Though the use of data for analysis is often framed as a way of answering questions about the world, it also helps us come up with new questions, questions more aligned to the mysteries that drive us to ask questions in the first place. We (Amanda and Cora) come from very different disciplinary backgrounds (History and Economics), but we share many of the same interests and broad questions about how race and inequality shape the world. 

Our different backgrounds and expertise have been an asset throughout our collaborations. While we are both able to read work from each other’s fields to bolster our own scholarship, our partnership has helped teach each of us about the unique methods we, and our fields, use. This collaboration has helped us bring new questions to light, inspired by the insights from each other’s work.  

Beyond our own research, these experiences of learning from each other have informed the way we approach our data literacy pedagogy. Since we met, we have hypothesized that integrating qualitative and quantitative research methods simultaneously into our teaching could help students better understand how to interpret and use data and how to form new questions about inequality.  

Same Topic, Different Approach 

One way in which we’ve tried this is through a flexible workshop about redlining in the U.S. in the 1930s. Redlining as a topic has gotten more attention over the last decade or so, and the term is used colloquially to refer generally to racial inequality in access to services, especially as they align with geography. But what we call redlining today originated with the Second New Deal and the Home Owners Loan Corporation (HOLC).  

In 1933, during the Great Depression, the federal government created the HOLC to prevent foreclosures and expand home-buying opportunities. Over many years, the HOLC helped restructure the US mortgage lending market by creating a home appraisal system to value homes, which standardized the lending process required to buy a home and get a mortgage. Like so many New Deal policies, the HOLC appeared at first glance to be race-neutral, but, as many scholars have since written, it created new forms of racial inequality.  

The HOLC appraised neighborhoods in major US cities and created blue, green, yellow, and red color-coded maps where red neighborhoods were denoted as least valuable and, more often than not, where residents were poor, immigrants, or Black (Katznelson 2005; Rothstein 2017; Michney 2023; Boakye et al. 2025). (To learn more about this topic, we recommend Parts I and II of the Stuff You Missed in History Class podcast.) 

We both teach redlining in our courses, but for different reasons. Cora teaches a unit on redlining in her Economics of Racial Inequality course, focusing on the ways in which redlining practices appear in government policy and the potential long-term consequences of these practices beyond their initial use. Amanda teaches a unit on redlining as part of The New Deal in her introductory U.S. history survey, U.S. History Since 1865.  

In these sessions, we ask students: How can scholars use data to explore whether redlining in the 1930s created measurable, intentional inequalities between Black and white Americans, and whether those inequalities continue to affect Americans today?  

Understanding Data Creation (History) 

We incorporate insight from scholarship on data equity, digital humanities, and critical quantitative methods to help students understand primary sources and biases embedded within data. The Mapping Inequality: Redlining in New Deal America digital humanities project at the University of Richmond digitizes and shares searchable maps made by the HOLC depicting red-, yellow-, blue-, and green-lined areas of different cities across the United States and provides the assessor forms for each neighborhood. The maps make the concept of redlining visible through the geospatial data visualization and lend themselves well to in-class exploration of a complicated and nuanced topic.  

Before class, we assign a reading or podcast that provides students with background on the HOLC. Over the years, we’ve learned to spend less class time lecturing about the New Deal and more time discussing the appraiser forms. In class, we review the reading as a large group, paying particular attention to the origins of the HOLC and actions of neighborhood assessors to flag the ways that humans influenced the data we’ll review next. Then, we ask students to analyze the original archival sources used to create the database.  

Using the SOCC method for primary source analysis, students learn how to question the creation of data and place it in its historical context. Together, we complete a SOCC analysis of the original assessor form of one neighborhood. As we go step-by-step through the analysis, students apply historical context to the form. They realize that people created these ratings based on their own impressions and evaluations, which were influenced by not only HOLC policies, but also an individual’s determinations.  

Each document is signed and dated by an assessor, highlighting how one person’s perception of race and a neighborhood was baked into the grading system. Students see how individuals work within institutions to affect policy, troubling the false binary between personal viewpoints and widespread impact.  

