 Hello, good afternoon everybody and welcome to the second part of this pair of lectures by Rash Chetty, the Hitchcock Lectures. So I am a manual science professor of economics and I'm delighted to introduce Rash Chetty who is currently a professor of economics at Harvard. They actually met Rash Chetty first, you know, 25 years ago at Harvard University when he was a grad student and I was teaching public economics. And it was indeed, you know, in my long years teaching public economics the best student I've ever had so I'm really happy to introduce him and not only that, we were able to attract him to Berkeley just a couple years right coming out of the PhD so then I got the opportunity to teach with him public economics and see his passion for the field and today he's gonna tell us, you know, based on his analysis about policies to restore the American dream and so what struck me also about Rash, beyond his passion for public economics is also his incredible ability to assemble teams of workers. Right there, you know, as a young assistant professor he was able to get our grad students, many of them older than him to work on his project and he also loved the hard sciences perhaps because his wife was in molecular biology so he always had the idea economics should imitate the hard sciences and indeed he found his vocation there by studying, you know, big data for understanding our social world and designing better policies. Big data is hard to access, hard to work with so it's really an area where you need a big team of dedicated workers and any one of those projects that Rash has done is really like a super production, you know, like really a true like a hard science project, you know, made with a big lab and we are delighted to have him today, you know, present to us just the key substance, the results, how the results can be distilled, you know, for broad audience today but those of us who've worked on some of those know the tremendous amount of work, you know, that each of those projects required so Rash is going to talk for about 50 minutes then we'll take Q&As and then Jane the organizer told me, you know, to emphasize that there is a reception just after the end of the Q&As right in the room here next door where you are all welcome to come in and continue this conversation with Rash. Rash, take it on. So thank you so much Emmanuel for the very generous and warm introduction and thank you all for being here so Emmanuel of course as he mentioned my teacher and mentor for many many years one couldn't have a better role model somebody I look up to in numerous ways and so it's really an honor to be here presenting today so I know a number of you attended the lecture yesterday but some of you did not so I'm going to start by first just briefly recapping the central focus of these lectures and the problem that I'm interested in and then transition to today's lectures really focused on potential policy solutions to create more opportunity so just to pick up, you know, with the first slide that I started in the previous lecture for those who weren't there the big picture problem that I've been interested in with many many others in our field is trying to understand why the American dream captured here in one way is measuring the fraction of children who go on to earn more than their parents how much upward mobility is there in America why that notion of the American dream is fading over time why you know back in the middle of the last century for kids born in the 1940s virtually everyone was growing up to earn more than their parents did but if you look at more recent data for kids born in the 1980s who are turning 30 around now when we're measuring their incomes as adults it's become a 50-50 shot as to whether you're going to achieve the American dream and so that broad trend is of course you know something we're interested in understanding the determinants of and we'd like to figure out and what I'm going to focus on this lecture is you know how you might reverse that trend looking forward how you might create more opportunities for upward mobility in the U.S. as a whole and in particular for kids growing up in lower-income families where I think these issues of upward mobility are most relevant and so what I did in the last lecture was tried to disaggregate this national picture and break it down into subgroups and geographic areas and try to understand what might be driving this trend and what more broadly are the key determinants of economic opportunity and just to briefly toggle over to that and motivate what we're going to talk about today you know I showed this map from the opportunity Atlas which is data on children's rates of upward mobility constructed from tax records in the modern era for children born in the 1980s dividing the U.S. into different metro and rural areas and in each of those areas calculating for the set of kids who grew up in low-income families defined here as families at the 25th percentile of the national income distribution where did those kids themselves end up in adulthood and we constructed those statistics by using data from those kids own tax filings and if you look at this map and you can go to this website yourself opportunity Atlas.org you can see that in much of the center of the country kids growing up in low-income families had very high rates of upward mobility even in the current era you know those kids growing up in families making twenty seven thousand dollars a year are on average making forty five fifty thousand dollars a year but then if you look at other parts of the country you have much lower rates of upward mobility and a key point here is that not only do you have a lot of geographic variation in children's chances of rising up across broad regions but if we then zoom in and I'm going to zoom in here to show the data for Berkeley given where we are if we now zoom in and look at these data at a much finer geographic level we see that that variation is arising not just across states or across cities but at a hyper local level in the north side of Berkeley versus the south side of Berkeley different parts of Oakland Alameda etc and so we talked about in the previous lecture is what might be driving this showing that it really seems to be the causal effects of childhood environment where people are growing up related to factors like the quality of schools social capital access to higher education and so forth so with that background in today's lecture I want to turn to the question of what we can do to make a difference going forward in increasing economic mobility and so motivated by the findings from the previous lecture on the science of economic opportunity which just to summarize in a nutshell what I think we've learned from the research we've done in our team and many other scholars and economics sociology and other fields is that the roots of economic opportunity are hyper local it's about at least to some extent differences in childhood environment at a neighborhood level and what really matters is where you're growing up from birth to something like age 22 or 23 for a host of different reasons from things related to who you're connected to who you're interacting with to the types of education you get and so forth okay so if you have that world view I think you would naturally think about three different ways to increase economic mobility going forward the first is to reduce segregation if I know that two miles down the road here in Berkeley kids in low-income families can have much better opportunities of rising up you might just ask well why don't we try to help more kids move to those neighborhoods or create less segregation in our cities so more low-income kids can grow up in neighborhoods where they have better chances of rising up and that is one potential approach that one can take and I'll spend some time talking about how one might do that from a policy perspective now of course helping people move or reducing segregation is not going to be a completely scalable approach you can't move everyone to a different neighborhood and not everyone wants to move to a different neighborhood so I think it's equally if not more important to think about a place-based investment approach how can you take the red colored areas and the maps that I was showing you and turn them into blue-green colored areas how can you bring opportunity to people rather than bringing people to opportunity in some sense and so I'll spend some time talking about that second approach as well and then finally recognizing that the key touch point for most kids after age 18 is not the home in which they're growing up but the institution of higher education that they're attending there are also I think lots of things that we can do in our higher education system to make colleges more effective engines of upward mobility so the way I'm going to structure today's lecture is to talk about each of these three different buckets sequentially starting with the reducing segregation approach and really show you how with modern research and large scale data we're not only able to study these issues but actually make a difference in people's lives on the ground so to start let me take you back to the opportunity Atlas data and show you a different snapshot here the opportunity Atlas data in the Seattle metro area where you see a pattern that is similar to what we saw in the Bay Area what we see in many other cities this checkered pattern where there's some places in Seattle that have very low rates of upward mobility there are other places in Seattle where kids growing up in low-income families have much much better prospects of rising up now what we've done in this version the map is overlaid in the bright green dots the most common places where families receiving housing vouchers from the federal government currently live so as a bit of institutional context in the United States we spend about 45 billion dollars per year on various affordable housing programs the largest component of which are housing choice vouchers which go to lower-income families about two and a half million families and they basically provide rental assistance for families to find more stable housing and housing in better neighborhoods that might ultimately enable them to break the cycle of poverty those vouchers are quite valuable they're worth about fifteen hundred dollars a month in Seattle and so they can really make a dent in helping families afford better housing now when you look at this map and look at those bright green dots in terms of where families use getting these vouchers currently live you might notice a quite puzzling pattern which is those bright green dots are colored are clustered in the red and orange colored parts of the map rather than the blue and green colored parts of the map so that is even despite