By Chris A Garrington, on 29 October 2021
by Fran Abrams
Where you grow up still has a significant effect on your life chances, according to new research using Census data. Evidence from the 1971 to 2011 censuses shows that those who moved out of poorer areas were more likely to move up the social ladder than those who stayed – but, for later cohorts, those from the North or Wales were more likely to thrive in their own region than in London.
A major study of social mobility confirms that not only who your parents are, but also where you grow up, substantially influences subsequent life chances. But there have been significant changes in recent decades, it says – suggesting that some long-standing assumptions about social mobility chances across the country may need to change.
The government’s ‘levelling up’ agenda is based on the notion that deep divides exist between the North and South of Britain. The study, by researchers at the University of Westminster and the London School of Economics, shows this still holds true.
But within regions there are big variations in social mobility, the study confirms. And while those who leave their birthplace to live and work elsewhere tend to do better as a result, heading for the Capital does not confer the same advantages as it did in the past.
The study focuses on three cohorts who were aged between eight and 18 in 1971, 1981 and 1991, and who were followed up after 20 years when they were aged between 28 and 38. The data is a particularly rich source, the researchers say, because it enables them to link people’s occupations to those of their parents. The sample covers a total of almost 170,000 people over a fifty year period and reveals both where they lived as adults and what they and their parents did for a living.
In general, upward social mobility in Britain increased between the mid-1950s and the early 1980s. But some regions had higher rates of mobility than others during that time, and the upward trend tailed off for those born later. So while there were small increases in mobility in every region over time, there were persistent and substantial inequalities.
In all regions of England and Wales, children born to managerial and professional parents were at least two and a half times more likely to end up in those occupational groups than children from working class backgrounds.
For those born between the late 1950s and the early 1980s, there was a clear advantage to starting out in London for upward social mobility. For the first cohort, the West Midlands had the next highest upward mobility followed by the North East, Wales, the North West, and Yorkshire and Humberside.
Stayers and leavers
The study compares those who moved away with those who stayed close to their birthplace and finds that overall, those born outside London and the South East did better if they moved away.
The study divided those born in the North or Wales into four groups according to where they lived later in life: those who stayed in the same region in the North or Wales, those who moved a new region within the North or Wales, those who moved to a different region outside London and those who moved to London.
Three quarters of those in the study stayed in their region of origin, while the remaining quarter moved. And in all regions outside London and the South East, movers had higher rates of upward mobility compared to stayers.
There were significant changes in these patterns over time, though. Among those who were children in the early 1970s and 1980s, social mobility was highest among those who moved to London. But for those born a decade later a move to the capital was not associated with any greater upward mobility when compared to people who stayed in their region of origin.
The highest level of upward mobility for this latter group was among those who moved to a different region within the North or Wales or who moved to another region outside of London.
Overall, London stands out as the most socially mobile region. But when the figures are broken down to the more fine-grained level of local authority areas, a more nuanced picture emerges: within London, there are areas with both very high and very low levels of social mobility. Indeed, most authorities in London have a border with a district whose social mobility is substantially different. Similarly, for all areas in England and Wales there is substantial variation in social mobility both within as well as between regions.
What should politicians do?
So what does this mean for the Government’s ‘levelling up’ agenda? The study suggests that while some redistribution of resources from London and the South East to post-industrial areas in Wales, the Midlands and the North is justified, a more fine-tuned approach is needed.
This research shows patterns of social mobility are changing over time, and also that they are greater within regions and cohorts than between them.
And while it confirms that people who move out of their region of origin tend to advance higher up the social ladder, it also highlights the other side of the same coin: those who are born in low-mobility areas but who stay there have lower chances of occupational attainment.
Incentivising people to move is one approach – but that will not solve the problem of ‘left-behind’ towns and cities. An alternative solution would be both to improve opportunities for salaried jobs in those areas, and also to improve the working conditions —autonomy, employment rights and security— of those lower down the class structure.
Spatial and social mobility in England and Wales: A sub-national analysis of differences and trends over time is research by Franz Buscha, Emma Gorman and Patrick Sturgis and is published in the British Journal of Sociology.
