Auten, Gerald, and David Splinter. “Income Inequality in the United States: Using Tax Data to Measure Long-Term Trends.” The Journal of Political Economy, vol. 132, no. 7, 2024, pp. 2179–2227, https://doi.org/10.1086/728741.
In “U.S. Income Inequality Trends, 1960–2019,” authors Auten and Splinter reevaluate long-term inequality in the United States using administrative tax and survey data. They find that “our estimates for pretax income, based on distributing total national income, show that the top 1% share declined from 11.1% to 9.4% from 1962 to 1979 and then increased to 13.8% by 2019” (p. 2218), suggesting a more modest rise in inequality than previous studies like Piketty and Saez. Their analysis emphasizes that “after taxes and transfers, [incomes of] the bottom half of the distribution… increased by two-thirds” (p. 2218), challenging the perception of stagnant middle- and lower-class income growth. The authors conclude that including transfers, tax reforms, and unreported income “provides a more complete picture of economic resources available to individuals” (p. 2203), thus offering a more balanced understanding of U.S. income distribution over time.
Berman, Yonatan, et al. “Modeling the Origin and Possible Control of the Wealth Inequality Surge.” PloS One, vol. 10, no. 6, 2015, p. E0130181.
https://doi.org/10.1371/journal.pone.0130181.
The authors of this scholarly article use a simulation based model to study how wealth inequality in the US changed from the 1930s to 2010. They found out that inequality declined through the mid 20th century but began rising drastically again after the 80s, where the economy “gradually switched from being income-dominated to being capital-dominated” (15). Their results show that falling private savings and growing dependence on capital income allowed wealth to concentrate among those who already owned assets, especially among the wealthy. They also note that “access to high return investments and the alleged ability to effect regulation due to wealth naturally contribute to a growing gap even within the top 10%, 1% or 0.1%” (16). This study explained how capital ownership replaced education as a major source of wealth inequality by showing that wealth increasingly depends on investment income and asset accumulation rather than wages or human capital, making financial resources and existing wealth stronger predictors of economic success.
Broady, Kristen, et al. “Racial wealth gains and gaps: Ten economic facts about the disparities.” Journal of Economics, Race, and Policy, vol. 8, no. 1, 16 Sept. 2024, pp. 40–56, https://doi.org/10.1007/s41996-024-00154-2.
Broady et al. review factors that contribute toward existing racial wealth gaps in the United States, looking at investments, education, debt, and worker discrimination and displacement. They begin by exploring the history of wealth calculation, starting with the 1860 U.S. Census, where the number of black people owned was a factor when calculating wealth; then, they pivot to using the Survey of Consumer Finances (SCF) to examine more current trends, especially 2019-2022. They note a marked difference in post-COVID income growth between White households and non-White households: the former group mostly profited from investment income (including interest, dividends, and capital gains), while the latter group were more reliant on government transfer programs, which seem to be a less sustainable method for growth. Moreover, they investigate the correlation between educational attainment, income, and wealth. They found that although the number of Black and Hispanic adults with a college degree has increased over 100% since 2000, White and Asian Americans were still more likely to earn bachelor’s and master’s degrees. Another issue was that “Black and Hispanic students are less likely to study science, technology, engineering, or mathematics (STEM), fields which typically lead to higher paying jobs relative to other fields” (Broady et al. 48). This is important because it implies that the relationship between education and pay may not be so intuitive for those groups. For our project, this source provides a framework for analyzing SCF data, which is integrated in our dataset. Additionally, this provides potential causal links for why the intersection of educational attainment and income is not so clear when comparing Black and Hispanic Americans to their White counterparts.