receiving the assistance from the federal government which is intended to help families find housing potentially in better areas families are not actually using those vouchers in that way you know we know from the research I shared in the previous lecture that their kids would do much better if they lived in some of these other neighborhoods that offer much better opportunities for upward mobility but in fact when you look at the data they're not living in those places so when we put out the opportunity Atlas data initially we actually got a number of inquiries from housing authorities in Seattle and across the country and the housing and urban development agency who had overlaid these their data on these kinds of maps and said you know this is odd we're spending all this money billions of dollars to try to help families move to opportunity in some sense but that doesn't seem to be happening so why is that the case and what can we learn about how to maybe make these programs more effective you know what are the key reasons that families are not moving to higher opportunity neighborhoods so what came out of that was a collaboration with the Seattle and King County housing authorities that were calling creating moves to opportunity which we set up as a randomized trial a pilot study to help families that are getting these housing vouchers move to higher opportunity neighborhoods if they wanted to do so through a combination of counseling services connections to landlords and forms of liquidity that I'll describe in further detail in a second and before I get into how this experiment was set up let me just spend a minute talking about what I think the scientific question here is and what we're trying to test is basically whether the pattern you see in this map is driven by preferences so the standard neoclassical models in economics would say you know people optimize if they're choosing to live in these places despite the fact that you're giving them money it must be that they prefer those places for some reason maybe it's closer to their family closer to their jobs they feel more comfortable in those neighborhoods who knows a different possibility less consistent with the standard models we would write down in economics is that there are barriers that these families might face that are preventing them from moving to these higher opportunity neighborhoods even though they would like to do so so maybe landlords don't want to rent to them it's very complicated to find housing in a neighborhood unfamiliar to you now maybe they're frictions that are preventing you from making that move that you would really like to make so that's conceptually basically what we're trying to test in this experiment we're trying to test between these two hypotheses by removing some of these frictions that the families might face and seeing if that ends up affecting where they move where they choose to live where their kids end up growing up so how do we go about removing those frictions so this program was designed in partnership with the local housing authority that had worked with many clients and had some sense on the ground you know there's a good example where I think had we tried to come up with this in our own offices we would have not you know and sitting in our academic institutions I don't think we would have thought of these particular interventions partnering with people who are actually doing this kind of work makes an enormous difference and what they hypothesized was that you needed three elements to try to make these moves more successful so first they set up partnered with a nonprofit to provide search assistance to these families in the process of housing search so much as at the high end of the market if you want to buy a house there are lots of people who want to help you find the best house at the low end of the market there's no money there so there's no kind of brokerage service and you can think of this nonprofit is trying to provide that brokerage service saying you know let me help you identify the high opportunity neighborhoods here's a place that would work for you in terms of your commute you know here's a listing that seems like it could fit for your family and so on the second component was then direct engagement with landlords so connecting people with landlords and also recruiting landlords to join the housing voucher program and really making that network work and helping represent clients with landlords saying you know here's a tenant who we think could be good for you and coming and advocating for folks sometimes who may not have a great credit history things like that and then finally we provided a little bit of short-term financial assistance basically to overcome liquidity constraints sometimes people don't have the money to pay an application fee or a security deposit varies across people had a little bit of assistance for that the way I think about this kind of intervention in broader more abstract terms is it's sort of a social capital intervention we're trying to provide people additional support in the housing search process we're trying to connect them with landlords and build those networks basically we're trying to do a little bit more in terms of surrounding support beyond just giving you the cash and saying you know good luck find housing wherever you would like okay so what does this cost this program costs about two thousand six hundred dollars per family that's not cheap by any means however to put it in perspective remember that the voucher is fifteen hundred dollars a month of rental assistance that adds up over time because the typical family is living in this housing for you know several years eight or ten years and so it's about a two percent incremental cost relative to the cost of the voucher itself so from that perspective it's not incredibly expensive I want to emphasize that families are not required to move to high opportunity areas here so the whole point of this is we're providing the support and then we're kind of trying to enable choice we're saying you know we want you to move wherever you're happiest and we're going to provide this additional support if you want to move to a high opportunity area great if you don't that's also fine so what happens randomized trial you follow people over time you can see where they end up choosing to live in the control group fourteen percent of families moved to high opportunity areas which we defined as places in that initial map that I showed you that were in the top third of the distribution in terms of their rates of economic mobility so basically the blue green colored places in that map in the treatment group as you can see here that number jumps up to fifty five percent so this relatively modest one-time intervention substantially changes where families choose to live suggesting that this is more about barriers than about preferences and you can just see very clearly how the sort of intervention is starting to desegregate the city so these pins here and the red colors show you where families in the control group chose to live and you can see that those families are clustered in the south side of Seattle you know in other places that are low opportunity areas the high opportunity areas are shown in the shaded blue here whereas the green pins for the treatment group are scattered throughout the blue shaded region the high opportunity places to a much greater degree now one thing you'll notice here is also you know quite importantly as you think about scaling these programs all of the green pins are not in the same exact neighborhood so it's not like we took everyone in the treatment group and ended up moving them all to a single apartment complex or something like that if that's what happened you would worry that as you try to bring this to scale you're going to change the character of the neighborhood to which people are moving and this is not going to actually work in equilibrium but you can see what actually happens is it's way more scattershot than that it's more consistent the view that you've kind of eased the constraints that people face and they end up choosing now to live in many different places that are in higher opportunity areas so we were happy to see these initial reduced form treatment effects of the program but naturally the next question then to dig deeper and understand how you might scale these programs you know what the key mechanisms are here is to dig deeper and try to learn about why this program is working what barriers are important to overcome why is it that these families were not moving to high opportunity places to begin with so here I want to talk about different methodological approach the theme of these lectures and as Emmanuel discussed in the introduction has been about big data and how quantitative methods with large scale data are enabling us to make progress on many questions but I also want to acknowledge that there are some questions that are difficult to answer with quantitative data approaches and sometimes it's useful to use complimentary other methodologies as well and so in this case what we did is teamed up with sociologist a fantastic sociologist at Johns Hopkins Stephanie DeLuca who does qualitative research and she interviewed hundreds of families who participated in these experiments and compiled about 10,000 pages of transcripts from interviews with these families each of which were about two hours long and then systematically coded those interviews with her team to identify from a qualitative perspective what seem to be the key drivers that enabled families to move to high opportunity areas in the treatment group and the types of things that come out I'm just highlighting some of the main themes here are very different from what we typically discuss in traditional economic models emphasis on things like emotional and psychological support so here one of the people who moved emphasizing the support of nature of having lots of conversations with Megan who is the housing counselor who helped in the search process talking about the importance of brokering with landlords you know having somebody to help fill out the application saying I will come with you I will talk with the landlord having that kind of advocate many people identified as being very important and then some people pointing out that the financial assistance you know getting the money at a point where it was really useful made a big difference and so these kinds of qualitative interviews made us think that it's really this combination of wraparound support that made a difference rather than just giving people financial incentives in and of themselves which is the way we would traditionally think about it in economics or just giving people information about high opportunity areas another common hypothesis you know maybe it's