Hi, my name is Chris Hernandez from the Cloud Platforms Business Unit over at Red Hat. In this video, I'm going to be running a Windows container on OpenShift 4 using an OpenShift Managed Windows node. It's important to note that running Windows containers on OpenShift 4 is currently a dev preview and won't get full support until it goes full GA. Before we get started, we want to make sure we have some of the prerequisites out of the way. You want to make sure you're running Ansible version 2.9 with the WinRM Python module installed, and an RDP client is also helpful. The AWS CLI is also required. You want to make sure you have your credentials stored under .aws slash credentials. The OC CLI needs to be version 4.4, and the corresponding OpenShift-install CLI needs to also be at 4.4. OpenShift installs all its installation artifacts in a directory, so we'll create a directory called cluster for this purpose. Next we'll need an install-config file, so we'll run OpenShift install create install-config and specify the directory. This will start a wizard where we need to choose a SSH key, and I'm installing on AWS, so I'll choose that as the platform, and the US East 1 region. I'll choose a domain that I own on AWS, and I'll give this cluster a name, WC in this case. This pull secret I put in my copy buffer can be obtained at try.openshift.com. Once this is done, this will create an install-config.yaml file in that cluster directory. We need to make a change to this file. We need to take the default OpenShift SDN and change it to be OVN Kubernetes. Verify this change is made, and as you can see, my network type is set to OVN Kubernetes. Next, we need to generate the manifest file for the installation. You do this by running OpenShift install create manifests, specifying the directory. Once this is done, we need to make a copy of a configuration and change that network configuration. In this file, we need to make a few changes. You need to make sure the API version is set to operator.openshift.io, and then we need to set the OVN Kubernetes settings. Here you need to take note that my CIDR for my Kubernetes OVN does not overlap my cluster network. Now we're ready to begin the install. To install OpenShift, you run OpenShift install create cluster, specifying the directory where the manifests are. The install can take some time, so I'll pause here and return when this is done. Once the cluster is installed, you'll need to run the export kube config command displayed. Once you run this command, you're now logged into the cluster. You can verify this by running oc get nodes. This will display the node count. Note that there's three masters and three workers. Also run oc version to see that you're running 4.4. Verify that the network configuration was set to the OVN Kubernetes. You can do this by doing oc get network operator cluster dash OEML. We will need to now install a Windows node. For testing purposes, we'll use the WNI CLI tool provided by the OpenShift engineering team. Please note that this is for testing only and is not suitable for installing production grade Windows servers. Once I have this downloaded, I would change, mod it, and make it executable, and I'll run the WNI command in order to create my cluster. Before I do this, though, I need to create a directory where the artifacts need to be installed. Now that I have this directory created, I can go ahead and create the Windows node. The installation of the Windows node can take some time, so I'll pause here and I'll come back when this is done. Now that the Windows server is finished installing, you will see in the output that I'm given the administrator name and the password to use to connect to this Windows server. We will be using this information to create an Ansible Inventory file. This inventory file will be used against a playbook to install the node. The information needed for this inventory file are the IP address of the node, the Ansible password, which is the administrator password, and the username, which is administrator. Note that you may need to single quote the password. Another configuration you need to keep in mind is a cluster address, which is just your cluster ID plus your domain name. Note that all the information you need was in the output of the command. Now we will run an Ansible win underscore ping command in order to verify connectivity. Once you see the green success pong, you know you have good connection. Next, we will get clone the installation playbook. Once this playbook is cloned, you can run the Ansible dash playbook command to bootstrap this machine. This will run some basic checks before the installation starts. While we're waiting for this, I have launched and logged in with an RDP session to my Windows server. Here you can run the winvert command to see that we're running Windows server 2019. Also, you can launch the PowerShell from the start menu to verify that Docker is running. Back in the installation window, you see that this may be taking a while. So I'll pause here and return when the playbook has finished bootstrapping. Once the playbook is finished running, the Windows node is bootstrapped and joined to the cluster. You can see this Windows node by running oc get nodes dash o wide. We now have a new node with the OS image of Windows server 2019 data center. We can also get this node by specifying a label selector. To specify a label selector, you run the same oc get node command with a dash L and specifying the label kubernetes.io slash os equals windows. We're now ready to deploy a sample application. This sample application has a Windows container with a web server in it. Once we deploy this application, we can monitor the rollout with the oc rollout command. Once this application is finished rolling out, we can monitor this by switching over to our RDP session. In our RDP session, we can run a Docker PS command to see the container is running. Note that this container has a prefix of kates signifying that it's managed by Kubernetes. When we open up the task manager, we can see a few Kubernetes specific processes running. The kubelet and the kuproxy is running, as well as the hybrid networking as well. Switching back over to the CLI, we can see that this application also deployed a service. We can get the service by running oc get SVC. Note that the service win-webserver deploys a service type load balancer. The service type load balancer creates an ELB with the DNS name. DNS can take some time to propagate. In my case, it took about five minutes. Once DNS returns, we can curl this endpoint. Run oc get SVC to get the name, and run a curl against the DNS name. This application is up and running. Thank you for watching this video about Windows Containers, and invite you to try out Windows Containers on OpenShift4 dev preview. Thank you.


Maybe you're a homeschooling parent looking to make one at home. The problem with making a real-life scale version is that you can't nail all the attributes at once. So here we are, we're going to make a digital scale model that uses Scratch. In this video specifically, we're going to head over to NASA and grab their planetary fact sheets so we can use some real data. We'll clean it up in a Google Sheet and then import that into some lists in Scratch ready to use and scalify. What's more, if you're a teacher or a homeschooling parent, maybe you're looking for some credible solar system worksheets that you can use with your group of learners. In that case, I've got your cover. Just go check the description for a link to those. All right, I can hear the countdown, so let's blast off together in just a sec. Hello world, it's The Surfing Scratch here, teacher-surfer programmer, and I help curious learners just like you along on their learning journeys. Welcome to this series on a solar system model that we're going to make in Scratch. Just a bit of housekeeping first up to orient us to what we're going to be doing. Here, I've created an empty sprite and I've called it readme, and I'm going to try and use this as a sort of a best practice going forward because I know when I jump into projects, it can be really hard to sort of orient yourself to what's going on. And in this readme empty sprite, I've got here a bit of a plan or an outline of what I'm hoping to cover in this video. So I've just broken down all these sort of videos into these comments. You'll see the first videos are a little bit more padded out than the later videos because we need some wriggle room. We need to be able to adjust and become flexible. So this video is a little bit different in that I haven't actually made this project yet. I'm going to be doing it on the fly with you. And that's kind of new for me because I sort of like to make these projects first. I've had a bit of a play around, but I haven't gone ahead and created the finished product. So I think that'll be a great opportunity for all you out there to just see how I go about creating a project. And I reckon there's going to be some debugging stuff that will come along the way. And I think that'll be a useful video as well. It's surfing scratchy from the future here. And in fact, we get an opportunity to check out one of the blunders