By Chris A Garrington, on 19 October 2021
by Fran Abrams
Across the developed world, populations are ageing and policy makers are wondering how to keep people in work for longer. But at the same time, greater numbers of older people are claiming sickness benefits. So what can the Census tell us about the true picture, and about the types of policy interventions that might help?
Heated debate has raged for years around the issue of disability and sickness benefits. More people are claiming them – and a key response from the UK Government has been the use of ‘Nudge’ techniques to encourage the reluctant to return to work.
But a range of studies which use data from the ONS Longitudinal Study suggest popular assumptions on the topic may be flawed.
One such study challenges the assumption that the number of people claiming sickness benefits is growing because they are becoming available to people whose conditions are less serious: that claiming has become easier and that those with milder illnesses are doing so.
Bola Akinwale from Public Health England and colleagues from the ESRC International Centre for Lifecourse Studies at University College London compared Census data from 1971 to 2001.
There had been big changes in the labour market positions of 60-64 year-old men, they found:
- Working – 78.4 percent v 47.5 percent
- Retired – 7.2 percent v 24.7 percent
- Permanently sick – 9 percent v 19.7 percent
The proportion of permanently sick men had doubled in 30 years, but the trend was even more striking among women: 12.4 percent of 55-59 year-old women described themselves as permanently sick in 2001 compared with 3.4 percent in 1971.
And yet in the last 30 years of the 20th century, life expectancy for those aged 65 increased more than it did in the previous 70 years, and the risk of dying just before State Pension Age decreased substantially – by more than 60 percent for men and by more than 50 percent for women. This increase in life expectancy benefited the permanently sick as much as those in work, with both living longer than their counterparts 30 years previously.
Are sick people less sick nowadays?
But the researchers found that statistic did not tell the whole story: yes, people were living longer and healthier lives by the turn of the century. But if the ‘permanently sick’ were in fact less sick than in the previous generation, the gap between their chances of dying prematurely and that of someone in work would have got smaller over the 30-year period. It didn’t.
Permanently sick men aged 65-69 were three times more likely to die prematurely than their working peers in 2001 – an increase on the 1971 figure. For women, the figure was between four and five times higher.
The life expectancy of the permanently sick increased in line with others’ as medical and social advances were made. But their likelihood of dying when compared to working people if anything, increased slightly.
So, Census data confirms the United Kingdom has an ageing population that contains more people with long-term and life-limiting illnesses. It also gives us a richer picture of who those people are, and where they live.
Dr Emily Murray and colleagues* used census data to look at who lives longest after leaving work, and they found wide disparities in health and life expectancy between different social classes.
They compared data on people who were aged 50-75 at the time of the 2001 census and who had stopped work by 2011 – the average age of stopping was 58 for women and 60.2 for men.
The study showed those in professional occupations could expect to live and enjoy good health for longer than those in manual jobs: the average 50 year-old man in a professional job could expect 25 years of good health, while a man in a manual occupation could expect only 18: a seven-year difference. And that explains why lower social class groups are more likely to find themselves on disability benefit.
Among the sample group of 50-75 year-olds from 2001, 14.6 per cent of the women and 25.1 per cent of the men died within 10 years. For both genders, those in lower social classes tended to die younger – professional women lived two years longer than unskilled women, and professional men three years longer than unskilled men.
But despite these longevity gaps, those from lower social groups faced more years between leaving work and being able to draw their state pensions – because they left work earlier.
The researchers estimated that if two women were 65 in 2001, the woman who had worked in an unskilled occupation would live five years longer after leaving work than the professional woman with good health – because the unskilled woman would have left at a younger age. Two men in the same circumstances would live on average 25.0 and 19.5 years from stopping work to death.
The most likely explanation, they said, is that poor health has a greater impact on the ability of manual workers to continue working than it does on non-manual workers.
There is a clear message for policymakers: a uniform state pension age disproportionately affects the poorest because they must wait longer between stopping work and qualifying for their state pension, at a time when they are likely to be in poor health: over half of women and two-fifths of men fall out of the labour market before state pension age.
A two-year earlier pension age may be more appropriate for individuals who work in manual occupations, the researchers say, in order to improve the financial security and health of the most vulnerable in society. Such occupation-specific pensions already exist in some other countries, along with pensions based on duration of employment – people in manual occupations generally start work earlier so they work more years if they retire at same time. The issue was raised in the Cridland review on the state pension age.
