Data Sources
[1] Board of Governors of the Federal Reserve System. “Distributional Financial Accounts.” Federal Reserve Board, 1989–2024, www.federalreserve.gov/releases/z1/dataviz/dfa/
[2] United States Census Bureau. “Median Household Income in the Past 12 Months (in 2024 Inflation-Adjusted Dollars).” American Community Survey 5-Year Estimates, 2019–2024, data.census.gov/.
[3] “2024 Best National Universities.” U.S. News & World Report, 2024, www.usnews.com/best-colleges/rankings/national-universities.
Annotated Bibliography
[4] Baker, Scott R. “Debt and the Response to Household Income Shocks: Validation and Application of Linked Financial Account Data.” Journal of Political Economy, vol. 126, no. 4, 2018, pp. 1504–57, doi.org/10.1086/698106.
In this journal, Scott Baker discusses how household debt affects the way families respond to changes in income, primarily during economic turndowns. He argues the amount of debt someone was acquired is not the only important measure, but whether households have access to liquid assets and credit is too. To test this, Baker uses financial account data to track income, spending, debt, and assets, he analyzes relationships between them and any noticeable patterns with respect to the economy. He also uses unexpected firm level shocks, such as layoffs and earnings, as instruments to measure income changes more accurately. His results show that highly leveraged households tend to reduce spending more after negative income shocks. When he controls for liquidity and credit access, debt alone does not explain most of the response. This suggests that financial constraints are a bigger factor than debt levels themselves.This journal is important because it helps explain why some households are more vulnerable during recessions. It supports our claims by showing that differences in financial structure and access to credit play a major role in shaping economic inequality over time.
[5] Bartscher, Alina K., et al. “The College Wealth Divide: Education and inequality in America, 1956-2016.” Review, vol. 102, no. 1, 2020, doi.org/10.20955/r.102.19-49.
In this journal article, Baartscher, Kuhn, and Schularick use historical data and data from the Survey of Consumer Finances to study how household wealth in the United States has diverged from 1956 to 2016, with the main point of comparison focusing on education. The main points that the authors make are that the divergence in wealth gap between college-educated and non-college-educated households is mainly linked to portfolio composition. In general, households with a college education tend to hold more shares in businesses and companies through investments, allowing them to take advantage of another stream of income in addition to just one wage from a job. Higher education may also teach business practices and general financial literacy, which could explain the general correlation of college-educated individuals having a larger share of wealth. Importantly, the authors mention that these findings are correlational and different; longitudinal studies must be conducted to establish a causal relationship. In summary, the authors present a comprehensive overview of the educational wealth divide, which can be useful to illustrate historic trends in education’s influence on historical wealth gaps in the United States.
[6] Behrman, Jere R, et al. “How Financial Literacy Affects Household Wealth Accumulation.” American Economic Review, vol. 102, no. 3, 2012, pp. 300–04, doi.org/10.1257/aer.102.3.300.
In this article, the author argues that financial literacy, which is the ability to process economic information and make informed decisions about household finances, is a major reason why some people accumulate much more wealth than others. The article uses the Chilean Social Protection Survey and a two-step weighting approach called “PRIDIT” to score how financial knowledge directly affects total net worth and pension savings. This source is important because it proves that financial literacy is a more significant factor when it comes to building wealth than a traditional education, showcasing that better financial knowledge usually leads to higher savings as well as better retirement planning. Ultimately, this article demonstrates how teaching people how to manage money is a necessary step to closing the wealth gap, as it provides individuals the ability to increase their wealth regardless of their background.
[7] Buckner, Elizabeth, and Yara Abdelaziz. “Wealth-Based Inequalities in Higher Education Attendance: A Global Snapshot.” Educational Researcher, vol. 52, no. 9, 2023, pp. 544–52, doi.org/10.3102/0013189X231194307.
This article argues that wealth-based inequality in college is not only a US problem, but a global one, particularly in low-income countries where wealth inequality is much larger than the gaps seen in high school graduation rates. In the research, the authors prove this by analyzing data from 117 different countries using the World Inequality Database on Education (WIDE) and calculating four indicators of unconditional educational inequality to measure how wealth affects someone’s chances of going to college. This research provides a global comparison of higher education inequality, demonstrating that as the country’s national wealth increases, the enormous gap in higher education between the rich and the poor tends to decrease. Ultimately, the article supports the claim that educational success is often driven by family wealth rather than just earned through grades by showing a clear pattern where the wealthiest students always have a better chance of finishing college, regardless of which country they live in.