I made in this video and debug it. So let's shift our focus to this particular video. And in this video, focusing on this comment up here, we're going to be using the NASA fact sheet to create a scale model of a certain attribute of the planets in our solar system. There we go. So the first thing we're going to do is head over to the NASA website and obtain this fact sheet. So let's jump over to the browser. Now that I'm over here in the browser, I'm just going to type in NASA planets fact sheet. And the very first one with a link down here with planetary fact sheet. If you're in the US and you want to use the US units, go ahead and do that. But I'm going to be using the metric system because I'm based here in Australia. And that's what's familiar to me. That'll take us to a page with all the planets that stood out here. Of course, Pluto isn't now classified as a planet, it's a dwarf planet. I've also got the moon in here as well. Down the left hand side, we've got all these different attributes of these planets that we're going to visualize in scratch. So I'm just going to go ahead and select all of this and copy it. Hopefully know how to do that. You can just control or command C. And I'm going to head over to Google Sheets. I've already signed in here. If you don't have a Google account, no drama, just open up Excel. You can do the same thing or just go quickly sign up now to get a Gmail account. So I'm just going to type into the browser sheets.new, which is a nice little hack and that will just open up to a default new Google Sheet. Then what I'm going to do is I'm just going to control V and paste that information that we grabbed. And I've just pasted in that data. Okay, you may have noticed here that our headings are just offset by one column. So I'm just going to select all of those headings.  Then I'm going to go ahead and delete the moon, sorry moon, but we're not going to be working with you. But we're going to keep Pluto because we like Pluto. Okay, now I'm going to do something that's going to seem a little bit strange, but it will make sense as we progress the video. So I'm just going to select from Mercury. So that says B1 cell B1. And I'm going to select it all the way down to the bottom of cell here of J19 Pluto and their global magnetic field. Going to copy that. Going to create a new sheet. I'm going to add it. Then I'm going to paste special here and I'm going to paste transposed. And what that has done is instead of having the planets listed out in all these columns, we've listed out the planets in rows. And this will make sense surely. Okay, I'm just going to add a row above here. I'm going to go back to that original sheet, select all these values. And then I'm going to copy those again. Head over to the sheet. And in the B1 cell here, you guessed it, I'm going to paste transpose. And now we've got all of our attributes here lined up with the columns. And I'll just go across here to make sure that they're all in there. Great. This is what we're looking for. Okay, so in this video, we're going to focus on the sizes of the planets and the particular attribute that we're going to use is this diameter column. So I'm going to go ahead and just copy all the values from Mercury down to Pluto. Control C, Command C. And then I'm going to open up a text editor. You can use any text editor you want, but the one here on the Mac is called text edit. And I'm just going to open that one up, create a new document. Now I'm just going to convert this into a plain text file. You don't need to do this, but I'm just going to do it there just to get rid of all the styling. And I'm just going to paste the values. So we have here just the direct values of the diameters of Mercury Venus that are down to Pluto. We're going to save this file. Now I'm going to save it here for this folder structure set up. And I'm working on the scratch desktop version. So I'm going ahead and creating different versions of my files, which is good to just to keep some backups. If you're working with the online editor, you do some auto saving, but it could be good practice capture some versions of your particular file in case something happens to it. So I'm going into data here. And I'm going to rename this file. I'm going to call it diameters. There we go. Now I can just close that. We'll head back across to scratch. Now we're back over here in scratch. I'm going to jump over to the variables category here. We're going to make a list and we're going to call this list, you guessed it diameters. Now the cool thing about that text file is when we right click on this list over here on the stage and we go import, we can find that file that we just created in diameters. We can open it up and it will import all those values in for us. Of course, you could just eyeball it and have both of those documents open side by side and enter those values in, but I'm a little bit lazy and we're going to be doing this for lots of values. So I'm going to do it this way. Now I've just done that. I've just realized something and we've got a problem with our data here. It's not what I thought it would be. Let me show you what I mean. I'm going to jump back into the browser and go back to our fact sheet here. And you'll see that we've got the fact sheet that's ratio to earth values, which means earth is all one for everything. And what we've done is make every value of the celestial body, the planet or the moon, we've made them relative to the value of the earth. And that isn't what we want. If we go back over to the Google result and we click on the first result of NASA planetary fact sheet, we'll get a different fact sheet and we're going to get the metric one for all the value. Now you could probably just go through and repeat the process of what we've done of copying everything, but I'm just going to do it a little bit differently just to show you how you can do it just for the diameter. So I'm going to go in here and copy all these diameter values, head back over to our fact sheet here. I'm going to paste those values only. And I'm going to cut those values. And over here in our diameter, in our first cell beneath the diameter header, I'm going to paste special and we'll paste the transposed. And you'll see here that we've got all those values and it's formatted it nicely. Now which one is the moon? Well, hopefully you know the order. You've got Mercury, Venus, Earth, and this one here is the moon. We don't want the moon, remember? So I'm going to right click it and we're going to delete the cells and shift up. And there you go. You can see that we've also shifted up all these values too. Now we can just go ahead and delete those values that we had. Great. So now that we've got all those values, I can just copy those, open up text edit, get that diameter file, repaste in all those values. Now if you've got some commas here, you'll need to go ahead and remove those commas because scratch will only import the first numbers up until that comma. So you want to make sure that you get rid of those commas there. We're going to resave that and then I can just jump back over and import and we'll re-import that file again. And now it's overridden those values and we've got all the diameters of our planets. Of course, the rest of our fact shape is all out of whack because all these other values are still relative to the earth. So you might want to go ahead and recreate this based on that new fact sheet. That's a good opportunity to reinforce what we've just done. Okay. So I actually just went ahead and recreated our second sheet here just to be wary that there is an extra attribute here for the new planetary fact sheet. You'll notice that we've got some commas here in ourselves. The way to get around that is if you select all the values, just like I'm doing here, go to format, number, and you want to click plain text. The next thing that you want to do is do command or control F. We're going to be finding. If you've got some options here, you might just go this sheet, but because I've selected those cells, it's only going to look in those cells. We're going to find the commas and there we go. There's the first one and we just want to replace them with nothing. So you can see it stripped it. Now that it works, we know we can just replace all and that's how we can just get rid of the commas. Sweet. Let's jump back over to scratch. Great. We're back over here in scratch. Let's check in when we're at. So we've got that NASA web sheet. We've imported that data into a Google sheet, exported the frozen or plain text file. We've imported that now into scratch lists. Now we want to create a list of planets and variables. We're going to do that in the next video because this one's getting a little bit long and it's nice to just sort of chunk it out to keep one video doing one particular thing. Cool. So in that next video, we're going to be using all these data values of the planets to create some sprites and display the sizes relative to these values.


Hi, my name is Chief Petty Officer Tolle from Annapolis, Maryland with NSN Naples. In honor of the Navy's 239th birthday, I'd like to thank my friends and family from the U.S. and France for their support.


