good afternoon uh I'm Vincent rash and I'm a professor of the graduate school and the chair of the Hitchcock professorship committee uh we're this afternoon we're very pleased uh to present Professor Raj chedy our fall speaker in the Charles and Martha Hitchcock lecture Series this lecture is being SP co-sponsored by the the department of economics and also the College of data science uh uh Computing data science and Society and also The Graduate Council as a condition of the original request we're obligated and pleased to tell you about how this endowment came to UC Berkeley it's a story that exemplifies many ways that this campus is linked to the history of California and the San Francisco area Dr Dr Charles Hitchcock was a physician with the US Army uh he came to San Francisco during the gold rush and established a very thriving practice and in 1885 he established his professorship uh it was an expression of his long-held interest in education especially higher education and his daughter Lily Hitchcock KO also a very well-known uh name in San Francisco uh greatly expanded his F her father's original gift uh to establish this professorship ship and making it possible to present a series of lectures the Hitchcock fund is really one of the most cherished and oldest funds and endowments on the Berkeley campus in terms of bringing Scholars to to Berkeley as we will have h a talk this afternoon uh it's we have brought if you look at the program uh dozens of speakers over the years and uh really it was this original gift of uh Charles Martha and Lily hitchc CH KO that gave us this this chance to do it so we want to thank them again now our speaker Raj chedy is the William a acman Amman professor of public economics at Harvard University and he is the director of opportunity insights which uses big data to understand how we can give children from disadvantaged backgrounds better chances of succeeding ched's research com combines empirical evidence with economic theory T design more effective government policies his work on topics ranging from tax policy and unemployment insurance to education and affordable housing has been widely cited in academic media outlets and Congressional testimony Professor chedy received his PhD from Harvard in 2003 and as one as the the youngest tenure professor in har Harvard's history before joining The Faculty at Harvard he was a professor at UC Berkeley and Stanford he's received numerous awards for his research including MacArthur fellowship and the John Bates Clark's model medal given for The Economist under 40 whose work is judged to have the most significant contribution to the field he will also be presenting a lecture tomorrow uh and that is announced in your program so without further delay we're pleased to to welcome professor chedy and the uh presentation this afternoon will cover the science of Economic Opportunity new insights from Big Data thank you very much for joining us today thanks so much Vince for the very warm welcome it's really a pleasure to be here with all of you today thank you all for coming and I just want to say before I start that it's a particularly a joy for me to be back here at UC Berkeley uh I vividly remember uh back in 2003 getting a call from Rich Gilbert who's here in the audience today saying that I'd gotten an offer from the economics department at UC Berkeley when I was completing my PhD at Harvard and uh and um uh you know that was really a transformative experience for me I came to Berkeley when I was 23 years old and you know being influenced by people like David card and Emanuel SZ who's also here who've been tremendous mentors to me uh over the years has really I think shaped my own career and had a had a big impact on many of those around me as well and so much as I'm grateful for you see Berkeley as a great public institution I'll show you some data showing the tremendous impacts of public institutions like UC Berkeley on economic mobility in the United States as I've seen that in my own research going back one generation in my family my parents came to the United States having grown up in low-income families in India to another great state institution the University of Wisconsin Madison where my dad uh did his PhD and my mom did her medical training and like many immigrants my parents came to the United States in search of the American dream which is of course a multifaceted concept that has different meanings but I want to start this talk by distilling that notion of uh the American dream to a single statistic that we can measure systematically in the data which I think captures a Cornerstone aspect of the American dream which is the idea that we at least aspire to be a country where through hard work any child has the chance to move up in the income distribution relative to their parents certainly it was that sort of notion that Drew my own parents to come to this country and countless other immigrants and so in a paper some colleagues and I uh including Jimmy narang who's a graduate student here at Berkeley uh wrote a few years ago we set about to assess the extent to which America actually lives up to that aspiration by simply measuring the fraction of kids who go on to earn more than their parents did measuring both kids and parents' incomes in their mid-30s and adjusting for inflation now there's some further technical details in the background in particular you don't have longitudinal historical data going this far back in time so it's not that easy to compute the statistic and so we develop some methods in the paper that we think allow us to get reliable estimates I'll skip those details here but let me just note more generally I'm going to present this at focusing on the key takeaways but for those interested in discussing the technical details further I'm more than happy to take questions on that when we get to the Q&A so just jumping to the punchline of this analysis you can see it you know quite clear for yourself back in the middle of the last century for kids born say in 1940 it was a virtual guarantee that you were going to achieved the American dream of moving up 92% of children by our estimation born in 1940 in America went on to earn more than their parents did but if you look at what has happened over time you can see that there's been a dramatic fading of the American dream such that for children born in the middle of the 1980s who are turning 30 around now when we're measuring their incomes as adults it's become essentially a 50-50 shot a coin flip as to whether you're going to achieve the American dream of moving up and so that broad trend is of course of great interest to economists because it reflects a fundamental change in the US economy that we'd like to understand but I would argue it's also fundamental social and political interest because I think it's this very Trend that underlies a lot of the frustration that people around the United States are expressing that this is no longer a country where it's easy to get ahead and that's reflected in a lot of the political outcomes we see recently and so forth and so motivated in part by that Trend and other similar statistics I think there's a broad interest in the public and Academia and policy circles in understanding what's driving trends like the one that I just showed you and more broadly in creating equality of opportunity help giving kids better chances of rising up particularly those growing up in more disadvantaged circumstances and increasing social Mobility or intergenerational income Mobility there's a very large literature as some of you here likely know in the social sciences in economics in sociology and other adjacent Fields analyzing the determinance of intergenerational mobility and equality of opportunity uh but and while that literature you know I think has made quite a bit of progress over the years there's recently been a surge of work in this area which is what I'm going to focus on in this pair of lectures here where the key technological breakthrough if you will is the recent availability of large-scale longitudinal administrative data things like tax records or other kinds of Big Data like social network data that allow us to follow people over time and understand the determinance of economic Mobility with a Precision that we've never had in the past and so in particular as I'll show you in this lecture uh those data allow us to study the determinance of Economic Opportunity much more sharply for I think two key reasons first they permit disaggregation of the national picture that I just showed you in very fine ways across different subgroups so for example by race and ethnicity across Geographic areas uh by income group so forth and so on and second they because of their scale and scope they permit the use of various quasa experimental and experimental techniques that allow us to understand mechanisms much more precisely than we were able to in the past so what I'm going to do here is present an overview of a series of papers that we've written in our research team at Harvard opportunity insights on these issues with many many co-authors John fredman Nathan hendin Matt Jackson Teresa kler Pat kleene here at UC Berkeley Larry Katz Emanuel Siz who I mentioned earlier uh Yanna Strobel Denny jagen also here at Berkeley and many many others I'll site those papers along the way so let me start um in this first map here by showing you one form of disaggregation which is to disaggregate the picture of intergenerational mobility in America geographically I'm going to first describe how we construct this map the original version of it was from a paper in 2014 with Emanual Pat Klein and Nathan hendin and since we've done since then we've done further work on it so what I'm going to do is first just construct describe how we construct the map and then tell you what I think we learn from it so what we're doing here is taking data on 20 million kids essentially all kids born in America in uh the early 1980s and we're using data from anonymized tax returns to link them back to their parents and back to the specific place in which they grew up we divide the us into 740 different Metro and rural areas and in each of those areas we compute a very simple statistic a simple measure of upward Mobility we ask what is the average household income at age 35 for kids who grew up in low-income families and for the purposes of this map and much of this talk I'm going to Define low-income families just to pick a specific number as families at the 25th percentile of the national income distribution which corresponds to a household income of $27,000 a year okay so for example here in the San Francisco Bay Area what we see using tax record data is if we take a set of kids who grew up in families makinging $27,000 a year on average if we look at those kids tax returns when they're age 35 they are making about $37,000 a year okay so similarly we can compute that statistic for all the different areas of the United States and we color the map so that blue green colors represent areas with higher levels of upward Mobility where kids are more likely to achieve the American dream in some sense and red orange colors represent areas with lower levels of upward Mobility so if you start by looking at the scale in the lower right hand side of this map you can see that even in the current generation kids born born in the 1980s who are entering the labor market in recent years there's a tremendous amount of variation in children's chances of rising up across different parts of America there are some places like much of the rural Midwest take places like Iowa for example where kids growing up in families making $27,000 a year on average one generation later are making more than 45 or $50,000 a year so substantial amount of upward mobility in a single generation yet you have other places like Atlanta Georgia or Charlotte North Carolina much of the southeastern United States where kids growing up in families at that exact same income level $27,000 a year one generation later are actually making less than their parents were and that's kind of remarkable given the amount of economic growth that has occurred in America over the past 30 years so motivated by this map you know you can see the broad Geographic patterns for yourself right much of the rural Midwest has high levels of upward Mobility parts of the coasts have high levels of upward Mobility much of the southeast and the industrial Midwest cities like Cleveland and Cincinnati and Detroit have very low rates of upward Mobility you know I think this map has motivated a lot of research and interest in our field