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In Episode 5 of Series 2 of our podcast, we talk to Alejandra Rodríguez Sánchez from the University of Berlin and Susan Harkness from the University of Bristol about research from the DIAL funded Equal Lives project on the gendered division of housework during lockdown and whether or not changes that happened were temporary or long-lasting.
Gender division of housework during the COVID-19 pandemic: Temporary shocks or durable change?
is research published in Demographic Research by Alejandra Rodríguez-Sánchez, Anette Fasang and Susan Harkness.
Transcript
Christine Garrington 0:00
Welcome to DIAL a podcast where we tune in to evidence on inequality over the life course. In this series, we’re discussing findings from DIAL’s Equal Lives Project, which looks at how inequality impacts the lives of young adults. Our guests in today’s episode, are Equal Lives’ Principal Investigator Susan Harkness, and Aleja Rodríguez Sánchez. They’ve been looking at the gender division of housework during the COVID-19 pandemic, to see whether any changes are temporary or longer lasting. I started by asking Susan to explain the backdrop to the research.
Susan Harkness 0:32
So this research fits into a larger body of research looking at how inequalities evolve over the lifecourse. And so when COVID came along, we realised it revealed that there are really important changes going on and new inequalities and growing inequalities we needed to think about so a part of that we’re looking at what is happening within families, and in particular, how households divide their labour because that has implications for what happens in the labour market and what happens to broader inequalities.
Christine Garrington 1:01
Aleja what exactly was it that you’re hoping to get a better handle on, a better understanding of in all of this?
Alejandra Rodríguez Sánchez 1:09
Yeah, we started with the idea that maybe the effects of the pandemic and the lockdown on the division of labour were going to affect some people more than others. We intended to look at what couples would do differently if they had children or didn’t have children. We also further wanted to look at whether the age of the child would make a difference in how the pandemic impacted the division of labour. But then we realised that there were a lot of things going on at the same time and it wasn’t going to be easy to sort of look at one moment in time and before and after. And so we wanted to give it a more dynamic look and see how the situation evolved over time as these further changes in the furlough scheme, childcare and school closures were put in place and then lifted.
Christine Garrington 1:55
Can you talk us through exactly what it was that you that you did?
Alejandra Rodríguez Sánchez 1:58
We opted for a very, first, very descriptive type of study, looking at the share of housework done by women, and here we look at the percentage or the proportion or the share that it’s done by women of the total amount of number of hours spent on housework, cooking, cleaning, but does not include childcare in this case. So we want to know how that share of the work done by women changed. So we wanted to look at this quantity at different moments in the pandemic. We wanted to look at it first shock but then we also wanted to look at what happened after that first shock, whether those same things, the same effects would have stayed over time or were they, whether there would need some sort of change. And for that we use a technique called kernel density estimation, which helps us to see how the distribution of a variable in the population has shifted over time. So we wanted to first estimate pre-pandemic distribution and then we wanted to see how the distribution has shifted over time. Then secondly, we wanted to sort of further those descriptive analysis with a fixed effects type of regression, which basically we tried to compare couples to their own selves in the past to sort of get rid of some of the heterogeneity that it’s unobserved.
Christine Garrington 3:13
You got your information from an especially conducted COVID survey, what sorts of things were people asked in that that made it possible to look really closely at these questions?
Alejandra Rodríguez Sánchez 3:23
Yeah, the COVID survey – it’s really like a special very special or very unique study, in a way even for other in comparison to other UK studies that were done during the first year of COVID. The sample is basically a sub sample of this Understanding Society study, which is this much larger study that has followed UK households for more than 10 years. And there were lots of questions on how the COVID pandemic affected sort of the general welfare of households and individuals and there were questions about socio-economic conditions, I think, unemployment stuff, and there were also things about family dynamics. So it’s quite comprehensive. And I think there’s a lot more to see in there.
Christine Garrington 4:02
How and why do surveys like this help us gain such important insights into what is going on in people’s lives and how that’s changing over time?
Susan Harkness 4:11
Okay, so there’s two things that are really valuable about surveys like the UK household Longitudinal Survey, or Understanding Society, as we call it. First of all, it’s a survey that’s conducted at the household level so we can see what’s going on. Not just amongst individuals, but how, how relationships within the household pan out. And then the second thing that’s valuable about it of course, is the longitudinal aspect. So that really allows us to look at changes over time. And that allows us to understand much more about why we have inequalities and how they affect different people. So in the case of the COVID studies, we can see where change happened, where they relatively, families where things were relatively equal before became more unequal? Or which particular groups were most affected? So we can see these changes over time. And I suppose if we look at the COVID Understanding Society surveys, and what’s also really valuable about this survey is that whilst there have been a lot of other surveys which have given us a sort of snapshot of what’s happening at a particular point in time, with the COVID surveys with Understanding Society, we can see how these inequalities are emerging as we move through the pandemic so we can see who is being affected and how that is changing for them over time.
