 We weren't able to get data on, unfortunately, so it could be even more terrible. Next slide, please. Then we also looked at innocent rates of people who have been incarcerated, who were later found to not have committed the crimes that not only were they arrested for, but actually were imprisoned. The National Registry of Exonerations, which looks at this data, found that African-Americans were 12 times more likely to be convicted, who were not guilty of drug crimes, than white people who were not convicted. And so while some of the focus is on making sure that there is absolutely fair treatment and justice for individuals who may have committed no offense, and as they go through the system that they're treated equally and fairly, but we also focus on people who have absolutely committed no crime at all, who end up in prison. And that may be hard to imagine, but it certainly happens all the time. It happens in a very racially disparate way. Similar, if you look in Manhattan, for example, again, when you go down to the local levels, you see how the disparities become even more charged when you go through the data. Next slide, please. And even for those who are not necessarily arrested or sent to jail, traffic stops. One study that we found that was stunning, actually, was looked at 95 million traffic stops. It basically looked at traffic stops over a long period from all over the country, a total of 95 million. And what it was able to discover was that not only are African-Americans stopped at much, much, much higher rates, but that is clearly driven by racial profiling. Because when it was broken down into drivers who would stop during the day versus drivers who would stop during the evening, in other words, when it was easier to see what the driver actually looked like, the numbers became very close in the evening versus during the day where these disparities existed, which indicated that it was really looking at the driver and then pulling these drivers over and doing these traffic stops. Next slide, please. So we also looked at food and housing insecurity. These are areas, again, that have not gotten a lot of attention. They've gotten more attention this year because of the pandemic. And the data we looked at as bad as it appears is before we have reached this crisis now. You do not have to go far to see the long, long, long lines of individuals and families who are lining up for food because people are very desperate kinds of situations. But even before the pandemic, this exists. And again, it was racially disparate. Black children, about three times more likely Latino children, about 1.6 times more likely to be food insecure. And by food insecure, it means that people are not sure where their next meal is coming from. Certainly not with their meals and food they will have for the coming weeks or for the coming months. And so it's a very startling situation to be in a country, one of the most advanced countries in the world in 2020. And you've got millions of people. And again, you can see them lined up every day who are food insecure. And if you look at the chart on the screen, this has been persistent over a longer period of time. You see some degree of drop with Black and Latino communities in between 2015 and 2018. But those numbers have risen again and again, even before the pandemic. And now they are sharply rising as a result of the situation people find themselves in. Next slide, please. One area that has contributed to this situation has been the loss of land by Black farmers as well as by Indigenous communities. Indigenous communities, of course, as Elena alluded to at the very beginning, lost virtually all of the land that was originally in tribal hands. But even after, even in the 21st century, that land loss is still continuing. And that has had an impact on people's ability to grow food and to develop provisions for themselves. And in a similar way, African-American farmers, which at the beginning of the 20th century owned hundreds and hundreds of thousands of land, peaking at almost a million by 1920, that's fallen down to about 45,000 Black farmers to date. And you still have a number of important organizations that are working to save Black farms. But that's going to require some policy intervention, which I'll talk about in one second. Next slide, please. Can we go back? Yeah, there. All right, thank you. And then coupled with the crisis that we're seeing every day around food insecurity, there's also housing insecurity. And it's pretty well documented that by the end of this year, both renters as well as homeowners all face the possibility of being out on the street. And that, again, is disproportionate, about 53% of Latino households, 55% of Black households. And again, this data was collected prior to the current crisis compared to other households. And if we look in certain areas, New Mexico, Hawaii, Montana, and Vermont, more than 2 1⁄3 of Black households are rent burdened. As Elena alluded to in her remarks, the purpose of looking at what she referred to as repair, the necessity of addressing these issues requires public policy. And what we hope this report will do, and my colleagues will go over this more, is it will lay the basis and the groundwork before beginning to deal with policy solutions that can address these concerns. So now I'm going to turn it over to my colleague, Helen, and thank you so much for listening. Thank you, Clarence, for that. And thank you, all of us, again, for joining us today. I want to say before we get into the racial disparity aspect of it that we used as a case study, that we did not forget the domain of education. We just didn't have enough time to actually cover it. Just two things about that, the when you log into the report, or you see maybe the original slides, that there were two disparities in education in terms of the racial and ethnic demographics of the teacher population in comparison with the student population. And we also looked at the racial disproportionalities in school discipline that