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You're listening to IOE insights, the UCL Institute of Education podcast at University College London. This is Research for the Real World. Conversations about education and social science research and its impact on policy practice and our everyday lives. Welcome. This is Research for the Real World. Hello, I'm Doctor Amy Harrison and I'm an associate professor at the Department of Psychology and Human Development. In this season of Research for the Real World, we're highlighting the UCL Centre for Longitudinal Studies and its role in developing powerful resources and evidence for research and policy development, and to inform and shape the world we live in today. Today, I'm delighted to have the IOE's Doctor Sam Parsons. Sam is a principal research fellow at the Social Research Institute. Much of her research experience has involved working with Britain's longitudinal birth cohorts, the nineteen fifty eight National Child Development Study and the nineteen seventy British Cohort Study, as well as the Millennium Cohort Study. We're particularly interested in how Sam's research explores the antecedents and consequences of poor adult basic skills, and how that results in social exclusion and socioeconomic disadvantage, and some of her learning from the more recent data set, the Millennium Cohort Study. I'm really interested to hear your insights and theories about this topic, Sam. So a very warm welcome to the podcast. Thanks, Amy. Good to be here. Thanks so much for joining us, Sam. Could you start by taking us on a bit of a journey through your career? You know what got you interested in this area of research particularly. So I've been working with the British birth cohort data since the mid nineties. And when I first joined, I was working with Professor John Brunner, who sadly recently died. But he had a great passion for literacy and numeracy. And that was the first project that I started working on, and that became a twelve year sort of in and out project. And it was basically, as you said, as you mentioned, looking at the antecedents and consequences for adults with poor basic skills and, um, the astonishing struggles that they experience and also how relatively prevalent it was. And so we assessed people, they, they did a, you know, a literacy and numeracy assessment. So in the nineteen fifty eight cohort, they were thirty seven years old. And in the younger, younger nineteen seventy cohort, they first did one when they were twenty one years. And then we got extended funding. Later on when they were thirty four. And we were able to everybody in the survey. So that was over ten thousand people did a did an assessment of their literacy and numeracy, which was an amazing thing to. And that was back in the day. Down to the then Labour government. We had a big drive. Skills for life and yeah to look into that. So that was as I said, that was a twelve year programme that ended in the noughties. And since then, you know, you just move on to different areas of research because the studies are so broad and they cover so many different topics. So we're able to delve deep and wide into all aspects of life. For those listeners who don't know what are these longitudinal cohort studies, we've got three of them hosted by the centre for Longitudinal Studies. So what do these cohort studies involve? They are absolutely amazing. So the first one. So basically the first one was introduced or started, sorry, in nineteen forty six. And that is one that we actually don't run. And then twelve years later, there was a second one, nineteen fifty eight. Then in nineteen seventy, and then there was a gap and we started the millennium one in around, you know, in two thousand. So the earlier studies, the one in forty six, fifty eight and seventy essentially followed every baby born in one week in Britain, which is roughly around seventeen thousand babies, and said the mother was was interviewed by a health visitor. And it was essentially these these early ones were never meant to be longitudinal. It was just, let's look at the health and wellbeing of families and how they varied by socio economic status and different health, particularly post-war post-Second World war. And then bit by bit, there was extra funding. And so that's particularly if you look back in the early years of those studies, there's different gaps in the periods of data collection. So, for example, in the nineteen fifty eight, the families were interviewed at the time of the birth, but then it was not until they were seven. There was an extra pot of money available and it was like, oh, let's go back to these families. And they were interviewed at seven, and then it was eleven and sixteen. And then in their twenties and in their thirties. And from then on, it's been very much more organised and longitudinal funding, very similar for the nineteen seventy cohort. They were interviewed when they were five, ten, sixteen. Then there was a huge gap, not until twenty six, etc. and the most recent waves, they were the nineteen fifty eight were on average sixty two when they were interviewed. And for the nineteen seventy cohort there were fifty one. And when we get to the Millennium Cohort study that was planned longitudinally and they. And it was much more about child development. So they were interviewed. They and their families were interviewed much, very much more