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Welcome to the LSE Events podcast by the London School of Economics and Political Science. Get ready to hear from some of the most influential international figures in the social sciences.
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Hi, good evening. Welcome. Welcome to LSE and this hybrid event. My name's Coretta Phillips and I'm a Professor of Criminology and Social Policy here at the lse and I'm here in my capacity as the head of Department of Social Policy. I'm really delighted to welcome our esteemed guest and speaker, Professor Hilary Hoynes, and also to welcome our online audience and also, of course, to welcome all of you that are present in the theatre today. It's really fantastic to see so many of you and I think it's a testament to how excited we are to hear about the ideas and thinking of our speaker. This evening is the fourth in our annual lecture series where we invite eminent thinkers in the field of international social and public policy, where we're keen to hear from those with ideas that can move our thinking forward in our disciplines. Before I introduce our speaker more fully, I need to just do the usual admin housekeeping things. So in the event of a fire alarm, the fire assembly point is opposite this building on the corner of Lincoln's Inn Fields, where if we have a fire alarm, it's bound to be raining, isn't it? So hopefully people have umbrellas. For those ex users, the hashtag for Today's event is LSESocialPolicy. The events being recorded and will hopefully be made available as a podcast subject to technical difficulties. Please can I ask everyone to put their phones on silent so as not to disrupt the event? I also would like, before I introduce Professor Hoynes, to offer thanks to colleagues who've made this event possible. As ever, these things take a huge amount of work. So I'd like to thank Professor Almadena Svea and also Doctors Eva Tetova and Robtel Nij Paley, who are our seminar organisers. I'd like to extend thanks to our brilliant events and comms team to Maria Schlegel and Pippa Iger, and of course to our stewards. Thank you for supporting this event. So let me introduce Hilary Hynes. Hillary is Chancellor's professor of Economics and Public Policy at the University of California, Berkeley. She's also the Faculty Director of the Opportunity Lab at Berkeley. Professor Hoynes has a very large number of accolades which will take me a long time to read out, so I'm going to just summarise a few of those. So Professor Hoynes is an elected member of the National Academy of Sciences, the American Academy of Art and Sciences, and the National Academy of Social Insurance, and is an elected fellow of the Society of Labour Economics. She's a member of the National Academy of Sciences Committee on National Statistics. She served as the editor of the leading journal in economics, American Economic Review. She's the recipient of the Daniel M. Holland Medal from the National Tax association, honoring her lifetime achievement in public economics, and also a recipient of the Carolyn Shaw Bow Award from the Committee on the Status of the Economics Profession of the American Economic Association. And that really is just a small number of the accolades. Thank you for bearing with me. So Professor Hoin's talk this evening is entitled the Social Safety Net as an Investment in Children, and she'll speak for around 45 minutes and then hopefully we'll have a good 20 to 30 minutes for questions afterwards. And I'll remind everyone of the format at the end of the presentation. So thank you.
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Thanks so much for having me. It's wonderful to be here. I just dropped something on the ground, but that's okay. I think it was only a pen. I'm really excited to talk to you today about some work that I've been doing and many others have been doing that sort of fits all under this sort of category of the social safety net as an investment in children as a sort of starting point. When you're learning economics, studying human capital models, it's a very kind of familiar setting that we think about certain investments in society as ones where there's some kind of upfront investment. You have to make, say, arguments in favor of preschool education and that in thinking about those policies, we kind of think of them as sound policies in the sense that they're going to return, there's going to be a return on that investment that might take some time to play out. Children might enter school more prepared because of this preschool that then makes them able to take advantage of more in school and that might pay rewards in the long term to society, not just themselves because they may earn more, pay more taxes and that sort of thing. And what's kind of interesting is I'm a scholar who studied the social safety net most my whole career and for most of my career we've spent time in thinking about the safety net very much with an eye towards how we structure the safety net and how it affects parents decisions. How much do you work if we set up the program this way or that way, if we're giving resources to households, how does it affect attachment to the labor Market, how does it affect decisions around marriage, fertility? And in that discussion, which are all good things to do, I've written many papers that study those things, but we do very little and much less that studies what good these programs do. And so what I'd like to talk to you about is sort of growing evidence that shows that public investments like cash assistance in kind assistance, that are focused on families with children, you've got to pay up front for them. You're transferring these resources to those families. But those families, the children, do better off in the long term, and they in turn sort of pay back to society in adulthood as they're earning more, have higher education, less connection with the criminal justice system. And so I want to just take this 45 minutes to kind of give you a little bit of a sampling of what we've learned sort of in this space. Some work by me and some work by many others. So to start with just a little history or maybe a table setting, this scatter plot here just gives you at a point in time, the relationship between how much different countries are spending on what the OECD calls family benefits. For those of you familiar with the OECD data, expressed as a share of GDP to account for the fact that of course, some countries are much richer than others, others. And then on the vertical axis is the poverty rate of children in those countries. And you know, this is just a correlation. It's just data at a point in time. But it shows a rather striking relationship that is not very surprising that the more money we spend on family benefits, the lower the child poverty rate in that country. So the United States famously spends relatively little compared to other countries and certainly compared to other rich countries. And we have a kind of persistently quite high child poverty rate. Just for comparison, you can see the child poverty rate and the spending here in Great Britain. There was a time where Anna Iser and Adriana Yaras Mooney and I were sort of staring at this data and talking over coffee one day of like, why is it that that's just been so persistent for so long? Why has the United States been in that upper left hand quadrant of spending for so long? One of the things that we did to just try to investigate one of the many hypotheses that we talked about over coffee, or maybe it was beer or wine one day was we all came from the perspective that for a long time, as I said in my kind of open, opening statement, that economists, myself and many others have studied these programs through a lens of looking at the effect of the programs on the decisions of the parents and what I would put in the bucket of like the costs of a program. So if I distribute income to you and as a response to that, you work a bit less, that may be good, that may be bad, but it adds to the cost of the program. It's we call like the leaky bucket, right? I give you $100, you work $25 less. I've now spent $100 to only increase your income by $75. So we care about that. Like that's something we want to measure and we spend an awful lot of time measuring it. What we did is we went back through 50 years of articles in, you know, basically top journals and then journals and labor and public economics to see whether or not our what we thought was true was indeed true. And we took each paper that was published on an anti poverty program and we tagged it as to whether the paper focused on measuring costs or whether paper focused on measuring benefits or what we incentives, costs, incentives. I'm using those two things interchangeably. And what you can see is, and I'm like purposefully just dropping the last decade because I'm going to show you that with great drama in a few slides. But what you can see is that indeed we've mostly spent time quite quantifying the incentives of programs. That's the red line. And spent much less time quantifying the benefits of the program. Now, is that the reason why we don't have as robust a safety net for children in America? That part I can't say. But it is true that as a discipline, we've spent a lot more time measuring deadweight loss essentially than we've measured what the benefits of these programs are. And when we first released this working paper, we got a lot of emails from colleagues kind of saying, oh, wow, I feel like that's true in my field too. Like minimum wages, we study effects on employment, but what good does minimum wage does? That work is obviously blossomed, but it's not where our research started for sure. And so if we spend most of our time quantifying the costs of the program, which I am not here to say is not valuable, but it's a little bit hard to know what to make of the cost of a program without also knowing what