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Paul Mattessich: Welcome to Talking Through The Numbers, a podcast produced by Wilder Research. Our goal, to provide insight on significant issues, to combine sound information with expert knowledge, to enrich our understanding of things that affect our communities and our world. I'm Paul Mattessich, executive director of Wilder Research.
Paul: In this episode, our topic is data disaggregation. Two experts have come to the studio for this conversation. KaYing Yang is the director of programs and partnerships with the Coalition of Asian American Leaders. KaYing began her career as a community organizer and executive manager providing social services and advocacy for protection of refugees and immigrants. In the mid-'90s, she served as executive director for the only national Southeast Asian American advocacy organization in the United States based in Washington D.C.
Nationally, KaYing has worked in coalition with Asian American civil rights groups to address alarming gaps in educational achievements, lack of desegregated data, and economic and health disparities that plagued large sectors of the Southeast Asian American community. To address these issues at institutional levels, she co-founded several organizations, such as the National Asian Pacific Women's Forum, the Asian and Pacific Islander-American Scholarship Fund, and she worked closely with the White House Initiative on Asian Americans and Pacific Islanders. KaYing is a recipient of a 2019 Bush Fellowship from the Bush Foundation.
Nicole MartinRogers is a senior research manager who has been with Wilder Research since 2001. She provides research and evaluation services to a wide range of programs and organizations, including community-based applied research and evaluation using a range of qualitative and quantitative methods and evaluation frameworks. She specializes in culturally responsive approaches and supporting organizations and community groups to use data to inform their decisions around program improvement, strategic operations of an organization, public policy advocacy, and other actions.
Nicole has a bachelor's degree in psychology and sociology from the University of Minnesota, a master's degree in public policy from the Humphrey School at the University of Minnesota, and a doctorate in sociology from the University of Minnesota. She's currently the president of the Minnesota Evaluation Association, and also serves on the boards for the Saint Paul Children's Collaborative and the Tiwahe Foundation.
So again, welcome. Our topic is data disaggregation, which may sound like a complex statistical term, but basically I think it refers to constructing more detailed categories for race or other characteristics of people who live in our communities. But Nicole, what is data disaggregation? What does that term mean?
Nicole MartinRogers: Yeah, well you got it right, Paul. I mean, it's pretty much taking a broad category and breaking it into smaller or more detailed categories, subcategories. So the example of race that you gave. If we take the broad racial category of Asian American, that can be broken down into a lot of different cultural groups, like Hmong, Karen, Khmer, Chinese, Asian-Indian. Same thing is true for American Indian, there's in Minnesota many different tribes are represented, Ojibwe and Dakota. So all of these racial groups can be broken out basically into smaller subcategories that are sometimes of more interest in terms of whatever social issue or variable we're looking at.
Paul: Okay. I know race is probably the major characteristic we'll discuss today, and it's probably the one that's most in the news and discussed right now locally and nationally. Can you talk a bit about how race and ethnicity has been measured, who decides how to define it, what data are used to measure it?
Nicole: Sure. Well, one of the main groups that defines how we think about race in the country is the U.S. Census Bureau. Obviously, they both define the categories that we measure race by in this country, and they also try to respond to the current social and political trends about what groups of people we care about and therefore measure.
So race is a social construct, it's not a biological fact. You can't measure someone's DNA and determine what race they are based on that DNA. It's something that changes over time and really should change over time to reflect the current social and political realities that we're facing.
So the Census Bureau is one organization, but also any group that really does a survey and asks someone about their race has to decide at some point what categories they're going to use. In the health care setting, we have to ask people sometimes about their race. In an education setting, we ask parents about their child's race and ethnicity. So there's a lot of different institutions that measure race.
Paul: Is coming up with additional categories, disaggregating data, more important than it used to be in either of your opinions?
Nicole: I don't know if it's more important than it used to be, but it's definitely gotten more attention recently than it used to be. We have better approaches for how to do it and more advocates, like KaYing and the Coalition for Asian American Leaders and other groups like that that are advocating for data disaggregation in different settings. So I think there's a lot more attention.
Paul: And why is this? Why has more attention come to this topic? What's caused the change?
KaYing Yang: Well, I think that we all know that the changing demographics in the state of Minnesota, but all over the United States really, require that we have better data on each of our communities and distinguish one group and one individual from another. So I think just by having the racial category now is not enough. People who come from Southeast Asia are very different from people who come from South Asia and/or China. People who come from Somalia have different needs than people who come from the Caribbean. So I think that to understand our community and to ensure the disparities are not hidden, we need to have this kind of data. I think it's good for all of us in the state of Minnesota to really understand who is in the state.
CAAL, the Coalition of Asian American Leaders, we work on education issues. So for us, I think to ensure that all students are successful, we need to know who are those students and not just hide them under the racial category anymore.
Paul: Sure. Well, you're bringing us, KaYing, into a topic that I was hoping we could cover in this episode. For research purposes, for political purposes, for community purposes, what are the advantages of having more disaggregated data? Maybe starting with research purposes, why does it matter whether data are organized into more categories? What are the advantages?