Christine Garrington 5:30
Yeah, really important and Aleja so let’s get down into the nitty gritty then – when you, when you looked over time at how men and women were dividing housework or sharing housework at different times during the pandemic. What did you find?
Alejandra Rodríguez Sánchez 5:44
Yeah, well, we found a remarkable first data set that is seen as a sort of shift in the distribution towards more equality between couples. So the original pre-pandemic distribution it’s quite was quite unequal and therefore sort of skewed looking like more women, we’re doing more. So visually, it really looks like a bump around like 65-70%, which means really, women do the majority of this housework. And so the first shock is that the shape of that bump to the left, so to say towards more equality, so it’s close to 50/50 but it’s not quite there yet. And then after, after that first shock with the data for further and further waves revealed was a gradual, but a clear return to previous levels, that we’re seeing pre-pandemic where women started to do again a higher percentage of the total housework over time. And this sort of return to normal was especially clear for couples with children, whereas couples without children, we saw that this higher equality achieve after their lockdown or during the lockdown was more sustained. And these results were also confirmed with the fixed effects regression that I mentioned. So we think that the results basically show us that there were different trends, depending on the lifecourse stage in which the family was.
Christine Garrington 7:07
Yeah, so it looks to be fairly robust doesn’t it? And Susan, is that what you expected to see? Or were you surprised that having children made a difference to whether couples continue to share housework more equally, or revert to their pre-pandemic habits as it were in this, this area?
Susan Harkness 7:23
Right, well of course, I mean, COVID has been an enormous shock, I think. Especially in the early months where we had people’s work was affected, and for those who had children schools were closed. And I think that shock meant that couples changed their behaviour, whether they had children or not. And of course, there was a lot of early optimism that, that those changes would mean a bit more gender equality. But what we’ve seen is I think that as people have adapted, we’ve seen the sort of patterns emerge in a way that’s quite similar to what happens for example, when children, children are born. And we have this specialisation, where women tend to revert back to taking on more of the roles involved in sort of looking after the house whereas men tend to specialise more in work. So sadly, and I think for those with children, I think we see these greater specialisation – so children are often a trigger for more specialisation within households with, with men doing more paid work and women doing more of the housework. And I think to some extent, what we’re finding reflects these, these old patterns that we’ve seen previously in the data before COVID. So perhaps early optimism was, was not as well placed as it might have been.
Christine Garrington 8:31
Hmmm I will ask you a bit more about the implications of that in a moment, but Aleja what would you say that we’ve learned from this piece of research that you’ve done and I wonder if you have plans to look further at this particular area, this particular aspect of people’s lives?
Alejandra Rodríguez Sánchez 8:45
I think one key message besides what Susan has already said is that before and after comparisons, sort of they can be informative, but they’re only showing us like a snapshot of what actually happened. And longitudinal data is really key because for us, we gained a lot of information by actually looking at how things evolved over time, which gives a different take on this, on how, when we claimed, those early claims on how the pandemic would change at all for the better or worse we are definitely looking at working already further on the COVID-19 effects. But this time, we’re going to, we’re shifting the perspective from the couple to the children in particular to teenagers. We’re interested in understanding what factors may explain some preliminary findings on what was found as the serious deterioration of mental health among teenagers, especially teenage girls. We want to see what factors about their families but particularly about mothers who were also having, were having a hard time during the pandemic may explain what happened to teenagers. And so we’re working with Susan and Annette on this.
Christine Garrington 9:52
Yeah, no really interesting, more really important work to come by the sounds of things. So Susan, just to wrap up really a lot has been said about how COVID-19 has negatively affected the lives of women more than men. Fears have been expressed around the reversal of any sort of pre-pandemic trends towards a greater gender equality. Would you say that this piece of research and, and other work that you’re doing tells us anything about that, do you think?
Susan Harkness 10:17
Well, I think certainly other studies have, have shown that women’s mental health has been particularly adversely affected by the pandemic and particularly by the lockdown measures, so we know that some of the responses to the COVID crisis have really negatively affected women more than they have affected men. We’ve also, I think seeing some trends in the labour market where particularly mother’s employment is perhaps recovering a bit more slowly than that, that of men to pre-pandemic levels of employment and hours of work. So I think there are some indications that there are implications for gender equality, and that those are not necessarily going to be reversed in the short term. I think the longer-term consequences are much harder to tell of course. You know, what effect this, these, these closures has on women and their careers is, it’s something that we don’t yet know, but it’s, it’s not hard to imagine reasons that parents may feel that they’re going to fall, fall behind in the labour market because of the extra roles they’ve had to take on over this crisis. And I suppose the question remains about whether in the longer term, they’ll be able to catch up with where they may have been otherwise.
