
by the recruitment of new births and immigrants born on LS birthdays and so remains nationally
representative.
The SLS is a 5.3 per cent representative sample of the Scottish population based on 20 birthdays
in the year. A sample of approximately 265,000 SLS members was identified from the 1991
Census, with information linked in from the 2001 Census and other sources, including vital events,
cancer registrations and hospital episodes.11
The NILS is also modelled on the ONS LS and includes approximately 500,000 sample members
(around 28 per cent of the population of Northern Ireland). As with the ONS LS and the SLS, the
sample is maintained by recruitment of new births and immigrants born on the 104 NILS birthdays.
The NILS sample differs slightly from the ONS LS and SLS in that the initial sample was drawn
from the Health Card Registration System and then linked to the census, whereas in the other two
studies the initial sample was drawn from the census. Northern Ireland has a second census-based
dataset that links the 2001 Census returns for the entire enumerated population to subsequently
registered mortality data. However, the smaller NILS dataset was used for this study to maximise
comparability with the other UK longitudinal studies.
All three studies have associated user support services, which facilitate use of the data for
authorised researchers subject to disclosure control procedures. Further details of the data sets
and these support services are available elsewhere.12 Access to anonymised individual level data
is only possible in the respective statistical office safe setting (ONS for the ONS LS, The General
Register Office for Scotland for the SLS, and the Northern Ireland Statistics and Research Agency
for the NILS).
Dataset development
This study is based on analyses of those aged 35–74 at the 2001 Census and their mortality from
the time of the 2001 Census until 30 June 2006. This age range was chosen because in younger
groups levels of poor health and rates of mortality are very low, and in age groups 75 and over
fewer indicators of socio-economic status are available in the data sets. We excluded those living
in communal establishments, students not at their term time address and those lacking information
on self-rated health or marital status in the 2001 Census. Proportions excluded because of
non‑response to these questions in the census accounted for 1.3 per cent of the ONS LS sample,
1.4 per cent of the SLS sample and 3.2 per cent of the NILS sample. We created datasets for
both individual level and aggregated analyses. For the individual level analysis, we constructed
equivalent separate datasets for the ONS LS, SLS and NILS.
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Table 1Variables and categories used in individual level and
aggregated datasets, ONS LS, SLS, NILS 2001
Variable
Variable categories
Individual level datasets
Aggregated datasets
Self rated health
Good
Fairly good or not good
Good
Fairly good or not good
Gender
Male
Female
Male
Female
Age/Age group
Age–single years
35–49
50–64
65–74
Marital status
Married
Separated or divorced
Widowed
Never married
Upper secondary or degree
Lower secondary
None
Other* (ONS LS only)
Missing
Manager or professional
Intermediate **
Lower ***
Never worked, unemployed, student, other
Missing
Owner occupier
Social rental
Private rental or other
Missing
Yes
No
Missing
Married
Not married
Highest educational qualification
NS-SEC
Housing tenure
Car access
–
–
–
–
Socio-economic status score ****
–
0 (Highest)
1
2
3
4
5 (Lowest)
Missing data
Country
–
England and Wales
Scotland
Northern Ireland
Notes:
* This category includes City and Guilds, RSA/OCR and BTEC/Edexcel qualifications which cover qualifications from
entry to degree level.
** This group includes intermediate occupations, small employers and own account workers.
*** This group includes lower supervisory, technical, semi-routine and routine occupations.
For the aggregated analysis, we created aggregated count datasets for each LS and then
combined them. In aggregated datasets such as these, cells comprise counts of individuals with
a particular set of characteristics, (for example, being female, living in owner occupied housing
and aged 35–49), rather than individuals themselves. Disclosure control guidelines meant that
Office for National Statistics
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Population Trends 139
Table 2
Spring 2010
Socio-demographic and socio-economic characteristics of
the population aged 35–74 in England and Wales, Scotland
and Northern Ireland, ONS LS, SLS, NILS 2001
LS Sample
Variable
Categories
Age (years)
Mean
ONS LS
SLS
NILS
52.2
52.1
51.7
0.022
0.032
0.025
35–49
44.5
45.3
47.0
50–64
37.4
36.8
36.2
65–74
18.0
17.9
16.8
Men
48.8
47.9
48.3
Women
51.2
52.1
51.7
Married
69.3
68.1
71.5
Separated or divorced
14.1
13.9
10.4
5.9
6.9
6.6
Never married
10.7
11.0
11.5
Highest educational qualification
(per cent)
Upper secondary or degree
22.5
35.2
18.1
Lower secondary
None
Other
Missing
28.5
34.9
8.7
5.5
19.6
40.1
–
5.2
23.7
51.0
–
7.3
NS-SEC (per cent)
Manager or professional
30.0
28.9
25.2
Intermediate occupations, small employers and own account
19.8
18.9
19.8
Lower supervisory, technical, semi-routine and routine
33.5
39.7
35.1
3.8
3.4
5.4
Missing
12.9
9.2
14.6
Owner
78.4
72.3
78.2
Social housing tenant
13.3
20.7
14.2
Private housing tenant and other
5.9
5.1
4.0
Missing
2.4
1.9
3.6
Car
83.9
77.5
82.7
No car
14.5
21.2
14.8
Missing
1.6
1.2
2.5
Standard error
Age group (per cent)
Gender (per cent)
Marital status (per cent)
Widowed
Never worked, unemployed, student, other
Housing tenure (per cent)
Car access (per cent)
Socio-economic score
Mean (excluding those with missing values)
2.4
2.5
2.7
0.004
0.006
0.004
0 – Least disadvantaged
13.6
18.5
11.5
1
13.4
10.7
9.7
2
14.7
12.8
12.3
3
16.5
13.6
14.9
4
15.4
16.0
19.2
9.3
15.6
12.0
17.2
12.8
20.5
100
100
100
254,918
122,753
192,251
Standard error
Socio-economic score (per cent)
5 – Most disadvantaged
Missing
Total (per cent)
Number in analysis
Source: Analysis of ONS LS, SLS and NILS
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Spring 2010
cell counts of less than three were not permissible.13 For this reason in the aggregated analysis
we used age groups rather than single year of age, combined marital status categories and
created a socio-economic score derived from several variables rather than using each variable
separately. This score was derived from separate indicators as follows: car access (0), no car
access (1); home owner (0), private or social housing tenant (1); highest educational qualification
upper secondary or degree (0), lower secondary or other (1), none (2); manager or professional
(0), intermediate occupations (1), lower occupations, never worked, unemployed and students (2).
Higher scores thus indicate a greater level of disadvantage.
The main advantage of using the aggregated data set was that we could also include a variable
indicating country (England and Wales, Scotland, or Northern Ireland) and compare effects across
these directly.
Variables used in the analysis
In all analyses we dichotomised self-rated health into a variable, distinguishing those who reported
good health from those reporting ‘fairly good’ (termed ‘fair’ in some of the text below) or ‘not good’
health (hereafter referred to as ‘poor’ health). Mortality was measured from the census date, 29
April 2001, until 30 June 2006, the latest date that mortality data was available in all three data
sources, giving five years and two months of follow-up.
Table 1 shows the variable categories used in the individual and aggregated analysis.
Demographic variables comprised single year of age, or age group, gender and marital status.
Indicators of socio-economic status included individual-level highest educational qualification and
National Statistics Socio-Economic Classification (NS SEC), derived from information on
occupation and employment status, and two household-level variables, housing tenure and
household access to one or more cars or vans. Variables and categories of variables were identical
Figure 1
Percentage of the population aged 35–74 with fair or poor
self-rated health by age group, gender and country, ONS LS,
SLS, NILS, 2001
70
England and Wales
Scotland
Northern Ireland
60
50
40
30
20
10
0
35–49
50–64
65–74
Men
35–74
35–49
50–64
65–74
35–74
Women
Source: ONS LS, SLS, NILS 2001
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Population Trends 139
Spring 2010
in all three sources with the exception of highest educational qualification. The ONS LS education
variable included an additional category of ‘other’ which the SLS and NILS did not have.
Statistical methods
We undertook preliminary descriptive analyses of the three samples using the individual level
datasets. We used multivariate logistic regression to analyse differentials in self-reported health by
socio-demographic characteristics using the individual level datasets, and by socio-demographic
characteristics and country using the aggregated dataset. In the latter analysis we also present
results for each country separately, in order to allow comparison between the two methods.
Survival analysis, using Poisson regression, was undertaken to investigate associations between
self-rated health and socio-demographic characteristics with subsequent mortality. Known
emigrants were excluded from date of leaving the respective study. In both analyses of self-rated
health and mortality we present results from models controlling for age and sex (Model 1), and
results from models additionally controlling for socio-demographic characteristics (Model 2). In the
aggregated analysis, country was included in both models. In the mortality analysis we also show
results from a third model including self-rated health. All analysis was carried out in the statistical
office safe settings and produced in accordance with disclosure control guidelines.
Descriptive results
Socio-demographic sample characteristics were broadly similar for England and Wales, Scotland
and Northern Ireland (Table 2). The samples were similar in age and gender distribution, except
that the Northern Ireland sample was slightly younger and included slightly more married and
fewer divorced members. Differences between the three study samples in the distribution of
sample members by educational level reflect both the separate identification of those with ‘other’
qualifications in England and Wales, and the different educational system in Scotland. Scotland
had the highest proportion in the highest education category at 39 per cent, compared with
25 per cent in England and Wales, and 21 per cent in Northern Ireland. In the Northern Ireland
sample, 51 per cent had none of the educational qualifications asked about, compared with
40 per cent of the Scottish sample, and 35 per cent of those in England and Wales. The Northern
Ireland sample also included a slightly lower proportion in managerial and professional occupations
and a slightly higher proportion in the category of never worked, unemployed, students or
other. The proportion in lower supervisory, technical, semi-routine or routine occupations was
largest in Scotland. In England and Wales, and Northern Ireland, 78 per cent of the sample were
owner-occupiers compared with 72 per cent in Scotland, where a larger proportion lived in social
housing. Those in Scotland were also slightly less likely to have access to a car or van. For the
socio-economic score, used in the aggregated dataset analysis, the NILS sample had the highest
proportion with missing values at 20 per cent, compared with 17 per cent in England and Wales and
13 per cent in Scotland (this illustrates one of the main disadvantages of using summary scores such
as this – the high proportion with missing values on at least one of the variables used to construct
it). The mean socio-economic score was lowest (representing a lower mean level of disadvantage)
in England and Wales at 2.4, and highest in Northern Ireland with a score of 2.7. Scotland had the
highest proportion of the sample in both the least and most disadvantaged categories.
Figure 1 shows the proportions with fairly good or not good self-rated health by gender, age group
and country. These proportions were higher among women than men and higher in Northern
Ireland than in Scotland or England and Wales.
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Population Trends 139
Figure 2
Spring 2010
Mortality rate by gender and country for those aged 35–49,
ONS LS, SLS, NILS, 2001
Rate per 1000
2.5
2
1.5
1
0.5
0
England and
Wales
Figure 3
Scotland
N. Ireland
Male
England and
Wales
Scotland
N. Ireland
Female
Mortality rate by gender and country for those aged 50–64,
ONS LS, SLS, NILS, 2001
Rate per 1000
12
10
8
6
4
2
0
England and
Wales
Scotland
Male
N. Ireland
England and
Wales
Scotland
N. Ireland
Female
Multivariate results
Self-rated health
Table 3 and Table 4 show results from logistic regression analysis of differentials in the proportions
reporting not good or fairly good self-rated health. In both individual level (Table 3) and aggregated
analysis (Table 4), the odds of poorer self-rated health increased with age, and were significantly
higher for women than men, although the gender difference was smaller once marital status and
socio-economic status were controlled (Model 2). Inclusion of single year of age in the individual
level models was a better control than in aggregated models which only included three age
groups, as confirmed by a comparison of r-squared values for Model 1 individual level versus
aggregated dataset analysis (r = 0.042 for individual analysis and r = 0.037 for aggregated
analysis, for Scotland). Unmarried people were more likely to report poor or fair self-rated health
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Population Trends 139
Figure 4
Spring 2010
Mortality rate by gender and country for those aged 65–74,
ONS LS, SLS, NILS, 2001
Rate per 1000
35
30
25
20
15
10
5
0
England and
Wales
Figure 5
Scotland
N. Ireland
Male
England and
Wales
Scotland
N. Ireland
Female
Mortality rate by gender and country for those aged 35–74,
ONS LS, SLS, NILS, 2001
Rate per 1000
12
10
8
6
4
2
0
England and
Wales
Scotland
Male
N. Ireland
England and
Wales
Scotland
N. Ireland
Female
Source: ONS LS, SLS, NILS 2001
than the married. In the individual level analysis, in which we were able to distinguish between
unmarried groups, we found that the separated, divorced and never married, but not the widowed,
were significantly more likely to report not good or fairly good health than the married. In England
and Wales, and Scotland the widowed were in fact marginally less likely to report not good or
fairly good health than the married (Odds Ratio (OR) for England and Wales 0.96, 95 per cent
confidence interval (CI) 0.92–0.99). In all countries, those living in social housing, with no car,
with no recorded educational qualification and in lower status occupations or not employed were
the most likely to report not good or fairly good health. Reported health differentials by tenure
appeared weaker in England and Wales than Scotland or Northern Ireland, whereas health
differentials by NS-SEC appeared stronger in England and Wales than the other countries. For
example, in England and Wales the odds of reporting not good or fairly good health among
those who had never worked were 89 per cent higher than among managers or professionals
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Population Trends 139
Spring 2010
(CI 1.80–1.99), whereas the equivalent figure for Scotland was 55 per cent (CI 1.44–1.67).
Differentials in health status by educational level appeared smaller in Scotland than in England
and Wales or Northern Ireland. In general, those with missing data were more likely than the
most advantaged reference category to report not good or fairly good health, but did not appear
to have the worst health. Results from analysis of the aggregated datasets (Table 4) showed that
in each country increasing socio-economic score (indicating a higher level of disadvantage) was
associated with poorer reported health. This association appeared to be the strongest in Northern
Ireland, where those in the most disadvantaged category had 5.4 times the odds of reporting not
good or fairly good self-rated health than the least disadvantaged (CI 5.19–5.66). In England and
Wales the equivalent ratio was 4.4 (CI 4.20–4.52) and in Scotland, 4.7 (4.47–4.89).
After adjusting for age and gender (Table 4, Model 2), those in Northern Ireland were 10 per cent
more likely to report not good or fairly good health (CI 1.09–1.11) than those in England and Wales,
but there was no difference in this regard between Scotland and England and Wales. However,
after additionally adjusting for marital status and socio-economic score (Model 2), the odds of
reporting not good or fairly good self-rated health were slightly lower in Scotland than in England
and Wales (OR 0.96, CI 0.95–0.97).
Mortality
Figures 2, 3, 4 and 5 show mortality rates (deaths/person years of exposure) by country, age
group and gender. In all age groups, men had higher rates of death than women. Those in
Scotland had higher mortality rates than those in England and Wales or Northern Ireland, although
in the youngest age group, in which the numbers of deaths observed were lowest, country
differentials were small and not statistically significant. Age and sex standardisation demonstrated
that for those aged 35–74, mortality rates in Scotland were 24 per cent higher than in England and
Wales, and Northern Ireland’s mortality rate was three per cent higher.
The main aim of the mortality analysis was to examine the association between health status and
subsequent mortality in the three countries. Results show risks of death relative to a reference
category. First, we briefly describe associations between other co-variates and mortality.
In all countries rate ratios of mortality increased with age, and were higher for men than for women,
a difference that increased once marital status and socio-economic status were controlled for
(Table 5). Although widows and widowers were no more likely to report not good or fairly good
health than the married, in all countries their risks of death were higher. Indeed in England and
Wales, relative risk ratios for the widowed were as high as for the separated, divorced and never
married.
Consistent with the analysis of variations in self-rated health, mortality was highest for: tenants in
social housing; those with no educational qualifications; and for those who had never worked, were
unemployed, students or unclassified. Analysis of separate country aggregated datasets showed
that there was a stronger association between socio-economic score and mortality in Northern
Ireland than in the other countries. After control for self-rated health status, the association
between socio-economic status and mortality weakened in all models and for all countries, but
remained significant. In other words, while strongly related to survival, variation in health status
only partly explained the association between socio-economic status and mortality.
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Spring 2010
Analysis of the combined country aggregated dataset demonstrated that, after controlling for age
group and gender (Table 6), the Scottish sample had significantly higher risks of death in the 5
years and two months following the 2001 Census than those in England and Wales (RR 1.23,
CI 1.19–1.27). In Northern Ireland, mortality risks were not significantly different from England and
Wales (RR 1.01 CI 0.98–1.05). After control for socio-economic and marital status, the ratio for
Scotland decreased marginally to 1.19 (CI 1.15–1.23) and the rate ratio for Northern Ireland fell to
0.95 (CI 0.92–0.98) indicating a significantly lower risk of death than in England and Wales (after
control for marital status and socio-economic status). Additional control for self-rated health status
(Model 3, all countries) did not alter the differences between countries in terms of mortality risks.
Those reporting not good or fairly good health in 2001 were more than twice as likely to die in the
follow up period than those reporting good health, after controlling for socio-demographic and
socio-economic factors (Model 3, Tables 5 and 6). However there was some variation in the
association found using the different analysis strategies, with rate ratios associated with reporting
poor or fair health being 7–9 per cent higher in the analysis of the individual level data than
in the aggregated dataset. This is probably because of poorer control for socio-demographic
and socio‑economic factors in the analysis of the aggregated data, because of the need to use
collapsed and less detailed indicators (age group rather than single year of age, two rather than
four categories of marital status, and socio-economic score instead of separate socio-economic
indicators). The association also varied by country. Using both analysis strategies we found that
the association between health status and mortality was stronger in Scotland, after control for all
other factors (aggregated analysis RR 3.01, CI 2.81–3.22) than in England and Wales (RR 2.57
CI 2.45–2.70) or Northern Ireland (RR 2.69 CI 2.54–2.86).
Summary of results
Consistent with previous studies, these results showed that in all constituent countries of the UK,
women were more likely than men to report not good or fairly good self-rated health, but were less
likely to die in the follow up period. The never-married, divorced and separated were also more
likely to report not good or fairly good health. All unmarried groups, including the widowed, were
more likely to die in the follow up period than the married. Living in social housing, not having a
car, having no educational qualifications and having never worked or being unemployed were all
associated with higher levels of self-reported not good or fairly good health and with mortality,
as was overall worse socio-economic score. There was some variation in the strength of these
associations by country. Analysis using the socio-economic status score, for example, suggested
that socio-economic differentials in health and mortality were larger in Northern Ireland than in
Scotland or England and Wales.
We found a strong association between reporting of not good or fairly good health and mortality in
all countries. This association appeared stronger in Scotland than Northern Ireland or England and
Wales. This reflects our finding that members of the Scottish sample were no more likely to report
not good or fairly good health than those in England and Wales, but that they had higher relative
risks of death. This might indicate variations in pre-death health status in different parts of the UK
or differences in the thresholds at which people in different parts of the UK report not having good
health, or a combination of both. This would account both for the apparently lower risks of poorer
health in Scotland, despite higher mortality, and the stronger association between self-rated health
and mortality in Scotland.
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Spring 2010
Discussion
In this article, we explored different strategies for comparative analysis of the ONS LS, the SLS
and the NILS. All three studies have a very similar design and, even though each country has its
own census form, most questions are identical and there is UK-wide co-ordination on census form
development, data collection and data processing.14,15 Registration of deaths and processing of
mortality data are also co-ordinated and comparable. There are, however, some minor differences
in categories used which need consideration, namely the inclusion of an additional educational
qualification category in the England and Wales Census. There are also differences in the
distribution of the populations by educational and housing tenure indicators, reflecting the fact that
in Scotland upper secondary level qualifications are gained a year earlier than in England, Wales
or Northern Ireland and that the social housing stock (relative to population size) is far larger.
These differences may explain why differentials in Scotland ,in health by education appeared
weaker and by housing tenure stronger, than in England and Wales or Northern Ireland.
These country differences in education and housing tenure also influenced the comparability of
the socio-economic score used in the aggregated datasets, which was based on all four socioeconomic indicators. For example Scotland had the highest proportion in the least disadvantaged
category of the socio-economic score, which is likely to have been in part a result of the large
proportion in the highest education category. Therefore, care must be taken in interpreting country
differences, especially by socio-economic status. The other factor affecting comparability of results
between countries is the differing proportions of non-respondents for the socio-economic status
variables. This was a particular problem when combining socio-economic variables to produce
the score used in analysis of the aggregated data set in which the proportions with missing data
ranged from 13 per cent in Scotland to 20 per cent in Northern Ireland.
Strengths and weaknesses of each analysis strategy
Development of the individual level datasets involved standard application procedures, and so
they were quicker and easier to prepare and use than the datasets for the aggregated analysis.
There were no limits on the variables and categories used in the individual level datasets because
all analysis was carried out in the safe setting for each longitudinal study. Preparation of the
aggregated datasets was much more time consuming and logistically complex. It took time to
obtain approval for release of aggregated NILS and SLS datasets from their respective safe
settings to the ONS safe setting, where analysis of the aggregated data set was undertaken, and
for the statistical offices to put into place secure data transfer systems.
Data set preparation also took much longer than for the individual level datasets, because of the
iterative process necessary to ensure that all datasets met disclosure control protocols of each
longitudinal study and ensure that they were also identical in terms of the variables and categories
included.
Statistically, the individual level datasets provided more detailed, richer information than the
aggregated datasets, including individual year of age instead of three age groups, four marital
status groups instead of only two, and separate socio-economic variables instead of a combined
socio-economic score. We therefore obtained more detailed country comparisons of the
associations between different socio-economic and demographic indicators associations using the
individual level datasets, and variables (particularly age) were more completely controlled than in
Office for National Statistics
24
Widowed
Never married
0.090
1.63
1.89
1.34
1.11
1.00
1.48
1.46
1.72
1.16
1.00
1.07
1.49
1.00
1.21
1.25
1.84
1.00
1.08
0.96
1.20
1.00
***
***
***
***
***
***
***
***
***
***
***
***
***
*
***
Model 1
1.04
1.20
1.00
0.042
***
***
Office for National Statistics
Source: Analysis of ONS, GROS, NISRA
1.17,1.23
1.04,1.05
Model 2
0.101
1.53
1.55
1.34
1.11
1.00
1.42
1.60
1.23
1.00
1.20
1.54
1.00
1.38
1.40
1.99
1.00
1.10
0.97
1.31
1.00
1.13
1.00
1.04
***
***
***
***
***
***
***
**
***
***
***
***
***
***
***
***
Model 1
1.05
1.19
1.00
0.050
***
***
1.16,1.21
1.05,1.05
Model 2
0.110
1.50
1.62
1.39
1.12
1.00
1.49
1.88
1.30
1.00
0.83
1.45
1.00
1.29
1.41
2.10
1.00
1.11
1.01
1.35
1.00
1.13
1.00
1.04
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
1.45,1.56
1.54,1.70
1.35,1.44
1.08,1.15
1.42,1.56
1.82,1.95
1.26,1.35
0.78,0.89
1.40,1.49
1.22,1.37
1.34,1.48
2.03,2.17
1.08,1.15
0.97,1.06
1.30,1.39
1.11,1.15
1.04,1.04
Odds Sign Confidence
ratio
limits
Northern Ireland
Odds Sign Confidence
ratio
limits
192,251
1.45,1.61
1.44,1.67
1.29,1.38
1.07,1.15
1.33,1.52
1.54,1.65
1.18,1.27
1.07,1.34
1.48,1.59
1.26,1.51
1.32,1.48
1.92,2.06
1.06,1.15
0.92,1.02
1.26,1.36
1.10,1.16
1.04,1.04
Odds Sign Confidence
ratio
limits
Scotland
Odds Sign Confidence
ratio
limits
122,753
1.58,1.69
1.80,1.99
1.31,1.38
1.08,1.14
1.42,1.55
1.41,1.51
1.68,1.77
1.13,1.19
0.99,1.15
1.45,1.53
1.14,1.28
1.21,1.30
1.79,1.89
1.05,1.11
0.92,0.99
1.17,1.23
1.07,1.11
1.09
1.04,1.04
***
***
1.00
1.04
Odds Sign Confidence
ratio
limits
Model 2
* p < 0.05 ** p < 0.01 *** p < 0.001. Model 1: Age. Model 2: Additionally includes marital status and socio-economic score
R2
0.040
Never worked, unemployed, student, other
Missing
254,918
Lower supervisory, technical, semi-routine
and routine
Number in analysis
Intermediate occupations, small
employers and own account
NSSEC (Reference: manager or
professional)
Other
Missing
Lower secondary
None
Education (Reference: upper secondary
or degree)
Missing
No
Car access (Reference: yes)
Missing
Social housing tenant
Private rental and other
1.15,1.19
1.04,1.05
***
***
Housing tenure (Reference:
owner occupier)
Separated or divorced
1.17
Women
Marital status (Reference: married)
1.00
1.05
Odds Sign Confidence
ratio
limits
Model 1
England & Wales
Odds Ratios from logistic regression analysis of variations in the proportion of the
population aged 35–74 with poor or fair self-rated health in 2001 by socio-demographic
and socio-economic characteristics in England and Wales, Scotland and Northern Ireland.
