life course

The dynamics of inequality: what have we learned?

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In the final episode of the DIAL podcast we’re looking at what’s been learned from DIAL projects about how and when inequality manifests in our lives and what its longer term consequences might be. We’re joined by Elina Kilpi-Jakonen from the University of Turku in Finland. Elina is the Scientific Coordinator for DIAL and, as the programme draws to a close she reflects on some of the programme’s highlights,  key findings and implications for the future.  

Transcript

Christine Garrington  0:00 

Welcome to DIAL a podcast where we tune in to evidence on inequality over the life course. In series four, we’re looking at what’s been learned from DIAL projects about how and when inequality manifests in our lives, and what its longer-term consequences might be. For this final episode of the series, we’re delighted to be joined by Elina Kilpi-Jakonen, from the University of Turku in Finland. Elina is the scientific co-ordinator for DIAL and today, as the programme draws to a close, she’s here to reflect on some of the program’s highlights, key findings and implications for the future. So welcome, Elina thank you very much indeed, for joining us. Now, first of all, I’m guessing it’s been no mean feat and indeed, I know, it’s been no mean feat, keeping an eye across 13 fantastic research projects with researchers based all over Europe. But just take a minute or two, if you would to remind us of what exactly the DIAL programme is and what it’s involved over the last few years.

Elina Kilpi-Jakonen  0:57 

So thanks a lot, Chris. The DIAL programme is, as you said, kind of transnational programme. And we’ve had 13 research projects involved. And all of those involve international collaboration. And it’s based in the social sciences and behavioural sciences, financed by NORFACE, which is a research organisation bringing together different funding institutes across Europe. And so the focus of DIAL has been on inequality and in particular inequality across the life course and trying to understand some of the structures of inequality cross nationally and some of the mechanisms kind of producing inequality and and what that means to people and societies as a whole.

Christine Garrington  1:41 

Wonder if I can ask you why it has been so important to look not just at inequality, per se, as you were saying there, but at how inequality manifests itself over the life course, because this is an important thing, isn’t it? And indeed how, when and where it sort of accumulates?

Elina Kilpi-Jakonen  1:57 

Inequality is a really complex and multifaceted issue. And so I think one one part of it is that inequality comes across in many different domains. So it’s important to take into account inequalities, for example, in education, labour market, health, and so on. And then I mean, to really understand where it comes from and what it means it’s important to look at the determinants across time, I mean, both across time for an individual and their parents, and so on, kind of that life course aspect, but also, for countries to see how it develops across time. Inequality isn’t something that just is, I mean, it develops. And so kind of building on that kind of developmental process to really kind of inform us about how we can do something about it, or how we can really kind of understand where it comes from, it’s important to take that into account.

Christine Garrington  2:58 

Now, you talked about the programme largely being based in the social sciences. But one of the key things about the project is that we’ve seen researchers from different disciplines as well as different countries coming together to try to tackle, as you say, as you rightly say, this incredibly complex area around inequality, what’s been the thinking there?

Elina Kilpi-Jakonen  3:20 

Well, I mean, inequality is something that interests a lot of academics working in different disciplines. And, and they come from it from from kind of different angles. And I think, because it is kind of a complex issue, and it’s an issue that kind of manifests itself in different ways. So really building on on the strengths of different disciplines, I think is a key key strength here. So we don’t only look at inequality in one domain, for example, say, say something like education, which would then be kind of a subset of disciplines that tend to be interested in inequality in education, but also how education is linked to inequalities in in other aspects and, and in addition to the kind of different domains that come from different disciplines, also, the ways in which we, we analyse it and building on the strengths and knowledge of different disciplines. I think is key here, key to, to just building a comprehensive picture and learning from each other, as well as as then taking that knowledge forward.

Christine Garrington  4:34 

It would be remiss of us not to talk about COVID. And in some ways, it was something of a setback for plans to to stage events and meetings around the the programme of research to get the word out there about it. But it also provided in some respects, a rather unexpected opportunity, didn’t it to use the programme to look at inequality in the context of COVID. So, so tell us a bit about that.

Elina Kilpi-Jakonen  4:58 

Yeah, so obviously the research programme began before COVID. And so the projects had their their kind of plans of what they wanted to do and the analysis that they were going to do. But given this massive impact that COVID had on on society and and on inequality as well. A lot of projects then decided that this would be a really important aspect to look at and an opportunity also to learn about inequality in a changing societal context. So different projects have taken this into account in different ways. But for example, there’s been kind of really important work on on just what happened to inequality for example, due to lock down and and the economic upheaval of COVID, not just the health implications, but then also using that upheaval, to think about how inequalities might be changed. And for example, so work by Alejandra Rodríguez Sánchez, Suzanne Harkness and Anette Fasang, looking at what happened to housework, during COVID. People were having to stay at home, both parents and children and seeing what happens to inequalities between men and women. And how the, the the number and age of children influences that and kind of what they saw was that obviously, this change in in family habits changed house work habits, but at the same time, when locked down ended a lot of couples returned to normal. So so even though there was a massive shift, and and people behaved differently for a short period of time, we can see that these kind of entrenched habits, then then go back to normal quite quickly.

Christine Garrington  6:43 

Yeah, really interesting piece of work that so. And also, despite COVID, you were able to, nevertheless, to involve a great number of stakeholders in in the research, what messages did you receive from them, I wonder about what was emerging?

Elina Kilpi-Jakonen  6:58 

So yeah, we’ve had some really interesting discussions with stakeholders, both policymakers and then kind of non-governmental organisations involved in both practical work and lobbying as well. And they’ve been really interested in in the work that we’re doing. So in particular, we’ve talked to stakeholders involved in kind of gender inequality work, and how participation in the labour market is unequal between men and women, and in particular, between mothers and fathers. And then we’ve also talked a lot to stakeholders involved in kind of childhood disadvantages, and how different types of children are put at a disadvantage. And what are some of the mechanisms kind of potentially either alleviating those disadvantages, or that are currently making those disadvantages larger, and that would kind of be important to look at. So we’ve kind of talked both about the the bigger picture of inequality, but also some of the mechanisms and obviously, stakeholders are, are often interested in what they can do. And then we’ve also had really good discussions about especially with policymakers also about the kinds of data that going forward, would be needed to, to kind of really analyse these things further. And I think there’s a lot of kind of shared interest in collecting data or making administrative data available for researchers to be able to address inequalities in the future.

Christine Garrington  8:28 

Yeah, now a major part of your role, Elina has been to pull together all of these different strands of work in some way to ensure that we get to a, what we hope is a coherent picture of what’s been learned from the programme as a whole. And I wonder whether it’s possible in the short period of time that we have to say what has been learned from the programme as a whole?

Elina Kilpi-Jakonen  8:48 

Well, that’s no mean feat. To then kind of say what’s been learned because I think there’s such richness in the research coming through and I mean, we’ve only kind of touched upon some of the aspects just now. And so we what we’ve been trying to do is, is bring together kind of thematically, things we’ve learnt in terms of, for example, gender inequalities, as I just mentioned. So So really looking at further at kind of motherhood, penalties and how, how those might be potentially for example, by by further training ameliorated although at the same time, we need to remember that women tend to nowadays have higher education levels than men. So education isn’t always the key here. So also looking at kind of gender and sexual minorities, even though we’ve been making progress in terms of legislation and policy. The discrimination can still be kind of an ongoing issue for people and and kind of the legacy of the past is still a major issue for for LGBT citizens across Europe and even though legislation has progressed a lot to still the practices in terms of, of workplaces or educational institutions aren’t aren’t really catching up necessarily, to such a large extent. And then moving on to kind of a different area, I think there’s been a lot of really interesting work in terms of, of the role of genetics, which is a big new area of research in terms of social sciences, and how that plays into the reproduction of inequalities across generations and over the life course, and how that changes depending on the environment that people live in. So, so we’re learning a lot about so called gene environment interplay, and which is obviously kind of something that social scientists are really keen to look at is the the environmental aspect of, of how genes play out. So so we’re learning a lot about the fact that genes aren’t our destiny as such, but but the the context or the environment matters a lot for that.

Christine Garrington  11:00 

Yeah, lots of really fascinating and very, very innovative work that’s going on in that area, for sure. Now, you’re talking about stakeholders a moment ago, you’ve been responsible also for helping to ensure the dissemination of this research to to those non-academics as well as other researchers. So I’m interested to know and I think others will be interested to know what sorts of resources there are available. For those interested to know more, aside from the obviously, the dozens, and I know, there are dozens of journal articles and working papers that have have been produced if you’d like for the scientific community. But what else is there.

Elina Kilpi-Jakonen  11:34 

Starting from those journal articles, I think we’ve tried to make a kind of effort to make those more accessible in terms of both bringing them all to our website, but also providing summaries that are not just the academic abstract. So even looking at the journal articles, starting from from summaries that are more accessible to everyone involved, and not just researchers in those fields, I mean, abstracts can sometimes be a bit difficult to disentangle. Then bringing together the research we’ve we’ve been producing policy briefs that, I mean, obviously are aimed at policy audiences but I think those bring together thematically some of the research as well in a really nice way. So those are available on the on the website, then obviously, this podcast series, I think is has been a great way of disseminating the research. In addition to that, so we had our final conference last autumn. And some of those videos from the presentations are available still through the website. I mean, there’s both recordings of presentations that bring together entire projects, but also kind of individual, more finely specified research topics. But But in particular, there’s there’s videos of researchers presenting their whole project at the final conference. So I think those are also a great resource.

Christine Garrington  12:57 

Yeah, indeed, a wonderful library of materials that people can dip into at their leisure and really catch up on and get to grips with the important things that have emerged from this, this work. So finally, Elina, the ultimate aim of a programme like this is obviously to improve our understanding and knowledge on the one hand and influence change for the good with the understanding on the other. And I wonder if you’re able to say how, I know it’s very difficult, but if you can say how all this important work might feed into the thinking and policies of those seeking to reduce inequalities today, and in the future?

Elina Kilpi-Jakonen  13:32 

At the same time as advancing academic knowledge, we definitely have wanted these research results to be relevant for policymakers and to reach policymakers and indeed, kind of other organisations interested in in these types of inequalities and processes. I mean, on the one hand, there has been really great comparative work on the kind of institutional influences that policies in different countries have and I think that’s a really important thing to draw from in terms of, for example, education policy, or family policies for work life balance and the gender inequality in pay so, so looking at the across national differences and comparing countries and then learning from that. But then also, I mean, there’s been really detailed work into kind of the mechanisms of inequality and more specific interventions for example, and how those influence inequality and and then really digging more deeply into how inequality is reproduced and what we might be able to do about that. So for example, work on on parenting and how that reproduces inequalities among children and and then thinking about well, how we might be able to to provide more equitable parenting for children and what we can do about that. So I think there’s, there’s been work on multiple levels that hopefully we’ll be able for policymakers to draw on in terms of developing these things in the future.

Christine Garrington  15:07 

Thanks to Elina Kilpi-Jakonen, DIAL’s scientific co-ordinator for joining us for the final episode of this fourth series of the DIAL podcast. You can find all the resources that Elina mentioned in this episode on the DIAL website at www.dynamicsofinequality.org. We hope you enjoyed this episode, which is produced and presented by Chris Garrington of Research Podcasts. And don’t forget to subscribe wherever you find your podcasts to access all our earliest series.

A level playing field for children: why it matters in tackling inequality over the lifecourse

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In Episode 5 of Series 4 of the DIAL Podcast we’re in conversation with Andreas Peichl, Professor of Macroeconomics and Public Finance at the University of Munich and Principal Investigator of a DIAL project looking at the impact of childhood circumstances on individual outcomes over the life-course (IMCHILD). 

Pre-term children: how do they get an equal chance to thrive?