and around two sorts of questions so first this sort of data gives us a new granular lens sort of a microscope if you will to understand the determinance of Economic Opportunity the science of upward Mobility with a Precision we never had before if you just look at the initial graph that I showed you you might have various explanations for what changed in the United States over the past 50 years but it's going to be very difficult to test between those explanations because many things have changed in the US over that period in contrast here you can start to ask you know what is it that's different about Salt Lake City versus Atlanta versus debuk Iowa versus Boston Etc and you can start to look for instance at people who move between these places to uncover what the mechanisms might be through which you're seeing these differences and outcomes across places so this gives you a powerful way to understand uh the the determinance of Mobility just from a scientific perspective but a second I think equally important reason to be interested in these data is from a policy perspective which I'll turn to in the second of these lectures where the motivation there is if I can figure out what is different in a place like Salt Lake City relative to Atlanta maybe I can start to replicate some of those lessons in these cities that are in the redder colors and increase upward mobility in the US as a whole so motivated by that logic the way I'm going to structure this first lecture today is basically to walk through a series of potential explanations you might already have some in your mind for what is driving the variation you're seeing in this map and systematically test a bunch of hypotheses to understand the science of what's going on and then in the second lecture I'll turn to think about what kinds of policies might actually make a difference on the ground in light of what we've learned about the determinants of opportunity so let's start with the first hypothesis that I think is very natural uh particularly in coming from economics which is that maybe this is about differences in the types of jobs that are available in different places you know take the Bay Area for example with the tech sector booming in recent years in the past few decades you know maybe that's why you have relatively High rates of upward Mobility here so to evaluate that explanation let's turn to this scatter plot here where we're going to take the data on upward Mobility from the map that I just showed you and put that on the y axis for the 30 largest cities uh here in in the United States and we're going to plot those measures of upward Mobility against a simple measure of job growth rates from 1990 to 2010 the period over which these kids were growing up in those same cities so if you look at this graph you can see that this basically looks like a cloud there's basically no relationship between these two variables and in particular you have cities like Charlotte and Atlanta which you might know are some of the most rapidly growing cities in America kind of viewed as the engine of jobs in the southeastern United States if you look at any measure the number of jobs the number of high paying jobs the average incomes Charlotte and Atlanta will rank at the top of the list in terms of cities that are doing really well yet if you look at these longitudinal measures that we're now able to construct with these tax records looking at Upward Mobility for the low kids growing up in low and middle- income families in Charlotte you can see that Charlotte actually ranks the lowest among large large cities in America in terms of rates of upward Mobility so you might first ask you know how is that even possible arithmetically if you look at repeated cross-sections or snapshots of data it looks like Charlotte is systematically getting richer over time but if you follow the set of kids growing up in Charlotte they don't seem to be getting richer over time like how does that add up so the way I think about it is that Charlotte imports Talent lots of people move to Charlotte to get high-paying jobs at firms like Bank of America for example which is headquartered in Charlotte but what we're seeing in the longitudinal data is that that doesn't necessarily translate to the kids in Charlotte getting those jobs and in particular the kids growing up in low-income families and disadvantaged neighborhoods in Charlotte getting those jobs and so the first you know I think very simple lesson from this analysis is you know obviously jobs matter at a macroeconomic level and jobs can influence the strength of a local economy as well as shown by enrio Moretti here at Berkeley and many others but Simply Having more jobs in your city for instance getting the Amazon headquarters to move to your city is not in and of itself the solution to have more upward Mobility for your residents one has to think I think more deliberately about how you equip people with the skills how you develop the human capital needed to actually get those high paying jobs so that was the first hypothesis maybe this is about jobs that doesn't seem to be what's going on so let's come back to the big map and consider a second potential explanation this time uh coming not from economics but from demography so anyone familiar with the demographic structure of the United States would recognize that there's a potential link to race here so in particular the red and orange colored parts of the map are places with larger African-American populations like the southeast Cincinnati Cleveland and so on we all know that there's a long history of racial disparities in America and so you might wonder how much of the differences that we're seeing in this map are really about differences by race rather than differences by place so to get at that what we did next is took the tax records and linked them to census data for everyone in the US population which gives us data on everyone's race and ethnicity in the US and that allows you to construct this pair of maps here the same statistics on upward Mobility but separately now for black men on the left and white men on the right now if you look at these two maps initially your reaction might be it looks like they've put these maps on two different color scales kind of a green blue color scale on the right and an red orange color scale on the left but in fact if you look at the bottom the slide you can see that we have not done that it's just that there is such an enormous difference in rates of upward mobility in the United States between black and white men that it's almost like you have two different countries you basically have non-overlapping distributions here right so in particular if you take a place like Boston where black men growing up in low-income families have average incomes of about $25,000 in adulthood it's one of the best places in terms of upward Mobility for black men they have worse outcomes there than say white men in Atlanta which offers some of the poorest opportunities for Upward Mobility for white men so it's really like two disjoint distributions in terms of economic possibilities for black and white men so that is to say there's no understating the importance of race in America even today and importantly even conditioning on class you're taking a set of kids all of whom start out at exactly the same income level and seeing dramatically different prospects for black versus white boys now you'll notice when I started to split the data by race I also began to split it by gender so I showed you the data specifically for men here why did I do that turns out if you make the exact same comparison for women you get a very different picture if you look at rates of upward Mobility for black women on the left versus white women on the right the spectrum of colors in the two maps looks much more similar and more broadly if you look at any measure of economic Mobility you know rates of rising up average income rates of college attendance things like that once you control for Parental income black and white women have very similar outcomes black and white men have extremely different outcomes and so what that's telling us is there's something about the interaction or intersection between race and gender that's extremely important in understanding racial disparities in America you might think of things related to the criminal justice system you might think of discrimination that's particularly affecting black men in the labor market I don't know if we know for sure yet exactly what the answer is but it's clear that whatever it is has tremendously Divergent implications by gender which I think is useful in narrowing down the possibilities so what we can see from these Maps is that race is undoubtedly important but even conditional on Race I want to emphasize that there's a lot of difference across places right if you look at the map on the right uh and so you know our takeaway from this and other related analyses is that race is an incredibly important determinant of Economic Opportunity but Place matters as well and I will into that in further detail in a second but before I move on from these racial comparisons I want to make one further Point throughout this talk I'm going to focus mainly on upward Mobility because I think most people are interested in that phenomenon how can you help more kids rise up and achieve the American dream but I think it's equally important particularly in understanding the Persistence of racial disparities in America to think about the converse phenomenon of downward Mobility that is kids who start out in high income families and ask where they end up in the distribution and to show you that data I'm going to turn to I think a powerful visual that the New York Times put together using our data that captures this point you know very saliently so the way to think about this is it's basically a depiction of a transition Matrix we're going to take a set of kids who start out in high income families in the top fifth of the income distribution and ask which quintile of the income distribution they themselves end up in as adults do they drop all the way to the bottom fifth do they stay in the top Fifth and so on purple dots are for black men green dots are for white men and what you can see is there's an enormous Divergence here for black versus white men for white men if you grow up in a high-income family you can basically expect a float at the top of the distribution you're likely to be at the top or in the upper middle class for black men I think tragically even if you grow up in the highest income families in America odds are you will end up in the middle class or even at the bottom of the income distribution in the Next Generation so this result which I think is very disturbing about the United States and certainly was a great surprise to me when we first saw this in the data you know I had expected to see when we began to study racial disparities that there would be some Divergence by race but I thought that race might start to matter less as you had sufficient income you know if you were from a very high income family you could go to the best schools live in the best neighborhoods Etc maybe race would become less important so that hypothesis turns out to be totally wrong uh there are enormous differences in outcomes even for black and white kids who live in the most affluent neighborhoods go to the same schools Etc and I think recognizing that is extremely important to think about how you tackle racial disparities in the US because in some sense you know make a visual analogy for white Americans achieving the American dream is like climbing a ladder where you kind of start off in the Next Generation where you left off in the previous one whereas for black Americans it's more like being on a treadmill where even after you rise up in one generation there are these tremendous structural forces that push you back down only to have to make the rise again why is that important to recognize if you think about a model of income Dynamics and how income is going to evolve across Generations if you've got this force of downward Mobility no matter what you do to lift the fortunes of black kids who are currently at lower levels of incomes that gap between black and white outcomes is just going to reemerge in future Generations as people fall back down this treadmill so you know the key takeaway here I think is if we want to address racial disparities in a sustained way in the long run we not only need to focus on the most disadvantaged neighborhoods and schools that have less resources and so forth but also think about why black kids