Christine Garrington 11:31
“Gender Division of Housework Furing the COVID-19 Pandemic: Temporary Shocks or Durable Change” is research by Alejandro Rodriguez Sanchez, Annette Fasang and Susan Harkness and is published in Demographic Research. You can find out more about the Equal Lives project at www.equal-lives.org. Thanks for listening to this episode of our podcast, which is presented and produced by Chris Garrington. Don’t forget to subscribe to the DIAL podcast to access earlier and forthcoming episodes.
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.
In the fourth episode of Series 2 of our Podcast looking at research from the Equal Lives project, we talk to Michael Grätz from the University of Lausanne and Swedish Institute for Social Research. He discusses research published in Demography involving Equal Lives team members Jani Erola and Aleksi Karhula which looks at siblings to to see whether educational opportunities are equal for all in and across 6 countries.
Transcript
Christine Garrington 0:00
Welcome to DIAL a podcast where we tune in to evidence on inequality over the life course. In series two we’re discussing emerging findings from DIAL’s Equal Lives project. For this episode, we’re talking to Michael Grätz from the University of Lausanne and Swedish Institute for Social Research about findings from research using siblings to see whether educational opportunities are equal for all in and across six countries. I started by asking him how studying the lives of siblings helps us to understand issues around equality of opportunity.
Michael Grätz 0:30
The simple argument of the study actually, of this approach of looking at the similarity of siblings in life outcomes is that this can be used as one measure of equality of opportunity. So equality of opportunity is something we’re interested in, which means that how much control people have over their chances in life. The traditional approach to measure equality of opportunity is you take some indicator of the parents like parental education and parental occupation, parental income and relate it to some indicator of the children like their educational attainment. And then you get some measure which which is which has something to do with the equality of opportunity. However, the problem of that approach is that we know that it’s going to overestimate equality and it’s going to underestimate inequality, because there are also factors affecting life chances, which are not observed parental characteristics. The idea of this using the similarity between between siblings and their education to measure equality of opportunity as some of these factors that are not observed are actually shared by siblings. So siblings do have the same parents, they live, they grew up in the same neighbourhood. They go to the same school often and all these factors influence their life chances and they are they are combined if we produce an estimate of the similarity of siblings and education as a measure of equality of opportunity. Now, I should also say that this is still not a perfect measure of equality of opportunity because we also know that there are factors that vary between siblings that influence their life chances, but still we know also from the empirical side, that using sibling similarity is a better measure of equality of opportunity than relating some kind of characteristic to the child characteristic, which is the most often used.
Christine Garrington 2:24
Okay, understood. And so for this research then, Michael, what was it that you were looking at specifically and why?
Michael Grätz 2:29
So the main thing we were interested in is the traditional question whether equality of opportunity varies across society. So we were producing estimates of sibling similarity in education for different countries. And then we were also interested in whether sibling similarity in education varies across different outcomes. So there are different educational outcomes that we were interested in, and we’re looking at three of them. So we’re looking at cognitive skills. We’re looking at school grades, and we’re looking at educational attainment. Of course, these three are related to each other, but still, they capture slightly different things. And it’s interesting to compare the cost and, and then the third thing we were interested in is whether they are some differences in sibling similarity across different social groups in society. So the idea is do the offspring of some sort of groups in society is more similar among each other than then the offspring of others.
Christine Garrington 3:31
Okay, took us through what you actually did to try to look at these, these issues then?
Michael Grätz 3:35
We asked the people we know to give us access to data and to produce these estimates. And this is really mainly a project where we produce comparative estimates of sibling similarity in education for different countries. So in our case, we have six countries. And then it’s mainly data work, where you have to put together all these datasets the different data sources we use for the different countries come up with different estimates, try to harmonise the data, which is a very challenging thing because we are talking about data which has been collected for different purposes with different strategies in different countries. And we want to have something comparable, and then you have challenges of making those data after it has been collected comparable to each other.
Christine Garrington 4:28
Yeah, you talked about the data there. So where did that information come from and why were those data particularly good sources? For this type of research?
Michael Grätz 4:38
There are two strategies you can have if you want to do a comparative research comparing different countries. So the one strategy is you use data sources which have been collected with the purpose of comparing them so collected in different countries. That’s, for instance, the PISA data that some of the listeners may know and the other approach is to use really what we think is the best data source for each country and compare them and it’s the second approach we have been taking in this paper. And one reason also we have to take these papers, because we were interested in siblings and siblings, information on siblings is not what is something that is often collected. So what we’re using in the data is really what we think are the best data source for each country. And this means that we’re using different data sources in different countries. So we using in Finland, Norway and Sweden using administrative data, because those countries have administrative data sets which can be used for research. In Germany, in the US and the UK we using a survey data sets – these are surveys that have been collected in those countries for a long time. And they are very large and they’re very reliable, and they have information on certain things.