ONS LS, SLS, NILS 2001, using parallel datasets
Gender (Reference: men)
Age
Table 3
Population Trends 139
Spring 2010
25
65–74
3.02
Missing
R2
Number in analysis
Northern Ireland
0.073
4.36
5 (most disadvantaged)
0.037
2.70
4
254,918
1.86
3
Scotland
122,753
***
***
***
Sign
1.17,1.23
3.15,3.36
1.86,1.96
Confidence
limits
1.33
1.00
1.12
1.00
2.53
1.75
Odds
ratio
***
***
***
***
***
2.92,3.12
4.20,4.52
2.62,2.79
1.80,1.92
1.46,1.56
0.037
0.085
3.30
4.67
2.67
1.96
1.60
1.34
1.51
Country (Reference: England and Wales)
1.20
1.00
3.25
1.91
Odds
ratio
1.23
1.19,1.28
1.19,1.23
1.06,1.09
2.66,2.79
1.73,1.80
Confidence
limits
Model 1
1.00
***
***
***
***
***
Sign
Model 2
Scotland
1.00
1.21
1.00
1.08
1.17
2.72
1.00
1.15,1.19
3.30,3.46
1.77
Odds
ratio
1.00
***
***
3.38
1.85,1.92
Confidence
limits
2
Socio-economic score
(Reference: least disadvantaged)
1
Not married
Marital status (Reference: married)
Women
Gender (Reference: men)
***
Sign
1.88
Odds
ratio
Model 1
England and Wales
***
***
***
***
***
***
***
***
***
***
Sign
Model 2
3.15,3.45
4.47,4.89
2.56,2.79
1.87,2.05
1.52,1.67
1.28,1.41
1.30,1.37
1.10,1.15
2.44,2.62
1.71,1.80
Confidence
limits
Odds Ratios from logistic regression analysis of variations in the proportion of the
population aged 35–74 with poor or fair self-rated health in 2001 by socio-demographic
and socio-economic characteristics in England and Wales, Scotland and Northern Ireland
and for all countries combined. ONS LS, SLS, NILS 2001 using combined aggregated
datasets
Age group (Reference: 35–49)
50–64
Table 4
Population Trends 139
Spring 2010
Office for National Statistics
26
Continued
***
3.75
2.93
5.42
3.13
4
5 (most disadvantaged)
Missing
Source: Analysis of ONS LS, SLS and NILS
Model 2: Additionally includes marital status and socio-economic score
Model 1: Age (and country for all areas combined)
* p < 0.05 ** p < 0.01 *** p < 0.001
R2
0.046
0.040
569,922
1.10
Number in analysis
0.99
3.01,3.25
5.19,5.66
2.82,3.04
2.00,2.17
1.62,1.77
Northern Ireland
***
***
***
***
***
Scotland
0.090
2.08
3
Country (Reference: England and Wales)
***
***
***
***
Sign
0.98,1.01
1.17,1.19
3.42,3.52
1.96,2.01
Confidence
limits
1.28
1.00
1.10
1.00
2.76
1.84
Odds
ratio
1.09,1.11
0.081
1.01
0.96
1.00
3.09
4.77
2.76
1.95
1.58
1.27
1.18
1.00
3.47
1.99
Odds
ratio
1.28
1.22,1.34
1.31,1.37
1.09,1.13
2.88,3.04
1.96,2.05
Confidence
limits
Model 1
1.00
***
***
***
***
***
Sign
Model 2
All UK countries
1.00
1.34
1.69
192,251
1.11
1.00
1.19
2.96
2.01
Odds
ratio
1.00
1.17,1.21
3.65,3.85
2.13,2.22
Confidence
limits
1.00
***
***
Sign
2.18
Odds
ratio
Model 1
Northern Ireland
2
Socio-economic score
(Reference: least disadvantaged)
1
Not married
Marital status (Reference: married)
Women
Gender (Reference: men)
65–74
Age group (Reference: 35–49)
50–64
Table 4
***
***
***
***
***
***
***
***
***
***
***
Sign
Model 2
0.99,1.02
0.95,0.97
3.02,3.16
4.66,4.88
2.70,2.82
1.90,1.99
1.55,1.62
1.24,1.30
1.26,1.30
1.08,1.11
2.72,2.81
1.82,1.87
Confidence
limits
Population Trends 139
Spring 2010
Office for National Statistics
27
Population Trends 139
Spring 2010
the aggregated dataset analyses, as confirmed by the r-squared values. Additionally, it was only
possible to carry out an exploration of the characteristics of non-respondents to certain census
questions using individual level and not aggregated datasets, because of small numbers that
would have precluded clearance of such an aggregated dataset. There are therefore a number of
advantages to using the individual level datasets. However, the major drawback was the difficulty
in formally ascertaining country differences in the outcomes of interest. Using the combined
aggregated datasets, we were easily able to ascertain country differences in health and mortality
controlling for all co-variates and so add considerably to our knowledge of UK inequalities in health
and mortality, and associations between self-rated health and mortality.
In summary, the individual level datasets provided much richer data with more variables and less
time taken for dataset development (although for this project, this involved travel to three UK
locations). However there was no easy way to make statistical comparisons between the countries.
The combined aggregated datasets were logistically much more challenging and time consuming
to prepare, had less variable detail, but enabled direct analysis of country comparisons. Both
methods therefore have benefits, and the choice is likely to depend on the focus of research.
Stringent disclosure control procedures on cell release of data from statistical office safe settings
also means that this strategy would not be suitable for those wishing to analyse rare outcomes or
more detailed variable categories.
Although it is not possible at present, the ability to combine subsets of individual level data from
the three studies would combine the benefits of both of the methods currently possible – there is
no question that this approach would be scientifically stronger. Given that the census offices pass
census data between them, we would hope that it should be possible to develop relevant protocols
and legal agreements to make the passing of longitudinal study data a future possibility. Finally,
in the course of this project we developed a number of resources, including a technical working
paper, comparative data dictionary and a comparative overview of database structure that we hope
will be useful for others wishing to pursue UK comparative analyses. These are available via the
web sites of all three user support services.
Acknowledgements
The research reported here was funded by the Economic and Social Research Council, grant
reference RES-348-25-0013.The permission of the Office for National Statistics to use the
Longitudinal Study is gratefully acknowledged, as is the help provided by staff of the Longitudinal
Studies Centre – Scotland (LSCS); the Northern Ireland Longitudinal Study (NILS) and the Centre
for Longitudinal Study Information and User Support (CeLSIUS) service. The LSCS is supported
by the ESRC Census of Population Programme (Award Ref: RES-161-25-0001-01), the Scottish
Funding Council, the Chief Scientist’s Office and the Scottish Government; the NILS is funded by
the Department of Health, Social Services and Public Health, and the Research and Development
Office of the Health and Personal Services in Northern Ireland. CeLSIUS, is supported by the
ESRC Census of Population Programme (Award Ref: RES-348-25-0004). The authors alone are
responsible for the interpretation of the data. Census output is Crown copyright and is reproduced
with the permission of the Controller of HMSO and the Queen’s Printer for Scotland.
Office for National Statistics
28
Population Trends 139
Table 5
Spring 2010
Rate ratios of mortality for the population aged 35–74 by
socio-demographic and socio‑economic characteristics and
health status in England and Wales, Scotland and Northern
Ireland. ONS LS, SLS, NILS 2001 using parallel datasets
England & Wales
Model 1
Model 2
Model 3
Rate Sign Confidence Rate Sign Confidence Rate Sign Confidence
ratio
limits ratio
limits ratio
limits
Age
1.10
***
1.10,1.11
1.09
***
1.09,1.10
1.09
***
1.08,1.09
0.65
***
0.62,0.68
0.58
***
0.55,0.60
0.58
***
0.55,0.60
1.26
***
1.18,1.34
1.23
***
1.16,1.31
Gender (Reference: men)
Women
Marital status
(Reference: married)
Separated or divorced
Widowed
1.23
***
1.14,1.32
1.24
***
1.16,1.33
Never married
1.24
***
1.15,1.34
1.26
***
1.16,1.36
1.51
***
1.43,1.60
1.38
***
1.30,1.46
Housing tenure
(Reference: owner occupier)
Social housing tenant
Private housing tenant and other
1.25
***
1.14,1.37
1.19
***
1.09,1.30
Missing
1.39
***
1.23,1.56
1.34
***
1.19,1.51
No
1.49
***
1.41,1.58
1.40
***
1.32,1.48
Missing
1.17
*
1.01,1.36
1.17
*
1.00,1.35
1.00,1.19
1.06
***
1.30,1.52
1.26
***
1.17,1.36
Car access (Reference: yes)
Education (Reference: upper
secondary or degree)
Lower secondary
1.09
None
1.41
0.98,1.16
Other
1.27
***
1.16,1.40
1.17
**
1.07,1.29
Missing
1.50
***
1.36,1.65
1.37
***
1.24,1.51
0.97,1.13
1.02
0.95,1.10
0.98,1.12
NSSEC (Reference: manager or
professional)
Intermediate occupations, small
employers and own account
1.05
Lower supervisory, technical,
semi-routine and routine
1.11
**
1.04,1.19
1.05
Never worked, unemployed,
student, other
1.34
***
1.21,1.50
1.21
***
1.09,1.35
Missing
1.21
***
1.12,1.31
1.11
**
1.03,1.20
2.38
***
2.26,2.50
Self-rated health
(Reference: good health)
Fair or poor health
Total person years analysed
R2
1,251,009
0.09
0.11
0.12
Office for National Statistics
29
Population Trends 139
Table 5
Spring 2010
Continued
Scotland
Model 1
Model 2
Model 3
Rate Sign Confidence Rate Sign Confidence Rate Sign Confidence
ratio
limits ratio
limits ratio
limits
Age
1.11
***
1.10,1.11
1.10
***
1.09,1.10
1.09
***
1.09,1.09
0.67
***
0.63,0.71
0.61
***
0.57,0.64
0.60
***
0.57,0.64
Gender (Reference: men)
Women
Marital status
(Reference: married)
Separated or divorced
1.36
***
1.25,1.48
1.30
***
1.20,1.42
Widowed
1.14
**
1.05,1.25
1.15
**
1.05,1.25
Never married
1.28
***
1.16,1.41
1.29
***
1.17,1.42
Housing tenure
(Reference: owner occupier)
Social housing tenant
1.52
***
1.42,1.63
1.34
***
1.25,1.44
Private housing tenant and other
1.36
***
1.21,1.54
1.26
***
1.12,1.43
Missing
1.44
***
1.22,1.69
1.34
***
1.14,1.58
No
1.40
***
1.30,1.50
1.28
***
Missing
1.25
*
1.02,1.53
1.19
0.97,1.46
0.95,1.18
0.99
0.89,1.11
Car access (Reference: yes)
1.19,1.37
Education (Reference: upper
secondary or degree)
Lower secondary
1.06
None
1.32
***
1.21,1.45
1.18
***
1.07,1.29
1.38
***
1.21,1.58
1.26
***
1.11,1.43
0.92,1.14
1.02
Other
Missing
NSSEC (Reference: manager or
professional)
Intermediate occupations, small
employers and own account
1.03
0.92,1.14
Lower supervisory, technical,
semi-routine and routine
1.20
***
1.09,1.32
1.13
*
1.03,1.24
Never worked, unemployed,
student, other
1.36
***
1.16,1.61
1.27
**
1.08,1.49
Missing
1.26
***
1.13,1.41
1.17
**
1.05,1.31
2.82
***
2.63,3.01
Self-rated health
(Reference: good health)
Fair or poor health
Total person years analysed
R2
597,711
0.10
0.13
0.15
Office for National Statistics
30
Population Trends 139
Table 5
Spring 2010
Continued
Northern Ireland
Model 1
Model 2
Model 3
Rate Sign Confidence Rate Sign Confidence Rate Sign Confidence
ratio
limits ratio
limits ratio
limits
Age
1.11
***
1.10,1.11
1.10
***
1.09,1.10
1.09
***
1.09,1.09
0.64
***
0.61,0.67
0.58
***
0.55,0.61
0.57
***
0.54,0.60
Gender (Reference: men)
Women
Marital status
(Reference: married)
Separated or divorced
1.28
***
1.18,1.40
1.21
***
1.11,1.32
Widowed
1.13
**
1.05,1.23
1.13
**
1.04,1.22
Never married
1.26
***
1.16,1.36
1.25
***
1.16,1.35
Housing tenure
(Reference: owner occupier)
Social housing tenant
1.53
***
1.43,1.63
1.36
***
1.27,1.46
Private housing tenant and other
1.23
***
1.09,1.39
1.16
*
1.02,1.31
Missing
1.27
***
1.13,1.44
1.21
**
1.07,1.37
No
1.47
***
***
Missing
1.12
Car access (Reference: yes)
1.38,1.57
1.39
0.96,1.30
1.16
0.99,1.34
1.30,1.48
0.98,1.24
Education (Reference:
upper secondary or degree)
Lower secondary
1.16
*
1.03,1.31
1.10
None
1.39
***
1.25,1.54
1.20
***
1.08,1.33
1.49
***
1.31,1.69
1.32
***
1.16,1.50
0.97,1.18
1.04
Other
Missing
NSSEC (Reference: manager or
professional)
Intermediate occupations, small
employers and own account
1.07
0.95,1.15
Lower supervisory, technical,
semi-routine and routine
1.18
***
1.08,1.29
1.09
*
1.00,1.19
Never worked, unemployed,
student, other
1.43
***
1.27,1.62
1.31
***
1.16,1.48
Missing
1.27
***
1.15,1.39
1.16
**
1.05,1.27
2.50
***
2.35,2.66
Self-rated health
(Reference: good health)
Fair or poor health
Total person years analysed
R2
928,238
0.11
0.13
0.15
* p < 0.05 ** p < 0.01 *** p < 0.001
Model 1: Age.
Model 2: Additionally includes marital status and socio-economic score.
Model 3: Additionally includes health status indicator
Source: Analysis of ONS LS, SLS and NILS
Office for National Statistics
31
Population Trends 139
Table 6
Spring 2010
Rate ratios of mortality for the population aged 35–74 by
socio-demographic and socio-economic characteristics and
health status in England and Wales, Scotland and Northern
Ireland, and for all countries combined. ONS LS, SLS, NILS
2001 using combined aggregated datasets
England & Wales
Model 1
Model 2
Model 3
Rate Sign Confidence
ratio
limits
Rate Sign Confidence
ratio
limits
Rate Sign Confidence
ratio
limits
50–64
3.78
***
3.50,4.09
3.69
***
3.41,3.99
3.28
***
3.04,3.55
65–74
13.02
***
12.08,14.03
11.19
***
10.37,12.07
9.17
***
8.50,9.90
***
0.56,0.61
Age group
(Reference: 35–49)
Gender (Reference: men)
Women
1.00
0.65
1.00
***
Marital status
(Reference: married)
0.62,0.68
0.58
1.00
***
0.56,0.61
1.00
Not married
0.58
1.00
1.42
***
1.36,1.48
1.38
***
1.32,1.45
1
1.26
***
1.12,1.41
1.20
**
1.07,1.35
2
1.39
***
1.25,1.55
1.26
***
1.13,1.41
Socio-economic score
(Reference: least
disadvantaged)
3
1.57
***
1.41,1.74
1.35
***
1.22,1.50
4
1.94
***
1.76,2.15
1.56
***
1.41,1.73
5 (most disadvantaged)
2.93
***
2.65,3.25
2.16
***
1.95,2.40
Missing
2.48
***
2.25,2.73
1.94
***
1.76,2.14
2.57
***
2.45,2.70
Self-rated health
(Reference: good health)
Fair or poor health
Country (Reference:
England & Wales)
Scotland
Northern Ireland
Total person years
analysed
1,251,009
* p < 0.05 ** p < 0.01 *** p < 0.001
Source: Analysis of ONS LS, SLS and NILS
Office for National Statistics
32
Population Trends 139
Table 6
Spring 2010
Continued
Scotland
Model 1
Model 2
Model 3
Rate Sign Confidence
ratio
limits
Rate Sign Confidence
ratio
limits
Rate Sign Confidence
ratio
limits
50–64
4.25
***
3.83,4.72
3.98
***
3.58,4.42
3.52
***
3.17,3.91
65–74
13.97
***
12.63,15.45
11.23
***
10.14,12.45
9.18
***
8.29,10.18
***
0.57,0.64
***
1.30,1.46
Age group
(Reference: 35–49)
Gender (Reference: men)
Women
1.00
0.67
1.00
***
Marital status
(Reference: married)
0.64,0.71
0.61
1.00
***
0.58,0.65
1.00
Not married
0.61
1.00
1.46
***
1.38,1.55
1.38
1
1.17
*
1.00,1.38
1.10
2
1.41
***
1.23,1.63
1.24
Socio-economic score
(Reference: least
disadvantaged)
0.94,1.29
**
1.08,1.43
3
1.61
***
1.41,1.84
1.36
***
1.19,1.56
4
1.94
***
1.72,2.19
1.51
***
1.34,1.71
5 (most disadvantaged)
2.97
***
2.65,3.33
2.06
***
1.83,2.31
Missing
2.70
***
2.41,3.04
2.01
***
1.79,2.26
3.01
***
2.81,3.22
Self-rated health
(Reference: good health)
Fair or poor health
Country (Reference:
England & Wales)
Scotland
Northern Ireland
Total person years
analysed
597,711
* p < 0.05 ** p < 0.01 *** p < 0.001
Source: Analysis of ONS LS, SLS and NILS
Office for National Statistics
33
Population Trends 139
Table 6
Spring 2010
Continued
Northern Ireland
Model 1
Model 2
Model 3
Rate Sign Confidence
ratio
limits
Rate Sign Confidence
ratio
limits
Rate Sign Confidence
ratio
limits
50–64
3.90
***
3.57,4.26
3.69
***
3.38,4.04
3.20
***
2.92,3.50
65–74
13.54
***
12.43,14.74
11.27
***
10.34,12.29
8.99
***
8.24,9.81
***
0.54,0.60
***
1.28,1.42
Age group
(Reference: 35–49)
Gender (Reference: men)
Women
1.00
0.64
1.00
***
Marital status
(Reference: married)
0.61,0.68
0.58
1.00
***
0.55,0.61
1.00
Not married
0.57
1.00
1.41
***
1.34,1.49
1.35
1
1.26
**
1.06,1.50
1.18
2
1.60
***
1.37,1.87
1.40
Socio-economic score
(Reference: least
disadvantaged)
0.99,1.41
***
1.20,1.64
3
1.80
***
1.56,2.08
1.49
***
1.29,1.72
4
2.12
***
1.84,2.43
1.62
***
1.41,1.86
5 (most disadvantaged)
3.44
***
2.99,3.95
2.37
***
2.06,2.72
Missing
2.71
***
2.37,3.10
2.03
***
1.77,2.33
2.69
***
2.54,2.86
Self-rated health
(Reference: good health)
Fair or poor health
Country (Reference:
England & Wales)
Scotland
Northern Ireland
Total person years
analysed
942,434
* p < 0.05 ** p < 0.01 *** p < 0.001
Source: Analysis of ONS LS, SLS and NILS
Office for National Statistics
34
Population Trends 139
Table 6
Spring 2010
Continued
All
Model 1
Model 2
Model 3
Rate Sign Confidence
ratio
limits
Rate Sign Confidence
ratio
limits
Rate Sign Confidence
ratio
limits
50–64
3.93
***
3.74,4.14
3.76
***
3.57,3.96
3.31
***
3.14,3.49
65–74
13.41
***
12.77,14.09
11.24
***
10.70,11.82
9.12
***
8.67,9.59
***
0.64,0.67
0.59
***
0.57,0.60
0.58
***
0.57,0.60
1.43
***
1.39,1.47
1.37
***
1.33,1.41
1
1.25
***
1.15,1.35
1.18
***
1.09,1.28
2
1.45
***
1.34,1.56
1.29
***
1.20,1.39
Age group
(Reference: 35–49)
Gender (Reference: men)
Women
1.00
0.65
Marital status
(Reference: married)
Not married
Socio-economic score
(Reference: least
disadvantaged)
3
1.64
***
1.52,1.76
1.39
***
1.29,1.49
4
1.98
***
1.85,2.12
1.56
***
1.46,1.67
5 (most disadvantaged)
3.06
***
2.87,3.28
2.18
***
2.04,2.33
Missing
2.57
***
2.41,2.75
1.97
***
1.85,2.10
2.71
***
2.62,2.80
Self-rated health
(Reference: good health)
Fair or poor health
Country (Reference:
England & Wales)
Scotland
Northern Ireland
Total person years
analysed
1.00
1.23
***
1.01
1.19,1.27
1.19
***
1.15,1.23
1.2
***
1.16,1.24
0.98,1.05
0.95
**
0.92,0.98
0.94
***
0.91,0.97
2,791,153
* p < 0.05 ** p < 0.01 *** p < 0.001
Source: Analysis of ONS LS, SLS and NILS
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References
1 O’Reilly D, Rosato M et al. (2005) ‘Self reported health and mortality: ecological analysis based
on electoral wards across the United Kingdom’. British Medical Journal 331: 938–9.
2 Idler E and Benyamini Y (1997) ‘Self-rated health and mortality: a review of twenty-seven
community studies’. Journal of Health and Social Behaviour 38: 21–37.
3 DeSalvo K B, Bloser N et al. (2005). ‘Mortality Prediction with a Single General Self-Rated
Health Question: A Meta-Analysis’. Journal of General Internal Medicine 21(3): 267–275.
4 Singh-Manoux A, Dugravot A et al. (2007) ‘The association between self-rated health and
mortality in different socioeconomic groups in the GAZEL cohort study’. International Journal of
Epidemiology 36: 1222–1228.
5 Mitchell R (2005) ‘Commentary: The decline of death – how do we measure and interpret
changes in self-reported health across cultures and time ?’ International Journal of
Epidemiology 34: 306–308.
6 Rees P (1993) ‘Counting people: past, present and future’. University of Leeds. Review 36:
247–273.
7 Boyle P J, Gatrell A C et al. (1999) ‘Self-reported limiting long term illness, relative deprivation,
and population stability in England and Wales’. Social Science and Medicine 49: 791–9.
8 Bardage C, Pluijm S et al. (2005) ‘Self-rated health among older adults: a cross national
comparison’. European Journal of Ageing 2: 149–158.
9 Breakwell C and Bajekal M (2006) ‘Health expectancies in the UK and its constituent countries,
2001’. Health Statistics Quarterly 29: 18–25.
10Hattersley L and Creeser R (1995) ‘Longitudinal Study 1971–1991. History, organisation and
quality of data’. Series LS no. 7. London HMSO.
11 Boyle P J, Feijten P, Feng Z, Hattersley L, Huang Z, Nolan J and Raab G (2008) ‘Cohort Profile:
The Scottish Longitudinal Study (SLS)’. International Journal of Epidemiology 38: 385–392.
12More information available at: http://census.ac.uk/
13Under the NILS Disclosure Control Policy, outputs containing tabular data with counts lower
than ten are not released from the secure setting. However, as an exception and to facilitate
analysis by the research team, it was agreed that NILS would securely transfer data with counts
lower than ten to the ONS LS secure setting, though the final product contains no counts lower
than ten.
14UK Census Committee (1999) ‘The 2001 Census of Population’. UK Census Committee,
HMSO.
15Office for National Statistics (2005) ‘Census 2001 General report for England and Wales’,
HMSO. Available at: www.statistics.gov.uk/census2001/cn_143.asp
Office for National Statistics
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Population Trends 139
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Do partnerships last? Comparing
marriage and cohabitation using
longitudinal census data
Ben Wilson
Office for National Statistics
Rachel Stuchbury
Office for National Statistics and CeLSIUS (Centre for Longitudinal Study Information and User
Support), London School of Hygiene & Tropical Medicine
Abstract
The stability of couple partnerships is of continual interest to policy makers and many
users of official statistics. This research used a sample of adults (from the Office for
National Statistics Longitudinal Study) who were in a partnership (married or cohabiting) in
the 1991 Census of England and Wales, and then explored whether these individuals were
living with the same partner in 2001.
Marital partnerships were found to be more stable, even when additional factors were taken
into account. Of adults aged 16 to 54, around four in five adults (82 per cent) that were
married in 1991 were living with the same partner in 2001. The equivalent figure for adults
cohabiting in 1991 was around three in five (61 per cent), of whom around two-thirds (of
those remaining with the same partner) had converted their cohabitation to a marriage
by 2001. Long-running partnership stability was also found to vary according to the
socio‑demographic characteristics of individuals and their partners and a summary of these
variations is discussed.
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Contents
Abstract............................................................................................................................................ 37
Introduction....................................................................................................................................... 40
Previous research and different sources of data.............................................................................. 41
Analysis............................................................................................................................................ 42
Results............................................................................................................................................. 45
Changes in partnership status: cohabitation.................................................................................... 46
Comparing marriage and cohabitation............................................................................................. 47
Factors associated with stability....................................................................................................... 47
The influence of multiple factors....................................................................................................... 49
Further modelling of partnership outcomes...................................................................................... 53
Discussion........................................................................................................................................ 53
Key Findings..................................................................................................................................... 54
Acknowledgements.......................................................................................................................... 54
Appendix.......................................................................................................................................... 55
References....................................................................................................................................... 59
List of figures
Figure 1 Changes in partnership status.................................................................................... 40
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List of tables
Table 1
Partnership status and legal marital status................................................................. 43
Table 2Partnership status by sex, percentage in each age group in 1991............................. 43
Table 3Partnership status in 2001 by age in 1991 (percentage in each age group).............. 44
Table 4Partnership status in 2001 by partnership status in 1991 (percentages)................... 45
Table 5Partnership status in 2001 by partnership status and age in 1991 (percentages)..... 46
Table 6
Probability of having the same partner in 2001.......................................................... 48
Table A1Whether enumerated in the 2001 Census by de facto status in 1991........................ 55
Table A2
Whether enumerated in the 2001 Census by age in 1991......................................... 56
Table A3aPartnership status by sex, percentage in each age group in 1991............................. 57
Table A3bPartnership status by sex, percentage in each age group in 1990/91........................ 57
Table A4Probability of being married to same partner in 2001 (if cohabiting in 1991)............. 58
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Changes in partnership status
Figure 1 B
Not in a
partnership
Married
E
D
F
A
C
Cohabiting
Formation
Dissolution
A: Cohabitation formation (residential partnership, not with a spouse)
B: Marriage – without prior cohabitation (may or may not live with spouse)
C: Marriage – with prior cohabitation (may or may not live with spouse)
D: Cohabitation dissolution (end of residential partnership)
E: Divorce/Separation (end of marriage and/or living with spouse)
F: Divorce/Separation (moving in with a new partner, may still be legally married)
Introduction
There have been notable changes in UK partnership behaviour over the last 40 years. Divorce
rates rose considerably during the 1970s1, remained broadly stable after the mid-1980s, and
more recently have fallen since 20042. At the same time, there has been a long-term fall in
marriage rates since the beginning of the 1970s, and a steady increase in the proportion of adults
cohabiting3. For unmarried men in Great Britain aged 16 to 59, the proportion cohabiting increased
from 11 per cent in 1986 to 27 per cent in 2007. There was a similar change for equivalent
unmarried women, from 13 per cent to 28 per cent4,5.