Burt, Brian A., Karl L. Williams, and George J. M. Palmer. “It Takes a Village: The Role of Emic and Etic Adaptive Strengths in the Persistence of Black Men in Engineering Graduate Programs.” American Educational Research Journal, vol. 56, no. 1, 2019, pp. 39–74.https://doi.org/10.3102/0002831218789595
This research journal by Burt, Williams, and Palmer undertakes a more qualitative approach to draw a narrative about economic mobility from education. An analysis of an underrepresented ethnic minority; black men in graduate engineering, is conducted, arguing that the differences within institutional support and representation limit the economic mobility that is promised by advanced degrees. It draws information from a series of in-depth interviews as its evidence, aligning with broad patterns of educational classification seen in wealth distribution data. A statement provided from these interviews was, “Black men at PWIs…are consistently made to feel as if they do not belong and must prove that they have the talent and skills necessary to be in graduate school” (Burt et al. 50). This resource is important more for creating our own narrative, in which here will be provided more of the social context highlighting the difficulties in the process of educational attainment. Social networks, mentorship, and cultural identity are essentially what will be explored within the context of the thesis, where these factors significantly aid in overcoming the wealth gap.
Chetty, Raj, et al. Measuring Distribution and Mobility of Income and Wealth, The University of Chicago Press, Chicago, 2022, pp. 641–677.
In this chapter, named “The Distributional Financial Accounts of the United States,” Chetty et al. describe the construction of the Distributional Financial Accounts (DFA), a dataset that provides measurements of US household wealth from 1989 to 2025. The DFA combines and interpolates data from the Financial Accounts of the United States and the Survey of Consumer Finances, building analogs for variables that only exist in one of the datasets. The Financial Accounts provide quarterly data on the balance sheets of the major sectors of the U.S. economy, distinguishing between households, non-profit organizations, federal and local government, and financial and non-financial uses. The Survey of Consumer Finances contains triennial microdata on the assets and liabilities of a representative sample of U.S. households, using weighted estimates of family income, net worth, credit use, and other financial outcomes. This source is important because combining those two different measures allows for a variety of investigations that examine the relationships between various factors and the distribution of income. Furthermore, understanding the construction of the data set allows us to look at the silences and the implicit assumptions that are built into the data we analyze.
Gibson-Davis, Christina M., and Christine Percheski. “Children and the Elderly: Wealth Inequality Among America’s Dependents.” Demography, vol. 55, no. 3, 2018, pp. 1009–1032. https://doi.org/10.1007/s13524-018-0676-5
As the article title suggests, Gibson-Davis and Percheski analyze wealth inequality among the American population of dependents, which mainly involves children and elderly. It argues that higher education attainment does not necessarily correlate with overcoming financial disparities involving wealth inheritance between generations. The data that they draw from is part of the Survey of Consumer Finances (SCF), demonstrating the increasing gap in wealth between generations as younger generations of people obtain assets slower in comparison to older, previous generations. They find that, “In 2013, the median elderly household held 12 times more wealth than the median household with children” (Gibson-Davis and Percheski 1010). This article is important because it defines a huge factor of the differences in household wealth, which is the demographic and how that demographic is limited by their initial economic disadvantage as it transfers over time. When addressing a connection between education and upward economic mobility, it is important to note from this article that educational attainment often fails to alleviate the inheritance of economic disadvantage, indicating that intergenerational transfer of wealth is more of a factor towards obtaining long-term household wealth.
Glaeser, Edward L., and Joshua D. Gottlieb. “The Wealth of Cities: Agglomeration Economies and Spatial Equilibrium in the United States.” Journal of Economic Literature, vol. 47, no. 4, 2009, pp. 983–1028, https://doi.org/10.1257/jel.47.4.983.
In “The Wealth of Cities: Agglomeration Economies and Spatial Equilibrium in the United States,” authors Glaeser and Gottlieb explore how cities create economic advantages through the concentration of people, talent, and industries. They argue that “the existence of human capital externalities does suggest that attracting skilled workers may increase local productivity and local growth” (1015), emphasizing the central role of education and talent in fostering regional prosperity. The authors caution, however, that “the mere existence of agglomeration economies does not imply any particular policy action” (1015), underscoring the complexity of translating these findings into effective economic policies. Overall, the article highlights how the clustering of skills and ideas drives productivity while raising critical questions about the balance between market forces and government intervention in urban development.
