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The dynamics of inequality: what have we learned?

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

Transcript

Christine Garrington  0:00 

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

Elina Kilpi-Jakonen  0:57 

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

Christine Garrington  1:41 

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

Elina Kilpi-Jakonen  1:57 

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

Christine Garrington  2:58 

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

Elina Kilpi-Jakonen  3:20 

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

Christine Garrington  4:34 

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

Elina Kilpi-Jakonen  4:58 

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

Christine Garrington  6:43 

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

Elina Kilpi-Jakonen  6:58 

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

Christine Garrington  8:28 

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

Elina Kilpi-Jakonen  8:48 

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

Christine Garrington  11:00 

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

Elina Kilpi-Jakonen  11:34 

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

Christine Garrington  12:57 

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

Elina Kilpi-Jakonen  13:32 

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

Christine Garrington  15:07 

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

Why rich parents have rich children

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by Sreevidya Ayyar, Uta Bolt, Eric French, Jamie Hentall MacCuish, Cormac O’Dea

The children of rich families tend to go to better quality schools, have higher cognitive skills, and complete more years of schooling. This blog exploits unique data from the National Child Development study to determine these early childhood factors go on to have long-run impacts on an individual’s lifetime earnings, perpetuating a cycle of wealth. These results suggest that policies that equalise investments, such as improving school quality, could promote income mobility.

Rich parents have rich children. Why is that the case?

The children of rich families tend to differ from their poorer peers in multiple ways. They have fewer siblings and more educated parents. Their parents spend more time with them and send them to better quality schools. Their cognitive skills are higher, and they complete more years of schooling. All of these channels have been found to affect an individual’s earnings. However, in order to design policies to improve intergenerational mobility, we need to understand how these channels interact with each other in generating correlations in lifetime income across generations.

Take the example of school quality. Attending a high-quality school may have direct long-run effects on an individual’s lifetime earnings by creating a more valuable professional network, for example. However, attending a higher quality school can also have indirect effects on lifetime earnings through improved cognitive skills and/or the student staying in education for longer. Each of these channels are more likely to benefit the kids of richer parents, who tend to have access to better schools. In a new paper we use mediation analysis to quantify the different channels through which parental income can impact an individual’s lifetime income. We find that intergenerational earnings persistence is mainly explained by differences in investments received during childhood, which in turn drive differences in cognition, years spent in education, and ultimately lifetime earnings.

We exploit unique data from the National Child Development Study (NCDS), which initially surveyed families of the entire population of children born in one particular week in 1958 and has followed them up until today.  The NCDS contains rich information on: family income and circumstances during childhood, indicators of quality time that parents spent with the children, proxies for the quality of schools that they attended, measures of cognitive skills, as well as final educational outcomes and earnings throughout the lifecycle.

Table 1 shows gradients for some of our channels of interest by parental income tertile.  The table shows that children from high-income households have fewer siblings and more educated parents than those born to lower income parents. Teachers report that high-income parents are more interested in the education of their children.  Furthermore, children of high-income parents are more likely to go to schools where: parents attend educational meetings at age seven, student-teacher ratios are low at age 11, and a high fraction of students are doing GCEs at age 16 (an optional exam for progressing to further education).  As a result, children from richer households develop greater cognitive skills; at age 16, reading scores were 21% of a standard deviation higher on average for children with high-income parents compared to children with low-income parents.

Table 1 describes only a subset of the variables we use.  We combine these variables using a factor analytic approach to predict latent time investments, school quality and cognition, similar to Heckman et al. (2013).  The factor analytic approach allows us to use all measures available to us by treating them as noisy measures of school quality, parental time, and cognition.

Table 1 Sample means, by parental income

Table 1 Sample means, by parental income

Note: The final column reports P-values from the F-tests testing the null hypothesis of equality of means across parental income tertiles. The time investment measures are teacher-reported measures asked when the children are 7, 11, and 16. Teachers can evaluate parents as very interested, a little interested, not interested at all. We report the fraction of mothers and fathers who are very interested.

We find the intergenerational elasticity of earnings, or IGE (which is a measure of the relationship between parental and child lifetime income), to be 0.32 for men and 0.24 for women. The first part of Figures 1 and 2 shows the fractions of this relationship explained by differences in family environment, time investments, school quality, cognition at 16, and completed years of schooling when we only allow for direct effects of each variable on lifetime income. These variables explain over half of the IGE –– 54% and 62% for men and women, respectively (the remainder is explained by factors beyond the ones we consider, such as better job networks).

Figures 1 and 2 summarise the main for results for men and women respectively.

Figure 1

Figure 2

In the first panel of each figure, we find that years of schooling and cognition explain significant and large fractions of the IGE, both for men and women.  We then investigate whether schooling and cognition are driven by earlier life investments and family background.

In the second panels of Figures 1 and 2, we allow for indirect effects via years of schooling. For example, for cognition, we now additionally account for its effect on lifetime income via its effect on years of schooling. Doing so, we find that the fraction of the IGE that was previously explained by differences in years of schooling can actually be explained by differences in cognition, instead. This suggests that it is not parental income per se, but the higher cognitive levels of children of high-income parents that encourages higher educational attainment.

The next level of our analysis, illustrated in the third panel of each figure, addresses the sources of cognitive skills.  We allow for parental time investments, school quality, and family background to affect the IGE not just directly and via years of schooling, but also via cognition. We then find that the fraction of the IGE that comes from the cognition gradient can largely be explained by differences in time and school quality investments received during childhood. This is consistent with previous literature that has found significant effects of parental investments on cognitive development.

Lastly, we let family background – which comprises mother’s and father’s education, and number of siblings – have an indirect effect by affecting investments. Once we do so, family background explains 19% (34%) of the IGE for men (women). This contrasts with the zero effect of family background that we find in our baseline analysis. In other words, family background matters, but only because it affects investments, which then affect cognition and years of schooling. This result is consistent with Carneiro et al and Akresh et al. who find that increases in parental education lead to more favourable child outcomes. However, we also find that even if we control for family background, the remaining parental income gradient of investments explains 28% of the IGE. This suggests that higher parental income directly leads to higher investments in children, and not only runs through family background, a point which we have developed in greater detail in a forthcoming paper. This supports Bastian and Lochner’s (2021) conjecture that the increase in financial resources from programmes such as the Earned Income Tax Credit are what drives improvements in child outcomes.

Thus, we conclude that the main driver of intergenerational earnings persistence are differences in investments received during childhood which subsequently leads to improved cognition and more years spent in education.  Many of these investments, such as school quality, are the subject of public policy debate.  Our results suggest that policies that equalise these investments could improve income mobility.

The Intergenerational Elasticity of Earnings: Exploring the mechanisms, is research by Uta Bolt, Eric French, Jamie Hentall-MacCuish and  Cormac O’Dea.

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

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

 

Transcript

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

 

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