I am Emmanuel Saez, a colleague of Hillary Hoyns in the Economics Department. And I'm also the chair of these MOSES Lectureship Committee. And so it's great to be back here after a three-year break to have Hillary Hoyns give the MOSES Lectures. So I have to say a few words about the lectureship. It honors the memory of Bernard MOSES, a famous professor of history and political science at the University of California here, when there was only one campus more than a century ago, a building, bears his name. He did pioneering research on the Latin American republics and served on the famous US Philippine Commission of the early 20th century that really set up the Philippines as a new independent country. So those lecture series started in 1937, almost a century ago, a few years after Bernard MOSES died. So they are hopefully given every year by a colleague in the social sciences whose work has really inspired colleagues across the division. And I am delighted tonight to welcome Hillary Hoyns to give the lecture. So let me say a few words about Hillary. She's a professor of economics and public policy and also holds the HASS Distinguished Chair in Economic Disparities. So she completed her PhD at Stanford in 1992, after which she immediately joined actually the faculty here at UC Berkeley. So she's had three decades of scholarship. And her work has focused on poverty and inequality, and especially about the role of the safety net in alleviating both. So you'll hear tonight talk about the social safety net as an investment in children. If I were to put an arc on her research trajectory over the decades, which I have followed because I'm almost the same cohort as Hillary, she started perhaps more narrowly as an economist focusing on the effects of the social safety net on labor. If you give people transfers, if they are low income, is this going to discourage work at the time or welfare reform in the United States in the 90s was really big in the debate. And over the years, she has really broadened her analysis of the social safety net to focus on the broader picture and in particular, the good that such programs can do in particular for the next generation that is helping kids growing up in poverty. And I'm delighted to hear you talk about this tonight. And her work has obviously been directly relevant to public policy. As you know, the Biden administration, one of its goals was to expand the safety net that in the United States is not quite on par with what we have in other advanced economy. Her work featured prominently. And she was also tapped by the administration to try to convince people of the good that such programs could do so. Unfortunately, we didn't get as much as many of us had hoped for, but her work and yours, it's going to be part of the debate in years to come. And you'll see that it's powerful stuff that really can inspire others to work on it and hopefully policy makers to improve the social safety net in our country. Thank you very much, Hillary, for coming. Tonight, we look forward to your talk. I forgot, but in terms of the logistics, so she's going to talk for about 45, 50 minutes. Then we'll have questions. There will be a mic that will be passed on.  Hillary will call upon people for questions. And after the questions and answer, the room is going to open. And right behind us, there will be a reception so that we can have further discussions with Hillary and the audience. Thank you. Thank you very much. It's great to be here. I'm actually on sabbatical this semester in New York at Columbia. And you know, on the one hand, it'd be nice to not have to come all the way back here, but on the other hand, it's just such an honor to be giving this talk to be helping to, I don't know, bring campus life back. And I just want to say thank you all for being here. I know there are some things we're just not quite used to doing. And maybe coming to talks outside of our department are things that, you know, we're not used to doing. And I just want to say thanks for being here. And it's just really a delight. Thank you so much for that kind introduction. And it really gives a really good introduction to want to talk to you about today. And you don't know how close your description of where my work has come to the theme of what I want to talk about today. So this talk is a little bit more what we might call naval gazing than one normally does as an academic. So thank you for indulging my own naval gazing, but it's interesting to try to step back. And when you get asked to do something lovely like this and give a general talk, it really makes you think about trying to put things together. And I'm now looking for the clicker, which may or may not be here, but Jane will look forward if it's not here. So thanks so much for being here. So let me just start out by doing a little bit of context setting. So as Emmanuel mentioned in his remarks, the United States is not a really big spender when it comes to programs aimed at reducing poverty for children. So this is a graph that is just OECD data. And on the horizontal axis of the graph states how much of a country's GDP is spent on spending on families with children. And we express this as a share of GDP because obviously there are big countries and there are small countries, there are richer countries and less rich countries. So expressing it as a share of GDP sort of puts it on a similar sort of footing. And then on the vertical axis is the child poverty rate in those countries. And what you see is a pretty compelling set of data that shows, not really surprisingly, that the more a country spends on benefits for families with children, the lower the child poverty rate. So that's the first thing that you see. This is not, this is just data, but it's a pretty, it's not a very surprising feature. And it's a fairly striking one when you just look across countries. The second thing that you note from looking at this data is the United States really lives in the upper left-hand quadrant of this graph. We do not spend very much of our GDP on programs aimed at families with children and we have a very high poverty rate. So there are two countries on this graph that have higher poverty rates in the United States, Turkey and I can't remember what CRI is. So somewhere else. So we are an outlier, you might say, with respect to spending less and having a high child poverty rates. Interestingly, this is not necessarily a feature that is common across the entire United States sort of social safety net. So just to show a countervailing set of data, this shows also from the OECD, also for a set of countries in the OECD database, the United States spending also as a share of GDP on the elderly and so this is pension benefits as a share of GDP and what you can see is that we're kind of middle, a little less than middle, but we're certainly not an outlier with respect to our spending on the elderly. And so in a recent paper that I wrote with my colleagues Anna Eiser and Adriana Yeris-Muni, we spent some time sort of thinking about why that is. Why is the United States such a low spender on children in particular? And what explains that? And I would be here a very long time if I went through all the things that we talked about in that paper and probably many other things that we didn't think about in putting our thoughts together on that. But the one thing that I wanna talk about sort of relates to something that Emanuel brought up in his introductory comments and that is as economists, we spend a lot of time estimating and quantifying how the social safety net affects decisions that parents make. And so in particular, what we've spent a lot of time quantifying is the question of if we provide a benefit for families with children, how is it gonna affect the labor supply of the mom? Or how is it gonna affect marriage decisions or fertility decisions? And I would put all those things in the bucket of quantifying the costs of doing redistribution. And so I wanna talk about that today and the implications of doing that. And so in this paper that Anna and Adriana and I wrote, we had both, all three of us, had sort of done work on these, what we would call negative or disincentive effects of programs or maybe what sometimes people call unintended consequences. And that's something like economists love that, we love unintended consequences. And I'm not here to say that that's not something worth doing. We absolutely need to do that. But when we went back, Anna and Adriana and I, we went to almost 60 years of data on academic journal publications. So we started in 1968 and we went to 2020. And over all of those years, we looked at each journal article published in a set of sort of top academic outlets. And that included the top five economic journal publications and then another set of sort of top field journals where people like me would do this work or Emmanuel and others who would do this work on estimating the effects of the social safety net. And every paper that we found in those academic journal publications, we put in a category of, is this studying the benefits of programs or is this studying the costs of programs or the incentives? And what you can see from this analysis is that over this period, particularly in the 60s and the 70s and the 80s and the 90s and the 2000s, we were mostly spending time quantifying the costs of these programs. And so this is something that anybody working in this area would recognize, but it was kind of useful to just put a number to it. So over this time period of these first four decades of the data, four and a half decades of the data that we're showing here, about a quarter of the articles were focused on quantifying what good these programs do. And the rest of it was the vast majority of the work was focused on what the costs of these programs were. And why is that important? Why am I here talking about this? Why is it useful to kind of recognize that? Well, I mean, I think if you're thinking about taking academic research and applying it to public policy, which is certainly something that I do and I think many people in this room do, looking at who's around and sitting in this room. And you wanna ask the question, should we do more of this program or should we do less of this program? Or what is the right mix of programs with a goal of reducing poverty and increasing economic mobility? In order to do that right, in order to do that fully, we need to quantify not only how much programs cost, but it doesn't make any sense to think about how much programs cost if we're not also quantifying what the benefits of those programs are. And that sounds pretty obvious. And how many people in this room seem like that's intuitive? Like, I don't know anything about economics, but that sounds right to me.  And she'd be like, wow, Hillary, you spend all your time thinking about that. That seems pretty obvious. And it is, but my dissertation work was on measuring the costs of welfare programs. And for the first 20 years of my career, that's what I did without really thinking anything of it.  