just that we told them where the high opportunity places were and that's what led them to move the qualitative evidence doesn't seem to be consistent with that so motivated by those qualitative findings what we did next is went back to the field and ran a second round of experiments where we disaggregated that treatment that I showed you initially with the bundled set of services but now we had multiple treatment arms we had a control group that didn't receive any of these services we had a treatment group that received just the financial support to move to a higher opportunity place and information about where the high opportunity areas are then we had what we call a partial support group that didn't get the customized counseling with somebody texting you about new listings and things like that but more kind of general non customized support and then we had the full blown intervention that we did in the first round and here again we're plotting the fraction of families who ended up moving to high opportunity areas in each of these groups and very consistent with the qualitative evidence you can see exactly the result that I was alluding to earlier that it's really the customized support that makes a difference here providing any one of these things you know just by itself for example just financial incentives and information doesn't really get you there and so you know I think a key takeaway from that is that it's really this customized you know meeting people where they are providing the social support seems critical in making this program more effective and those are the kinds of barriers that people face in moving to higher opportunity places so what one can then do you know so we've seen that this program is successful in helping people move to higher opportunity areas so that's helpful as a first step but if you'll remember from for those who attended the previous lecture to really see long-term benefits we need people to stay in these neighborhoods it's obviously not enough if you just move there and then end up leaving and this is also a very important test of the hypothesis that this is about preferences rather than constraints because if it's actually about barriers then you would expect that the people who ended up moving they would be satisfied with the place that they move to that they want to stay there for a long time whereas if they found after they got there this actually is not such a good fit they would end up leaving so we ended up we continue to follow these families and we find first not shown in the slide but you know you could just track these families and look at those same rates of what fraction of families live in high opportunity areas over time and those rates of persistence are incredibly high 90 95% of families who move to the high opportunity places are staying there two years later three years later and so on but what's more if you just use subjective measures of how happy people are with their new neighborhoods or how confident they are that they're going to want to stay in these new neighborhoods that they just moved to systematically those rates are much higher in the treatment group than the control group very consistent with the idea that people actually expose strongly prefer living these neighborhoods and again when you do qualitative interviews it's totally obvious you know families talk about the much better environments for their kids the lower rates of crime now they're much happier feel safer healthier etc so we were very happy about the results of this program it seemed like through a relatively small intervention remember 2% incremental cost you can significantly change where families end up choosing to live and tying it back to the research that I showed you in the previous lecture we estimate that the families who got lucky and ended up in the treatment group based on the neighborhoods they moved to we estimate that their kids are going to grow up to earn about 200 thousand dollars more over their lifetimes than the kids in the control group who ended up living in the low opportunity areas and so that $2500 incremental investment has an enormous rate of return in the long run so in other words the 45 billion we're currently spending if we add a little bit of additional support services to that or tweak it a little bit on the margin I think we can make that program much more effective so just to show you now how from doing basic research of the type that we were describing the last lecture to this more applied pilot type of study to then finally having an impact on policy on scale you know for the students here and others interested in this type of work you might wonder in this polarized era where politics is very divided does this kind of empirical evidence science really make a difference and I've been very heartened to see in my experience working on these issues that indeed it does seem to make a difference and so just to give you an illustration of that in this context what that led to the work in Seattle was first a bill that was passed in Congress a few years ago with bipartisan support the Democratic and Republican Center co-sponsoring it that led to 80 million dollars of funding to replicate what we did in Seattle and eight other cities in America so that is currently happening at the moment in these eight other cities you want to understand whether these things can be scaled but then much more importantly you know 80 million dollars obviously in the grand scheme of things is not going to make a difference what is potentially going to make a difference in this context is this new bill the family stability and opportunity vouchers act which is another bill again co-sponsored by Republican Democratic Senator that's currently working its way through Congress which proposes to expand the housing voucher program by five billion dollars per year and have more vouchers and provide more of the types of services that we've been talking about and again just to show you how I think empirical evidence and science can have a very big impact I just want to show you some of the text drawn from this bill and show you how it directly relates to the evidence that we've been talking about in these lectures so this bill proposes to create an additional 500,000 housing vouchers targeted at families with kids under the age of six so where do they get that those of you attended the previous lecture you might remember that dosage curve where we showed that the earlier you move to a better neighborhood the more of a game you get given that logic you want to target these vouchers at families with young kids because that's where you're gonna get the biggest bang for your buck so that's what motivates that with access to counseling and case management services that comes from the Seattle evidence that I was just showing you and then focusing on engaging new landlords in the voucher program again based on that type of evidence and subsequent work suggesting that these connections to landlords are extremely important and so that gives you one example of kind of the arc from research to policy where you know this bill is passed and our sense is given the bipartisan support for it at some point that it is likely to be passed that's gonna have an impact on thousands of kids lives across America and is gonna be one step towards creating more integration now housing vouchers are of course just one way to create more integration I want to just give you one other example of a very different type of policy that can also make a difference in this domain so again when we put out some of this data and I mentioned how Charlotte North Carolina for example came out as the 50th out of the 50 largest cities in terms of rates of upward mobility various things happened in Charlotte and in many other cities one of the things a number of those cities did is change zoning regulations so they recognize that part of what was limiting opportunity for low-income families what were zoning regulations for single family homes that basically made it difficult to move to a higher opportunity area place with a better school district greater social capital etc if you allow denser building you can potentially relax that constraint if you move to multi-family zoning and that's what a number of cities have done in the United States and you know we don't yet know exactly how effective that's gonna be but that may be another way to create more integration down the road okay so in this first part of the talk I focused mainly on this focused on this first approach of creating integration by helping families move to opportunity and changing where housing is located but you know of course you may be thinking well that's all well and good and it's gonna help you know a certain number of kids but surely it's not gonna be fully scalable and so what I want to turn to next now recognizing that is a second approach of place-based investment so trying to transform the places that are currently lower opportunity into higher opportunity places in the long run I really think this is the scalable path to creating more opportunity it is also a much more difficult path and conceptually that's because you actually have to understand what it is that's making some of these places lower opportunity relative to others and then address those problems and I don't think you know frankly from a scientific perspective we actually know the answer to that question yet whereas for this first approach of helping people move to opportunity or reduce segregation at some level you don't need to understand exactly why certain parts of Berkeley provide higher levels of upward mobility than others that's a fact and if you help families move to those places you're gonna see better outcomes so that's what makes the second approach you know more challenging but I'm gonna show you is the progress that we and others have been making in this space but as you'll see this is going to start to look a little bit rougher in terms of providing a completely complete picture and you know for the students here I think this is the type of area where we're basically at the frontier of the sort of research I think doing more work in this sorts of these sorts of spaces can be very impactful okay so the question I want to ask in in this segment is how we can improve kids outcomes in lower opportunity areas as some of you might know there are a lot of place-based policies in the U.S. that seek to improve areas but they largely focus on adults in the labor market so for example there are lots of tax credits given for businesses to locate in lower income areas or areas with lower levels of employment things like empowerment zones or promise zones etc but as we saw in one of the first slides I showed in the previous lecture just bringing more jobs to a given place does not in and