A third paper addresses Government responses to these issues, which have tended to focus on behavioural techniques for encouraging older people to stay in work. It asks whether official publications, which have suggested there may be resistance to continuing in work among some groups, are correct in their assumptions.
Nicola Shelton and colleagues* used census data to look at what happened in 2011 to adults aged between 40 and 49 in 2001 and found significant regional differences: men in the North East were significantly less likely to extend their working lives than others, for instance, while women in all regions apart from London and Wales were significantly more likely to stay in work than those in the North East.
But further analysis showed that for men at least, other social factors could explain these differences. Put bluntly, men in the North East leave work earlier because they tend to have fewer qualifications and less favourable employment status – both of which are associated with shortened working lives.
For women, some additional factors affected the likelihood of staying in work. Those in lower-skilled jobs were less likely still to be in work by 2011, but those working for larger employers, for long hours or away from home were also more likely to have left.
So, what can governments do? Given a good work environment, choosing to remain in work may have positive benefits such as maintaining good health and functioning and providing a sense of purpose- so working conditions are important, the researchers suggest.
The biggest single factor in determining whether workers stay on for longer is prior employment – and that is not likely to be changed by behavioural approaches such as the ‘nudge’ theory of behavioural economics, which is popular with policy makers, they say.
Policies that do not address issues such as low levels of education and high levels of unskilled employment can only be partially successful in enabling people to work for longer. Indeed, some groups who may have the most financial need to remain in work are most likely to leave earlier. This is particularly an issue for women.
Policies that increase skills and education in later life, rather than simply targeting those ‘receptive’ to extended working, will be more likely to make a difference, they conclude.
By Chris A Garrington, on 20 July 2021
by Fran Abrams
Census data from the ONS-LS has helped shed new light on outcomes for children who have been in care in a major research project led by the UCL Institute of Epidemiology and Healthcare. The study uses Census data to push forward the boundaries of knowledge by looking at what happens in mid-life to those who have been in care as children. It finds disturbing disparities – with some groups faring much worse than others.
Care-leavers have long been known to suffer worse outcomes than their peers. Now a programme of research funded by the Nuffield Foundation has highlighted the ways in which these issues continue throughout the lives of those affected. At an event to share the project’s findings with those interested in improving the lives of children who cannot live with their parents, Josh MacAlister, who is leading an independent review of children’s social care, said the research had already had a major impact on its work.
It is actually breaking new ground in our understanding of where care-experienced people end up in later life,” MacAlister told an audience of more than 200 practitioners, policymakers and academics.
“One of the questions we pose is: ‘How many parents don’t know where their own child is at 30, 40 or 50?’ It’s a common refrain that the state is the corporate parent, and actually not knowing really basic things about destination and outcomes for care-experienced people later on into life has been a huge blind spot. So this study is very significant in that respect,” he said.
Raising the bar
The Government review led by MacAlister was launched earlier this year with the aim of ‘raising the bar for vulnerable young people across England.
The UK’s National Statistician, Sir Ian Diamond, who chaired the event, told the audience the care research was an exemplary use of data from the ONS-LS dataset.
Sir Ian highlighted a key issue for those studying children’s social care: the evidence shows those who have been in care suffer worse outcomes than their peers – for example higher than average levels of unemployment, lower levels of educational qualification and early pregnancy. But that research tends to focus on recent care-leavers while this project follows them into mid-life.
The project, led by a team of researchers at University College London, used Census data from the ONS-LS to explore outcomes for those who experienced care from the 1970s onwards, up to the age of 50.
The ONS-LS has gathered information on approximately a million people, and from this the researchers were able to look at a sample of just under 500,000 children. Of those, around 3,500 were looked after by family members other than their parents, 2200 were in formal foster care and 900 in residential care. Over time, residential care had become less common and family or kinship care more so.
There were four main aims of the research:
- To determine whether children who had experienced care had worse health and social experiences from 10 to 40 years later, compared to others.
- To explore whether children in residential care did better or worse than children in foster or kinship care.
- To look at care differences related to gender, ethnicity or migration status.