[8] Datta, Biplab Kumar, et al. “Long COVID and Financial Hardship: A Disaggregated Analysis at Income and Education Levels.” Health Services Research, vol. 60, no. 2, 2025, pp. e14413-n/a, doi.org/10.1111/1475-6773.14413.
In this journal, Datta and his coauthors discuss whether long COVID is tied to financial hardship and whether this relationship is different across income and education groups. Using national representative data from over 271,000 adults in the 2022 BRFSS survey, they measure three different levels of hardship: food insecurity, inability to pay bills, and the loss of utilities. They use logistic regression models and mediation analysis to estimate how long COVID affects these outcomes. Something they found was that long COVID is associated with a 1 to 11 percentage point increase in financial hardship across most income and education groups. These hardship financial effects are the strongest among lower income households, especially those with income to poverty ratios below 2. They also show that loss of employment or reduced work hours explain around 6 to 20 percent of the relationship. This article is important because it shows that long COVID has economic consequences beyond health impacts, especially for more vulnerable groups in society. It supports our claims by showing how health shocks can deepen existing economic inequality, especially for lower income households.
[9] Hamilton, Darrick, and William A. Darity Jr. “The Political Economy of Education, Financial Literacy, and the Racial Wealth Gap.” Review (00149187), vol. 99, no. 1, Jan. 2017, pp. 59–76. EBSCOhost, doi.org/10.20955/r.2017.59-76.
Hamilton and Darity argue that education and financial literacy are often incorrectly framed as the primary causes of wealth inequality, when in fact structural forces and intergenerational asset disparities determine who is able to convert education into long-term wealth. They show that the process of obtaining education can reinforce inequality rather than reduce it through unequal graduation rates by race and institution type, higher exposure to debt, and differences in post-college labor market outcomes. These claims are supported by empirical wealth data, statistics on graduation rates by race and four-year institution type, and historical analysis of discriminatory housing, credit, and labor market policies that shape asset accumulation across generations. This resource is important because it shifts the analytical focus from individual educational attainment to the structural systems that shape financial outcomes over time. Connecting to our project, this article helps us interpret patterns in wealth distribution across education levels as potentially reflecting broader economic and social inequality, and it provides a framework for examining whether periods of widening gaps are tied to structural and historical forces rather than simply differences in schooling.
[10] Harvey, Laura A, et al. “Inequality in an Equal Society.” Oxford Bulletin of Economics and Statistics, vol. 86, no. 4, 2024, pp. 871–904, doi.org/10.1111/obes.12611.
Laura A. Harvey, Jochen O. Mierau, and James Rockey in their article titled “Inequality in an Equal Society” primarily argue that some inequality is natural because individuals save and earn money at different rates throughout their lives, but recent increases in inequality are mostly due to differences within age groups, not just age differences. The authors utilize data from the U.S. Current Population Survey, the Luxembourg Income Study, and the Luxembourg Wealth Study to calculate the overall inequality across multiple developed countries over time. This resource is important because it helps us better understand what is actually causing inequality and shows that recent rises in inequality cannot be explained by demographics alone. For the thesis, this source is able to convey and strengthen the argument that demographics are not the only sole reason for inequality in the U.S. and other developed countries, meaning that there are other factors such as education levels, political and institutional factors, or the COVID-19 virus that lead to increasing levels of inequality in the U.S.
[11] Hout, Michael. “Social and Economic Returns to College Education in the United States.” Annual Review of Sociology, vol. 38, 2012, pp. 379-400, doi.org/10.1146/annurev.soc.012809.102503.
This review synthesizes research on the “returns” to college in the United States, emphasizing that education is strongly associated with major outcomes such as economic success, health, family stability, and social ties. Hout also addresses long-standing concerns from stratification and selection perspectives namely, that because schools and colleges select who advances, the apparent benefits of education might simply reflect preexisting advantages rather than education’s causal effects. Surveying the evidence, the article argues that the balance has shifted away from purely selection-based explanations and toward education having substantive, direct effects on life outcomes. At the same time, Hout stresses that educational returns are heterogeneous: the magnitude of education’s direct impact varies across individuals and demographic groups, and may be larger for those who are less likely to attend college in the first place. The review further highlights a smaller but important literature on “social returns,” suggesting that communities and states may benefit from population-wide educational gains, with some estimates implying that social returns can exceed private returns. For our project, this article provides a conceptual foundation for treating education as both an individual level stratifying mechanism and a potential source of broader social spillovers, while also motivating analyses that explicitly test for heterogeneous returns across groups and historical contexts.