of itself guarantee that you're gonna have higher levels of upward mobility there instead I think the set of results I've shown you call for a place-based focus on human and social capital development during childhood rather than simply bringing jobs to places for adults so what might that look like you know it could be things like early childhood interventions and preschool and so on improvements in K through 12 schools job training programs mentoring programs ways to build social capital and so forth and again as I said earlier this is a place where we would like to sort of identify the recipe for how you turn a red-colored place in the map into a blue-green colored place but I don't think we have that recipe yet and so we're going to show you kind of how we're trying to figure that out at the moment so I'm going to start by some ongoing work we're doing looking at what is probably the most ambitious neighborhood revitalization program that's been undertaken in the United States called the hope six program housing opportunities for people everywhere so what the government did here in the 1990s and 2000s is spend about six billion dollars to take high poverty neighborhoods typically with large public housing projects and turn them into mixed income communities and provide various additional services like create a preschool in the area and have career development programs and community centers and so forth so people have been interested for a while and trying to figure out whether hope six actually was successful in achieving that aim and this is a classic example of a program where we've literally spent billions of dollars and we're not totally sure if this worked or not and modern data can help us figure that out so to show you how we're approaching this we're again going to go to the longitudinal tax records where we're able to follow people over time and we're going to start with this chart here and again I'm just going to illustrate illustrate this with an example rather than getting into the statistical details of the study what I want you to think about is a revitalization of a neighborhood that happened in say 2000 so they took a large public housing project they demolish it they completely rebuild the neighborhood this obviously takes a couple of years to happen and what we're going to do is plot here the difference in earnings between people in one of these sites that was revitalized and a control project that we find based on looking at other public housing developments that have very similar characteristics but did not end up getting selected for the hope six revitalization program okay so it's basically a matching approach comparing treated sites with control sites and what we're doing here is plotting the change in earnings for the adults who were living in these hope six sites before the program and then just following them using tax records over time so when you look at this you can see this line is totally flat there is absolutely no change in the earnings of the adults who were living there to begin with now if you naively looked at the data with repeated cross sections which is what people were able to do before you just look at average rates of poverty or average levels of income in these neighborhoods you would see that average levels of income went up a lot but the longitudinal data are showing you that that's largely because of resorting you have a different set of people living in those neighborhoods not because the people who were living there to begin with are actually doing any better so that obviously looks quite disappointing we spent six billion dollars and it looks like we reshuffled a bunch of people and didn't actually end up changing their economic outcomes but we've learned our lesson about what might be going on here those of you who saw the earlier lecture and what I was saying earlier about the importance of childhood environment you might remember that in prior studies when people move across areas we find that when adults move to better neighborhoods we see no change in their economic outcomes and so you know are there impacts potentially on the kids that's naturally a you know reasonable question to ask in light of the evidence we've seen in other contexts and so here in this plot now we're plotting kids incomes in adulthood measuring kids incomes when they're 30 years old again with the same type of design comparing now kids who grew up in these areas before the revitalization versus kids who grew up in these areas after the revitalization and so each of these dots is basically taking the set of children who lived in those areas between the ages of you're defining kid as people between the ages of 1 and 18 follow them until they're 30 years old and measure their earnings in adulthood and you can see very clearly that relative to the control group there's basically no trend before and then afterward as the neighborhood gets revitalized and maybe all these programs come online and so forth the kids who are growing up there have substantially higher earnings than than the kids who were growing up in those neighborhoods previously and so what we're doing now is trying to understand the mechanisms through which these gains are occurring what is making these programs effective what can we learn about how to revitalize neighborhoods going forward and so forth but you know what I think is very encouraging here is this shows that you can actually change this like this actually makes a difference in quite a big way if you were to calculate a rate of return on those six billion dollars given the many cohorts of kids who are now going to have better outcomes lower rates of incarceration higher levels of earning better health and so forth the social benefits are potentially enormous and so that I think is a very good initial indicator okay so that the hope six program tries to do something on a kind of a massive scale so if you were just driving around the city it would be totally obvious to you that there was a hope six revitalization that happened in a particular place you know that the place would look completely different so that may not be something that we can accomplish everywhere it's a very expensive approach even if it has a high rate of return so it's useful to start to think about more distilled versions of that intervention that you might be able to do on a narrower scale and so I want to give you a couple of examples of that focusing on one channel that I've repeatedly emphasized throughout these lectures is potentially being quite important I think it might be quite important in driving these effects which is the idea of creating more social interaction across class lines and I'm here not going to show you evidence because we really don't have empirical evidence yet but I want to show you some creative things that people are doing in the field to try to make a difference on these issues so let me start with this example here I'm going to give you a bunch of examples from high schools so here's an example from a school in Dallas where they sought to tackle what we're calling friending bias disconnection between low and high income kids in a very intuitive way so it turns out this big high school in Dallas called Lake Highlands they had two different cafeterias one cafeteria where they served free and reduced price lunches and one where they did not so you know you don't have to do a lot of statistics to figure out that that might not be good for creating cross-class interaction and so they teamed up with this architectural firm to redesign the school create a single cafeteria create more interaction like physically change the school and they argued that this actually leads to you know meaningful change in how people are interacting and potentially could lead to better outcomes here's another example closer to home for all of you the Berkeley high school which I mentioned to you before is one of the most segregated high schools in America internally within the walls in terms of levels of friending bias despite being a very diverse high school so again they recognize this and you know recognize that it's a very segregated school and so on and so one thing Berkeley is experimenting with recently maybe some of you are more familiar with it than me is the creation of a ninth grade that places incoming students into intentionally diverse communities smaller houses that basically cut across the different tracks that student and students end up going on which are often correlated with socioeconomic status and race as a way to create more of that cross-class interaction again will be interesting to study with Facebook data or perhaps Instagram data because younger kids don't use Facebook anymore what that kind of interaction looks like and then give you one more example this time outside the school setting again creative idea be interesting to see if it works so Atlanta is another incredibly segregated city you can see if you look at the opportunity Atlas map low opportunity here high opportunity there completely segregated by race completely segregated by income so what they're trying to do in Atlanta is connect communities by taking the train stations turns out the train runs exactly along that dividing border and creating soccer fields at each of these stops and having a you know a soccer league where you try to get kids from different backgrounds to essentially meet each other at these different soccer fields across the city so urban design approach to trying to create more interaction again don't know if it will work and lead to meaningful interactions and change outcomes but you can see the kinds of things that people are trying to do in the urban design planning space okay I want to give you one final set of evidence which I think illustrates a very different approach to tackling these problems so everything I've shown you so far has the flavor of public policy change public intervention but I think there's also potentially an important role for the private sector to play in this context and I want to give you an example of that by again going back to Charlotte which I've mentioned a few times here you know the when we put out the data that it was 50th out of the 50 largest cities in terms of rates of upward mobility they paid attention to that in Charlotte there was an article in the local newspaper saying this is a wake-up call for Charlotte Mecklenburg you know how can we be such a rich and vibrant city yet be ranked dead last in terms of rates of opportunity for our kids what came out of that was a task force and a commission to try to understand this problem try to make changes involving local policy leaders local business leaders and so forth they did many different things including the zoning thing that I mentioned earlier changes in affordable housing and so forth