the benefits of it are. And so I think we've learned a lot because we've started to quantify the benefits of these programs. And if there's little impact of a program on the intended group, say children, then maybe we don't want to tolerate much in the way of cost, which seems quite reasonable. But if the program generates a lot of benefits, maybe we're willing to tolerate some costs along the way. And we really need to bring them both together in order to think about something around optimal policy and whether we're spending enough on these programs or whether we're spending too much and we should claw some back or sunset some programs. So I think that some of the first work that started to work in this to try to quantify the benefits of the programs, we were a couple of papers by Janet Curry and John Gruber which actually studied the U.S. medicaid program, which provides public health insurance for poor families with children. And that to me was kind of the birth of thinking about, oh wow, we should be thinking about what these programs do. And those kinds of analyses kind of started by looking at really proximate things like when a mother has access to Medicaid, are her births healthier? Very proximate to that? Well, we now have work that's like two generations beyond that that shows that when a woman has access to Medicaid, not only is her birth healthier, but that person's say their daughter's birth subsequently is healthier. So it's kind of like a 3 gen analysis showing that spending money on public health insurance, I know a crazy thing, you would never do that in other countries, I'm sure, yields these benefits that are very long lasting and as I'll show you, are not just confined to things like birth weight, but translate to higher completed education, higher earnings in adulthood and so on. And so Medicaid is the program among everything I'm going to talk about today that has the highest rate of return. In fact, the rate of return on Medicaid providing health insurance for pregnant women and small children is so high that the program fully pays for itself in the long run through subsequently higher taxes and lower social spending, which is kind of amazing. It's kind of a high bar that we're not going to get many programs that do that, but they can still be a really good investment even if they don't fully pay for themselves. So we now have just kind of surface level quite a lot of evidence that maybe started from those Curry and Gruber papers and so on. Here's like the initial research was looking at things that were shorter term, more proximate to the delivery of the benefits. And on the left hand side is kind of a list of sidebar. Everything I'm going to talk to you today is really focusing on just to kind of make it tighter and more finite on US Programs. There's certainly work outside the United States, work here in the UK and in other developed countries in Europe. But today I'm going to be focusing on this work in the U.S. so there's a lot of different programs that are studied. I'll tell you more about some of them in this talk. And you can see sort of a range of different kinds of outcomes that people have looked at. You know, when a family gets food assistance, do their children attend school more often? Are they healthier? Does the mother have less stress? Is her mental health better? When there's a bit more resources in the family, for example. And then the work actually extended beyond these shorter term effects to really look at, you know, children are, they grow up and there's, you know, there's opportunity to have a whole lifetime returns on this investment, as it were. And so with these, you know, same kinds of programs, some different, some overlap, you tend to see evidence where people look at the effects of these programs on completed education earnings. Where people live the quality of their neighborhoods. Are they able to live in neighborhoods that generate more upward mobility? Do they have less contact with the criminal justice system? Are they less likely to have a teen birth? Are they more or less likely to get married in the long term? What is their adult health and mortality in adulthood? So it's a wide range of outcomes that have been investigated. And what I want to do is give you a little bit of a sampling, just a little bit of an idea about what some of these studies show. And so it really is a fantastic time to be doing this work. So the data that we, when we wrote this paper, we had data through articles through 2020, and you can see that in the last decade of our data, the kind of underlying context of where scholars are working in this very narrow space has completely flipped. And so you see this real blossoming of work that are quantifying the benefits of the social safety net. And, and I'm going to end this talk by kind of bringing together the strands of this new work on the benefits of the program and combining it with the existing work on the cost of the program to try to kind of put it together, to kind of indicate what we've learned. Personally, you know, taking off my economist hat and putting on my policy hat, one of the things that I've been thinking about is that if in fact these benefit programs generate returns, but it takes a while to get there, that could really influence policy support for these programs. And so I think the dynamic that you see, say in the United States and you know, you'll tell me in the Q and A when you think that's that's also true here is that, you know, more progressive policymakers want anti poverty programs, but they talk very much in the present like we need to reduce poverty. That's a bad thing, we need to reduce it. And then on the other side, maybe less progressive policymakers say, but if we give more assistance to these families, they'll withdraw from the labor market. And we want people to be able to take care of themselves on their own, pull themselves up from the bootstraps with the classic American expression. And so that's the dynamic that's been at play a long time. And so the question I have in the kind of policy part is if we're able to make the argument and switch the aperture a little bit to the focus on children who hopefully we can all agree are awesome and we want them to be better in the long term, that maybe that could shift the dynamic and sort of move away from what I would call as, as a present bias, like we got to spend this money today and there's other things we could do with it and move it towards thinking a little bit longer term. So that's sort of my own kind of wondering, musing, you might say, about how this work might change things. So what is the kind of theoretical, kind of quote, unquote with a lowercase T? Not formal theory, but theoretical reasons. What do we know about why we think it should matter that resources could matter for kids in the long term? Well, it's great to be sitting here in London and have as the first bullet the fetal origins, or Barker hypothesis, as was born here in London, where there was this strong connection made particularly in prenatal setting. So essentially focused on learning about how resource, you know, not having enough resources when you're pregnant not only affects the child at birth, but can have, you know, systematic quantifiable reductions in the children's ability to earn and so on kind of in the longer term. So that's kind of a very kind of narrow in utero development period. There's also important theories around the importance of chronic stress and what it's like to live in a setting with more stress that can come from different kinds of environments and the impact on your physiological development because of stress. And a lot of this early work was very much focused on famines, like something really bad happens. And then we trace what happens to these people who were exposed to these really negative shocks. And there's been an incredible wide range of both health and economic outcomes that have been linked to these. The resource environment, in utero and in childhood. And quite later into that research, many decades into into that research, folks started to think about the social safety net sort of within the same sort of context. And I think there were sort of two things that maybe the research on the social safety net really helped kind of push forward. One is shocks can be positive as well as negative. You know, a lot of this original research was all about super bad things happening like famines and you know, bad weather events. That means the crops go down. So you can study some agricultural kinds of settings and many, you know, pollution and toxins and you know, many bad things. So if bad things are bad for the long run, could positive things like increasing social supports be positive in the long term? And I think here was really one of the places where a lot of this work extended beyond the focus on the in utero period pre birth and focused on childhood. And are there particular periods of childhood that are more, you know, you're more vulnerable that these investments kind of generate a higher return question mark. So that's sort of the warm up the context, the theoretical structure or underpinning to this. And so really what I want to do with most of the rest of the time is just to walk you through some studies to give you kind of an idea about what kinds of things that people are doing. And then I'm going to put them into different buckets. So we're first going to talk about the evidence on the long term effects of cash assistance. And then we'll talk about the long term effects of the tax credits which are also very much in use here in the uk. And then we'll talk about Medicaid and then we'll finish with in kind food benefits, which is the area where I've done a lot of work. And we'll go from there. So let's start with cash assistance. There's not a lot of evidence on the long term effects of cash assistance in the United States. And one study I'm just a huge fan of, so