actually affects the achievement gap between groups. But we're going into now the racial disparity in COVID-19 in the United States. Thank you, Alina, if you would advance that slide, please. Thank you. We used, looking at this data, we use COVID-19 and the situation on the ground as the lived experience of this data. What does this data look like when it's experienced among a pandemic? And we use the notion of looking at the disparities across the groups and how they correlated in people that were being infected and dying of COVID-19. And the data that we used for this came from the COVID tracking project that's in partnership with the Center for Anti-Racist Research now at Boston University. And to let you know that the data that we are using for this is actually the data that is used by John Hopkins, as well as many of your news stations. And you have open access to this data. And what I want to talk about, I want to show a little bit, even though, and if we'll go to the next slide, I think will be a good one to introduce that next point. Thank you, Elena. This is what we've been hearing about nationwide, that black and brown and indigenous people are dying at so much of a higher rate. And I just downloaded the slide literally last night, because the data on the COVID tracking project is updated daily. And this is the latest. And you can see just the visual disparity of who is actually dying. Black people at two times the rate of white people. And what I've decided to do with this section is to look at some state-by-state disparities, not just looking at how many people are dying per state, but look at how that data is being collected and the inconsistencies across the data collection around race and ethnicity. And while we have this picture before us, if we had better data, if we had more consistent data, going to show you where some states aren't collecting data on some of these groups at all. They might be collecting it. Some might not be publishing all of it. But there are really disparities across that. And so when you look at this picture, you wonder what would this picture actually look like. I think we do see the trends. But if there was more consistent data collection across the state, so we'll go to the next slide. Thank you. Just to give you just a quick overview, you can see this on the COVID racial data tracking site on how they calculate a racial and ethnic disparity. And it's flagged as suggestive when it meets the following three criteria. Is at least 33% higher than the census percentage of what they represent in the population remains elevated, whether we include or exclude cases or death with unknown race or ethnicity. And it's based on at least 30 actual cases or deaths. So there is definitely some patterns, even in this universe of really inconsistent data and data collection. Thank you. Next slide, please. What's the start out with the District of Columbia? Because, and if you go to the COVID data tracking project, you can look at this data state by state and to see the reported race data, the reported ethnicity data. And I wanted to start out with a part of the country that was actually doing the best around reporting this. And it actually is District of Columbia, even though it's not a state, but it is doing the best in terms of reporting. If you look, 100% for race data, 100% of cases and 99% of deaths. I didn't include the ethnicity part, but they also reporting very high percentages of that. So when you look at the disparate outcomes, you have some confidence in them. Black or African-American alone, 47% of the population in Washington DC, 47% of cases and yet 75% percentage of deaths. And what I have outlined in red, that's where the COVID data tracking project has actually flagged a racial and ethnic disparity as being likely. So we'll go to the next one. And I've given some more case studies, as we move forward. Next slide, if you would. We're gonna go to Idaho. These states weren't picked just randomly. They really, I wanted to show you the inconsistent picture across the United States. Reported, so they report ethnicity data. That's for the Latinx and Hispanic population. For only 51% of cases and 98% of deaths. So deaths, almost 100%, but only 51% of the actual cases. So when you look at the disparate outcomes, 12 American Indian, Alaska Native, representing 12% of the population, get 30% of the cases and 12% of the deaths. And you see the very high racial disparities there. And certainly there needs to be more representation in reporting the number of cases there. If we could go to the next slide. Great. This is Montana. Interesting here. In terms of race data, 70% of cases, but only 65% of deaths. I was actually comparing this last night to the last time I presented this. And for Montana, in terms of ethnicity, they're reporting 61% of cases, but you notice 0% of deaths. They're actually not reporting deaths at all. And the last time that I presented on this, they actually have improved. And they were reporting 54% of ethnicity. Now they're reporting 61%. But still 0% of deaths. And look at the impact on the disparate outcomes. How well can you get a picture of that when the reporting is so poorly? For the American Indians, they represent 6% of Montana's population, but 13% of cases and 28% of deaths. To get a better picture of that, we need better data on the left-hand side. Thank you. We'll go to the next state, just a few more, and we'll look at some patterns very quickly. North Dakota, similar situation, but worse, really. The missing data, they reported race data for 71% of cases, but 0% of deaths. 