Nicole: Well, there's not always an advantage to organizing data into more categories. Really what as social scientists what we're looking for, is we want to understand what are the meaningful categories that help us explain whatever social issue or situation or problem we're trying to understand. So if we ask a student and understand that they're Asian American, that doesn't necessarily help us understand their needs or preferences or assets, community strengths that they might bring to a educational setting.
Whereas, if we know they're Hmong, that might help us understand both some of their language and cultural needs in the school, as well as how to engage them through their community with being successful in education. In that particular case, it might be helpful to disaggregate data. In other cases, it might not be helpful.
Paul: I suppose it's not just one constant, invariant set of categories. It can change a bit over time depending on-
Paul: ... the needs for information.
Nicole: Exactly. Well, if you think even about gender categories, up until very recently, we almost always used binary gender categories, male and female. More recently, now we've started recognizing and understanding that there's people that don't identify just as male or female that also have a whole range of different gender identities. And depending on the population that you're serving, it may be more appropriate to just add one more category, like transgender. Or if you're serving a population that you know has a lot of gender variation, you might want to add three or four or five different categories to help people really reflect their identity.
Paul: Sure. What about organizations serving the community or the communities themselves?
KaYing: Sure. I agree completely with what Nicole was just saying. I'm not a researcher, but I think from our lived experience and also practical services and advocacy, I think breaking down student achievement data by race and ethnicity will uncover trends and some bright spots in disparities as well. Really we're trying to make the data more actionable for educators, families, and policymakers, because I think that they also need this data in order to better equip themselves to ensure that resources are targeted appropriately to different communities.
Paul: Sure, have you seen good examples of that, where they've taken these data,-
KaYing: Oh, yeah.
Paul: ... taken action?
KaYing: I think beyond education, hospitals and government agencies have used data to determine how many bilingual staff to hire. In other states, I know that disaggregated data can lead to protection of voting rights by providing mandated translations of materials for different pockets of communities, including large populations like Chinese in the East Coast and the West Coast. So these are the practical ways that policymakers can use this data.
Paul: Nicole, any more thoughts about that, the use of the information, making it actionable, and actually getting action to happen?
Nicole: Yeah. I mean, I think you have to, again, just be sort of aware of what the current political and social realities are and line up the categories that you're asking about with the things that matter to the people in the community that's affected by the issue, which is going to be different in every case. So there's not one right answer or one right way to do this. It is a really contextual problem for social scientists and advocates to figure out.
Paul: Sure. So our census approaches, 2020. Census has been in the news quite a bit. Either of you have thoughts about how this data disaggregation process is important for the upcoming census?
Nicole: Well, one thing is we know that there have been budget cuts to the census, which makes it difficult to fund outreach efforts to make sure that groups that tend to be underrepresented or under-counted, because they don't participate or aren't found to participate in different ways. That's going to be really important for us in Minnesota to make sure that we're focusing on outreaching to those communities and helping them to feel safe and understanding what the uses are of the census and why it's important for them to participate.
Then also, for some groups, it's just really important to help them understand that the way that they get their group counted is they have to actually write in their cultural group or ethnicity on the “other – specify” line when they are asked for more detail.
For example, if they're African American or African born and they don't see their particular group, Oromo or whatever it might be, represented, there's an “other – specify” line that they have to know that they actually have to write their group in. That's how we will know the size of different groups that maybe aren't reflected with the boxes on the census. So there is some outreach to communities in instruction that's really required in order to effectively count people at this disaggregated way.
KaYing: Yeah, and I just want to add that I think historically the census does want to get country of origin from people. In fact, the census already disaggregated some of the data, because the census can track how many ethnic groups are coming or are in the United States.
But I think from Minnesota, it's very important, because our populations look very different from the rest of the national population. So while the census is very important, it disaggregates large populations that's represented nationwide. In Minnesota, we also have to be mindful that we have one of the largest Liberian communities here, one of the largest Somali community and the largest Southeast Asian population. In fact, Southeast Asians represent about 60% of the Asian Pacific islander community here in the state of Minnesota, which is very different from the East Coast and the West Coast. So while the census is really great at doing some of that work right now, we need to ensure that our local researchers also understand the diversity within the state of Minnesota.
Paul: Sure. Okay. Let me ask you a question, a different type, coming at this from a different angle. Would you say that sometimes in the past, the available data have been used by the dominant group in any society, United States, other places, in ways that have not been appropriate, that might have produced some systemic inequities, but that data disaggregation offers an opportunity to correct that or counter that?
Nicole: Well, I absolutely think that data about disparities has been used to reinforce stereotypes and reinforce current systems of power that set some people up to be more likely to be successful than other people in education or employment systems or housing or whatever it might be. I think that data disaggregation isn't necessarily a solution for that, and whether we're using broad categories or disaggregated categories, we always have to keep in mind the narrative around what is it that we're talking about in terms of a disparity and what was the cause of that disparity.