it turns out that the first sort of cash assistance program for mothers and children in the United States actually started in the early part of the 20th century called the Mothers Pension Program. And really it sort of was the beginning points of what became AFDC or traditional cash welfare in the United States, which happened in the Social Security Act in 1935. But this predates it. And Anna Iser and colleagues basically went back into the archives, studied this Historical program, which is super cool because it's long enough ago that you can see the whole life cycle, which we don't always see. And I should say another sidebar here. It's kind of frustrating that a lot of these benefits take a long time to show up. You know, you might have a new policy that comes online. You might expand your tax credit today. And somebody wants to know what's it going to do in the long run. And sometimes our evidence is kind of patchy because we need to wait a while. Nonetheless, they go back in time, and the research design that they use is pretty good. There's probably better ones, but this was what they were able to do. They were able to actually look in the archive and they saw these. These, like, cards that had, like, somebody's name, like Hilary Hoynes, you know, had a daughter named Sarah. My daughter Sarah is actually in the audience tonight. So shout out to her and it'll have some things about us, material supports and so on. And then the social welfare person will write, like, accepted or rejected. And they basically took those. And that's who they compared. They compared the people who were accepted to those who were applied, who were rejected. And, you know, you kind of maybe think that, on average, the folks that were rejected were maybe a little bit better off, maybe that's why they were rejected. But you don't really know. So it's not perfect, but it's pretty interesting identification strategy. So they do that. They then find these children, and then they use a huge mishmash of data from decennial censies to some military records from. For the men, because they were kind of the right age to be enrolling for military service. And what they find is that receiving cash assistance as a child for these cohorts leads to greater longevity. They live longer. This is particularly important. This is they measured by the men who were signing up for military service. You could be excluded from military service if you were too underweight, kind of a marker of malnourishment, reduced the probability of being underweight by half, increased educational attainment and increased income. So these are not necessarily like transformative numbers, but they kind of add up. And that's going to be something we see kind of as a theme across these studies. And just to look at one graph from their paper, you can see that the blue are kind of the treated ones, the accepted, the ones who got the cash benefit. And the red bars are the rejected ones. And you can see basically in this simple graph that there's sort of less mass here. So fewer people getting eight years of education. This was kind of before the growth of the high school movement, and more people getting 12 years of education. So you can kind of see in the data where this seems to be transformed, transforming education. So that's one study. Another study that sort of complements this is much more recent, and this work is still in progress. Randy Aki has a series of papers that uses the fact that in the United States, Native American communities, at some point, I can't remember the year Congress allowed them to operate casinos on their Native American land. And this brought a lot of new resources into the community. And they'd gone into the archive. And apparently the United States government required all these Native communities to basically tell the Bureau of Indian affairs, the government agency that oversees this, what they were going to do with the money. And amazingly, I did not know about this. I don't think many people did. Many of these communities used them as a sort of basic income program for their community. They would raise revenue and they would just deliver it as a demogrant to their community. And so they basically. Their very first paper took advantage of a longitudinal data source that was already in the field in North Carolina that happened to encompass a Native American community. And they essentially compared children of different cohorts in this community, some of which were too old to get the benefits of this new revenue source and others that were younger and got the benefits of it. And there was a control group of folks that weren't part of that Native American community, but in the same labor market. And what they found is that having access to this new cash transfer led to an increase in their education and a reduction in credit. Criminal activity in young adulthood was kind of a small sample. They've now collected enough data across many different Native American communities and linked to internal admin data from tax records in order to do a much more comprehensive study across in much bigger samples. And so I'm really excited for that work. I think it's very close to being released. So a similar theme. More. More cash, more education in the long term. So now let's talk about something that's very similar to that. It's also cash, but it's cash that's received through tax credits as opposed to just kind of welfare type programs. So the main policy that does that in the United States is the Earned Income Tax Credit, which is like the Working Families Tax Credit. Is that what it's called? No, I know it's changed names a few times. Somebody help me out to get the right name here. Is that Right, that's right. It's universal credit now. Was before that. Okay, big go. All right. I was only one generation off universal credit. So it's been around for a long time in the United States. And it's sort of a policy that has been kind of embraced, embraced by both more progressive and less progressive presidents and Congress. So it's increased a lot, you know, different times, which is helpful. And so there's kind of a whole kind of period of changes in the earned income tax credit that, you know, depending on how old you are, you would have access to different kind of amounts of benefits at the family level. And many, many states, and increasingly states are topping up the federal credit with state credits that sort of look very similar. And so then you've got variation not just across sort of child cohort, but also across different geographic areas in the United States. And there's tons and tons of work on the earned income tax credit. And interestingly, the kind of mechanism for the effects of the of the earned income tax credit operate not only through income, but through maternal labor supply. Because the earned income tax credit is only available if you work. It's a work promotion program where you get these tax benefits which are not small. It could be $6,000 if you have a family with two children, which, if you're a minimum wage worker in America, is going to be about 40% of your earnings. So it adds up to a lot, but you have to be working to get it. So we know that the earned income tax credit leads to more work. So whatever we see in the long run, it's sort of a combination of mom working more and having more resources. And so you need to think about it. And we don't have a lot of, like, causal estimates of the effect of mom working separately from income, because they tend to be bundled. So we don't know, but there's a lot of work. And just to kind of give you a bit of a life cycle view on what this work has shown, we find that children having access to this tax credit leads to better test scores when they're in school, higher levels of completed education, better labor market outcomes. The folks that are affected by this aren't that old yet, so. So we don't have lifetime earnings or anything like that, increases in income. And when we look at the mom, you see biomarkers showing less stress in the mom, which could be one of the mechanisms possibly. And so let me just give you just a taste of just one of these studies where the results are just really clean to see so there's a study by Andrew Barr and co authors that was published recently in the, in the Quarterly Journal of Economics. And here's what they do. It's very simple. It turns out when you file your taxes, who is your dependent depends on who is born by the end of the year, end of the tax year, December 31st. And so if it's, say, my first child and my kid is born on December 31, my first year of tax benefits, benefits for having a child arrives just a few months later. You file your taxes, you get your tax refund in general in February, my kid is born in early January. I don't have a dependent in that tax year the year before. It's going to take a year and a couple months for me to get my first child benefit. It's so sweet. It's so simple. So essentially what they do is they take all the data on all births linked in the tax data, and they look at those that are born near the end of the year, so this is like 30 days before the end of the year. And then compare them to those born at just the beginning of the year. And this first graph is just showing you that indeed those born in December get on average, a tax refund that's, you know, something like $1,300, not $6,000, because some have some earnings, some don't, blah, blah, blah. I mean, higher earnings, lower earnings, et cetera, compared to those whose first child is born in the next year. And so that's your treatment. Kids that are born in December get $1,300 that they, they don't get it. The family gets it in their first months of life. And those that are born just a month later get that same amount, but they have to wait a year before they get it. Okay, so that's it. That's the treatment. So here's a flavor of what the results are. So they then follow these children into