0% of deaths, let me say that again, was reported in terms for race data out of North Dakota. And for ethnicity, or Hispanic and Latino populations, 0% of cases and 0% of deaths. And I'm just looking down, I was just noting the last time that I presented, that has not improved, or actually the reporting percentages are exactly the same. Look at the disparate outcomes on the next picture there, moving over. How much we can trust that? Are we seeing the full picture? Certainly not when you're not reporting data for some groups, but for African-Americans, 3% of the population in North Dakota, and 5% of cases. And of course, we have no idea of the deaths because they are not being reported. Next slide, please. South Dakota, reporting race and ethnicity data for race and ethnicity, 100% of cases, 100% of deaths. American Indian, Alaska Native, we have more confidence in the disparate outcomes when we look at what's being reported. They are 8% of the population, 12% of cases, 18% of deaths. And what's highlighted in red is where there is a racial and ethnic disparity likely. Just a couple more just to give you a range around the United States. Next slide, if you would, please. Thank you, Texas. Really interesting here. Reporting race and ethnicity data, only 6% of cases, but we do get 99% of deaths, but we need a higher percentage of cases. And you can see the disparate outcomes among Indian and Alaska Native. Huge disparities in Texas for American Indian and Alaska Native. 39% of population of Texas, representing 45% of cases, although we have some misreporting there, and 54% of deaths, a really huge racial ethnic disparity certainly there. Next slide and we'll be wrapping up here shortly. Vermont is an example of, although they're not at 100% across the board, they seem to be doing a fairly good job reporting race data, 94% of cases, 100% of deaths. Ethnicity, Hispanic, Latino, 88% of cases, 100% of deaths. We do have however, disparate outcomes in the Asian American population that represents 2% of the population in Vermont get 4% of the cases and 3% of the deaths. So a disparity in terms of cases, the reason why we need to make sure that we have full reporting of all those cases. Next slide if you would, I have another minute or so. I wanted to include West Virginia, it's an interesting state in terms of reporting 100% of cases, 100% of deaths, yet 00, reporting no percentage of cases in terms of ethnicity, 0% of deaths in regards to ethnicity. And yet they do have quite disparities in terms of some other race alone and how they calculate and understand that particular category. Okay, I'm about a minute or two over, so if I can just take a minute to wrap up, we'll go to the next slide. The reason, and I think this has been reiterated very well by my colleagues, why we need consistent and timely data collection to really understand the pandemic to its fullest extent. And you can see just by sampling, if you look at all the states across our wonderful union, you're going to see the same discontinuous inconsistent pattern. We need to focus efforts and messaging that are culturally responsive and we need the correct information, the data to do that, ensure equitable access to testing and treatment and to ensure equitable distribution in terms of resources, in terms of preparedness, even as we move forward with the vaccine distribution. Let's see what we have for the next slide there. And our conclusion, and I'll let you read this if you want a copy of the slides on your own. But what I did was to look back and look internationally at the calls for disaggregated data, 2007, the United Nations Economic Commission for Europe, calling for attention to data regarding subpopulations, regarding gender, committee on the elimination of the forms of racial discrimination, calling governments to provide racial and ethnic data regarding education and employment. And again, the committee on the elimination of discrimination against women, being asking states for data stratified by both gender, race and ethnicity. So the need to do this has been ongoing and internationally. I'll end with that and turn it back over to my colleague. And thank you very much for joining us today. Thank you, Helen. I'm clear and excited to invite you to turn back on your video as well. Yeah, now turn it over to everyone on the call. And I'd love to hear questions that you might have around this project or things that you'd be interested in learning more about. We also have some questions for you also. After that, we'll turn back to you. Maybe as we get started, maybe I'll turn it to you first clients and ask, what do you see as the next steps in terms of moving us into policy? Do you see there to be a policy opening available? Thank you. Yes, I do. One of the things that we do with data, and I would mention that I worked on Capitol Hill for about seven or eight years working on a different range of issues. One of the things that's critical for developing policy is data. You have to have the right information. You have to have correct information. And one of the things that we need to do right now is what I would call a racial policy audit where at the federal level, the state level, and local level, we look at not only existing policy but proposed policy to address these issues and assess that policy for its adequacy. And if it's unable to address these questions, then we need new policy. And so that's where we're at now. And this involves not just policymakers but people who have the vested interest in these policy changes from church leaders, to community leaders, to academics, to community activists, to educators. And so there's a broad spectrum of stakeholders that need to be brought together. Again, around not our report, but data again that begins to assess all of the, and identify the reality of the experiences that people are having. Thanks for that. Helen, I don't know if there's anything you'd like to add. No, I, along with that racial audit, I would like to say would be a unique call and for more consistent data reporting and gathering, which I didn't include in the presentation, there was a researcher from Harvard that actually termed it as a form of discrimination in and among itself. If you are not collecting, reporting, and acting on, we included that Harvard professor reported that if you're not reporting or collecting, but I'd like to add, if you're not acting on data that you know where there are disparate