This change in partnership behaviour is likely to persist. According to demographic projections, the
long-term rise in cohabitation will continue, with the number of cohabiting couples in England and
Wales projected to rise from 2.25 million in 2007 to 3.70 million in 20316. The same figures show
that the proportion of the adult population that is legally married is projected to fall from 49 per cent
in 2007 to 41 per cent by 20317. Official statistics provide considerable information on the
estimated and projected population by partnership status. However, there is limited comparative
information on the stability of different partnerships8. Furthermore, although the characteristics of
married and cohabiting couples are available from various sources3, information on the factors
associated with stability is also limited, largely due to a lack of suitable data (discussed later in this
article).
Information about partnership stability is important for many different users of official statistics.
For example, discussions about the legal rights of cohabiting couples might be informed by
comparing the stability of marriage and cohabitation9. This comparison also has implications
for policy areas concerning children in different family types. Knowledge of partnership stability
therefore informs policy connected with fertility, education, poverty, and any aspect of child welfare
(including maintenance and contact with parents). In addition, as the prevalence of cohabitation
and divorce has increased at older ages10, it is of interest to consider the impact that changes in
partnership stability might have on older people. The UK is an ageing society11, and any changes
in older people’s partnership histories or those of their progeny may affect family networks, care
arrangements, or retirement income. From a research perspective, it is of great interest to discover
how far the predictive power of marital status (for morbidity, mortality, socio-economic wellbeing
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and other outcomes) can also be attributed to cohabitation status (and for whom). For all of these
topics, it is not just stability that is of interest, but also the extent to which cohabitation transitions
differ from marital transitions.
Previous research and different sources of data
The study of partnership stability ideally requires data on partnership formation, dissolution, and
transformation (from cohabitation to marriage). Cohabitation may end when two partners cease
to live together (dissolution) or when two partners decide to marry (formation), but a marriage will
only end when it dissolves (see Figure 1)12. In this case, any analysis must take account of those
who cohabit and then marry. Considering all this, two ways to gather information on stability (or
partnership transitions) are by:
1. collecting retrospective partnership histories, and
2. using prospective longitudinal data13
It is also desirable that marriage can be reliably distinguished from cohabitation, and that the
results should be valid for the whole population14.
The General Household Survey (GHS) has included annual questions on partnership history –
including cohabitation – since 1979 (for women), and 1986 (for men)15. Research using this source
shows that in Great Britain there have been long-run increases (since the 1950s) in the proportion
of married women cohabiting before marriage16. Among those cohabiting in their first union, a
majority will marry their partner, although this proportion declines for more recent first unions17.
Current cohabitations, that is, those cohabiting at the time of the survey, tend to have begun more
recently than current marriages (although this compares partnerships that are not yet completed)18.
Nevertheless, it should be noted that the median duration of cohabitation increased between 1979
and 199519.
There are issues with research (such as that quoted above) using partnership history data.
Marriage and cohabitation histories from cross-sectional data (such as the GHS) have the
disadvantage that it is only possible to examine the partners by their characteristics at one point in
time. Also, retrospective history data can suffer from respondent recall problems, which are known
to be more likely with informal events such as the start or end of a cohabiting relationship20.
On the other hand, partnership stability can be researched using longitudinal birth cohort studies21,
although it takes several decades before the subjects themselves have acquired sufficient
experience of partnerships. It is possible to examine parental partnerships in birth cohort studies.
For example, results from the Millennium Cohort Study (MCS) showed that children living with both
their natural parents at nine months were more much likely to remain so at five years if the parents
were married to each other at nine months rather than cohabiting22. Of course, this result does not
consider partnerships where neither partner has children in the household, and like other birth
cohort studies it is only valid for a single cohort of children born between 2000 and 2002.
Longitudinal data where the panel is continuously refreshed can offer a reliable sample for the
whole population in any year. The British Household Panel Survey (BHPS) is one such source, and
has the advantage that partnership histories have been collected from most respondents. Previous
research has combined these histories with data from different waves of the survey to analyse
partnership transitions. For example, it has been estimated that within 10 years about three-fifths of
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first cohabitations turn into marriage, while just under a third dissolve23. The BHPS has also been
used to show that cohabiting couples are more likely than married couples to separate24.
One problem with the BHPS is its relatively small sample size. This is the case particularly when
looking at the cohabiting population (which is much smaller than the married population). An
alternative source (used for the research reported in this article), is the Office for National Statistics
(ONS) Longitudinal Study (LS). This has a much larger sample, one per cent of the population,
and has been used in previous research to explore partnership stability25. This research showed
that adults in couples (either married or cohabiting in 1991) who had a dependent child in the
household (in 1991) were more likely to be lone parents in 2001 compared with couples who had
no dependent children in the household (in 1991). They were also less likely to be ‘not in a family’
(that is. not partnered or a lone parent). Other research using the LS has shown that only a fifth
of cohabiting adults in 1991 were still cohabiting with the same partner in 2001 (although a further
two-fifths had married their 1991 partner)26. The research in this article follows on from this analysis
to compare cohabiting and married partnerships, and to explore the factors associated with
stability.
Unfortunately, apart from information on dissolutions due to widowhood, the LS only contains
partnership information for respondents every 10 years (for more information on the LS see the
section Analysis below). This means that it is not possible to know exactly when partnerships start
or end, or to consider each individual’s amount of exposure to the different partnership states.
It also means that some partnerships can be missed altogether because they begin and end
between two censuses. Of course, even when data are collected annually, changes within the year
may be missed27, and this should be considered when interpreting the results presented here and
elsewhere. Thus the term ‘stability’ is used here to refer to long-term changes in partnership status,
and the results only apply to a selected cohort of individuals (those enumerated at the 1991 and
2001 censuses of England and Wales).
Bearing these restrictions in mind, the questions addressed by this research are:
• What proportion of individuals remain with the same partner over a 10-year period?
• What are the differences between the stability of marriage and cohabitation?
• What are the characteristics associated with partnership stability?
• To what extent does cohabitation end in marriage, and what are the associated factors?
Analysis
This research uses the ONS Longitudinal Study (LS) to explore what happened to a cohort of
individuals who were married or cohabiting in 1991. It examines their partnership status 10 years
later in 2001, whether they are still living with the same partner, and what factors are associated
with changes in partnership. As with all of the LS results in this article, the data are for England
and Wales. The LS sample is selected by birthday, and continually replenished as new members
with LS birthdays are born or migrate into England or Wales. Data comprise linked census records
from 1971, 1981, 1991 and 2001 for sample members plus census records for those in their
household at each census. Data from vital events are also added, including birth or death of a
sample member, births and deaths of children to sample mothers and widowhoods to sample
members. Vital event information on marriage and divorce registration cannot be included in the
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Table 1
Spring 2010
Partnership status and legal marital status
Longitudinal sample, England and Wales, All adults aged 16+ in 1991
Partnership status
1991
Living with a partner
%
2001
194,092
61
194,712
61
213,554
Married and living with spouse
Cohabiting – single
%
220,117
12,343
4
14,251
4
Cohabiting – married (separated)
1,077
0
243
0
Cohabiting – divorced
5,653
2
10,166
3
Cohabiting – widowed
389
0
745
0
Not living with a partner
104,979
Single
Married (separated)
98,416
67,811
21
35,280
11
6,302
2
2,832
1
Divorced
14,425
5
27,921
9
Widowed
16,441
5
32,383
10
318,533
100
318,533
100
Total
Note: These frequencies are for the same sample of individuals in 1991 and 2001
Source: ONS Longitudinal Study (authors’ analysis)
LS, as date of birth, the key variable for matching data sources, is not asked on the registration
forms. In addition, since cohabitation (formation or dissolution) is not registered in any way there is
no corresponding way of including inter-censal information on cohabitation.
To begin with, a sub-sample of the LS was taken, giving over 435,000 adults (aged 16 and over)
who were enumerated at the 1991 Census28. After removing those living in communal
establishments and visitors to private households in 1991, the sample was reduced to 417,000.
It was further reduced by the selection of those who were also enumerated at the 2001 Census.
Table 2Partnership status by sex, percentage in each age group in
1991
Longitudinal sample, England and Wales
16–24
25–34
35–49
50–59
Total
(16–59)
Lone
45
22
20
12
100
Cohabiting
33
40
23
5
100
5
27
46
23
100
Lone
49
25
18
8
100
Cohabiting
20
44
29
7
100
2
24
48
26
100
Women
Married1
Men
Married1
1 Married and living with spouse
Source: ONS Longitudinal Study (authors’ analysis)
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These numbered 318,533 and formed the sample for this study, referred to henceforth as the
‘longitudinal sample’29.
Table A1 in the Appendix shows the initial sub-sample by partnership status at 1991 and whether
they were present at the 2001 Census. Over three-quarters of adults present in 1991 were also
present in 2001, with 14 per cent having died or embarked between 1991 and 2001, and the
remaining 11 per cent ‘missing’. The latter represent all individuals unaccounted for in the 2001
Census. There are many possible reasons for this, but the most likely are non-response in the
2001 Census or migration to a location outside England and Wales (without notifying a General
Practitioner)30.
Compared with women, men were more likely to be missing in 2001. This was particularly the case
for men who were cohabiting or not living with a partner in 1991. Compared with married women,
married men were more likely to have died or embarked. Around 97 per cent of the 60,000 deaths
and embarkations (of men and women) were deaths, so it is likely that this largely reflects the
fact that a marriage is more likely to end by the death of the male partner rather than the female
partner31. There are also variations in whether initial sub-sample members were ‘missing in 2001’
by age (see Table A2 in the Appendix).
Partnership status variables for 1991 and 2001 were constructed for this analysis. It should be
noted that they were intended to represent actual partnerships in the household, so adults were
only classified as married if the spouse was present in the household at census, and the same
of course applied to cohabitation. A few spouses and partners will not have been recorded by
the census (in 1991 or 2001), and therefore both married and cohabiting adults will be slightly
undercounted in favour of people not living with a partner. Since there was no direct question
about cohabitation in the 1991 Census and no household relationship grid, partnership status was
derived from information about relationships in the family and household (as explained below).
This means that there will also be a slight tendency throughout this research to undercount those
cohabiting32. Partnership status in 1991 was derived from the LS member’s position in the family33,
the relationship of other household members to the LS member, and the sex, age and marital
status of all household members. In 2001 it was derived from the same factors in 2001, as well
Table 3Partnership status in 2001 by age in 1991 (percentage in
each age group)
Longitudinal sample, England and Wales, All adults cohabiting in 1991
Partnership status in 2001
With the same partner
16–24
25–34
35–44
45–54
55–64
65+ Total (16+)
51
62
67
70
67
51
61
Cohabiting with the same partner
11
20
33
38
42
35
23
Married to the same partner
41
43
34
32
25
16
39
49
38
33
30
33
49
39
Partnership has ended
Cohabiting with a new partner
13
8
6
4
3
1
8
Married to a new partner
15
10
6
4
4
1
10
Not living with a partner
21
20
21
22
27
46
21
100
100
100
100
100
100
100
All individuals in age group
Source: ONS Longitudinal Study (authors’ analysis)
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as the LS member’s partnership status in 1991, and the widowhood records in the LS for 1991 to
2001. Other people in an LS member’s household are not linked from census to census, so there
is no cross-census identifier for them. The sex, date of birth, marital status and relationship to LS
member of the LS member’s partner from 1991, were used to determine whether that person was
still in the LS member’s household 10 years later.
Results
Table 1 provides a summary of partnership status for 1991 and 2001 respectively. In both years,
around two thirds of adults are living with a partner. These may be different individuals in different
years (the table does not show changes in individual partnership status). Nevertheless, the table
indicates that partnership is more common than not living with a partner, and that the majority of
partners are married. In 2001 there are larger proportions of divorced and widowed adults not living
with a partner, but this is to be expected given the fact that the sample is older in 200134.
Before investigating changes in individual partnership status, it is worth looking more closely at
the distribution of sample members by partnership status in 1991. Table 2 shows that in 1991,
cohabiting men and women tended to be younger than those who were married and living with
their spouse. Lone adults (that is not in a partnership) tended to be younger still. The raw data
from Table 2 was also compared with published GHS data for 1990/199135. Tables A3a and A3b
(in the Appendix) provide a summary of the comparison, which shows that the adult population by
partnership status has a similar age distribution for both sources (LS and GHS). It may therefore
be assumed that the sample is broadly representative of the 1991 adult population (by age and
partnership status), despite the fact that non-response will affect both sources, and non-response
may be different for the GHS and the 1991 Census. (For information on adults not responding to
the 2001 Census that were excluded from this sample, see Appendix Tables A1 and A2.) There
are additional issues that may affect both sources, but the comparison provides verification that
cohabiting adults were successfully identified from the 1991 Census.
Table 4Partnership status in 2001 by partnership status in 1991
(percentages)
Longitudinal sample, England and Wales, All adults aged 16 to 54 in 1991
Partnership status in 2001
Cohabiting
in 1991
Married
in 1991
All
partnerships
in 1991
With the same partner
61
82
79
Cohabiting with the same partner
22
0
3
Married to the same partner
39
82
77
39
18
21
Partnership has ended
Cohabiting with a new partner
9
3
4
Married to a new partner
10
5
5
Not living with a partner
21
10
12
100
100
100
Total
Source: ONS Longitudinal Study (authors’ analysis)
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Table 5Partnership status in 2001 by partnership status and
age in 1991 (percentages)
Longitudinal sample, England and Wales, All adults aged 16 to 54 in 1991
Partnership status in 2001
16–24
25–34
35–44
45–54
Total
(16–54)
Married in 1991
With the same partner
64
77
84
87
82
With a new partner
19
12
7
4
8
Not with a partner
Total
17
12
9
9
10
100
100
100
100
100
51
62
67
70
61
Cohabiting in 1991
With the same partner
With a new partner
27
18
12
8
18
Not with a partner
21
20
21
22
21
100
100
100
100
100
Total
Source: ONS Longitudinal Study (authors’ analysis)
Changes in partnership status: cohabitation
As indicated in Figure 1, cohabiting partnerships may end due to marriage, separation or death,
whereas marriages end in separation (and/or divorce) or death. To consider this additional
complexity, Table 3 shows only the population that were cohabiting in 1991, and what their
partnership status was in 2001. Of all cohabiting adults in 1991, 61 per cent were living with the
same partner in 2001 – 23 per cent cohabiting and 39 per cent married. Another way to summarise
this is that over the 10-year period, almost two in five cohabiting partners separated, and almost
two in five married their partner, while the remainder were still cohabiting.
Table 3 also shows considerable variation by age. Cohabitants aged 45 to 54 years were most
likely to remain with the same partner (compared with other age groups). The youngest cohabitants
aged 16 to 24, and the oldest aged 65 and over were the most likely to have separated. However,
although the youngest age group were the most likely to be living with a new partner (married
or cohabiting), the oldest were the most likely not to be in a partnership. These differences no
doubt reflect the influence of mortality at older ages. In addition, cohabitation among the young
might be expected to be more transient, and this is reflected in both the high level of separation
(cohabitation as a trial relationship) and the high level of cohabitants that marry (cohabitation as
a precursor to marriage). At ages over 35, the higher proportions of cohabitants that remain in a
cohabiting relationship with the same partner may be indicative of cohabitation as a substitute for
marriage at these ages (although it is not possible to state this with certainty).
Further analysis was carried out looking at the differences between male and female cohabitants.
Overall and at all ages female cohabitants were found to be more likely to have separated from
their partner over the 10 years compared with male cohabitants. They were also more likely not
to be living with a partner in 2001 (24 per cent, compared with 17 per cent for men), a fact that
is partially explained by mortality differentials between the sexes, and the likelihood that a male
partner will on average be older than the female partner36.
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Comparing marriage and cohabitation
Considering the above results, it is possible to compare the stability of couples who were
cohabiting in 1991 with those who were married (Table 4). For this comparison the age group (in
1991) has been restricted to 16 to 54-years-olds. This restriction does not materially affect the
distribution of partnership outcomes (as illustrated by comparing the total column in Table 3 with
the cohabiting column in Table 4). However, it does allow widowhood to be largely discounted as
a reason for partnership dissolution, which is important given the younger mean age of cohabiting
adults compared with the married population.
Table 4 shows that adults aged 16 to 54 in 1991 were more likely to be living with the same partner
in 2001 if they were married. Around four in five married adults (82 per cent) were living with the
same partner in 2001, compared with around three in five cohabiting adults (61 per cent). Of those
that were no longer living with the same partner (having been married or cohabiting), a little more
than half were not living with any partner at all. The remainder were living with a new partner, with
a slightly higher likelihood of being married rather than cohabiting.
Table 3 showed variations in the stability of cohabitations by age, and Table 5 shows similar results
for all partnerships in 1991. Previous research has shown that adults who marry at younger ages
are more likely to divorce, and the results in Table 5 do not contradict this finding37. However, it
should be remembered that the duration of existing partnerships in 1991 is not known, either for
marriage or for cohabitation. Importantly, the effects of age are similar for both marriage and
cohabitation, with young adults in partnerships in 1991 more likely to be separated from their
partner in 2001.
Despite the general finding that marriage is more stable than cohabitation, it is interesting to note
that the youngest married adults (aged 16 to 24 in 1991) were less likely to be living with the same
partner in 2001 compared with older cohabiting adults (aged 45 to 54). Despite this, marriages were
more stable when comparing partnerships in each age band. As with those cohabiting adults that
separated, married adults that separated were more likely to be living with a new partner if they were
young (aged 16 to 24), and more likely to live without a partner if they were older (aged 35 to 54).
Factors associated with stability
Table 5 shows the influence of a single factor – age on partnership stability. However, it is likely that
other socio-demographic factors will influence whether individuals remain with the same partner.
These other factors may also explain the variation by age. For example, younger partnerships may
be less stable, but this may be because young people are more likely to have other risk factors
associated with instability.
Reviewing the results of previous research, it is difficult to prepare an exhaustive list of potential
factors, partly because factors vary over time and according to which population is being studied.
In addition, much research focuses on marital stability (partly because of data constraints), and
caution should be exercised when considering the similarity of marital and cohabiting stability. With
this in mind, it is useful to mention a review published by the Lord Chancellor’s Department, which
stated that socio-demographic factors affecting marital stability may be placed in three groups:
characteristics of the individual’s parents, marital factors (demographic factors associated with the
couples’ partnership history and childbearing experience), and the individual’s own socio-economic
characteristics38.
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Table 6
Spring 2010
Probability of having the same partner in 2001
Longitudinal sample, England and Wales, Adults aged 16–54 and in partnerships in 1991 (Model 3 & 4 are sub-samples)
MODEL 1:
individual
characteristics
(n = 156,739)
Variable
Age in 1991
MODEL 2:
MODEL 3:
including partner cohabiting couples
characteristics
(in 1991) only
(n = 156,739)
(n = 18,501)
MODEL 4:
women only
(and if they
had a baby)
(n = 82,467)
Odds
ratio1
Sig.
level2
Odds
ratio1
Sig.
level2
Odds
ratio1
Sig.
level2
Odds
ratio1
Sig.
level2
1.05
***
1.05
***
1.04
***
1.06
***
0.95
***
0.97
***
0.96
***
Age gap in absolute years (0 = man 2 years older)
Married in 1991
Cohabiting (reference category)
1.00
n/a
1.00
n/a
1.00
n/a
Married
1.83
***
1.73
***
1.78
***
Female (ref.)
1.00
n/a
1.00
n/a
1.00
n/a
Male
1.11
***
1.11
***
1.13
***
No (ref.)
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
Yes
1.07
***
1.07
***
1.12
***
1.05
**
Yes (ref.)
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
No
1.25
***
1.10
***
1.16
*
1.10
**
Single or widowed or married (ref.)
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
Remarried or divorced (or married if cohabiting)
0.62
***
0.73
***
0.79
***
0.72
***
Degree or higher
1.38
***
1.21
***
1.18
**
1.12
**
Other professional or vocational qualification
1.21
***
1.13
***
1.14
*
1.15
***
No degree or professional qualification (ref.)
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
One: professional
1.20
***
1.12
**
1.12
1.15
Two: managerial or technical
1.05
***
0.98
0.97
0.95
*
Three: skilled non-manual
1.20
***
1.11
***
1.12
**
1.12
***
Three: skilled manual
1.14
***
1.09
***
1.11
**
1.01
Four: part-skilled, unskilled, other (ref.)
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
Unemployed (ref.)
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
Not economically active
1.31
***
1.25
***
1.14
*
1.25
***
Self-employed
1.33
***
1.21
***
1.20
**
1.22
***
Employed
1.38
***
1.24
***
1.26
***
1.16
***
Single or widowed or married (ref.)
1.00
n/a
1.00
n/a
1.00
n/a
Remarried or divorced (or married if cohabiting)
0.90
***
1.04
0.92
***
Yes (ref.)
1.00
n/a
1.00
n/a
1.00
n/a
No
1.61
***
1.17
*
1.33
***
Sex
Dependent children in household in 1991
Has limiting long term illness in 1991
Previous dissolution (marital status in 1991)
Higher qualifications in 1991
Social class (Registrar General’s) in 1991
Economic activity in 1991
Partner: previous dissolution (marital status in 1991)
Partner: has limiting long term illness in 1991
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Table 6
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Continued
Longitudinal sample, England and Wales, Adults aged 16–54 and in partnerships in 1991 (Model 3 & 4 are sub-samples)
MODEL 1:
individual
characteristics
(n = 156,739)
Variable
Odds
ratio1
Sig.
level2
MODEL 2:
MODEL 3:
including partner cohabiting couples
characteristics
(in 1991) only
(n = 156,739)
(n = 18,501)
Sig.
level2
MODEL 4:
women only
(and if they
had a baby)
(n = 82,467)
Odds
ratio1
Sig.
level2
Odds
ratio1
Odds
ratio1
Sig.
level2
Degree or higher
1.23
***
1.12
1.32
***
Other professional or vocational qualification
1.16
***
1.33
***
1.17
***
No degree or professional qualification (ref.)
1.00
n/a
1.00
n/a
1.00
n/a
One: professional
1.19
***
1.19
*
1.22
***
Two: managerial or technical
1.09
***
1.08
1.20
***
Three: skilled non-manual
1.10
***
1.07
1.07
*
Three: skilled manual
1.09
***
1.02
1.15
***
Four: part-skilled, unskilled, other (ref.)
1.00
n/a
1.00
n/a
1.00
n/a
Unemployed (ref.)
1.00
n/a
1.00
n/a
1.00
n/a
Not economically active
1.35
***
1.33
***
0.84
***
Self-employed
1.35
***
1.55
***
1.43
***
Employed
1.43
***
1.56
***
1.57
***
No (ref.)
1.00
n/a
Yes
1.28
***
Partner: highest qualification in 1991
Partner: social class (Registrar General’s) in 1991
Partner: economic activity in 1991
Had a baby between 1991 and 2001
Note: For Registrar General’s social class, other includes armed forces and missing
1 Reference categories are shown with an odds ratio of 1.00
2 * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level
n/a = reference category (significance is not applicable)
Source: ONS Longitudinal Study (authors’ analysis)
In the case of this research, the limits of the LS data mean that it is not possible to explore either
parental characteristics or some of the marital factors, such as age at marriage39. The same can be
said for psychological factors, such as behavioural and emotional problems, or wider social factors
(such as the effects of legislation on divorce and the rights of cohabiting couples). A final restriction
relates to unavailable socio-economic characteristics that would ideally be of interest, such as
income and religious belief40.
The influence of multiple factors
The next stage of this research uses logistic regression to create four models. Each of these
models explores the influence of multiple factors on a single outcome. that is whether an individual
who is partnered in 1991 remains with the same partner in 200141 (for an example of logistic
regression using the LS, see the online training module42).
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The first model explores the effect of individual characteristics; the second extends this to include
the characteristics of their partner; the third looks at 1991 cohabiting adults in isolation (that is
the model excludes those who were married in 1991); and the fourth looks at women only – both
married and cohabiting in 1991. It was decided to use 1991 data for all explanatory variables so
that circumstances prior to the outcome were being investigated.