regularly. So they were first interviewed at nine months, three years, five, seven, eleven, fourteen, seventeen and then most recently, they've just been interviewed at age twenty three. And that data will become very will very soon be available. And that's a really interesting data collection point, um, around the pandemic. Yeah, yeah, yeah. So there was planned. So if I say like the nineteen seventy cohort were interviewed when they were fifty one, it should have been for their fiftieth birthday, but that was twenty twenty. That was the pandemic. Ah, and so it was. And likewise for the millennium, you know, yeah, the millennium that was delayed and delayed and also for the nineteen fifty eight. So there was a much more extended period of field work for for the latest waves. However, actually, during the pandemic, online questionnaires were sent to everybody to try and capture the particular Covid experiences. And so there were three surveys done through the first year from March twenty twenty Twenty twenty two. Whenever you know it's an amazingly rich data set and the defining features are that the same group are followed up at lots of different time points. And it gives us these really rich data that over time paint a picture of things like development and social influences. And I guess I'm interested in what your findings tell us about some of the data that have been collected. So for example, what your findings tell us about how well or how poorly people approaching retirement are actually prepared for it? So that's one of the data points that you've been looking at. Yes. So this was a hasn't been published yet, but it will be very soon. And this was a research project that I've carried out with colleagues, Vanessa Moulton and Zena and George. And as I said, that's going to be published soon. So this was looking at, you know, how prepared for retirement are people as they coming up to. So this is people born in nineteen fifty eight. And I say that on average, they were interviewed when they were sixty two. And their state retirement age for that cohort is age sixty six. And so it was like lots of questions about what private pensions do you have and their value, etc., but also what knowledge do you have of the state pension? And I found it quite fascinating that, you know, one in five didn't know when the state pension age was and then one in three didn't know how much the state pension was, was for. And although many of the study members at oh, like eight in ten had a private pension, the values of those private pensions varied enormously with, as I'm sure people are aware, with many pensions values being so much higher than women's. So just some things you kind of know. But to see all the evidence and the disparities of. By people say health status or their family income was quite astonishing in terms of their preparedness for this retirement, which meant that now, now they are actually of time and age. Yeah, it's interesting how you're able to do that because you get such a large sample. You can look at these patterns across the sample and see that variety. And what about psychological outcomes that were people talking about how they felt about retirement, or was it more focused on kind of practical preparation? It was this report because it was for the Department of Work and Pensions. It was more practical, sort of how much money have you got? Have you paid off your mortgage? What kind of work histories you've got? Because that's something that these studies have got, which is amazing. We know they're basically economic activity histories from the moment they left school to what they're doing now for every single month of their lives. So you can see how much time they've spent working or out of work due to ill health or in a home caring role, particularly for women of that generation. So you can kind of group those experiences and see, you know, the people who've spent the most time out of paid work are perhaps the least prepared for retirement and don't have such advantaged pensions, but quite a lot. I mean, because these are people born in nineteen fifty eight are part of the baby boomer generation, which is thought to be very privileged in terms of pensions and retirement. But many of them are underprepared, should we say, for retirement. And in terms of what money they will have at their disposal is, you know, it's not it's not true to say that this is a blessed generation and that many will be reliant on the state pension for a good portion of their income once they've retired. Sounds like there's quite a bit of variability in the sample. And that's that's important to, you know, there's different lived experiences. And you mentioned sort of the men in the sample having in the cohort, sorry, having perhaps more in their private pensions and gender inequalities in pension wealth really do seem to be very stark in your research. And have you got a sense of what might be driving that gap? I think it's a lot to do with maybe a women working more part time in occupations that potentially pay less. Well, you know, and because men have, on average, spent far more time in paid work than women in this generation, they've had far more time to build up their private pensions. And particularly a lot of this generation have a defined benefit pension, which are increasingly less around, and many more people have a defined contribution. And so I had to