Beyond that, redlining assessor documents are starkly racialized in ways students—even those familiar with redlining as a concept—may not fully understand without facing it head on. For example, one document corresponding to the modern day Hayti neighborhood in Durham, North Carolina (a predominantly Black neighborhood and cultural hub today) reads: 

White population largely on McMannen Street for about 2 blocks South of railroad and some on South Street, and on McMannen Street are several fairly nice homes. This was formerly a good white residential street but negroes are gradually taking up the area.

Each time we’ve taught this, some students will ask us how common such stark language was. Typically, someone will note that even if all red-lined neighborhoods mention the Black population, this is not sufficient for showing a correlation between racial language and being red-lined. We praise them for asking a relevant and important question for data analysis: is this form representative of other forms? And is this sufficient evidence to show that designations were racialized? We’re honest in our answer: we haven’t read all the assessor forms in the database, but we also didn’t cherry-pick an assessor form to suit our conclusion.  

To figure out the patterns of this kind of racialized language, students will need to gather more data. By looking at additional forms for other red-lined neighborhoods, they can see how commonly Black and immigrant populations are mentioned. By exploring descriptions in forms for blue- and green-lined neighborhoods, they can see how descriptions and invocation of race change in these higher-rated neighborhoods. When we follow through, they identify the absence of Black people or immigrants in the descriptions of higher-rated neighborhoods and note the positive descriptions, concluding that, yes, race is playing a role here. 

After reviewing the assessor forms (and discussing historical racialized language), we then guide students to explore the maps themselves, along with the  “summary statistics” about neighborhood ratings and population statistics in a city of their choice. After spending time with the primary sources, students begin to notice the ways that transforming information into data is itself interpretive, requiring a critical lens even if the information is numerical, appears to the user as simply and logically organized, or appears unbiased.  

Connecting to Today (Economics) 

While this first step helps students learn more about the creation of this data, it’s quite another topic to understand the long term consequences of this data. Ultimately, this comes down to how the data was used. As Cora often points out to students, if the appraiser had simply written these comments in their journal and then tucked them away, we would have no expectation that this information would continue to shape inequality today. The way these practices have far-reaching consequences involves people acting within institutions and systems. Already, this means that our questions have gotten more complex. To connect the historic redlining maps, and context of those maps, to modern day, Cora then goes over the logic behind and limitations to the research question at hand.  

First, we have to acknowledge that the question we’d like to answer about the long-term consequences of redlining practices is actually more broad than the one we’re likely able to answer given the data we have. The HOLCs were not the only instance of redlining practices, and historians and economists alike debate whether or not the HOLC maps were even the most influential in terms of lending practices (HOLC maps were not believed to be utilized by the HOLC itself in granting loans, but may have been influential in creation of other maps and influencing other practices [see Aaronson et al. 2021]).  

So the question we are able to ask and try to answer with the data we have about the long-term impacts of HOLC redlining maps is more specific than the one we’d like to know about the long-term impacts of redlining practices in home loans more broadly.  

Next, we can explore the ways in which modern neighborhood maps reinforce what students have seen in their redlining map explorations using the Opportunity Atlas tool, which visualizes modern neighborhood-level characteristics such as median rent, poverty rates, and measures of intergenerational mobility of residents. Common trends regularly emerge across many different cities, where students see the mirroring of redlining maps today.  

But here, again, we must acknowledge that this alone is not necessarily evidence that the redlining maps created these inequities, particularly as redlining maps were in most cases documenting inequities that already existed at the time. So the continuation of these patterns is not necessarily a result of redlining maps but could be from the same common sources of segregation that led to the creation of the maps in the first place. Importantly, to chock all of modern day segregation up to redlining maps implicitly ignores all the precursors that laid the groundwork for redlining practices to exist in the first place! 

To break this down further, Cora goes over the ways in which some economists and sociologists have gone about trying to study and understand these long-term impacts using innovative methodologies. For example, the City Survey that created the HOLC maps was only conducted on cities with a population over 40,000, so Aaronson et al. (2021) compare modern residential racial disparities in cities that were just over that 40,000 population limit to those that were just under it to try to get at how the practice influenced long-term inequality at a city-level for cities along this margin.  