outcomes that is in a form of inequality of racial and ethnic discrimination in and among itself. Thank you. Thank you for a really important point. I see we have a question in the Q and A. It says, how is this presented in terms of SDG metrics and terms? Maybe I'll take a stab at it and then I'll turn it over to the two of you. So we started by grouping this thematically by SDG and we use the basis of our state index, which is grouped thematically by SDG. So the underlying backbone of this report is the SDGs and in the index and dashboards that will be published in the new year, that is organized by SDGs as well. So I don't know if you're familiar with previous reports, but we organize by score and SDG and we give a score for each SDG as well as overall. So that's the underlying backbone, but this report was really for a general audience. So we kept the SDG backbone and when we talk about food and housing insecurity, we talk about how that relates to goal two as well as goal 10 and all of those pieces, as well as the idea of this underlying framework and the SDGs of the leave no one behind agenda, which really requires that those who are furthest who have been the least served by their governments receive the most attention and resources first. And so we believe this is a way to move forward in that agenda by understanding which groups have been the least served and how to prioritize that reparation. I don't know if Helen or Clarence, you wanna add anything? The only thing I would add is that in the report, to a limited degree, we also do some international comparison. So we look at how incarceration rates and other data compares on a global scale. And I also would like to add, this is, we haven't seen many in terms of research using the SDGs as a metric for accountability for racial inequality. And so in a sense and in spirit, we are arguing that the SDGs are certainly a metric for as they are defined, peace, prosperity for all, looking at environmental and sustainability issues, but understanding that the backbone of that requires racial and ethnic equality as one of the fibers that actually hold those clinics, the SDGs across the 17 indicators. So thank you for that question. There's another question or it seems like a comment and it was saying that this project will not just look into data, but also policies that have led to disparities and that mission to actively point to policies, which have caused disparities will be great. Yeah, I think in this report, we only scratched the surface of probably all of those policies. I don't know if Helen or Clarence, you wanna talk about that approach to talk about the policies that have led to these disparities. Absolutely, thank you for that comment. That's absolutely right. So for example, when we look at housing and we look at Helen did a lot of research and we look at redlining, there are a number of what were federal and state and local policies that contributed for decades to what we see as the outcomes now of the housing insecurity, the wealth gap, the income gap that exists. There's a very good book called Color of Law, which traces in some detail the federal policy that shaped how the country looks in terms of housing and the absolute hard line segregation that was part of that policy. And so the comment is really appropriate that it's not just the absence of policy to address these issues, but the history of policy that has led to the disparities that we're witnessing. Absolutely, and I'll add a little bit to that, even though we didn't have time to talk in depth about the education section, and Elena illuminated that we have some missing data around segregated schooling. But what data would we have suggest that schools are more segregated now than the time of Brown v. Board? And when you look at Brown v. Board, of course, and we actually discussed policies that were sort of paralleling at the time with the Hispanic Latino population, similar court cases that were going on, but Brown v. Board has been challenged and really broken down in the courts and have been watered down till in fact, even though we celebrated, and it is one of the greatest civil rights edicts that we celebrate all the time, but in practice, it's been broken down through the courts and what you see actually on the ground is schools being very much so segregated with white students as being one of the most segregated populations. And you're seeing this coupling together of African-American, black and brown students being coupled together through. And when you think about policies, how wide reaching that is, you're looking at how neighborhoods and gentrification and white flight and just the way schooling now is structured having charter schools and private schools. And so there are a lot of factors there that actually play a role, but you can definitely trace all of that back to specific and certain policies that have played out and you can see the impact on the ground. And I'd also like to say along with that, that we were able to pull all that and we did some policy analysis as much as we could within the confines of the report, but we use a social determinants of health model. And when you look at that, take for instance in education and people are thinking, well, how does education actually connect with what you're seeing happening in COVID-19? And we were able to show that people with less education were more likely to be in customer or consumer-facing roles such as cashier or a bus driver and therefore more highly exposed. People with less education, poor people, less likely to have health insurance. So there's a connection, a web, if you wanna think about it, sort of as a web and how things all sort of come together and produce this, what I like to call it, constellation of inequalities. And that's another connection back to the SDGs. The SDGs 17 indicators, you have 169 sub indicators that sort of pull a lot of these things together. And we were able to see what was contributing to COVID-19 on the ground was, you could just draw a