who you know apparently are growing up in relatively advantaged families why they are not maintaining those positions that's really the only way ultimately one will narrow uh racial disparities in the long run okay so I've shown you a few broad facts about the United States these broad Geographic comparisons highlighting the importance of race not so much the importance of differences in job availability and so forth so now what I want to do next is dig in deeper to understand uh what the roots of these differences across places are conditional on R uh and in order to do that the the next thing we did is began to look at these data at even a finer Geographic level so if you look to the sociology literature and the long literature on the potential influence of neighborhoods and environment on people's outcomes nobody would think of your neighborhood at the level of the Bay Area or Boston or New York City you'd think about the specific town you live in or the specific School District you're in or specific group of people you're associated with and so on so to do that uh what we did is went back to these data and began to look at the data at a finer Geographic disaggregation and the easiest way to show you that is to toggle over to this website let me just see if this is going to Yep this comes up here called the opportunity outlas which you yourself can access just a freely accessible website opportunity atlas.org and the way this works is like a Google map you can enter in any address you like and literally zoom in to to see the data in that area so given where we are I'm going to enter an address in Oakland which I'll tell you about in a second and so now we're going to zoom in and see these same statistics uh that I've been telling you about uh before but now at a census tracked level so there 70,000 census tracks in America Each of which has about 4,000 people there's enough data here because you have data on the entire US population you can construct quite reliable statistics on these rates of upward Mobility for every census tract in America and so that's what we're doing it's the same exact statistics I've been showing you but now you know just in the Berkeley Oakland uh bay area and so the first observation I want to make before I talk about you know specific cells of data here is just a simple one the spectrum of colors that you're seeing on the screen here is the same as the spectrum of colors that you were seeing in the National map that itself tells you something about the origins of differences in Economic Opportunity this is not about state level differences or differences across cities in terms of opportunity and rates of upward Mobility no it's really about you know going two miles down the road in Oakland and going over to Alam or differences between one part of Berkeley and parts of Emeryville uh in terms of rates of upward mobility in this area as in areas you know cities across the United States you can drive 2 miles down the road and it's like you're going from Alabama to Iowa in terms of rates of upward Mobility so what that tells us I think is that the roots of differences in Economic Opportunity are hyperlocal it's not about we shouldn't just look to Broad uh differences in state level policies or I was talking with Aon earlier you know is it about Federal policies I actually think these data suggest that there's a lot that that can be done at a local level if we can just figure out what is different about these environments that seem to have much better outcomes relative to other nearby areas that have much worse outcomes even in the current ERA in the same time period so this particular address that I uh entered in is a census it's in a census tract in the center of Oakland and that happens to be a place called The Adeline Lofts it's an apartment complex in Oakland which was built with low-income housing tax credit ltech funding so it's affordable housing okay and so the kind of problem I want to think about and I'll get more into this in the second lecture when we think about policy Solutions is does it make sense to have tax dollars going to build affordable housing here as opposed to other places where you might be able to also site affordable housing for instance you know just to pick you know an example of another census trct which I'll come back to in a second you know take this place you know over here in Alam where you see much higher rates of upward Mobility so now one thing I just want to note when you look at this map you might wonder how much of these differences are due to differences by race right I just pointed out earlier that there sharp differences in upward Mobility by race you all know that the Bay Area like many other places is quite racially segregated so one thing you can do with this tool which is helpful in that regard is we can subset the data to different subgroups so if I click black here first of all a lot of the map is going to disappear because there are no black people in a lot of uh the bay area but where you do have a black pop you can see that even conditional on Race we see very different outcomes for kids growing up in the center of Oakland versus those tracks in Alam uh which it turns out are also the site of low-income housing tax credit developments and so you know one wonders like would outcomes be very different if more low-income F kids were to grow up here rather than here and so this level of granularity can you know give us a sharper sense of what the determinants of opportunity are so motivated by that I'm going to now come back to the slides and dig in to understand in a little bit more detail uh what is going on at that census trct level of granularity uh and in order to do that you so I mentioned the Adeline Lofts before and here's another uh ltech development called the pla Del Alam apartment complex in order to understand what is different about these two places the next thing I want to do is look at families that move across these different neighborhoods so why is that interesting to do so before we get into what that analysis reveals you know just from a social science perspective there's been a longstanding interest in understanding how much of the difference in outcomes we see across people is due to nature versus nurture environment versus other factors that may be harder to to manipulate and so you may wonder you know how much of the outcomes we're see difference and outcomes we're seeing across these places are just that different types of people live in these different places and maybe that's why we're seeing differences and outcomes and how much of it is actually about the causal effect of growing up here versus there so if you think about it conceptually what you'd like to be able to do if we were able to run experiments is basically randomly assigned kids to grow up in these blue colored areas versus the red colored areas and compare their outcomes over time that is obviously a very difficult experiment to run and practice but the power of large scale Big Data administrative data is that we can start to approximate these experiments with the data we have in hand the observational data that we have and so to show you that uh I'm going to talk about a study where we look at 5 million families that move across neighborhoods but rather than getting into the statistical details of the study here I'm going to summarize what we find in the context of a simple example that example of those two places in Oakland and Alam that I was showing you on the map so imagine a set of kids who move from Oakland to Alam at different ages starting with kids who move when they're exactly two years old so what we're doing in this first dot year is think of it as take the set of kids who move from Oakland to Alam when they're two track them forward 30 years in the tax data and measure their own incomes when they're adults and what we can see consistent with the the kind of average levels of earnings we see for kids who grow up in birth from Alam is that those kids earn about $2,700 per year in adulthood okay so that's for the kids who move when they're exactly 2 years old now let's repeat that analysis for kids who move when they're three four five and so on make the exact same move you get this very clear downward sloping pattern the later you make the move from Oakland to Alam the less of a gain you get and if you move after you're in your early 20s the relationship becomes completely flat and you get no further gain at all so what do you see from this chart I think there are three key takeaways all of which are predicated on a key identification assumption which I will come back to in a second for those who may be wondering about how we're identifying causal effects here but if you just take this at face value for a moment I think there there three key lessons the first is that where you grow up really matters right so it's not just that the kids who live in Oakland are different from the kids who live in Alam apparently if you take a given kid and move that kid particularly at a young Agee from Oakland to Alam you see very different life outcomes for that child now in order to make that strong causal claim I have to make the key assumption that the types of families who are moving from Oakland to Alam or more generally from a red colored place on the map to a blue green colored place on the map which is how this is identified with millions of families who are moving across such places I have to make the Assumption of constant selection the types of families who are moving from better to worse neighborhoods it's fine if if they're selected families but the nature of that selection cannot vary with the age of the child at the move if the types of families we moving to better places when their kids are young or different more educated wealthier Etc from the types of families who are moving when they're older then this is going to be confounded and we may not be able to we may not be picking up the causal effects of place so as you can imagine this is really the core focus of our work in the space trying to understand the validity of that assumption I'll give you one simple example that makes us confident that this is assumption assumption holds so one thing we can do is replicate this figure looking its siblings within the same family you have enough data here that you can look at a family that moves with say a 2-year-old and a six-year-old from Oakland to Alam and what is amazing you can look at the paper for this figures you will you get exactly the same figure back if you put in family fixed effects and only rely on sibling comparisons so these effects are emerging within family showing that it can't just be that you know different families are moving at early versus late ages that kind of test you know rejects that possibility and there are various other tests we do along those lines which I'm happy to say more about so Point number one really seems like where you grow up matters Point number two what really seems to matter is childhood environment than where rather than where you live in adulthood if you move in your early 20s or after that we find in this study and many other studies that that has very little impact and the third point is that there's sort of a dosage effect here every extra year that you spend growing up in a better environment in Alam in this case or more generally in a blue green colored place on the maps I've been showing you the better you do in the long run if you move to that place when you're two instead of three five instead of 10 there's a cumulative gain from moving earlier that result I think is important in light of recent policy discussions on early childhood education a lot of focus on programs like preschool intervention Universal preschool Head Start Etc are our sense is those kinds of programs can be quite valuable but we don't want to take it to the extreme of saying everything is determined you know by the time you're 5 years old and there's not much value in investing beyond that clearly there's an enormous amount that you can do even Beyond those very earli stages so one of the nice things uh in this literature as we and others have been working with these data is to see you know how our understanding of these questions evolves as other people do work on related topics and so this dosage effect that I just showed you established in the US data turns out to be an incredibly robust pattern established now in many different studies I'm giving you a sampling here from different countries using different research designs in some cases a pure experimental research design going back to a