adulthood. And here you can see they're measuring them at ages 26 to 28. And it's men on the right and women on the left. And they're organized in the same way that we have this first stage organized. And you can see over here on the left, for women, there's not much difference. The women are earning. I'm sorry, and what are we showing you? We're showing you their earnings in this young adulthood. And you can see that women earn about $29,000. And it doesn't vary much at all between those that got the benefit that year versus don't. But you really see this emerge for men. And there's sort of a growing literature that men's boys seem to be more vulnerable and maybe these resources on the margin seem to help boys a little more than girls in some studies. And so this is, you know, again, it's like a modest effect, it's like an 8 or 9% effect. But these things aggregate over time. And in fact, when they take this result, I think this is boys and girls together. You can see that as these children age, you see these earnings effects sort of growing over time. So $1,300 in the first year of life, a modest treatment leads to a quantifiable, not life changing, but a quantifiable effect on earnings 25 years later. In another study by McInnes et al. They show that the sort of aggregated amount of the earned income tax credit over a kid's lifetime, I think age 0 to 17, leads to increases in the probability that your income is above a certain threshold. So for example, this point here is, is your income above 100% of the poverty line? And you can see here's a zero line that having more access to the earned income tax credit increases your income in adulthood at some age that I don't remember. But it really doesn't change anything above something like 300% of the poverty line, which maybe isn't surprising, but you can sort of see where those gains and income are. So that's a sampling of the earned income tax credit, Medicaid. So again, public health insurance for children. To just give you a little bit of an idea about what has been happening in the United States over the past couple of decades. Back in the early to mid-1990s, we had very high rates of not being insured children, not having any health insurance. And through a sequence of expansions of Medicaid that actually started under the first Bush, President, George Bush Sr. And then continuing on through a couple of presidents, Medicaid was expanded to sort of COVID all children up to 100% of the poverty line. And you can see that sort of over this time period, this real convergence of the poor and near poor families rate of children. I know this crazy to think about it, but it's just the reality in the United States that we don't have universal health insurance. So it's a patchwork system. We've really improved it for children, but we're not there yet with adults. And with the tax act that was just passed, it's going to get worse. But that's a story for another day. This is a good story. Health insurance, the rate of folks being uninsured went down a lot. And so there's many, many, many studies that look at this, and I think the original thinking was, well, this should improve health. But turns out it's had really quite dramatic effects on economic outcomes as well. So likewise, over the kind of life cycle, we see that expanding health insurance to children improves their health in their teen years, it improves their health in young adulthood, it improves the economic outcomes. We've got higher earnings, lower criminal justice involvement, higher rates of education. And this is the one I mentioned at the very beginning. And one generation down, the children of the children that had access to public health insurance are born healthier. So again, to give you just a little bit of a sense of, of what this looks like, some of the changes in the policy that led to this reduction in the uninsurance rate had another one of these amazing, like, opportunities to look like that. Births in December versus January. And the reason was when the first law was passed to expand Medicaid, they didn't grandfather in all the children. They said if you were born after September 30, 1983, then you would need to be covered. And they told the states they had to do that. But if you were born before that, you wouldn't have to be covering these children. And so that's pretty cool because if you look in data today and see when someone's born, you can have a good estimate of how much their health insurance coverage was born improved by these policies. And so here's. They're not nearly as tight an estimate as the ones I showed you before, but here are some estimates from that work. So, for example, over here you can see here's that October 1983, that first month of additional coverage. So kids over here to the right had more access to Medicaid and kids over here to the left had more, less access to Medicaid. And you can see that at ages in these teenage years, this is for Black children ages 15 to 19, they had lower rates of mortality. That's a pretty extreme outcome. Obviously, if they had coverage, health insurance coverage. And the nature of this expansion of Medicaid really affected kids like ages 7 to 14 for reasons that are not interesting to go into. And that's why they start measuring it at 15. So it's not about having coverage today, it's about having coverage before. And the reason why it's argued that these results may show up more for black children than white children is they don't. In their data Observe who's actually receiving Medicaid, that that longitudinally isn't available in the data. And on average, black children are raised in families with lower income and are more likely to have income below the poverty line where these policies were expanded. Or at least that's one explanation. So you see that having access to health insurance lowers mortality and important it lowers mortality for external causes. I'm sorry, internal causes, not external. External is like violence, homicide, things that we don't think should be affected by health insurance. And these are internal causes, more physiological causes. Subsequent work now as these kids get older, this is work by Wary et al. And what it does is first looks at visits to the hospital as a measure of health. And this is all visits to the hospital and emergency department. And these are visits for chronic conditions. So maybe things like asthma might be a very common one at the emergency department. And likewise you can see these estimates to the right of the zero. And these are much tighter. This is not important for me to tell you, but these are the folks that got access to the Medicaid because they were born after September 30, 1983. And these are those that did not. And you can see a quite sharp reduction. And these attendance of the emergency department at ages age 25. So you're starting to see these things accumulate over time. And you can this. I've circled the ones for the black young adults and you can see that the impacts really load in the chronic conditions opposed to non chronic conditions, which we think is more related to having regular access to care. Another recent paper by Ehrenberg et al. Shows that those that have access to Medicaid have lower connection to the criminal justice system in adulthood. I think this is by age 25. So you know, a variety of outcomes that show once again essentially providing more of this assistance when children are young lead to improvements in their outcomes in adulthood. So some of the work that I've done is on what's called the food stamp program or SNAP in the United States, which is, you know, essentially it's a voucher program. You get an EBT card that's loaded up with dollars. And how much you get depends on your family size and how much income you have because it's phased out as your income goes up. And it is, you can use it to buy any food in the grocery store. So it's pretty close to cash, but it's technically not cash because I say close to cash because we all got to go to the grocery store and buy food. And so this is displacing some of what we would spend on food with the, with the debit card. It turns out that when the food stamp program was first introduced in America, it took about 15 years from the first counties that got it to the last, last counties that got it. And so if I observe in the data when someone was born and where they were born, I can sort of have an estimate of how much access to food stamps they had in their childhood. Again, I don't know that their family was on food stamps, because that data doesn't exist longitudinally back to the 1970s and 1960s when this expansion took place. So it's a very much of a kind of reduced form, intent to treat, sort of analysis of studying the rollout of this program across the United States. So to give you a sense of what we found, this is slightly more complicated than the last graph that I showed you. What we're doing here is I observed someone in the data. I observed what county they were born in, and I observed when they were born. And that along with the archival data that we found about when their county of birth started their food stamp program, we can get an estimate of how old I was when food stamps was introduced in my state. And so this is like an event study graph, except the event is in how old I was. And all of these, like, in all event studies, it's relative to zero at some normalized year, which arbitrarily we chose as year age 10. Like our hypothesis was, this program might make more difference in early childhood compared to later childhood. So that's just what we did, not important. And so what you can see here and what we're looking at in this graph is a measure of your human capital. So more human capital is better, higher education, more likely to have a professional job, and so on. And so the way to read this graph is as I move to the left, I'm tracing through what the data say about how somebody's human capital changes as they