Using 1991 data, the following individual factors were investigated:
• age – which indicates birth cohort and will be correlated with length of partnership up to 1991
• whether married or cohabiting – one of the main factors of interest
• whether dependent children were in the household. In 1991 a dependent child was a child aged
under 16 years, or a never married, economically inactive, full-time student aged under 19 years
• limiting long-term illness – to measure health
• marital status – indicating previous marital dissolution
• highest qualification – to measure socio-economic potential43,
• social class – to measure socio-economic circumstances, and
• employment status – to measure economic circumstances
Partner characteristics included the same variables used to measure individual factors. Age of
partner was not included because this was measured by looking at absolute age difference
between partners44. Sex of the LS member was also included for all models except the fourth,
which looked at women only45. To investigate the influence of childbirth on stability in the fourth
model, a variable was added showing the effect of whether women gave birth to a living child
between 1991 and 2001. This was the only factor using data from between the two censuses, and
was made possible because annual birth registrations are linked to individual data in the LS.
The results of all four models are shown in Table 6, which compares the influence of multiple
factors on stability. Table 6 also shows the effect of a single factor, for example age, when other
factors are held constant, that is, net of other factors46. In all the models, a reference category is
chosen for each categorical variable. The other categories of this factor are then interpreted in
comparison to the reference category. Therefore the reference category itself has an odds ratio of
one. For example, in Model 1 the odds ratio for adults with no limiting long-term illness in 1991 is
1.24. This means that the odds of remaining with the same partner in 2001 are 1.24 times higher
for those without a limiting long-term illness (compared with those who do have a limiting long-term
illness), all other factors being equal47. For the two continuous variables, age and age difference,
an odds ratio shows the effect of a change in one unit, that is one year48.
Model 1
Model 1 shows the likelihood of an individual remaining with the same partner in 2001 according
to individual factors. The model includes both men and women, aged 16 to 54 in 1991, who were
either married or cohabiting in 1991. Notable results are as follows:
• Marriage remains more stable than cohabitation after controlling for individual factors. Those
who were married were more likely to remain with the same partner (the odds of remaining with
the same partner if you were married in 1991 were 1.83 times the odds if you were cohabiting).
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• Adults were less likely to remain with the same partner if, in 1991, they were:
-- younger
-- cohabiting
-- had no dependent children living in the household
-- had a limiting long-term illness
-- had previous experience of partnership dissolution
-- had no higher qualifications
-- had a low social class, or
-- unemployed
• The fact that there is a significant difference between men and women suggests that the sample
may be affected by attrition. That is, given that there were equal numbers of men and women
in the population of opposite-sex residential partnerships in 1991, there should be no sex
differences. According to the model, men have more stable partnerships, but they are also more
likely to be missing from the sample (see Appendix Table A1). This suggests that men in less
stable partnerships may be more likely to be missing from the sample49.
Two points are worth mentioning when interpreting these results. The first is that possible selection
effects should be considered. For example, those adults who are more likely to have stable
relationships may also be more likely to marry (rather than cohabit). The married and cohabiting
populations have different characteristics, and it may be these different characteristics, rather than
the partnership arrangements themselves, that result in the differences in stability. Without a more
refined model, it is not possible to be certain about the impact of selection effects on these results.
The second point worth mentioning is that all of the factors in the model are significant at the
1 per cent level. However, in some respects this is unsurprising given the very large sample size
(almost 157,000 adults).
Model 2
Model 2 is the same as Model 1, but also includes characteristics of each individual’s partner in
1991. Notable results are as follows:
• The inclusion of partner’s characteristics does not materially affect the difference in stability
between married and cohabiting partnerships
• Most of the individual factors remain broadly the same (in magnitude and direction). However,
the effect of limiting long-term illness is reduced, and the effect of social class becomes less
clear50
• A larger age difference between partners reduces the likelihood of remaining with the same
partner in 2001
• Partner’s characteristics are all significant and are similar in direction to individual factors. Adults
were less likely to remain with a partner who in 1991 had:
-- a limiting long-term illness
-- previous experience of marital dissolution
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-- no higher qualifications
-- a low social class, or
-- was unemployed
It is worth considering that there will be some correlation between an individual’s sociodemographic characteristics and their partner’s. As such, the effect of some of these factors may
be overstated and would be reduced by the inclusion of interaction effects.
Model 3
Model 3 is the same as Model 2, but excludes all adults who were married in 1991. In other words,
it includes only those who were cohabiting in 1991. Notable results are as follows:
• Individual factors that remain highly significant and increase the likelihood of stability are:
-- being older
-- the presence of dependent children
-- no experience of previous marital dissolution
-- economic activity also remains fairly significant with a relatively strong effect – being
employed increases the likelihood of stability.
• For partner’s characteristics, age difference and partner’s socio-economic activity remain highly
significant. That is to say, being employed or self-employed, and having a smaller age difference
increase the likelihood of stability.
• Partly due to the smaller sample size, many of the factors reduce in magnitude and become
far less significant (or insignificant). There is a large fall in the effect of whether a partner has
a limiting long-term illness, as well as a reduction in significance. Previous marital status and
social class of partner also cease to be significant.
Model 3 aims to show which factors are associated with cohabitation stability, in isolation from
marriage. A model for married adults only is not shown because it is very similar to Model 2. This is
partly due to the far larger number of married adults in the Model 2 sample. This means that data
for cohabitants has a smaller influence on Model 2. Apart from the overall reduction in significance
for many of the variables, the odds ratios for cohabiting adults (Model 3) are not very different from
those in Model 2. This suggests that the factors influencing cohabitation stability are somewhat
similar to those influencing marital stability, particularly those that remain significant in Model 3.
Model 4
Model 4 is the same as Model 2, but excludes men. In other words, it includes only women who
were married or cohabiting in 1991. Notable results are as follows:
• Compared with women who did not have a baby between 1991 and 2001, those that did have a
baby were more likely to remain with the same partner in 2001
• Despite the introduction of this new childbirth factor, and a slight fall in the significance of some
factors, the model for women only is very similar to the model for both men and women – Model
2. As with the model for both sexes, women who were not economically active were more likely
than either working women or unemployed women to be with the same partner in 2001
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• Apart from a considerable reduction in the effect of partner’s limiting long-term illness, the main
difference is for partner’s economic activity. Women whose partners were not economically
active were less likely to remain with the same partner, compared with those whose partners
were unemployed.
Further modelling of partnership outcomes
There is limited space in this article to discuss further modelling that was undertaken. However,
one additional question is: ‘what are the characteristics of cohabiting adults that go on to marry
their partners?’. Table A4 (in the Appendix) shows the results of an additional model with the
outcome: ‘Was the cohabiting adult in 1991 married to the same partner in 2001?’ The sample for
this model was the same as Model 3 – all cohabiting adults in 1991. A preliminary model was run
for this new outcome, with all the factors in Model 3 used as covariates. Categories that were not
significant were then either removed, or combined with other categories in the same variable. The
results are shown in Table A4.
It is interesting to note the different factors that are associated with whether cohabiting adults
marry their partner (between 1991 and 2001). They are more likely to marry if they or their partner
have experienced previous marital dissolution. They are less likely to marry if they or their partner
are unemployed, or if dependent children are present in the household in 1991. In addition, limiting
long-term illness is not significant for either an individual or their partner.
Compared with the previous models, this suggests that the presence of dependent children
increases the likelihood of remaining with the same partner, but reduces the likelihood of
cohabiting couples becoming married (between 1991 and 2001). Experience of previous marital
dissolution has the opposite effect, reducing the likelihood of remaining with the same partner, but
increasing the likelihood of cohabiting couples becoming married (between 1991 and 2001). This
suggests that factors may act in different directions when considering different types of change
in partnership status (for example. formation versus dissolution). In this case, and for this cohort,
couples who have children and have not experienced marital dissolution may be more likely to be
cohabiting as a substitute for marriage. There may of course be other reasons for this difference,
and it should also be noted that cohabiting couples with children are different from married couples
with children51.
Discussion
This research provides an overview of long-term partnership stability between 1991 and 2001.
It shows that marriage was more stable than cohabitation, even when controlling for a variety of
factors. Despite this difference, the majority (61 per cent) of cohabiting adults aged 16 to 54 were
living with the same partner in 2001. Of those 1991 cohabitants that were living with the same
partner, two thirds had married this partner by 2001. This suggests, at least for those cohabiting
in 1991, that cohabitation may be (or rather, may have been), more likely to be a precursor to
marriage, rather than a substitute. However, this conclusion might change if those that cohabit as a
substitute to marriage are (or were) less likely to remain with the same partner.
Although the exact timing and order of events are beyond the scope of this study, the stability of
partnerships between 1991 and 2001 is shown to be associated with both the presence of children
in the household and the birth of a child. In addition, looking at cohabiting adults in isolation, it
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appears that social factors which are known to be associated with marital stability (for example
age, economic activity and previous experience of partnership dissolution) are also associated with
cohabitation stability. Further research is required to elaborate these conclusions, in particular to
measure partnership transitions that occur both within and beyond a ten year period52.
Key Findings
• Of adults aged between 16 and 54 in 1991, around four in five married adults (82 per cent) were
still living with the same partner in 2001, compared with around three in five cohabiting adults
(61 per cent).
• Marital partnerships were found to be more stable than cohabitations, even when additional
factors were taken into account. After controlling for the characteristics of both individuals and
their partners, married adults were more likely than cohabiting adults to remain with the same
partner between 1991 and 2001.
• Adults were less likely to remain with the same partner if, in 1991, they were younger, had
no dependent children living in the household, had a limiting long-term illness, had previous
experience of partnership dissolution, had no higher qualifications, or were unemployed.
• Partner’s characteristics also have an impact upon partnership stability. Adults were less likely
to remain with the same partner in 2001 if, in 1991, their partner had a limiting long-term illness,
had previous experience of partnership dissolution, had no higher qualifications, had a low
social class, or was unemployed.
• Compared with women who did not have a baby between 1991 and 2001, those that did have a
baby were more likely to remain with the same partner in 2001.
Acknowledgements
The authors would like to thank all those who commented on this article and all members of the LS
team who provided assistance with this project.
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Appendix
Table A1Whether enumerated in the 2001 Census by de facto status
in 1991
All adults (aged 16+) enumerated in the 1991 Census, England and Wales
Count
Partnership status in 1991
In the
Dead or
LS sample embarked1
in 2001
Percentages
Missing
In the
Dead or
in 2001 LS sample embarked1
in 2001
Missing
in 2001
Males
Married and living with spouse
Cohabiting
Not living with a partner
In a communal establishment
Visitor
All males
93,373
17,859
9,917
77
15
8
9,344
521
1,989
79
4
17
46,088
8,399
12,010
69
13
18
1,192
1,087
734
40
36
24
3,481
707
1,076
66
13
20
153,478
28,573
25,726
74
14
12
100,719
10,297
9,470
84
9
8
Females
Married and living with spouse
Cohabiting
10,118
326
1,312
86
3
11
Not living with a partner
58,891
16,846
9,419
69
20
11
In a communal establishment
1,047
2,992
503
23
66
11
Visitor
3,807
994
730
69
18
13
174,582
31,455
21,434
77
14
9
328,060
60,028
47,160
75
14
11
All females
All men and women
1 This category combines those who died between 1991 and 2001 and those who migrated (out of England Wales).It
should be noted that only known migrants are in the embarked category. Some in the “missing in 2001” category will
be undeclared migrants.
Source: ONS Longitudinal Study (authors’ analysis)
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Table A2
Spring 2010
Whether enumerated in the 2001 Census by age in 1991
All adults (aged 16+) enumerated in the 1991 Census, England and Wales
Count
Partnership status in 1991
In the
Dead or
LS sample embarked1
in 2001
Percentages
Missing
In the
Dead or
in 2001 LS sample embarked1
in 2001
Missing
in 2001
16–34
Married and living with spouse
45,084
655
6,197
87
1
12
Cohabiting
13,025
213
2,316
84
1
15
Not living with a partner
60,543
1,150
14,756
79
2
19
1,160
64
745
59
3
38
In a communal establishment
Visitor
All adults aged 16–34
4,591
108
1,365
76
2
23
124,403
2,190
25,379
82
1
17
111,292
5,822
9,979
88
5
8
35–59
Married and living with spouse
Cohabiting
Not living with a partner
In a communal establishment
Visitor
All adults aged 35–59
5,892
283
894
83
4
13
24,951
2,283
4,022
80
7
13
655
147
275
61
14
26
1,534
166
281
77
8
14
144,324
8,701
15,451
86
5
9
37,716
21,679
3,211
60
35
5
545
351
91
55
36
9
19,485
21,812
2,651
44
50
6
424
3,868
217
9
86
5
60+
Married and living with spouse
Cohabiting
Not living with a partner
In a communal establishment
Visitor
All adults aged 60+
All adults 16+
1,163
1,427
160
42
52
6
59,333
49,137
6,330
52
43
6
328,060
60,028
47,160
75
14
11
1 This category combines those who died between 1991 and 2001 and those who migrated (out of England Wales). It
should be noted that only known migrants are in the embarked category. Some in the “missing in 2001” category will
be undeclared migrants.
Source: ONS Longitudinal Study (authors’ analysis)
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Table A3aPartnership status by sex, percentage in each age group in
1991
Longitudinal sample, England and Wales
16–24
25–34
35–49
50–59
Total
(16–59)
73
27
18
21
32
12
11
5
2
7
Women
Lone
Cohabiting
Married1
All women
Men
Lone
Cohabiting
Married1
All men
14
62
77
77
60
100
100
100
100
100
86
33
16
14
33
8
12
6
3
7
7
55
78
83
59
100
100
100
100
100
1 Married and living with spouse.
Source: ONS Longitudinal Study (authors’ analysis)
Table A3bPartnership status by sex, percentage in each age group in
1990/91
Cross-sectional sample, Great Britain
16–24
25–34
35–49
50–59
Total
(16–59)
Lone
70
26
18
21
31
Cohabiting
14
10
5
2
7
Married1
16
64
77
77
62
100
100
100
100
100
86
30
16
16
33
7
12
5
2
7
Women
All women
Men
Lone
Cohabiting
Married
1
All men
7
58
79
82
60
100
100
100
100
100
1 Married and living with spouse.
Source: General Household Survey (GHS); 1990 and 1991 combined
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Table A4Probability of being married to same partner in 2001
(if cohabiting in 1991)
Longitudinal sample, England and Wales, All cohabiting adults (aged 16–54) in 1991
Variable
Odds ratio1
Significance level2
Age in 1991
0.98
***
Age gap in absolute years
0.97
***
Female (ref.)
1.00
n/a
Male
1.18
***
No (ref.)
1.00
n/a
Yes
0.84
***
Single or widowed or married (ref.)
1.00
n/a
Remarried or divorced (or married if cohabiting)
1.14
***
No qualifications after age 18 (ref.)
1.00
n/a
Has qualifications after age 18
1.14
***
Professional, managerial, technical or skilled non-manual
1.18
***
Skilled manual, part-skilled, unskilled, other (ref.)
1.00
n/a
Unemployed (ref.)
1.00
n/a
Not economically active
1.19
**
Self-employed
1.28
***
Employed
1.43
***
Single or widowed or married (ref.)
1.00
n/a
Remarried or divorced (or married if cohabiting)
1.15
***
No qualifications after age 18 (ref.)
1.00
n/a
Has qualifications after age 18
1.17
***
Sex
Dependent children in household in 1991
Previous dissolution (marital status in 1991)
Qualifications after age 18 (in 1991)
Social class (Registrar General’s) in 1991
Economic activity in 1991
Partner: previous dissolution (marital status in 1991)
Partner: qualifications after age 18 (in 1991)
Partner: social class (Registrar General’s) in 1991
Professional, managerial, technical or skilled non-manual
1.17
***
Skilled manual, part-skilled, unskilled, other (ref.)
1.00
n/a
Unemployed (ref.)
1.00
n/a
Not economically active
1.40
***
Self-employed
1.58
***
Employed
1.74
***
Partner: economic activity in 1991
Note: For Registrar General’s social class, other includes armed forces and missing.
1 Reference categories are shown with an odds ratio of 1.00.
2 * significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.
n/a = reference category (significance is not applicable).
Source: ONS Longitudinal Study (authors’ analysis)
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References
1 This rise is often attributed to changing legislation (the Divorce Reform Act 1969 and
Matrimonial Causes Act 1973) and changing attitudes in society. Considering the long-term
trend, and ignoring minor fluctuations, this increase can be seen as a step-change. For more
information see: Smallwood S & Wilson B (2008) ‘The proportion of marriages ending in
divorce’, Population Trends 131, pp. 28–36. Available at:
www.statistics.gov.uk/downloads/theme_population/Population_Trends_131_web.pdf
2 In 2007 the provisional divorce rate in England and Wales fell to 11.9 divorcing people per
1,000 married population, compared with the 2006 figure of 12.2. The divorce rate is at its
lowest level since 1981. See also: www.statistics.gov.uk/cci/nugget.asp?id=170
3 For example see: Smallwood S & Wilson B (2007) ‘Understanding recent trends in marriage’.
Population Trends 128, pp. 24–32. Available at:
www.statistics.gov.uk /downloads/theme_population/ PopulationTrends128.pdf and
Wilson B (2009) ‘Estimating the cohabiting population’. Population Trends 136, pp. 21–27.
Available at: www.statistics.gov.uk/ downloads/theme_population/Popular-Trends136.pdf
4 Both figures are from the GHS. For 1986 results see: OCPS (1989) General Household Survey
1986 (Series GHS no.16), London: HMSO. For 2007 results, see: Results from the General
Household Survey (GHS), 2007 (Table 5) available at:
www.statistics.gov.uk/StatBase/ Product.asp?vlnk=5756&Pos=&ColRank=1&Rank=256
5 In addition there has been a long-term increase in adults living alone, and an increase in lone
parent families. For more information see Social Trends 39: Chapter 2 Households and families,
available at: www.statistics.gov.uk/downloads/theme_social/Social_Trends39/ST39_Ch02.pdf
6 Office for National Statistics (2007) 2006-based marital status projections. Available at:
www.statistics.gov.uk/pdfdir/marr0309.pdf
7 The proportion of adults who have never married is projected to rise from 34 per cent to 42 per cent.
It should be noted that some of these will be cohabiting. Therefore there is an overlap with the
projected numbers of cohabitants.
8 Although there is good information on marriage and divorce, statistics on the formation and
dissolution of cohabiting partnerships are not collected routinely. In order to consider
partnership stability adequately, it is desirable to have comparative information on partnership
transitions. These transitions are important because they go beyond stock estimates at a
given time point, to suggest how (and why) partnership estimates change over time. In some
respects, this can be considered equivalent to the importance of births, deaths and migration
when considering changes in the population. (Of course, mortality and migration may also
change an individual’s partnership status.)
9 For example, see the report published to Parliament by the Law Commission on 31 July 2007.
Available at: www.lawcom.gov.uk/cohabitation.htm
10Wilson B (2009) ‘Estimating the cohabiting population’. Population Trends 136, pp. 21–27.
Available at: www.statistics.gov.uk/downloads/theme_population/Popular-Trends136.pdf
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11 Dunnell K (2008) ‘Ageing and Mortality in the UK – National Statistician’s Annual Article on the
Population’. Population Trends 134, pp. 6–23. Available at:
www.statistics.gov.uk/downloads/ theme_population/Population-Trends-134.pdf
12As far as legal status is concerned, a marriage ends in either death or divorce, however it is
also important to note that couples often separate prior to divorce (that is there is a residential
dissolution prior to the legal decree). Separated individuals may therefore begin to cohabit with
a new partner prior to divorce (which is one of several explanations why a married couple might
not be living together).
13Although marriage and divorce statistics have been collected by the registration system (and
the courts) for over a century, there is currently no requirement for cohabiting couples to
register the formation or dissolution of their partnerships. As such, there are limited sources of
information on partnership transitions. It is not possible to use simple cross-sectional surveys
because we need to explore changes in individual partnerships over time.
14It is also important that cohabitation can be distinguished from simply sharing accommodation.
In addition, any attempt to identify cohabitants can be affected by misreporting. For example,
prevailing social attitudes have (at least in the past) attached a stigma to cohabitation.
15The coverage of topics has been developed and extended over the years: initially in 1971 a few
questions were addressed to women aged between 18 and 44; additional subjects – including
cohabitation – were introduced in 1979; and the age range was extended, firstly going up to
age 49, and then from 16 to 59 in 1986, when men were first asked questions on cohabitation.
For more information (and the source of the previous sentence) see: Haskey J (2001)
‘Cohabitation in Great Britain: past, present and future trends – and attitudes’, Population
Trends 103, pp. 4–25.
16Haskey J (2001) ‘Cohabitation in Great Britain: past, present and future trends – and attitudes’.
Population Trends 103, TSO London, pp. 4–25.
17Haskey J (1999) ‘Cohabitational and marital histories of adults in Great Britain’. Population
Trends 96, TSO London, pp. 13–24.
18Haskey J (2001) ‘Cohabiting couples in Great Britain: accommodation sharing, tenure and
property ownership’. Population Trends 103, TSO London, pp. 26–36.
19Murphy M (2000) ‘The evolution of cohabitation in Britain, 1960–95’. Population Studies 54(1),
pp. 43–56.
20Lilly R (2000) ‘Developing questions on cohabitation histories for the General Household
Survey’. Survey Methodology Bulletin 46 (January), ONS, pp. 15–22. Available at:
www.statistics.gov.uk/ssd/ssmb/smb_46.pdf
21Berrington A and Diamond I (2000) ‘Marriage or cohabitation: a competing risks analysis of
first-partnership formation among the 1958 British birth cohort’. Journal of the Royal Statistical
Society: Series A (Statistics in Society) 163(2), pp. 127–151.
22Calderwood L (2008) Chapter Three: Family Demographics. Millennium Cohort Study
Third Survey: A User’s Guide to Initial Findings, by Hansen K & Joshi H (eds.), Centre for
Longitudinal Studies, Institute of Education, University of London, pp. 22–50.
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23Ermisch J and Francesconi M (2000) ‘Cohabitation in Great Britain: Not for Long, but Here to
Stay’. Journal of the Royal Statistical Society: Series A (Statistics in Society) 163(2),
pp. 153–171.
24Buck N and Ermisch J (1995) ‘Cohabitation in Britain’, in Changing Britain: Newsletter of the
ESRC Population and Household Change Research Programme 3, pp. 3–5, October 1995.
25Clarke L and Buxton J (2006) ‘Cohabitation: Changes over the 1990s and longitudinal evidence
on transitions in status’. Presentation at 2006 BSPS Annual Conference.
26CeLSIUS (2008) Downloadable tables from the ONS Longitudinal Study. Available at:
www.celsius.lshtm.ac.uk/download/wt020400.html
27Wolf DA and Gill TM (2009) ‘Modelling transition rates using panel current-status data: How
serious is the bias?’ Demography 46(2), May 2009: pp. 371–386.
28Essentially, this was all adults in the LS that were both present in 1991, and aged 16 or over in
1991.
29The date of extraction for the sample was June 2009 (LSLOAD62).
30Embarkation is only flagged when an individual notifies their GP.
31For deaths by marital status see DR Table 4 (ONS), available at:
www.statistics.gov.uk/downloads/ theme_health/DR2007/DR_07_2007.pdf
32No direct question was asked about cohabitation in the 1991 Census, although marital status
was asked. This means that a cohabiting partnership involving an LS member must be
identified using the relationship questions on the census form. Because only relationship to
the head of household was collected in 1991, in complex households or where the LS member
is not the head of household some partnerships are likely to have been missed. Moreover,
for people who were enumerated at an address which was not their usual place of residence,
marital status will be known but whether they were cohabiting will not be known.
33Strictly speaking, the Minimal Household Unit (MHU), which is a subdivision of the Census
category ‘family’. A MHU comprises either an unmarried individual, or a lone parent with his/her
dependent children, or a couple (married or cohabiting) with their dependent children.
34Being an adult present at both censuses is the criterion for inclusion in the sample. As such,
there will be no sample members in 2001 aged between 16 and 25 (since they are under 16 in
1991).
35OPCS (1993) General Household Survey 1991 (Series GHS no. 22), HMSO London.
36For a distribution of age differences at marriage see: Wilson B and Smallwood S (2008)
‘Age differences at marriage and divorce’. Population Trends 132, pp.17–25, available at:
www.statistics.gov.uk/downloads/theme_population/Population_trends_132.pdf
37For an example with recent results see: Smallwood S and Wilson B (2008) ‘The proportion of
marriages ending in divorce’. Population Trends 131, pp. 28–34, available at: www.statistics.
gov.uk/downloads/theme_population/Population_Trends_131_web.pdf
38Clarke L and Berrington A (1999) ‘Socio-demographic predictors of divorce’. Published in:
Simons J (ed.) High divorce rates: The state of the evidence on reasons and remedies:
Reviews of the evidence on the causes of marital breakdown and the effectiveness of policies
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and services intended to reduce its incidence. (Lord Chancellor’s Department Research Series,
1 2/99) London.
39As the initial LS sample ages, it will be increasingly possible to explore the influence of parental
characteristics.
40Although the LS contains information on religion, it was decided not to include this because
information was only available in 2001 and even then the question was not compulsory.
41Many models were created to test partnership stability, but the four most important are shown in
this article.
42See: Online training module for users of the ONS Longitudinal Study. The logistic regression
example starts at the below link. Follow links at the bottom of the page to continue the example.
Use: www.celsius.lshtm.ac.uk/modules/analysis/an030200.html
43It is worth noting that in 1991, only information on degree and professional qualifications was
collected, not information on school qualifications.
44Adjusted for ‘normal’ age difference so that zero represents a man two years older than his
female partner.
45The LS is not a household based sample, which means that non-response is at the individual,
rather than the household level. It was therefore deemed important to consider differences by
sex, which might link to any non-response issues.