famous experiment called moving to opportunity that was conducted in the 1990s to other quasa experimental designs that use public housing demolitions and so forth there's really this quite robust I think consensus now in the social sciences that environment matters and it matters through this dose effect in particularly in childhood okay so having established that that this really seems to be about the causal effects of childhood environment as a key driver of economic Mobility a natural next question that you're probably wondering about is what is it that's causing these differences and outcomes across places why is Alam producing higher rates of upward Mobility than other parts of Oakland and so to get at that there's now quite a large literature trying to investigate that question basically taking the data from the opportunity outlet that I just showed you and correlating it or using quasa experimental techniques to try to understand the determinance of that variation now that it's we know that it's largely due to the causal effects of place and there are many many different factors that people have identified I won't read all these off but you know from ranging from things like poverty rates to the level of income inequality in an area to levels of segregation measures of family structure and so on and I think you know the picture here is complex my reading is is all of these things can be quite important and you know there many different things that matter what I'm going to do in the talk here is go into more depth on one particular Factor Social Capital that many people have you know speculated over the years might be very important for determining many outcomes going back to the famous work of people like Glen Lowry uh James Coleman at the University of Chicago my colleague Bob putam at Harvard um that the idea that the strength of your community who you're connected to your networks might matter for economic outcomes that's something you know a lot of people have theorized about had small scale evidence on but I think it's been hard to systematically measure and test partly because we've not really had data on social capital on who people are friends with on on the structure of networks and so on to evaluate these sorts of hypotheses and so I'm going to spend some time on that here both because it happens to be the most recent thing that we and others have been working on kind of is at the frontier of the field but also I think as I hope to convince you is one of the most important factors and cuts across a lot of these other explanations that are listed here so to get into this I'm going to show you a different map first uh drawn from a completely different data source so when we decided to study social capital we set up a collaboration with meta the company that operates the Facebook platform which you know of course gives you tremendous social network data on who people are interacting with and we constructed various measures of social capital using that that network data and I'm going to start by focusing on one particular measure uh that I think turns out to be particularly important which I'm going to call economic connectedness basically a measure of the degree of cross-class interaction in a society so again let me describe how we construct this map and then tell you what I think the lessons are so what we're doing here is taking Facebook data on everyone between the ages of 25 and 44 in America that's 72 million people uh it's about 84% of the US population in that age range so Facebook is not a universal data set like the tax records it doesn't literally cover the population but 84% it's pretty good and our sense is there isn't a huge selection problem here okay so we take that and here at the county level for every County we first use at the at the national level a machine learning algorithm to assign everyone incomes and then in each County we construct a simple measure of the degree of cross-class interaction we have ask if you are a below median income person what fraction of your friends have above median income how much are you interacting with high income people basically on Facebook red colors are places where there are fewer cross-class uh friendships disconnection across class lines blue green colors here are places with more cross-class interaction so you probably immediately recognize that this map looks incredibly similar to the map of upward Mobility from the tax records that I started out with right and so indeed you can do a scatter plot of the data on upward Mobility from the tax records against the data on cross-class interaction from the Facebook uh data and you can see those two things are incredibly highly correlated with a correlation of 0 65 very different from that job growth scatter plot that I started out with so there is indeed a tight relationship between these two variables now you'll note I've picked one particular measure of Social Capital here those of you familiar with this literature might recognize there are many other ways you can think of measuring Social Capital how cohesive networks are how many friends you have how much people are volunteering levels of trust there are many different concepts that people have talked about over the years in this paper we construct many other measures that have been suggested in the literature and we correlate all of those measures with the data on upward Mobility I showed you the correlation of0 65 in the previous slide with this measure of economic connectedness it turns out there's a very Stark result here all of these other measures of uh social Capital that sociologists Network theorists and others have proposed over the years are basically uncorrelated with levels of Mobility so this is not to say they are not important for other outcomes I want to be clear on that but at least for the topic we're focused on here today it seems like this cross-class interaction phenomenon in a predictive sense is particularly important so why is that coming back to this plot here so we have seen and we've shown in in this work that if you do that same movers analysis I was showing you earlier if you move to a place where there's more cross-class interaction at a young age you have better outcomes in adulthood and you can tell various causal stories for why that might be true if you're connected to people who have higher incomes we know many jobs in America are obtained through referrals so maybe that kind of network helps you directly get a referral or an internship that makes a difference but I think even more importantly a very plausible mechanism here is that your aspirations or what you aim to do in life what you think is possible is greatly influenced by who you're around if you've never met anybody who's gone to college maybe you never even think about the possibility of going to college if you live in an environment where some of your friends parents went to college became scientists became lawyers you know whatever it might be those possibilities start to become things you aspire to do start to becom things you things you work toward so that I think is a plausible explanation for at least part of this correlation but there are other explanations for this correlation that have nothing to do with the causal effects of networks or social capital so give you an example take San Francisco on the far right there which looks like a place with a lot of cross-class interaction overall and very high rates of upward Mobility one potential story is that there's a causal effect of those cross fast interactions on upward mobility in San Francisco another possibility is that relative to other places in America like Indianapolis the Bara is a relatively Rich place it's going to have more funding for schools other types of public goods other types of resources you know maybe it's just the average levels of income which are what are leading to these greater interactions with high income people to begin with but maybe it's just the average levels of income and the resources associated with that that are leading to higher levels of upward Mobility so to show you how we can disentangle between those explanations want to turn to this chart here which I really see as the key thing from this analysis of Social Capital so let me walk through it in a couple of steps so what we're doing here is each dot represents a different zip code in America and we're plotting first just the share of high income friends that low-income people have in that zip code so our measure of economic connectedness from the Facebook data against just how rich people in that zip code are median incomes in that zip code and you can see there's a very clear upward sloping relationship here as you might expect intuitively because you tend to be friends with the people around you if you live in a place where people are richer you tend to have more High income friends so that kind of makes sense and is a is a bit mechanical now notice despite that relationship there's still a lot of dispersion vertically in this chart so at a given with a given income distribution there's still big differences in the share of high income friends that people have in some zip codes versus others so now I want to get to the key Point Let's now color these dots by the uh data from the opportunity Atlas tax data measures of upward Mobility where let me remind you red colors are places with lower levels of upward Mobility and blue colors are places with higher levels of upward Mobility so what you see in this chart I think is a very clear and striking pattern which is that if you take any vertical slice of this chart you see that the colors change systematically from red to blue as you move up so that is to say if I take a set of zip codes all of which have median household incomes of about $50,000 a year they all have similar resources in some sense but I go from the zip codes where low-income people are not connected to not interacting with high- income folks to the places where there is a lot of cross-class interaction I see rates of Mobility changing systematically in contrast if I do the converse and take any horizontal slice of this graph I go from a place that has much fewer resources to a much richer Place holding fixed the level of cross-class interaction there's no change in levels of economic Mobility so that suggests quite strongly that what matters is not just average incomes in an area but actually the degree of cross-class interaction conditional on the level of income right which points more in the direction of the view that this cross-class interaction really matters now this of course is just a non-parametric depiction of a two variable regression right that that's what we're doing here and so to show you that this phenomenon actually turns out to be pretty robust across different explanatory variables I'm now going to in a more compact way just show you a couple of regression results uh that I think further illustrate the power of this cross-class interaction variable so I'll give you another example of a similar exercise there's a long literature in economics that shows that there's a cross-sectional link between the level of income inequality in a given generation and rates of Mobility across Generations so this was first established by an economist named Miles korak and popularized by the late Alan Krueger in what he coined the Great Gatsby curve the link between inequality and mobility and that's what's replicated in this first regression here of upward Mobility on the genie coefficient within a county in the second column we repeat that regression but now control for the level of economic connectedness this new Facebook based variable and you can see that two things happen first economic connectedness is a very strong predictor of mobility and second the relationship between inequality and Mobility basically disappears exactly like I was showing you in the previous chart with poverty rates so in that sense statistically connectedness explains the link between inequality and Mobility that's been widely discussed in the public discourse and the prior academic literature another example so my colleagues David Cutler and Ed Glazer have a famous paper uh in the quarterly Journal of economics in 1997 where they noticed using you know more limited survey data that correctly you know segregation is extremely harmful for blacks if you look at black kids growing up in racially segregated neighborhoods large black share they tend to have lower rates of upward Mobility but they know it in their paper in the conclusion but we do not have an exact understanding of why this