get more access to food stamps. In other words, it started when I was younger, it starts when I'm younger. It also means I had access to it for more years. Those two things are the same in our setting. What you can see is in this later childhood period, having more access to food stamps doesn't change your human capital in the long term. But somewhere around this early childhood period, maybe before children go to school, when they also have food assistance at school, that could be why you tend to see that if I had access to food stamps at age 4 or age 3 or age 2, or age 1. So more years, my human capital improves. And it almost is, it's like almost kind of linear and a kind of what we call like a dose response. Way more months, more years better. And over here, in this range between minus 5 and 0, it's like it shouldn't matter if food stamp was implemented five years before I was born or three years before I was born. I had access to it the whole time. And so it's kind of handy that this graph flattens out over there. It's sort of a test of whether this is exogenous. So bottom line, more access to food stamps improves your human capital. And in graphs I won't show you, we have sort of similar graphs that shows that you end up in better neighborhoods, you earn more, you're less likely to die early. You know, these people are only in their 50s, lower rates of incarceration. They're not huge, but they're significant. And they, as I said, they sort of accumulate in another paper just very quickly. We do exactly the same thing, except it's a smaller sample, so there's kind of fewer dots here. And we look at metabolic syndrome. And the reason why we looked at metabolic syndrome, which is like heart disease, high blood pressure, diabetes, these kinds of risks, is because that original Barker hypothesis was very much about if you have bad nutrition early in life, you've got worse metabolic health in adulthood, like higher disease, risk of heart disease, and so on. And so here you get a very similar story, except metabolic syndrome is bad, whereas human capital is good. And so having access to food stamps in early life sort of reduces this risk of metabolic syndrome and then it flattens out. And likewise, having access originally in that later childhood more or less doesn't seem to change that. So it seems in particular, early childhood is real important for this health program. So before I wrap up, let me very quickly just talk about a few puzzles and I think some key places where we need to learn more. First of all, not all safety net programs generate long term benefits. So there's a recent paper by Miller et al, or maybe Hawkins is the first author on a cash program called ssi. And this cash program is particularly targeted at disabled people. But for many decades they've accommodated children in that disability definition, even though disability is generally related to your inability to work. And so they study this program for children and it turns out if your birth weight is below 1200 grams, which is really small, I had to calculate the pounds because I don't think in grams is 2.6 pounds, you're automatically given access to this program. It kind of like defines the disability simply by your birth weight because it's such an early birth, such a premature birth. You automatically get this benefit if you also income test into it because it's income tested. And so they match birth certificates with long term outcomes from tax data as these kids are in adulthood. And on the top of this slide you see all these things that basically show indeed if you're born below 1200 grams, you're more likely to get SSI. So the rules are set that way. And in fact it's true in practice. So you see these sharp, like for example on the left it says do you get an SSI benefit in your first year of life and infant? So if you're born to the left of 1,200 grams, the answer is yes, on average $250. If you're born to the right, it's much lower. So yes, that works. They match the birth certificates to these later life outcomes and find, I mean they brought so much data to this project. I'm just showing you one slide. It's just zero everywhere. So why might that be? Well, we have much more to learn. The authors kind of miss or think in their conclusion that there may be certain kinds of disadvantages where cash is just not sufficient to get over what those disadvantages are. So that's one observation. Not all safety net programs translate to improvements in long term outcomes. And the second observation is we know very little about mechanisms. About why what? You know, I'm looking at people who get food stamps in the 60s or 70s and then I observe them in the tax data, you know, in the 2000s, and I don't observe a lot in between. And one of the reasons why I think it's really important today to point out that we don't know about mechanisms is there is a ton of attention in the United States right now to a couple of different studies that have showed null effects of basic income, cash, RCTs, parents and children's development. So how many people have heard about babies, first years? Maybe people are talking about it here, maybe they're not. There was also a, that's one of the studies that had been going on for a couple of years. And a recent study, which I think is mostly being tagged as open research as the name people are calling it, had a quite excellent RCT where they were randomizing whether people get $1,000. These are unconditional benefits for a fixed term. So there's a bunch of studies on this and the bottom line is there's just, there's very little evidence that it's moving child development, how parents interact with children, how parents spend their time. You know, they may buy a bit more of child oriented kinds of things for the home, but that doesn't seem to be changing things along, along the way. And there was this very high profile piece by an excellent reporter for the New York Times, Jason DeParle, who's been writing on sort of cash welfare for 40 years, where the title is study may undercut the idea that cash payments to poor families help child development. So it's really like, you know, really made people think a lot about all this work. And so the question is, do these studies tell us that everything I've just shown you must be wrong and flawed and error prone? I don't think so. They're all short term studies. But what they do tell us is a lot of our hypotheses about where the mechanisms are in terms of how this additional resources translates to better education and earnings in the long, long run just might not be right. And that we need to do more to try to understand what the mechanisms are. I don't think it necessarily says that my whole lecture is irrelevant, but anyways, there's like a lot of talk about this right now in the US where there's just a real proliferation of an interest in basic income and it's being tested all over the place. So it's kind of interesting. So stepping back to wrap up, so there's this thing called the marginal value of public funds, which is a way to take all these kinds of benefits of these programs that I've been talking about and compare them to the costs. And the numerator of this measure is essentially like how much better the family is off because we provided this benefit. So my kids earn more in the long run, they've got higher education. That's all good for the family. That's kind of in the numerator. And the denominator is how much does it cost the government to provide this benefit? And back in the day before we measured the benefits to these programs like we were doing a lot in the denominator and not much in the numerator, basically. And so if you take my food stamp program as an example of, of this, we calculate that the benefits to the family of having food stamps are $56 for every dollar of government costs. Now that doesn't mean that the program pays for itself because the numerator is my own private value. And the big calculator in this to be clear, is by mortality going down and with the value of a statistical life being high, that's a lot of this $56. But if we hadn't have done all of this work that shows that kids who have access to food stamps, have higher education, are less likely to die young in life, we would have a marginal value of public funds that's less than one. And the reason it's less than one are exactly those. When I give parents food stamps, they work a bit less. And so that's that leaky bucket. So we lose thousands, 30 cents on the dollar in leakages when we fund this program. And if this is all we looked at, we'd be like, dang, 30 cents on the dollar is a lot to lose. Maybe we shouldn't have this program. But that doesn't seem fair if you're not also calculating the benefits. And so it's just kind of striking to look at that difference. And so I think that's what this work has done. And if you're kind of interested in this rate of return, I highly recommend this paper by Hendren and Sprung Kaiser that does this marginal value of public funds calculation for 166 different. I don't know how long it took them to do this, but they've calculated it for different programs that are like different kinds of programs, cash assistance, et cetera. And also whether the program hits somebody young or, you know, older. It's really, there's kind of a lot to, to take there. So I just want to end here by saying I hope that this has given you a little bit of a glimpse into this kind of emerging literature that shows that spending more of a society in basic kind of safety net program, kinds of income support programs can both generate improvements and outcomes for families, but also sort of pay back that tax investment in the long term by these improvements in outcomes in the long term. And I think that we should be thinking about that when we think about setting policy. So I will stop there and I will just leave this up here as some other questions that I think are unanswered. So thank you very much.