46Table 5 (which looks at a gross relationship) does not hold any other factors constant when
considering stability and age. In fact, Table 5 does not consider the influence of any factors
other than age. When interpreting both statistics, it is important to remember that neither one
is more accurate, but that they each offer a different perspective on the same results. For more
information see: Murphy M (1985) ‘Demographic and socio-economic influences on recent
British marital breakdown patterns’. Population Studies 39, 441–460 as cited in Clarke L and
Berrington A (1999).
47Alternatively, those without a limiting long-term illness in 1991 are 24 per cent more likely
to remain with the same partner between 1991 and 2001 compared with those who have a
limiting long-term illness in 1991, all other factors being equal. The last part of this statement
(all other factors being equal) means that the effect of limiting long-term illness on partnership
stability (for this sample) has been shown controlling for all the other factors in the model (age,
qualifications etc). It is important to note that any factors not in the model are not considered.
As such, any variations in stability by limiting long-term illness may be explained by these
(exogenous) excluded factors.
48For example, in Model 1 the odds ratio for age difference is 0.95. This means that for every
additional year of absolute age difference between partners, the odds of remaining with the
same partner between 1991 and 2001 are 0.95 (or 5 per cent lower). Absolute age difference is
the total age difference irrespective of which partner is older.
49Some of the difference between men and women will reflect the typical partnership age gap
where the man is on average 2 to 3 years older than the woman. Some older men will therefore
fall above the 16–54 age range when women in an equivalent partnership will not. However, the
effect of age difference was investigated and found to explain only a minority of the difference
between men and women.
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50In particular, the odds ratio for the managerial or technical class ceases to be either material or
significant.
51A number of selection effects might be considered here, and further research would be required
in order to draw more definitive conclusions.
52For example, further research is needed to explore the effect of partners that separate and then
reform their partnership with the same person (including those that are married and not living
together at any given point).
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Households and families:
Implications of changing census
definitions for analyses using the
ONS Longitudinal Study
Emily Grundy, Rachel Stuchbury and Harriet Young
Centre for Longitudinal Study Information and User Support (CeLSIUS), London School of Hygiene
& Tropical Medicine
Abstract
The ONS Longitudinal Study (LS) includes information from the 1971, 1981, 1991 and 2011
censuses. This article explains definitional differences over time, and their implications for
household and family classifications.
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Contents
Abstract............................................................................................................................................ 64
Introduction....................................................................................................................................... 66
What is a child?................................................................................................................................ 66
Family definitions.............................................................................................................................. 66
The impact of changes in 2001........................................................................................................ 67
References and key publications..................................................................................................... 68
List of tables
Table 1Distribution of ONS Longitudinal Study members by family/household type in 2001
using the 2001 and 1991 (and earlier) definitions of a child....................................... 68
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Introduction
The strengths of the ONS Longitudinal Study (LS) of England and Wales include the fact that
information on all co-residents of LS sample members is available at each census point, together
with information on family and household type (more information on family and household
definitions and classifications is available in the CeLSIUS training module on households and
1
families) . The LS is thus a valuable resource for those interested in, for example, changes
over the life course in the types of household people live in and those who want to compare
distributions of household and family types at different time points in order to investigate period
changes. For researchers interested in either approach, consistency of definitions is important.
The ONS LS now includes information from the 1971, 1981, 1991 and 2001 Censuses. Changes
2
in definitions between the first three of these censuses were relatively minor , but in 2001 there
was a more substantial change arising from a revised definition of a child. In this paper we explain
this difference, its implications for household and family classifications, and offer a link to code (in
STATA) which can be used by those wanting consistent definitions over time.
What is a child?
In the 2001 UK Census a child was defined as an individual of any age or marital status, not
themselves part of a co-residing couple or a parent, grand-parent or step-parent of anyone else
in the household, who lived with one or both of their own parents. This differed from the definition
used in previous censuses in which a child had to be never-married, as well as meeting the other
criteria specified above.
This change affects a number of the classifications that researchers may use, such as the
statistical definition of a family. Prior to 2001, ONS defined a family as either a co-resident
couple; a couple and never-married child(ren); a lone parent and never-married child(ren); or a
grandparent and never-married child(ren) if the intervening generation was absent. Households
refer to co-resident groups sharing common living space, or at least one meal a day, and may
include one or more families, or none.
In 2001 the change in the definition of a child meant that the definition of a family also changed,
as did descriptions and definitions of households based on the families within them. In 1991 for
example, a widowed mother and divorced daughter living together with no-one else would not
have been classed as a family, and their household would not have been described as a family
household. In 2001 however, the same two people would have been classed as a lone-parent
family and their household as a lone-parent household. This change presents difficulties for those
undertaking longitudinal analyses who may want to analyse changes in household and family
status over census points, or for those who are interested in looking at period changes between
different censuses. This article investigates and quantifies the impact of this change.
Family definitions
In previous research on households, we have derived and used a variable describing the
3,4,5,6
household and family circumstances of LS members at the 1971, 1981 and 1991 Censuses .
This takes into account the position of the LS member in the family and household in which they
live and relationships with other family and household members. The variable, which we have
named ‘housefam’, includes the following categories: living alone; couple only; couple and children;
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couple and others; couple and children and others; lone parent; lone parent and others; two or
more families; not in a family but living with others; child (including adult children) in family; and
living in a communal establishment (although in most previous work a collapsed version of this has
been used).
Constructing a 2001 version of this using the 2001 definitions of child and family will produce
slightly different results from equivalent analyses using the earlier definition. For example, in
2001 a divorced female LS member living with her parents would have been classed as a ‘child in
family’, where previously she would have been classed as ‘not in a family but living with others’.
If the LS member was the mother of the divorced daughter in the same configuration in 2001 she
would be classed as living in a ‘couple and child’ family/household, but in 1991 or earlier, as living
in a ‘couple and others’ family/household. Fortunately the LS includes information on all those in
sample members’ households and on intra-family and intra-household relationships, including in
2001 a full relationship grid. It is therefore possible to produce classifications for 2001 using the
old (pre 2001) rather than the new child definition. Details of the algorithms, and relevant code
7
(in STATA) for doing this are available on the CeLSIUS web site
The impact of changes in 2001
The table below shows the distribution of LS members in 2001 by family/household type using
alternative definitions of ‘housefam’ based on either the 2001 or the earlier definition of a child.
In each case, if there is any imputed relationship in the household, the family/household type
has been set to ‘unclassifiable’. Using the wider 2001 definition of a child obviously results in the
numbers in categories which include a child being larger than when using the more restrictive 1991
definition. For example using the 2001 definition, 3.5 per cent of LS members were classed as
living in a lone parent family, compared with 3.3 per cent using the earlier definition. This difference
may seem slight, but differences in the numbers concerned are considerable, given that the LS is
a one per cent sample of the population. It is recommended that the impact of these changes be
considered when making any comparison between the 2001 Census and previous censuses for
statistics relating to households, families and children.
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Table 1 Distribution of ONS Longitudinal Study members by family/
household type in 2001 using the 2001 and 1991 (and earlier)
definitions of a child
2001 definition
1991 definition
Number
%
Number
%
65,033
12.22
65,053
12.22
Couple only
120,830
22.70
120,830
22.70
Couple and children
107,386
20.17
106,095
19.93
3,721
0.70
5,098
0.96
Solitary
Couple and others
Couple and child and others
4,658
0.88
5,207
0.98
18,495
3.47
17,725
3.33
Lone parent and others
1,939
0.36
2,058
0.39
2 or more families
4,284
0.80
4,326
0.81
Lone parent
Not in a family, with others
Child in family (including adult children)
Communal establishment
Unclassifiable
Total
10,850
2.04
12,270
2.31
127,400
23.93
125,954
23.66
7,922
1.49
7,922
1.49
59,773
11.23
59,773
11.23
532,311
100
532,311
100
Notes:
1 The definition of a family includes grandparent(s) living with a grandchild whose parents are not resident in the
same household. We did not account for these families when making the new housefam variable. There were only
approximately 40 such households who had an ever-married child in the same family, and so a decision was made to
leave these families in their original categories.
2 We found that there were approximately 900 never-married children not in the same family as the LS member. We
left these as they were, and assumed that they are likely to be never-married children with children of their own, who
are therefore part of a separate family.
3The table above excludes imputed values in the source variables. We have also derived versions of the housefam
variables using imputed values. For further information
on these and on general derivation of these variables, please
7
see our information pages on derived variables .
References and key publications
1 CeLSIUS (2009) CeLSIUS training module on households and families. Available at:
www.jcelsius.lshtm.ac.uk/modules/hhfam/hf010000.html
2 Brasset-Grundy A (2003) ‘Researching households and families using the ONS Longitudinal
Study’. LS User Guide 20. Institute of Education, University of London, London. Available at:
www.celsius.lshtm.ac.uk/documents/userguide20.pdf
3 Grundy E (1987) ‘Household change and migration among the elderly in England and Wales’.
Espace, Populations, Sociétés 1, 109–123.
4 Grundy E (1999) Household and family change in mid and later life in England and Wales.
Published in McRae S (ed.) Changing Britain: Families and Households in the 1990s. Oxford
University Press, Oxford.
5 Glaser K and Grundy E (1998) ‘Migration and household change in the population aged 65 and
over, 1971–1991’. International Journal of Population Geography 4, 323–339.
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6 Grundy E and Jitlal M (2007) ‘Socio-demographic variations in moves to institutional care
1991–2001’. Age and Ageing 36(4), 424–430.
7 CeLSIUS (2009) Derived variables: household composition. Available at:
www.celsiusdev.lshtm.ac.uk/private/forclearance/derive/hhcomp.html
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Ten year transitions in children’s
experience of living in a workless
household: variations by
ethnic group
Lucinda Platt
Institute for Social and Economic Research, University of Essex
Abstract
Over the last few decades, there has been an increase in the proportion of children growing
up in workless households, that is households in which no adult member is in paid work.
This proportion has stabilised, and has declined slightly in recent years. Worklessness
among households with children is viewed as a cause for concern for two reasons: firstly,
because children in workless households are much more likely to be growing up in poverty;
secondly, because of concern that worklessness in families with children may be subject to
intergenerational transmission.
We know surprisingly little about children’s experience of household worklessness over
time, particularly over their childhood as a whole, even though worklessness is heavily
implicated in higher poverty risks. Children from most minority ethnic groups are at
substantially higher risk of household worklessness than those from the majority. For
some ethnic groups, children’s rates of living in a workless household are associated with
high rates of lone parenthood. For others it is worklessness in couple parent families that
predominates. This article uses the Office for National Statistics (ONS) Longitudinal Study
to explore differences in risks of worklessness over time, among ethnic groups within a
single cohort of children who are observed at two time points, 10 years apart.
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Contents
Abstract............................................................................................................................................ 70
Introduction....................................................................................................................................... 72
Data and study design...................................................................................................................... 77
Results............................................................................................................................................. 80
Discussion........................................................................................................................................ 85
Acknowledgments............................................................................................................................ 86
Appendix.......................................................................................................................................... 87
References....................................................................................................................................... 87
List of figures
Figure 1 Proportions of children in workless households at 1991 (aged 0–5 years) and
2001 (aged 10–15 years) by ethnic group, England and Wales................................. 80
Figure 2 Movers and stayers, children in workless households
1991–2001, by ethnic group....................................................................................... 81
Figure A1 Employment status by gender and ethnicity (percentages)........................................ 87
List of tables
Table 1 Recent estimates of proportions of children living in a workless household and
living in a poor household by ethnic group (percentages).......................................... 74
Table 2 Children in lone parent families by age group and ethnic group; risk of living in a
workless household for children in lone parent family................................................ 75
Table 3Children aged 0–5 years in 1991 and observed aged 10–15 years in 2001 by
ethnic group, England and Wales............................................................................... 78
Table 4 Relative chances of being in a workless households in 2001 conditional on 1991
workless household status, by ethnic group............................................................... 83
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Introduction
Children living in workless households
The last decade has seen a growing research and policy interest in workless households, that is
1,2,3
households where no one of working age is in work. A particular concern has been the welfare
4,5,6
and future prospects for children in such households. Attention has focussed on the differential
5
risks of living in a workless household faced by children from different ethnic groups. We still
know little about how worklessness is experienced over time, and how that may or may not differ
by ethnicity. This is of particular concern, since children from many minority ethnic groups are at
7
relatively high risk of living in a workless household and of the poverty stemming from that.
This article focuses on a particular cohort of children, born around the end of the 1980s, and
investigates the currently unexplored question of whether risks of remaining in or moving into a
workless household during their childhood are comparable for children from different ethnic groups.
It describes absolute differences in risks of remaining in or entering a workless household across
groups, and examines the extent to which any differences are mediated by household structure
and characteristics. For the purposes of this article, a workless household is defined as one where
8
no adult member is in work.
The increase of work work-rich and work work-poor households has been well documented.
3
Gregg and Wadsworth have shown how the share of workless households increased over the
last two decades of the 20th century with some levelling off by 2001, and that this was the case
for households with children as well as for all households. Indeed, in 1996 the UK had the highest
proportion of workless households with children in the member countries of the Organisation for
Economic Cooperation and Development (OECD). At the same time there has been a longstanding
interest in, and concern with, the potential transmission of various forms of economic disadvantage
9
between generations, and with how children’s experience of such disadvantage, including
worklessness, can have long term impacts.
Children’s risks from living in a workless household tend to be higher when they are younger and
10,11
tends to decline with age, but experience of a workless household can have negative
consequences at any age, and growing up in a persistently workless household is likely to be
particularly detrimental to future outcomes.
Children living in workless households face very high risks of living in poverty, and the associations
12
13
between childhood poverty and future outcomes, as well as their development, are well attested.
Moreover, persistent poverty both tends to represent more severe poverty and to be associated
14
with more negative outcomes than short-term or transient poverty. Thus, to the extent that it
implies long-term poverty, long-term worklessness is likely to be of particular concern in relation to
children’s welfare. The timing can also have implications for children’s later life outcomes. Poorer
outcomes associated with poverty and worklessness tend to be greater for younger rather than
10, 11
and it is also at younger ages that the risks of living in a workless household are
older children
11
greatest. Nevertheless, Ermisch et al. have shown that the experience of worklessness in later
childhood (11–15 years) is associated with increased chances of smoking and of psychological
distress.
While there has been substantial emphasis on the detrimental effects of long-term poverty or
worklessness, increasing attention is being paid to the negative impacts of socio-economic
15,16,17,18
Instability provides the opportunity for periods of relative advantage
instability in its own right.
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compared to remaining persistently below a given poverty threshold such as 60 per cent of median
19
equivalent household income, as used in UK low income statistics. However, those who fluctuate
between states are likely to be in more marginal positions – on the borders of poverty or on the
16
margins of work , while the actual variation in circumstances may introduce its own costs, such
as uncertainty, the need to reclaim benefits with the consequent possibility of periods without any
support, the need to change arrangements for care, and so on. Therefore vulnerability to poverty,
as evidenced by subsequent moves into worklessness, raises concerns for family welfare.
In addition to the welfare implications of growing up in a workless household, the experience or
persistence of worklessness among families with children, which typically implies a need for
support by state benefits, has raised concerns about the extent to which there is intergenerational
9
transmission of worklessness and benefit dependence. Evidence from the US provides supporting
21
evidence for intergenerational transmission of ‘welfare’ participation, over and above income
effects, though the exact mechanisms are not clearly understood. Both lack of role models and
limited access to networks and opportunities for pursuing employment – or a combination of these
– have been offered as explanations for intergenerational transmission.
There are good reasons for being concerned about children’s rates of living in a workless
household, their risks of being persistently in a workless household and their vulnerability to ending
up in a workless household from a working household. However, there is little understanding of the
extent to which these risks differ for children according to their ethnicity, and the factors implicated
in differential risks. This is despite the fact that it is well known that there are higher risks of poverty
for children from certain ethnic groups, and that the risks of being in a workless household also
vary substantially by ethnicity, as the next section discusses. Although there are some indications
22
of differences in persistence and instability in economic circumstances across ethnic groups, we
have little understanding of how transitions into workless households, or persistence in growing up
in a workless household across childhood over an extended period, varies by ethnic group. The
contribution of this article is to explore precisely these questions for a particular cohort of children
of the same age and over the same period.
Ethnicity and worklessness
Individual employment rates are well known to vary by ethnicity, particularly for women, with high
rates of inactivity among Pakistani and Bangladeshi women, relatively high levels of participation
among Black Caribbean women, relatively high rates of inactivity (compared to other men)
among Bangladeshi men and above average unemployment risks for all minority groups (see
Appendix, Figure A1). A substantial body of research has shown that some minority groups are
disadvantaged in the labour market, even taking account of variation in qualifications and other
23
job-relevant characteristics . However, far less is known about the duration of unemployment
or worklessness across ethnic groups even at the individual level; nor do individual levels of
24
employment – analyses of which abound – tell us about the same phenomenon as household
3
experience of work and non-work.
Cross-sectional analyses indicate that there are clear differences in workless household rates by
ethnic group; we also know that there are substantially higher rates of cross-sectional child poverty
among all minority groups compared to the majority. Table 1 shows that for most recent estimates,
children’s risks of living in a workless household were particularly high for Black African children
and lowest for Indian children. There are substantial differences between the groups and the rates
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Table 1 Recent estimates of proportions of children living in a
workless household and living in a poor household by
ethnic group (percentages)
Children’s household
workless (per cent) by
ethnic group of child
Children’s household poverty
(per cent) by ethnic group
of head of household
2009
2001
2004/05–2006/07
2001/02–2003/04
White
15.4
14.8
20
20
Mixed
27.9
28.6
–
–
Indian
8.6
10.4
27
28
25.1
34.8
54/58
59/72
Black Caribbean
30
27.4
26
31
Black African
42
50.1
35
38
Other Black
26.8
36.8
–
–
Chinese
13.9
23.4
–
–
Pakistani/Bangladeshi
Notes: ‘–’ = figures not available due to small sample sizes. Children refers too children aged under 16.
Sources: Column 1: ONS Statistical Bulletin ‘Work and worklessness among households 2009’, Table 3(iv) (from Labour
Force Survey) UK data; Column 2: Platt 2009, Table 2.1 (from Family Resources Survey), data for Great Britain
for minority groups are significantly different from those for the White majority. Given that, in this
article, rates of living in a workless household are considered for 1991 and 2001, Table 1 also
illustrates the rates that pertained across groups in 2001. While the rates are rather different for
some groups at the earlier period, the ranking is very similar for the two time points, with the major
change being the reversal of the relative positions of Black Caribbean and Pakistani/Bangladeshi
children.
It can also be seen from the right hand panel of Table 1 that the ranking of workless household
risks does not map precisely onto poverty rates. Worklessness is of concern in part because it
6
brings high risks of poverty, but poverty is not fully accounted for by worklessness. As Nickell
pointed out in his discussion of children and workless households, 53 per cent of poor children
lived in workless households in 2000/01, and those living in workless households had a 70 per cent
chance of being poor. However, there is not a complete overlap. Nevertheless, worklessness may
have implications for future welfare over and above the material deprivation that it is likely to bring.
It is known that family structure varies substantially between groups. For example, Black
Caribbean and Black African children experience high rates of lone parenthood, and children from
South Asian groups are much less likely to live in a lone parent family (see Table 2). The trend with
age is towards higher risks of living in a lone parent family, but this is counteracted by the greater
likelihood of lone parents with older children being in work. Recent policy changes are intended
25
to enhance this pattern. Since we know that family structure, in particular lone parenthood, is
heavily implicated in risks of worklessness, we might therefore expect that such variations in family
structure would influence absolute risks of worklessness, despite the greater propensity of Black
Caribbean lone parents to be in employment compared to other lone parents. This is reflected in
Table 2, where children’s risks of living in a workless household, given that they are in a lone
parent family, is shown.
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Table 2 Children in lone parent families by age group and ethnic
group; risk of living in a workless household for children in
lone parent family
Ethnic group
Percentage in lone parent family
All children
[C.I.]
Age 0–5
[C. I.]
Age 10–15
[C. I.]
Risk of household
worklessness in
lone parent family:
percent [C. I.]
White British
24
[24–25]
21
[21–22]
27
[26–27]
45
[44–46]
37,362
White & Black Caribbean
52
[44–61]
–
–
58
[47–69]
141
White & Asian
16
[11–22]
–
–
–
166
10
[8–12]
8
[5–11]
12
[9–17]
51
[41–61]
887
16
[14–19]
11
[8–14]
21
[16–27]
62
[52–70]
923
11
[8–15]
7
[4–12]
–
–
417
Black Caribbean
56
[52–60]
54
[47–62]
58
[52–66]
39
[34–45]
583
Black African
46
[43–50]
40
[34–46]
51
[45–58
65
[59–70]
760
Indian
Pakistani
Bangladeshi
Number
Note: Figures are weighted. ‘–’ indicates that sample sizes are too small to allow for reliable estimates.
Source: Family Resources Survey 2001/02–2006/07, pooled. Author’s analysis
Whether family structure does account for differences in overall risks of worklessness over time,
when considering children of a comparable age and a common cohort, is a question addressed by
this article.
There are also variations in average family size according to ethnic group, with Pakistani and
Bangladeshi families in particular having larger family sizes on average. Additional children may
make moves out of worklessness more difficult, both as a result of the demands they make on
parental time and as a result of the structure of benefits and the impact on marginal tax rates. On
the other hand, as children grow up they may provide additional sources of labour market income
7
for families as they remain in the home.
It is therefore not clear what the different chances are likely to be of moving into, or remaining in, a
workless household over the childhood years, for different groups.
What neither Table 1 nor Table 2 reveals are the risks of worklessness for children of particular
26
ages and family circumstances, nor is there information on risks of long-term worklessness, or the
nature of transitions to and from worklessness. These are addressed in this article.
Aims of current analysis
This article sets out to map the patterns of children’s workless household transitions, for children
from different ethnic groups. It asks:
• What are the differences in risks of worklessness for a single cohort of children according to
their ethnic group?
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• What are the chances of remaining in a workless household (persistence) or ending up in a
workless household after having been in a working household (entry), for children from different
ethnic groups?
• To what extent are experiences of workless household persistence and entry significantly
different for children from particular minority groups, compared to White majority children?
• To what extent are such differences mediated by family and household context?
• And conversely, to what extent do greater risks appear to exist over and above the contribution
of relevant household and family characteristics?
The analysis is motivated by the implications raised, by the differential chances of remaining
workless, for children from minority ethnic groups. In absolute terms, any differences in
vulnerability to remaining in or entering a workless household may have implications for the future
wellbeing of children from those groups, and therefore merits attention. Understanding the role of
family and household characteristics can inform and reinforce strategies to address these areas.
If those differences are largely mediated by household and family characteristics, such as the
emphasis on moving lone parents into work, then there is less argument that policy should be
differentiated to address the risks of different groups.
Conversely, if there appear to be ethnic differences in children’s risks of staying in or moving into
a workless household even after taking account of relevant family and household characteristics,
27
then such ethnic penalties in children’s risks of worklessness require further explanation, and
possibly targeted intervention. It should be noted however, that the extent to which the long-term
impacts of worklessness are themselves comparable across ethnic groups, including transmission
of deprivation, is as yet untested and is an area for future research.
The following analysis explores transitions into and out of workless households over a ten year
interval by ethnic group, using a unique data set, the ONS Longitudinal Study (see Box one). It
examines the risks of living in a workless household for a cohort of children born between 1986
and 1991, when they are young (0–5 years old) at the beginning of the 10 year window in 1991
and when they are older (10–15) at the other end of the observation window in 2001. It explores
their chances of remaining in, moving into or moving out of a workless household between
these two time points, and how those chances vary by ethnic group. It cannot be assumed that
the households will have been workless throughout the whole period demarcated by the two
measurement points. Indeed, we can expect substantial fluctuation in family and household
circumstances. However, those who are continuously workless will be overrepresented at the
second time point compared to those moving in and out. Moreover, it is relevant to observe that
there is an association between worklessness at a ten year interval, even if there have been
shorter moves out of worklessness within the period.
The article estimates these chances, controlling for both household and family characteristics
associated with the chances of living in a workless household, such as family composition, parental
qualifications, access to a car and housing tenure. It also examines the contribution of changes
in circumstances during the observation window, such as parental separation, change in family
composition, or geographical mobility.
By estimating models, both with and without these additional explanatory and control variables, it is
possible to measure the extent to which family and household characteristics account for observed
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differences in patterns of worklessness between ethnic groups, or conversely, the extent to which
residual ‘ethnic penalties’ remain.
Data and study design
Data and sample
This article makes use of an extract based on a cohort of LS members who were children aged
0–5 in 1991 and who were linked to their records in 2001 when aged 10–15. Information on the
households and those enumerated in the households (the non-members in the data) at which the
study members were living at either point in time also formed part of both extracts.
The children had to be observed at two time points in order to be included in the sample. This means
they will not precisely reflect the overall populations of children aged 0–5 in 1991 or aged 10–15
in 2001. However, those children who join the LS during the decade (via immigration or return)
are not a concern of this analysis of transitions, and any potential bias stemming from systematic
28
differences in those observed at 1991 but not responding in 2001 is anticipated to be marginal.
Box one ONS Longitudinal Study data
The ONS Longitudinal Study (LS) contains linked census and vital event data for one per cent
of the population of England and Wales. Information from the 1971, 1981, 1991 and 2001
censuses has been linked across censuses as well as information on events such as births,
deaths and cancer registrations. The original LS sample included 1971 Census information
for people born on one of four selected dates in a calendar year. These four dates were
used to update the sample at the 1981, 1991 and 2001 censuses and to add new members
between censuses. New LS members enter the study through birth and immigration. Data are
not usually linked to a member after their death or after de-registration from the NHS Central
Register but these members’ records remain available for analysis.
Census information is also included for all people enumerated in the same household as an LS
member, but only information on LS members is linked over time.