is true so again we replicate their analysis but now control for the level of economic connectedness cross-class interaction and once again you see that exact same pattern right the lack of connectedness in those predominantly black communities can in a statistical sense explain why you see much lower levels of Mobility there and so you know based on uh that uh set of results uh our sense is that this measure of cross-class interaction is extremely important in understanding uh economic mobility in combination with other factors like the quality of schools and other things for sure but this is certainly something that that warrants attention and so I want to make one final set of points on this before uh turning to a couple of other things and concluding today's lecture so i' I've shown you that there are these big differences there's this link between economic connectedness and mobility and so you might wonder okay if we think this cross-class interaction is important how can we get more of it right so I don't have a definitive answer for that but I think it's useful to start at a conceptual level by thinking about two different factors that matter for the level of cross-class interaction or connectedness in a society the first is just simply exposure so to give you a simple visual ual example imagine you've got two schools and imagine that all the high-income kids shown with the green circles go to the first school and all the low-income kids go to the second school so since you can't be friends with people you never meet obviously this is going to generate a very disconnected Society across class lines so that's one potential explanation for why we have stratification by class in terms of social networks in America just segregation but that's not the only possibility you could also have this situation a situation with what we're calling friend bias where you have perfectly integrated schools yet if you look at the friendships that are formed shown here by these lines you still don't have any cross-class friendships because all the high income kids in a school hang out with each other and all the low-income kids in a school maybe hang out with each other which of these two things is driving the lack of connectedness is extremely important to understand from a policy perspective because if it's the former then you can think about policies like changing zoning laws redistricting busing Etc that create more exposure if it's the latter that's not going to you know that's not where the problem is you've got to figure out what's happening that's creating a lack of interaction within a given room so the power of the social network data the Facebook data is that we can actually start to disentangle these phenomena and the way we do that is by taking the friendships that we see the billions of friendships and it turns out that you can you actually have enough information to make a pretty good guess about where those friendships were formed so we can make a good guess about whether you made your Facebook friends in a high school in a church in a specific College Etc by looking at you know where you both were at certain times and so on so I'll again spare you the details so we do that for all of the friendships and using that we're able to construct these very precise measures here I'm showing you the data by High School of these two key axes that matter for connectedness the friending bias measure so conditional on a level of exposure how many high income friends do you make one way to think about it statistically is are how much are you sampling nonrandomly from the set of friend set of peers you have in your school that's what's on the y- axis and the x-axis is just how many high income students you have in your school just a measure of exposure so again take a local example Berkeley High School Berkeley High School on the surface looks like a very diverse high school it's got a pretty mixed income distribution if you look in the Facebook data Berkeley public high school is one of the schools in America with the greatest level of friending bias it's the most separated by class among schools in the United States and so you see that pattern more generally among big public diverse high schools where at some level you have integration but in practice you actually don't have integration when you look at what the networks look like and as we've seen in the slides I was showing you earlier it's really the actual cross-class interaction not just being in the same building that's predicting uh good outcomes for kids from low-income families so using this kind of data and again we've made this publicly available so for the students here you know if you're interested in understanding what's driving this variation and so on I think there are lots of interesting questions here can be analyzed with these publicly available data um we can take that and go back to the question I raised initially how much of the social disconnection in America by class is due to exposure versus friending bias and it turns out if you do various reting exercises basically the answer is 50/50 half of the disconnection is due to a lack of exposure the fact that poor and rich kids live in different neighborhoods go to different schools go to different colleges and half of it is due to uh friending bias um so even conditional on exposure they're being disconnection in terms of who people interact with now at some level this all seems maybe a little bit abstract and we're figuring out seems like we're figuring figuring this out with these new data but at another another level I think these are totally familiar ideas to anyone just in their own life from introspection just to give you a sense of that I want to give you a quote here from uh Carmelo Anthony the famous basketball player from his recent Memoir which I think captures this idea in very simple terms he talks about his experience growing up in Baltimore where he notes that millionaires could live on one side of a street and the projects could be on the other side in that sense was a very integrated place if you looked at like that census tract it would be very integrated by income but he notes those two worlds would never cross never make friends never acknowledge each other in our jargon he's basically saying there's a lot of friending bias um everybody was okay with it especially the rich and I think it's that kind of situation that explains why Baltimore in particular is actually a place with really poor outcomes for kids from low-income families so I think this friending bias phenomenon how you actually get people from different uh backgrounds to interact with each other is very important to try to think about how we address ress going forward much as how I think we devote a lot of attention in policy circles to thinking about issues of exposure and segregation right they're numerous policies focused on zoning laws and other kinds of things that are about getting people into the same sorts of neighborhoods but I think the latter component is also important I want to show you one final piece of data here which is I don't want to leave the impression that this friending bias is somehow some intrinsic thing that cannot be changed through policy and only exposure is manipulable and to give you a flavor for that and I'll come back to this more in the second lecture as well just want to show you a couple of pieces of evidence that I think support that view so one point to note is that the level of friending bias varies substantially across settings if you give take a given set of people and look at the friends they that same set of people make in different settings they exhibit much more friending bias in certain settings like the friendships they make in their neighborhood or their college to say their religious institution or recreational group where people are much more likely to make friendships that cut across class lines another example relevant to the schools context and consistent with the example of Berkeley High School that I gave you if you look at the size of a school or the size of a group more generally and the level of friending bias people tend to come apart in big groups so in very big schools you find the other kids who sort of look like you and make your own uh subgroup in smaller groups you kind of end up interacting with everyone by the end and that leads to more of these connections this is not to say you know this is in and of itself the solution you know maybe creating smaller cohorts which actually turns out Berkeley High School is trying to do at some level you know maybe that can make a difference but more generally my point here is this seems malleable to some extent we should be thinking more about how we can make a difference on that Dimension okay so I want to open it up to questions but before doing that I want to show you one last set of dat data which I think is particularly relevant in this context which is about the role of higher education in economic Mobility so to this point I've been focused largely on disaggregation across neighborhoods and schools K through2 schools and so on of course a natural next important Junction in the pipeline to opportunity is where we're all sitting right now right institutions of higher education many people view institutions of higher education as the realm where the playing field gets leveled where you can really create equality of opportunity and give people Pathways to Upward Mobility so the last set of data I want to show today is to to explore whether that's actually the case in practice try to understand how colleges affect economic mobility and here we're going to use data again from the anonymized tax records that we've used in the other studies Linked In this case to Department of Education records and data on everyone's SAT and ACT scores for everyone who took the SAT and ACT uh in the US so let me start with this chart here which comes from a paper with Emanuel and Danny Jen and others where first I'm just going to show you some data on what Mobility looks like across colleges in America so when we're thinking about colleges there two Dimensions that matter for a College's contribution to economic Mobility the first is what I'm going to call the upward Mobility rate if you take a set of kids from low-income families in the bottom 20% of the income distribution and ask what fraction reached the top 20% of the income distribution that's a measure of how upwardly mobile the student body is right like how much are you helping the low-income kids on your campus rise up when you measure their incomes 10 years after college using tax data and on that measure you can see that a lot of the highly selective colleges in America like my own institution currently Harvard Stanford Princeton Etc look terrific as does UC Berkeley uh but that's not the only measure that matters for a College's contribution to economic Mobility what also matters of course is how many low-income kids you have on campus to begin with and on that measure if you look at places like Princeton and Harvard only two or 3% of kids at those colleges are coming from the bottom 20% of the income distribution you're about 80 times more likely to be at Harvard if you're from the top 1% than if you're from the bottom 20% so these colleges are enormously skewed towards kids from high income families Berkeley has more low-income kids than Harvard it's not enormously large but it's still significantly more more like 8 or 9% as opposed to 3% but then as you go over to the right side of this distribution you can see that there are many colleges in America each one represented by a different dot that do serve many many low-income kids right but there you have a different challenge which is if you look at a lot of those institutions typically two-year institutions or community colleges you don't see very good outcomes at those places and so at some level one way to think think about what the problem is in the higher education system in the United States in terms of contributing to economic Mobility is that we basically don't have many dots in the upper right here we don't have a lot of places that serve a lot of low-income kids and have excellent outcomes now one reason that might be the case is that uh you know there's a limited capacity for colleges to do anything about these issues because of all the other disparities that we've talked about starting at Birth from the ne neighborhoods you live in to who you interact with to the schools you attend so forth and so on you know one view you might have is maybe there's just very little capacity for these