So somehow it's not obvious or it hasn't been obvious. And just as a little bit of a side note, after writing this paper and sort of getting it out in this very sort of applied journal, we had many colleagues come to us and say, that's true in my field as well. I study social security or I study health insurance. And it is a kind of theme, I think, that economists spend a lot of time talking about costs and not about benefits. That's interesting. And I can't explain all of that, but I wanna talk about the implications of it. So there's a little bit of good news. I like buried the lead a little bit purposefully. That's changed. And so if we extend this data out to the last decade and through the year 2020, which is when we are collecting our data for this, what you can see is like things have really changed. And we now see many more papers that are working on quantifying the benefits of programs compared to working on quantifying the cost of these programs. And so something has changed. And it's not necessarily the focus of this talk for me to give you a pine about why I think it's changed, although we definitely thought about it a lot and sort of talked about it in our paper. But what I wanna talk about today is what are the implications of not quantifying benefits? And can I give you an example under which I can illustrate just how much these policies actually do generate benefits? And we need to be patient because these benefits tend to not show up until children who are in these households that are receiving more benefits or not, most of the benefits don't show up until those kids are in adulthood. And so that's what I wanna talk about today. So to sort of pitch the tent around this, it's very common in many settings, very maybe typical of think about preschool, advocating for public preschool in a state, expanding public school to go to preschool as is being debated and partially happening in California. The justification around that is often given this sort of investment lens. It's gonna cost us some money today to expand our K through 12 system to cover another year of school. We need teachers, we need classrooms. That costs a lot of money. And that's an upfront cost that's incurred in expanding to pre-K. But that's justified oftentimes when you see this debated through the lens of the fact that this program is going to kind of pay us back in the long run. These kids are gonna show up in kindergarten, more ready to learn, it's gonna be maybe less costly to educate them. They're gonna have higher education ultimately, be more tax paying when they're older, less likely to engage in criminal justice and many, many things that have been quantified actually about the effects of pre-K. And with that lens, we can sort of think about is it worth paying the money today in order to generate these benefits in the long run and we can have a debate about that. But what's interesting is in the context of the social safety net and sort of redistribution more broadly, this isn't the lens that people think about. It's not usually debated in that context. And I think what's pretty great is I think we now have the research base and I'll talk about a little bit of it today to really make a pretty compelling argument that we can and should talk about the social safety net as an investment in children. So that's where we're going. So what I wanna do with the rest of the talk is to step back a little bit for those of you who don't know the facts about poverty and social safety net, talk about that a little bit. And then I'm gonna do a little bit of a kind of whirlwind summary of work that I've been doing to try to ask this question, how does more assistance for children affect where they end up in adulthood? And I've been studying the food stamps or SNAP program or what's called in California, CalFresh. And so I'm gonna talk about that a little bit and then come back to this question about what if we ignore the long-term benefits? What kind of cost benefit number are we gonna have? What if we incorporate the long-term benefits? How does it change that sort of perception of the program? And then I wanna finish by kind of thinking about this story about the balance of disincentives for labor when we do redistribution compared to the benefits in the long run and think about that within the context of the child tax credit and the expansion in 2021. Okay, so first of all, back to this first figure that I showed you, that was a snapshot as a kind of point in time comparison across countries. And I do not want you to leave this room with the idea that we've done nothing for child poverty in the United States in the 50 years since the war on poverty. And so let me show you a graph that plots child poverty over time all the way back to the mid-60s when we first measured poverty, started measuring poverty in the United States. And if you look at this graph, and just there's also all individuals, but if you look at the time trend for children, you would certainly not look at that and say we haven't made any progress on child poverty in the United States. And a little bit more analysis of this graph over time would lead you to the conclusion, and certainly the research backs this up, is that in the kind of time period of poverty measurement in the United States for children, we tend to see declines in child poverty when one of two things happen or both. The first is we expand the social safety net in some way, that's kind of like the first graph where I said it's not surprising that if we spend more, we generate some, you give people money and poverty goes down, it's not very surprising. So when we expand the social safety net, we tend to see reductions in child poverty. And then also we tend to see reductions in child poverty when we've got a very strong labor market where we have real wage gains because maybe you know this, maybe you don't, but most poor children live in families where there's a worker. And so rising wages have a very powerful impact on child poverty and that's one of the reasons why we see a real decline in child poverty in the years leading up to the COVID recession. So the other thing is just to point out that there's been some attention to this decline in child poverty kind of recently. There was a pretty high profile piece put out by an organization in Washington called Child Trends and this graph is from a New York Times piece that the very prominent Jason DeParle wrote about child poverty and the story in particular about the role that the social safety net played in the declines in child poverty that we have seen since the early 1990s. So this is something that I think is starting to get a little bit more traction. Jason DeParle told me when he was, I talked to him when he was working on the piece and I basically said, I'm not sure you need to write this, I think people generally know this and he just starts laughing. He's like, honey, I've talked to poverty researchers that don't know this and I thought, wow, okay, that's interesting. So anyways, obviously it's something that some of us know, but it's encouraging that they're starting to be more awareness of we know how to do this. We know how to make improvement and that's the reason why I sort of point this out. So for those of you who don't know much about what our quote safety net is in the United States, there's a lot of different people to use that term in a lot of different ways. What I mean in this context is basically transfers to families or individuals and those transfers can be in cash like unemployment insurance is a cash payment or they can be what we call in kind which would be like Medicaid or food stamps or housing vouchers. And so the whole bucket of those include benefits that are more universal like social security or unemployment and benefits that are particularly targeted at low income individuals. And I'm sort of the thrust of this talk and the research that I'm gonna summarize is primarily focused on those policies that are targeted at the low income population. So what we call means tested transfers and that's how I'm using the term safety net here today. So in general the social safety net for the poor in the United States, I guess if I were to say one thing about it, I would say other than we don't fund it at a very high level, the thing that I would say is it's not very universal. So we tend to identify particular groups that we target aid to and those groups tend to be the elderly, the disabled and children or families with children. And that's where most of our assistance is and the two really big groups that are left out are able bodied adults, prime age adults and the undocumented population which throughout many, many, many elements of our social safety net are categorically ineligible. A conversation for another day, an important conversation. For poor families with children, my focus today, I would say two additional things. One is very little of the assistance takes the form of cash, which is another interesting thing about the social safety net in children in the United States. I think there's a lot of lack of trust of what parents do with the money. That's certainly what Joe Manchin said when I was asked to talk to him to try to convince him to support the child tax credit. So there's a lot going on I think into why there isn't full support for more higher spending for families with children. And the result, I think, is that we do very little in the way of cash assistance. They're basically tax benefits or in kind benefits largely. That's fact one. Fact two is we make very heavy use of what we call conditionality. And what does that mean? That means, well, you only get this if you do something that society sees as good, which generally means work in the United States. So most of our social safety net for poor families with children is conditioned on the parent working, which you see the problem right away. If you can't work and you don't have resources, where's the safety net? And that is particularly true after welfare reform and the rise of the sort of tax credits as the ways of doing redistribution for the lowest earning American families with children is it really sort of took away the out of work safety net, which is a pretty critical element of kind of any definition of what you'd think a safety net is. So in terms of what that safety net looks like for children, this is a graph from the Urban Institute and it classifies spending on children by type of program. And so one way to think about what are the big programs for children is just to count the dollars. And so you'd see from that that our biggest spending on children is through tax credits. Probably most people wouldn't know that. The second biggest is health insurance through Medicaid. And then after that comes some of the other programs including the bucket that they call income security or all sort of cash benefits. And then comes the nutrition benefits. That's where CalFresh or Snap is and then others after that. So that's sort of counting the safety net based on the dollars that we spend on it. Another way to quantify the different parts of the social safety net would have to do with, well, how much do they reduce poverty? If that's the goal, if that's like the short run goal, another way to say, what is our social safety net is to measure it that way? And so if we look at the social safety net, again, for children, this is child poverty rates and just ask, do a kind of simple simulation. If we eliminated this program and nothing else changed for the family, how many more kids would fall into poverty? This is as a rate. And so what you can see is the biggest anti-poverty programs for children in America are the tax credits. The earned income tax credit and the child tax credit. And then the second biggest is actually the Snap or what's called CalFresh in California. So the food and nutrition program is quite targeted at the low income population and does do a lot of anti-poverty effectiveness. Okay, so that sort of gives you a sort of lay of the land as to the social safety net for children, what it is, how is it effective, and so on. And what I wanna do now is to just dive in a little bit to give you