learn all this quite recently for this. So basically the DB pension is more like the that's the gold standard where you get an income every year for your life, whereas the DC is like a lump sum and you've got much more autonomy over it, which is a good thing in one way. But it also requires you to have more pension knowledge about being able to make decisions. But yeah, we found a huge, huge gender gap in pension wealth, but this report is very broad. It's a huge beast, but it's very descriptive. So we need to do a lot further work to really get at yeah, you know, behind these these differences. Yeah. So looking at kind of the different subgroups, it sounds like that would be really interesting. And half of the nineteen fifty eight cohort won't maintain their pre-retirement standard of living. Is that something we should be worried about perhaps as a society? So maybe what are some of the implications of these data? So you have these things called target replacement rates. And it's based on. So how much income you. What's your earnings and how much. If you say like the bottom income target replacement rate is, is like if you earn up to fourteen and a half thousand pounds. So if you're in that group, you would need about eighty percent of your income for you to have the same standard of living in retirement. I hope I'm being clear, and most of that, you see will be paid when you get a state pension. However, if you're earning, say, above sixty thousand, you only need about fifty percent of that. And so say a state pension is about twelve thousand pounds. You need to have obviously a private income from your private pension to make up to be able to live theoretically as you were doing when you were working. HMM. That's interesting. And many people, you know, and we found that in this data, half of the people weren't whichever band they were in weren't making it. Overall, half were below. So it is. Yes, I think it is quite I don't know if alarming is the right word, but I think it's about. There needs to be more. Obviously, I think there's a knowledge gap that needs to be filled. In contrast, in the nineteen seventy cohort, poor health was deeply tied to low income and have inequalities like this actually worsened across the generations? You mentioned the sort of baby boomers being blessed, although that wasn't necessarily born out by your analyses around pensions. But is that something that's emerging from these cohort data sets that inequalities are increasing in some instances? So in some of the more recent work, what we've done is we've looked at health, certain health outcomes. And looking at the nineteen seventy cohort when there were fifty one and then looking at the nineteen fifty eight cohort when they were fifty, to essentially see, well, how are they at different periods of time, but at the same age. And we find quite a similar pattern. So like in self-reported physical health, the proportions of overall or men and women are, you know, have the same amount of say percentages reporting physical health. And if you look at it across income, a similar, it's extraordinarily related. So there's far fewer people in, say in the top income quintile with poor health, and there's far more in the bottom. But that is very similar pattern across the generations, although there's more in the younger cohort, there's more women in the more recent cohort with mental health problems, and there's more in the lower income groups with mental health problems. That's in the nineteen seventy compared to when they were age fifty. So there is some evidence of increasing inequalities. Um, and another thing that we think is rising more generally in society is psychological distress and particularly among women. Yeah. And I guess I'm interested in your insights into what might be driving that trend. Well, again, this is really sort of hot off the press research and we've just finished that and it's very descriptive at this point. It was just both of these new data sets for the fifty eight and seventy are available for, for for all the research community now. But we were just looking into, I was part of the team that was looking into some initial findings. And so they are purely descriptive. And so we need to delve deeper into, you know, what lays behind potentially these increasing inequalities. That would be for our next podcast episode. That sounds really interesting. And even though women in the nineteen seventy cohort are working more hours than previous generations, domestic tasks are still overwhelmingly completed by women. And what does that tell us about gender equality today? Well, you have to be careful with this because the questions were asked of study members. Do you your partner or do you share? Like, who does most of it? And so I've looked at well, we've looked at it by if you are a woman study member or a male study member. And so to look at, I do most of it is the response. I mean, because everyone's saying they do most of it regardless of their gender, because we all think we do loads. Exactly. Men are more so for the traditional women's jobs in the home, like cooking, shopping, cleaning and doing the laundry. Over half of women say that they do most of it, and no more than one in five men say that they do most of it. Okay. And men are more likely to say that it's shared equally. However, because they're men and women in different households. What would be