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In Episode 4 of Series 4 we’re talking to Professor Sakari Lemola from the University of Bielefeld and formerly from the University of Warwick. Sakari is one of the Principal Investigators of the DIAL project PremLife, which has been looking at what factors can provide protection and increase resilience for preterm children’s life course outcomes. 

Transcript

Christine Garrington  0:00 

Welcome to DIAL a podcast where we tune in to evidence on inequality over the life course. In series four, we’re looking at what’s been learned from DIAL projects about how and when inequality manifests in our lives, and what its longer-term consequences might be. For this episode, we’re delighted to be joined by Professor Sakari Lemola. He’s from the University of Bielefeld and formally from the University of Warwick, and one of the Principal Investigators of the DIAL project, PremLife, which has been looking at what factors can provide protection and increase resilience for preterm children’s life course outcomes. So Sakari, thank-you so much for joining us today. It’s great to have you on the DIAL podcast. I wonder if you can start by telling us a bit more specifically what this project has been investigating and why?

Sakari Lemola  0:45 

So the PremLife project has been particularly focused on the role of protective factors for social and educational transitions after preterm birth. Preterm birth is defined as birth before the 37th gestational week. Then there are two further categories one distinguishes between moderately to late preterm children at its birth between the 32nd and 36th gestational week – moderately and late preterm children. But they’re also very preterm children who are born before the 32nd gestational week. So in the PremLife project, we specifically look at both of these groups – the very preterm children and moderately and late preterm children compared to term born children and try to figure out what are their disadvantages they have in their lives? And also, what are protective factors that may improve their outcomes? In some domains they actually do really well when certain protective factors are present.

Christine Garrington  1:50 

Can you tell us something about how common preterm births are?

Sakari Lemola  1:54 

The incidence of preterm birth has been rising in the last few decades. So in the UK, around 7% of all babies are born preterm each year. This means that two children in an average sized primary school class are likely to have been born preterm and in spite of the advances in neonatal care of preterm birth in the last few decades, and also decreasing mortality rates, which is a very good thing. Negative long term, sequels and consequences of preterm birth have still remained, particularly for very preterm children, those born before the 32nd gestational week that means eight weeks too early or even earlier than that. That leads to medical complications, which often require distressing but life saving treatments frequent are, for instance, neonatal asphyxia, hypoxia due to immature lungs. Necessary treatment involves ventilation, continuous positive airway pressure, surfactant treatment, but also treatment with stress hormones, prenatal corticosteroids treatments to accelerate the long development.

Christine Garrington  3:10 

And so Sakari what does life look like for those children compared with their full-term born peers?

Sakari Lemola  3:16 

They often have an increased risk for poor cognitive development, they show poor educational outcomes, less favourable employment outcomes in adulthood and increased risk for developing mental health problems. And in the PremLife project, we try to specifically answer the question, first of all, of course, what are protective factors for those born preterm. But also we try to focus also to figure out out about what are the social and emotional development of the preterm birth, particularly related to social relationships, wellbeing and things like self-esteem and self-confidence.

Christine Garrington  3:58 

Now, there’s considerable policy interest across Europe and indeed elsewhere and ensuring that obviously, that children get the best possible start and in helping those children who for whatever rate, whatever reason may not get off to the best start. How has your work tied into that sort of policy context would you say?

Sakari Lemola  4:17 

In the PremLife project, we particularly aim to answer what can be done by policymakers, by practitioners, stakeholders to improve preterm children’s and adolescent development? So two focal points were, one was on preschool training in math and literacy. The second point was about how schooling should be organised in general. So we compared school systems in Germany, where so called school tracking takes place. That means children are sorted into higher or lower tracks after the first few school years and we compared Germany with the UK and Finland where no school tracking takes place. That means better and the lower performing children remain in their school classes in the UK and in Finland. But children with special needs they receive remedial teaching but they are not sorted into a different school or different school classes. A third focal point was related to physical activity in childhood and adolescence and what role physical activity actually plays for mental health and social emotional development.

Christine Garrington  5:32 

A key piece of work from the project involved the assessment of adults who had been born preterm. What was sort of the main thinking, the main driver for for this work?

Sakari Lemola  5:42 

Previous work has shown that preterm children have an increased risk for poor cognitive development and they also show poor educational outcomes. And particularly, most work has focused on childhood, but less work on later outcomes like adolescence and also in adulthood. In the PremLife project we have now also focused on adolescence and adulthood. And also particularly, we focused on differences in socio-emotional outcomes in adulthood, particularly regarding social relationships, a topic that has previously been neglected So, children who were born preterm in adolescence and in adulthood, they seem to be less satisfied with their social relationships, they are less likely to be partnered in adulthood, and they are also have decreased fertility so they are less likely to have children on their own later in life.

Christine Garrington  6:44 

Okay, and what were the key things then to emerge about how those people who were born preterm faired later on in life?

Sakari Lemola  6:52 

So we found out that children born preterm to still show differences compared to their term born peers, when they are grown up particularly. Yeah, they show more mental health problems, particularly anxiety disorders, they show lower wellbeing then full term born children in friendship relationships, they are less likely to experience intimate relationships in adulthood, they are less likely to become parents on their own. Somehow, it is likely that anxiety and shyness play a role which is increased in preterm children, they are more anxious about making a step for instance, in social relationships, and that may lead to lower rates of being partnered and becoming parents themselves.

Christine Garrington  7:47 

Okay, now, you made some key recommendations from this. Can you talk about those recommendations and just how practitioners, policymakers and those people born preterm might benefit from from those recommendations?

Sakari Lemola  8:00 

With regard to schooling and education outcomes a key recommendation is the importance of early training and early support in math and literacy. So what we found is that preterm children, they appear to disproportionately benefit from preschool training in math and literacy. So, preterm children who perform well in math, reading and writing when entering into school, so very early on age of five, six years, they were more likely to receive GCSE grades that qualify later to go to university than their term bond peers actually. However, it was exactly the other way around for preterm children who perform poorly in math, reading and writing at school entry, they were less likely to get sufficient GCSE grades compared to their term born peers with similar preschool skills. So their skills at school entry, the skills and math, reading and writing appear to be more important for preterm children than for term born children. And that highlights how important early support and rhythm medial teaching plays there. A second point is that school tracking as it happens currently, is it’s the current policy in in Germany, is a negative thing for preterm children probably also for for other children with early difficulties. Where people from a migration background who are not as fluent in German, for instance, as German children so children with more difficulties in school should rather receive remedial teaching but they should not be sorted out into lower performing school tracks as it is currently the case in Germany. We compared it with the outcome of preterm children who go to school in Finland and in the UK, and they seem not to have that. There is no such a negative effect of the school tracking because there is a different policy in the UK and in Finland.

Christine Garrington  10:11 

And what about the social and emotional side of their lives? What did you find there Sakari?

Sakari Lemola  10:15 

Here we had focused on two factors that appear to be relevant for preterm children. So this involves sensitive parenting on one hand and physical activity and playing sports in childhood and adolescence and preterm born children benefit from both from sensitive parenting and physical activities, such as playing sports. So both factors seem to increase self confidence and have to be considered as protective factors against the negative outcomes of preterm birth, particularly negative outcomes regarding social and emotional development.

Christine Garrington  10:53 

So much interesting research to emerge from this project Sakari. I wonder what the key things have been for you, things that have really caught your eye or have been of particular interest to you, things that maybe surprised you?

Sakari Lemola  11:05 

I think the key findings and surprising findings are that this early training in math and literacy, writing and reading are disproportionately important for preterm children compared to term born children. And this may generalise also to other children who may have a more difficult start in school for them. Most probably it is important to have early support. A second important finding was related to the school tracking that means the grouping of the children to higher and lower performance levels in school. So this has a particularly negative effect on preterm children, as we found out in Germany with an effect that, of course, isn’t present in the UK where there’s no such good tracking.

Christine Garrington  11:53 

Okay, that’s really helpful. Now, you’ve been really active in sharing your findings, not not just with academics, but health practitioners and policymakers. I know what has been the response from them, I’m interested to know.

Sakari Lemola  12:03 

So overall, we had very positive feedback from practitioners and policymakers and we are also confident that the messages will be heard, but of course, time will tell what will be applied and what not.

Christine Garrington  12:20 

Thanks to Sakari Lemola for discussing the findings and implications of DIAL’s PremLife project. You can find out more about this and other DIAL research on the website at dynamicsofinequality.org. We hope you enjoyed this episode, which is produced and presented by Chris Garrington of Research Podcasts. And don’t forget to subscribe wherever you find your podcasts to access earlier and forthcoming episodes.

  

Tackling inequalities in adolescence and working life

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In Episode 3 of Series 4 of the DIAL Podcast, we are in discussion with Richard Blundell. Richard is the Ricardo Professor of Political Economy at UCL, director of the ESRC Centre for the Microeconomic Analysis of Public Policy at the Institute for Fiscal Studies and the principal investigator of a DIAL project looking at human capital and inequality during adolescence and working life. In this episode we explore the work done by this project tackling inequalities in adolescence and working life.

 

Transcript

Christine Garrington  0:00 

Welcome to DIAL a podcast where we tune in to evidence on inequality over the life course. In series four, we’re looking at what’s been learned from DIAL projects about how and when inequality manifests in our lives and what its longer-term consequences might be. For this episode, we’re delighted to be joined by Richard Blundell, David Ricardo Professor of Political Economy at UCL, and director of the ESRC Centre for the Microeconomic Analysis of Public Policy at the Institute for Fiscal Studies. Richard is also the principal investigator of a DIAL project, looking at human capital and inequality during adolescence and working life. So welcome, Richard, thank you very much for joining us today.

Richard Blundell  0:40 

Thank you, Christine.

Christine Garrington  0:41 

I wonder if you can just start by telling us a little more specifically what this project has been investigating and why.

Richard Blundell  0:48 

Yeah, I’d be delighted to. What we’re looking at in this project is the evolution of inequality through adolescence and working life. Relating to the education streams, people choose how it affects their outcomes going forward into working life, what happens during working life, what kind of training seems to work, what routes to better jobs are for people who don’t, for example, go to higher education, university. Whether training can offset some of the gender gaps that we’ve been seeing opening up in the labour market, and whether choices in higher education matter for future labour market outcomes. So it’s very much about not the early years of school – there’s another project looking at that, that runs in parallel with our project, similar investigators, we’re working together with them. What we’re looking at here then is from adolescence onwards, and how the inequality evolves during adolescence and working life.

Christine Garrington  1:58 

So one area of focus has been women and work really very, very interested in in this, you’ve looked at the gender pay gap, the role of childcare, on women’s ability to return to work, and indeed, on the role of job training, among other things. So what would you say for you are the key things to have emerged from this particular area of work Richard?

Richard Blundell  2:18 

Yes, this is obviously absolutely central, the kind of pay gap between men and women and how it opens up through working life is something that’s been really hard to tackle and getting behind this, what are the drivers of it, and how to address it is really key to solving some of the most important inequalities that we see in working life. We’re working with researchers, mainly economists, and education researchers in Norway, in the UK and in France. That’s rather good, because those three countries have rather different systems of routes through education, into work, and different opportunities for women and men as they progress through their working life. And we wanted to understand what those differences could tell us about the gender pay gap. And therefore what policies could be perhaps most useful in addressing the gender pay gap.

Christine Garrington  3:25 

There are a couple of key things to come out of this one there.