some sort of insight into what we think we know about how the question is, if we spend more today on children, does it change where they end up in adulthood? That's the question. And I wanna give you a little bit of data and results on that. And there's an enormous amount of work going on in this area, but since you're listening to me today, I'm gonna tell you about my work. That's sort of how it goes. And so I'm gonna talk to you about this work that I've been doing on the Snap program. So before I do that, what do we think about in terms of the kind of theories, theory theories, about why the social safety net and spending more when children are young might change where they end up in adulthood? Like why might that be and what kinds of theories do folks have that they are thinking about that explain that? Well, I would say that this work very much started in thinking around something called the fetal origins hypothesis. And the fetal origins hypothesis, which started with the study of famines and the recognition that when a, like a woman is pregnant during a famine and if the child survives that bad nutritional environment and exists in the world, in adulthood, those children as adults are more likely to have metabolic health disorders like diabetes, high blood pressure, heart disease and also interesting, a higher risk of things like schizophrenia. And so this has had been very well documented sort of in the public health developmental psychology literature. And economists sort of came to this idea and really it just sort of, this literature and research has really grown and we find very consistently that not just nutritional shocks early in life, but pollution, other kinds of disease environment when you're in utero or in early life and these other kinds of neighborhood contexts of where you live and the conditions around you have just time and time again been quantified as showing that they not only might cause some harm today but you see a significant amount of harm in the future. And when economists started working on this they sort of looked beyond these kinds of health outcomes and also found very consistently that when bad things happen, when kids are young you also see differences in completed education and therefore earnings and other things in adulthood. And so this is one framework that's used and this framework very much comes originally from a more of a nutritional basis. So that kind of makes sense for thinking about food stamps but it's certainly something that exists much more widely in the data beyond just kind of nutritional interventions. So there's other theories around the importance of stress as a pathway. There's a lot of interesting work on kind of bandwidth poverty and when you don't consistently have access to predictable sources of funds, how that affects your decision making as a parent and the stress environment that seems to be very consistently showing long-term effects on the kids. So I wouldn't say that we've nailed the mechanism here that's one of the really open questions I think but I wanted to just spend a little time talking about that before kind of diving into the results. So in a series of papers I've been working with different co-authors kind of trying to ask this question in particular how does having access to food stamps in early life change, does it generate benefits and what might they be? And so for those of you who don't know what these benefits are, food stamps or SNAP or CalFresh is a income condition program. You need to have low income to be eligible for the program and as your income goes up, your benefits are sort of phased out and it's a voucher that is distributed as an EBT card and you can essentially buy anything in the grocery store with it. And the average benefit is about $250 a month so it's like $4 per person per day. So that's what food stamps is. And it was started as part of the Great Society set of programs in the 60s but interestingly and actually many aspects of these Great Society programs were not just immediately available across the United States at one time. And food stamps has its own story. Every program has its own story. Food stamps story is that it took about 15 years from the first parts of the United States that got food stamps to the last parts of the United States that got food stamps. And so essentially in a series of papers, my co-authors and I have basically kind of leveraged this variation in access to this program across the United States. And so in essence what we're trying to do is compare two people that are similar and one happened to grow up in a place where food stamps was available when they were young and the other one, it turns out it wasn't available until they were in their later childhood or something like that. And so we do this in the context of event study models or difference and difference models that essentially use some children as controls for other children using this variation in when the program was launched across the United States. And whenever you do this kind of work, obviously the first thing you need to do is study the actual role out of the program and convince yourself that there's some randomness there because you certainly wouldn't want to just assume that the places that got the program early were randomly assigned, which again, there's a different story for every program. The story of the food stamp program in the very first places that got access to the program was under a pilot and they were definitely non-random places. They were very poor parts of the United States in Appalachia and some native reservations and one city, Detroit. So we exclude those places. They were very explicitly chosen for pilot counties and sort of used the period starting in 1964 when Congress sort of launched the food stamp program. I'd be happy to talk more in the Q and A if anybody's interested to sort of learn more about that. But for this conversation, we're gonna just like take that this is kind of, there's a lot of variation across places, a lot of it's pretty random and then we use these kind of econometric models to control for things. People are born at different times in different places. I don't need to talk about all those details. That's another great thing about giving a general talk. I don't need to go with equations and convince you that everything's causal. I'm just gonna say it is and you can pester me about it later if you like. So in the first paper that we wrote on this, was with Doug Almond and Diane Schaunsenbach, we looked at the effect on children at birth. So often time, if you look across many different areas where people have been studying, quantifying the benefits of programs, almost always the first thing people quantify is the effects on children at birth. So the idea here is that a woman is pregnant, some women live in areas that have food stamps, some women live in areas that don't and we're gonna compare, controlling for things, that's the model, how healthy their baby is at birth. Which the main thing that we use when we look at that is how much is the baby weight. And particularly if the baby is below 2,500 grams, that's called low birth weight or 1,500 grams very low birth weight. And we know that those are adverse outcomes that can have long, lived costs associated with them. And the reason why I think the research often starts there is it's like it's a perfect little setting, pregnancy, nine months, it's a concrete period, we know birth weight's important, the data's very good and has been for many, many decades. So it's often the first thing that is quantified. So what do we find? We find that having access to food stamps leads to much healthier births, more so for African American children than for white children. And most of the effects are concentrated at very low weights of birth weight. So it improves outcomes and particularly for the sort of most vulnerable children, that is, as measured by their birth weight. So that's the first set of findings in this work. And I put on the same page, this is just sort of like short run. There's like short run benefits and long run benefits. This is a short run benefit. Another paper by Chloe East uses a somewhat different identification strategy, but she finds what I might call medium term effects. She finds that having access to food stamps leads to children being in school more regularly. So when you start to think about what are the mechanisms by which we might see long run outcomes, studying what happens during the school years is obviously very critically important. So she finds a sort of reduction in poor health and more school presence or less school absences. So now the long run benefits. And so the first paper that we wrote on quantifying the long run benefits, we wanted to look at this metabolic health, which maybe seems like a weird thing for an economist to study, but the reason why we did it was because this fetal origins hypothesis was very focused on this finding that having less good nutrition in early life leads to this increase in the risk of metabolic disease in adulthood. And so that's why we decided to study this first when we started working on this project because it was such a direct theoretical prediction from this early fetal origins hypothesis. So what do we do? So this is like the takeaway graph from the study. I'm gonna walk you guys through it. You're gonna totally get it. And then we're gonna talk about some other work. So this is what you call an event study graph. And it's plotting out the effects of a policy. Usually it's over time, like a minimum wage goes up and we track through how it affects people's wages and employment as time progresses and the minimum wage happens and we watch what happens to people's outcomes. This isn't quite that because the challenge with this looking at the effects on the long run is everybody's treated eventually when food stamps is available everywhere. So what this event study graph is, is trying to focus on how food stamps affects metabolic health in the long run by focusing on how old were you when food stamps was introduced in your county of birth. So we're gonna observe a bunch of people contemporaneously today. And I'm gonna know from the data where they were born and when they were born. And then I can go to my data to find out when food stamps was introduced in your county of birth and I can assign for each person how old were you when food stamps was introduced? Were you three? Were you five? Were you 10? Were you 12?  