[12] Jackson, Margot, and Brian Holzman. “A century of educational inequality in the United States.” Proceedings of the National Academy of Sciences, vol. 117, no. 32, 11 Aug. 2020, pp. 19108-15, doi.org/10.1073/pnas.1907258117.
This article tests the “income inequality hypothesis,” which proposes that rising income inequality increases class-based advantages in access to and completion of college. Using all available nationally representative datasets, Jackson and Holzman track long-run trends in family-income gaps in college enrollment and completion (“collegiate inequalities”) for birth cohorts spanning 1908 to 1995. They show that, over most of the past century, collegiate inequalities moved in close tandem with overall income inequality, offering strong evidence that rising income inequality is tightly linked to unequal educational opportunity and, by extension, unequal life chances. The authors identify a notable exception for cohorts exposed to Vietnam War draft risk: during this period, collegiate inequalities were high even though income inequality was low, and these education gaps were substantially larger for men than for women, which is consistent with a distinct “Vietnam War” effect that temporarily disrupted the usual relationship between income inequality and educational inequality. For our project, the study directly supports examining whether major historical shocks and policy regimes correspond to shifts in education based inequality, and it provides a clear empirical template for identifying long run co movement and meaningful deviations between macro level inequality and education stratified outcomes.
[13] Kakar, Venoo, et al. “Does Student Loan Debt Contribute to Racial Wealth Gaps? A Decomposition Analysis.” The Journal of Consumer Affairs, vol. 53, no. 4, 2019, pp. 1920–47. JSTOR, www.jstor.org/stable/48564442.
This study argues that student loan debt plays a significant role in suppressing household wealth and sustaining racial wealth disparities, particularly between Black and White households. Using cross-sectional Survey of Consumer Finances data from 2013 and 2016, years following the Great Recession during a period of credit expansion, they conduct quantile regressions and Oaxaca-Blinder decompositions to show that the mean net worth of households without student debt was three and a half times higher than that of indebted households in 2013, a gap that widened to roughly four times by 2016. The disparity is especially pronounced in the lower wealth quintiles, where debt burdens compound financial vulnerability and restrict opportunities for saving, investment, and asset accumulation. They find that student debt contributes to the Black-White wealth gap but does not significantly explain the Hispanic-White gap, suggesting that debt affects groups differently. With student loans being the second-largest category of household debt amid rising tuition costs and the increasing necessity of a college degree, the study highlights how the financing of education can generate long-term wealth divergence. Overall, this article links education to widening inequality over time and suggests that periods of expanding borrowing or rising costs may coincide with observable increases in wealth gaps across and within education levels, which will be helpful for our project.
[14] Killewald, Alexandra, et al. “Wealth Inequality and Accumulation.” Annual Review of Sociology, vol. 43, no. 1, 2017, pp. 379–404, doi.org/10.1146/annurev-soc-060116-053331.
This article titled “Wealth Inequality and Accumulation” by Alexandra Killewald, Fabian T. Pfeffer, and Jared N. Schachner argues that rising levels of inequality in the United States is driven primarily by structural changes of institutions, policies, and labor markets rather than individual differences in skill. The authors review decades of sociological research, economic data, and changes in policy in order to explain long-term trends in rising levels of inequality in the United States. This source is very important because it shows how inequality is primarily shaped by social and political systems in the United States, highlighting that rising levels of inequality is not inevitable but rather socially and politically constructed by policymakers and institutions. This article is great for the thesis, as it is a way of displaying how there is a rise of inequality in the United States that is caused by political and institutional factors. This article also gives the opportunity for us as narrators of the final project to find a solution to the problem of the rising inequality in the United States through political, educational, and social reform.
[15] Kim, ChangHwan, et al. “Field of study in college and Lifetime earnings in the United States.” Sociology of Education, vol. 88, no. 4, 4 Sept. 2015, pp. 320-39, doi.org/10.1177/0038040715602132.