Ethnic group
Children’s ethnic group was allocated on the basis of their (non-imputed) ethnic group in 2001.
Where ethnic group information was missing for 2001, the 1991 response and parental ethnic
group were used to allocate ethnic group as far as possible. The approach for adding information
from parents’ ethnic group was carried out on the basis of the observed patterns of parents’
and children’s ethnic group in the non-missing data. Therefore, where couple parents had the
same ethnic group as each other, the child was given the ethnic group of the parents. Among the
remainder, where two parents were from different white ethnic groups, the child was attributed
White British ethnicity. Where the two parents were from different ethnic groups, these were
mapped onto the appropriate mixed categories. This left some missing cases where only one
parent was present. It is not possible to assume that lone parent and child share the same ethnic
group, and so these few cases were excluded from the analysis.
Table 3 shows the number of children included in the analysis by ethnic group. There were
rather small numbers of children from some ethnic groups, rendering them unsuitable for detailed
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Table 3Children aged 0–5 years in 1991 and observed
aged 10–15 years in 2001 by ethnic group,
England and Wales
Ethnic group
Total in group
Per cent of sample
33,166
90.2
White Irish
119
0.3
White Other
304
0.8
White and Black Caribbean
394
1.1
75
0.2
White and Asian
228
0.6
Other Mixed Group
161
0.4
Indian
757
2.1
Pakistani
564
1.5
Bangladeshi
212
0.6
Other Asian
123
0.3
Black Caribbean
262
0.7
Black African
135
0.4
Other Black
97
0.3
113
0.3
63
0.2
36,773
100
394
1.1
37,167
100
White British
White and Black African
Chinese
Other ethnic group
Total
Missing ethnic group
Total including missing
Source: ONS Longitudinal Study, author’s analysis
consideration, though they were included in the estimations for completeness. Small sample sizes
were particularly an issue for White Irish, White and Black African, and Chinese children, as they
were for the heterogeneous ‘other’ groups: Other Mixed, Other Asian, Black Other and Other.
The illustration of results and the discussion therefore focus on the larger groups: White British,
White Other, White and Black Caribbean, White and Asian, Indian, Pakistani, Bangladeshi, Black
Caribbean and Black African.
Workless household
For the purposes of this article, the definition of a workless household is that no member of the
household was in paid work, either full-time or part-time. To construct the workless household
variable, the non-members file was used, providing information on those co-resident with the LS
member at each measurement point.
Additional explanatory and control variables
The non-members file was also used alongside the members file to enable the construction of
variables to indicate whether:
• the sample member was living with both parents or just one at either time point
• whether the co-resident parent(s) were UK born, and their educational level
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• the age of the mother
• whether the parents had experienced separation, widowhood or divorce within the decade
• how many siblings were co-resident, whether this changed between the two time points, and
whether there was a child aged under five in the household at the later time point
Research suggests that all these are likely to influence the chances of adult household members
being in work, and therefore the chances of a household being or becoming jobless.
Household level variables on car ownership and housing tenure were also included, as was a
measure of change in housing tenure. These variables are indicative of financial resources which
may assist maintenance of family work and protect against adverse circumstances. They have
been shown to be more directly related to employment outcomes. Access to a car, or at least
possession of a driving licence, has been shown to be important in facilitating labour market
29
(re)-entry, including among lone parents. Housing tenure is known to be strongly associated
30
with employment status, as well as a range of other unfavourable outcomes. While the causal
relationship and direction between housing tenure and other outcomes is hard to determine
precisely, it does appear that living in social housing is not solely a consequence of disadvantage
23
in other domains, but may also shape outcomes. The analysis also included a measure of
whether the family had experienced a geographical move between the two time points, and the
distance moved. The economic variables are likely to be protective against joblessness, and
geographical relocation may also imply a change in socioeconomic circumstances (including being
associated with a move into work). The child’s own age and sex were also included.
The variables included focused on the family (or parental) circumstances of the child and the more
general household context. However, they were not exhaustive. This was partly for reasons of
parsimony and the risks of overparameterising the model, given the small sample sizes of some
ethnic groups, and partly to aid more direct interpretation. Other analysis of workless households
(not focusing on ethnicity, and with a richer set of variables to choose from) has included a more
31
complex range of variables, but that can come at the risk of rendering individual variables hard to
interpret. Key variables that may be relevant to consider in future analysis are regional effects and
household size/number of adults.
Analytical approach
Following inspection of the simple distributions of worklessness by ethnic group across the two
time points and transitions between workless and non-workless states, binary logistic regression
models were estimated for the probability of being in a workless household in 2001 conditioning
on workless household status in 1991, and both with and without controlling for the household
and family characteristics. This enabled entry, exit and persistence effectively to be summarised
in a single model. By constructing a set of dummies that combined ethnic group and workless
household status in 1991, the estimation allowed the association between workless household
status in 1991 and 2002, that is, patterns of entry, exit and persistence, to vary by ethnic group,
while still using the full estimation sample. Creating individual dummies for the combinations of
ethnic group and household workless status avoids the problems of interpreting interaction effects
32
in a logit model, while not forcing the impact of prior worklessness to be constant across groups.
Not allowing for interactions would mean, given the numerical dominance of the White majority
group, that the effect of worklessness in 1991 on workless household status in 2001, would be
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driven by the association for the white majority. In the results section, odds ratios are provided for
combined ethnic group and 1991 workless households status effects. These capture entry rates
relative to the reference category of White British children not living in a workless household in
1991, and persistence rates relative to the same reference group.
Given that persistence rates for those workless in 1991 are likely to be higher for all ethnic groups,
including the White majority, compared to White majority children not workless in 1991, evaluation
of whether there are differences in persistence between minorities and the majority was attempted,
by testing the equality of the coefficients for each minority dummy for those in workless households
in 1991 with the coefficient for White majority children workless in 1991.
Results
Patterns of children’s experience of workless households by ethnic group
Figure 1 shows the simple proportions of the sample of children who experience worklessness at
either time point. Overall, a substantial 21 per cent were living in a workless household in 1991 and
33
this declined to 17 per cent by 2001, a statistically significant change. Given that these are the
same children who aged over the decade, this could be partly an age effect (that as the children
become older, other members of their household become workers). For example, lone parents
34
become freed for work or older siblings still living in the household begin work. It could also be a
structural effect related to the improvement in the economy and reduction of unemployment over
time. The role of family and household characteristics in contributing to household worklessness
and workless transitions is explored below. It is worth noting the very different rates of
worklessness experienced across the groups. For most groups, except White Other and Pakistani
children, there is a decline in household worklessness risks over time, though it is not statistically
significant in all cases.
Figure 1 Proportions of children in workless households at 1991
(aged 0–5 years) and 2001 (aged 10–15 years) by ethnic
group, England and Wales
Percentages
60
51
Workless in 1991
Workless in 2001
50
49
45
40
34
34
26
24 24
20
21
19
17
36
36
32
27
30
44
18
15
13
12
10
0
All
White
British
White
Other
White and White and
Black
Asian
Caribbean
Indian
Pakistani Bangladeshi
Black
Caribbean
Black
African
Source: ONS Longitudinal Study, author’s analysis
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Figure 2 Movers and stayers, children in workless households
1991–2001, by ethnic group
Percentages
9
8
8
8
12
11
10
13
25
8
11
11
4
7
26
27
9
11
17
17
9
15
18
18
10
21
14
19
27
72
73
22
80
64
65
40
53
51
In a workless household at both time points
Moves into a workless household
Moves out of a workless household
Not in workless household at either time point
All groups
White
British
White
Other
White and
Black
Caribbean
White and
Asian
45
34
Indian
Pakistani
Bangladeshi
Black
Caribbean
Black
African
Source: ONS Longitudinal Study, author’s analysis
Figure 2 divides these overall risks of living in a workless household at the two time points by
looking at the actual patterns of movement in and out of a workless household for the children at
either end of the decade. It shows those who were in a workless household at neither time point,
those who moved out of one over the decade (exits), those who moved into one over the decade
(entries) and those who were living in a workless household at both time points (persistence).
Is it that the majority of those who are living in workless households in 1991 were also living in
workless households in 2001, or is there a significant movement between one time point and the
next?
It is clear from Figure 2 that experience of living in a workless household across the decade is a
minority experience since 72 per cent of children lived in a working household at younger and
older ages. Only nine per cent were living in a workless household at both ends of the decade. Of
course, for the former group it cannot be assumed that they never experienced worklessness, nor
that the latter group was continuously living in a workless household. For example, the extensive
35,36,37
dynamics in poverty was documented by Jenkins and others.
38
However, those with continuous experience will be over represented in either group. Aside from
these two groups of ‘stayers’, however, a fifth of young children (20 per cent) have either moved
out of or into a workless household by the time they are aged 10–15.
There is additionally, substantial ethnic group variation in these patterns. With the exception of
children in Indian families who are less likely to have experienced worklessness at either time
point, the minority groups are all more likely than the white majority to have been in a workless
household at one or other time, as well as to have experienced persistence, that is being in a
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workless household at both time points. The small sample sizes mean that not all the differences
in persistence are statistically significant, but they differ significantly for Pakistani, Bangladeshi
and Black African children compared to White British children. The proportions of all children
who are persistently in workless households are particular high for White and Black Caribbean,
Bangladeshi and Black African children: a quarter or more were living in a workless household
at both time points. However, if we take the proportion in workless households as a proportion of
all those workless at the first time point (that is the top green section over the top grey plus the
first green sections combined) to be the persistence rate, that is the proportion of those children
in workless households who are also in workless households at the second time point, we find a
slightly different pattern. Indian children have the lowest persistence rates at 31 per cent, followed
by 37 per cent for White and Asian children, 42 per cent for White British and Black Caribbean
children, 48 per cent for White and Black Caribbean children, around 55 per cent for White Other,
Pakistani and Bangladeshi children and to 58 per cent for Black African children.
The patterns of transitions are also varied. Pakistani and Bangladeshi children have high rates of
entry into worklessness: 17 and 18 per cent respectively of children from these groups moved into
a workless household. As a proportion of those not workless at the first time point, this amounts
to entry rates of 25 per cent and 35 per cent respectively, compared to only ten per cent for White
British children.
While in general there is a slight tendency of workless household rates to reduce with time (and
age) – and this is particularly true for White and Black Caribbean children – Pakistani children
(where moves into worklessness outweigh moves out of it) have in fact higher risks of living in a
workless household at the later time point.
Estimating ‘ethnic penalties’ in children’s workless household persistence
Table 4 shows the results from models estimating the impact of ethnic group and workless
household status on chances of being in a workless household in 2001. In model 1 only the
dummies created by interacting all ‘ethnic group’ categories with ‘1991 workless household status’
(in working household 1991 / in workless household 1991) were included, whereas model 2 also
included the full set of household and family characteristics.
Variables which were particularly strongly associated with persistence in or entry to workless
households status in 2001 included family structure. ‘Presence of a father in 2001’, was found
to be negatively and significantly associated with workless household status (odds ratio = 0.34).
The variable ‘number and increase in siblings’ was also positively associated with living in a
workless household in 2001. Parental qualifications at every level decreased chances of living in
a workless household in 2001 relative to having no qualifications. Housing tenure was strongly
associated with worklessness. Both private tenancy and social housing had odds ratios of over
5 relative to living in owner occupation. Change in housing tenure (that is into owner occupation)
moderated this effect slightly as it was negatively associated with remaining or becoming
workless by 2001 (odds ratio = 0.81). Car ownership in 1991 was also negatively associated with
workless household status ten years later, consistent with expectations. While it is not possible to
disentangle the causal relationships in every case, the indication is that prior household resources
as well as parental qualifications and family structure are all implicated in children’s vulnerability
to worklessness over time. These are areas that are already recognised as affecting children’s
opportunities.
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Table 4 Relative chances of being in a workless households in
2001 conditional on 1991 workless household status,
by ethnic group
Ethnic group
Household work status in 1991
Model 1
Simple model
Model 2
with household
and family
characteristics
In workless household 1991 (odds ratio)
in working hh 1991 [relative entry compared to
White British] (odds ratio)
7.9***
1.4
2.14***
1.70*
In workless hh 1991 [compared to White British
not workless]
15.3***
4.43***
6.27(1)*
5.47(1)*
2.56***
10.07***
2.38(1)
1.42
1.89***
0.53(1)
Reference: white not workless
White British
White other
Wald test of difference from White British
workless [relative persistence compared to
White British] (Chi2 (df))
White and Black Caribbean in working hh 1991 [relative entry] (odds ratio)
In workless hh 1991 (odds ratio)
Difference from White British workless
[relative persistence] Chi2 (df)
White and Asian
in working hh 1991 [relative entry] (odds ratio)
In workless hh 1991 (odds ratio)
Difference from White British workless
[relative persistence] Chi2 (df)
1.26
5.98***
0.99(1)
1.08
2.25*
0.02(1)
Indian
in working hh 1991 [relative entry] (odds ratio)
Workless (odds ratio)
Difference from White British workless
[relative persistence] Chi2 (df)
0.79
4.95***
3.69+
0.99
3.01***
1.32(1)
Pakistani
in working hh 1991 [relative entry] (odds ratio)
3.25***
2.56***
14.15***
8.92***
10.98(1)***
38.96(1)***
In workless hh 1991 (odds ratio)
Difference from White British workless
[relative persistence] Chi2 (df)
Bangladeshi
Black Caribbean
Black African
Number in analysis
in working hh 1991 [relative entry] (odds ratio)
5.05***
2.11**
In workless hh 1991 (odds ratio)
12.43***
2.41*
Difference from White British workless
[relative persistence] Chi2 (df)
4.32(1)*
0.18(1)
in working hh 1991 [relative entry] (odds ratio)
1.95***
0.9
In workless hh 1991 (odds ratio)
7.92***
1.83*
Difference from White British workless
[relative persistence] Chi2 (df)
0.00(1)
0.38(1)
in working hh 1991 [relative entry] (odds ratio)
2.83***
1.43
In workless hh 1991 (odds ratio)
9.30***
2.72*
Difference from White British workless
[relative persistence] Chi2 (df)
0.24(1)
0.36(1)
33,051
P values: + <0.1; *< = 0.05; **< = 0.01; ***< = 0.001.
Values that are not statistically significant are indicated in italics.
Note: Model 1 includes ethnicity and workless household status in 1991.
Model 2 adds the household and parental characteristics outlined in Section 2.
Source: ONS Longitudinal Study, author’s analysis
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For estimating the relative impact of combined ethnic group and 1991 household work status,
the reference category is White British children living in a working household in 1991. We would
expect that for all children the chance of living in a workless household in 2001 is higher if they
were already living in a workless household at the earlier point, both in absolute terms and when
holding family characteristics constant. The ‘workless’ rows in the table show that this is true for
White British children who are workless in 1991, and for other groups who are living in workless
households in 1991, relative to White British children not living in a workless household in 1991.
This then simply tells us about the tendency of workless household status to persist over time.
The ‘in work’ row in Table 4 for each minority group illustrates the relative chance of being in
a workless household in 2001, given that the child was living in a working household in 1991,
compared to the White British children in working households in 1991. It shows whether minority
groups face a greater risk of entry into worklessness compared to the White British majority. In
a situation of ethnic equality we would expect all these coefficients to be statistically no different
from the reference category of White British children living in a working household at a young age.
Instead, it was found that Pakistani, Bangladeshi, Black Caribbean and Black African children
faced greater risks of entry into a workless household in absolute terms (Model 1), consistent
with what we saw in Figure 2. However, on controlling for household and family characteristics,
the Black Caribbean and Black African children no longer face relatively higher risk of entry; that
is, their greater risk can be explained in terms of living in family types or experiencing changes in
household or family characteristics placing them at greater risk of entry, but they appear to face no
additional vulnerability to becoming workless. By contrast children in the White Other group appear
to live in family types that tend to be less vulnerable to becoming workless since, when family and
household characteristics are controlled for, they experience greater risk of workless household
entry compared to their White British counterparts. Pakistani and Bangladeshi children also had
significantly higher rates of entry into worklessness in model 2 than their White British comparators,
but for these children the effects were reduced when family and contextual characteristics were
held constant.
The third row for each minority group in Table 4 indicates the differences in the risk for children
of remaining in a workless household over time, by ethnic group. Any statistically significant
difference between the coefficients implies that the chance of remaining in a workless household
(persistence) is greater for the minority group. This statistically significant result suggests that
exit from workless household status over the ten-year period is harder to achieve for that group
compared to the majority of children.
For White Other, Pakistani and Bangladeshi children, there was a significantly greater absolute
risk of persistence. In Model 2 the increased risk remains for White Other and Pakistani children,
but was not observed for Bangladeshi children once controlled for household and family
characteristics. This suggests that White Other and Pakistani children face an ‘ethnic penalty’
in their chance of remaining in (or returning to) a workless household over a ten-year period.
Their increased persistence in a workless household cannot be fully accounted for by family or
household characteristics placing them at greater risk of worklessness. By contrast, the greater
persistence experienced by Bangladeshi children would appear to be more readily accounted for
by family and household characteristics that make them more vulnerable to worklessness.
To illustrate the magnitude of these effects, while a White British child with average characteristics
from this cohort has a six per cent chance of entering worklessness by 2001, a Pakistani child with
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the same family and household characteristics has a 15 per cent chance. A White British child with
average characteristics but living in a workless household in 1991 has a 13 per cent chance of still
being there in 2001, but for a Pakistani child living in comparable family circumstances, the chance
39
is 27 per cent.
Discussion
This article has exploited the unique features of the ONS Longitudinal Study to examine the risk
of living and remaining in a workless household for a single cohort of children over a common
period and at a ten year interval. It set out to examine the chances of worklessness over time and
for a group of children subject to comparable economic circumstances when growing up, and to
investigate the extent to which there appeared to be differential risk of remaining in a workless
household over time by ethnic group.
Understanding of ethnic group differences in child welfare, particularly in duration and persistence
of deprivation, remains limited, despite some recognition of the labour market penalties
40
experienced by minority groups. This article set out to understand the extent to which differences
were mediated by family characteristics and household circumstances, which are subject to direct
4,5,18
and the existence of ethnic penalties.
policy interest,
Analyses have shown that there are dramatic differences in the chance of remaining in or entering
a workless household for this cohort of children by ethnic group. Indian children had the lowest
chance of having lived in a workless household at either time point, while mixed White and Black
Caribbean children had the highest. For those children who ever lived in a workless household,
there were differences in their vulnerability to remaining in a workless household and to entering a
workless household from a working household.
Estimating the chance of being in a workless household in 2001, when controlling for a range of
family and household characteristics and conditioning on household work status at 1991, showed
that for children from other groups, the absolute difference in chances of living in a workless
household were mediated by family structure and household circumstances. Nevertheless,
Pakistani and White Other children faced statistically significantly greater chances of both
remaining in a workless household and of entering a workless household when such family and
household characteristics were held constant. Bangladeshi children also experienced higher rates
of entry into worklessness than otherwise similarly situated White majority children. The findings for
White Other children were not apparent in other labour market studies. For Pakistani children the
21,41
increased risk is consistent with findings elsewhere despite increases in educational level and
other apparently protective factors.
Of course, estimating ‘ethnic penalties’ across ‘otherwise similar’ children does not take account of
family characteristics that may operate in different ways for different groups. In particular, housing
tenure has been shown to have different meanings for some groups, with owner occupation
42
operating as a potential constraint as much as an economic resource. Moreover, ethnic groups’
geographical distribution leads to greater concentration of some groups in higher unemployment
areas. Part of the ‘ethnic penalty’ can stem from living in a location where there are fewer – or
declining – opportunities more generally. Future analysis could explore the role of regional effects.
43
However, consistent with analysis by Simpson et al., preliminary area-based investigation did
not indicate that this was the only factor accounting for the additional risk of living in a workless
household faced by children from ethnic minority groups.
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This article studies just one cohort of children, avoiding problems of changing labour market
conditions which could influence outcomes. But in doing that it can only really speak to this cohort.
The ONS Longitudinal Study enables persistence in worklessness to be examined over a relatively
long period, allowing early and middle/late child experience to be compared. This comes at the
cost of being able to study only two time points, with intervening experiences remaining unknown.
The discussion of ‘persistence’ is therefore subject to some caveats. The advantage of the ONS
Longitudinal study is that its sample size allows direct analysis of ethnic difference; however, when
exploring specific cohorts, some sample size issues still arise.
Despite limitations, these findings shed new light on children’s experience of living in a workless
household over time by ethnic group. If the experience of worklessness in childhood has longterm effects, there should be concern for the future of these groups, particularly those at risk of
persistent (or repeated) worklessness. The evidence for some groups suffering ‘ethnic penalties’ in
worklessness should lead to questioning the extent to which these penalties will be addressed by
current policies to increase workforce participation in families with children.
Acknowledgments
The permission of the Office for National Statistics to use the Longitudinal Study is gratefully
acknowledged, as is the help provided by staff of the Centre for Longitudinal Study Information and
User Support (CeLSIUS). CeLSIUS is supported by the ESRC Census of Population Programme
(Award Ref: RES-348-25-0004).
This work contains statistical data from ONS which is Crown copyright and reproduced with the
permission of the controller of HMSO and Queen’s Printer for Scotland. The use of the ONS
statistical data in this work does not imply the endorsement of the ONS in relation to the
interpretation or analysis of the statistical data. This work uses research datasets which may not
exactly reproduce National Statistics aggregates. The author alone is responsible for the
interpretation of the data.
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Appendix
Figure A1 Employment status by gender and ethnicity (percentages)
Percentages
32
Chinese women
17
37
Black African women
16
Pakistani women
12
8
13
2
8
38
White British women
39
6
40
3
Pakistani men
35
18
4
9
0.20
36
5
12
6
17
0.30
6
11
20
22
11
19
28
7
30
12
5
0.50
10
12
7
0.40
26
37
9
10
3
6
11
60
White British men
4
27
58
Indian men
Employed Full Time
Employed Part Time
Self-employed
Unemployed
Other/Inactive
71
49
Black Caribbean men
34
31
73
53
Bangladeshi men
6
5
Black African men
0.10
39
15
36
Chinese men
4
2 5
Indian women
0.00
2
45
Black Caribbean women
Bangladeshi women
8
0.60
14
0.70
6
18
4
0.80
17
0.90
1.00
Source: Longhi and Platt (2008) ‘Pay Gaps Across Equalities Areas’, Figure 2.1
References
1 Department for Work and Pensions (2005) Department for Work and Pensions five year
strategy: opportunity and security throughout life, London: Stationery Office.
2 Gregg, P and Wadsworth, J (1996) ‘More work in fewer households?’ in J. Hills (ed) New
inequalities: the changing distribution of income and wealth in the United Kingdom, Cambridge:
Cambridge University Press.
3 Gregg, P and Wadsworth, J (2001) ‘Everything you ever wanted to know about measuring
worklessness and polarization at the household level, but were afraid to ask’, Oxford Bulletin of
Economics and Statistics 63: 777–806.
4 Child Poverty Unit (2009) Ending Child Poverty: Making it happen, London: Child Poverty Unit.
5 HM Treasury, Department for Work and Pensions and Department for Children, Schools and
Families (2008) Ending Child Poverty: Everybody’s business, Budget 2008 Report. London: HM
Treasury.
6 Nickell, S (2004) ‘Poverty and worklessness in Britain’, The Economic Journal 114, C1–C25.
7 Platt, L (2009) Ethnicity and child poverty, Department for Work and Pensions Research Report
No 576. Leeds: Corporate Document Services.
8 In the vast majority of households children are living with at least one parent, and for children
of the age range covered in this analysis (0–15), most will be living with a parent of working
age. A small number of children will be living with adults other than parents, and a small
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number will be living only with adults over pension age, whether parents or not. But from the
perspective of this analysis, which is the impact that worklessness has on children, these
situations are not excluded. It is a moot question whether it is more appropriate to investigate
workless households or workless families (HM Treasury, 2008). It is conventional to look at
the household as a whole and accords more with the aim of the article, where any household
employment, even if not within the nuclear family, is likely to have an impact on children’s
poverty risks and will provide them with some role model or familiarity with a employment.
Separate analyses were conducted using a family definition, and in order to answer somewhat
different questions, but it is beyond the scope of this article to cite them here.
9 Such, E and Walker, R (2002) ‘Falling behind? Research on transmitted deprivation’, Benefits
10(3): 185–192.
10 Duncan, G G, Brooks-Gunn, J, Yeung, W J and Smith, J R (1998) ‘How much does childhood
poverty affect the life chances of children?’ American Sociological Review 63(3): 406–423.
11 Ermisch, J, Francesconi, M and Pevalin, D J (2004) ‘Parental partnership and joblessness in
childhood and their influence on young people’s outcomes’, Journal of the Royal Statistical
Society A 167(1): 69–101.
12 Vleminckx, K and Smeeding, T M (eds) 2001 Child Well-Being, Child Poverty and Child Policy
in Modern Nations: What do we know? Bristol: The Policy Press.
13 Schoon, I, Sacker, A and Bartley, M (2003) ‘Socio-economic adversity and psychosocial
adjustment: a developmental contextual perspective’, Social Science and Medicine 57: 1001–
1015.
14 Bradbury, B, Jenkins, S P and Micklewright, J (eds) (2001) The Dynamics of Child Poverty in
Industrialised Countries, Cambridge: Cambridge University Press.
15 Hills, J, McKnight, A, Smithies, R (2006) Tracking Income: How working families’ incomes vary
through the year. CASEreport 32. London: Centre for Analysis of Social Exclusion.
16 Smith, N and Middleton, S (2007) A Review of Poverty Dynamics Research in the UK. York:
Joseph Rowntree Foundation.