Richard Blundell  3:28 

Some of its, you know, in some sense, pretty obvious. That is that work experience is really important for pay and for earnings as you go through your career for career progression. And of course, when children come along, women spend a fair amount of time not in work, perhaps still in employment on maternity leave, but not actually gaining the work experience that turns out to be so important in career progressions. We’ve kind of known that. But it’s become really acute, even part-time work is really not sufficient for women to keep up at work with their male colleagues. There are two kind of routes to addressing this. One is to provide good quality childcare, that can have two major benefits. One is it can provide good quality inputs and care for children, which is particularly important, especially in disadvantaged families. But it can also allow women to spend more time at work and developing their career profiles. There’s also a very large importance of mothers and fathers spending time with their children. And so when children come along, it’s kind of inevitable, really, that work may take second place, and that there’ll be less time engaged in work experience in progression. And remember, it’s exactly these years in the 20s and early 30s, where all the big career progression is made in working life, and women really fall behind there. So an alternative we’ve been looking at, and it turns out to be rather interesting is to work instead of on work experience, but on the human capital itself, once women come back into work.

Christine Garrington  5:25 

So what might that look like in reality, then Richard?

Richard Blundell  5:27 

So you can imagine the following scenario, a woman or a man, but unfortunately, it’s particularly typically, the woman who takes time off, once she returns to work, you can imagine her engaging in a training programme, and that can make up some of the loss. Well, we weren’t that optimistic about that to begin with. But we’ve become more optimistic for two reasons, particularly in the UK and in Norway. In Norway, using the Population Register, we can follow people, right the way through their working careers, we can follow the whole of the Norwegian population. It’s an exhaustive data set on everything everybody does – their qualifications, where they’re working, their family structure, and so on. And what we found is that it’s particularly successful for women to who’ve had a child early on in their career to return to some kind of schooling qualifications, and that can have a big boost to their career profiles and address some of the gender gaps that occur. In the UK it turns out similarly, women who returned to work spend quite a bit of time in training. And we found that that training, work related on the job training, it has to be accredited, and it has to be work related, those things have a payoff. And we feel that there’s real room for improving this type of training. It’s all part of designing education and training routes, during your working career, that work much better than the ones we currently have. And boy in in the UK, we’ve been training way behind in the organisation of formal routes into education and training through your working life, especially for those who don’t go to university.

Christine Garrington  7:29 

Now, I want to move on to talk about COVID. And obviously, although not expected when your project began, the pandemic, obviously, as well as being a terrible thing for us all did provide, however, what I’m guessing was quite a fascinating and important opportunity to look at the impacts of COVID on on people’s lives in this context of inequality. So what did you, what did you get to focus on there?

Richard Blundell  7:52 

Once we were into the first major wave of COVID, it was clear that it was going to exacerbate a lot of the inequalities during adolescence, during education and during working life, let alone health of course. The longer run impact that we’re seeing is on learning – the loss of learning, the loss of school time, the loss of engagement in learning, because of being not able to go to school, those children from deprived families have had much, much more learning loss over this period, than the privilege than children in more privileged families. It suddenly became clear that space was really important. But for learning for children, it was absolutely critical. If children didn’t have a quiet place with good digital access, a good setup for engaging in online classes, then that already put them behind behind. And there’s many studies showing there’s a huge gradient in space, in digital access, in access to these kinds of technologies across the income and and socio economic gradient. Losses have been extremely large, up to half a year of schooling loss for many, many children. The second point is that if you’re at home with educated parents, who are working from home and still have time to interact with you, you’re going to get that input from them. schooling is the great equaliser. It puts children from deprived backgrounds in an environment where they can learn perhaps things that they couldn’t learn at home. And that was taken away. The work on Norway and France shows exactly the same there. So learning loss, huge. This doesn’t usually happen in recessions by the way. This was very, very specific to COVID.

Christine Garrington  9:55 

And what about when you looked at matters related to work.

Richard Blundell  9:58 

All on the job training, apprenticeships just didn’t happen. In fact, for those in their early careers, you know – 18, 19, 20 – there was an almost complete end to apprenticeships. Apprenticeships fell back by 70% or more for that younger group, exactly the group that I was mentioning before. It’s vital that we get this on the job, accredited training, because they’re the ones not going to university, those going to university have been served rather better. I know from my experience here that we’ve at UCL, we’ve been keeping online classes and activities going at a pretty high level, actually. And the kind of students that we have here, can engage in that quite fully. But that’s very different for a student who didn’t make it to university, and who’s trying to gain their experience and training through apprenticeships, there’s just been no engagement. So this loss of learning has been huge.

Christine Garrington  11:11 

I’m interested to know whether women were worse affected than men in this context?

Richard Blundell  11:16 

We thought it might affect women more but in fact, overall in employment and what have you, it’s been pretty neutral in the UK, that’s just because of the structure of industry we have here. But it hasn’t been neutral at home. We’ve seen, of course, mothers and fathers both having to do more childcare, because schools have been closed during lockdown, or children have been at home during self-isolation, even in periods without lockdown. But mothers of taken, have borne the brunt of the childcare at home, we followed women and families in surveys throughout COVID. And found that although childcare activities have increased for both male and female parents, there really has been an extra load on women. And again, that’s going to affect their careers, and other aspects of their life going forwards. All those things that we were concerned about before COVID. And that were the absolute centre of this project have all become all the more heightened through COVID. And I think the policy recommendations that have come out of this project are very, very relevant for the post COVID world that we’re now entering.

Christine Garrington  12:41

Yeah, I wonder how how easy it has been? Or how difficult I guess it’s probably the better question to to feed those recommendations in such a fast moving event that COVID has been and, you know, was it possible for that to feed through all of those findings, all of those important things into the policy sort of making cycle in order to try to mitigate some of those impacts? Or, or was that that must have been very challenging.

Richard Blundell  13:09 

For policy makers, at least civil servants have been very open, of course, to try and to figure out what’s been going on. And remember, the initial policy responses, at least on simple measures of inequality have been remarkably successful. You know, we haven’t ever had a recession, really, where there’s been so much support thrown into the economy, of course, we’re gonna have to pay for that. But some of the short run impacts, I think were mitigated, what we’ve focused on here, are the longer run ones, you know the the loss of learning, the loss of training, the loss of work experience, they’re not showing up even yet, they’re going to show up in the next few years. And it’s critical, we have an opportunity now to address them. And there is a lot of interest across the whole policy world, and government and around the world. In addressing this. In fact, as part of this project, we fed into the G20 meetings last year in Rome, and a major part of our work was used to suggest a kind of coordinated approach to designing the best interventions now to address what’s been going on with loss of education, and loss of work experience and training across more or less the whole developed world.

Christine Garrington  14:27 

Really great to hear that there’s been such an appetite for findings like these important findings to feed into policy, but I guess the devil is in the detail, right?

Richard Blundell  14:37 

Unfortunately, these are gonna have to be huge programmes. And the thing about huge programmes is that they can be hugely expensive and not necessarily very effective. We need to get this right. We need to get these education interventions and these training interventions done in the most efficient and effective way. And that’s where we can learn from other countries that do at least some things better, some things worse, we’re all learning from each other. And this project which brought in, you know, Norway, which has a pretty effective system of education and training right across the board, not just for those going to university, which is where we tend to focus. And France, which has, again, a very different system. So we can learn, we can learn from that. But yeah, I see a long impact of COVID, not just long COVID. But it’s hidden a bit at the moment, by the way, because of the uptick in the economy. You know, there’s quite a demand for certain types of jobs, as you’d expect, when there’s, you know, we’re coming out of a big, big recession like that, but I’m pretty sure that that’s hiding these big losses, they will turn up over time. So yeah, there’s, there’s a big hunger for this. We’re feeding a lot of a huge amount and working a lot with Department of Education here with the Treasury on what what should be done with other policy groups. And similarly in Norway, and France.

Christine Garrington  16:08 

Now, I know we’ve talked about the labour market a bit, but I wonder whether there’s anything else that you really would like to stress about that side of things, because this was a major part of your work?

Richard Blundell  16:19 

We had to invent things on the hoof and everyone was involved in that the furloughs remember, the furlough system didn’t exist. In fact, in the UK, and in many other economies, we’ve not, we’ve not been particularly good at providing general what one might call social insurance. That is, if people fall on hard times get reduced earnings, you know, do we make up the difference? At least in the in the shortish run, we don’t particularly do that very well, in the UK, we target very low incomes. We have a very targeted universal credit and benefit system. So it does prop up incomes at the bottom. And it does that actually quite well. Not always administratively perfectly, but it does it. But if you look at someone who’s on a kind of lower middle income, which is the group that really was hit during COVID, there’s very little support for them. Universal credit doesn’t do a great job, it just doesn’t replace their incomes – the furlough system did it replaced 80% of their income. And, and it was very successful in doing that, to the extent that as I said, you know, income falls and inequality increases didn’t happen in the way they often do during recessions. So in that sense, these policies have been very successful. On the downside, you know, they’re the things I mentioned, they’ve been very good at short run income support, at least for for many groups. But they’ve not been very good yet at addressing these losses in, in human capital investments. And work is about two things. It’s about earning money today. And it’s about in investing in skills that will earn you even more, or give you a better career profile, at least in the future. And it’s those longer term investments that I feel, or a fear of being really left to one side.

Christine Garrington  18:17 

I wonder whether you’ve seen anything that relates to how these inequalities manifest in respect of where people live, where they come from, is there something around place that’s quite important as well?

Richard Blundell  18:28 

We kind of knew there were geographical differences and differences by family background, it just, you know, we can see that in workings of our society. But I didn’t realise how big they were. And I think it’s been quite a shock to us. It’s not surprising, you know, that the emphasis now is on levelling up, at least it’s suggested it is in education is very important. What we found in this research, you know, looking at how well people do at school, and then into university, if they go there, and then into work is really striking, you know, some areas of the UK, for example, and this would be true in other economies as well, by the way, very few children actually make it to university. Take areas like Grimsby or Skegness those kinds of places we almost think of as left behind communities, children just don’t do so well. And not only that, if they do manage to get into higher education, they often don’t return to those communities. So those communities, once you look at people in work that just have many, many fewer people with higher education qualifications and skills to other areas. Let’s call them the thriving areas, many of which are in the southeast or in the more successful cities. And these differences are really important because they’re having huge impacts in the way people think about their well being levelling up political discourse.

Christine Garrington  20:07 

You talked earlier a bit about their fabulous data in Norway that you had available to you. But we’ve also got some great data here in the UK, haven’t we, particularly when it comes to tracking young people through education?

Richard Blundell  20:20 

We have the National Pupil database that follows all children through school, through higher education, or through their education and training and into work right up to about the age of 28/29 now. So we’re, and that will go on. So this is a remarkable, a remarkable dataset of the kind that you would typically think of finding only in a Scandinavian country. So this has allowed us to do these differences. And we can look at two children doing exactly the same courses in the same university, and just look at the differences of outcome by parental background and they’re still there, they’re still quite important. So parental background really matters. But so does course choices and university choices. These things, I guess we knew that have a big impact. All these things that people are doing through their their education, and early working lives and at university have a long lasting impact. And many of the differences you can take back to geography, and parental background, and the early education investments. This is really providing a real detail in what’s driving the inequalities that we see at least in working, working careers.

Christine Garrington  21:47 

Yeah, on that note, I’d like to put a final question to you really about, you know, for those interested who in tackling inequality, obviously, including yourself and your fellow researchers, the wonderful team that you’ve talked about there. But for those who have responsibility for creating interventions through policy or practice, are there any essential takeaways, implications or recommendations for your project that you’d like to share?

Richard Blundell  22:11 

If there’s something we’re going to really have to address the in the UK and elsewhere it’s these geographic divides. It’s what is creating a lot of the political turmoil, I think, whether it be almost in any elections, we’ve seen the left behind areas. You know, the evidence is clear, these geographical divides, by socioeconomic background, and by areas are really important and long lasting. And it’s really up to us to figure out the best ways now, to address them as quickly as possible. They’ve been exacerbated through COVID and so they become even more urgent, I think, in the policy debate.