but I want to focus here on something different which is what Bank of America did so Bank of America is headquartered in Charlotte and they recognized the result of this evidence that while they thought they were bringing a lot of jobs to Charlotte they weren't actually helping the local community in terms of creating upward mobility so they made a commitment to hire a thousand kids who grew up in disadvantaged communities in Charlotte itself and importantly they recognized that there was a reason they weren't hiring those kids to begin with you know they presumably didn't have the skills needed to get the jobs that they were advertising so they took a very deliberate approach of trying to solve that problem they teamed up with a group called Year Up which is a non-profit sectoral job training provider that's based in Boston actually and they teamed up with Year Up and a local community college to implement a program that Year Up is designed which is a one-year job training program that again has this flavor of providing social capital and social support in addition to technical skills so they connect people with employers looking to hire for a specific set of skills they provide them some training to do that particular job could be IT could be you know something else and then they provide mentoring and various social skills and try to get you to really succeed in that job so here we have some prior data where there's a randomized trial that's been run of the Year Up program not in Charlotte itself because that's very recent but in other sites and so you know just to show you the key results that you get from that here what we've done is taken the data from that Year Up randomized trial that was run by others back in 2014 and we've linked that data to tax record information to be able to follow people's earnings over time and you can see that this Year Up program which targets kids who've completed high school but don't go down traditional college pathway we randomize into the control group shown in the orange and then the treatment group shown in the green year minus one is the year you're enrolled in the program so you get this dip in earnings in the treatment group because they're actually in the program and they're not working at the same rate but then if you look at what happens afterward you have the surge in earnings post program and earnings remain high for many years afterward you have a 35% increase in earnings for the kids in the treatment group relative to the control group and so that's a type of program that I think is incredibly effective it turns out in providing trajectories for upward mobility and when targeted at places like disadvantaged communities in Charlotte can be another way harnessing the forces of the private sector to get more talent for companies and potentially transform those areas to some extent that again I think is an approach that can be potentially scaled across the United States okay so I'm gonna skip a couple of details here and then move finally in the last few minutes to talk about the third bucket on improving higher education so these first two buckets have been very much much focused on kind of the environment from birth till age 18 and to be clear I've just given you a few examples here right there are numerous other things that you could think about like changing the quality of schools teacher hiring class size you know preschool interventions there are many many other things that are relevant here but in the last part of this talk I want to spend a little bit of time focusing on higher education in particular I think particularly relevant in places like UC Berkeley here and to set the stage for that I'm again going to show a chart that I showed in the previous lecture which shows you how much each college in in America is contributing to economic mobility by showing you know one dot for every college in America using data from tax records linked to Department of Education records in a paper with Immanuel and Danny Yegan here at Berkeley and others where we're plotting what we're calling upward mobility rates so if you take the fraction of kids at a given college who come from a low-income family in the bottom 20% of the income distribution what fraction reach the top 20% and you can see on that margin places like Harvard Stanford Princeton Berkeley look terrific but then on the X axis we're plotting the other key dimension that matters for a college's contribution to economic mobility which is the number of low-income kids or the fraction of low-income kids that you have on campus obviously if you don't have many low-income kids you can't be contributing a whole lot to upward mobility and so on that dimension as we discussed in the last lecture of these colleges don't look so great and you have other colleges that are serving many more low-income kids but they unfortunately don't have very high rates of upward mobility and so that the problem basically is that you don't have many dots in the upper right side of this chart and so one way to think about the policy problem here is first how do you move these dots over to the right right the colleges that are up here how can you expand access for low-income kids and then second how can you bring those dots that are over on the right side upward and so what I'm going to do in the remaining time is walk through the work we've done on each of these margins spending more time on the first one because that's really the part that's more developed at this point and then I'll talk briefly about what people are starting to do on the other margin of improving outcomes at places like community colleges and to your institutions okay so to diagnose first what is going on why do you have so few low-income kids especially at places like Harvard Stanford Princeton etc the elite private colleges whereas I pointed out in the previous lecture there there seems to be a particular dearth of low-income students even importantly conditional on their pre-college qualifications so if you take a set of kids all of whom have identical SAT scores and SAT score of exactly 1510 out of 1600 and plot your chances of attending an elite private college in America one of the Ivy League colleges or Stanford Duke MIT or Chicago you are two and a half or three times more likely to be attending one of these colleges if you come from families making more than six hundred thousand dollars a year in the top 1% of the income distribution relative to families and what you might think of as the middle class of the applicant pool you know families making eighty thousand dollars a year even conditional on SAT scores so what I want to do now I've showed you that fact before I want to understand what is driving this why are high-income kids more likely to be attending these elite colleges even conditional on their SAT scores so it turns out when you dig into the data the key thing that's driving this is not where kids are choosing to apply or where they're choosing to attend it's really about who's getting in it's all about the admissions margin and in the admissions margin there are three key things that drive that uptake at the top and this U shape the first as Steve Todellus anticipated in the previous lecture is related to legacy admissions so the practice of giving an admissions advantage to kids whose parents attended the college which is common practice at many institutions in America and many private colleges in particular so why does this create that tilt in favor of kids from high-income families well there are two reasons one is very simple and obvious the legacy kids are much more likely to come from high-income families than from lower-income families so if you know that makes sense if your parents went to one of these colleges which tend to produce very good outcomes they themselves tend to be quite rich and so the fraction of kids who are legacy applicants tends to be much higher if you're from a high-income family then a lower-income family so that's going to create some of that tilt towards high-income folks but there's a second factor shown in this chart here which is that the legacy advantage itself is larger if your parents are rich than if your parents are lower-income legacies so here what we're plotting is the admissions rate for kids with comparable SAT scores for kids of legacies who come from very rich families in the top 1% 44% of those kids are getting in that compares with 30% for legacy applicants with parents in the bottom 95% and just 9% for the typical non-legacy applicant okay so it's nearly a five-fold difference in terms of the advantage for legacy applicants relative to non-legacy applicants particularly legacy applicants from very high-income families so one interpretation of this is that there's just a direct advantage given to legacy applicants and that's creating this greater you know inequality by income in terms of who's showing up on these college campuses another interpretation that's often raised when people see these kinds of data is well you haven't controlled for everything in a person's application file right like if a person's if someone's parents went to a top college you know presumably they got a lot out of that they may have stronger credentials they may have you know gone to better schools they may be more qualified applicants and that's why they're getting in at higher rates so the power of these data is you know what we've done here is linked data on admissions records for 400 different colleges across America to the tax records and to the SAT and ACT data for everyone in the United States so what that allows you to do is evaluate that hypothesis in the following way so if it were true that being a legacy say at Yale you know as a legacy applicant you're a more qualified applicant in general you might think that kids whose parents went to Yale would also be more likely to get into Stanford say or get into Princeton because if you truly are more qualified you know that's what you'd expect to see so we can essentially do that test and ask what your rate of admissions are at other Ivy plus colleges so if your parents were legacies at one college what are your odds of admission to other Ivy plus colleges and the answer is absolutely not there's no advantage in admissions at other colleges this seems like a pure college specific legacy preference you have essentially no advantage at other places it's much much smaller okay so that's one key factor that's driving this and of course you might wonder and Steve raised some questions about this from a fairness perspective from an equality of opportunity perspective does this make sense I think that's but you know whatever you think about that that's definitely one key margin that's contributing to the admissions advantage for kids from high-income families second factor is