- To investigate trends and look at whether longer-term outcomes had improved over time.
Amanda Sacker, Professor of Lifecourse Studies, Epidemiology and Public Health at UCL, who led the study, told the launch one of its strongest findings was that inequalities within the cared-for population were as great as inequalities between that group and those in parental care.
Impacts of a childhood in care
The research found the impact of being in care on health, socio-economic circumstances, family life and living arrangements varied according to the type of care experienced – those who were cared for by relatives had the best outcomes and those who had been in residential care the worst, with foster care sitting between the two.
This did not necessarily mean the type of care had led to better or worse outcomes, Professor Sacker explained at the event – those who were most vulnerable or damaged by early experiences might be more likely to go into residential care rather than family care or a foster home.
In relation to employment there was some evidence these inequalities between care groups reduced with age, though adults in their 20s were twice as likely to be employed if they had been in their parental home rather than in residential care.
Social inequalities between care groups also extended to the age of death: while those brought up by parents had become less likely to die prematurely in the past 30 years, this was not true for those in non-parental care. Those in care in 1971 were 30 per cent more likely than average to die but by 2001, this ratio had grown to more than 300 per cent, most commonly from preventable causes such as car accidents, alcoholism and assault.
When the researchers looked at education, they found a similar picture: those in parental care had a 28 per cent chance of achieving an NVQ level 3 qualification compared with just 11 per cent for those in residential care. There was some good news, though – the care groups were more likely to be in education later in life, and this helped them to catch up with their peers.
The project’s findings on ethnicity were more mixed. White and south Asian groups were found to have worse outcomes if they had been in care, but for black children the study found having been in care did not seem to be associated with any additional detrimental effect over and above the inequalities seen for all black children. The researchers said more investigation was needed into this complex picture.
The final key findings of the study were on the children of those who give care. They too, it found, were affected by their experiences – there had been reports that the children of foster parents or of kinship carers had felt disadvantaged.
The research looked at five ‘transition milestones into adulthood: leaving education, leaving home, starting work, forming a relationship and becoming a parent. It found children of care-givers were less likely to have higher qualifications, more likely to be unemployed, more likely to be married early and less likely to own their own home than others. If they were female, they were likely to be younger when they had their first child.
“Our work in the field of life course social epidemiology has highlighted that inequalities in health cannot be reduced without tackling social inequalities too,” Professor Sacker said. “By shining a light on the life courses of children who have had to be placed in care, our aim is to get it right for care leavers.”
Sir Ian Diamond said the Office for National Statistics was keen to make more use of longitudinal data such as the ONS-LS to help inform policy on issues such as this one.
“Among the most disadvantaged in society are clearly those young people and children who have experienced care, and care leavers, we know empirically, have not had great outcomes. It’s always seemed to me that we needed better longitudinal data to understand the pathways that would help us to inform policy, which could improve the outcomes for people who are under-achieving because they have been let down early in their lives for all kinds of reasons. So it seems to me there’s an enormous need for this work,” he said.
Tim Gardam, Chief Executive of the Nuffield Foundation, said the research fitted very well with its aim of viewing social wellbeing primarily through the lens of the experiences of the most disadvantaged and vulnerable.
“What we find here is the ONS-LS using Census data from five decades, painting a vivid and complex picture of vulnerable people’s lives and their health and social outcomes that in places brings the reader, quite frankly, up short in shock.
“We have known for a long time that being in care as a child is associated with poorer outcomes in adulthood. But most research has previously ended with young adulthood and been based on small population samples. This research documents people’s lives in their 40s and enables us to understand the longer-term effects,” he said.
The lifelong health and wellbeing trajectories of people who have been in care: Findings from the Looked-after Children Grown up Project, is by Amanda Sacker with Emily Murray, Rebecca Lacey and Barbara Maughan. It is published by the Nuffield Foundation.
By Chris A Garrington, on 25 June 2021
by William P Ball
Again and again, research has shown that health outcomes vary between groups of people. These differences appear when we group people based on where they live, the type of job they do, their level of education, their sex or race/ethnicity.
When these differences are systematic, avoidable, and unfair we call them inequalities.