Haveman, Robert, and Timothy Smeeding. “The Role of Higher Education in Social Mobility.” The Future of Children, vol. 16, no. 2, 2006, pp. 125–150. https://doi.org/10.1353/foc.2006.0015.
Haveman and Smeedling analyzes the influence of higher education in upward economic mobility in the United States. By incorporating data from analysis conducted on the proportion of students from different socioeconomic statuses and other factors such as family average income-to-needs ratio, the authors emphasize that the system of higher education in the United States is not conducive in reducing wealth inequality, instead reinforcing existing socioeconomic stratification. This paper notes that there exists a clear pattern of educational attainment and family income, as said, “While only about 22 percent of youth from the bottom quartile of families attended college, 71 percent from families in the top quartile at least entered a college or university (131).” Furthermore the paper presents evidence against the notion of meritocracy in the process of college admissions with data regarding the allocation of educational resources, “half of all higher educational services necessary for attaining a college degree are allocated to youth from the richest quarter of the nation’s families, as against only 7 percent allocated to youth from the poorest 25 percent of families and only 3 percent to youth from the poorest 10 percent of families(132).” By providing context for the effect on socioeconomic status on college enrollment and graduation as well as the indicators of ability that are related to family income, this resource provides the narrative for the relationship between wealth and educational level in the dataset provided by the DFA.
Hubmer, Joachim, et al. “Sources of US Wealth Inequality: Past, Present, and Future.” NBER Macroeconomics Annual, vol. 35, no. 1, 2021, pp. 391–455. https://doi.org/10.1086/712332.
In the analysis of rising wealth inequality in the US, the authors of this article focus on the underlying economic factors that have widened the gap between the rich and the poor. They specifically point out that “since at least 1992, it has been well-documented that the education skill premium has risen” (393), showing that higher education has yielded greater income advantages over time. Yet the authors concluded that those differences in earning are not the main cause of growing wealth inequality, emphasizing that “the marked decrease in tax progressivity is by far the most powerful force for the cumulative increase in wealth inequality” (394). This suggests that as tax rates on the wealthy have declined, returns on capital/savings grew faster than wages, allowing those who were already somewhat wealthy to accumulate more.
Morley, Louise, et al. “Internationalisation and migrant academics: The hidden narratives of mobility.” Higher Education, vol. 76, no. 3, 25 Jan. 2018, pp. 537–554, https://doi.org/10.1007/s10734-017-0224-z.
Morley and colleagues on the “Internationalisation and Migrant Academics: The Hidden Narratives of Mobility” Higher Education argues that the already existing inequalities in power relations amidst those in academia revoke the narrative that education directly correlates to upward economic mobility. The information obtained for commentary stems from a more qualitative approach through interviews with migrant academics, stating that “academic mobility is not a level playing field, but one structured by hierarchies of language, status, and geopolitics” (Morley et al. 539). This article is important because although macro-scale by its perspective in internationalization, the American correlation of education and economics follows the same pattern. Higher education both promotes diversity and at the same time reinforces the divisions between those who come from a more privileged economic background. For the point in our thesis that there is a decline in education being a pathway to upward economic mobility, this demonstrates that even in achieving the highest point of academic success in education, it not only fails to prevent the gap between those with already existing privileges, but in fact promotes it.
Painter, Matthew A, and Zhenchao Qian. 2016. “Wealth Inequality Among Immigrants: Consistent Racial/Ethnic Inequality in the United States.” Population Research and Policy Review 35 (2): 147–175. https://doi.org/10.1007/s11113-016-9385-1.