In this journal article, Kim, Tamborini, and Sakamoto highlight the stratification between one’s field of study in college and lifetime earnings. The authors used respondents’ data from the Survey of Income and Program Participation and tax earning records to follow earning trajectories for individuals to analyze and estimate the distribution of career earnings by majors in college. In the paper, the authors divide cumulative earnings into 10-year chunks for analysis and define lifetime earnings to total around the 40-year mark. Essentially, instead of analyzing one single person’s career end-to-end due to the timeline of the study, cohorts are broken up into 10-year age gaps. Some of the main findings the article details are that earning gaps between fields of study in college are substantial. The report generally indicates a correlation between high lifetime earnings for STEM, business areas, and graduate fields. In summary, the journal article emphasizes that not only does the binary presence of education or no education widen wealth gaps, but divisions within educational fields themselves correlate with significant wealth gaps that can be observed in one’s lifetime.
[16] Kuhn, Moritz, et al. “Income and Wealth Inequality in America, 1949–2016.” The Journal of Political Economy, vol. 128, no. 9, 2020, pp. 3469–519, doi.org/10.1086/708815.
In this journal, Kuhn, Schularick, and Steins argue that changes in asset prices, regarding things such as housing and stock prices, have historically played a huge role in shaping wealth inequality in the United States since the beginning of World War II. Using a newly constructed long run dataset that links historical and modern patterns of the Survey of Consumer Finances, they analyze the joint distribution of income and wealth from 1949 to 2016. The show that middle class households mainly hold housing as their primary asset. However, wealthy households hold most of their wealth in stocks and business equity. Because of these differences in assets, rising house prices tend to reduce wealth inequality, while stock market booms increase it. They also demonstrate that from 1971 to 2007, much of the wealth growth for the bottom 90 percent came from asset price appreciation rather than savings, which helps explain why wealth inequality did not rise as sharply as income inequality during that period. This article supports our claims by showing that structural economic forces strongly shape long term inequality trends in the United States.
[17] Pfeffer, Fabian T. “Growing Wealth Gaps in Education.” Demography, vol. 55, no. 3, 2018, pp. 1033–68, doi.org/10.1007/s13524-018-0666-7.
In this article, Pfeffer argues that while gaps in high school graduation and college accessibility have stabilized, the wealth gap between college graduates has increased significantly between the 1970s and 1980s birth cohorts. The author supports this claim by using long-term data from the Panel Study of Income Dynamics (PSID) to track educational outcomes across different wealth quintiles. This article identifies family wealth as something independent and an increasingly powerful factor in graduating from college, separate from family monthly income or parental occupation. This is significant because it highlights how wealth acts as a financial guarantee, unlike a monthly paycheck; wealth acts as an extra security one would resort to when they incur unexpected costs that might otherwise force students to drop out. In summary, this article shows clear evidence that unfairness in education is tied to family property and assets, implying that schools must find ways to help students overcome financial obstacles to graduation rather than just helping them to get admitted.
[18] Pfeffer, Fabian T., et al. “Wealth disparities before and after the Great Recession.” The ANNALS of the American Academy of Political and Social Science, vol. 650, no. 1, 25 Sept. 2013, pp. 98-123, doi.org/10.1177/0002716213497452.
This journal article, authored by Pfeffer, Danziger, and Schoeni, discusses how the Great Recession resulted in a restructure of wealth inequality in the United States. The journal article uses data from the Survey of Consumer Finances and Panel Study of Income Dynamics. The main points the authors cover are that less advantaged individuals in society experienced the highest percentage of losses during the recession itself. The effects were lasting, with a large share of the bottom quarter of wealth continually declining and even going negative following the Great Recession. Ultimately, the data highlighted correlations for severe losses disproportionately concentrated among less educated households. The article supports the argument that the Great Recession, an influential economic event in the United States, did not affect households evenly. It illustrates a trend in which education was a dividing factor for how vulnerable individuals were to the effects of the recession.
[19] Singh, Arsh, and Nirvikar Singh. “The 0.0003 Percent: Short‐Run Dynamics of Extreme Wealth in America.” Review of Income & Wealth, vol. 70, no. 3, Sept. 2024, pp. 723–46. EBSCOhost, doi.org/10.1111/roiw.12660.
This article focuses on the short-run dynamics of extreme wealth accumulation among the top 0.0003 percent of Americans, examining how education, self-made status, and macroeconomic conditions shape wealth growth at the very top of the distribution. The study centers on the Forbes 400, which has selection bias since it is not a random sample, so the authors incorporate variables such as advanced degrees and whether wealth is inherited or self-made to better understand how characteristics interact with structural economic forces. Using dynamic panel econometric modeling, they analyze wealth changes from 2004 to 2015, with particular attention to periods during and after the Great Recession. Their findings show that while advanced degrees were associated with faster wealth growth before the 2008 crisis, this advantage weakened afterward, underscoring that the relationship between education and wealth is historically contingent. This study is important because it demonstrates that business cycles and economic shocks can reshape the returns to education, even among the extremely wealthy. For our project, the article supports examining whether major historical events coincide with shifts in wealth distribution by education level and how broader social conditions affect wealth gaps.