Geographic inequalities, often measured by the level of deprivation experienced by groups of people in small residential areas, are clear. Recent figures show that life expectancy in the least deprived areas of England is considerably higher than in the most deprived areas – although women live longer, both sexes can expect to live into their seventies in the poorest areas and into their eighties in the richest.
Understanding the mechanisms which produce these inequalities is incredibly important for researchers and policymakers. If we understand what drives these avoidable and unfair differences in health, we can start to design policies which aim to reduce them.
My research project looks at the self-rated health of nurses – understanding this group, and how they experiences inequalities, is important for a number of reasons. It’s useful to understand their health in terms of planning around health care workforce and service delivery, but also to understand factors influencing health for the rest of the population.
Nurses are the biggest health workforce in the UK, with over 680,000 currently registered with the Nursing and Midwifery Council, and theyare a central part of the health service. Even with recent growth in the profession, the NHS in the UK has around 40,000 vacancies for nurse jobs and the workforce is ageing. If we can understand the health of nurses and reduce the number leaving the profession due to ill health, that will contribute to maintaining and improving patient safety. In the current context of COVID-19, where the NHS and its workforce has experienced huge pressure, the relevance of studying the health of healthcare professionals, for their sake, has become even more clear.
But the health of nurses isn’t only relevant to nurses themselves. Nurses have socioeconomic characteristics which should protect their health and they are also rather similar to one another – roughly 90 per cent are female and they are generally in a stable job which requires degree-level education. They also have higher levels of home ownership and are less likely to live in the most deprived areas.
The design of my project uses this similarity in socioeconomic characteristics of nurses to act as a sort of control – reducing the impact of variation between individuals to better isolate the effects of other variables, like area deprivation, on health,. This is like studies looking at differences in health outcomes in siblings or twins, which assess the health trajectories of people living with similar characteristics, in very similar conditions. This type of design is chosen to reduce the effect of confounding, where other variables influence the exposures and outcomes being studied.
Nurses also have very high levels of health literacy and knowledge of healthy behaviours. Their curriculum includes teaching on modifiable risk factors for disease, such as smoking, or lack of exercise – and many Nurses see first-hand the outcomes which are caused by such behaviours. We might expect that such a population would generally have better health and that its characteristics would protect it against inequalities. We can also investigate a counterfactual question – what would population health look like if we all had similar characteristics as nurses?
The Office for National Statistics and Scottish Longitudinal Studies (ONS LS and SLS) offer the opportunity to use individual-level data from on Census records, which have been linked to administrative data such as births and causes of death. They offer detailed data on a large and representative sample of the British population which can also link individual records to other sources of information such as deprivation measures.
Although the data held in each LS is only accessible in safe settings due to data protection concerns, a process called eDatashield has been developed to allow researchers to simulate ‘combined’ analysis. Raw data cannot be removed from the Secure Research Service computers in the ONS building in London or the safe haven at the National Records for Scotland offices in Edinburgh. However, eDatashield allows some types of analysis to be conducted by passing anonymised summary statistics to a computer which holds none of the original data. This approach has been used in my project to conduct cross-national analysis, to understand the role area deprivation plays in health differences between Scotland and England & Wales.
Preliminary results from this study suggest that self-reported health is patterned by area deprivation for non-nurses and nurses alike and that inequalities are present for both groups.
Roughly 90% of nurses appeared in the highest grouping for a measure of educational level, largely due to the inclusion of professional registration in this group. Nurses also had higher representation in the least deprived areas, but crucially nurses still lived in areas covering all levels of deprivation. 58% of nurses stated they have ‘very good’ health, with only 51% of non-nurses replying the same. Nurses reported a higher proportion of ‘very good’ self-rated health at all levels of deprivation compared with non-nurses. However, both groups displayed a social gradient in this – the proportion self-reporting ‘very good’ health increased when comparing from most to least deprived quintile. However, the gradient for nurses in less ‘steep’, meaning the differences between most and least deprived areas is smaller.
From these early results we can see that nurses generally have better health outcomes than non-nurses. That makes sense given their individual characteristics and that levels of wealth in the area where we live also affects our health. The crucial finding though is that social inequalities in self-rated health, although reduced, still exist within the nursing profession – even in a highly educated group with high health literacy the conditions in which we live influence patterns in health.