Christine Garrington  22:56 

And I guess my final final question, is there something specific that we should be focusing on?

Richard Blundell  23:03 

There’s a lot, but let me just pick on one, it’s a kind of old topic, it’s the it’s the point about good jobs. You can have successful interventions for people who come from, you know, backgrounds or haven’t been quite successful at education investments, you can make better choices during education. And we’ve seen how, with the data and work we’ve been doing, how that can be improved. But it’s really the match of the skills, the firms and the kind of work related nature of these training investments that’s so important. And what we have learned here is that, you know, small interventions on one aspect of this are not going to solve the problems. So you can think of the example of the, of just providing a job. What we’ve seen here is that just providing a job, say, Amazon warehouse job is not really going to help much with career profiles, you really need to match workers, develop their skills, and bring the right kind of firms that can enhance career profiles into these more left behind deprived areas. If we can get that to work, then there’s great hope that we can do something for the careers and wage profiles of people who’ve been doing rather less well than we’d like in society.

Christine Garrington  24:39 

Thanks to Richard Blundell for joining us for this episode of the DIAL podcast. You can find out more on the DIAL website at dynamicsofinequality.org and also on the IFS website at ifs.org.uk. Much of the work of Richard and his colleagues has also fed into the Deaton Review on inequality so do take a look there as well. We hope you enjoyed this episode, which is produced and presented by Chris Garrington of Research Podcasts.

‘Queer Millennials’ and ‘Gay Boomers’: Rethinking the Generational Narrative for LGBTQI+ Lives.

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By Matthew Hall, Research Fellow with CILIA-LGBTQI+ at the University of Surrey

The media, policymakers and marketing organisations all love their generational labels. Over the last decade all-to-familiar terms like ‘Boomer’, ‘Millennial’, ‘Gen X, and now ‘Zoomer’, have come into popular use, narrowly classifying vast groups of people who share their formative years during the same approximate socio-historical period.

Recently, the credibility and use of these classifications have been rightly scrutinised. This is largely due to their non-empirical origins in consumer marketing strategies, their use as a political smokescreen and their tendency to overstate intergenerational conflict and pit social groups against one another.

Sensationalist and provocative headlines such as “Why millennials’ distaste for Baby Boomers is justified”, “Our Weak, Fragile Millennials” and “Boomer v broke: why the young should be more angry with older generations” have become commonplace within a time when swathes of mainstream news outlets have become caught in a tide of propagating the so-called ‘Culture Wars’.

However, one equally relevant, yet often overlooked, concern is that these narratives tend to overstate shared generational experiences within each birth cohort. They disregard ways in which historically marginalised groups of people, such as those identifying as lesbian, gay, bisexual, trans, queer or intersex (LGBTQI+), may share completely different generational experiences, marked by completely different formative historical, social and political developments.

Our research conducted over the last three and a half years, as part of the CILIA LGBTQI+ (Comparing Intersectional Life Course Inequalties amongst LGBTQI+ Citizens in Four European Countries) project, highlights just how poor these marketing classifications are for understanding LGBTQI+ lives. The project undertook extensive lifecourse interviews with 48 LBGTQI+ citizens within England from an array of ages, social and regional backgrounds, ethnic identities, and dis/abilities. The results suggest that whilst there are some distinctive generational differences between those identifying as LGBTQI+, these do not map neatly at all upon traditional frameworks.

Firstly, socio-historical developments are often used to define generational cohorts – for instance, a dramatic population spike and increase in living standards typically marks the Baby Boomer generation. However, for our respondents, these were set against more LGBTQI+-specific social, historical, and political developments.

For example, the Stonewall riots of 1969 and subsequent decriminalisation of homosexuality across Western countries during the late 1960s and early 70s, as well as the introduction of civil partnerships and equal marriage, appeared far more formative for generations of LGBTQI+ individuals. Likewise, less-progressive developments such as Section 28 – restricting any promotion of homosexuality as a ‘pretended family relationship’ by UK local authorities (including schools) from 1988 until 2000 (in Scotland) and 2003 (in England and Wales) – also featured heavily as important generational markers with longstanding legacies.

Meanwhile, other events more specific to transgender and queer individuals, such as the Gender Recognition Act (2004) in the UK, were equally formative for their queer generational identifications. Even ostensibly universal socio-historical developments, such as the information age and widespread digital literacy, can be experienced differently based on one’s position and needs within society. For example, fairly recent concerns about the impact of digital technologies on the dating economy for Millennials and Zoomers, appears to ignore many gay men and women’s reliance on technologies (and previously newspaper ‘personals’) for the best half of a century. Perhaps ‘Gay Boomers’ were just Millennials ahead of their time?

Secondly, our respondents’ queer generational identifications did not always correspond with neat and discrete birth cohorts, nor shared turning of adolescence, as has come to be expected of generational groupings. When discussing the introduction of civil partnerships in 2004, a gay trans male respondent, who had been working in the armed forces, puts it aptly: “[…] civil partnerships came in, and you were allowed to marry and get married quarters and all that sort of stuff […] So, there was definitely changes, and people were a bit more open about it. But that didn’t really affect me because I wasn’t same sex attracted, and I didn’t understand the gender thing then. Like I say, I didn’t understand it at that point, so…”.

Cases like these raise the question as to whether queer generational cohorts may be better framed by when individuals first become self-aware of their same-sex attraction, begin questioning their gender identity, ‘come out’ to others, begin identifying with a wider LGBTQI+ community or when they enter ‘queer spaces’ for the first time. A 50-year-old who has only recently begun to think about themselves in terms of sexual identity, for example, may share more formative experiences with much younger LGBTQI+ individuals than with their own peers. As such, queer generations could be understood as the mutual phasing between queer-specific socio-historical developments and a personal queer lifecourse.

Overall, despite more recent social change, many LGBTQI+ lives remain outside of what we might think of as normative generational experiences. It is important that pervasive and universalising narratives about generational difference do not further marginalise queer lives and render them invisible. How we effectively talk about generations should account for both the diversity of LGBTQI+ lives and varied experiences of social, historical and political developments.

King, A. and Hall, M.A. (forthcoming 2022) ‘Re-thinking Generations from a Queer Perspective: Insights and critical observations from the CILIA-LGBTQI+ Lives in England project’, in H. Kingstone and J. Bristow (Eds.) Studying Generations: multi-disciplinary perspectives, Bristol: Policy Press. The book is part of the Bristol Policy Press open access series.

Nature, nurture and our later life outcomes: new insights on inequality over the lifecourse

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In Episode 2 of Series 4 of the DIAL Podcast, we are in discussion with Professor Hans van Kippersluis from the Erasmus University in Rotterdam. Hans, Professor of Applied Economics, is the Principal Investigator on the DIAL project, Gene Environment Interplay in the Generation of Health and Education Inequalities, which has used innovative methods and data to explore the interplay between nature and nurture in generating health and education inequalities.

 

Transcript

Christine Garrington  0:00  
Welcome to DIAL, a podcast where we tune in to evidence on inequality over the life course. In series four, we’re looking at what’s been learned from some of the DIAL projects about how and when inequality manifests in our lives, and what its longer term consequences might be. For this second episode of the series, we’re delighted to be joined by Hans van Kippersluis, Professor of Applied Economics at the Erasmus University in Rotterdam. And Principal Investigator of the DIAL project, Gene Environment Interplay in the Generation of Health and Education Inequalities – put more simply nature versus nurture. So Hans, welcome to the podcast. And I wonder if you can start by talking us through what researchers working on this project have actually been looking into.

Hans van Kippersluis  0:42  
What we’ve been doing in this project is essentially incorporating the recent availability of genetic data into social science and most prominently economic analysis. And so most of our work has focused on the interplay between genes and the environment. So in the introduction, you mentioned nature versus nurture, but actually more accurately, what we’re doing is nature and nurture jointly into how they shape essentially education and health outcomes. And I think this is also the main innovation of our project, because biologists have studied nature before; social scientists have  of course, extensively studied nurture, but not many have studied the interplay, the interaction between the two. And I think this was sort of the main innovation for why we got the funding some five years ago. And so what we have done is mostly studying this interplay. But along the way, we have also made some methodological contributions to a field which is very new. Then we’ve also used genetic data to test all their theories, and also, I think, enrich the framework of equality of opportunity.

Christine Garrington  1:35  
Yeah, fantastic project. And as you’ve just said, you’ve made unprecedented use of genomic as well as survey data in the research, tell us a bit more about the information that you’ve been able to access? And how you’ve been able to use it?

Hans van Kippersluis 1:47  
Yeah, sure. So the interesting thing is that more and more social science datasets, so data sets that have been traditionally used by social scientists, and these are mostly extensive surveys,  are now collecting DNA information from their respondents. And this is often from blood or saliva. And what they did is basically, so more than 99% of DNA is the same across human beings. And so what we are using is only this remaining less than 1% of the variation. And these are called snips. And snips are points of your DNA that differ across human beings. And there’s roughly 1 million of them. And so what we do, basically also other people have done is sort of aggregating these tiny effect sizes into an index. And this is called the polygenic index. And this is telling us something about your genetic predisposition towards a certain outcome. And this is quite interesting, because this data, this new variable, essentially can be added to existing datasets. And so we have a wealth of information that has been collected in the past on surveys on existing data. And then we simply add one indicator, one new variable. This is telling us something about people’s genetic predisposition. And just to be clear, this is not like a deterministic variable. It also exhibits quite a bit of measurement error and noise. But at the group level, and that’s what we have been doing is it sort of does tell us something about your genetic predisposition, and it can help us understand how certain life outcomes like education, like health, are shaped by the interplay between your genetic predisposition and your environment.

Christine Garrington  3:07  
Indeed, let’s talk a little bit now then about some of the research findings. And you know, what’s come out of this now, one piece of research we’ve spoken about this actually, in an earlier podcast episode, actually drew links between mothers smoking in pregnancy and their baby’s birth weight. I wonder if you can just sort of summarise that for you what actually came out of that what we learned

Hans van Kippersluis 3:28  
this was work with with my PG students, Rita Dias Pereira and colleague Cornelius Rietveld . And for birthweight we knew that maternal smoking is one of the key environmental risk factors. And we also knew from genetic studies that genes matter in determining your birth weight. And so what we did here was essentially looking at the interaction between the two. So can higher polygenic indices protect against maternal smoking? And the answer, unfortunately, perhaps was no, in the sense that we found very, very little interaction between genes and the environmental exposure of maternal smoking. So it seems that both matter, but there doesn’t seem to be any meaningful interaction between the two. So that was, to some extent surprising, but on the other hand, also perhaps logical in the sense that maternal smoking is apparently such a devastating environmental exposure that even higher genetic predisposition cannot protect you from this.

Christine Garrington  4:16  
Yeah, really interesting. And anybody who’s interested in that can listen to Rita actually discussing that in series three, Episode Seven, of our DIAL podcast called Mums Who Smoke and their Baby’s Birthweight. So do check that out if you’re interested to know a little bit more about what Rita and all of the all of your colleagues did. Now, there have been some interesting findings Hans from the project around the role of genes in a child’s education and specifically around parental investments. I wonder if you can explain a bit more about what you were looking to understand there. 