By introducing evidence from real economics studies, broken down for students across disciplines and training to understand, students both get a sense of the evidence at play and the complexity that goes into answering such questions. They see how race and racism shaped inequality at the same time that they learn to question the nuances in the data that helps researchers establish those relationships. 

Conclusion 

These lessons with students—whether in a history or economics classroom—serve a dual purpose. Yes, students learn about redlining, but perhaps more importantly, they learn how to use tools from both history and economics to think about the creation and use of data to answer questions about how the past impacts the present. Without historical source analysis, students would not see that data is only as reliable as the people who create and interpret it into new forms.  

The lesson challenges the idea that racism is the product of individual bad actors or the collective work of institutions; instead, we help students see how institutions such as the federal government and the HOLC shape and compound the work of individual actors. Students also learn to view the interplay between qualitative and quantitative data and archival and modern sources. On the other hand, economic methods help them understand the long-term consequences of redlining, which studying the HOLC maps from the 1930s-1940s can’t prove alone.  

Economic methods also remind us that it’s hard to prove the long-term consequences of redlining, even with reliable quantitative and qualitative data. From the interdisciplinary lesson, students gain language to interrogate the causes and consequences behind racial inequality and practical tools that answer some questions while raising new ones. These lessons demonstrate the value of interdisciplinary interrogation to understand complex causalities and legacies.  

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Suggested Lesson Plan 

This lesson plan is meant to be an adaptable starting place; please add, adjust, and change to suit your own goals and expertise.  

Preparation

Assign podcasts and/or readings for background. Some suggestions include: 

• Mapping Inequality: Redlining in New Deal America: Introduction, How and Why the Home Owners’ Loan Corporation Made Its Redlining Maps, and Agents of the New Deal Mapping State (readings) 
• Stuff You Missed in History Class: A Brief History of Redlining, Parts I and II (podcast) 

Part I: Historical Context 

Guide students through a single assessor document for a single neighborhood from the Mapping Inequality: Redlining in New Deal America digital records. Use the SOCC analysis method to consider the source.  

Guiding Questions 

• Who is the author, and what is their motivation or purpose? (Also prompt students to pull from what they learned in the podcast/readings) 
• What kind of document is this? Who is the intended audience, and how might that affect who sees and uses the document, for what purposes? 
• What is this document a reliable source for? 
• What isn’t this document a reliable source for? 
• What kinds of questions can we answer with this document and others like it? 

Part II: Exploration 

Let students spend some time exploring the maps and accompanying assessor documents and summary statistics for a city of their choice. 

Guiding Questions 

• What patterns do you see emerge? 
• How does mention of race show up in different assessor comments across the different color-coded areas?  
• What language shows up disproportionately in red and yellow neighborhoods as compared to blue or green neighborhoods? 

Part III: Connecting to the Present 

First, guide students through the logic of how the HOLC maps could have realistically led to racial inequality today. Make sure this is historically grounded and specific to the HOLC maps, not all redlining practices in general. Highlight the complexities and ask students to use their unique disciplinary knowledge and lived experiences to add to the theorizations. 

Next, introduce the Opportunity Atlas and give students time to explore the same city they looked at in Part I using these modern maps.  

Guiding Questions 

• What similarities do you notice between the modern and HOLC maps? What differences do you see? 
• What does and doesn’t the correlation between HOLC and modern maps reveal about the effect of the maps. 
• What else could explain the correlation?  
• What kind of evidence would help you better understand the effect the maps had? 

Part IV: Introduce the Research 

Review empirical papers (some suggestions below) that have tried to answer the question of how redlining from the 1930s impacts racial inequality today, including an overview of methods.  

• Aaronson, Daniel, Daniel Hartley, and Bhashkar Mazumder. 2021. “The Effects of the 1930s HOLC “Redlining” Maps.” American Economic Journal: Economic Policy 13 (4): 355–92. https://doi.org/10.1257/pol.20190414.
• Aaronson, Daniel, Jacob Faber, Daniel Hartley, Bhashkar Mazumder, and Patrick Sharkey. 2021. “The long-run effects of the 1930s HOLC “redlining” maps on place-based measures of economic opportunity and socioeconomic success.” Regional Science and Urban Economics 86. https://doi.org/10.1016/j.regsciurbeco.2020.103622.