non-academic ratings so a lot of these colleges look not just at your SAT scores or academic credentials but as many of you know at various non-academic factors so these are things like extracurriculars measures of character and overall strength and so on and here it turns out if you look at these ratings and plot them by parental income holding fixed SAT scores again you get that exact same pattern of an uptick at the top the kids from the highest income families are much more likely to have strong non-academic credentials often because they've gone to private schools or have the kind of support that's leading to them to succeed on these margins and then the third important margin is athletic recruiting it turns out so recruited athletes who are actually quite a significant portion of students sometimes 10 or 15 percent of students on IV plus college campuses are much more likely to be from high income families than from low-income families and that also contributes to that gap interestingly if you contrast that with what recruited athletes look like at places like UC Berkeley there first of all they're much smaller percentage because Berkeley is much larger and so you need a smaller fraction of athletes to fill your sports teams but also it's completely flat across the distribution and so those three things together they each are you know roughly a third legacy is maybe 40% in explaining the overall picture that explains why you have such such a large excess number of students from high income families at these IV plus colleges and that also points to things that you might be able to do there's nothing set in stone saying you have to have legacy admissions saying you have to consider non-academic factors or you have to recruit athletes those are things that colleges could consider changing down the road that could potentially create more mobility now in order to make that that final step that these kinds of changes might actually create more mobility there's one further point that one has to establish which is whether these colleges actually have a causal effect on kids outcomes or whether we're seeing such good outcomes at places like Harvard and Berkeley and so on just because they're very selective and those kids would have done well no matter where they would have gone so Rich Gilbert asked a question about exactly this in the last lecture are these colleges actually having significant value added or is it largely just a selection effect and in fact there's some prior literature which suggests that the selection effects might be very important so the last step I want to show you on this analysis in the last main piece of data is getting at that question and so the way we're going to do that you know it's tricky to figure out the causal effects of attending say Harvard or Princeton because you know ideally what you'd like to do is run an experiment where you randomly admit some kids to those colleges and randomly don't admit others and then compare their outcomes obviously you can't run that experiment practice so but how can you come close so what we're going to do is look at students who are placed on the wait list so I want you to think about two students placed on the wait list who tend to have very similar application credentials and we sat in on a bunch of admissions committee meetings to try to get a sense of what the process is for determining who gets in off the wait list and it turns out that you know various things go into it but there are lots of idiosyncratic factors that seem to matter to give you one example suppose it turns out you know the orchestra needs a trumpet player in the year that you're applying you might have you might get in that year if I play the oboe and they didn't need an oboe player that year I don't get in and so in so far as whether you play the trumpet or the oboe isn't really relevant for your long-term outcomes you basically have what looks like an experiment here so this is the idea we developed more formally in the paper isolate this kind of idiosyncratic variation from factors like this where you have one student who ends up getting rejected because of the needs of the college in that particular year and another student who gets up except it gets ends up accepted and we're basically going to compare outcomes using this quasi-experimental approach looking at post college outcomes okay in a nutshell that's essentially what we're doing so what do you end up seeing so I'm going to show you a few different outcomes let's look at the fraction of kids who end up when we look at their earnings 10 years after college in the top 1% of the income distribution for the kids who don't get in that's 12% for the kids who do get in barely just by that flip of the coin that number goes up to 19% so a 50% increase in your odds of reaching the top 1% that's actually not even though the largest effect that you see if you look at sorry so before I get to other outcomes let me point out so this is a this is a fairly large effect on the share of kids who are reaching the top 1% of the income distribution right the upper tail of the income distribution now interestingly tying back to the question that rich asked prior research which had suggested that there's not a big causal effect of attending these top colleges a lot of that work focused on just average incomes rather your chance rather than your chances of reaching the very top because they didn't have enough data basically to look at it in that sharp of a way and if you just look at the average income percentile of the kids who get in versus the kids who don't it's actually not that different so put in simple terms what I think you see in the data is that if you get into one of these elite private colleges were versus go to a top state flagship school it doesn't matter so much in terms of average outcomes or even you know high-end outcomes like becoming you know a professional things like that but if you think about reaching the very top of the distribution as measured here by income as measured by your chance of attending a top graduate school as measured by your chance of working at a very prestigious firm immediately after college it has an enormous impact now why does this matter at the end of the day so what we're showing you here is if we change the admissions policies and admit a different set of kids from middle-class families by making the three changes I described that could meaningly meaningfully affect economic mobility in America but more importantly you know these colleges at the end of the day these 12 Ivy League colleges they're not that big they educate a relatively small share of Americans less than 1% of all Americans attend these colleges so there'll be some impact overall on mobility but it won't be all that big but despite that and this is I think really the key punchline I think that kind of change can have an enormous impact on what society in the US looks like because these colleges end up playing in its incredibly disproportionate role in shaping what the leadership of America looks like in various ways so as I just pointed out less than 0.8% of college attendees in America went to an Ivy Plus college so they're very small however if you look at the fraction of Fortune 500 CEOs who went to one of those 12 colleges it's 12% you look at the fraction of people who are receiving prestigious awards like MacArthur grants 30% went to one of those 12 colleges and then if you look at people who are leaders in public service like senators presidents and so on or Supreme Court justices literally off the charts 71% of Supreme Court justices went to one of these 12 colleges now you can start to see why these causal effects that we're finding you know we're not able to directly look at the causal effect on your probability of becoming a senator or Supreme Court justice but if you kind of trace that out it suggests that if we were to pull back on practices like legacy admissions weight placed on non-academic factors athletic recruiting etc we could end up with a very different composition of what the Senate looks like in the US who scientific leaders are who CEOs are and that obviously has enormous ramifications for lots of people across the country and so that's another margin where you know we will see what happens in the coming months and years colleges are gonna have to revisit admissions decisions because of the Supreme Court decision but my sense is we're gonna see more movement in this direction which could again really meaningfully affect many kids lives so let me just briefly touch upon one final point which is I focused you know in this discussion on higher education on moving these dots to the right it's also I think equally important if not more important on a national scale perspective to figure out how to move those dots up how to improve outcomes at community colleges and so on we're in an earlier stage and figuring out how to do that want to give you one example from other nice research done by a different research team vice at all where they look at a program at the City University of New York where they call it accelerated study and associate programs and it again has the same flavor of the programs I've been talking about where they take students at CUNY and they give them additional support while they're studying to navigate the curriculum choose a major if they face you know a challenge getting a bad grade or face a financial shock provides them additional support basically have a counselor to help you through the program ran a randomized experiment followed them over time and look at the fraction who graduated which is a key margin in these colleges in terms of producing good outcomes and you can see that the people who are in the treatment group have much higher graduation rates than people in the control group this kind of program really makes a difference so my sense is we're gonna see a lot more analysis and evaluation of these types of programs and that has a lot of potential going forward so let me conclude by you know again showing you lots of different results from different studies what are some of the main takeaways I hope you'll you'll get from this first I think there's substantial scope to improve the effectiveness of existing policies to create opportunity and often that can lead to bipartisan support so it's not a debate about more dollars for this or less dollars for that I think often if we think about it carefully and use modern science and data and reasoning we can think about ways to spend the existing dollars much more productively second as I've shown you in many different contexts the programs that are most effective we're finding repeatedly are ones that pair social support with financial resources and then finally you know at a broader level I think the kinds of granular observational