The authors of this article highlight the disparities in wealth inequality along racial and ethnic lines, noting that immigrants from minority demographics struggle to integrate into U.S. society due to a variety of institutional constraints and social boundaries. The dataset utilized by the authors include Survey of Income and Program Participation (SIPP) from the years 2001 and 2004 which consists of financial information on a large sample size of immigrants. This source is relevant because it explores the intersectionality of wealth inequality in the United States as well as using the framework of assimilation theory to examine wealth distribution along racial lines, as said, “we found that blacks consistently had the least amount of wealth, and Asians and Latinos were at the middle and had similar levels of wealth across most of the conditional wealth distribution(169).” While the dataset specifically focuses on the distribution of wealth among immigrants of different racial and ethnic backgrounds, it provides evidence for the contribution of race and ethnicity to economic status in the United States.
Saez, Emmanuel, and Gabriel Zucman. 2016. “Wealth Inequality in the United States since 1913: Evidence from Capitalized Income Tax Data.” The Quarterly Journal of Economics 131 (2): 519–578. https://doi.org/10.1093/qje/qjw004.
This article presents empirical evidence strengthening the argument that wealth inequality has greatly increased since the mid-1980s, mirroring the pattern observed in the early twentieth century. The financial dataset used by the authors include annual household balance sheets from the US Board of Governors of the Federal Reserve System as well as individually reported capital income on individual tax returns, investment income, and untaxed assets. The quantitative analysis of wealth distribution conducted on numerous factors that build wealth investigates the difference in wealth distribution between the top 1% and middle class, observing the decline of middle-class wealth, “rising income inequality matters a lot at the top. The share of income earned by families in the top 1% of the wealth distribution has doubled since the late 1970s, to about 16% in recent years (565).” This source establishes the empirical foundation for understanding the growth of wealth inequality present in the U.S., supporting the argument that neoliberal policies and changes in the tax structure during the Reagan administration have disproportionately benefitted households in the top 0.1% and 1%.
Sun, Stephen Teng, and Constantine Yannelis. “CREDIT CONSTRAINTS AND DEMAND FOR HIGHER EDUCATION: EVIDENCE FROM FINANCIAL DEREGULATION.” The Review of Economics and Statistics, vol. 98, no. 1, 2016, pp. 12–24. JSTOR, http://www.jstor.org/stable/43830329.
Sun and Yannelis use the Panel Study of Income Dynamics (PSID) in combination with the 1979 National Longitudinal Survey of Youth (NLSY) to determine that lifting restrictions on giving credits to families increased college enrollments, especially amongst lower and middle-income families. PSID is a longitudinal panel survey of American families, measuring economic, social, and health factors, while the NLSY includes data on whether families took out loans to finance educational expenses. This source provides an alternative examination of the impact of loans on college enrollment, placing it in conversation with other sources. Also, it could provide us with a potential solution if we want to provide a path toward higher education attainment for lower-income families while balancing education and long-term financial stability.
Torche, Florencia. “Is a College Degree Still the Great Equalizer?: Intergenerational Mobility across Levels of Schooling in the United States.” American Journal of Sociology, vol. 117, no. 3, 2011, pp. 763–807. https://doi.org/10.1086/661904.
In this article, Torche examines whether higher education continues to serve as a pathway to social mobility in the US. In her research, she found that “the intergenerational association is strong among those with low educational attainment; it weakens or disappears among bachelor’s degree holders but reemerges among those with advanced degrees” (763). In other words, an individual’s family background has a tremendous impact for people with lesser education, and has little if any for those with a college degree, but at the graduate level higher family wealth/connections have a significant influence on outcomes. This pattern shows how class advantages reappear at the top of the educational ladder, as Torche notes, “these extraoccupational resources are central at either extreme of the economic distribution—among the ‘underclass’…and among the ‘overclass,’ whose income largely depends on returns to capital” (774). This emphasizes that inequality is not only about education, but also about who has access to what financial/social resources beyond the general labor market, that enable wealthy families to maintain their social/economic standing even without relying solely on education.
Weller, C. E., & Hanks, A. (2018). The Widening Racial Wealth Gap in the United States after the Great Recession. Forum for Social Economics, 47(2), 237–252. https://doi.org/10.1080/07360932.2018.1451769.