colleges to admit more qualified low-income kids while maintaining their selectivity standards and there's just limited capacity for these colleges to do better given all the challenges uh kids have faed to that point so to interrogate that further one way we can do that is by using data on SAT scores to get a sense of kids qualifications kind of where they are at the point that they are applying to in entering college and so at some level there is quite a bit of Truth in that view there are a lot of disparities that emerge by the time you're 18 if I plot for instance the fraction of kids who are scoring above, 1500 out of 1,600 on the SAT that puts you in the top 1% of test scores on the SAT by parental income it is indeed the case that there's a very steep gradient here you're about 30 times more likely to have an SAT score about 1500 if you're from the top 1% relative to being from the bottom Fifth and so at some level you know you take a place like Berkeley trying to admit very highly qualified kids because of this fact whatever you think about whether the SAT is biased or not and whether we should go test optional I'll touch on some of those issues in the next lecture but there's some content in this as I'll I'll show you direct data on in in the subsequent lecture but you know given that there's going to be a limit to how much you can do do at uh at colleges but that being said I don't think we should let colleges completely off the hook because there are still significant differences uh in attendance rates even conditional on SAT scores and so just to give you uh a quick flavor of that let me toggle over to one last thing here so I'm going to go over to this um website uh that the New York Times has uh constructed to look at these college level data and here what you can do is type in the name of any college so let's type in Berkeley so if first what we're going to do is just look at attendance rates for kids who attend Berkeley for kids at Berkeley in general by parental income based on information from parents incomes from tax records right and so you can see at Berkeley there's kind of a flat distribution and then it does take up pretty sharply at the top where Berkeley has many more kids from high income families than from middle class and lower income families but what you can now do is ask using the SAT data suppose we take a set of kids who have the same qualifications when they're applying as measured by the SAT so we can click on this button here and see what happens to the distribution at Berkeley turns out it totally flattens so at Berkeley there doesn't seem to be much of a difference in attendance rates conditional on SAT scores in some sense there's a limit to what UC Berkeley can do further in terms of creating Equity unless of course one wants to have sort of class-based affirmative AC and have more kids from lower inome families uh admitted but Berkeley and a number of other public institions are somewhat distinct in that way so now if I type Stanford okay so Stanford looks completely different where you can see Stanford even conditional on SAT scores has this kind of u-shaped distribution with much fewer kids from the middle class and then a real uptick at the very top of the distribution where even if you take two kids with the exact same SAT score you are far more likely to be attending Stanford if you come from families making more than $600,000 a year than if you're from the middle class or lower income family and so just to come back to that here and and wrap up for today you know if you look at that more systematically uh and look at IV League IV plus colleges the IV league and schools like Stanford elite private institutions look at your probability of attending these colleges four kids all of whom have say exactly the same SAT score an SAT score of 1510 which is exactly the 99th percentile threshold you see this very clear upward sloping pattern where you're like two or three times more likely to attend these colleges even holding fixed sat if you're from a high- income family and what that shows you then is that actually there is probably scope going back to this plot to do something at the college level to move these dots to the right and then in a different vein to think about how you move those dots upward and potentially amplify the impacts of colleges on upward Mobility as well so you know more broadly you know the point here is I think piece by piece by looking at this Pipeline with these granular data we can figure out where the shortfall sort of are and make progress in tackling uh these issues going forward and so I'm going to skip a couple of things here uh and conclude for today by just highlighting three takeaways I've shown you lots of different data but you know I hope you will take three main messages away first you know I think childhood environment plays a central role in shaping prospects for Upward Mobility through a dosage or exposure effect second I think a lot of the reason environment matters is because of Social Capital often in economics and in policy debates we focus solely on Financial Resources but I think there's an important complimentarity between financial resources and Social Capital that can be very influential and you'll see how that guides a lot of the policy interventions we're thinking about going forward in tomorrow's lecture um and then finally in some separate work that I didn't talk about here our senses focusing on creating equality of opportunity can be very useful from the perspective of reducing inequality which is what I think motivates a lot of people to be interested in these issues but can also have payoffs in terms of increasing economic growth this is not a zero sum game uh you know you bring more people through the pipeline they start new businesses they invent new things that benefit everyone and I think there's clear data supporting that as well so that's what I have to say for today tomorrow I'm going to take the set of findings of shared today and try to think about what we can do to increase upward Mobility on the ground talk about some policy pilots and interventions that we and others are implementing uh in this space and I'll focus on three areas reducing segregation making strategic place-based Investments to kind of turn the red colored places into blue colors on the maps that I've been showing you and what we can do to improve higher education the last set of topics that I focused on but let me stop there for today and thanks so much whatever whatever makes sense I'm I'm Flex so if anybody has any questions we have a microphone up in the front if you could please stand in line up there and we'll take them thank you and please try to make them brief uh as a question hi um so if I look at what we at an institution of higher education can do um Zach blemer has recently been coming out with data which shows that uh limiting the enrollment in lucrative Majors like economics like computer science actually causes seems to be causally related to racial stratification and limitation of upward Mobility um you know in a way that is correlated with that racial stratification so what's your thought about that and about how that might give us a way to move this in a different direction yeah great thanks so much so you know we're Zach is a terrific graduate of the Berkeley PhD program we're actually working with Zach on these issues now uh with these data and you're absolutely right so you know as you start to interrogate what is going on at some of these colleges where you're seeing lower levels of Mobility part of it is about the types of things that people are majoring in which may be partly about major restrictions like Zach documents in one of his papers it may also be about other factors that lead people for example to be discouraged from pursuing a class you often see gender disparities for instance emerge where women if they get a there's some evidence which address that if women get a be in an introductory class they may choose something else men are happy to plow ahead you know with with the be or whatever the great is and so I think there are lots of things underneath the hood like that to try to unpack I'll show some evidence on this tomorrow take City University of New York which is a little bit of an outlier in that graph that I'm showing you what are they doing they have a number of interesting programs that try to really support kids in the pipeline help them graduate help them choose Majors that can work well for them uh Etc so I think all of these things are the types of things to be thinking about hi I'm Carissa I'm a student in the econ Department here um I think your research is so interesting and so necessary um and what I see in it what I identify with is an enormous love for this country um anyone who hated America wouldn't spend nearly this long thinking about it um so I wonder after all of this at a very high level um if you think there's any Vision that you have of a new American dream or if you think um we should move away from it alt together yeah thanks so yeah I mean my view is the US has traditionally been very focused on being a capitalistic engine kind of maximizing growth and that can have certain benefits of course in terms of creating more Innovation more Discovery entrepreneurship and so on as I think we're all well aware the fruits of those returns are very unequally distributed as shown clearly in work by Emanuel sayz here and Gabriel zman and others and part of what I think we're trying to show here is that thinking about the dynamic process the equality of opportunity in particular can be useful lens to try to restore the American dream in some sense and as I'll emphasize in tomorrow's lecture I think that way of thinking about it as opposed to focusing on inequality of outcomes the distribution of income which may also be important obviously in its own right when you focus on equality of opportunity it can be a broad tent that brings people together so in my experience you find people on the right people on the left all of whom are very interested in trying to figure out how to tackle this problem um because I think a lot of people believe it's just a fundament Al ideal in America and beyond that we want to be a place where people can rise up whether you know one can get back to where we were in the 1950s I don't know but my view is step by step with the kind of evidence we have here making scientific progress I think one can make progress in the right direction at least hi um my question has to do with that first graph that you showed in terms of the decreasing um uh American dream and I'm curious over that time from the 1940s until I guess it was the 1980s a larger percentage of generations were going to college so since College connects with income level is there something where you're kind of tapping up against how educated the children can be right yeah great question so part of what I think is going on here at a macroeconomic level there's a nice book by my colleagues Claudia golden who just won the Nobel Prize and Larry cats called the race between education and techn techology that kind of captures what you were getting at in your question where they point out that if you look at Trends in levels of Education just measured say by the number of college graduates or total years of education up until 1980 it was going up steadily and then it completely plateaus and falls off the trend line and so the way they think about it is there's constantly technological progress globalization there are these forces that sort of human beings have to compete with in terms of getting wages up until 1980 we were keeping steady in that race after 1980 we fell behind and you know one way you can look at it is that is one of the core reasons the American dream has really faded here and my view is it's not just about years of education but the quality of Education the nature of the childhood environment America has become more segregated there are many elements related to what I've been showing you in the more modern data that I think have changed in the time series as well now you raised the question of whether there's a ceiling I don't think there's a ceiling here that kind of says you know we've hit the limit now only 50% of people are going to do better than their parents and the way you can see that and we actually show this in this paper is the overall size of the pie in America is still growing very rapidly growth rates are very large average incomes are going up quite a bit it's just that the distribution of that income growth is far more skewed than it used to be uh in the