Christine Garrington 5:48
Fantastic. So time to dip into that data and talk a little bit about what you find. Let’s talk about cognitive, cognitive skills first. What were the key things to emerge in and across the countries that you looked at there?
Michael Grätz 5:59
The main finding actually is that for cognitive skills, there isn’t very much variation across countries. So meaning that equality of opportunity with respect to cognitive skills, something that was sort of similar across countries. And what we also saw because for cognitive skills we had two data sets for the US which we could use and those two data that gave slightly different results. And we see that the these differences which we find between the two datasets for the same country, about the same size as differences that we find between countries, so meaning that it’s not necessarily easy to tell what the differences then across countries whether they are really there, or whether they are just due to the fact that we have used different data sources.
Christine Garrington 6:46
Okay, now one of the other outcomes was school grades. What did you see there?
Michael Grätz 6:50
So school grades was an interesting outcome because that’s actually the only outcome for which we find that sibling similarity is lower in the US than in the Scandinavian countries. Meaning that equality of opportunity and school grades is higher in the US than in Sweden and Norway, which are the other two countries for which we have taken this outcome. The differences are not really large, but still this is this is something which goes in a different direction of what you usually expect and what you also have found for educational attainment.
Christine Garrington 7:21
On that note that was going to be my next question to you, so what overall education attainment – what did you see?
Michael Grätz 7:27
So I think educational attainment in some sense, it’s the outcome we care more about most of all, because this is really the final educational attainment that respondents have observed. And this is this is also the outcome where we find a pattern of growth across national variation which is aligned with what we usually think in both as scientists but also in the policy discourse. So meaning is that we find that sibling similarity in education is highest in Germany and in the US, meaning that equality of opportunity is lowest in these countries. On the other hand, sibling similarity in the education is lower in Norway, Sweden and the UK. So these three countries are both on the same level in that respect was maybe a bit surprising is that in Finland, sibling similarity in education is once more again much lower. So meaning that they also within the group of Scandinavian countries – Sweden, Norway and Finland – there’s some variation. And there’s more equality of opportunity in Finland with respect to educational attainment in Finland than Norway, and in Sweden.
Christine Garrington 8:36
Did the siblings’ family background matter at all and if so, how does that sort of play out?
Michael Grätz 8:42
So the idea was that it could be that especially socioeconomically advantaged families can invest resources in the way that they make sure that all their children are succeeding well in life and getting a high level of education. So this is why we were speculating that there could be more sibling similarity among socioeconomically advantaged families. However, our empirical results were not really in line with this finding. So we didn’t we didn’t find much variation in that and the variation that we found was not really very robust across the different outcomes.
Christine Garrington 9:17
So when it came to the question of which countries were providing the highest level of inequality of opportunity, what did you see there?
Michael Grätz 9:24
So one thing is that the finding is not totally the same if you understand education and different things. So if you’re looking at the use different outcomes, but if you focus on final educational attainment, so which is really the highest decree from school that children obtain, then inequality is highest in Germany and the US, equality is highest in Finland, which is which is a result of our study, which is also I think, in line with the general perception. However, there’s also still two points I think that I would like to emphasise. So the first one is there’s still a lot of variation within the Scandinavian countries. So there’s more equality in Finland than in Norway in Sweden, meaning that probably, we see that the way to get to high equality is not totally obvious when see these results because even within a lot of countries with similar educational systems, and similar features of society, there is still variation. And the second point is that is cross country differences are not that large, meaning that in all societies actually there is a lot of inequality of opportunity. And this is something which in the policy discourse in some countries get lost. So I’m from Germany, and I know the policy discourse in Germany quite well and I always get the impression that the ideas are that in Finland, there would be perfect equality, but that’s certainly not the case. So there’s still a very high level of sibling similarity in education also in Finland. So we’re talking about differences between countries – Yes, they are, they are there, but compared to the overall level how much similarity across siblings is there? In a society these cross-country differences are not that large.
Christine Garrington 11:07
It is complex research. You know, you talked earlier about the harmonising of the data sets, etc. Really, really complex work with some complex results. But would you say for policymakers, particularly there are any clear messages or takeaways here around that policy discourse that you were alluding to?
Michael Grätz 11:24
I think, probably the clearest message coming out from that results is that these simple ideas. And there are some simple ideas about around so for instance, this idea that inequality in education is largely related to income inequality in a country. The findings of our, our research, they are basically saying that these simple ideas don’t work in the sense that they may explain, they may be explaining something but they’re not going to explain everything. So there’s probably so there’s more inequality in education opportunity in countries where there is income inequality, but still, this doesn’t explain all the cross country variation. That’s what I wanted to say. The second point, I think, for policymakers is also to be aware of that in all countries, there is inequality in education. And that probably has to do with the fact that this is has also to do two processes happening outside of the school system. So this is why reforms of the educational system, they will never fully address equality of opportunity because there are processes occurring within families. They are also contributing to inequality of educational opportunity. And I think this is something that policymakers should be at least aware of.
