Racial Fluidity and Inequality in the United States
Abstract
The authors link the literature on racial fluidity and inequality in the United States and offer new evidence of the reciprocal relationship between the two processes. Using two decades of longitudinal data from a national survey, they demonstrate that not only does an individual’s race change over time, it changes in response to myriad changes in social position, and the patterns are similar for both self-identification and classification by others. These findings suggest that, in the contemporary United States, microlevel racial fluidity serves to reinforce existing disparities by redefining successful or high-status people as white (or not black) and unsuccessful or low-status people as black (or not white). Thus, racial differences are both an input and an output in stratification processes; this relationship has implications for theorizing and measuring race in research, as well as for crafting policies that attempt to address racialized inequality.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Racial categorization operates both as an input and an output to racial inequality.
“Saperstein and Penner find that racial categorization operates both as an input and an output to racial inequality.”
From Towards a critical race methodology in algorithmic fairness (2020) · cites this paper as [105]
In survey responses, individuals who experience an increase in social position may be more likely to lighten themselves, while those who become more disadvantaged may darken themselves.
“Individuals who experience an increase in social position (e.g. increased income) may be more likely to “lighten” themselves in survey responses, while those who become more disadvantaged (e.g. experiencing unemployment or incarceration) may “darken” themselves [89, 105].”
From Towards a critical race methodology in algorithmic fairness (2020) · cites this paper as [105]
Cites
Ethnic identity in adolescents and adults: Review of research.
Phinney, J. S. (1990) · Psychological Bulletin · doi:10.1037/0033-2909.108.3.499 · Cited by 3,480 · see citing works
Racial Measurement in the American Census: Past Practices and Implications for the Future
Snipp, C. M. (2003) · Annual Review of Sociology · doi:10.1146/annurev.soc.29.010202.100006 · Cited by 188 · see citing works
Eberhardt, J. L. (2005) · American Psychologist · doi:10.1037/0003-066x.60.2.181 · Cited by 131 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676 · see citing works
Glenn, E. N. (2015) · Sociology of Race and Ethnicity · doi:10.1177/2332649214560440 · Cited by 531 · see citing works
The cost of color: Skin color, discrimination, and health among African-Americans
Monk, E. P. (2015) · American Journal of Sociology · doi:10.1086/682162 · Cited by 386 · see citing works
Towards a critical race methodology in algorithmic fairness
Hanna, A., Denton, R., Smart, A., & Smith-Loud, J. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372826 · Cited by 295 · see citing works
“encing unemployment or incarceration) may "darken" themselves [89, 105].”
Racial categories in machine learning
Benthall, S., & Haynes, B. D. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287575 · Cited by 110 · see citing works
Ancestry, Color, or Culture? How Whites Racially Classify Others in the U.S.
Schachter, A., Flores, R. D., & Maghbouleh, N. (2021) · American Journal of Sociology · doi:10.1086/714215 · Cited by 74 · see citing works
Hall, J. C. (2017) · Health & Social Work · doi:10.1093/hsw/hlx001 · Cited by 55 · see citing works
Black women’s experience of colorist microaggressions
Hall, J. C., & Crutchfield, J. (2018) · Social Work in Mental Health · doi:10.1080/15332985.2018.1430092 · Cited by 26 · see citing works
- Authors
- Saperstein, A., & Penner, A. M.
- Affiliations
- Stanford University (US)
- University of California, Irvine (US)
- Venue
- American Journal of Sociology
- Publisher
- University of Chicago Press
- Published
- 2012-11
- Volume
- 118
- Issue
- 3
- Pages
- 676-727
- ISSN
- 0002-9602 (ISSN-L)
- 1537-5390
- OpenAlex
- W1963835707
- Cited by
- 464
- References
- 3
- Citations by year
- 2026: 30 · 2025: 55 · 2024: 32 · 2023: 32 · 2022: 39 · 2021: 38 · 2020: 26 · 2019: 51 · 2018: 43 · 2017: 36 · 2016: 27 · 2015: 30 · 2014: 17 · 2013: 8
- FWCI
- 58.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- American Journal of Sociology (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Racial and Ethnic Identity Research
- Social Sciences › Sociology and Political Science › Intergenerational and Educational Inequality Studies
- Social Sciences › Sociology and Political Science › Race, History, and American Society
- Keywords
- racial fluidity, racial inequality, social stratification, racial classification, self-identification, racialization, racial disparities, racial policy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review