So it was around your whole life. So that's kind of like the data object. And with any event study, there's always an omitted group. Everything is always relative to something. It's just a normalization, not important, but we normalized the event study to age 10, 11. The reason was from this kind of fetal origins hypothesis, we had a pretty somewhat strong idea that early life was gonna be more important for this translation of this kind of nutritional intervention on long run outcomes. So as you move from the right to the left part of this graph, from age 10 to age eight to age six to age four, you're essentially saying, if someone was first exposed to food stamps when they were age 10, compared to someone who was first exposed to age four, what is the difference in what? In their long run metabolic health outcome. And metabolic health is bad, so we want less of it. And what we measure this as is in our data, we observe obesity, high blood pressure, diabetes, heart attack, heart disease. And we add them all in an index and everything is in a standard deviation unit. I realize that's not very helpful for most people. So for the purposes of this conversation, we'll just kind of say it lowers the risk of metabolic health, but everything the units here are in standard deviation units. So like 0.2 standard deviation units, which is actually a lot. So what do we find? What's the takeaway? That's like the setup, the description of what this is. What this work shows is that having access to food stamps in later childhood doesn't change much around your long run outcomes of metabolic health. But once we get to these earlier ages, age two, age three, age four, and as we move this way, we're kind of thinking about the experiment of someone getting access to food stamps at age two versus age four, or at age zero versus age two. And as we go through that time period, we see that the risk of metabolic syndrome goes down. And interestingly, it looks pretty linear. Like it's like a straight line. And we often call that a dose response, like a linear dose response. Like having more just gives you a little bit better outcome in a kind of equal way. I didn't have any theory. I didn't have any prior that it would look like this. But the bottom line is that having access to food stamps in early childhood between kind of in utero and around age four, you get more of that, you get better metabolic health and adulthood. And so this is gonna quantify to some important benefits. After all, everybody ends up on Medicare. That's funded half out of general funds. And that's gonna generate a lot of benefits. These folks aren't quite Medicare age yet. These folks, my friends in the data. This is like my age. I was born in 1961. I'm only 61. I'm not Medicare age yet. So we need to wait another 10 years if I'm still doing this work in 10 years. We'll get the Medicare data and that'll cover everybody and we'll learn a lot more. This could be a short run effect. Could be that we all get metabolic health eventually. These metabolic conditions, I'm looking at Cave, he'll explain to me whether that's true or not. But that's what we know from this first work. So given that I set that all up, I'm gonna show you some similar graphs with some different outcomes. So in a later paper with Martha Bailey, Maya Rossenslater and Reed Walker, my colleague here at Berkeley, we were able to merge together some data that comes from the US Census. And it's a one in five sample of all Americans. I didn't say what this data was. It's the panel study of income dynamics. It's great data, it's very rich, but it's not, there's not very many data points in it. So it's sparse data, but really rich outcomes. So the advantage of the census data is, we've got one out of five Americans. So the samples are really big. And that data has been merged to data at the Social Security Administration that records place of birth and data birth from birth certificate and Social Security sign up information. And so we can take data from this really big data file and do something very similar where we're able to look at sort of economic outcomes that you have in the census, like what is your education? What is your income? What is your earnings? What is your wages? Are you working? Where do you live? We know in a very micro level where you live. So sort of the quality of your neighborhood. And we also have it merged to data from the Social Security Administration on mortality. So we can also look at effects on mortality. And there's a way in the census data to also have a sort of a proxy for being incarcerated because the census trying to count everybody even if you're in institutions. And so there's a way to sort of capture some proxy for kind of contact with the criminal justice system with this data. And so I wanna show you some of these findings. I sort of tell you here what I think we learned. And it's a very broad range of findings. Having more access to food stamps in early life leads to higher education, higher earnings, better neighborhoods that individuals live in, reductions in mortality, reductions in incarceration for African American men and increases in mobility. So I wanted to show you a little of that. So these graphs are made in the same way. The only thing you'll see is like there's more dots on the graph and that's simply because our data is so, we've got our data is so much bigger and so much so many more sort of observations that we can measure it year by year as opposed to putting it in those bins. And so this outcome is human capital. So like education, completed education. Do you have a higher degree? Things of that sort. That's a good thing obviously. So increasing that is good. And what you tend to see is a very similar pattern. So having more access to food stamps in later childhood doesn't change those long run outcomes very much. But having more access to food stamps in early childhood leads to an improvement in education. And I guess what I didn't say on the last graph but I'll say here is what's happening over here in this furthest left part of the graph where remember I told you there was event time, how old are you when food stamps was first introduced? Well over here the numbers are negative. What does that mean? Well it means that I was born after food stamps was introduced. So from the standpoint of me, it means it was available my whole life. It shouldn't matter if I was born five years before the program was introduced, three years before or two years before, I'm treated the whole time. So the fact that this is a flat set of data points is actually super good. We call this like a placebo or a pre-trend test when we're doing these kinds of models. Like we want some sort of part of the data where we have a strong reason to think that more or less access shouldn't change the outcome.  So the fact that this gets flat here is like actually really confirmatory that this is doing something that we think it is. So you see that again here. So more access to food stamps in early childhood means better, higher education. More access to food stamps in early childhood leads to an improvement in the quality of where you live. So this index is do you own a home? Is it a single family residence? What is the poverty rate of the census tract that you live in? What is the teen pregnancy rate of the census tract you live in? And so on and so forth. And so we pool all those things together and again measure them in these very unintuitive standard deviation units, but I'm gonna give you some real numbers in just a second. And here they are. So what we find is that compared to not having food stamps, having food stamps for that whole sort of in utero period and through age five leads to a 11% reduction in mortality, although these guys are pretty young, so we could learn more about that. If you're only in your 50s, mortality rates aren't that high up until that point. A 0.5 percentage point increase and not being incarcerated, a 6% increase in having some college, some like you at least start college, a 7% increase in earnings, you can see the rest, you're less likely to be poor, less likely to be receiving welfare income in adulthood because their income is higher and a 5% increase in home ownership. So not huge numbers, but no one even thought to look at this before and they're very wide, like just very consistent across a wide range of outcomes. So that's just a little bit of a summary of the work that I've been doing on the food stamps program, but there's so much other work going on by many other scholars. So there are folks that are doing work on the earned income tax credit on other cash assistance programs that pre-existed the sort of war on poverty. There's an enormous amount of work that shows that Medicaid, or what's called MediCal in California, has incredibly important long-run impacts that don't just operate through health. Like if the kid has access to Medicaid in early childhood, they're more likely to graduate high school. Like who would have thought that? Are more likely to have higher earnings in adulthood and the Medicaid work is probably shows the greatest return on investment of any of the programs that have been studied. And on the right just gives you kind of a laundry list of the different kinds of outcomes that folks have looked at with respect to these data. So putting it all together, we want to sort of like give you some number that's like, okay, we did all this work, we quantified all these long-run benefits, what do you make of it? Well, there's different ways to do this, but one measure that's very commonly used is something called the marginal value of public funds, and it's a ratio of benefits to costs. So the numerator is what kinds of benefits is this program generating sort of for the family? And the denominator is how much did it cost us as a society to generate those benefits? And what's cool about this marginal value of public funds is it highlights the fact, and I'm not sure I said this out loud earlier, but it was probably on the slides. If in fact a program where we give more resources to kids when they're young, if that improves their education and earnings in adulthood, part of the cost of that program is sort of paid back, because my earnings are higher, that means I'm gonna be paying higher taxes and we're gonna be spending less as well on criminal justice and healthcare and so on. And so this denominator is kind of like a dynamic thing that builds in what you might call as a feedback loop. If we spend more today and improves people's outcomes in the long run, then more taxes, less spending, that sort of lowers what we think of as the net cost of this program. And so the marginal value of public funds takes the benefits and divides by these net costs to get like a ratio. So if you do that for food stamps, you get a number of 62, which is actually a pretty big number. It's $62 of benefits to the family relative to $1 in terms of government costs. So that's a pretty high return on the investment. And the things that I talked about before is where those benefits and the reduction in costs come from. Higher earnings in adulthood mean more taxes, which means the program is less costly. One thing that adds to the cost to get back to the very beginning of my talk is if we give folks food stamps, what we see is on the margin a few less people work and they work a bit less. It's really practically impossible to devise a system that gives folks money, that doesn't sort of on the margin to scourge work. And so this is like what I started with, just the focus and the research on quantifying these disincentives. And then they're important, absolutely, but they're not important without also thinking about the benefits of the program. So this calculation builds in the fact that there are costs associated. It's like the cost of doing business is how I think about it. If you ignored all these benefits and just quantified the cost of the program, sending out these food stamp vouchers, plus the cost because parents work a bit less, you would end up with a marginal value of public funds of 0.69. So one dollar of benefits, in other words, the family gets a dollar of food stamp benefits per 69 cents of food stamp benefits per dollar of government cost. So it's not one to one because there's some responsive labor supply that makes the program more costly. And so if we were to just do this and say, wow, is food stamps a good investment? You might say, well, I'm not sure it is. Like it generates 69 cents worth of benefits for one dollar of cost, that seems like I'm not sure we should do that. And so this is what we did for a long time. We didn't calculate these numbers quite this way. But the point is that calculating the benefits can make a very big difference