past and so you know in the limit if it's just one person get getting much richer then of course you're going to end up in a situation where 50% of people do better than their parents but it's not like we've hit the ceiling in terms of economic growth it's just that we've gotten closer to that situation of one person capturing all the growth and we show in the paper that if you were to distribute the modern growth rates more equally like we did in the past maybe through some of these types of solutions that I've been talking about here creating more equal opportunities you would reverse 2/3 of the decline in Mobility showing you that most of this is not about hitting a ceiling in terms of Education or potential progress hello my main question was on your propositions when when it comes to in specific the importance of integrating People based on socioeconomic levels not just on race and one of my questions because I think one of the main push backs especially behind closed doors when we're trying to push this forward from a policy perspective is is there a negative effect when you bring in people from lower income yeah uh demographics into the communities that are from higher income because I think the higher income families I think in the back of people's heads what we see on camera is very different than when we vote behind closed doors and so I was wondering in terms of bringing this to light and sharing this with people is there a negative correlation if you introduce people from lower socieconomic groups into higher and then does that affect those children's ability to still be high performers in further Generations excellent question and of course a critical one in understanding whether any of this is going to be actionable going forward and so what can we say in the data setting aside what people's perceptions might be so couple things come out and you can look at this for yourself in the opportunity Outlet so if you look at those maps that I've been showing you you know support was I was to show you the same map not looking at kids who started out in low-income families but kids who start out in high income families what would it look like spatially Fact one is that there's much less dispersion across areas in outcomes for kids who grow up in high-income families than low-income families if you look at kids who grow up in high-income families Atlanta for example they do just as well as kids who grow up in Salt Lake City or the Bay Area and I think that's intuitive basically what we find is where you grow up matters much less if you're rich than if you're poor and it's kind of because you can insulate yourself from the local conditions you can send your kids to the best schools Etc you have networks that cross many boundaries and so forth so that's one way of looking at it that the outcomes of the rich sort of are always good regardless of of the environment to some extent but then more directly you can ask if we look at these more integrated places we see low-income kids are more likely to rise up do we see worse outcomes for the high- income kids there and the answer there is a little bit more nuanced so if you just look at that in the Raw data you see slightly worse outcomes for kids from high income families that turns out to be driven largely by the fact that the average incomes are different in that place why might that matter in the US with property tax local financing of schools if you live in a more integrated Place obviously your school is going to have less funding and that might have a negative effect larger classes etc for kids from high- income families once you control for that in the way that I showed you with that color Dot Plot hold fix the level of resources and look at changes in composition there's absolutely no effect of more cross-class interaction on the outcomes of kids from high-income families while kids from low-income families do better so it is not a zero some game by any means and I think think trying to convey that publicly now coming to the core of your question to actually change people's perceptions I think that would be a very valuable thing to figure out how to do thank you thanks for a fascinating talk Raj um I have two quick questions the first relates to what this young gentleman just talked about when you have that 5050 split between how much of it is segregation and how much of it is friend bias yeah um then at least it's suggests that uh creating less segregation more cross area busing or policies like that will build off of that 50% and do something yeah it's possible however that the areas where there's less of that are areas with you know intrinsic attitudes so that if you would mix them the friend bias would become much more severe right exactly so it's it's not clear that we would get 50% if we did that right and in fact you see that in the data so in in the more diverse communities that are are more mixed income you see more friending bias and so there's kind of a catch 22 and that's what shows you you need to think it's not just about integrating people you need to think about how you actually Foster that interaction this is speculation moving beyond data in a way that usually I don't feel comfortable doing but one hypothesis is that people look for something in common like if you look at this data on religious groups or recreational groups if you find kind of the common denominator the shared Faith or the shared sports team it overcomes other forms of stratification and so maybe it makes sense to think intentionally about that when one thinks about integration fascinating the second question when you looked at that u-shape uh with the you know you compare Berkeley to Harvard I'm wondering if a big part of that isn't part of the say private Elite University's business model of Legacy admits yeah because that is a tremendous engine for donations and if I were the president of Harvard I would look at and say I'm probably not changing anything if I want to keep on getting the money I'm getting so Steve I'll get into this in more detail actually in in tomorrow's lecture to decompose exactly what's driving that uptick because I think that then points to potential policies one might undertake to create more Equity there you're absolutely right that legacies are part of it turns out it's not all of it it's about 40% of it but let me emphasize one key thing I think about legacies anticipating what else tomorrow which is at the scale of these private institutions at this point the vast majority of legacies who are admitted quite honestly do not have enough money to make a genuine difference for an institution that has a $55 billion and Dam it and so yes there are some people who give $350 million and that you know does obviously matter to an institution but that's a tiny sliver of the population and it relates fundamentally to the work in manual and Tom and others have done that the distribution is of wealth is so skewed you really need to admit 10 kids to get the the vast majority of your donations and so my view is it's not obvious even from that very economic perspective that uh this makes sense we can talk more about that thanks again Raj thank you so much so glad to see you here at Berkeley again uh so my question has to do with the value added effect of a university or college if my memory is correct maybe it's not I I I think there was a paper by Alan Krueger that said that once you control for the individual and where the individual uh you know the course courses taken the majors of the individual that the college effect uh almost disappears and how do I square that with your value added yeah numbers yeah well thanks Rich so um I feel like the questions you all are asking perfect quickly set up for the next lecture where I'm going to tackle that as well so our most recent paper actually is about the causal effects of colleges and why kids from high andcome families are more likely to attend some of these colleges and just in a nutshell our view is that we've updated our views since Alan Krueger's paper for two reasons it turns out to what Alan Krueger was doing in this very famous paper is taking kids who got in to say their local state Flagship school and a highly selective private school comparing the outcomes of a child who chose to go to the private school versus a child who go to the chose to go to the local state school and found that on average their incomes weren't that different turns out that if you use the tax records with now a tremendous amount of precision and you look at the fraction of children who reach the upper tail of the distribution there's an enormous difference in your odds of reaching the top of the distribution if you go to one of these highly selective private colleges versus less selective Public Schools so in that that range it matters tremendously and then there's other work including by Zack bleemer and other research designs we're using um where if you look at the State flagships versus say like the CAL States or the cunis the next year of schools even on average incomes you start to find really significant differences and so our sense is that you know what has actually become quite a popular takeaway a lot of people have read that paper and it's been Incorporated in the general view like maybe College doesn't matter so much I actually don't think that that's right we're able to replicate those findings reconcile them with more modern data and my sense is there is actually a real value out of here and I'll show that data more in tomorrow's lecture thanks hello my name is Leah I'm an undergrad I actually um was assigned one of your lectures to watch on YouTube in like freshman year and econ one um and I've been a fan ever since so I'm um right well you excited but um so as I'm many people will uh know and recognize when we use meta platforms this I have a question regarding your economic conducted dis variable um we use meta platforms a lot of the time we get a little you know some some little box that says people you may know um and I think this contributes to what people discuss in literature and just in pop culture is the echo chamber effect and so I'm wondering given this very significant coefficient you got and your research overall like how does the echo chamber effect impact the significance and the meaning of your economic connectedness variable and that data Etc great great question so you basically wonder you know how much of the friendships we're seeing on the Facebook data are sort of generated algorithmically because of the algorithm used to recommend friends uh or because of the nature of online interactions to generalize a bit and so on and so we're concerned about that as well and we try to tackle that in VAR I ways so one example is we try to look at your closest friends so you can use various proxies like the messages people are exchanging and where they're located various groups they participate in things like that to get a very clear sense of who people's closest inperson friends are and who are kind of broader contacts who may have been algorithmically recommended and it turns out if you subset to those five or 10 closest friends you get results very similar to what I've been showing you so why is that that might not seem totally intuitive you know average person has like 350 friends on Facebook why does it turn out if I look at your 10 closest friends I get a similar picture if I look at the 350 basically it's an empirical result in the data that if I tell you the socioeconomic status of your first 10 friends it is very highly predictive of the 350 friends you have and so you end up getting a pretty sharp signal of probably the contexts who genuinely matter not the very broad Network and so that's why our sense is despite those kinds of issues this is actually giving you a pretty reliable signal okay and to follow up on the um the economic connectedness we talked about the you talked about the channels um through which this variable is impacted we have like the friending bias and exposure yes um but I'm wondering about potentially other channels like time to use social media that people who work full-time jobs might not have and how that's going to also impact your data if there are other channels that you're currently discussing yeah totally you know I think a very interesting question for students and others who are interested in these issues is what is the effect of online networks themselves on these kinds of connections and how is that going to impact outcomes down the road we are basically using the Facebook data as a proxy to get at offline interaction more or less as I was