Hans van Kippersluis 4:50  
Yes, yeah, so this is one of my favourites studies. It’s joint work. Also with another PG student   Muslimova and my colleagues Stephanie von Hinke, Cornelius Rietveld and Fleur Maddens. And the starting point there was actually a theory of human capital formation from economics. And it dates back all the way to the work of Nobel laureate Gary Becker. And one of the crucial assumptions in that model is that parental investments are complementary to your genetic endowments. And this assumption is actually very hard to test because often we do not have a good measure of endowments. And if we do, it may already be contaminated by parental investment. So many people, for example, use birth weights. But of course, well as we just learned, maternal smoking may have a large effect on your birth weight, so it’s not fully free of your parents’ behaviour. And the other thing is that your parental investments often respond to endowments. So if you have a child with specific needs, of course, parents respond to this. So the problem of testing this assumption is that endowments and investments are actually always very closely entangled. And that makes it very hard to test whether they are complementary or not. So what we did here was using one’s genetic endowment, and that is actually has a very nice property and that it’s fixed at conception, so it cannot be affected by your parental investments. And what we did was using the child’s birth order to proxy for parental investments. So what we know from earlier studies is that firstborns tend to get more parental attentions on average than later points. So this is one after all, because they have undivided attention until the arrival of later borns. And this extra parental investment is actually independent of your endowments. It simply derives from the fact that you have more time if you have one child as opposed to multiple children. So what we did in this study is looking within families comparing siblings that were first born to later borns, and then further analysing whether this firstborn advantage was stronger for firstborn siblings who randomly inherited the higher polygenic index for educatio. I think this was a nice, very unique setting to test this theoretical assumption that parental investments are complements to genetic endowment.

Christine Garrington  6:45  
What did you find here? Then what do we learn about the role of genetics in affording in affording certain children advantages later on in life?

Hans van Kippersluis 6:53  
So what we found was that indeed, the firstborn effect seems to be stronger for siblings who randomly inherited higher polygenic indices. And I think this is evidence in favour of this theoretical assumption of complementarity between endowments and investments. And it also means that your genetic predisposition cannot just give you a direct advantage. But it also means that this advantage may be kind of amplified by your parental or your teacher investments. And this complementarity, I think also suggests once again, that for disadvantaged children, so the other side of the coin, we need to start very, very early and follow up these early investments also with data investments to make them as productive as possible.

Christine Garrington  7:29  
So Hans, some fascinating research and findings. I wonder if there’s been a standout or surprising finding for you from the project. 

Hans van Kippersluis 7:36  
I  think methodologically, what we’ve learned is that there’s still a world to explore in terms of using genetic data in social science, because what we have seen is that polygenic indices can be a great tool to improve our understanding of the things we just talked about. But I think the way we use these polygenic indices, are shall I put this sort of a bit naive, in some sense, because what we do is we first construct a score or an index by regressing an outcome on all of these 1 million individual genetic variants. And as you can imagine, if you do these 1 million regressions, then it will be a lot of noise in  these coefficients, and these estimates also come with some uncertainty. And what is surprising to me, what I’ve learned is that many researchers simply sort of seek to use this polygenic index as if it’s some kind of a transferable and deterministic index. And there’s hardly any account in the literature on the uncertainty in this index. And I think what we have done in one paper is actually showing how this uncertainty is sort of leading to different conclusions, because what we did is basically looking at the polygenic index for cardiovascular disease. And in cardiovascular disease, more and more people are using these polygenic indices, this genetic data for personalised decisions regarding, for example, the use of statins. And what we did was sort of constructing six different polygenic indices using different discovery sample using different methods of constructing this polygenic index. And what was fascinating and actually maybe astonishing to see is that only 6% of the individuals are in the top quintile of the polygenic indices, if you look across these six different ways of constructing the same polygenic index. And I think this is fascinating, because it shows that even though polygenic indices are now increasingly being used, apparently it matters a great deal about how you construct these things. And this is one thing we have shown, I think this is quite remarkable, and also an important methodological contribution.

Christine Garrington  9:19  
A really important contribution to how this research might develop in the future. Right, absolutely. And then just finally, Hans, I wonder what this all of this work tells us about the interplay between genes in our environment, or, as we’ve talked about nature and nurture, not nature versus nurture, in better understanding and in tackling inequality.

Hans van Kippersluis 9:41  
So it’s very hard, I think, to give sort of direct policy leads or implications, but there’s a few leads. One thing is that I think we need to start early. We knew already that inequalities arise early in life. And I think this focus on genetics gives us yet another clue that it’s very important to start early. And also because of the work I mentioned about complementarities, it’s very clear that later investments are more effective if the person has had already more investments early in their life. So that’s clearly one more general policy implication, I think. And I think our work is also showing how sort of genes and environment shaping jointly inequalities. And I think this has important implications for the discussions about equality of opportunity. I mean, if you look at politicians across the entire political spectrum, everybody seems to be agreeing that equality of opportunity is a great thing, and that your health and your income should not depend on your parental background. But let me ask two questions about this. One is, what about your genes? There’s hardly any discussion about whether inequalities that are deriving from genetic advantages or disadvantages are fair or not. And what we’ve also shown in this project is that parental background seems to reinforce genetic advantages. So even if you believe that parental background should not be leading to inequalities and your genes may, then how do you treat the interaction between the two? So I think we should have a clear discussion here a societal discussion about what is fair here. And I think that’s why our research is very important, because 30 years studies have already shown that people’s preferences for redistribution, for example, depends strongly on whether they perceive inequalities as fair or unfair. So I don’t think we are political activists here. But I do think that showing how genes and the environment jointly shape outcomes such as health, education, income, but really help people to make up their own mind as to what they regard as fair or unfair inequalities.

Christine Garrington  11:23  
Hans thank you very much some some big advances here. But still some big questions to answer, I guess is the is the summary but fascinating work and thank you for taking time to share it with us. So finally, thanks to Hans van Kippersluis  for discussing the findings and implications of DIAL dial project Gene Environment Interplay in the Generation of Health and Education Inequalities. You can find out more about this and other dial research on the website at www.dynamicsofinequality.org. We hope you enjoyed this episode, which is produced and presented by me Chris Garrington of Research Podcasts. And don’t forget to subscribe wherever you find your podcasts to access earlier and forthcoming episodes.

 

 

Ability grouping: does it affect UK primary school pupils’ enjoyment of Maths and English?

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In Episode 16 of Series 3 of the DIAL Podcast we’re discussing ability grouping in UK primary schools and how it affects children’s enjoyment of certain subjects. Our guest today is Queralt Capsada-Munsech from the University of Glasgow, who as part of DIAL’s LIFETRACK project has been looking at primary school children’s enjoyment of English and Maths at age seven, and later at age 11 to see whether ability grouping positively or negatively impacts their enjoyment of those subjects.

 

Does ability grouping affect UK primary school pupils’ enjoyment of Maths and English? is research by Vikki Boliver and Queralt Capsada-Munsech, and is published in Research in Social Stratification and Mobility.

 

Transcript Christine Garrington  0:00 

Welcome to DIAL a podcast where we tune into evidence on inequality over the lifecourse. In this episode, we’re talking about how grouping children by ability at school affects their enjoyment of certain subjects. Our guest is Queralt Capsada-Munsech from the University of Glasgow, who as part of DIAL’s Life Track project has been looking at primary school children’s enjoyment of English and Maths at age seven, and later at age 11 to see whether ability grouping positively or negatively impacts their enjoyment of those subjects.  

Queralt Capsada-Munsech  0:29 

There are people who are advocates of ability grouping, and their main argument is usually that high ability pupils improve their attainment, while there is no detriment in lower ability, once their academic performance mainly. But you know, the opponents in the general debate of ability grouping, what they say is that high ability students only do marginally better when they are grouped with a, yeah with a group of students that are homogenous to them. While lower ability ones are the ones that are substantially worse off from these ability grouping. And what we have seen mostly in previous research is that there is the main mechanism that we call that is the self-fulfilling prophecy of low attainment that you know, because you are grouping the low ability grouping and people, and students are aware of that. So they just know that they are not doing as well. And they continue to do more poorly while those that are in the high ability grouping, so they think better of themselves. And that leads them to better academic achievement. And usually, the way it has been measured has been based on what we call academic self-concept that basically is asking students, how good are you at maths, at English or at school in general? 

Christine Garrington  1:48 

So talk us through some of the policy context here – ability grouping as an education policy.

Queralt Capsada-Munsech   1:53 

In the gaze of the UK ability grouping was encouraged by the New Labour governments in the 1990s and in the 2000s. And the main idea was that it would raise standards generally, with higher grades in brightest kids in particular, that was something that was quite influential in past decades. And that had clearly an effect in policy because, you know, the prevalence of grouping practices still remains in place. And it increased quite a lot for the past few years. And we see in the UK, even that ability grouping is becoming increasingly common in early years, you know, at ages three and four and even in Key Stage One ages five to seven was, which was something that we didn’t see in the past. While you know, in the 1990s, there were fewer than 3% of primary schools who reported that they were streaming students by 2008 16% of seven year olds, were being streamed by ability for all subjects and 26% were being taught in ability sets for English and Math so that’s quite the change.

Christine Garrington  3:01 

What was it for you that you wanted to look at exactly about the way in which children are grouped by ability at primary school? What was it you wanted to look at and why?

Queralt Capsada-Munsech   3:10 

Empirical evidence what did show us that at least for the UK suggested that there weren’t many benefits of practising ability grouping. Mainly at the secondary level was most of the studies and it did little to raise the school’s standards, and it was more detrimental for socioeconomically disadvantaged students. And it was through that, you know, measured ability at the early ages is predictive of ability group placement. But so were also a lot of socio-economic indicators. So there were also a few mechanisms, you know, that people were looking at. So for instance, teaching practices or teaching learning environments. So the reason why this happened, that it was more detrimental for some instruments to the individual than others because teacher quality is correlated with ability grouping, meaning that mainly you know, teachers that are more qualified, more experienced tend to be a placed with a high ability grouping, while less experienced in the lower one, which maybe should be the opposite. But also because of students self-perception, so they internalise these labels of consequences for their self-esteem. We could see that there were many studies that had been undertaken at secondary level, but not that many at the primary level. So what we want to talk that was that academic enjoyment so that it would be different question like, how much have you enjoyed reading or doing number work or English or maths more, more precisely.

Christine Garrington  4:45 

So why was it important to look at how much children enjoyed subjects? What was it about that particular concept or idea that was was important in your research?

Queralt Capsada-Munsech  4:56 

Academic self-concept, the question how good you are at? It’s informed by students awareness of their test scores or their ability group placement, but it’s also a relational construct, you know how good you are compared to the rest of your pupils in your group or in your class. While academic enjoyment, we thought that it was a more an intrinsic motivation and is more of a personal preference, you know, you might like or dislike reading, even if you are very good at English or not. So it’s more of an independent one. And it’s less relational. So it’s not that you enjoy reading compared to your peers, it’s more they do you enjoy it more? Yes or no, or to what extent.

Christine Garrington  5:39 

So talk us through what you actually did.

Queralt Capsada-Munsech  5:40 

The question that we wanted to look at was looking okay, for instance in primary school at age seven, how much did pupils enjoy Maths or English and school in general? And then to look later on, you know, at age 11, before going to secondary school, how much they enjoy again, Maths, English and school in general. And to see to what extent it had changed from age seven to 11, depending on the ability group that they were in. So we were expecting that, okay, maybe your academic enjoyment of Maths might be different to those that are in the top or bottom ability group. But our hypothesis was that, theoretically, there is no reason for people to change how much they enjoy or not enjoy reading, for instance, from age seven to eleven, regardless of the relative group they are in.

Christine Garrington  6:38 

So where did you get your information from? And why was it a good source of data to help you address these questions that you were interested in?

Queralt Capsada-Munsech  6:47 

So we use one of the cohort studies that are called, that its the Millennium Cohort Study. But basically, it’s a longitudinal survey that follows about 19,000 people born in the UK in 2000 and 2002, approximately. You have the same people, the same individuals, and they have been surveyed throughout their lifetime. What it was important for us in order to make this comparison is that we would have that data about the same individuals, but also that we would have like similar questions at two points in time. So in this case, for instance, because if we want to check if there is a change or not in academic enjoyment, from age seven to eleven, we needed to have this very same question of academic enjoyment at those two points in times. And in addition, obviously, we had some information about ability grouping. And so.