to how we think about the program. And that's sort of the point. For those who are kind of interested in this, there's a very nice paper by Nathan Hendron and Ben Spring-Kaiser that takes this marginal value of public funds, this measurement thing, and goes to, I can't remember, 100 different policies, some educational interventions, many different kinds of interventions, and puts them on the same kind of metric of this, what are the benefits relative to the cost? And there's no way you're gonna get anything out of this picture that I have up here. That is not the idea. But the point is to say, look at the scale of this. The marginal value of public funds goes basically from zero to five, and then it is infinity at the top. And the reason why it goes to infinity is that if the denominator of the marginal value of public funds goes to zero, like all of those feedback loops are so incredible that we pay for the program simply, we've paid for the program. That's what gets you an infinite marginal value of public funds. And Medicaid is the safety net program that generates it fully pays for itself in the long run. You even need to care about the benefits for families. It just pays for itself, which is interesting. And the other kind of takeaway from this graph that you might be able to see is that the horizontal axis here is how old are the people that are getting these benefits, or this program, whatever is being analyzed? And you tend to see a pretty common pattern that the younger the folks, the higher the marginal value of public funds. Because there's like more lifetime over which for these to grow. Okay, so in the last whatever, 10, five, five minutes. Manual's keeping me on time here. I wanna take this and try to bring it to this sort of current discussion about the child tax credit. So in the beginning I said, we're not so good. We don't spend much on children. And in the context of that, we did a pretty amazing thing in 2021 by expanding the child tax credit as part of the American Rescue Plan. And what we did is the following. So this is what the child tax credit looked like before the 2021 expansion. On the vertical axis here is just income. This is just the schedule, as we say, for the program. And the horizontal axis is how much money do you get? And the child tax credit, which was most recently expanded in 2017, although it's been around since 1997, essentially gave families $2,000 per child. And if you look out here, this is the schedule for a single parent. You would get that $2,000 until your income was about $200,000 a year and then it was phased out. It's pretty high. If you're a married couple, it was phased out starting at $400,000 a year. So only 2% of children lived in families whose incomes were too high to have this child tax credit. Yet down here, at the bottom of the income distribution, the child tax credit had a pretty significant, what we call minimum income requirement that not only required that you work to get this benefit, but you actually had to have earnings above a certain level to even get it.   Oh, tell me about it. Well, it's supposed to help families cover the cost of children. Oh, really? Well, tell me about it. Like, who gets it? Well, the poorest Americans don't get it. And then your mom is like, what, that doesn't seem to make any sense. So 35% of children were in families whose incomes were too low to receive this child tax credit. Interesting. So the American Rescue Plan comes in and essentially does two things. One, it makes it more generous. So it goes from $2,000 to $3,000. Or if your child is under age six, $3,600. And I think that this work, the work I showed you and the work that others have done was part of what led to this higher benefit for those under six. Because I think the research shows that that matters more. Also, there's a lot of attention to the cost of childcare and that's very expensive. And so that's the first thing it did. It increased the generosity of the program. But more fundamental than that is it made everybody eligible even if you had zero earnings. You still had to file taxes. So that's a very costly hurdle and we had to get we, not me we, like the Royal we, needed to bring people into the filing, you had to file taxes to get it. And maybe some of you know about that from having to file taxes to get your rebate during the COVID crisis. Same thing. So this is pretty fundamental. And if it were made permanent, some simulations would say that this would reduce child poverty by 35% in the United States. And it would definitely be the biggest single thing we ever did to reduce child poverty in the United States. Kind of similar to what social security did for reducing elder poverty. So back in the day before social security, elder poverty was the highest, like the elders had the highest rates of poverty in America. After social security, they, their poverty was really brought down and now it's children that have the highest rates of poverty in the United States, which is just a fact. And so going back to my first graph, this is my best estimate of how much where we would move on this graph. We'd spend more. It kind of puts it in the context that this is pretty big, but it still doesn't even move us to like close to the middle of the pack. But it's a real change is the point of showing you this. Okay. So interestingly, perhaps tragically, despite all of this new research, the debate about the child tax credit then and now is still primarily about how it affects parents' employment, which is just kind of amazing. And, but I'm a kind of optimistic person, people in this room who know me, know that I just am. I'm from Wisconsin. Why don't we're optimistic people in Wisconsin? It is true though. And I think it'd be very interesting if someone were to do like a text analysis of like words spoken on the floor of Congress or something. There was more discussion about the benefits of these things in the long run and some sort of language around investment. At the end of the day, that didn't do anything or it didn't seem, it hasn't, but at least it was talked about more. And there was a lot more like press and articles about it. And I said, yes, to way too many press calls because I felt like, well, this was the time. You got to talk about it. Like we've been doing all this work. Let's get the ideas out there. So what do we know? And then I'll wrap up. So what we saw from the child tax credit coming out is that when the monthly benefit checks started to drop, child poverty fell. When they stopped, child poverty went up. That's perhaps not that surprising. It's hard in the short run to find things that change that we think are gonna be good predictors of what these things are gonna do in the long run. That's tricky and that's like one of the open research questions that I wanna leave you with. But what we did see is that food insecurity fell when these checks started to drop. And they fell in particular for households with children and not for households without children, which is kind of hopefully more confirmatory that this was this policy and not something else that was going on at this time. There's also of course some studies that looked at the effects on parental employment. And I would say that they're all very small in terms of the impacts on employment. And there's a handful of studies that show no effect on employment and one study that shows that employment goes down. Of parents when you give them this child tax credit. So the debate still is very much on this question of employment. So finally, the census every year releases annual poverty measures and those numbers dropped in early October, I think. And what you can see from those calculations and so the October numbers were citing data for 2021. That's the child tax credit year, it's gone now. It was just for that one year. I might not have said that, but that's true. It was just a one year expansion of the child tax credit and it was not renewed. So we're back to the old policy of 35% of kids are in families with income too low to get the child tax credit. But in 2021 when we had it, we saw the lowest child poverty rates ever measured in US history. And if you look at the gray line, that's like the census's best guest of what child poverty rates would have been if the child tax credit hadn't happened. And this got a lot of attention when it came out. And there was a lot of reporting about the role of the tax credit rather than just talking about the numbers, which I think is helpful.  It's my last slide, but a nice study out of the Columbia Center on Poverty and Social Policy took all this research, some of the research I've talked about in many, many other papers and tried to quantify the effect of this child tax credit increase on the benefits to families and the cost. Net, these net costs as I talked about. And so the child tax credit expansion costs about a 100 billion dollars a year. And when you calculate all that feedback loop of these better outcomes in the long run, that 100 billion dollars is reduced to 18 billion dollars. And they quantify the benefits, many things like we've seen already today, to be about 810 billion dollars. So that ratio of those two things is pretty close to that 67 that we had in our paper. And you can just kinda scan this and see where the real benefits are. So there's a lot of benefits relative to the cost of this program. So that's what I wanna leave you with is just the importance of doing the work to quantify these benefits in order to think about what optimal policy is. And while I'm taking questions, I will leave those in the room, perhaps the graduate students that are here. These are sort of my ideas about what we still need to know and where the real opportunities are for doing more work. So thanks again for coming. So we have time for questions and I guess you guys know this is being recorded and that's why we're using a microphone so that those out in the world who are watching this, who will watch it later, will be able to hear the questions. So feel free to just raise your hand so Jane can find you. I see someone in the middle over here, Jane. I'm gonna grab the microphone. I know you're being very kind. Thanks so much for coming. It's good to see you again. Turn it on. Okay. Is it in the end? Someone can figure it out. I'm gonna not do that part. Usually there's a button on the bottom. Where's the switch on that mic? I'm not good with tools. I have a helper next door. There we go. So I've worked on childcare policy for four decades and one of the things that strikes me as I'm looking at this is that I'm curious whether when you looked at SNAP or CalFresh, whether you were also looking at WIC because these are children who are the youngest ages and I'm kind of curious whether you get like double double whammo with having WIC plus CalFresh or whether that was not looked at at all. And I also just wanna comment that in the childcare arena, the whole push behind preschool was developed by this very method of looking at what are the benefits of high quality early childhood. We realize very early, low quality isn't gonna get you there, but high quality is. And one of the people who was most profoundly involved in this is James Heckman, economist, not child development specialist, who not only moved on from finding that early childhood high quality education had all these impacts on children. And I will say that his work built on other people from even earlier. So there's at least three or four really wonderful studies that were done. But what I wanna say is that he moved on to the health outcomes because he could see, wow, we're