- [56:44]
B
Hi, I'm interrupting this event to tell you about another awesome LSE podcast that we think you'd enjoy. LSEIQ asks social scientists and other experts to answer one intelligent question like why do people believe in conspiracy theories? Or can we afford the super rich? Come check us out. Just search for LSEIQ wherever you get your podcasts. Now back to the event. Thank you so much for a really stimulating talk. It's given us lots to think about and reflect on. We have lots of time for questions, so can I please ask for those that are online, if you want to just put your question into the chat function, it will work out its way to me. Please include your name and affiliation. And then for those of you that are here in the theatre, if you can just raise your hand, we'll have a roving mic that can come round and same thing there too, please, if you can say your name. We're really keen to hear from students and our alumni, so we're going to first start with the questions from our online audience. So from Iannis Bandunas, the question for Hilary is, do we see a difference in outcomes between conditional and unconditional assistance, such as those closer to ubi like policies?
- [58:22]
A
That's a great question. Everything that I talked about here, you'd categorize as consistent. I mean, there's different kinds of conditioning one can do. Like in more developing countries, there's conditional cash transfers that might be conditioning on things like my child needs to go to school or they need to get regular checkups. In the work that I'm talking about here, the main conditionality is that if you earn too much, you're going to earn your way out of the program. So it is conditional on your income. It's not unconditional on your circumstances. And for some of the programs, like the earned income tax Credit, it's also conditional on work. So these two, as it turns out, these two, the baby's first years and the open research RCTs are both unconditional. And so one of the things that folks have been talking about is particularly the baby's first years team has been talking about, okay, this is, this is very, it's very American perspective, is like seeing your mom work is somehow an important role modeling for a kid's future. And the reality is we actually don't know much about the causal effect of maternal employment separate from the things that comes with maternal employment means, which is more resources in the household. And so it could be that mom working is bad for kids because you can't get good daycare, or maybe your older children are left unattended when they need some supervision. Or it could be that mom working is good for kids because she's got good, she feels good about herself and that translates to the home. So we don't really know. But everything that I've shown you today, you do want to think of as conditional. And we don't have as much. We certainly don't have evidence on the long term effects of unconditional cash transfers because that's really a very new phenomenon. There's more evidence when you zoom out like these child benefits which are in many countries not tied to work. Sometimes they're phased out at very high incomes, but they're more universal. There's more evidence that shows that those create improvements in the long term. So that would be sort of like the closest evidence we have on the long term effects of unconditional transfers in a kind of cross country setting. So it's a good question. I do think that's one of the questions out there right now that people are trying to ascertain the role.
- [60:54]
B
Thank you. So we have another question, very topical from Anthony Valiant, a LSE alum on metabolic syndrome and also linked to thinking about the implications of the conflict and violence in Gaza. And the question is, what are the recovery chances for the malnourished kids in Gaza?
- [61:24]
A
Well, it's very difficult to even talk about this, but I guess what I would say is that there's a way in which everything that I've been talking about here is showing that if we do something more, it can change outcomes in the long term. None of them are transformational, but they add up. But you do see these very strong effects of malnourishment early in life, life in utero and in the first couple years of life that have, even if everything tomorrow returned to normal, which it has, not that the prediction would be that those children, particularly the young ones, would be at greater risk for metabolic syndrome in adulthood. But is it a risk that's 100%? No. So, I mean, this is where I'm not even sure I could tell you what that number is. But of course, what we also know from so much research is quite apart from the nutritional channel. We know about these chronic effects of stress, which you're not going to take away. Those have been experienced. So I am sure that people who are much more informed than me have estimates for just how dramatic these effects might be for these children. And there's no question about that from the research.
- [62:42]
B
Plus, as you say, the addition of impacts of conflict and the trauma.
- [62:46]
A
Yeah, absolutely.
- [62:47]
B
Okay, let's take a couple of questions from people in the room. So a keen questioner in this corner.
- [62:57]
C
Thank you for coming. Professor Hoynes, I'm Martha. I'm a student at LSE in the School of Public Policy and I recently came from Washington D.C. where I was working at Urban Institute. We really admire you, your work at Urban Institute. I'm sure you and I share concerns about the new work requirements that will be imposed on snap. And I wanted to ask you, when we have a political window in the future, perhaps with a new administration or a new Congress, to instead reduce barriers to access to the program or perhaps expand snap, what changes would you make to the program?
- [63:38]
A
Well, it's interesting, awesome that you're here and that you were at Urban. I was actually just at Urban Institute on Tuesday for an event that I co hosted with them that is basically kind of a like we should be continuing to study to learn about what these programs do. It was kind of focused on the 2021 child tax credit expansion. This kind of, of collaboration that I did with Urban, which was really awesome event. Wes Moore, the governor of Maryland, was our keynote speaker. It was really an awesome event. So there was a big tax bill that was passed in Washington a few months ago. And you know, you can think about that bill as sort of doing two things. One, it's really lowering taxes on firms and very high income individuals. There's a little bit of an expansion to our child benefits program, but you essentially need to earn about 20 plus thousand dollars a year to gain that benefit, which we could talk about later. It doesn't really make much sense that that's where you'd expand the benefits. But so there's that piece of the, of the tax bill. The other piece of the tax bill is maybe we should help to pay for this by cutting benefits to Medicaid and food stamps and the main ways. One of the main ways that they're cutting benefits to Medicaid and food stamps is implementing new conditionality which says that you need to work. You can only get these benefits for a certain amount of time if you're not working. And after that period of time you get kicked off. And what we know, we have some evidence about what this might do because food stamps already has a work requirement. It doesn't affect that many people, but essentially you need to be up till age 49, 54. 54 because the policy reduces it to 49 and you can't have any children and you're not disabled. So there's this population of people who are basically single, young, low income individuals that if they're below that age they get hit with this work requirement. And you see in the data so clearly that when they hit the work requirement they get kicked off the program. But guess what happens to their earnings? Doesn't change their earnings. So basically most people are working, but they might, might have. I don't know, like work that's a little precarious, you know, you're in, you're out. You have to go in and document this work. So it builds in these administrative burdens that you have to jump through the hoops in order to document your eligibility. And so the work, the existing evidence shows so clearly that this is not a mechanism to increase people's work. Most people who are able to work on these, you know, in these situations kind of are working, but it's a good way to kick people off programs. And so that's what's going to happen. I think I'm actually been in some conversations and meetings with my state, the state of California, to try to help them figure out how to make sure that doesn't happen. Like, can the office that administers SNAP partner with the Labor Department to get people's earnings directly? So I don't need you to come into my office and document that you're earning. I can see in the admin data that you are and automatically basically take the burden off you and put it on me. And so some states are going to do that and they're going to figure out clever ways to try to get their population to remain on the program. And some states won't. I think famously, when medication had a work requirement for a short period of time in Arkansas, the rules were that you had to call the office between the hours of like 6 to 7pm and that was it. That was the window. That's when you could call to go through the process of documenting that you're working. Well, guess what happens? You know, the phone lines are full, people don't get through, they get kicked off the program. So it's pretty brilliant, really, if you're looking for ways to reduce a program and somehow have the burden be on them and have the. Well, we're just trying to help people, you know, have employment. So anyways, I digress, but. So I think there are some things we can do. And at this urban event that I was at the other day, day, we ended the day with a conversation between CeCe Rouse and Doug Holtz Eakin, who had both chaired our Council of Economic Advisors. And the conversation was all about what we should be doing. And there was a lot of conversation about you got to do the work now to get your policy proposals on the shelf so that when the window opens, you're ready to act. And so I think there's a lot of thinking about that.