Christine Garrington  7:41 

And when it came to this question of whether being grouped by ability did in some way influence whether a child liked a subject or not, what did you see?

Queralt Capsada-Munsech  7:50 

If we start just with the descriptive statistics, we already saw, obviously, that there were some differences. So maybe it’s worth starting by saying that, you know, overall, there was quite a lot of academic enjoyment among students. So the majority of them like a lot Maths, or reading or English, and most of the students so in most cases above 35%, were placed in the high ability one. And students that were placing the low ability one was usually smaller numbers like below 20%. That already gave us an idea of the distribution. But more importantly, yeah, when just looking at some bivariate descriptive statistics, we could see that those that are in high ability groups, tended to enjoy more Maths, for instance, than those that were placed in the low ability group. And that has stayed like, quiet similar when we look at it both at age seven, and eleven. So that’s something that we could see just from the descriptive statistics.

Christine Garrington  9:00 

Now you had some very specific findings around maths didn’t you? I wonder if you can talk us through what it was exactly that you saw there.

Queralt Capsada-Munsech  9:07 

Being placed in a lower rather than a high ability group at age seven, depress the probability of coming to enjoy continuing to enjoy or even to increasing your enjoyment of Maths by age eleven. And that stayed like this way even after controlling for you know, students measured ability in Maths, sex and social background at age seven. So we found that yeah, really being placed in ability grouping has an influence in your academic enjoyment of Maths. However, we didn’t find quite the same for English and school in general. So, you know, there was this tendency, but once we control for socio-demographic and socio-economic variables, the results weren’t statistically significant. So, we would say that, it’s mainly for maths that we would find some differences or some effects of ability grouping.

Christine Garrington  10:05 

Yeah, really, really interesting, though, and I’m gonna take you right back to the beginning of our conversation when you were saying, you know, just how much policy interest there is in this area around, specifically around education policy. What do you feel that we must take away from from this piece of work? And do you think there are any key takeaways specifically for education policy and practice?

Queralt Capsada-Munsech  10:25 

Overall, I would say that our findings are very much in line with much of the existing research, which indicates that ability grouping is detrimental to those judged to be of lower ability. And at least we find it in relation to Math. So it’s something to be worried about. And ability grouping by measured ability has a negative influence for those that label as that, as lower ability. And we can see now that it’s for both for academic self concept this idea of how good I am at maths, but also for academic enjoyment is like how much do you enjoy maths? So that can be detrimental if you are put in a lower ability group.

Christine Garrington  11:08 

Okay, so that’s the key sort of takeaway message, but what recommendations might you have in this area then?

Queralt Capsada-Munsech  11:14 

We are aware that it’s difficult to be in a classroom and that we don’t mean that because they are doing ability grouping they are bad teachers or bad educators. But yeah, I guess it’s something that we’ll have to continue exploring. And while some parts of ability grouping might work for some students or for some teachers overall, I wouldn’t encourage the policy of ability grouping, at least in the UK.

Christine Garrington  11:45 

Does ability grouping affect UK primary school pupils’ enjoyment of Maths and English? is research by Vikki Boliver and Queralt Capsada-Munsech, and is published in Research in Social Stratification and Mobility. Thanks for listening to this episode of the DIAL podcast, which was presented by me Chris Garrington of Research Podcasts. You can find out more about all the DIAL projects at dynamicsofinequality.org.

Extending working life: what needs to change to make policies work?

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In Episode 13 of Series 3 of the DIAL Podcast, Professor Nicky LeFeuvre from the University of Lausanne discusses findings from DIAL’s DAISIE project (Dynamics of Accumulated Inequalities for Seniors in Employment, which has been exploring the gendered impacts of policies aimed at extending working life. 

 

 

Transcript

Christine Garrington  0:00  

Welcome to DIAL a podcast where we tune in to evidence on inequality over the life course. In series three we’re discussing emerging findings from DIAL research. For this episode, we’re talking to Nicky LeFeuvre from the University of Lausanne about findings from DIAL’s DAISIE project, which has been exploring the gendered impacts of policies aimed at extending working life. I started by asking her to talk about the policy backdrop to the research

Nicky LeFeuvre  0:25  

Over the last 10 or 12 years there has been this quite widespread consensus about the need to extend working lives and this consensus has existed at national levels, you know, government initiatives, also across international organisations like the OECD, and the dominant narrative has been based on kind of two or three basic assumptions this inevitability of increasing full pension age, to keep pace with life expectancy. Questions about the sustainability of our existing welfare systems, pension systems, and also a discourse around personal individual choice and personal responsibility. You know, this idea fielded by the OECD that people are increasingly free to construct their own biographies and so there has been this sort of focus on rewardingly later retirement or penalising early retirement in actual fact, and relatively little concern for the inherent, I would say dynamics of accumulated inequality that lie behind this policy objective. And so this was very much something that we wanted to address in the in the DAISIE project and of course, it was completely in line with the overall aims of the DIAL programme. And our sort of particular focus was to say okay, individuals are receiving this policy discourse which is relatively homogenous across the board, and what are they doing with it? Okay, how is this really impacting on their lives? And notably, how is this being interpreted and acted upon by their employers, by the occupations that they’re in? And to what extent their past life experiences, particularly their employment histories, and the way that their past employment has been articulated with their care duties if they happen to be women? How is all this coming together to produce a, what we presume would be quite a varied experience of what extended working life actually means for individuals and for organisations, to be honest.

Christine Garrington  2:28  

Yeah, now that brings me very nicely on to my next question, actually, because in recent years, policymakers have really recognised the need, the importance of taking, you know what we talked about as a life course approach to this issue, something you’ve just hinted at, and, and this is something that really resonates with your research. Tell us why this is so important.

Nicky LeFeuvre  2:45  

I think there has been it’s been slow to come, but it is there, I would say, perhaps emerging recognition that rather than full-fledged recognition at the moment of the need to avoid the accumulation of individual disadvantages over the life course in terms of extended working life and this is related to health issues. You know, we do not age equally, people are not as equally able to envisage extending their working lives and they’re also not as equally motivated to do so. So that this recognition has come or is coming slowly. And in the most recent OECD document, for example, which is entitled working better with age, there is this idea that perhaps there has been too, too much of focus on these financial incentives. So making it you know, financially penalising people for leaving the labour market too early with the too being in speech marks and the need to address what is often termed demand side barriers to extended working life. And I really do like this this quote from the OECD that now recognises the fact that we need workers who want to work longer and also employers who want to employ them, and I think this is really the crux of the issue here. You know, there’s there is this emerging shift towards the recognition that this is not just about individual choice that these extended work, working life issues are related to a whole host of structural constraints and opportunities. And this is really what we were wanting to do in the DAISIE project. It does really resonate with us because we were arguing that it is important to look beyond macro level policies and to study exactly how they are being interpreted how they’re being applied by employers and by individuals according to the you know, particular histories and their particular circumstances. So yes, this this really was very exciting for us, because we did see this policy shift on the horizon, as we were, you know, preparing to to address those issues in the project.

Christine Garrington  4:58  

Yeah, that’s great. That’s a really neat summary of the backdrop to this work. So let’s move on then. And if you could give us an outline an overview of what the DAISIE project has actually been doing?

Nicky LeFeuvre  5:12  

We set out to adopt what we called a multi-level research design, because we wanted to integrate as I just said, the policy background obviously you can’t completely abstract this out of your reasoning. So we did want to look at the similarities or the variations in these extended working life policies across countries and see whether there were some best practices or some policy recommendations that were being really adopted across the board and others that were perhaps, you know, more localised in certain contexts or certain countries. So we wanted this macro level analysis of working life practices, but also the objectives that were that were being defined by by different policymakers, either at the EU level or in particular countries. We also wanted to sort of micro socio sort of individual analysis. So we wanted to explore particularly the well-being, the health and the work life balance issues that older workers were facing. So we knew we wanted to use biographical accounts we wanted life history interviews, we wanted really to be able to get at the experiences of older workers, which are kind of slightly invisiblised in the policy documents. And in order to integrate those two levels or those two scales of analysis, we needed the mezzo level which is the sort of intermediate level the occupational and the organisational level, as I said previously, often sort of left out of existing analyses in order to look at what was happening in different employment sectors. And therefore, we adopted this case study method where we selected three different occupations or three different sectors: finance, health and transport. And with our sort of cross-national comparative perspective, we then looked at these three sectors that we could compare both within with other sectors within the same country and across countries to see whether these extended working-like issues were being addressed and implemented to the same extent and in the same ways in different sectors and in different countries.

Christine Garrington  7:20  

Such an ambitious programme of research and also at a very challenging time with everything going on with COVID.

Nicky LeFeuvre  7:27  

Carrying out empirical fieldwork during the COVID pandemic was a little bit challenging for us. But what we’ve actually done is cross country and cross sector comparative analysis using case studies that we carried out in the five countries and in three different sectors as I said, health sector, health sector, finance and transport. We have collected a huge amounts of empirical data, qualitative data to a large extent. We’ve used biographical interviews with older workers, male and female workers, and we have about 500 of these interviews across the board. We have of course prepared English language summaries of these interviews because we have to now compare them comparatively and they were not carried out all in the same language. So as you can imagine, this is slightly challenging. And we’ve also done about 60 expert interviews and we met up in each country and each company we went into we met with the human resource managers, we we talked to line managers, trade union representatives, and so on, to get a feel of how ageing at work was being framed in these different institutions. And we have a collection of about 500 life course grids, which enable us to kind of visualise the life course events that led up to people ageing at work, if you like. So the family events, the residential ability events and of course, their employment histories that frame the ways in which they are now thinking about whether they’re going to retire at what age they want to retire, whether they could envisage extending their working lives and under what circumstances. So quite quite a wealth of data. We’re starting to get to grips with along with a secondary statistical analysis that we did previous to the to the case studies.

Christine Garrington  9:12  

So what are the sort of the first things, most the things that you did early on Nicky I think was to map older workers employment trends, what were the key things to emerge there?

Nicky LeFeuvre  9:22  

We were really interested in looking at cross national differences here. And of course, we do see and that’s not you know, that’s unsurprising given given the convergence in the policy measures that I mentioned earlier. We do see a convergence of the employment rates, basically in the 55 to 64 year age group, I would say. So if we look at the data over the past 10 years, the differences between countries have been reduced. We still have about, you know, up to 20% difference in employment rates at 55 to 64. So this is not negligible at all, but the differences across countries are becoming smaller. So there’s there’s this idea of a kind of norm of extended working life, but remember, I’m talking about 55 to 64 here so what we’re really looking at is actually encouraging people to work up to full retirement age and very little actually about encouraging people to work beyond full retirement age. I think that’s a you know, quite an important point to remember when we’re talking about ageing at work. In actual fact, we’re looking at 55 to 64. And that’s where you know that the changes have been over recent years. The other important thing to remember is that there has been a slight reduction in the gender gap in retirement timing. Over the same period, women do continue to retire earlier than than their male counterparts in in almost all of the countries we’ve looked at. And of course, that’s why they they are to a certain extent the prime targets of many extended working life policies in recent years. But the the gap in the retirement, the actual retirement age of men and women has been falling. And that’s also very interesting because it means that the gender dimension of what it means to age at work becomes particularly important to look at in detail.

Christine Garrington  11:18  

So although as you said, you looked across a number of employment sectors, we’re going to be focusing in on the finance sector for our interview today. So my first question is why the finance sector?