jagged or somewhat straight line back to many of these policies and lived experiences on the ground. So I'll end with that. Hopefully there's some more questions around it. Thanks both. I would like now to ask you all, no, one of the important pieces of this work was to figure out how to move this into policy and actually make policy changes. And so I'm going to share my screen now and hopefully you all will see this Mentimeter at the top. There's a code to put in if you go to menti.com. We would love to hear what are your ideas for moving this into policy? Are there strategies at whatever level, at the local level, at the state level, at the national level, do you see openings? Are you working in places where you think there's a place where this could fit in? How do you think this type of work could move into the policy arena? And I'd love for you all to fill in your thoughts there. Maybe while I give everyone a minute to do that, I'll just answer one last question that popped up around the criteria. Hi, David. David is a colleague of ours at SDSN. When you include indicators, not an SDSN USA indicator set, what criteria guided your choices? So I'll say that we use the same criteria that we use for SGG indicators. So it had to be from a reputable source. The source had to be open access. It had to be easily and frequently updated. There had to be rigorous data collection processes. And there had to be an opportunity to set a target. So there had to be some way that you could say what would be the optimal level. And you can find, if you're interested in those details, it's in the methodology of our report. How exactly we did that? I'm refreshing the screen to see what's happening on menti.com. And we have a suggestion to establish a coalition of researchers and policymakers, which I'm very happy to see as a coalition of researchers and policymakers who are adjoined here today. So I don't know, that's great. Do any, let's hear feedback or thoughts from either of you on that idea. Yeah, I think that's right. It can't be done by policymakers sort of separate from the community and people who actually are experts in the areas. And then for folks who want to make policy change, just doing the research and sort of putting it on the shelf or bookshelf or any other shelf. It's also inadequate. So we really do need these crossovers. And again, I would involve the communities that are impacted as part of that coalition. I see, Dr. Gardeller. I see a comment around the collection of race-related data for every COVID patient should be a law. Definitely there needs to be certainly national leadership around the collection of this data, which is primarily state-based. You can see that, wow, the inconsistencies in regard to collection. Yeah, that's right. It absolutely should be mandatory because without the data, you just cannot develop effective policy. Absolutely. I see the suggestion around decision support tools. And I'd love to hear more about what that means for where you're sitting in your position. So if you're interested, please follow up with us because I'd love to hear what kinds of things might be useful in that way. I'll just switch quickly to, or maybe I won't because my screen is not frozen. You'd like to hear more about how you'd like to be involved. So Caroline, I think has posted a link in the chat. And we'd love to hear what might be interesting to you, how we might take this further, what other things haven't we done that we could do, how we might collaborate and be involved with the things you're already doing. And here's a link to tell us more about what we might do. Helen, I don't know if you'd like to speak briefly about some plans you have in the new year. Yes, I was just thinking about one of the comments to gather a coalition of policymakers and researchers. That is exactly what we have in mind at SDSN is to, we'll be looking forward to working with SDSN and developing a working group around racial inequality in respect to the metrics provided by the sustainable development goals. So look forward to hearing more about that. I look forward to leaving that and to maybe involving the policy community and that way more directly. So look forward to hearing more about that. I look forward to your participation and thank you. If I might just expand on that. I think Helen is going to be leading a really critical group and I'm very excited that it will be hosted here at SDSN. One of the things we're also thinking about are who are other organizations that are thinking about data gaps? How might we collaborate? Or are there researchers who are working to fill these gaps? I know that Data for Black Lives is one group that's doing really incredible work. The Black Mamas Matter movement is also doing really incredible work around that. If you or an organization you're part of or aware of is doing some work that we might collaborate with or get connected to, I'd love it if you share in this forum or connect via email, I think that'd be really, we'd love to be connected and work collaboratively on that. As well. Okay. Any closing remarks as we end this webinar? Thank you so much to everyone for your participation and your ideas. It's been an honor to be able to share this hour with you. Thank you for giving us your time. I know how precious it is, especially right now. And I'll turn it over to Helen and Clarence for the very last word. Yeah, I just want to say thank you as well. Each time we do this, we learn something new. We get new information, new insights. So we do see this as a dynamic and living project. So again, I just want to extend my thanks as well. Yes, I'd like to extend my thanks to all the listening audience, as well as to SDSN, as well as to all the researchers that SDSN and we have reached out to. And we do encourage you to go online. And if there's a link in the chat, I'm sure there is. Thanks to Caroline Fox for doing that. Please do go and read the report in full. And we'd love to hear your comments further. So thank you very much. Thank you all.