great is if we had it from the man and woman in those traditional heterosexual relationships. Say what is really going on? And we don't know that. We just know what the study member reports. But undoubtedly it's very gendered split because we have one question which is about doing DIY or, you know, those, again, traditional male jobs in the home. And only ten percent of women say that they do most of that compared to three quarters of men. So it's again, that needs to be unpacked a bit more, which is something that I'm currently working on with some colleagues. Yes. That's one of the things about self-report data, isn't it, that it's unless you can kind of corroborate and go in and observe, it's obviously the person's perception and evaluation of what they're doing. And those are the data we have to work with. But that may or may not be. But you find such consistency when there because those actually those questions on domestic division of labor have been asked repeatedly. Well, not repeatedly, but they have been asked before. And also in the older study. And the patterns are overwhelming. They're so consistent. That's really interesting. So across the the different sweeps of the cohort dataset collection are. Well, that's helpful, isn't it? So we've got the reliability of the. The consistency of the data. Fascinating. It just it makes me think, you know, how kind these participants are to keep participating, because to keep them in the study must be a really, really big job. And to actually collect all of that data. I mean, it's an amazing task. And, you know, just being feeling so grateful to these people to keep reporting and keep updating us on their, their life experiences and taking all these studies together. What do you think is the single biggest message that you want policymakers to hear right now? Wow, that's a big question. It is, isn't it? Well, there are some more I think the I don't know about necessarily the content because I think that is so much that can be learned, but it is to keep doing these studies. And actually there are some new ones Developing as we speak. We've got some more studies. Early life cohort for one, which is now off the ground. So it's great that they are so valuable. The longitudinal nature of the data is just something that you can't get from a cross-sectional study, which is obviously so much more cheaper to collect data from. But, you know, and traditionally, you know, these are interviewers going into somebody's home or earlier into schools or whatever. But more and more, I mean, that's still the preferred way, but particularly with younger cohorts. But, you know, at least different components of the survey are also done online now because you can do so much more. Or like in Covid, there was videos like this, there was video recordings because you couldn't go into people's homes. Also, the way the data are collected have changed over time. Yeah. Across. Yeah. Across the cohorts. And what about the Millennium Cohort study? What are some of your. Yeah. What things have you learned from those data? So one of my pet projects. It's one of my been working on that with colleague Ingrid Schoen from the same department. And it's funded by the Nuffield Foundation. And we've been looking into outcomes for children of mothers who had spent time in out-of-home care. So either in a residential care home or foster care. And it's like so there's much known about the poor outcomes for children who spent time in care. But what happens later? Like is that do you get evidence of intergenerational transmission of disadvantage? You know, how do the mothers cope when they become parents? Because by definition, they've had a disrupted time with their own parents. And the Millennium Cohort Study collected information on whether the mother had spent time in care. And so we were able to look at not only her own outcomes when she first had the millennium child. But also what are the child's outcomes of as they've developed? So we've looked at that up to age seventeen. And then also at age seventeen for the first time, the study member. So the cohort member for the millennium was also asked about if they had ever been in care in earlier years. And so we've been able to look at all these outcomes at age seventeen to see, you know, are they as disadvantaged as perhaps evidence, you know, sort of government evidence or statistics suggests. And in terms of if the cohort member herself has had out-of-home care experience, yes, they are they have much more contact with the police. They're far less likely to get good GCSEs to remain in education or training, you know, at seventeen and much more likely to be involved in risky behaviours and not smoke and have taken cannabis, etc. and but also for the children if their mum has been in care. A lot of those experiences are also there, particularly mental health problems across a range of a range of measures. However, we do find that once you control for their socioeconomic disadvantage conditions, they're just as likely to get good GCSEs and to want to go onto university, etc.. And so I'm currently trying to get some further funding to really look at how these outcomes, how it manifests into early, early adulthood, because I think I mentioned that there were last interviewed when they were twenty three. And so that data was is imminently available to be available. And so if I can really look at what