17 Jarvis, S and Jenkins, S P (1997) ‘Low income dynamics in 1990s Britain.’ Fiscal Studies 18(2):
123–142.
18 Platt, L (2006) ‘Social insecurity: children and benefit dynamics’, Journal of Social Policy, 35(3):
391–410.
19 Department for Work and Pensions (2009) Households Below Average Income, 1994/95–
2007/08. London: DWP.
21 Gottschalk, P (1992) ‘The intergenerational transmission of welfare participation: facts and
possible causes’, Journal of Policy Analysis and Management 11(2): 254–272.
22 Platt, L (2006) ‘Social insecurity: children and benefit dynamics’, Journal of Social Policy 35(3).
23 Platt, L (1997) Poverty and Ethnicity in the UK. Bristol: The Policy Press.
24 See, for example, the discussion of the literature on employment and ethnicity in Platt, L (2005)
Migration and social mobility: The life chances of Britain’s minority ethnic communities, Bristol:
The Policy Press.
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25 Department for Work and Pensions (2007) Ready for Work: Full employment in our generation,
Cm 7290. London: The Stationery Office.
26 Even though worklessness is defined at the household level, I have explored the association
of ‘family’ characteristics with that outcome (i.e. parental characteristics and family structure).
When it is the child (rather than the household or the family) which is the unit of analysis, this is
unproblematic (compare poverty analysis where poverty is defined at the household level but
family as well as household characteristics are invoked to ‘explain’ it). Throughout the article, I
have therefore used family to refer to family type (e.g. lone parenthood) or parental attributes,
even in the context of measuring outcomes at the household level.
27 Heath, A and McMahon, D (1997) ‘Education and occupational attainments: the impact of ethnic
origins’, in V. Karn (ed) Ethnicity in the 1991 Census: Volume Four: Employment, education and
housing among the ethnic minority populations of Britain, London: HMSO.
28 Platt, L, Simpson, L and Akinwale, B (2005) ‘Stability and change in ethnic group in England
and Wales’, Population Trends 121: 35–46.
29 Shaw, A, Walker, R, Ashworth, K, Jenkins, S and Middleton, S (1996) Moving off Income
Support: Barriers and bridges, Department of Social Security Research Report No. 53. London:
HMSO.
30 Hills, J, Brewer, M, Jenkins, S, Lister, R, Lupton, R, Machin, S, Mills, C, Modood, T, Rees, T and
Riddell, S (2010) An Anatomy of Economic Inequality in the UK: Report of the National Equality
Panel, London: Government Equalities Office / Centre for Analysis of Social Exclusion.
31 Hérault, N, Kalb, G, Mavromaras, K, Platt, L and Zakirova, R (2009) Dynamics of Household
Joblessness in Australia, Report of Melbourne Institute of Applied Economic and Social
Research for Australian Government Department of Education, Employment and Workplace
Relations. Melbourne: University of Melbourne.
32 Ai, C R and Norton, E C (2003) ‘Interaction terms in logit and probit models’, Economics Letters
80(1): 123–129.
33 The figure of 17 per cent for 2001 is slightly higher than the 16 per cent for the White majority
children shown in Table 1. But, while the rates are similar, the ONS LS sample is a specific
cohort and we would not necessarily expect its experience to parallel that of all children.
34 We already know that there is an age of child effect for lone parents’ labour market (re)-entry;
and this is evidence that the new Lone Parent Obligations (DWP, 2007) are building upon.
35 Jarvis, S and Jenkins, S (1997) ‘Low income dynamics in 1990s Britain’, Fiscal Studies 8: 123–
142.
36 Hill, M and Jenkins, S P (2001) ‘Poverty amongst British children: chronic or transitory?’
in B. Bradbury, S. P. Jenkins and J. Micklewright (eds) The Dynamics of Child Poverty in
Industrialised Countries, Cambridge: Cambridge University Press.
37 Jenkins, S P and Rigg, J A (2001) The Dynamics of Poverty in Britain, Department for Work and
Pensions Research Report 157, London: DWP.
38 Bane, M J and Ellwood, D T (1994) ‘Understanding welfare dynamics’, in M. J. Bane and D. T.
Ellwood (eds) Welfare realities: from rhetoric to reform, Cambridge, Massachusetts: Harvard
University Press.
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39 These predicted persistence rates are much lower than the ones we see in the raw data
(Figure 2) of around 42 per cent and 56 per cent, because the children more likely to be
workless in 1991 are not living in ‘average’ family circumstances.
40 Heath, A and Cheung, S Y (2006) Ethnic Penalties in the Labour Market: Employers and
discrimination, Department for Work and Pensions Research Report 341. Leeds: Corporate
Document Services.
41 Longhi, S and Platt, L (2008) Pay Gaps Across Equalities Areas, EHRC Research Report nr 9.
Manchester: Equalities and Human Rights Commission.
42 Phillips, D (1997) ‘The housing position of ethnic minority group home owners’, in V. Karn (ed)
Ethnicity in the 1991 Census: Volume Four: Employment, education and housing among the
ethnic minority populations of Britain, London: The Stationery Office.
43 Simpson, L, Purdam, K, Tajar, A, Pritchard, J A D and Dorling, D (2009) ‘Jobs deficits,
neighbourhood effects, and ethnic penalties: the geography of ethnic-labour-market inequality’,
Environment and Planning A 41(4): 946–63.
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2008-based national population
projections for the United Kingdom
and constituent countries
Emma Wright
Office for National Statistics
Abstract
The 2008-based national population projections, produced by the Office for National
Statistics in consultation with the devolved administrations, show the population of the
UK rising from 61.4 million in 2008 to 65.6 million in 2018 and 71.6 million by 2033. In the
longer-term, the projections suggest that the population will continue rising beyond 2033
for the full length of the projection period. The population will become older with the median
age expected to rise from 39.3 years in 2008 to 42.2 years by 2033. Despite the forthcoming
changes to state pension age, the number of people of working age for every person of
state pensionable age will reduce from 3.23 in 2008 to 2.78 by 2033.
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Contents
Abstract............................................................................................................................................ 91
Introduction....................................................................................................................................... 94
Projection period.............................................................................................................................. 94
Base population................................................................................................................................ 94
Underlying assumptions................................................................................................................... 95
Fertility.............................................................................................................................................. 96
Mortality............................................................................................................................................ 98
Migration......................................................................................................................................... 100
Results of the 2006-based national population projections............................................................ 101
Comparison with 2006-based projections...................................................................................... 110
Sensitivity........................................................................................................................................111
Key findings.................................................................................................................................... 113
References..................................................................................................................................... 113
List of figures
Figure 1abc Assumptions for the 2008-based national population projections............................ 96
Figure 2Actual and projected population of the United Kingdom and constituent
countries, 1971–2058............................................................................................... 101
Figure A
Projected UK total population, 2008 to 2083............................................................ 106
Figure 3
Actual and projected births and deaths, United Kingdom, 1971–2058..................... 107
Figure 4
Actual and projected age distribution, United Kingdom, 1981–2058........................ 108
Figure 5
Actual and projected old age support ratio, United Kingdom, 1981–2058............... 109
Figure 6Population of the United Kingdom according to principal and variant 2008-based
projections, 1981–2083............................................................................................ 112
Figure 7Proportion of the population aged 65 and over according to principal and variant
2008-based projections, United Kingdom, 1981–2083............................................. 112
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List of tables
Table 1Population change 2006–2008: actual change compared with 2006-based
projected change, United Kingdom.......................................................................... 95
Table 2
Summary of assumptions for individual countries.................................................... 97
Table A
Period and cohort expectation of life, United Kingdom............................................. 100
Table 3
Components of change: summary (annual averages), 2008–2033.......................... 102
Table A Projected population change, United Kingdom, 2008–33........................................ 104
Table B Projected population growth by component, United Kingdom, 2008–33.................. 104
Table A
Projected population growth by component, United Kingdom.................................. 105
Table 4
Projected population by age, United Kingdom, 2008–2033..................................... 108
Table 5Change in projected population at 2033 compared with the 2006-based
projections................................................................................................................ 110
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Introduction
National population projections are produced for the UK and its constituent countries every two
years. The latest set of projections is 2008-based; the principal projection and the key variant
projections were published simultaneously on 21 October 2009, with additional variants published
on 18 November 2009. Full results from the 2008-based principal and variant projections
1
are available on the ONS website, while results for previous projections are available on the
2
Government Actuary’s Department (GAD) website.
The 2008-based projections were produced by the Office for National Statistics (ONS) on behalf
of the National Statistician and the Registrars General of Scotland and Northern Ireland. The
underlying assumptions were agreed in liaison with the devolved administrations, following
consultation with key users of the projections in each country and advice from an expert academic
3
advisory panel.
The projections use an internationally accepted standard cohort component methodology involving
ageing on the population, adding projected births, subtracting deaths and adding assumed
numbers of net migrants. Normally, a new set of national projections is made every second year,
based on a full-scale review of the underlying assumptions about fertility, mortality and migration.
The availability of subnational projections is discussed in Box one.
This report provides an overview of the results of the 2008-based national population projections
and the underlying assumptions. More detailed information about the projections assumptions and
4
methodology is provided in the 2008-based National Population Projections Reference Volume.
Box one Subnational projections
Subnational population projections are the responsibility of the statistical offices of the
individual countries. The General Register Office for Scotland (GROS) published mid-2008
based subnational projections for Scotland, consistent with the national projections described in
5
this article, on 3 February 2010. ONS, the Welsh Assembly Government Statistical Directorate
and the Northern Ireland Statistics and Research Agency (NISRA) plan to release 2008-based
subnational projections for England, Wales and Northern Ireland respectively in May 2010.
Projection period
The main focus of these projections is on the 25 years to 2033. However, the results of longerterm projections are included in the graphs in this article and discussed where appropriate. In the
detailed results available on the ONS website, the projections are carried forward for 75 years
(that is, to 2083) for all countries. However, the long-term figures should be treated with great
caution as population projections become increasingly uncertain the further they are carried
forward, and particularly so for smaller geographical areas.
Base population
The projections are based on the official estimate (published on 27 August 2009) of the resident
6
population of the UK at mid-2008 of 61.4 million.
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Table 1
Spring 2010
Population change 2006–2008: actual change compared with
2006-based projected change, United Kingdom
Thousands
Population at mid-2006
Components of change (2006–2008)
Births
Deaths
Natural change
Net migration and other changes*
Total change
Population at mid-2008
England
Wales
Scotland
Northern Ireland
Mid-year
2006-based
estimates
projections
60,587
Difference
Number
(thousands)
Percentage
60,587
0
0.00%
1,548
1,535
13
0.82%
1,141
1,146
–5
–0.47%
407
389
18
-
388
435
–47
-
796
824
–29
-
61,383
61,412
–29
–0.05%
51,446
51,488
–41
–0.08%
2,993
2,993
0
0.00%
5,169
5,157
11
0.22%
1,775
1,774
1
0.08%
* Net movements of Armed Forces and other smaller changes.
Note: Natural change, net migration and total change can be positive or negative and hence it is not possible to express
change in percentage terms.
As Table 1 shows, the estimated population of the UK at mid-2008 was 29 thousand
(0.05 per cent) lower than envisaged in the 2006-based projections. This is largely explained
by a combination of three factors: an underprojection of births during 2006–08 (13,000), an
overprojection of deaths (5,000) and an overprojection of net migration and other changes
(47,000). There were differences at individual country level, with England having an estimated
population at mid-2008 that was 41,000 (0.08 per cent ) lower than expected from the 2006-based
projections. In contrast, the populations of Scotland and Northern Ireland at mid-2008 were
underestimated in the 2006-based projections, with the greatest relative error being for Scotland
where the actual mid-2008 population was 0.22 per cent (11,000) higher than projected.
7
A package of improvements for mid-year population estimates has been identified, and these will
be implemented in May 2010, when revised population estimates for England and Wales for mid2002 to mid-2008 will be published. The base population for the 2008-based national population
projections includes an adjustment for the expected national level impact of the revisions. The base
population for England is therefore 13,000 higher than the published mid-2008 population estimate,
whilst the base population for Wales is 3,000 lower than the corresponding published estimate.
Underlying assumptions
The assumptions used in the 2008-based national population projections are shown, for the UK as
a whole, in Figure 1, while those for the individual countries are summarised in Table 2.
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Figure 1a, b, c Assumptions for the 2008-based national population
projections
(a) Total fertility rate (TFR) and average completed
family size (CFS), United Kingdom, 1971–2033
2.50
2.50
Assumed TFRs
Children per woman
2.25
2.25
Replacement level
2.00
2.00
CFS*
1.75
1.75
TFR
1.50
1971
1.50
1981
1991
2001
2011
2021
2031
Year
* Completed family size (CFS) relates to cohort born 30 years earlier – 30 years being the approximate mid-point of the
childbearing ages. Projected CFS is given for cohorts who have not yet completed childbearing
Note: This figure is presented on a calendar year basis and for the TFR, shows a clear peak in actual fertility in 2008,
followed by a projected fall from 2009 onwards. When fertility rates are presented on a mid-year basis, this peak is
smoothed out due to the high fertility in 2008 being split between 2007–08 and 2008–09.
(b) Period expectation of life at birth, United Kingdom, 1981–2083
94
94
Assumptions
Expectation of life at birth (years)
92
92
Females
90
90
88
88
86
86
Males
84
84
82
82
80
80
78
78
76
76
74
74
72
72
70
1981
70
1991
2001
2011
2021
2031
2041
2051
2061
2071
2081
Year
Fertility
Fertility assumptions are formulated in terms of the average number of children that women born
in particular years will have. This cohort measure of fertility is more stable than the analogous
calendar year or period measure (the total fertility rate, TFR), as it is affected only by changes in
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(c) Total net migration, United Kingdom, 1991–92 to 2020–21
Thousands
275
275
Assumptions
250
250
225
225
200
200
175
175
150
150
125
125
100
100
75
75
50
50
25
25
0
0
–25
1991
1996
2001
2006
2011
–25
2021
2016
Year
the total number of children women have and not by the timing of births within their lives. Period
rates, in contrast, may rise or fall if births are brought forward or delayed for any reason. The
assumed average completed family sizes and resultant TFRs are both shown in Figure 1a, while
the TFRs for individual countries are summarised in Table 2.
The assumptions about completed family size are based on family building patterns to date and
other relevant evidence. For the UK as a whole, completed family size has been falling steadily
from an average of around 2.45 children for women born in 1935 to 1.94 children for those born
Table 2
Summary of assumptions for individual countries
Total fertility rate
England
Net annual migration (thousands)
2008–09
2011–12
2014–15
from
2017–18
2008–09
2010–11
2012–13
from
2014–15
1.94
1.88
1.85
1.85
162.5
173.7
166.2
157.0
Wales
1.93
1.88
1.85
1.85
5.5
9.1
10.8
10.5
Scotland
1.78
1.72
1.70
1.70
16.0
16.2
12.9
12.0
Northern Ireland
2.08
2.01
1.97
1.95
3.0
1.8
0.9
0.5
United Kingdom
1.93
1.87
1.84
1.84
187.0
200.8
190.8
180.0
Period expectation of life at birth (years)
Males
England
Females
2008–09
2012–13
2022–23
2032–33
2008–09
2012–13
2022–23
2032–33
78.1
79.7
82.0
83.4
82.2
83.3
85.5
87.1
Wales
77.2
78.9
81.3
82.7
81.5
82.8
85.0
86.6
Scotland
75.5
76.9
79.2
80.7
80.2
81.4
83.6
85.2
Northern Ireland
76.7
78.4
80.8
82.2
81.3
82.8
85.1
86.6
United Kingdom
77.8
79.3
81.7
83.1
81.9
83.1
85.3
86.9
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in 1962, the most recent cohort to have reached the end of their childbearing years. The family
sizes to be achieved by younger cohorts are highly conjectural, and there is some evidence that
falls in cohort fertility could be slowing down. For this projection it has been assumed that average
completed family size, for the UK as a whole, will remain below two children and eventually level
off at 1.84 children for women born after 1995.
For England and for Wales, long-term average completed family size is assumed to be 1.85
children per woman. A higher level of 1.95 is assumed for Northern Ireland and a lower level of
1.70 is assumed for Scotland. These long-term assumptions are the same as the 2006-based
assumptions for England, Wales and Northern Ireland, and slightly higher for Scotland. All the
long‑term assumptions remain well below ‘replacement level’ (see Box four).
Since 2002 TFRs have increased in all constituent countries of the UK. In 2008, the TFR (the
average number of children who would be born per woman based on the fertility rates for that
year) in each of the four countries was well above the long-term level assumed for the 2006-based
projections. For the 2008-based projections, the TFR for the UK has been assumed to decrease
from 2008–09 until reaching the long-term level around 2015. So, TFRs in the first few years of the
projections are above those assumed for the long-term.
Over the past six years, fertility rates have been rising faster among women in their thirties and
forties than for women in their twenties, so mean age at childbirth has continued to rise. The
average age at motherhood for the UK as a whole is projected to increase from 28.0 years for
women born in 1962 to a long-term level of 29.3 years for those born from 1990 onwards.
Mortality
The 2006-based projections assumed that mortality rates for most ages would converge to a
common rate of improvement of one per cent a year at 2031 and continue to improve at that
constant rate thereafter. However, for those born in the period 1923–1940 (who have experienced
greater rates of improvement in the last 25 years) rates of improvement above one per cent were
assumed from 2031 onwards.
The average annual rate of improvement over the whole of the twentieth century was around
one per cent for both males and females, although the improvement rates vary by age. There
continues to be considerable debate as to whether the impact of future lifestyle, medical and
technological changes will have a greater or lesser impact in the future than they had over
the last century. Therefore it was decided for the 2008-based projections to again assume an
improvement rate of one per cent a year from 2033 onwards for most ages, with higher assumed
rates of improvement for those born between 1923 and 1940 (rising from one per cent for those
born before 1923 to a peak of 2.5 per cent for those born in 1931 and then declining back to
one per cent for those born in 1941 and later).
As the projected age-specific annual rates of improvement prior to 2033 are generally higher than
one per cent , this produces averaged annualised rates of mortality improvement of 1.3 per cent
for males and 1.4 per cent for females over the next 76 years, which are about 0.1 per cent a year
higher than those experienced over the past 76 years:
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Actual and assumed overall average annual rates of
mortality improvement, England and Wales, per cent
Males
Females
Past
(actual)
Future
(assumed)
Past
(actual)
Future
assumed)
Last/next 26 years
2.17
1.99
1.50
2.15
Last/next 46 years
1.59
1.56
1.36
1.65
Last/next 76 years
1.27
1.34
1.28
1.40
Note: Historic estimates are based on comparison of the 2006–08 interim life tables with English Life Tables for 1930–32,
1960–62 and 1980–82.
For the UK as a whole, period life expectancy at birth, based on the mortality rates for the given
year, is assumed to rise from 77.8 years in 2008–09 to 83.1 years in 2032–33 for males, and from
81.9 years to 86.9 years for females. These expectations of life in 2032–33 are around 0.2 years
higher for males and 0.5 years higher for females than those assumed for the 2006-based
projections.
Assumed expectations of life to 2032–33 for the individual countries are shown in Table 2. Current
mortality levels differ between the individual countries. However, the same future improvements
have been assumed for all countries of the UK except that some differences (generally slightly
smaller improvements) in the period to 2033 have been assumed at some ages for males and
females in Scotland. Therefore, the relative differences in life expectancy between the four
countries are approximately maintained throughout the projection period.
The expectations of life shown in Table 2 are based on the mortality rates applying to a single
year and are examples of period expectations of life. However, expectations of life can also
be calculated on a cohort basis, allowing for known or projected changes in mortality rates in
later years. Box two gives further information on the differences between period and cohort
expectations of life.
Box two Period and cohort expectations of life
Expectations of life can be calculated in two ways: either period life expectancy or cohort
life expectancy.
Period life expectancy is the average number of years a person would live if he or she
experienced the age specific mortality rates for that time period throughout his or her life.
It makes no allowance for any later actual or projected changes in mortality. In practice,
death rates are likely to change in the future and so period life expectancy does not give the
number of years someone could actually expect to live.
Cohort life expectancies are worked out using age-specific mortality rates which allow for
known or projected changes in mortality in later years and are thus regarded as a more
appropriate measure of how long a person of a given age would be expected to live, on
average, than period life expectancy.
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For example, period life expectancy at birth in the year 2000 would be calculated using the
mortality rate for age 0 in 2000, for age 1 in 2000, for age 2 in 2000, and so on. Cohort life
expectancy at birth in 2000 would be calculated using the mortality rate for age 0 in 2000, for
age 1 in 2001, for age 2 in 2002, and so on.
In most official statistics, period life expectancies are given. Figures for past years provide a
useful measure of mortality actually experienced over a given period and provide an objective
means of comparing trends over time, between areas of a country and with other countries.
However, they are often mistakenly interpreted by users as allowing for subsequent mortality
changes. If mortality rates are projected to decrease in later years, then cohort life expectancy
will be greater than period life expectancy.
Period and cohort life expectancies at individual ages for 1981 to 2058 for the UK and its
constituent countries using historic mortality rates and projected mortality rates from the 20088
based national population projections are available from the ONS website. Expectations of life
at birth and at age 65 for the UK for the years 2008 and 2058 are shown in the Table A below.
Table A
Period and cohort expectation of life, United Kingdom
2008
2058
Period
Cohort
Period
Cohort
Males
77.6
88.6
86.0
94.8
Females
81.7
92.2
89.4
97.8
Males
17.5
21.0
24.3
26.0
Females
20.1
23.6
26.6
28.4
Life expectancy at birth
Life expectancy at 65
Note: The life expectancies in this table relate to calendar years, and therefore may be slightly different to the
mid‑year life expectancies shown in Table 2.
The table shows that male life expectancy at birth in 2008 was 77.6 years based on the
mortality rates actually experienced in that year. However, allowing for the future improvements
in mortality assumed in the 2008-based projections, a boy born in 2008 can actually expect to
live for 88.6 years. For females, the corresponding period and cohort life expectancies at birth
in 2008 are 81.7 years and 92.2 years respectively. For a person aged 65 in 2008, cohort life
expectancy (that is, taking account of assumed future improvements in mortality above age 65)
is 3.5 years higher than period life expectancy for both sexes.
The differences between period and cohort life expectancies in fifty years’ time are somewhat
smaller. This is because mortality at most older ages is currently improving by more than the
one per cent a year assumed from 2033 onwards.
Migration
Table 2 also summarises the annual net migration assumptions for each country of the UK. These
combine assumptions regarding international migration to each of the constituent countries of the
UK with assumptions about cross-border migration between each country. The new long-term
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assumption for net migration to the UK is +180,000 each year, compared with +190,000 a year
in the 2006-based projections. Although estimates of total long-term international migration for
2008 were not available when the long-term assumptions were decided, it was possible to include
provisional IPS estimates of long-term migration for 2008 within the long-term assumption setting
9
procedures.
For the first few years of the projection period, net migration is assumed to be above the long-term
annual level of +180,000 because of an allowance for additional net migration from the accession
countries which joined the European Union in May 2004 and January 2007. This allowance
reduces from +25,000 for 2009–10 to zero for 2014–15 onwards. This reduction in net migration
from the accession countries is in line with the published latest estimates of long-term international
10
migration.
Compared to the assumptions for the 2006-based projections, the long-term assumed level of
annual net migration to England is 14,500 lower, whilst the assumed levels of annual net migration
to Wales and Scotland are 1,000 and 3,500 higher respectively. These changes reflect the most
recent trends in both international migration to, and cross-border migration between, the four
countries of the UK.
Results of the 2006-based national population projections
Total population
The results of the new projections are summarised for the constituent countries of the UK in
Table 3 and Figure 2.
The population of the UK is projected to increase from 61.4 million in 2008 to reach 71.6 million by
2033. This is equivalent to an average annual rate of growth of 0.7 per cent during this period. In
the longer-term, the projections suggest the population will continue rising beyond 2033 although
at a slower rate of growth.
The population of England is projected to increase by 18 per cent by 2033, Northern Ireland by
14 per cent and Wales by 12 per cent . The projected increase for Scotland, where fertility and
Actual and projected population of the United Kingdom and
constituent countries, 1971–2058
Millions
(a) United Kingdom and England
85
80
75
70
65
United Kingdom
60
55
50
45
England
40
35
30
Projected
25
20
15
10
5
0
1971 1981 1991 2001 2011 2021 2031 2041 2051
85
80
75
70
65
60
55
50
45
40
35
30
25
20
15
10
5
0
6
(b) Scotland, Wales and Northern Ireland
6
Scotland
5
5
Projected
4
4
Millions
Figure 2
3
Wales
3
2
2
1
Northern Ireland
1
0
1971 1981 1991 2001 2011 2021 2031 2041 2051
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0
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Table 3
Spring 2010
Components of change: summary (annual averages),
2008–2033
Annual averages (thousands)
2008–2011
2011–2016
2016–2021
2021–2026
2026–2031
2031–2033
Population at start
Births
Deaths
61,393
781
561
62,649
782
544
64,773
801
544
66,958
801
562
69,051
794
598
70,933
794
629
Natural change
Net migration
221
198
238
186
257
180
239
180
196
180
165
180
Total change
Population at end
419
62,649
425
64,773
437
66,958
419
69,051
376
70,933
345
71,623
England
Population at start
Births
Deaths
51,460
663
462
52,577
664
448
54,472
683
447
56,433
686
463
58,334
682
492
60,071
684
519
Natural change
Net migration
201
172
217
162
235
157
223
157
190
157
165
157
Total change
Population at end
373
52,577
379
54,472
392
56,433
380
58,334
347
60,071
322
60,715
Wales
Population at start
Births
Deaths
2,990
35
31
3,024
35
30
3,104
36
30
3,187
35
31
3,263
34
32
3,326
34
34
Natural change
Net migration
4
7
5
11
6
11
5
11
2
11
0
11
Total change
Population at end
11
3,024
16
3,104
17
3,187
15
3,263
13
3,326
11
3,347
Scotland
Population at start
Births
Deaths
5,169
59
54
5,233
58
52
5,324
58
52
5,411
56
54
5,483
55
57
5,532
54
59
Natural change
Net migration
5
17
5
13
5
12
2
12
–2
12
–6
12
Total change
Population at end
21
5,233
18
5,324
17
5,411
14
5,483
10
5,532
6
5,544
Northern Ireland
Population at start
Births
Deaths
1,775
25
14
1,815
25
14
1,874
25
14
1,927
23
15
1,971
23
16
2,005
22
17
Natural change
Net migration
11
2
11
1
10
1
8
1
6
1
5
1
Total change
Population at end
13
1,815
12
1,874
11
1,927
9
1,971
7
2,005
5
2,016
United Kingdom
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life expectancy levels are assumed to remain lower than in the rest of the UK, is seven per cent .