Nicky LeFeuvre  11:28 

We were trying to decide which sectors to focus our case studies on. Several criteria were taken into consideration. One of them was of course the gender composition so we wanted sectors that was sort of contrasting in terms of the share of male and female workers. So healthcare was our sort of highly feminised sector transport our highly masculinised and finance our sort of gender balanced sector. Then we wanted sectors where the share of older workers was quite variable. And finance actually is the sector where we have the lowest share of older workers of the three sectors we looked at we have more or less depending on countries there is some variation around 20 to 30% of over 50 year olds in the finance sector, which is let’s to give an example half the rates of older workers in employment compared to the transport sector for example. So quite a low share of older workers in finance. And this is obviously related to the history of the of the sector of the occupation. Which is an occupation which has been downsizing and restructuring for the past 20 to 30 years. And where older workers have in most countries been targeted during these restructuring, downsizing operations and have often been offered various early retirement packages, or at least some older workers, mostly male managers, in fact, have been offered packages. And so the finance sector has kind of lopped off a whole generation. And this means that today, it is one of the sectors were that were older workers are relatively underrepresented. But it’s a sector that is now facing a number of challenges whether these rather generous early retirement packages have become financially non-viable, and where there’s also been some recognition that the accumulated experience of older workers, actually the banks actually needed that experience in order to face the new challenges. And so there is a shift in policy and this was also very interesting for us.

Christine Garrington  13:39  

Okay, let’s move on to some numbers then. What were you able to see in terms of the share or the proportions of the numbers of older workers in the finance sector?

Nicky LeFeuvre  13:47  

So, the moment we have a very, very small share of older workers, if I give you just you know, some figures for example, in in the Czech Republic, only 10% of men working in the finance sector are aged over 50 in 2019, but 20% of women in the Czech finance sector are aged over 50. So this is also interesting. It’s one of the rare sectors where, proportionally speaking, the share of older women, as compared to the whole female workforce is greater than the share of older men as part of the male workforce. And this is quite a rare configuration in sort of the European labour markets at the moment. So that was also something that we were very interested in exploring further.

Christine Garrington  14:31  

So let’s dig now into some of the rather wonderful data that you’ve been collecting, especially that qualitative data so when you asked workers about their experiences of being older employees in this sector, what were some of those key things that come out of that? Of what they told you?

Nicky LeFeuvre  14:46 

Well, perhaps I can, I can start with a rather eloquent quote from one of our Swiss interviewees, and I mean you have to take this against the backdrop of this very sort of systematic offloading of older workers over the past 30 years from the banking sector, and one of our interviewees said sort of almost in a whisper “ageing is something we just don’t talk about in this bank”. And this really I think, translates quite nicely how stigmatised the question of ageing is within the banking sector. We came across a lot of negative stereotypes about older workers. Older workers are systematically presented as lacking skills, being unable to keep up with the pace of organisational and technological change. And to a certain extent, this is of course the banks justifying their past age management policies which were almost entirely based on externalising ageing at work if you like, getting rid of their older workers, before they actually had to deal with ageing at work. And so in a lot of our interviews, ageing was seen as either something that could possibly enable one to leave the finance sector well before reaching retirement age. So you know showing that you were not able to adapt was seen as as a as a good reason to to leave early, or as something that was quite risky in that the bank could therefore consider you to be too old and and not flexible enough to face the challenges of this digital revolution that’s going on in in banking, and therefore you could be made redundant because you were not keeping up with the pace of things. So basically, there was this idea that age is something that we don’t talk about and older workers are a great pains to prove to their employers and to the themselves and to their colleagues, that they are actually not part of this terrible stigmatised group that one would call older workers.

Christine Garrington  16:56  

So some quite unexpected views expressed there about this whole issue of being an older worker, and what are the implications of that for any sort of age management policies that any business might want to put in place in this sector?

Nicky LeFeuvre  17:10  

Any kind of age management strategies that are adopted in this in this context, are destined to fail basically, because people refuse to be identified with a stigmatised group. That would be the target of you know, these age management policies, and therefore, even when there are minimal measures, I mean, we didn’t have a huge range of age management measures that we came across in our case study banks but most of them were related rather to the transition to retirement. So you know, accompanying workers in the transition to retirement, which we consider not really to be age management policies at all because this is you know, this is thinking about how to get rid of your older workers still, rather than thinking about what you do with them when they stay. But even in in those cases, there was a very, very low take up rate because nobody really wanted to run the risk of being identified as an older worker, because this was such a, you know a stigmatised category, and no one wanted to be demoted, or no one wanted to be encouraged to leave because they were part of that group.

Christine Garrington  18:13  

So Nicky, there seems to be a real disjunct – a real mismatch here between the sort of policy top level policy narrative that you outlined earlier and what you’re actually hearing on the ground from workers. You know, what, what’s your take on all of that? How does this chime with that policy narrative that you were outlining?

Nicky LeFeuvre  18:29  

Yes, so I mean, I think these results do, really do tell us that any serious attempt at extending the duration of our working lives requires the active involvement of employers in the business sector as a whole. It’s it’s quite incredible I think that we should find such a small share of older workers in a non-manual sector like banking, where the physical limitations to you know working longer in terms of health and well-being should in theory be far more limited than in the health sector or in the transport sector, but actually, we find the opposite. We find that finance, in finance we have a small share of older workers. So this really does confirm that postponing retirement is not only about health, it’s not only about financial incentives, and it is very much about how older workers are perceived within particular sectors, how they are treated by their employers, and particular I think in those parts of the job market that are looking to reduce staff costs that are looking to scale down their activities or who are confronted with technological change or organisational restructuring. Then, then we really do have to accompany I think companies in in looking at how they, how they frame the whole issue of, of ageing and working.

Christine Garrington  19:51  

Something I found quite extraordinary in in your research was that when you spoke to companies they expressed, expressed a reluctance or hesitation to introduce any age related policies for fear that this would somehow be seen as discriminatory in some way. What’s your take on that?

Nicky LeFeuvre  20:08  

We had a number of examples where HR managers line managers went to great lengths to explain to us that they couldn’t offer any positive support to their older workers because this would be seen to be discriminatory towards other groups of workers. And so we were kind of left a little bit speechless at this thinking, but what interpretation of equal opportunity is being developed here? And why is it that companies believe and they do apparently strongly believe that any accommodation of ageing in organisational structures in the way that work is shared  out or the way working time expectations or or shift work organisation – anything that would facilitate the experiences of older workers identified as a group with potentially some needs that are different to those of other age groups. This should not be seen as discrimination. This should be seen as equal opportunity policy, and it shouldn’t be seen as coming into conflict, for example, with gender equality policies or with parental support policies or whatever other objectives companies are seeking to meet. So I think, really applying or emphasising the need to think equal opportunities in an intersectional way looking at how gender and age and ethnic origin and disability and so on, interact across the life course and how employers can deal with these in innovative and creative and complex ways. I think this would be really something very useful because we were struck by this, this difficulty that that organisations were facing in thinking through what they could actually do to support their, their older workers.

Christine Garrington  22:05  

You did make a number of quite clear recommendations for employers and policy makers in the work that you’ve done. Can you just talk us through those?

Nicky LeFeuvre  22:12  

The need for organisations I would say, you know, policymakers at the national level, international level, but also within companies to recognise that there is a potential mismatch. In fact, in the older workers we are talking about as a target group for extended working life policies. Spontaneously when we when we talk to to employers about the kind of older workers that they would be interested in keeping on that they would be interested in training or that they would be interested to target for bridge schemes or for unretirement schemes as they call it and so this possibility of employing retirees back perhaps on a on a reduced rate. Companies do have quite clear, clear image of the kind of worker that they would be interested in in involving in those kinds of schemes. Unfortunately, those images do not equate very well with the profiles of the workers who are actually motivated and interested in extending their working lives. Who tend not to be the most highly qualified or who tend not to be the workers who have continuous employment histories. Who have had haven’t had any major health events during their adult life course, who haven’t had any significant care commitments that have taken them out of the labour market, who have relatively comfortable financial arrangements made for their latter years and so on and so forth.

Christine Garrington  23:37 

So among the people you spoke to then, who was motivated to extend their working lives and why? 

Nicky LeFeuvre  23:45  

Workers can also be motivated because for example, they have developed their careers quite late on in life. So maybe they are just moving into a management position because they’ve had, if they’re women, they have had some years out of employment for family reasons. We also had a number of very interesting testimonies from older women saying that continuing to work longer was actually very attractive to them, because it was one way of avoiding being swamped by care duties that they could be expected to take charge off in later life. So either you know, being on call for grandchildren or being available for elderly dependent relatives. This idea that continuing to work was actually a way of avoiding being overwhelmed by by these care expectations was something perhaps something we were not expecting to find to such a wide extent. And so quite clearly, women like that who are interested in extending their working lives as long as their working conditions are adapted to their needs are not being identified at the moment as resources that employers could call upon, and people that the employers could have in mind when they think about how they are going to manage age. You know what strategies they’re going to put in place and how they’re going to start, operationalizing their age management policies before people become old you know before people get to within two or three years of retiring.

Christine Garrington  25:20  

So is there a simple message in all of this?

Nicky LeFeuvre  25:22  

Making it clear that they’re the kind of people that employers would spontaneously think about as their target group for extending working lives are not necessarily primarily the people who are interested in extending their working lives, but there are other groups who are highly motivated to work for longer. I think this is you know, one message that we can pull out of the research that could translate into some quite exciting and quite innovative policy decisions on the ground as it were.

Christine Garrington  25:55  

Dynamics of accumulated inequalities for seniors in employment is a DIAL research project, looking at the gendered impacts of policies and an extended working life. You can find out more on the DIAL website at dynamicsofinequality.org. Thanks for listening to this episode of our podcast, which was presented by me, Chris Garrington and edited by Elina Kilpi-Jakonen.

Why and how do rich parents have rich children?

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In Episode 12 of Series 3 of our podcast, Jamie Hentall MacCuish from University College London and the Institute for Fiscal Studies discusses findings  from DIAL’s TRISP project on the intergenerational elasticity of earnings or why rich parents have rich children. 

The Intergenerational Elasticity of Earnings: Exploring the Mechanisms is a DIAL Working Paper. 

 

 

Transcript

Christine Garrington 0:00

Welcome to DIAL a podcast where we tune in to evidence on inequality over the life course. In series three we’re discussing emerging findings from DIAL research. For this episode, we’re talking to Jamie Hentall MacCuish from University College London and the Institute for Fiscal Studies. He’s been investigating why rich parents have rich children. I started by asking him to explain the background to the research.

Jamie Hentall MacCuish 0:26

If you will permit me I think it’s a bit hard to answer that question without very quickly saying what the paper is about. So in it, we decompose the intergenerational elasticity of earnings or the IGE, which is the correlation between parents and children’s earnings. And we do this to try and understand what mechanisms transmit privilege from one generation to the next. We were using this dataset – the national cohort data study or NCDS – for another paper with a slightly different focus, and we realised the data set offered a unique window into the mechanisms affecting the IGE. Now the NCDS follows a single cohort of people born in a particular week in 1958. From the moment of their birth, up until now as they approach retirement. And it really is a globally unrivalled resource for social scientists due to its combination of information about family background, parental effort and time investment in their children and children’s ability, educational outcomes and later life earnings. Having this information allows us to disentangle the relative importance of family background parental investments in children, further education and ability in explaining the correlation between parents’ and children’s earnings. Now, I mean, I’ve said it’s a globally unrivalled data set what really makes it globally unrivalled is how forward looking this policy was in 1958. I mean, other countries have since introduced similar datasets, but much later meaning that now we don’t have data that covers really most of the working life of these group of individuals, which really makes it a fascinating window into what explains that intergenerational correlation in earnings or IGE.

Christine Garrington 2:10

What exactly was it about having wealthy or poor parents that you wanted to get to grips with specifically in this piece of research?