We can't assume that people understand that explanatory chain of how we get back to a system that created housing disparities. That wasn't because some people are better at being homeowners than others. It was because there was a system set up to allow some people to become more successful at home ownership than others. So that's something that we have to make sure to just include in our narrative when we're talking disparities is that-
Paul: Sure, data might be a tool for that.
KaYing: Yeah, I think very specifically, like the Asian model minority is a great example of how aggregated data can be used to pit racial communities against each other. If we just look at the aggregate data for Asian Americans, it looks like we're doing better than any other groups. In fact, in some areas, we're even surpassing the white community. I think this is very dangerous and harmful to all communities, because again, it hides the disparities within our community. But on the other hand, Asian Americans are often not included in equity discussion, because the data doesn't show that we are experiencing those kinds of disparities.
I don't want to focus just on disparities, but I also say that when we disaggregate data, we also see the assets that people bring to this country. For example, our latest refugee population; in fact, they are NEL [Non-English language], but they also speak several other languages. So it's the multiculturalism and multilingual assets that people bring to this country, and yet we're not really seeing that as an asset. By disaggregating data, we will start to understand that a Karen student may speak Thai, their native language, and Burmese, right? Or a Hmong student could speak Thai, Lao, Hmong, English, sometimes Japanese and Chinese. That kind of asset, teachers and policymakers need to understand so that we can really be a strong state in Minnesota. Because our collective economy depends on the well-being of these young students.
Paul: Sure. Very powerful examples of how these data could have an important role in policy, funding, programming, representing underrepresented groups, communities, and so on. Any more thoughts about that?
KaYing: I speak a lot about the Asian American population, but I also have colleagues in the African community who are saying that if we just measure or collect data on the black or African American category, then we also lose the English learners and also the large numbers of refugees who are coming from other countries in Africa. I think that we need to offer the subcategories for people to check off, right, and to self-identify.
I think people should not be fearful of data disaggregation. They should see this as the ability for policymakers and people who are leaders to really do more with the limited resources that is out there.
Paul: Sure. It gives a tool to them to do more with limited resources.
KaYing: And there's a lot of measures already to protect data privacy. So people should not be concerned about that.
Paul: Okay. Well, speaking about what people might want to do, if people have an interest in promoting the gathering of and use of better, more useful data with more detailed categories, is there something you recommend that they do?
KaYing: Just for the education purposes and for the state of Minnesota, we did help pass a law called the All Kids Act Count in 2016. So if people want to know more about this, they can go to the Minnesota Department of Education website. It's kind of hidden under the families and special projects, but it's called Counting All Students. It has all the fact sheets about why the law has been passed and what is going to be collected. It is optional for parents to provide that kind of information.
The other source is really maybe the White House Initiative on Asian Americans and Pacific Islanders, because under both President Obama and President Trump, they have made data disaggregation a priority. The Asian American community for the last two decades have really been spearheading data disaggregation, so I think these are the two, at least, sites to look at, the national and then also at the local level and what's happening here.
Nicole: Yeah, a couple thoughts. One is, I always use Minnesota Compass at mncompass.org, which is a source of information about different racial and ethnic groups, as well as immigrant communities in Minnesota and can get good information about the numbers and the relative size of different groups in different geographic areas within the state. So that would help me to understand as I'm going in to maybe do a survey with a certain city or county of what ethnic groups I might encounter, so I could think about what languages I'd want to translate the survey into. Those are some of the considerations as a social scientist. Then the other project I want-
Paul: I'm sorry to interrupt you, but Minnesota Compass, of course, is a great source of information for a variety of purposes, developed by Wilder Research with the support of many foundation partners.
Nicole: Right. Yeah, and there's information about education, housing, employment, and all of these topics have some disaggregation by race and geographic subareas of the state. So yeah, it was a very helpful data source.
Nicole: The other source I wanted to mention is East Metro Pulse, which you can find out more about at eastmetropulse.org. That's a study that we did recently with Saint Paul & Minnesota Foundations. I just thought that that was a really great example of how to disaggregate racial and ethnic categories into more relevant cultural groups for people in a survey. Not because we were intending to necessarily report back out on those groups, but just because the Saint Paul & Minnesota Foundations wanted to make sure that people in the community that were participating in this survey could see themselves represented and to reflect the major cultural and ethnic groups that we have in our community was one way to do that. So that's a good example of how to do disaggregation well.
Paul: Well thanks. You both offered some great examples of what people can do and how they can get more information about the topic. Any parting comments for our podcast friends?
KaYing: Well, I really just want to reemphasize the high-quality, detailed data is essential to understanding community needs and challenges. It is vital to securing public and private resources to help communities that have needs. So it's very important that going forward, we need to dive deeper into data disaggregation, we need to do more and not less, and we can't be fearful of what the data tells us.
KaYing: In fact, we need to embrace it.
Paul: Okay. Well thank you to both of you, Nicole MartinRogers and KaYing Yang. We appreciate the opportunity to have a conversation with you.
Paul: To everyone, please visit our website, www.wilderresearch.org, for more information on this topic and others. If you have suggestions for a future podcast, please let us know. I'm Paul Mattessich from Wilder Research, and I look forward to talking through the numbers with you on other topics.
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