Christine Garrington 12:37
Sibling Similarity in Education Across and Within Societies is research published in Demography by Michael Grätz and colleagues as part of DIAL’s Equal Lives project. You can find out more on the project website at equal-lives.org and about the wider DIAL programme at www.dynamicsofinequality.org. Thanks for listening to this episode of our podcast, which is presented and produced by me Chris Garrington.
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.
In episode 11 of the DIAL podcast, Professor Gabriella Conti from University College London discusses two pieces of research part-funded through DIAL’s Growing up Unequal? The Origins, Dynamics and Lifecycle Consequences of Childhood Inequalities project. The first investigates socio-emotional inequalities in children born in the UK in the 1970s and the Millennium and the second investigates the long term health benefits of the UK Government’s high profile Sure Start programme.
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 Professor Gabriella Conti from University College London, about two pieces of research. The first compares the behaviour of children born in the 1970s with those born in the millennium, the other looks at the long-term health benefits of the UK government’s Sure Start programme.
Gabriella Conti 0:26
So we know that early human capital is a key determinant of lifecycle outcomes. And by now we also know that if there are early life inequalities that can perpetuate and amplify a person’s life cycle. And so it’s really important to document existence of early inequalities if they’re present, thinking about what we can do about them. So there was one starting point we ha. Another one was that we know that inequality has been increasing throughout the developed world and in particular in the US and the UK in the recent years. And there is likely less evidence for inequalities in child development. In particular, we were interested in the dimension of child development whose importance is being increasingly recognised which is child social emotional development. And so this is why then we started looking into data and see what we could do to document the evolution of inequalities in the social emotional development of children as early as we could.
Christine Garrington 1:26
So how did you go about the research tell us what you actually, what you actually did?
Gabriella Conti 1:30
You know, the documenting evolution inequalities for a long period of time requires that you have data which can be compared across time. And in the UK we’re fortunate enough to have British Cohort Studies, which essentially follow the life cycle of cohorts since birth. And in particular, we use the data from two of these cohorts. The British Cohort Study, which has followed the cohort born in one week in 1970. And the Millennium Cohort Study which has followed a cohort born throughout 2000. So these were like, a thirty years apart. And we had pretty big sample sizes more than nine thousand for the 70 Cohort, more than five thousand for the 2000, so called Millennium Cohort. So we were able to extract from these two data sets, relatively similar questions on child behaviour as to the mothers in the two cohorts. At age five so relatively early in life, and mothers were asked questions about whether for example, the child is restless? The child is solitary? Child is screaming or fidgety? And we construct the comparable skill so for two important dimensional social emotional skills, which have been used widely in an interdisciplinary literature on child development, namely, externalising and internalising.
Christine Garrington 2:51
Can you explain in simple terms, a little bit about what we mean by those types of things?
Gabriella Conti 2:55
Such emotional skills internalising refer to the child ability to focus their drive and determination and externalising relates to interpersonal skills. So a child with better externalising skills is likely less restless, hyperactive, less antisocial and a child with better internalising skills in less solitary, neurotic and worried. And one important thing that it’s good to notice at this point is that while you know the same questions have been asked to the mothers across the two cohorts, and we were super careful and just used the questions which were worded in a similar way of course it could be that what is perceived as a hyperactive child has changed in thirty years. And so we use reasoned methodological advances to take this into account to make sure that we’re effectively comparing the same constructs that showing the development of children and this methodology can be used in other settings. And we’ll show that it’s really important to use as we show in our application.
Christine Garrington 4:01
Just talk us through then the key things that sort of emerged once you’d gone through all of this, once you’d look so closely at that data, once you’d put those methods in place and once you’d used those really robust approaches.
Gabriella Conti 4:12
Yeah, so actually, we find that inequalities in social emotional development – this externalising internalising behaviour of these very young children of five years did increase in this 30 years between 1970 and 2000. So this kid we’re measured in 1975 and 2005. And it’s important to notice that we’re not able to say whether one cohort was better or worse than another because we weren’t able to compare the levels. Because thanks to our methodology, we were able to compare the difference so the inequality between these two cohorts but no whether one was better or worse than the other. But we saw very clearly that the inequality in the cohort born more recently, so the one born in 2000 was greater than one born in the seventies. So in particular, we see increasingly this early gap between the children with the highest and the lowest social emotional skills. So for example, no matter which measure we used we saw that for example, if you looked at the difference between the 19th and 13th percentile, this has widened substantially in 30 years. And this increase was particularly pronounced for boys. For example, for boys, the gap is increased by 19% for externalising skills and even more 30% for internalising skills. So inequality in the social emotional skills so very young children are five years of age was much lower among the children born in the 70s than among those born in the 2000.