[20] Tamborini, Christopher R., et al. “Education and Lifetime Earnings in the United States.” Demography, vol. 52, no. 4, Aug. 2015, pp. 1383-407, doi.org/10.1007/s13524-015-0407-0.
This study examines how educational attainment shapes lifetime earnings (the total earnings accumulated from labor market entry through retirement) arguing that, despite extensive research on education and wages, credible evidence on lifetime totals has been limited by data constraints. To overcome this, the authors link Survey of Income and Program Participation (SIPP) respondents to longitudinal administrative earnings records from the U.S. Social Security Administration, enabling them to trace individuals’ earnings trajectories over extended periods and estimate 50-year career earnings patterns separately for men and women. The paper provides detailed estimates of gross lifetime earnings by education, as well as “net” lifetime earnings that adjust for covariates associated with the probability of earning a bachelor’s degree; it also reports the net present value of education at age 20. Importantly, the analysis incorporates individuals with zero earnings and disability, reducing bias that can arise when focusing only on continuously employed workers. Overall, the findings confirm persistent positive effects of higher education on earnings over the work career and across a lifetime, but they also suggest that net lifetime effects are smaller than many prior estimates. For our project, the article is valuable because it reframes educational inequality as an accumulation process across decades shaped by employment stability and life-course events rather than a snapshot of earnings at a single point in time.
[21] Thomas, Alexis S., et al. “Disparities in Covid‐19–related stressful life events in the United States: Understanding who is most impacted.” Health & Social Care in the Community, vol. 30, no. 3, Dec. 2021, pp. 1199–1211, doi.org/10.1111/hsc.13671.
This article argues that COVID-19 exacerbated the levels of inequality in society, particularly with women and people with less wealth experiencing more pandemic related stressful life events across work, home, healthcare, and social-life. This article uses survey results from 374 U.S. adults from June-August 2020 in a follow-up study of the NICHD Study of Early Child Care and Youth Development, and analyzed the results by using a negative binomial regression while controlling for household size and local COVID transmission risks to see how gender, race, ethnicity, minority status, and wealth predict COVID related stressors. This article is vital because it shows who was hit hardest by the pandemic, and highlights that wealth can protect people from major hardships, and identifies women as constantly more vulnerable to the COVID pandemic, which is ultimately useful for shaping policy and objectives on how to handle and intervene with the pandemic. What the resource does specifically for the thesis is that it displays who was most impacted by the COVID-19 pandemic, and specifically how existing wealth gaps shaped who experienced the most hardships during COVID-19. This proves that crises deepen pre-existing economic inequalities rather than affecting everyone equally.
Sources from BruinLearn
[22] Trouillot, Michel-Rolph. “The Power in the Story.” Silencing the Past: Power and the Production of History, Beacon Press, 1995, pp. 1–30.
[23] Drucker, Johanna. “Digital Humanities Overview.” The Digital Humanities Coursebook: An Introduction to Digital Methods for Research and Scholarship, Routledge, 2021, pp. 1–5.
Banner Image & Media Credits
[24] “What 1970s School Was Like Before It All Changed.” YouTube, uploaded by Vintage America, 4 Nov. 2025, www.youtube.com/watch?v=lbHOsFK_BBE&t=10s.
[25] Piacquadio, Andrea, Karolina Grabowska, and NeONBRAND. Waitstaff and service industry images. Pexels and Unsplash, Fast Company article, 2023, www.fastcompany.com/90598244/a-15-federal-minimum-wage-would-reshape-the-lives-of-working-people-can-biden-deliver?utm_source=Pinterest&utm_medium=organic&epik=dj0yJnU9R3hyYkNHcmw5bmhWYjNpMG1nV2xkc0dTWXBUdnI5M3omcD0wJm49REEtUTV3S2puOC0xVHVhVzhjMjVQZyZ0PUFBQUFBR212cjdv
[26] Captain Pandapants. A “beet”-nik professor lectures to Harvard students. Flickr, The Atlantic article, www.theatlantic.com/technology/archive/2013/10/our-best-educational-technologies-are-just-spiffy-email/280755/?utm_source=Pinterest&utm_medium=organic