The challenge of health inequalities has been clear for decades and current trends in the United Kingdom are worrying. Improvements in life expectancy in the UK, growing consistently over the past century, began to stall from 2012 onwards. This pattern is largely a result of slower gains for those in the most disadvantaged groups, and we have even seen reductions in life expectancy for women in these groups. Reducing inequalities which are systematic, avoidable, and unfair is better for everyone, but especially so for the most vulnerable people in our society.
Highly detailed datasets like the ONS LS and SLS, which are largely representative of the total population and can be linked to other information, can play a central role in the future of health inequalities research. Their use also allows some innovative research design which can help us better understand population health in the UK and ultimately inform policy actions which can make our society fairer.
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 & User Support (CeLSIUS). CeLSIUS is supported by the ESRC Census of Population Programme under project ES/K000365/1. The authors alone are responsible for the interpretation of the data. This work contains statistical data from ONS which is Crown Copyright. 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.
By Chris A Garrington, on 19 March 2021
This weekend (Sunday March 21, 2021) we will all be asked to fill in our census forms. There’s a key difference this time: the Office for National Statistics, which runs the operation, is aiming for at least 75 per cent of our returns to be submitted online, and the early signs are that millions of people have already responded.
What difference will this make? We do have some information, because the option to complete the Census online was also offered in 2011 – around one in six people opted to do so.
And why does it matter that as many people as possible fill in their returns? I help to run the Office for National Statistics Longitudinal Study, a huge resource based on a one per cent of Census returns since 1971: more than a million individual records. Access to the Census data allows us to build a rich picture of people and places in England, to help us look at what’s happened over time and to anticipate what might happen in the future. So it matters a great deal.
It matters that we get as many people completing online as possible, because that will help the data to be gathered and processed more quickly than has been the case in previous censuses.
And it also matters because we know that without targeting, some groups could be under-represented in a Census which takes place mainly online. Prior to the pandemic, there had been plans to hold events – in schools and local libraries for example – to help people complete the census online if they couldn’t do it at home. Obviously now that can’t happen. The census agencies have provided paper forms to all people in areas that think have poor internet provision, or who are less able or willing to complete online. But there’s obviously a risk that some people might be confused, and may not realise how they can request a paper form.
By Chris A Garrington, on 18 March 2021
Back in the late 1960s there was concern that policymakers had too little information about births and deaths: death certificates recorded only limited information and even the occupation of the deceased could be recorded inconsistently. Similarly it was impossible to use information from birth registrations to look at patterns of fertility – how were children spaced within families, for instance? And so the ONS Longitudinal Study was born.
The 1971 Census had recorded respondent’s date of birth – as opposed to age – for the first time. And that allowed statisticians to record data on a one per cent sample of the population – all those born on four dates of birth which were, and remain, a closely-guarded secret.
The ONS Longitudinal Study now holds records for more than a million people, none of whom have any idea that they are a part of the study. It’s only possible to join through being born in the UK or through migrating into it, and it’s only possible to leave by dying or emigrating. The study also holds information for those living with its members, but it doesn’t follow them up in the same way from census to census.
Similarly, the information provided on birth certificates did not allow for studies of birth spacing. Although such data could be obtained from the General Household Survey (GHS), the total sample sizes were too small for detailed studies.
By Chris A Garrington, on 9 March 2021
By Nicola Shelton
When the 2021 census was first planned, we thought some of the biggest research questions to emerge from it would be around the effects of Brexit. But while those are still live, researchers and others will be watching with interest to see what this snapshot of Britain in 2021 will tell us about the effects of Covid-19.
It will be two years before new data begins to emerge from the March 2021 Census – and by then we hope the world will be quite a different place. But what will the ONS Longitudinal Study tell us about the pandemic, and about the changes it has wrought on all our lives?
One of the biggest questions will, sadly, be around mortality data. While the grim daily totals have told us about those who have died, and what their current or last occupation was when they died, the LS can link that mortality data with other information about the whole lives of those who have died. Because we have information going back to 1971, we can know where those people lived, what jobs they had done and what types of families and households they had lived in. It will give us a much richer picture of the complex reasons why some groups appeared to suffer more than others in the pandemic.