- [68:40]
B
Perhaps if I could just come in and follow up, because it was interesting to me that you referred to children, that there was this exciting time in terms of the availability of research data and that children are awesome, I think you said. And you know, I think they are. Yeah, I guess it depends on the child. But yeah, generally speaking, fair enough. But my question was going to be really around the possibilities of, in the kind of ideological climate that you're facing and is so varied across the US we might think of the power of social science research as being able to kind of leverage potentially political influence. And you've talked about in how California that works, what are the options where there might be less receptive kind of political audiences. What's the role of us as academics to, to persuade, to kind of work around the importance of evidence based policy making. And I'm just wondering if you've got anything to add beyond the kind of California experience.
- [69:46]
A
Well, I'll, I will paraphrase what Doug Holtz Eakin said in this event. He is a Republican and had worked for two different Republican administrations. And as he put it, we are living this, this particular administration is working in an information less environment. So it's, that's not what they want. They're doing what they want. They're not listening to advice, I think is from social scientists is that's what I hear. It's, I don't have, I don't have that experience. I don't know myself. And I'd be interested maybe when we all have a glass of wine in our hand out in the lobby to hear whether or not there's been the same sort of decline of faith in elites in Britain the way that there seems to definitely be in the United States. And I think that's kind of part of it. What the origin of that is, I don't know. But it is a kind of backdrop, I think, for the moment that we're living in now that I hope doesn't last too long for our policies.
- [70:48]
B
Thank you.
- [70:48]
A
Yeah.
- [70:50]
B
Back to the audience. So the person in the green top. Hi.
- [70:56]
A
Thank you so much.
- [70:58]
D
I'm sorry, my question is methodological, but so I was particularly interested in the Medicaid studies where it looked like there were some interrupted time series analysis where they were looking at the change in the slope between before and after the policies. And I'm wondering when you don't have the privilege of doing a, like a randomized control trial or having a comparator necessarily like, how do you account for large social phenomena that can serve as a confounder, particularly like in the, I think in the 1930s example that you gave with the maternal payments? Yeah, the program ended in 1935 with the maternal pension program. So like how in that example would you account for economic and social effects of the Great Depression? Or in the example with the low birth weight, you know, any child born below 1200 grams would also be admitted to the NICU by default and have like long term developmental consequences that would presumably alter their participation in the labor force. I'm a clinician, so. Yeah, like how do you account for these large scale confounders right now? For example, like if I'm looking at any data around 2020, I'm worried about how Covid influences Absolutely. Health outcomes.
- [72:10]
A
Yeah.
- [72:10]
D
Like how do you account for that in your models? Asking for a friend.
- [72:14]
A
Yeah.
- [72:17]
B
Can you just say your name, please?
- [72:20]
D
I'm Kavya. I'm in the social policy.
- [72:22]
A
Thank you. Thank you so much for that. Well, I would say what economists are really good at is figuring out ways that they're kind of creating quasi randomness that is sort of a transparent. I mean this isn't me like bragging. I just think it's like one of our comparative advantages is that. So for example, if we know that this policy affected, you know, just like changes the eligibility around a concrete point, whether it's time when Medicaid October 1, 1983, or whether it's the 1200 grams or whether it's the EITC payment that they get earlier because they were born in December versus January. What it does is it takes a lot of confounders off the table that at least don't vary locally around that threshold. And so it's kind of easy to see when, I mean, you might be worried like, well, births at the end of the year might be different because there's seasonality of births or you know, there's holidays and people don't want to go to the doctor on New Year's Day. So they're born, you know, planned C sections before. So there's all these kind of like things that you need to kind of worry about. But generally speaking, I think those estimates are good at just sort of like big picture, what do we see. But what they're not good at is, for example, I wouldn't necessarily call them confounders to the treatment effect, but they their important context for understanding the results. So if I'm comparing someone who was born a little below 1200 grams or a little above 1200 grams, you might say, well, how much does this tell me about much of anything? Because this is like a very Premature birth. And maybe it's not very generalizable to what cash means in generally like for small children. So a lot of times I think what we end up with in these economics experiences or maybe well identified things, but maybe not be that generalizable because you're really living in this, you know, kind of smaller space. And furthermore they don't allow us to unpack contemporaneous events that might be important. So if you have like 100 of these little micro experiments and some happen during COVID and some happen in other times, maybe you could compare across those in order to try to figure out how Covid impacted them. But you don't always have that. You know, it's like you got your one data set with your one experiment and so it can be very. They're not well suited, I think I would say, to the kinds of questions that you ask. But I think they're often a really good starting point for thinking about a problem, maybe not an ending point.
- [75:02]
D
And so I guess you could like look at different phenomena under the same time frame to account for like, different, like, like if you looked at only data during COVID then you could at least eliminate.
- [75:14]
A
Exactly right.
- [75:17]
B
Thank you. Can take Sunil.
- [75:26]
E
Thank you very much. Sunil Kumar, teach in the Department of Social Policy and student of the University of London. And I just was. I mean you've made a very eloquent argument for the instrumental reasons for investing in children. And I was wondering whether you'd say something about the intrinsic reasons. You mentioned a few. You mentioned reduction in stress, mental health and stress. I think that is an important one. You mentioned mothers working, which has issues of esteem and prestige and reduced reduction, stigma. And therefore from a social policy perspective, those more emotive kind of returns, wouldn't they be as important as the kind of economic returns you speak of? So in other words, then, therefore what kind of case would one make for those kinds of returns within families? So the esteem of working is I think a very important one. And the dignity associated with not being on food stamps is also an important one.
- [76:34]
A
Wow, there's a lot to think about there. I think first of all, some of the things you raised, like the evidence that shows that mothers are less stressed, I think in principle we could incorporate that into our kind of calculations of, you know, how much do you get out of this investment? You just need to figure out how to pardon my economic speak, like put a price on that. And some things are easier to put a price on than others. But I'm sure one could do that.
- [77:07]
E
The question was, do we have to put the price? That was the question. Because I see, if everything's reduced to a price, then I don't know.