Consequently, Scotland’s population is projected to increase until the mid 2040s and then start to
fall.
Of the expected 10.2 million increase in the UK population between 2008 and 2033, some
5.6 million (55 per cent ) is projected natural increase (more births than deaths) while the
remaining 4.6 million (45 per cent ) is the assumed total number of net migrants. However, the
projected numbers of future births and deaths are themselves partly dependent on the assumed
level of net migration. The overall effect of net migration on future population growth is considered
in Box three.
Box three
Migration and population growth
The population of the UK is projected to rise both because of positive natural change (that is,
more births than deaths) and because of positive net migration. However, the components
of population change are not independent of each other. In particular, the projected numbers
of future births and deaths are themselves partly dependent on the assumed level of net
migration.
An understanding of the overall effect of migration on population growth can be obtained by
comparing the results of the principal projection with those of the zero net migration variant
projection. The zero net migration variant assumes that net migration will be zero at all ages in
future, but makes the same assumptions about fertility and mortality as the principal projection.
In the analysis below, the effect of net migration on population growth in the period to 2033 is
considered.
Clearly if annual net inward migration to the UK was to average 180,000 a year (the long-term
assumption in the principal projection), this would lead to a total net inflow of 4.5 million migrants
in the period between 2008 (the base year of the projections) and 2033. In fact, the projected
total number of net migrants during this period in the principal projection is slightly higher
(4.6 million) due to the higher migration assumptions in the first few years of the projection.
The assumed fertility and mortality rates are the same in the principal projection and the zero
net migration variant projection. However, because migration is concentrated at young adult
ages, the assumed number of migrants affect the number of women of childbearing age and
hence the future number of births.
There is no comparable effect on deaths, at least in the period to 2033. At ages over 45,
assumed net migration flows are close to zero in the principal projection, and indeed, small net
migration outflows are assumed at some older ages. So the effect of the assumed level of net
migration on the number of deaths over the period to 2033 is very small.
Table A below shows the projected components of population change in the period to 2033 in
the principal projection and the zero net migration variant projection. Table B shows how the
projected population growth in the principal projection is broken down between the assumed
level of net migration and projected natural change.
The population of the UK is projected to grow by 10.2 million between 2008 and 2033. Some
4.6 million of this increase is directly due to the assumed number of net migrants; natural
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Spring 2010
change accounts for a further 5.6 million (the difference between 19.8 million births and
14.2 million deaths). Some 3.2 million of this natural increase would occur in the absence of
migration. The remaining 2.4 million is, therefore, the net effect of the assumed annual level of
net migration on natural change (almost entirely the effect on births).
Table A Projected population change, United Kingdom, 2008–33
Thousands
Principal
projection
Zero net
migration variant
61,393
61,393
Births
19,818
17,475
Deaths
14,175
14,208
Natural change
5,643
3,266
Net migration
4,586
0
Population at mid-2008
Population change (2008–33)
Total change
Population at mid-2033
Table B 10,229
3,266
71,623
64,659
Projected population growth by component,
United Kingdom, 2008–33
Thousands
Total population increase between 2008 and 2033
10,229
Resulting from:
Assumed net migration
4,586
Natural change assuming zero net migration
3,266
Additional natural change from assumed level of net migration
2,377
Some 45 per cent of population growth in the principal projection is therefore directly
attributable to the assumed number of net migrants. The remaining 55 per cent is attributable
to projected natural increase (of which 32 per cent would occur in the absence of net migration
and 23 per cent arises from the effect of net migration on natural change). In total, therefore,
some 68 per cent of population growth in the period to 2033 is attributable, directly or indirectly,
to future net migration.
It should be emphasised that these calculations are based on comparing alternative projections
which make the same assumptions about future fertility and mortality rates irrespective of the
assumed level of net migration. In practice, the fertility and mortality rates of migrants are likely
to differ, to some extent, from those for the existing population.
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By comparing some of the special case scenario variants with the principal projection, it is possible
to attempt a more detailed decomposition of future population change, distinguishing the separate
effects of the fertility, mortality and migration assumptions and also the effect of ‘population
momentum’ arising from the current age structure of the population. This is considered in Box four.
Box four Components of population growth
By comparing some of the special case scenario variants with the principal projection, it is
possible to provide a more detailed decomposition of projected future population change,
distinguishing the separate effects of the fertility, mortality and migration assumptions and also
the effect of ‘population momentum’ arising from the current age structure of the population.
The following four projections are used for this analysis:
Projection
Fertility
assumption
Life expectancy
assumption
Net migration
assumption
Stationary variant
Replacement
No Improvement
Zero
Zero net migration & constant mortality variant
Principal
No Improvement
Zero
Zero net migration variant
Principal
Principal
Zero
Principal projection
Principal
Principal
Principal
The stationary variant assumes ‘replacement level’ fertility. This is the level of fertility required for
the population to replace itself in size in the long-term given constant mortality rates and in the
11
absence of migration. Replacement level is now around 2.075 in the UK, that is, women would
need to have, on average, 2.075 children each to ensure the long-term ‘natural’ replacement of
the population. Under these conditions, the stationary variant will eventually produce a population
with an unchanging size and age structure, but this situation may take several decades to occur.
‘Population momentum’ is the phenomenon by which a population continues to rise or fall in the
interim and is a consequence of the initial age structure of the population.
By changing the assumptions one at a time from those used for the stationary variant to those
used in the principal projection, the separate effects of the fertility, mortality and migration
12
assumptions, and also population momentum, can be distinguished. This is done in Figure A
and Table A below. As the long-term fertility assumption remains well below replacement level,
fertility is still acting as a downward influence on total population size.
Table A
Projected population growth by component, United Kingdom
Millions
Total population growth compared with 2008
2018
2033
2058
2083
4.3
10.2
17.5
24.3
Due to:
population momentum from current age structure
2.1
2.7
1.0
0.4
–0.8
–2.1
–5.8
–10.7
assumed mortality improvement
0.7
2.7
5.7
7.1
assumed inward net migration
2.3
7.0
16.6
27.5
assumed below replacement fertility
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Figure A Projected UK total population, 2008 to 2083
90
90
80
80
Principal
70
Millions
70
Stationary
60
60
Zero net migration &
constant mortality rates
Zero net migration
50
40
2008
50
40
2018
2028
2038
2048
2058
2068
2078
Figure A shows that under the stationary variant conditions, the UK population would eventually
stabilise at around 62 million. However, this would not happen immediately. Indeed, in the
stationary variant, the UK population is projected to continue growing until the late 2020s, and
would be 2.7 million higher in 2033 than in 2008. The fact that the population increases in the
medium-term with replacement fertility, even with constant mortality rates and no migration, is
because of the present age structure of the UK population. Births would continue to exceed
deaths under these conditions over the next twenty years.
The ‘zero migration & constant mortality’ variant uses the principal fertility assumption of a longterm average of 1.84 children per woman but is otherwise the same as the stationary variant.
Comparison of these two variants therefore shows the effect of assuming that long-term fertility
will be about ten per cent below replacement level. Compared with the stationary variant, the
projected population of the UK at 2033 is 2.1 million lower because of the assumption of below
replacement level fertility.
Similarly, the ‘zero migration’ variant differs from the ‘zero migration & constant mortality’ variant
only in the mortality assumption used. Comparison of these two variants therefore shows the
effect of assuming that mortality rates will not remain constant but will continue to improve as
envisaged in the principal projection. The effect of the mortality improvement assumed in the
principal projection is to add about 2.7 million to the population at 2033.
Finally, the impact of the assumed level of net migration in the principal projection can be
assessed by comparing the principal projection with the zero migration variant. This shows that
the effect of the principal migration assumption is to add a further 7.0 million to the population
at 2033. This represents 68 per cent of the total projected population growth between 2008 and
2033. The impact of migration on population growth is considered in more detail in Box three.
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Population Trends 139
Figure 3
Spring 2010
Actual and projected births and deaths, United Kingdom,
1971–2058
1.0
1.0
Projected
0.9
0.9
0.8
Millions
0.8
Births
0.7
0.7
0.6
0.6
Deaths
0.5
1971
0.5
1981
1991
2001
2011
2021
2031
2041
2051
Births and deaths
Projected numbers of births and deaths are shown in Figure 3. With the single exception of 1976,
the UK gained population through natural increase (births less deaths) throughout the 20th century.
In the 2008-based projections natural increase remains positive throughout the projection period.
Of course, these projections are subject to considerable uncertainty. In particular, the projected
trend in births depends on the assumed future level of fertility (including that for women not yet
born) and has much greater uncertainty attached to it than the projected trend in deaths which is
largely determined by the age structure of the population alive today.
Age distribution
Table 4 and Figure 4 summarise the projected age structure of the population. The age structure
will become gradually older with the median age of the population projected to rise from 39.3 years
in 2008 to 42.2 years by 2033. Longer-term projections show continued ageing with the median
age exceeding 43 years by 2058.
The number of children aged under 16 is projected to increase by 6.2 per cent from 11.5 million
in 2008 to 12.2 million in 2018 and then to increase further to 12.8 million by 2033. After levelling
off for a few years, the increase is expected to resume around 2040 and reach 13.6 million by
2058.
13
Allowing for the forthcoming changes to state pension age (from 60 to 65 for women between
2010 and 2020, and then from 65 to 66 for both sexes between 2024 and 2026), the working age
population is projected to rise by 14 per cent from 38.1 million in 2008 to 43.3 million in 2033. The
working age population will become much older as the baby boom generations of the mid 1960s
age. In 2008, there were 1.5 million (8.3 per cent ) more working age adults aged below 40 than
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Projected population by age, United Kingdom, 2008–2033
Table 4
Age Group
2008
2013
2018
2023
2028
2033
All ages
61,393
63,498
65,645
67,816
69,832
71,623
0–14
10,753
11,001
11,550
11,851
11,942
11,963
15–29
12,293
12,700
12,269
12,057
12,301
12,850
30–44
12,978
12,498
12,826
13,821
14,181
13,757
45–59
11,795
12,660
13,170
12,638
12,185
12,533
60–74
8,798
9,444
10,036
10,498
11,366
11,871
75 & over
4,776
5,194
5,794
6,951
7,858
8,650
39.3
40.0
40.0
40.5
41.3
42.2
Median age (years)
Under 16 (A)
11,517
11,718
12,236
12,645
12,723
12,764
Working age* (B)
38,083
39,419
40,848
41,763
43,062
43,270
Pensionable age* (C)
11,794
12,362
12,561
13,408
14,047
15,589
3.31
3.36
3.34
3.30
3.38
3.39
Support ratios
Young (B/A)
Old (B/C)
3.23
3.19
3.25
3.11
3.07
2.78
Total (B/(A + C))
1.63
1.64
1.65
1.60
1.61
1.53
* Working age and pensionable age populations based on the state pension age for given year. Between 2010 and
2020, state pension age will change from 65 for men and 60 years for women, to 65 years for both sexes. Between
2024 and 2026, state pension age will increase from 65 to 66 for both sexes.
Figure 4
Actual and projected age distribution, United Kingdom,
1981–2058
100
100
75+
90
90
60–74
Per cent of total population
80
70
80
70
Projected
45–59
60
60
Median age
50
50
30–44
40
40
30
30
15–29
20
20
10
0
1981
10
0–14
0
1991
2001
2011
2021
2031
2041
2051
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were aged 40 and above. However, by 2033 there are projected to be 1.4 million (6.5 per cent )
more working age people above 40 than below 40.
Again, allowing for the forthcoming changes to state pension age, the number of people of state
pensionable age is projected to increase by 32 per cent from 11.8 million in 2008 to 15.6 million in
2033. In the longer-term, further increases in state pension age to reach 68 by 2046 will curb the
increase in the population of pensionable age, although a faster increase will again return after the
changes in state pension age are complete.
As the population ages, the numbers in the oldest age bands will increase the fastest. In 2008,
there were 4.8 million people in the UK aged 75 and over. The number is projected to increase to
5.8 million by 2018 and to 8.7 million by 2033, a rise of 81 per cent over 25 years. Over the same
period, the number of people aged 85 and over is projected to more than double (from 1.3 million
in 2008 to 3.3 million in 2033), whilst the number of centenarians is projected to increase more
than sevenfold (from 11,000 in 2008 to 80,000 in 2033).
Support ratios
These changes in age structure will, in time, have a marked effect on the future proportion of
pensioners in the population. Figure 5 shows the projected old age support ratio, that is the ratio
of persons of working age to those of state pensionable age. The ratios are based on the state
pension age for the given year and take account of the planned future changes to that age. It
should be emphasised, however, that demographically defined support ratios such as these,
whatever age boundaries are used, take no account of workforce participation rates and therefore
do not represent real levels of economic dependence. In reality, full-time education ends, and
retirement starts, at a range of ages.
Figure 5
Actual and projected old age support ratio, United Kingdom,
1981–2058
Number of people of working age per person of dependant age
4.0
4.0
Projected
3.5
3.5
High migration
3.0
3.0
Low migration
2.5
2.0
2.5
2.0
Principal, no change
to State Pension Age
1.5
1.5
1.0
1.0
0.5
0.5
0.0
1981
0.0
1991
2001
2011
2021
2031
2041
2051
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In 2008, there were 3.23 persons of working age for every person of state pensionable age.
Allowing for the forthcoming changes in state pension ages, this old age support ratio is projected
to fall to 2.78 by 2033.
Although the old age support ratio are projected to fall in the future, Figure 5 shows how the future
changes to state pension age help to moderate this decline. Allowing for all the changes between
2010 and 2046, the old age support ratio is projected to decline to 2.74 by 2058. However, were
state pension ages to have remained as they are today (65 years for men and 60 years for women)
it is projected that the ratio would fall considerably further – to 1.94 by 2058.
Comparison of the principal projection with the high and low migration variants show how future
net migration will impact upon the old age support ratio. If annual net migration to the UK were to
be 60,000 lower than assumed for the principal projection (that is +120,000, rather than +180,000),
the old age support ratio would fall to 2.70 by 2033. In contrast, if annual net migration to the UK
were to be 60,000 higher (that is, +240,000), the old age support ratio would be 2.85 by 2033.
Comparison with 2006-based projections
The projected total population of each country is compared with the 2006-based projections in
Table 5 and the difference between the two projections is broken down into changes in the base
population and changes in the projected numbers of births, deaths and net migrants. Decreases
in the projected numbers of deaths (as compared with the previous projections) are shown as
positive numbers in the table as they contribute to increases in the size of the population.
The projected population of the UK at 2033 is 153,000 (0.2 per cent ) lower than in the 2006-based
projections. This is due to a combination of slightly fewer births, fewer migrants and fewer deaths.
The assumed annual number of migrants is lower than in the previous projections, and this in turn
has led to slightly fewer births being projected despite the fertility assumption for the UK as a whole
remaining unchanged. The projected number of deaths is less than for the 2006-based projections,
as slightly higher life expectancies have been assumed for the current projections.
The projected populations of Wales, Northern Ireland, and particularly Scotland, in 2033 are higher
than in the 2006-based projections, due to higher net migration assumptions being made for
Table 5
Country
England
Wales
Change in projected population at 2033 compared with the
2006-based projections
2008-based 2006-based Total change
projections projections
Change due to
base
population*
projected
births
projected
deaths**
projected
migrants
60,715
61,085
–369
–28
–154
192
–380
3,347
3,311
37
–3
13
14
13
Scotland
5,544
5,371
173
11
63
5
94
Northern Ireland
2,016
2,010
6
1
2
1
1
United Kingdom
71,623
71,776
–153
–18
–76
212
–271
* Difference between the base population at mid-2008 and the 2006-based projection of the population at mid-2008.
** Decreases in the projected number of deaths (compared with the previous projections) are shown as positive
numbers as they contribute to an increase in the size of the population.
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Population Trends 139
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these countries in the latest projections, and also a slightly higher fertility assumption for Scotland.
In contrast, the projected population of England in 2033 is over 350,000 lower than for the
2006-based projection, due to the reduction in the net migration assumption for England.
Sensitivity
The one certainty of making population projections is that, due to the inherent unpredictability of
demographic behaviour, they will not turn out to be an accurate forecast of future demographic
events or population structure. One way of giving users an indication of uncertainty is by
considering the performance of past projections. An analysis of the accuracy of UK national
population projections made over the last fifty years was published in Population Trends 128 in
14
summer 2007. A second article looking at the accuracy of population projections made by 14
15
European countries, including the UK, was published in Population Trends 129 in autumn 2007.
Another way of illustrating uncertainty is by preparing variant projections based on alternative
assumptions of future fertility, mortality and migration. Since the 2000-based projections, an
extensive range of variant projections, at both UK and individual country level, has been produced
for each ‘full’ set of projections. Full details of the latest 2008-based variant projections are
16
available on the ONS website.
Compared with the principal projection assumptions, the high and low fertility variants assume
long term family sizes of ±0.2 children per woman. In the high and low mortality variants, projected
life expectancy at birth at 2033 differs by ±1.9 years for males and ±1.2 years for females from
the principal assumption. Finally, in the high and low migration variants, the long-term annual net
migration inflows are assumed to be 60,000 persons above and below the principal assumption.
These variant assumptions are intended as plausible alternative scenarios and not as upper or
lower limits for what might occur in the future. Figure 6 and Figure 7 show the total population of
the UK and the percentage of the population aged over 65 under these alternative assumptions.
It is clear from Figure 6 that there is considerable uncertainty regarding the future size of the
population. Under the alternative, but still plausible, fertility and migration assumptions, the
population at 2033 differs from the principal projection by around ±2 million. The uncertainty widens
with time and by 2083 the population would be over 10 million higher or lower than in for the
principal projection under the high and low fertility assumptions. Figure 6 shows that the population
continues to grow under all of the main variant projections. However, because the variant
assumptions are plausible alternatives rather than upper or lower limits, continued population
growth is not a certainty. Indeed, if a combination of the low fertility, life expectancy and migration
assumptions is considered (the ‘low population’ variant projection), it is projected that the UK
population would peak at 67.6 million in 2042.
Figure 7 demonstrates that significant population ageing will occur under any plausible set of
future assumptions. In 2008, some 16 per cent of the UK population were aged 65 and over.
The proportion aged over 65 is projected to increase to between 21.3 per cent and 24.6 per cent
by 2033. In the principal projection, the proportion aged 65 and over would continue increasing,
reaching over 27 per cent by 2083. In the high life expectancy variant, this would be over
30 per cent , and if a combination of low fertility, high life expectancy and low migration is
considered (the old age structure variant), over a third of the UK population would be aged 65 and
over in 75 years’ time.
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Population Trends 139
Figure 6
Population of the United Kingdom according to principal and
variant 2008-based projections, 1981–2083
110
Projected
105
HP = High population (HF, HL & HM)
HF = High fertility
HM = High migration
HL = High life expectancy
PP = Principal projection
LL = Low life expectancy
LM = Low migration
LF = Low fertility
LP = Low population (LF, LL & LM)
100
95
90
110
HP
105
100
HF
95
HM
85
Millions
Spring 2010
HL
90
PP
85
LL
80
80
LM
LF
75
75
70
70
65
65
LP
60
60
55
55
50
1981 1991
Figure 7
50
2001 2011 2021
2031 2041 2051 2061
2091
Proportion of the population aged 65 and over according to
principal and variant 2008-based projections,
United Kingdom, 1981–2083
36
36
Projected
34
Old
34
Old = Old age structure (LF, HL & LM)
HL = High life expectancy
LF = Low fertility
LM = Low migration
PP = Principal projection
32
30
32
HL
LF
LM
PP
HM
28
Percentage
2071 2081
26
HF
24
30
28
26
24
LL
22
22
Young
20
20
HM = High migration
HF = High fertility
LL = Low life expectancy
Young = Young age structure (HF, LL & HM)
18
16
18
16
14
14
12
1981 1991
12
2001
2011 2021
2031 2041
2051
2061
2071 2081
2091
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Population Trends 139
Spring 2010
The pattern and inevitability of population ageing is a consequence of the current age structure
of the population. This, in turn, is a result of changes in the past numbers of births. Thus over
the next few years, the number of older people will start to rise rapidly as the relatively large
cohorts born after the Second World War and during the 1960s baby boom enter the 65 and over
age group and replace the much smaller cohorts born before 1945. A new dynamic population
17
pyramid tool is now available on the ONS website which allows projected changes to the age
structure of the UK population to be compared between the 2008-based principal and variant
projections.
Key findings
Based on the assumptions underlying the principal projections:
• The UK population is projected to increase from an estimated 61.4 million in 2008 to reach
71.6 million by 2033
• Of the 10.5 million increase between 2008 and 2033 in the principal projection, some 5.6 million
(55 per cent ) is projected natural increase (more births than deaths) while the remaining
4.6 million (45 per cent ) is the assumed total number of net migrants. However, projected
births and deaths are partly dependent on the assumed level of net migration. Allowing for
the additional impact of migration on natural change, it is estimated that some 68 per cent of
projected population growth in the period to 2033 is attributable, directly or indirectly, to net
migration
• The UK population will gradually become older, with the median age expected to rise from
39.3 years in 2008 to 42.2 years in 2033
• In 2008 there were 3.23 persons of working age for every person of state pensionable age. By
2033, this old age support ratio is projected to decline to 2.78, despite the forthcoming changes
in state pension age
• Due to differences in demographic patterns, projected trends differ for the four countries
of the UK. The population of England is projected to increase by 18 per cent by 2033,
Northern Ireland by 14 per cent and Wales by 12 per cent. The projected increase for Scotland,
where fertility and life expectancy levels are assumed to remain lower than in the rest of the UK,
is 7 per cent .
References
1 Full results of the 2008-based national population projections are available at:
www.statistics.gov.uk/StatBase/Product.asp?vlnk=8519
2 Results for previous national population projections are available at:
www.gad.gov.uk/Demography%20Data/Population/index.aspx
3 Details of the membership of the National Population Projections Expert Advisory Panel are
available at: www.statistics.gov.uk/downloads/theme_population/NPP2008/NatPopProj2008.pdf
(see section 11)
4 Office for National Statistics (2010) National population projections: 2008-based. ONS Series
PP2 no.27. Available at: www.statistics.gov.uk/statbase/Product.asp?vlnk=4611
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5 2008-based subnational population projections for Scotland are available at:
www.gro-scotland.gov.uk/statistics/publications-and-data/popproj/2008-based-pop-proj-scottishareas/index.html
6 Office for National Statistics (2009) Population Estimates Statistical Bulletin (27 August 2009).
Available at: www.statistics.gov.uk/pdfdir/pop0809.pdf
7 Details of the package of improvements to population estimates are available at:
www.ons.gov.uk/about-statistics/methodology-and-quality/imps/updates-reports/index.html
8 Latest 2008-based period and cohort life expectancy tables available at:
www.statistics.gov.uk/STATBASE/Product.asp?vlnk=15098
9 Full details of the latest migration assumptions are provided in Chapter 8 of the 2008-based
National Population Projections Reference Volume, available at:
www.statistics.gov.uk/statbase/Product.asp?vlnk=4611
10Office for National Statistics (2009) Migration Statistics 2008 Statistical Bulletin (26 November
2009). Available at: www.statistics.gov.uk/pdfdir/miga1109.pdf
11 Smallwood S and Chamberlain J. (2005) ‘Replacement fertility, what has it been and what does
it mean?’ Population Trends 119, 16–27. Available at:
www.statistics.gov.uk/statbase/Product.asp?vlnk=6303
12In this analysis, the fertility assumption is changed first, then the mortality assumption and
finally the migration assumption. Because the components interact with each other, the
results would be slightly different if a different order was used. But this will not affect the broad
conclusions drawn.
13Details of the forthcoming changes to state pension age are available at:
www.statistics.gov.uk/downloads/theme_population/NPP2008/NatPopProj2008.pdf
(see section 6)
14Shaw C (2007) ‘Fifty years of United Kingdom national population projections: how accurate
have they been?’ Population Trends 128, 8–23. Available at:
www.statistics.gov.uk/statbase/Product.asp?vlnk=6303
15Keilman N (2007) ‘UK national population projections in perspective: How successful compared
to those in other European Countries?’ Population Trends 129, 20–30. Available at:
www.statistics.gov.uk/statbase/Product.asp?vlnk=6303
16Details of the 2008-based variant projections are available at:
www.statistics.gov.uk/downloads/theme_population/NPP2008/NatPopProj2008.pdf
(see section 4)
17United Kingdom interactive population pyramid: National population projections – principal and
variants. Available at: www.statistics.gov.uk/nationalprojections/flash_pyramid/default.htm
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