happens when they have made that transition into adulthood. So fingers crossed I get the money for that. That sounds really interesting. I really hope you get that funding. And it just points out the ways in which longitudinal data can really interrogate cross-sectional data. So cross-sectional data are where we've got kind of a snapshot in time, and we only know kind of what's happening there and then, and we can really follow that up and explore it further when we've got lots of data points over time. And here is such an interesting example, isn't it, of where we've got the different generations. And, and I think that's really, really useful and interesting. And it's also what I've been just to say sorry, just to say what has been sort of the real value of, of a lot of this data is if I just do a simple, you know, crosstab or a mean score of something looking at by either the child's or the mother's care experience and, and the outcome, you will see that most of them are all, you know, significantly different. But once you control for socioeconomic disadvantage, however you want to, you know, whether that be income or housing or area deprivation, a lot of the differences disappear. So that's something for policy to address. It's not the mother's care experience that is the problem. It's about the fact that perhaps she didn't have an opportunity to get qualifications. And her housing isn't so good in a disadvantaged area. Yeah. So what you're saying is that the socioeconomic disadvantage accounts for a lot of the outcomes that we see. Um, and then yeah, when we factor that in, that actually explains explains things really clearly. And so I'm just going, I'm just thinking about that question. I asked you about policy makers. It sounds like doing something about this disadvantage would improve society more broadly. Absolutely. And one of the there was a big drive, um, which wasn't taken up by the last government to get care experience to be to be recognized as a protective characteristic. But given the their disadvantage is so entrenched. But so that campaign is still rolling on because they didn't accept that, that it should be added on to, which is absolutely should. Yeah, it sounds like it's a really key data point. And are there other ways in which these cohort data sets have influenced policy or practice that really stick in your mind? Oh, well, back in the day, I mean, after a long, long time ago, it was actually it's from the nineteen fifty eight study that was the one that associated smoking during pregnancy as being very harmful for the child. That's where that data originally came from. And I know that's that's a long time ago. Now I need to brush up on that answers to that. Well, I think that was a really good one, isn't it? Like, yeah, that's an amazing wow. That is is a really good example of research for the real world and how research can change lives and influence outcomes in society. You know, in a really positive way. I'm wondering what you're looking forward to in your research. What things you've got planned that you're really excited about. The main thing is that what I've just mentioned is looking at the care experience and early adult outcomes in the Millennium Cohort, but also in earlier work. Again, using the millennium, we looked at outcomes for children with special education needs have been identified with special education needs, either by their parent or their school. I've looked at outcomes up to eleven and fourteen, so that would be something I would like to go back to, to see how they are again, how they are experiencing the transition to adult and what disadvantage they will have experienced. Amazing. Really looking forward to hearing more about that in the future. So thanks so much, Sam. It's been really interesting to get to know you and hear about your research. I've particularly enjoyed thinking about how outcomes have influenced policy and practice and the breadth of these data sets, in terms of the questions that they ask and how they ask them, and how that has actually evolved and changed over time. And I think I've learned something that I didn't know about how they started out, perhaps as a cross-sectional study. And then over time, more funding was given to keep going and going back to the participants and asking them more. And I found that really interesting. Thank you so much for coming on the podcast. Oh, it's been a been a delight. It's been great to have you. So you've just heard from Doctor Sam Parsons. Some of what we've covered today is also available in the episode notes. If you've enjoyed this episode, we have an archive of twenty five past seasons. Search IOE Insights to find episodes of Research for the Real World, as well as more podcasts from the IOE. And a quick favor before you go. If you're listening on Apple or Spotify, we'd really appreciate it if you could give the IOE Insights podcast a rating. Five stars would be nice if you're enjoying the show, and that will help us reach more people who are interested in hearing about such important work. I'm Amy and thanks for listening. Research for the Real World is brought to you by IOE Research Development and IOE Marketing and Communications at University College London. The series producer is Amie Liebowitz, the executive producer of the IOE Insights podcast is Jason Ilagan. Thanks for listening. 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