Part V: Conclude 

Ask students what questions they have now about redlining and racial inequality. Allow the big questions go unanswered. 

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References 

Aaronson, Daniel, Daniel Hartley, and Bhashkar Mazumder. 2021. “The Effects of the 1930s HOLC “Redlining” Maps.” American Economic Journal: Economic Policy 13 (4): https://doi.org/10.1257/pol.20190414.

Aaronson, Daniel, Jacob Faber, Daniel Hartley, Bhashkar Mazumder, and Patrick Sharkey. 2021. “The long-run effects of the 1930s HOLC “redlining” maps on place-based measures of economic opportunity and socioeconomic success.” Regional Science and Urban Economics 86. https://doi.org/10.1016/j.regsciurbeco.2020.103622.

Boakye, Brittney, Bianca Newton, Jehan Reaves, Austin Lansey, and Lawrence Brown, 2025. “Agents of the New Deal Mapping State: How Private Consultants Shaped the New Deal’s Redlining Maps.” Mapping Inequality. https://dsl.richmond.edu/panorama/redlining/consultants. 

Frey, Holly, and Tracy Wilson, hosts. 2015. Stuff You Missed in History Class. “A Brief History of Redlining, Part 1.” iHeartRadio, October 5th. Podcast, 37 min. 30 sec. https://www.iheart.com/podcast/105-stuff-you-missed-in-histor-21124503/episode/a-brief-history-of-redlining-part-1-30207658  

Frey, Holly, and Tracy Wilson, hosts. 2015. Stuff You Missed in History Class. “A Brief History of Redlining, Part 2.” iHeartRadio, October 7th. Podcast, 37 min. 5 sec. https://www.iheart.com/podcast/105-stuff-you-missed-in-histor-21124503/episode/a-brief-history-of-redlining-part-2-30208103  

Katznelson, Ira. 2005. When Affirmative Action Was White: An Untold History of Racial Inequality in Twentieth-Century America. W. W. Norton & Co. 

Kleintop, Amanda. 2025. “Historical Literacy as Data Literacy: An Intro to SOCC Analysis.” Center for Engaged Learning (Blog). Elon University. January 13, 2026. https://www.centerforengagedlearning.org/historical-literacy-as-data-literacy-an-intro-to-socc-analysis. 

Michney, Todd M. 2023. “How and Why the Home Owners’ Loan Corporation Made Its Redlining Maps.” Mapping Inequality. https://dsl.richmond.edu/panorama/redlining/howandwhy.  

Rothstein, Richard. 2017. The Color of Law: A Forgotten History of How Our Government Segregated America. Liverwright Publishing. 

University of Richmond, 2025. “Mapping Inequality: Redlining in New Deal America.” Webpage, version 3.5. https://dsl.richmond.edu/panorama/redlining 

US Census Bureau and Opportunity Insights. “The Opportunity Atlas: Mapping Economic Mobility Across the US.” https://www.opportunityatlas.org/  

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About the Authors

Cora Wigger is an assistant professor of economics and a 2025–2027 CEL Scholar. Her research focuses on the intersections of education and housing policy, with an emphasis on racial inequality and desegregation. At Elon, she teaches statistics and data-driven courses and contributes to equity-centered initiatives like the “Quant4What? Collective” and the Data Nexus Faculty Advisory Committee. 

Amanda Laury Kleintop is an assistant professor of history and a 2025–2027 CEL Scholar. She specializes in the U.S. Civil War, Reconstruction, and emancipation. Her book, Counting the Costs of Freedom (2025), explores debates about compensating former enslavers in the U.S. and profitmaking in slavery. It inspired her historical data and digital humanities project on African American soldiers in the Border States. 

How to Cite this Post

Kleintop, Amanda Laury, and Cora Wigger. 2026. “Where History and Economics Collide: Teaching on Redlining.” Center for Engaged Learning (blog), Elon University. April 28, 2026. https://www.centerforengagedlearning.org/where-history-and-economics-collide-teaching-on-redlining/.