data that I've been showing you throughout these two lectures can be incredibly helpful in informing decision-makers and monitoring progress importantly right like our neighborhoods getting better our colleges making a change in terms of who they're admitting being able to systematically put out these data at the level of individual schools colleges and neighborhoods can really have a transformative impact in and of itself I want to end by talking about some potential directions for future work especially given the students here you know I think we've made progress in this field as I hope I've shown you over the past ten years or so but there are many many remaining questions to be investigated I hope some of this has inspired questions in your own minds let me mention three things that we've been thinking about a little bit and our things where I would like to see more work so one is further evidence on dynamics everything that I've shown you in these talks is kind of a snapshot at a given point in time this is what opportunity looks like for kids who grew up in the 1980s and we break that up across different areas or here's our colleges look at in terms of mobility at a given point in time would be very useful to understand how these things are changing over time which will help us understand mechanisms much better and the next paper we're going to put out publicly is going to be a step towards that and I think there's much more to be done in that vein and then the last two things I'll mention here I think are the talks I've given are very empirical you know totally data driven I think obviously there's a lot to be learned from that but I think it's also useful to step back and think about the implications for theory and I've pointed out at various times how I think our standard economic models are not capturing important aspects of the data and by the same token I think it's important to formulate alternative theories that may better capture key aspects of the data and so two dimensions of that first on the positive side incorporating social interactions into equilibrium models of income dynamics I think is a valuable thing to think more about and then on the normative side you know Emmanuel Saez and others have done pioneering work on optimal taxation from an perspective of outcome-based inequality but I think a lot of people are very concerned about equality of opportunity independent of equality of outcomes and thinking about what that means in terms of optimal design of policies from a normative perspective for those of you working on those kinds of issues I think is a very valuable direction as well one final point throughout these lectures I focused on the United States partly because that's where our group has put its time and that's where some of these data are most developed but I want to conclude by emphasizing that these issues are by no means unique to America I think issues of upward mobility and opportunity are relevant around the world and I've been very inspired to see many other researchers in recent years doing similar work mapping opportunity and studying its determinants across the world of particular interest to me personally is the data for India shown in the lower right here where I started out the the first lecture describing my own parents you know trying to achieve the American dream and coming to this country but I was interested to see in the data in these maps constructed by other researchers my parents grew up in low-income families in the southern tip of India in a state called Tamil Nadu and if you look at that map you can see that that so happens to be one of the highest opportunity places in India that has given many people chances of rising up and I think that's in part why I'm able to do this work and standing here with all of you today thanks so much so I'm happy to take some questions another fascinating talk Raj thank you with the first study on giving people support to move around into better neighborhoods I'm wondering if there could be dynamic equilibrium effects where the places that are crappier using less than ideal term just become worse and worse and worse and you get these spirals of parts of the city that become very unfortunate areas have you given that any thought yeah yeah yeah so absolutely Steve so you know you worry like what about the people who get left behind yes you help some people who moved to opportunity but you know what about everybody else so they'll say two things on that so first the way you want to think about this program is not that we're taking a set of people who are happily living in a given place and kind of plucking them and having them move somewhere else what's actually happening is I think of it as redirecting the flows everybody who comes to apply for a housing voucher is moving anyway even in the control group 95% of people move to a different place so it's just a matter of where you're moving not whether you're moving and so from that perspective these people were not going to stay in their original neighborhood anyway we're just saying at the point you're going to move somewhere else let's help you move to a better neighbor so I don't think you're literally going to have further destabilization of the original neighborhoods now that being said I think you're absolutely right that there are a whole bunch of people in these original areas and the red colored places on the map you're obviously not helping with these approaches and that's precisely why I think the second approach place-based investment hope six program many other things that we've yet to fully evaluate our things we absolutely need to be focused on going forward so I think it's a complimentary I don't think one should focus on one versus the other I think both of these things really matter thanks very much for doing this work and the presentation I can ask a little more naive question because I don't I don't have a statistical base to question anything you've done but there's two things that really stood out for me one is and in my own field of consulting and research applied research and advocacy I understand only 16 percent roughly depends on where you are of these housing vouchers people look for housing and only 16 percent of their applications are accepted from the private sector landlords so most of them end up where affordable housing that's permanently subsidized in some way which tend to have been the low income neighborhoods because the upper income neighborhoods don't permit multiple yeah whatever the whole the whole nine yards so I just that's just one piece that I'd love to hear how that might have affected your results the second one is related so I'm a little unclear but it looked like the pre the pre family the early family the parents generation you are looking at about twenty seven thousand dollars a year twenty five percent of whatever the income percentile which is a little different than how the government usually does it which is by eighty percent of area median income you're considered low income section eight people are only the most poor and of course you lose your voucher once your income gets you out of poverty so there's this instability of housing yep so then the benefits forgive me for doing this but the benefits you saw for the next generation was about 30 an average of thirty four thousand okay so one is this adjusted for inflation what do you get for thirty four thousand twenty years later so my question is inflation and really asking is that a meaningful increase in income given what housing costs have done yeah thanks appreciate your experiments thanks just want to know thanks very much so let me wait keep your questions brief we have limited time yep I'll try to keep my answers relatively brief as well by that same token so those are excellent you know questions let me maybe address the first one and then say a little bit on the second so on the first one you're right that a big challenge here is that landlords don't want to take these tenants to begin with and that's part of I think the barrier that this program is helping overcome when you have somebody come with you and advocate for you etc that makes a difference in terms of landlords taking a chance and what I think is most telling is what we found we were worried that after this experimental program ended that this would all kind of collapse but what the housing authority reports to us is that landlords were actually reaching out to them afterwards saying we were very happy with these tenants you know stable source of income everything worked out great can you send us more tenants so that shows you even on the landlord side that once people have this experience you can change that part of the equilibrium as well on the second I'm happy to talk offline more detail about the calculations but can't exactly compare the numbers in that way across these different studies and I think the way you want to think about it is that statistics I was giving on about a $200,000 increase in lifetime income it does add up to a lot for these kids that is accounting for inflation now is it gonna completely transform everything in and of itself probably not but I think for many kids it's the difference between being unemployed versus having a stable job things like that but again happy to go into more detail there thank you for your interesting lectures I was wondering in the future will you be looking at children's outcomes for different groups like Latinx and Native Americans etc. yes so in the prior work which I briefly described in the first lecture we have broken these data down actually you can look yourself at the opportunity outlets that data for black Americans Native Americans Hispanic Americans and so forth and see how outcomes vary and part of what we try to do in each of these studies is understand heterogeneous treatment effects across those dimensions and reassuringly we're finding that many of the programs that I showed you here have quite positive effects irrespective of background so for the black kids who move to the better neighborhoods or black kids are also moving to better neighborhoods when we provide those services that I was describing and you know with the interventions like the year-up job training program and so on so there are many differences and disparities across these groups and that data has been made publicly available but very reassuringly the program is able to these kinds of programs can help across background thank you I'm curious if you could talk more about the potential role of private real estate development in perpetuating segregation especially in fast growth yeah metropolitan areas of the US like Florida or the Carolinas or Sunbelt cities and also how private real estate development may work sort of in tandem as a system with local government