just saying but you know one can think going forward in some sense there's a lot of potential to overcome exposure constraints you going back to Steve's question if you're not bound by physical geography and you can connect with people anywhere in the world in principle you can have much more diverse Connections in practice as you just said you end up getting a lot of echo Chambers and so are there ways that we can connect people meaningfully overcoming these Geographic boundaries I think that's a fascinating question with um social media thank you very much hi I'm Sean I'm a physics undergrad here uh incidentally physics is the major here with the most imbalanced standard ratio in favor of men uh could you speak a little more about the gender disparities we see in the maps yeah absolutely you know maybe the way I will do that is just briefly show one slide here that I skipped which is looking at where kids become inventors in America is very relevant to Fields like physics so uh what you see is on many dimensions by gender by Race by income there are many more kids who become inventors from certain backgrounds relative to other backgrounds more men than women more kids from high- inome families and low-income families white folks than underrepresent minorities Etc and to answer your question in an empirical way we can try to understand you know why that's happening and what's driving gender disparities in particular and I'll show you again a geographic dis disaggregation that turns out to be useful in figuring that out so here what we've done is linked patent data on the universe of patent holders in America to the tax records so we can look at the lives of who goes on to become an inventor in America and here we're looking at where inventors tend to grow up and you'll notice you know a lot of inventors grew up in the Bay Area a lot of inventors grew up in some other cities in the midwest what is this blip here in Texas that's Austin Texas right around UT Austin so you probably see a pattern it's around places of kind of where there is a lot of innovation and knowledge production among the adults living in that place so that's consistent with other ideas I talked about in this lecture that exposure might matter if you're growing up in a place where people are doing a certain thing like science and Innovation you might yourself have that on your radar screen but tying this now to your question about gender you see some very sharp patterns with this that speak for example to the origin of gender disparities so it turns out that as a girl if you grow up in an area with a lot of men who are innovating who are patent holders in a particular field it has uh very little impact on your probability of becoming an inventor if you're a boy growing up in a place with a lot of male inventors you're much more likely to become an inventor but as I was just saying if you're a girl growing up in a place with a lot of male inventors it's actually totally irrelevant if you're growing up in a place with a lot of female inventors it has a great influence on your probability of going into science and becoming an inventor and it turns out that also happens in a field specific man manner so you can classify p patents into the technology class like what type of patent you have and this happens in a very technology class specific way so if you grow up in the Bay Area you might be much more likely to have a patent in computers if you grow up in Minneapolis which has a lot of medical device manufacturers you tend to have a lot of patents in medical devices even if you yourself are living in some other place in adulthood very consistent with this exposure idea and it's totally gender specific and so coming back to your question you know why might we have so few women in physics I think part of it might be this intergenerational transmission where we've had so few women in physics and that itself leads to so few women in physics going forward and this is the type of problem that I think we really need to try to address going forward so if you look at the fraction of female inventors over time what fraction of people who are getting patents are women by Birth Cohort you can see that is indeed going up but it's going up at a rate of quarter of a percentage Point per year which means if you extrapolate it's going to take another 118 years to reach gender parity in terms of innovation and I think it's this kind of process that somehow one needs to figure out how to break out of to to have an impact um as an epidemiologist and a data nerd your talk was a real treat um thank you um my question actually relates a little bit to looking at this so virtually you're into entire talk had as the primary outcome um the longitudinal income objective but if you want to talk about the American dream or a humanistic dream income may not be the only relevant variable totally it's very convenient y um but I I'm interested what other variables outcome variables you'd looked at yeah like invention or or the Bhutan happiness index or yes great question you know despite being an economist happy to concede that many things matter Beyond income uh and uh you know I we've we've tried to make some progress on that you saw that with the Innovation work Something like Happiness and well-being is of course more difficult to measure on scale in the way that we're trying to do but here's another example of a type of outcome we're trying to study more systematically which is Health life expectancy for example and here you know from some other work we've done here we're just plotting life expectancy in America at age 40 versus income and the point I want to make here is there's an enormously strong link between income and life expectancy right so the poorest men in America we estimate using population Social Security death records live about 15 years shorter lives than the richest men in America which is a shocking disparity if you put it in context so the CDC estimates that if we were to eliminate cancer as a cause of death life expectancy would go up by 3.2 years so think about 15 years relative to to that uh as as you know well and so you know my takeaway from this is while income is not in and of itself surely the measure we care about there are lots of other things like health and other measures of well-being that are very strongly correlated with income and so it's a useful place to start and we find with when we start to look more directly at these other outcomes there's some nice work done by Stephanie Duca Johns Hopkins for example showing that that same kind of dosage effect I was showing you for income when people move to better neighborhoods also emerges in the context of health and adulthood so we're finding similar patterns for this constellation of variables but I think more work can be done on those other dimensions as well last two questions yeah I think this a last hi my name is Mike um I'm trying to reconcile a couple of big picture things with the kind of the social engineering kind of focus at your because it seems pretty obvious you move a kid into a situation where they're greater resources available for their self-improvement that they will improve I that seems fairly obvious um I mean when I went to college we didn't have tuition I went to UC there was no tuition now they're playing what 15,000 $20,000 a year so that seems to be a mechanism that would weed out some of the middleclass people like me that had the advantage but the question I have is in the last 40 years um there's been a huge shift in income and two years ago I think I read is the first time in US history that the majority of income national income came from return on Capital rather than return on labor in other words Investments dividends capital gains interest which obviously in that same 40-year period there's been a significant decline in the taxation of those forms of tax those forms of income so so and at the same time there's been a disinvestment in the public space public education Public Health all those kind of public Investments have declined dramatically so that people have access to fewer resources outside of their income so how do you how does this uh how do you connect the broad Trend which is away from investment in in in kind of mechanisms that might even up the which is driven by macroeconomic economic policies Taxation and accumulation of capital through investment how do you reconcile those with what your goals are is to even the playing field yep yep yeah great great question so I think some of those macroeconomic factors contribute to the trends that led to the very first draft that I showed right of the fading of the American dream and my view partly through the mechanisms that I'm digging into in more detail here so for instance take the example you gave the colleges that people attend we think have very significant causal effects on their future outcomes in the past it was maybe easier uh if you wanted to to attend an institution like UC Berkeley certainly a a private college now that's become much more difficult to do partly for financial reasons but partly for other reasons as well so if you take like the u-shaped graph I was showing you know with uh the IV League colleges here this is actually not largely driven by an issue of income and affordability because at this point many of these colleges have basically made it free to attend these institutions if you have an income below something like $80,000 or even $100,000 because of financial aid yet those kids are not attending those colleges at the same rates and in this casee it's because of who's getting in as opposed to just financial aid so my view is those macroeconomic factors that you're describing no doubt like the reduction in funding for public institutions have created some of the greater challenges that we're seeing they've led to more segregation they've led to a more unequal distribution of resources that limit opportunity but at the same time I think there are also other margins that are relevant in terms of creating more opportunity like who admitted to these colleges to take one example I'll give other examples in tomorrow's lecture of specific policies where we're spending billions of dollars to say try to reduce segregation in America but I think much less effectively than we could if we understood better you know what mechanisms are driving why people locate in different places so I don't view it as one hypothesis versus the other I think these things are connected and there's a value in addressing it at both levels hi I really enjoyed talk um one thing I was really moved by was the graphic showing outcomes of um black and white men uh from Rich families and where they ended up um in the in the percentile of income and it made me think of the three generation curse which is like the I I don't know if you're familiar the phenomenon where um nine and 10 uh families in third generation squander the wealth um in that family and uh I was wondering what role if any do you anticipate your work in economic Mobility um in ending the three generation curse especially in uh families of color yeah great yeah great question so yeah you're exactly right that the you're mapping out the Dynamics not to two generations but three generations if you kind of carry that process forward if you started out Rich you're even less likely to stay at the top when you look forward three generations now you used as I think as commonly used the term squander which kind of seems to implicitly I think people assign the responsibility to the families in the sense of you know you could have kept this wealth but somehow you ended up not keeping that wealth and my view is when you see those systematic patterns like I was showing for black Americans that it's not so much a choice or some sort of irresponsible spending or behavior they're more they're deeper structural factors that are leading to these vast differences and outcomes in a single generation further and subsequent generations and I think digging into what is causing that will help uh improve outcomes in a single generation in multiple generations and importantly in the long run in the steady state to use the term economists would use would lead to a convergence of incomes so I think that's absolutely right and that's why thinking about these various Solutions and getting into some of the policy Solutions as we'll do tomorrow is valuable great thank you so thank you so please join us tomorrow for the second of the lectures which will deal more with policy and thank you all very much for coming and thank you Professor cheddy for a great lecture