Christine Garrington 5:50
I will ask you what you make of all of that in a moment, but you also took a number of sort of different factors into consideration, didn’t you including the mother’s level of education, what did you see there? That was important?
Gabriella Conti 6:00
Yeah, indeed, because as we know there have been several changes in the composition or the population composition or the workforce but also changes in women’s experience, especially in the labour market. And so we wanted to look not only what happens in these gaps across the groups but also zooming in on particular groups. And so in particular, we found that even when we compare children or mothers with different characteristics, we found increases inequalities and in particular, while having more educated mothers or mothers with healthier behaviour was an important determinant to the skills in both cohorts. We found that the benefit of having the mother we higher levels of education or a mother in employment was significantly larger for both boys and girls in the most recent cohort than in the cohort born in 1970. So in other words, the difference between the children are more or less educated mother was greater among those born in 2000, as compared to those born in the seventies. And we found this for children’s mothers with respect to mothers education, with respect to mothers employment, but those inequalities increase between children and mothers who smoked and not during pregnancy.
Christine Garrington 7:19
Okay, so some really interesting findings. I’m interested to know whether you were surprised by what you found? And if so, how you explain what you found?
Gabriella Conti 7:26
I might sound a bit cynical but I am going to say I wasn’t actually surprised about the findings per se, because we do know that there have been many increases inequality across different dimensions. What I found really surprising was the extent of the increase and the fact we could see that so early across such an important measure of development. So we did spend a lot of time in trying to tease out the various determinants of this increase in inequality and as I mentioned before, there being significant societal changes in these thirty years so for example, it’s also documented in the paper across the two cohorts. The average age of women have children has increased by approximately three years from 26 to 29. The proportion of women in employment has increased in our data we see from 42% to 62%. And especially the proportion of unmarried mothers is increased dramatically from 5% in the 70 cohort to 36% among mothers in the 2000 cohort. So mothers are having children on one hand at an older age and when they’re more engaged in the labour market, which is good for social emotional skills. But on the other hand, mothers are also more likely to be unmarried at birth, which is more stressful, and so it’s less good for social emotional development. And we have used the methodology to decompose these factors. And we found the changes in these factors explain about half of the cross cohort increasing inequality when it comes to externalising skills.
Christine Garrington 9:02
Okay, so let’s consider them the, the implications of these widening inequalities that you’re hinting at here and talking about here, especially in the you know, recent context of the COVID-19 pandemic. What would you say overall Gabriella that we learn from this work?
Gabriella Conti 9:17
So first of all, let me remark that we’ve seen during the pandemic because there’s been increases in inequalities in learning experiences at all level by the children in the home environment. So the children would have been better able to have learning for example, at home or will have had the parents will be better able to cope. They certainly been affected less severely than children in a more disadvantaged situation. And so now it’s starting to be documented that in particular, children’s social emotional skills have been affected by the pandemic. And on the other hand, as have documented those in other work, support especially for the more disavantaged is also diminished during the pandemic. So for example, done work on the Universal Health Visiting therapist showing that they’re in addition to the cuts of course for many years, they’ve been an unequal in widespread deployment across local authorities. And so the pandemic there has been a double hit on the one hand families that have been unequally affected depending on individual circumstances. On the other hand, also, you know, public services so which are supposed to help families and to help preventing inequalities have been also unequally affected. And so it’s really crucial that the government takes stock of this and hopefully, in the upcoming annual review, it provides more support for the earliest and prevent at least the widening of these inequalities.
Christine Garrington 10:47
Yeah, on this whole idea of the importance of early intervention being key to reducing inequalities. We’re really fortunate to be able to talk to you today about another piece of research that you’ve been doing, and looking at one of the best known really policy innovation interventions in this era, particularly here in England, which is called Sure Start. Now, I think many people are familiar with it, but for those who are not I wonder if you can just talk us through what Sure Start is?
Gabriella Conti 11:11
Yes, thanks so much for this question. Indeed, with colleagues at the Centre for Fiscal Studies we have been working for a few years now on Sure Start and Sure Start is really a major early education initiative in UK, which was originally area based. And for those who don’t know, it has quite a long history. It was indeed first introduced by the Labour government in 1999, so called Sure Start local programmes, and the idea was to give quality services for under fives only in disadvantaged areas then it was so popular and so well accepted by families that the government gave it the major change in 2003 and changing it to Sure Start Children’s Centre which were gradually rolled out across England and at the peak they reached more than 3500 of these centres. And what was really nice about the centres was first they provided a physical place for parents to go to bring their children to interact with other parents and it has a wide variety of services which the parents can use. Ranging from early education services, parenting support, childcare, there were health visitors providing the visits there in the Sure Start centre. There were signposts for job search assistance. So think of it really, as a one stop shop for families with children under five. If you had a child and you needed help for health, for other reasons – so you wanted a childcare place, then you could just go to Sure Start centre. And it’s also to be said that at its peak in 2010 Sure Start also received a third overall earlier spending as much as £1.8 billion a year. But then unfortunately after 2010 spending has fallen by more than two thirds, many centres have been closed, they’ve been scaled back or they’ve been renamed children’s centres. Now you don’t even hear Sure Start or they have been integrated into family hubs.