getting outcomes in this area, we can get outcomes. So when you look at the entire child and you look at what are the outcomes, the benefits are like overwhelming if you can spell it out and message it to the American people that it is not only child development, reading all these things academically, but also health wise that you're not gonna get diabetes when you get older. And in addition, it's not going into special education, not going into prison. So it's enormous. But I'm curious about the WIC. So I'm gonna start with the second part. Actually in this paper that we wrote where we tried to tell the story or our best attempt to try to tell the story of what made people start focusing in this area on looking at the benefits, that's actually one of the things we cite is that that work was going on like in the 90s that was really getting out there in early 2000s, revisiting these interventions in the 60s, the Perry Preschool and the Abbasidari and all these things that you're talking about. And I think that was, again, our paper is a naval gazing, it's not a scientific exercise, but I'm quite convinced that that work was one of the things that spurred this work was just both seeing where and how large these benefits could be and how widespread they could be and causing other folks to think, huh, I wonder if dollars could do that too as opposed to educational intervention. So that was actually a really critical element, I think, in the intellectual history of how we got there. On WIC, I agree, I've done some work on WIC. No one has quite figured out there's no work on the long run effects of WIC. I would certainly think from the work on SNAP that it would be there and would be significant, but we just don't have the evidence yet. But your question about them working together is definitely one of the things that is on my list of exciting things to do more work on and that is kids don't just get one benefit, typically, they get multiple things. And one of the things I think we have to learn is does A plus B equal more than A plus B, if you know what I mean. So are there interaction effects between programs or not? Or what are the interaction effects between having SNAP and having a well-funded school? One of the findings I didn't talk about in our work on SNAP was that the results for the long run effects on the labor market were present for white children but not for African-American children. But the long run effects on the health outcomes were present more for African-American children and not for white children. And we're trying to see what can we learn about that? Why is that? Is that because of structural racism and discrimination in the labor market? Or is it because these kids were on average in different schools and maybe the school environment is critical for catalyzing those long-term impacts on human capital? I don't know, but it's all in this bucket of these interaction effects that we don't know very much about and is, I think, an important area for work. Okay, so let's get one over here and then we'll go over to the other side. That light is super bright, just gonna say. Thanks, Hillary. It's always so striking seeing that graph showing the impacts of SNAP. I remember seeing it. You probably saw it a very long time ago. Yeah, I think it was four years ago in your public econ class and every time it's just stunning. I guess my question to the mechanisms part here is what do we know about how much of those benefits flow through the mechanism of the fetal origins hypothesis versus maybe something about the social conditions of the family that the child grows up in. It just strikes me that both are so important, but I don't have any sense for which matters more or less. I think the reason that I think that it can't all be the strict fetal origins hypothesis, the one that says your metabolic health develops when you're little and that that's very much a nutritional story. And I obviously was studying a nutritional program, so fair. But the pattern that you see here is pretty replicated. I mean, it's not also striking in the early ages, so maybe that does tell us something about the importance of the nutritional channel. But the work on Medicaid shows that early life is incredibly important for having access to Medicaid, and that's pretty far from that nutritional story. So I don't think that this is all about a nutritional channel, but it's possible that the snap story is more so than others. And the sort of fetal origins hypothesis was really only about in utero, as I'm sure you know. And originally, people weren't studying what happened after birth, because the view was that that was all about in utero, it was all about the fetal development. But then they did some rat studies, and it turned out that you could basically generate these same kinds of effects by manipulating nutrition after birth. And it was like reading about those rat studies that got me to thinking about, we should think beyond the end of utero and we should think about after birth. And then you see that obviously in like effects of pollution in the long run, it's not all about in utero, it's also post birth. But maybe I'm taking fetal origins too narrowly, but I don't think it's all about that because not necessarily from what I've shown you, but I think from the broader work, there's gotta be other channels at play. But I don't think we know very much about the channels. Someone over here? My guess is that in this particular silo, your presentation was illuminating, but not surprising. You said you talked with Manchin, I assume you talked with others in Washington. Was it surprising but not illuminating? Was it not surprising, not illuminating? Clearly it's not illuminating because it's causing for all the presentations that go on in Washington, it seems to be causing more slippage than forward movement.  And you must have saturated him with this and more. He's a smart guy. What's keeping, and 62 to one is the best odds I've ever heard anywhere about anything. You certainly don't get that on any other government program. What's keeping him from saying, my God, we should be all over this. And then the other 99 senators and the 435 members of Congress, what's keeping them from steamrolling this right through Congress, as opposed to preventing it from getting out of committee? Well, I would say my overwhelming view of my very small toe into that world was, I like the world, I like the silo a lot better. That's why we all live here. No, and I don't mean a political silo. I mean, I have the privilege of being an academic that sits in my office and does my work, and that's what I mean. I don't know, I think at the bottom line, they're politicians and we're scientists. And so sometimes persuasion isn't going to be that kind of persuasion isn't gonna be what takes a day. I can tell you what he was concerned about was the fact that it was cash. And he was very concerned that people would spend it on the quote wrong things. And I would say, I wasn't the only one there. It was by Zoom. I wasn't the only one there, but I was the kind of the specialist. And so I would say, well, we have some studies. Here's the different evidence we have. Candidate, something really simple, similar. And this is what we know about what folks spent the money on. And that just didn't seem to, he had in his mind people were gonna buy drugs with it and that's bad. And then he was worried about the work disincentive. And I would say, well, actually, it's small. I relative to the big benefits and 62. And I just didn't seem, I wasn't good at it or he just wasn't persuadable. And I think that, you know, I, evidence is evidence. And I think my job or whatever, that when one cares to go outside this lovely world that we get to live in and to try to talk to people in the policy world, you know, there's a beginning and the end of what we're gonna do. But I will say that many, many months later, I was invited to testify before the House Budget Committee on this evidence.  And that's usually when you do testimony when there's something at play that you're weighing in on. And they said, we want it in the congressional record that there is this evidence and what it is. And we're playing a long game here and we need to just start to build, we need to regularize and make, you know, just part of the record. I mean, I don't know, maybe someone in this room can tell me that, I saw Paul Pearson here and I don't know if he's still here. Like, is the congressional record that important? I don't know, but that's what they told me when I came to do this testimony, that they're playing a long game and they want the evidence in there and there'll be another opportunity and you bring the evidence out then. So I don't know, I'm no expert on this. Thank you, so I just wanted to follow up because have you thought about using the one year experiment of the 2021 child tax credit to, for example, show how people probably did spend the money using perhaps the consumer expenditure survey. And have you also, maybe there are ways to get at other mechanisms, particularly stress and biomarkers and things like that. So any ideas on leveraging what we did do in the research world? Yeah, so there is already work on not from the consumer expenditure survey but from other surveys like the census pulse survey that we were doing real-time surveillance during COVID and other surveys that basically shows that people say, like this hasn't been like collecting the data from your bank account, which will come, I'm sure, is that people spend it on basic needs. Food, child items like it was like in the summer and your kids were going back to school and I got to buy kids new shoes that year and a lunchbox and all those things. So child expenditures, food, catching up on debt and housing, those were the largest things. So I do think we have evidence on that and I think that in academic speed, these studies have come out pretty quickly, I think in a couple of years, we'll know very, very, very much more. I mean, I just reviewed a study recently that showed that admissions to the child welfare system were dropping after the 15th of the month, the checks dropped on the 15th of the month and so they used very granular data, both like people showing up at the hospital and referrals to child welfare and you would see every month because these checks would drop on the 15th that the incidence of these bad outcomes would drop after the check. So we'll certainly know a whole lot more and whether that will help continue to build the evidence base for the next policy window that opens, I suspect it will certainly be there. Yes. Right, so there's a really exciting study going on right now in the field called Baby's First Year that's being led by Greg Duncan, who's a professor down at UC Irvine and they're giving kind of like a guaranteed unconditional income to women starting in their third trimester of birth of pregnancy and through the Baby's First Year of Life, $300 a month and it's a randomized control trial and they're gonna evaluate the kids for as long as they have funding to continue to evaluate them. I think the kids are in their second year of life right now and there's a lot of exciting kind of work that's very, very much on this question of do more resources in early life but through an RCT, which we don't have evidence on. So thanks, Rucker. Thanks so much for coming, everyone. Thank you. Thank you very much. Thank you everybody for coming. So I invite you to join the reception and continue the conversation. It's going to open up right behind Hilary. Thank you again, Hilary, for this really inspiring talk.