In “The Widening Racial Wealth Gap in the United States after the Great Recession”, Weller and Hanks argue that the racial wealth gap in the United States actually widened after the 2007–2009 Great Recession, leaving Black households with roughly one-tenth the wealth of white households and limiting Black economic mobility. They support this claim using post-recession household wealth data through 2016, and they show that the gap persists across age, education level, marital status, and income, meaning it cannot be explained away by individual traits or “good choices.” As they document in the body of the paper, “Median African-American wealth amounted to $13,460 in 2016 or less than 10 percent of the median white wealth of $142,180” (241). This source is important because it links the racial wealth gap to structural barriers in labor markets, housing, and credit, and shows that even in a so-called recovery, Black households were not able to rebuild wealth at the same rate as white households. For the project, the article gives direct evidence that racial inequality is actively reproduced by recent economic shocks, not just inherited from the distant past; it shows that the systems people have to move through — jobs, mortgages, credit — are rigged in ways that keep the gap wide.
Wolff, Edward. “Household wealth trends in the United States, 1962 to 2016: Has middle class wealth recovered?” National Bureau of Economic Research, Nov. 2017, https://doi.org/10.3386/w24085.
Wu, Yu-Tzu, et al. “Education and Wealth Inequalities in Healthy Ageing in Eight Harmonised Cohorts in the ATHLOS Consortium: A Population-Based Study.” LANCET PUBLIC HEALTH, vol. 5, no. 7, 2020, pp. E386–94, https://doi.org/10.1016/S2468-2667(20)30077-3.
This article argues that people with higher education and greater wealth enter older age with much better health and functioning, and that these advantages are already present by midlife rather than only appearing late in life. The authors support this by analyzing harmonised longitudinal data from 141,214 adults aged 45–106 across eight cohort studies in Australia, the USA, Europe, Japan, South Korea, and Mexico, using multilevel models to compare a 0–100 “healthy ageing” score based on physical and cognitive ability. As they report, “Participants with tertiary education had higher baseline scores … and the adjusted difference in healthy ageing score between lowest and highest quintiles of wealth was 8.98 points” (e386). This source is important because it shows that ageing inequality is socially structured — in fact, in the U.S. sample the health gap between high educated wealth groups and low-education and low-wealth groups is especially large. For the project it gives direct empirical evidence that social position does not just affect income or opportunity, it literally shapes how well people’s bodies and minds function later in life. I will use numbers from the study — for example, people with higher education scored 10.54 points higher in healthy ageing than those with only primary education, and the richest group scored 8.98 points higher than the poorest — to show that inequality leads to unequal ageing outcomes.
Zhang, Rui, et al. “The Impact of Household Wealth Gap on Individual’s Mental Health.” BMC Public Health, vol. 23, no. 1, 1936, 2023,
https://doi.org/10.1186/s12889-023-16871-6.
This research argues that a larger wealth gap within a household’s local environment is linked to worse mental health for individuals, meaning that inequality itself—not just absolute poverty—harms psychological well-being. The authors test this using nationally representative panel data from the China Family Panel Survey (2012–2018), apply two-way fixed effects and two-stage least squares models with instrumental variables to handle endogeneity, and run robustness checks like alternative inequality measures and propensity score matching. As the authors note in their results, “the household wealth gap significantly negatively impact individuals’ mental health,” (8) based on the 2SLS estimates. This source is important because it shows not only that economic inequality and mental health are connected, but also that the effect is stronger for socially vulnerable groups like people with less education, rural residents, and middle-aged and older adults. For the project it supports the idea that inequality is not just an economic statistic — it creates stress, lowers perceived security, and reduces access to protective resources like health insurance and supportive neighborhood ties, which then damages mental health. I will use this study to argue that the psychological cost of wealth inequality is a public health issue, not just a fairness issue, and that policy aimed at mental health needs to address structural wealth gaps, not only individual coping.