Christine Garrington 13:10
Yeah, so big changes over, over the piece there and your research on Sure Start has looked quite specifically at its effects on children’s health which is, you know, really important and you started by looking at hospitalizations and links with that. What did you, What did you find there?
Gabriella Conti 13:26
So we have a data set which contains the exact address and opening date of each Sure Start both local programs and children’s centres so we were able to look at the origin of the programme and its expansion up to 2010. And then in England, we have a very good dataset called Hospital Episode Statistics where there is collected data on the universal patients using English public hospitals. So this is important because we can really have a very comprehensive look at the effects across all Sure Start centres for all England the local authority so it wasn’t like a selected or a small sample. It was quite good administrative data. And so what we did – we essentially will be focused on the expansion period of Sure Start so as I say, 1999 up to 2010. So where there were all these centres opening up. And then we’re looking at what happened essentially when you have access to Sure Start since they’re in your area. And what we find, that we find very interesting effects which changed across the life cycle of the child. So first of all, greater access to Sure Start in your area initially increases hospitalization at age one and this shouldn’t be really surprising, given that it’s common in other studies as well and my reflect the fact that the programme would help referring the parents to proper health care in the case of childhood illness, but also brings their exposure to infectious illnesses from other kids being all in the same setting. However, what is really remarkable is that these early increases are more than outweighed by longer term effects. So as we look throughout childhood adolescence, we find that there is a reduction in hospitalizations.
Christine Garrington 15:18
And there were even longer term benefits for these children as they got older weren’t, weren’t there? Talk us through, talk us through what those benefits were.
Gabriella Conti 15:25
So we found is that exposure to an additional centre per 1,000 children at ages 0-4 average around 7% of hospital admissions at each file. Which goes up to 8% by the end of primary school, so age 11 and 8.5% by age 15, which is the final age of this study. Now this represents approximately 2800 fewer earlier hospitalizations at age five and over 13,000 hospitalizations prevented of 11 to 15 year olds each year. So that’s quite a substantial number. It’s pretty much an 8% reduction on the pre-Sure Start hospitalizations rate and this is all completely looking essentially at the peak level of Sure Start provision where there was one centre per 1000 children available throughout England. And what is really interesting, while Sure Start was for children under fives, we’re able to detect impacts which are increasing over time and are able to detect them essentially 10 years after the children have aged out of eligibility. And if we look at the drivers of these reductions hospitalizations, we see that it’s important conditions such as external causes, so things like injuries and also mental health related admissions in addition to infectious illnesses.
Christine Garrington 17:00
Those are just really striking findings Gabriella and I know that you also tried to get a feel for the potential cost savings that Sure Start would have generated. What were you, what were you able to say about that?
Gabriella Conti 17:11
Yeah, I think it’s quite important is this not only to provide evidence so that a programme is effective, but that it also provides enough bang for the buck. And with Sure Start it is particularly important given as I said before all the money, which was spent at the peak of the expansion. So what we did in this case, essentially, we had on the one hand, the money that the government had provided, and so we computed in this way a cost per child which is really more like the amount of money that the government spent on Sure Start per child, and we computed this amount to be approximately £415 per eligible child which is lower by the way than the cost of other programmes. And then what we did we looked at our estimates, and we considered first of all, we costed only the results, which for which we found significant impact. So as I said, subset of external conditions in particular injuries and also poisoning, then infections. So respiratory, parasitic and then mental health as you can imagine, you know, those have huge cost. And we considered three different types of costs. So on the one hand, the reduction in hospitalization has a direct cost saving in terms of the healthcare sector, so you will have less money essentially spent because fewer kids are going to be in the hospital. And this is more like a short-term cost, the cost at the time at which we estimate the impacts. Then there are assorted indirect costs, so if parents don’t have to spend time taking care of the sick child, they are not going to be absent for work. So it’s all savings in terms of averted the loss of productivity, and also in case the parents need, for example, to buy additional drugs. And on the other hand, we also included the long-term cost. So, for example, injuries experienced by a child can have a serious long-term consequence. Mental health conditions, especially experience in adolescence, which is the time at which you will find that Sure Start significant to reduce hospitalizations also have very costly long-term consequences. And so when we add up all this benefits together – all this averted cost, we come up with number which is around £330 million. Now of these, approximately a little bit over 10% like £3.9 million is attributed to the direct cost saving to the NHS and the rest is from the longer-term averted cost. And given that we’ve computed the cohort, this is going to be a total cost of a little bit over £1 billion then these represent approximately a third of the spending on the programme. So taken together, the savings from reduced hospitalization offset around 31% of spending on the programme. And importantly, this is only considering health benefits. Our calculations haven’t included yet any potential benefits in other domains such as for example education, social care or crime.