- [77:20]
A
Yeah, I mean, I don't disagree with you in a sense of, like, myself as a voter, like, what do I care about and what leads me to support some things rather than others. Probably not that dependent on what I'm doing here. Although I think on the margin, I might lean towards things that seem like they're more transformative than others, maybe, but I just basically want to live in a society where everybody has an even chance. And so that's kind of what I'm looking for when I'm thinking about supporting policies. But not everybody feels that way. And, you know, I. I don't know. I had a, like, dream that this kind of work, in addition to, like, publishing, well, in journals, might actually make a difference for the company conversation and bring more people along who are more stuck in not thinking about these intrinsic things. Now, I don't know. I don't know that that's actually true. Or maybe it'll take more time. But I think it doesn't hurt to be able to turn the debate, which might not be the debate here, but I can tell you what 100% is. The debate in the United States. States is, well, we might want to expand this program, but people are going to cut back on their hours and we don't want that. And that's where the discussion ends. Or we don't want to raise the minimum wage because employers are going to increase, you know, that's going to increase their costs and they're going to cut back unemployment. It turns out that doesn't happen. But nonetheless, that's where the debate kind of ends. And so somehow we need to continue to bring the social science to the table to show. Well, okay, that's fine. That's good. Good for you to think about that. But there's other things at play here. And if we don't put a price on it, I don't know, maybe telling stories is more important than a price, but I can't do that. So I put the price on it. But I think you got a good point. So I don't think we disagree very much. And I don't know what's effective to change people's minds. I know what I can do, so that's what I do.
- [79:23]
B
Thank you.
- [79:24]
A
Thank you for that.
- [79:25]
B
In the far corner in the white shirt.
- [79:30]
F
Professor, thank you so much for your lecture. It seems like such an obvious idea to invest in children, but probably very radical to produce better outcomes. I was hoping for your opinion on social impact bonds or social outcomes funds as a method of measuring success success of programs whilst they're being funded. Oh, and I'm Nathan and a former student here at the Department of Social Policy.
- [79:57]
A
Awesome, thank you for that. I remember once my, my mom somehow we got to talking about what I'm doing and she said, okay, let me see if I get this right. If I feed kids better, they do better. And I said, yep, mom, that's it. She's like, how many years did it take you to figure that out? And I said more than I care to, you know, quantify. So yeah, it does. It's not very surprising, you know, but there you go. I do think that, I think what's really cool about the social impact bonds that's usually being used when you're piloting something new, that's typically when this mechanism is used. And, and I think that there's something really powerful about pre stating what your metrics are that you want to try to move. And I think there's something very powerful, I think that's brought a lot of funders to the table, you know, very evidence based. You're kind of pre specifying like these are the things that we're trying to move, whether it's getting people to complete college once they start, you know, something that's kind of measurable and not. The tricky thing about these is the long term piece. One thing that there's a lot of interesting work on now is trying to identify sort of shorter term mediators, if you will, that might be predictive that a program is going to have benefits in the long term. And then maybe with that you could build in some longer term metrics to the, to, to, to the social impact bonds which tend to have short term impacts because you know, your investors and other supporters are operating on a timeline that's not a generation, which is really problematic, honestly about all of this. The other thing that's problematic about it, and I feel like one of the questions sort of got me to thinking about this, but I didn't answer it, is to measure the long term impacts you need to go back in time. And so you might say, well okay, so food stamps was rolling out in the 60s and 70s. That was a very different time. How much are the results you're showing me today indicative of the program today? And you know, it's kind of unknowable because I don't have that evidence. So I don't know Your question kind of brings up a couple things to me.
- [82:11]
B
Thank you. So I think we have time for. I'm going to return to our online audience. So from Nikraj Szerkak, Dear Hillary, how would you go about extending this analysis beyond the US and the Global north to assess the effectiveness of such safety nets in low middle income countries, perhaps those countries with less state power?
- [82:35]
A
Great idea. I think the thing about that is those programs are newer than ours and so there's definitely evidence on more near term effects. And I think this is where like the conditional cash transfers. To get back to the, the first question, you're kind of tracking these things on children and their movement through education, which is all great. So I think that work is being done to the extent to which it's possible given how long these programs are in place and some developing economies. They're much more recent programs. So there's just a bit less history to go back on.
- [83:12]
B
So a question from an anonymous user for metabolic considerations. Does the research take into account family history?
- [83:23]
A
No, no, because we don't have that all, you know, in this data, although there could be other data that does take into account family history. But in the data from my study you only have that individual's metabolic conditions. But I have no doubt that there's probably other studies that do that, that kind of longitudinal linkage across the generations, but not, not in the safety net world. Yeah. Yeah. Okay.
- [83:45]
B
Thank you. So we have time for just one last question. Kitty Stewart.
- [83:56]
A
Hi.
- [83:56]
F
Thank. Kitty Stewart from the Social Policy department.
- [83:58]
A
Hey, good to meet you.
- [84:00]
F
Thanks very much. I wondered if you had any reflections on why it's so hard to get this to cut through with policymakers given how strong the evidence is. It's a no brainer a lot of this stuff and I think certainly here they know this, it's just, it doesn't happen. And I suppose one thing is there's a political cycle issue clearly that they're not thinking 20, you know, they're not going to be here 20 years ahead. But it feels like that maybe there's also an accounting problem because these things. So here they were. Our government currently will borrow to invest in buildings, but child benefits are day to day spending. We can't possibly borrow for that even though, you know, we've got all this evidence that this is investment too. So yeah, I don't know if you've got any thoughts on whether, you know, can we make this evidence strong enough that we can somehow change the Treasury's accounting?
- [84:49]
A
That's so interesting. I Love that example. That is so true. Well, one of the things that at least is in the early stage stages at the, in the United States is, and I don't know, this might need Congress to pass this, I'm not sure. But all tax, all bills need to be, need to be costed out by the Congressional Budget Office. How much is it going to cost? And there's a fixed window. It's either five or 10 years. That's the window. So there's a lot of like I've given a couple talks to some of their teams there and other, other people have as well, and they're trying to think about how they would use the information they have from existing studies in order to do costs, like generational costs. And then meanwhile other people are trying to influence the system in order to make it mandatory to do costs beyond that window. So that would be one different kind of mechanism is like you would least need to put it in front of them because in addition to the like, the buildings are in a different bucket than the child space spending. There's also the, well, what is the window that the, that the debate is organized around what data they have in front of them that gets written up in the, you know, Financial Times. It says this is going to cost, you know, whatever X million pounds over Y years. What are those, what is the data that they need to be pushing out on each thing? That's kind of an interesting way to kind of like within the system, try to influence the information that's in front of folks. I will say that during COVID when there was a lot of policies that were happening in the United States that were very expansive, the reporting on it, and some of the policymakers were like, they, at least they said the words that, well, we know that this is going to improve outcomes. So I feel like that's a step in the right direction. But that's sort of where we're at. So maybe it just takes time.
- [86:46]
B
That's such a perfect place to end on the optimism.
- [86:50]
A
Optimism.
- [86:51]
B
So please join me in thanking Professor Hoynes. Thank you so much.
- [87:00]
A
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