Jamie Hentall MacCuish 2:19

Why wealthy parents have wealthy children and vice versa? So the children of rich families tend to differ from the poorer peers in multiple ways. They have fewer siblings and a more and more educated parents, their parents spend more time with them and send them to better quality schools. Their cognitive skills are higher at the end of compulsory education, and they complete more years of total education. All these channels have been found to affect an individual’s earnings. But in order to design policies to improve intergenerational mobility, we need to understand the relative importance of these channels and how they interact with each other to generate correlations in lifetime earnings.

Christine Garrington 3:00

Okay, so what did you actually do then once you know, once you sort of started digging into the data, what did you actually do?

Jamie Hentall MacCuish 3:06

A multi-level mediation analysis. Basically, that means we work backwards to see how much of the IGE is explained by each mechanism. So that’s probably pretty cryptic. But in the first level, that we started at we only allow for direct effects on a child’s earnings of the years in education, their cognitive ability, the quality of the school they attended, and the parental investment they received and their family background. So in this first level, for example, we find that education accounts for 43% of the IGE amongst females. However, in the next level, we account for the fact that other channels refer to events earlier in the child’s life, than total years of education because really, total years of education is determined by further education decisions. And so other events plausibly impact on the years of education. Once we account for both the direct effects as well as these indirect effects through years of education, the fraction of the IGE explained for females by education collapses to just 2% to continue with the example given earlier, and cognitive abilities at the end of compulsory schooling really explained most of this difference.

Christine Garrington 4:22

Can you help us unpack that a little bit Jamie? What does that actually imply?

Jamie Hentall MacCuish 4:25

Once you account for cognitive ability at the end of compulsory schooling, the fact that children of richer parents spend longer in education doesn’t account for much of the persistence in earnings between generations. And then we then extend the analysis back to more levels to account for the fact that parental investments and stalling might, like school quality, might impact cognitive ability at 16. And that family background might impact parental investment decisions, or the parents’ choice of school.

Christine Garrington 4:52

You mentioned a little bit earlier about, you know, the amazing data resource that the NCDS is. Are we able to sort of tease out a little bit more about the sorts of things that people are asked in that study that would help you with, with this research?

Jamie Hentall MacCuish 5:07

There are multiple things asked and multiple tests. So it’s not just survey questions. There were tests; reading tests, math tests administered to these children in schools. They measure their weight at birth, the researchers went into the children’s school and ask the teacher their impressions of how interested the mother and father are in the child’s education. With, it was asked how many outings the parents took their children on, and these are I mean, we combine all of these measures about parental investments into sort of using a latent factor analysis to tease out a measure of how much the parents invest in their children. And similarly, with the child’s ability, we have measures of reading scores, math scores, and then teacher ratings of these children on maths and reading ability. So I mean, it’s it’s a very, I could go on. It’s a very, very rich data set. And it’s yeah, it’s not just survey data. It’s tests administered medical information. Yes, it’s really quite detailed.

Christine Garrington 6:11

You’ve touched on this a little bit already. But when you looked at the data what were the key differences? Tell us more about the key differences that emerged between the children of wealthier and poorer parents.

Jamie Hentall MacCuish 6:23

The children of richer families tend to differ in multiple ways from their poorer counterparts: fewer siblings, more educated parents, better parental time investments and school quality investments, higher cognitive skills, and more years of total education. But for us, that was really just the jumping off point to then analyse which of these differences matter most to explain this correlation of earnings between parents and generations from this persistence of inequality from one generation to the next.

Christine Garrington 6:51

And you took into account obviously a range of other factors as well what factors mattered most in all of this and how did they play out?

Jamie Hentall MacCuish 6:59

So once we accounted for all the levels of analysis, so the for the effect of a family background and early investment on cognition and years of education, what we found is that family background and investment in early childhood mattered the most, the relative importance being different for men and women. For women, the most important was family background followed by school quality. And for men, parental time investment mattered most followed by family background.

Christine Garrington 7:24

So this is all very interesting, but I’m wondering now you know what are the important takeaways from the research about this relationship between how well off a parent is and their child’s life lifetime income prospects, for example?

Jamie Hentall MacCuish 7:38

It simply what factors we found mattered most? It seems that to explain the persistence of inequality in earnings across generations, early childhood investments and family background really matter the most. And higher educational choices, for example, aren’t one of the mechanisms generating persistence in inequality in earnings across generations.

Christine Garrington 8:02

Right, I wonder if there’s any more to say there about from a policy perspective, if you like for those committed to want to seek and create a more equitable, a fairer playing field for all children, regardless of their background and how rich their parents are, what sorts of things are most relevant? Are they the things that you’ve outlined already? Or is there anything more that they can take away from this?

Jamie Hentall MacCuish 8:21

Obviously, you want to be careful making too many policy suggestions off one piece of research, but that said, I think our research really is in alignment with a large and growing literature that says early childhood investments are one of the best levers available to reduce intergenerational inequality. I think anything the government, our research would say and so I think with a large growing body of research say, that anything government could do to reduce the inequality in investments in early childhood would be one of the most powerful mechanisms to reduce intergenerational inequality.

Christine Garrington 8:56

The Intergenerational Elasticity of Earnings: Exploring the Mechanisms is a DIAL Working Paper by Uta Bolt, Eric French, Jamie Hentall MacCuish and Cormac O’Dea from the Trends in Inequality: Sources and Policy or TRISP project . You can find out more on the DIAL website at dynamicsofinequality.org. Thanks for listening to this episode of our podcast, which was presented by me, Chris Garrington and edited by Elina Kilpi-Jakonen.

Education pathways: how do they affect young people’s job prospects?

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In episode 10 of Series 3 of the DIAL Podcast, Professor Steffen Schindler from the University of Bamberg discusses findings from DIAL’s LIFETRACK project which is looking at how different education pathways impact the type of job young people go on to secure. 

Further information

 

Transcript 

Christine Garrington  0:00  

Welcome to DIAL a podcast where we tune in to evidence on inequality over the life course. In series three of the podcast we’re discussing emerging findings from DIAL research. For this episode I caught up with Professor Steffen Schindler from the University of Bamberg, at DIAL’s final conference to talk about the LIFETRACK project, which has been looking at how the type of Secondary Education experienced by young people in seven different countries affects the type of job they go on to do.

Steffen Schindler  0:28  

This is the period when education systems start to sort their pupils or students into different paths, different tracks, different streams. And we were interested in how much the sorting that takes place in secondary education already predicts inequality in later life outcomes when the students are in the labour market when they’re grown up.

Christine Garrington  0:53  

Although you were looking at a number of different countries, you weren’t looking to compare across them but within them, weren’t you? So tell us about the thinking behind that.

Steffen Schindler  1:01  

There’s already some research out and we know that countries that are considered as having comprehensive education systems such as the UK for example. They tend to have lower levels of education inequalities than countries that track their students such as Germany for example, that have different schools where they go to. The new thing that we wanted to look at in our project was even though the level of inequality might be different between countries, we were interested does the differentiation in secondary education within a country still predict inequality in that country – is it important for the formation of inequality? So in the end, we have some sort of idea whether it contributes a lot or not so much to inequality.

Christine Garrington  1:50  

So working across seven countries must have posed some challenges – how did you go about organising all of that?

Steffen Schindler  1:57

Yeah well we had a very structured approach. So we had project meetings two times a year, and where we made a plan what we wanted to achieve till the next meeting. So basically, we started off with making a plan how we measure certain things such as social origin, or the labour market outcomes such as income or social class and we want to observe when people are grown up or in their 30s or 40s even So that we standardised across all the teams and then we made a plan for what we want to analyse. And we were very standardised in the beginning and the more the project was progressing the less standardised we were – the more freedom we gave to the project teams for their analysis.

Christine Garrington  2:44  

Yeah, that makes sense. So at first look all of the research teams in the countries that you were looking at, identify this differentiation between academic and vocational routes. Can you talk us through, through that? 

Steffen Schindler  2:58  

Basically, each education system has this distinction between academic tracks or streams that eventually lead into higher education on the one hand, and more vocational tracks that don’t lead into higher education. But the systems differ, how that looks like. So we have separate schools, for example, in Germany and Germany starts very early with that separation. We have separate schools in other countries such as Finland, for example, in Denmark, were the separations a bit later. Then we have England, which is a comprehensive system, but we also have some sort of academic stream, which is defined by taking a certain number of A Levels at the end. So the systems differ a bit – what the academic stream actually is, but each of them has one academic stream. And it turned out in our analysis that it’s always the separation between the academics and the non-academic stream, and that students that on academic stream always end up with better labour market outcomes in the end.

Christine Garrington  4:08 

Yeah, let’s talk about that a little bit. So when it came to those labour market outcomes for the young people concerned, what was the main implications of the differences?

Steffen Schindler  4:18  

Well, first of all, the people from the academic streams end up let’s say higher social classes with a better higher income, a better paid job with a more prestigious job in the end. So this is one result, which was pretty obvious. But another implication is that it’s also related to social inequality or social reproduction. Because if we consider who enters those academic tracks and who doesn’t, and then this is again, a question which is related to social origin. So the selection into the tracks is heavily based on social origin in all of the countries.

Christine Garrington  4:56  

And I’m interested to know if there were any advantages to being on the vocational path compared with being on the academic path.

Steffen Schindler  5:03  

I think the distinction is not so much between the academic and the vocational path but between being in upper secondary education or completing upper secondary education. So we have many countries which have an upper secondary stream, which is a vocational stream. And if we compare those students to students who haven’t been in upper secondary education, they might even have done vocational training, but the distinction is between upper secondary vocational training and lower secondary careers. And there we see advantages where people in the vocational stream in upper secondary education, indeed have labour market advantages. 

Christine Garrington  5:45  

So some of the studies also look back over people’s life courses to their social backgrounds. That’s really interesting. What were the key things to emerge around that?

Steffen Schindler  5:53  

What we saw in all of the countries is that differentiation in secondary education is a mediator of social reproduction, as we call it. That means that on the one hand, the selection into the academic secondary tracks is highly based on social backgrounds, family background. So this is the one part and the other part as I already said, since academic tracks lead into the better labour market outcomes, and this produces social inequality in the end.

Christine Garrington  6:25  

Yeah, indeed. And it really is a fascinating and important body of work that’s been carried out. What would you like those who are interested in reducing social inequalities to take away from all of this?

Steffen Schindler  6:38

I guess the core message of our project is that we have to look at differentiation in a broader sense. So usually, we were distinguishing educational systems based on very formal criteria, whether it’s a formally comprehensive system, or whether it’s a track school system. But what we’ve seen is that even in comprehensive systems there is some sort of internal differentiation, such as ability grouping, or other things that we call the hidden differentiation. That’s not very obvious. So another example would be the distinction between private education or state funded schools which is another dimension of differentiation. And I guess the core message would be to look at more carefully, those more hidden things, the more hidden differentiation in school systems

Christine Garrington  7:32 

Yeah and where do things need to go from here then? So more work to be done as always, presumably?

Steffen Schindler  7:37  

Yes, it is always more work. Our project was heavily based on longitudinal data where we could observe students from the day the entered the school system into adulthood and this is quite complex data. And what we saw is that when we are interested in those hidden forms of differentiation, we need better measures. So I think if we want to follow up on this path, we should think about how we could incorporate those measures in in those data so that would be a message also evolving from our projects that is more directed to our research.

Christine Garrington  8:15 

Educational differentiation in secondary education and labour-market outcomes is a special issue of Longitudinal and Life Course Studies by Steffen Schindler at the LIFETRACK project team. Thanks for listening to this episode of the podcast, which is presented and produced by me, Chris Garrington and edited by Elina Kilpi-Jakonen.