Christine Garrington 20:34
Goodness me so just looking at health benefits alone some, some really important good things that would change and, and help children grow up to live better, healthier, happier lives, but also an incredibly cost-effective programme. One which really doesn’t sort of exist in the same way anymore. So what are the key takeaways for policymakers here would you say?
Gabriella Conti 20:56
Yeah, unfortunately it’s not been good in my opinion, that Sure Start has been dismantled so quickly. So first of all, I think a key takeaway. So one key message for policymaker and not that this is a hard one to deliver. Because there are policy cycles and usually policymakers work based on these policy cycles but good programmes might take time to deliver their benefits, but it’s crucial that when a policy is decided, it has to be looked at the evidence and also it has to consider the long term benefits not only the upfront cost. Another important lesson I think, is that what probably made Sure Start so successful. Well, there are different components. I think one key component is to have everything in one place and to make it easier for the parents to access it. So having this suite of possible interventions and providing a place for parents to congregate and have access to resources. I do believe it’s key and it’s also the case that if you provide a variety of services, they are you know, their combined effects is not only the sum of the different ones, there are interactions and there are synergies. Among them. So now, you know unfortunately, Sure Start does not exist anymore. So there is this family hubs which are being shaped up and at least my hope is given that Sure Start no longer exists all these lessons will be considered into the shaping on the family hubs. And a particular part in this regard, there is a lot of talking about proportion at universities. Now one key finding that we have is that all the benefits that we find, for Sure Start are concentrated in the poorer neighbourhoods. So we don’t find the particular benefit in the richer ones. And so this is important why? Because on one hand Sure Start did help to reduce inequalities but also because going forward an important lesson for the new services such as family hubs is that the model combining universal services with a narrow base focus of disadvantaged neighbourhoods can be a successful approach to earliest interventions.
Christine Garrington 23:10
Inequality of socio-emotional skills: a cross-cohorts comparison and The health effects of Sure Start are research part funded by DIAL’s Growing-up Unequal? The Origins, Dynamics and Lifecycle Consequences of Childhood Inequalities (GUODLCCI). More information is available on the DIAL website. Thanks for listening to this episode of our podcast, which was presented by me, Chris Garrington and edited by Elina Kilpi-Jakonen.
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.
In Episode 9 of Series 3 of the DIAL Podcast, Professor Andrew King and Matt Hall from DIAL’s CILIA-LGBTQI+ research programme discuss their work exploring how Agent Based Modelling (ABM) can contribute to the study of LGBTQ lives, and conversely, how theory and insights from LGBTQ studies can inform the practice of ABM.
Read moreModelling the LGBTQ workplace for new insights and understanding
In Episode 8 of Series 3 of the DIAL Podcast, Katri Räikkönen from Helsinki University and a member of DIAL’s PremLife project, talks about her research investigating whether the babies of mothers who whilst pregnant are prescribed steroid drugs, because of concerns around premature births, are more likely to develop behavioural and mental disorders later on.
Associations Between Maternal Antenatal Corticosteroid Treatment and Mental and Behavioural Disorders in Children is research published in the Journal of the American Medical Association.
Read moreAntenatal steroids: are there links with mental and behaviour problems later on?
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In Episode 7 of Series 3 of the DIAL Podcast, Rita Pereira from the Erasmus University in Rotterdam and a member of DIAL’s Gene Environment Interplay in the Generation of Health and Education Inequalities(GEIGHEI) project, talks about her research looking at the links between mothers’ smoking and their baby’s birthweight.
The Interplay between Maternal Smoking and Genes in Offspring Birth Weight is a DIAL Working Paper by Rita Dias Pereira, Cornelius Rietveld and Hans van Kippersluis.
About the DIAL Podcast
The increasing gap between rich and poor, exacerbated by the recent financial and economic crises, is a key concern for us all.The DIAL Podcast helps us better understand the causes and consequences of those inequalities, providing new evidence and insights into the complex ways in which they play out over the lifecourse.
In a series of accessible audio interviews focusing on research emerging from the NORFACE funded Dynamics of Inequality Across the Lifecourse (DIAL) programme, we talk to those with an interest in getting to grips with inequality and trying to create a fairer and more equal society for all.
Series 1 of the podcast is co-edited and produced by DIAL scientific co-ordinator Elina Kilpi-Jakonen and former BBC journalist, Christine Garrington of Research Podcasts.