policies yeah public schools yeah so private real estate development I think just in an unfettered market equilibrium with no government intervention is likely going to lead to quite a bit of segregation where you tend to have certain types of housing clustered in some places other types of housing clustered in other places and that contributes to segregation I think you're exactly right to ask how do government incentives interact with that so you may know about the low-income housing tax credit which is another multi-billion dollar program that tries to address these issues and I think a key design issue in that program it's actually one very relevant here in California and their folks at Berkeley doing work on this is how we target those incentives so do we give incentives for developers to build affordable housing in high poverty areas or in low poverty areas traditionally actually it's been more tilted towards building in high poverty areas where it's easier to do and in some sense the incentives were larger but there's a push towards giving more incentive to build in these more blue green colored places precisely with the goal of creating access to opportunity and so I think there is a big role for the private sector to play but the government has to create the right framework to harness the market forces there thank you for two awesome lectures quick question on the data itself what does it take to convince the IRS and Facebook to share their data with you that's a good question there's a whole other talk I could give on that but you know all of this work is done with various safeguards as you can imagine it's all anonymized and scrutinized in various ways and you release statistics with tools from the computer science literature called differential privacy techniques so there's a whole layer of navigating all this and then a contracting layer in terms of you know making everybody comfortable with the privacy concerns and so forth which are obviously serious and so you know unfortunately at this stage and it relates a bit to what Emmanuel was saying in his introduction it basically motivates having a team-based approach to research because it's impossible to do this kind of work without some infrastructure and I think part of what we're seeing is the social sciences are becoming a little bit more of a lab science model to be able to do the type of work that I was showing you there are an enormous number of people who've contributed to that effort on our team and so for students what I would suggest is trying to connect with folks doing this type of work there are many here at Berkeley and elsewhere and join those teams and use that as a pathway to be able to access these data unfortunately it's a little bit difficult and I don't think you'd want to on a one-off basis do all the back-end work and you did to get set up. Yesterday you mentioned that the fronting segregation and the positional segregation are equally weighted in the outcomes and so you talked about national bills in reference to the position I was wondering if there's also any national solutions you talked about the local ones but any national solutions to the actual like fronting at that level type solutions. Yeah so you're saying national solutions to creating more cross-class interaction basically? Yeah I mean this is where I think creative ideas are useful people talk about in other contexts things like not as a policy solution but as an illustration of the national kind of thing that can make a difference military service is something that brings a lot of people together you could think of Peace Corps you know are there ways to bring people from different backgrounds together towards some common good those have other costs and may have other benefits and there are many other things to consider but those are the kinds of things that come to my mind. Thanks. Thank you for this very enlightening talk professor I wanted to ask so from your lecture I think it's pretty clear that some of our political and economic forces are very well informed and they have a very nuanced understanding of all these different ideas and proposals I was just curious so obviously society just came out of a pandemic and I fear that the COVID-19 pandemic really exacerbated some of the you know adverse effects that were already associated with unequal access to opportunity I was wondering do you feel like society are you optimistic that society is on track to sort of remedy those effects and recover you know it's full might. Yeah thanks so you know I think you're right in your observation that the COVID pandemic likely worsened all of the issues that I've been showing you like the fading American dream chart that I started out the lecture with my best guess would be that trend has gotten only worse if you look at any educational data things like that during the pandemic those gaps widened dramatically during the pandemic where rich kids continue to learn at a similar rate kids from lower income families did not and that's only gonna those effects are gonna be with us for years to come on a more positive note my sense of when change occurs in society is when you come out of big crises at pivotal points so if you look at you know the era of very high rates of upward mobility 1940s 1950s in the United States coming out of the Great Depression and World War two fundamental changes in US society that I think led to decades of inclusive growth you might think that we're at you know the big crisis of this century the COVID-19 pandemic is potentially a spark that could lead to those kinds of changes I think it's partly up to all of us to try to make that happen going forward Hi Professor Chetty I was wondering how much of the positive outcomes associated with IV plus attendance have to do with the lag of lag defect of policies like legacy-based emissions and whether there'd be a way to measure that using like the change in some sort of premium on announcement day of a new admissions policy yeah that's a good question maybe I mean I know we're running out of time happy to just take these last questions together and then I can just thank you professor Chetty for the talk I was I recall reading in your 2018 paper that high opportunity neighborhoods have a high fatherhood presence I was wondering how significant of a factor is this and reducing opportunity in low opportunity neighborhoods and in general family instability which is higher in these neighborhoods as well as how effective are policies that promote family stability and I don't know if there any policies that like reduce fatherlessness but how effective if you've studied these are they in promoting mobility like how effectively these place responses thank you Raj thank you so much for this talk and this is really brilliant for understanding outcomes for the population at large I wanted to ask a question specifically about your career I mean this inspiration this presentation was a total inspiration and I'm curious for students and early professionals in the audience what have been the biggest contributors to your outcomes thank you so let me take each of those in turn now very quickly so on legacy admissions I think the nature of the question is is that having legacy admissions at the moment you know you have certain types of kids from higher-income families legacy backgrounds on campus is having those kids on outcome creating higher value added at these colleges and is that why they're producing good outcomes for everyone else I don't know I mean I think it's an interesting empirical question I would like to think that the great professors we have at these institutions are part of the reason we have a lot of value added but surely there are other factors as well that said you know I I think it's hard to imagine that simply being around legacy students is the key reason one ends up succeeding even if connections matter my sense is a lot of the connections that might matter are being connected to people who go on to be successful in various different ways some of whom might have come from much more diverse backgrounds and shape your perspectives in broader directions but that's an example of an empirical question that I just you know the honest truth is we don't have the answer yet and I think needs to be investigated further on the question on family stability certainly from a correlational perspective a very important predictor one of the strongest predictors of differences in mobility in the maps that I've been showing you besides the social capital variables I've emphasized are the share of two parent families in an area and that seems to matter in particular for boys rather than girls there's a series of papers which have now shown that the presence of fathers is particularly important for boys outcomes rather than girls outcomes could be related to role modeling could be related to mentoring seeing certain pathways and so on now I think your second part of your question really gets to the core issue there is you know that may all be true but what does that mean from a policy perspective and I don't think anyone really has come up with an effective policy solution to create greater family stability it's not to say it's impossible but I think that's the way in which we need to be thinking about this you know family matters undoubtedly family matters you know independent of neighborhoods and the other factors I've emphasized but the reason I focused on the factors I emphasized is they're more naturally in the policy domain and so I think it's interesting to think about your question again and further work and then finally thanks for the question on my own personal background you know I think for me as I look back at this research I see many of these junctures that have as having made a difference in my own life and looking back one generation as I mentioned to my parents the opportunities they had I'll give you one example my mom grew up in a small town in South India where it so happened that the year she graduated high school there was a women's college that opened for the first time in her town so had that college opened a year later there's no way she would have gone to college she ended up going to college and became the first woman to become a doctor in our community in South India and that you know really changed the trajectory of my life my sister's life certainly the opportunities we've had and we've seen that play out over the generations and it's one example of the many you know I think circumstances having opportunities to have great mentors go to great schools and as I mentioned at the beginning of the last lecture I view as a very important part of my own journey being an assistant professor here at Berkeley as having shaped my trajectory so I'm grateful to have a chance to talk with all of you thank you