No. A racial disparity is evidence of a difference in outcomes, not, on its own, proof of what caused it. Systemic racism can be a well-founded explanation when a specified mechanism is defined, measured, and shown to contribute to a particular disparity. It should be tested rather than assumed—and a failure to find evidence for one proposed mechanism does not rule out every other pathway.
What does it mean to call systemic racism an explanation?
A disparity describes a pattern: groups have different outcomes on a defined measure, such as health, education, or contact with the criminal justice system. A causal explanation goes further. It identifies a process that could produce the difference and provides evidence connecting that process to the outcome.
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“Systemic” and “structural” racism are not single, measurement-ready variables. In a 2025 Annual Review of Public Health article, Tyson H. Brown, Hedwig E. Lee, Margaret T. Hicken, Eduardo Bonilla-Silva, and Patricia Homan describe systemic racism as “a complex, interconnected, and dynamic system.” They argue that researchers must specify the logics, properties, and mechanisms behind racial inequities, then choose measures that actually represent them. A mismatch between a broad concept and a narrow or indirect measure can undermine the validity of a conclusion.
The terms can also be defined differently by different researchers and institutions. The UK Commission on Race and Ethnic Disparities’ 2021 report, for example, described systemic racism in terms of discriminatory processes operating across interconnected organizations or wider society, and structural racism in terms of historic processes whose legacies continue to shape institutions and societies. Those are the Commission’s definitions, not universally accepted boundaries between the terms.
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How can a disparity be assessed without presuming its cause?
Begin with a specific claim, not a general label. A useful assessment makes clear what outcome differs, for which population, where and when, and what proposed process might produce the difference. Then ask whether the evidence measures that process or merely uses a proxy for it.
- Define the disparity. Specify the outcome and how it is measured, the groups being compared, the geography, and the time period. A conclusion about one outcome or place does not automatically apply to another.
- Name the proposed mechanism. Identify the policy, institutional practice, geographic condition, or other process said to contribute. “Systemic racism” alone does not say which mechanism is at issue.
- Check what the measure captures. Ask whether a measure represents the proposed process directly or stands in for it. Explain what the measure cannot show, too.
- Consider relationships among causes. Geography, class, sex, family experiences, and policy conditions may be relevant to some outcomes. Depending on the question, they could be part of a causal pathway, a factor that influences both exposure and outcome, or a factor that interacts with another cause. An association with an outcome does not establish that a factor fully explains a disparity.
- Look for convergence and limits. Compare findings across appropriate data, methods, and theoretical frameworks. A result that does not support one proposed pathway is not evidence that all systemic pathways are absent; conflicting results also require examining whether studies measured the same thing in comparable settings.
This is a practical synthesis of measurement guidance in the 2025 reviews by Brown and colleagues and by Brown, Homan, and Ray in the Annual Review of Sociology. The latter emphasizes theoretically grounded measures and methodological pluralism, including triangulation across frameworks, data, and methods.
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What approaches do researchers use to measure structural racism?
The Urban Institute’s 2023 report, Measuring Structural Racism, reviewed quantitative empirical literature on structural racism and health, focusing on its use as an independent variable. It examined 70 articles from the preceding 10 years. That is a count of articles reviewed, not 70 studies proving a causal effect.
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|---|---|---|
| Geographic | Geographic patterns or conditions used to operationalize structural racism. | A geographic measure must be connected to a specified mechanism; a pattern across places alone does not establish why outcomes differ. |
| Self-reported | People’s reported experiences, used as a measure of relevant exposure. | The measure and the claim need to match: a reported experience can inform an explanation, but does not by itself capture every system-level process. |
| Specific-policy | A defined policy or policy-related exposure. | Conclusions apply to the policy and context studied; they should not automatically be generalized to other policies or pathways. |
The report presents these as broad operational approaches, not interchangeable tests. It cautions that no single approach can capture all the ways structural racism may operate—directly and indirectly, at different social and geographic levels, and across historical and contemporary settings. It recommends theory-driven, historically rooted, policy-specific work, with contributions from people with lived experience and scholars from multiple disciplines.
What else might contribute to a disparity?
There is no universal alternative cause that applies to every racial disparity. Depending on the outcome and setting, relevant factors may include geography, class, sex, family experiences, or specific policies. Their relevance is an empirical question: the fact that a factor is associated with an outcome does not show that it explains the group difference, still less that it explains all of it.
The UK Commission on Race and Ethnic Disparities argued in its 2021 report that geography, class, and sex can demonstrably explain some persistent differential outcomes, and that differences in family life and structure may explain many disparities in education and crime. Those are claims made within the Commission’s framework; they do not establish the cause of any particular disparity without evidence about that outcome, population, place, and period.
The Commission proposed calling persistent differential outcomes with no conclusive evidence about their causes “unexplained racial disparities.” That category is a way of describing what is not yet established, not proof that a disparity has no cause or that systemic racism is absent. The report’s approach is a contested official position, not a universal scientific conclusion, and its UK scope should not be treated as a finding about US data or every policy domain.
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The measurement literature is developing, especially in health. A 2024 Epidemiologic Reviews scoping review of work from 2019–2021 describes research linking structural racism to racial health inequities as early-stage and identifies continuing uncertainty about mechanisms and quantitative measurement. That does not mean there is no evidence; it means claims should match what has been measured and established.
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- Most important of all, it has spawned a whole generation of criminal justice reform activists and organizations motivated by Michelle Alexander’s unforgettable argument that “we have not ended racial caste in America; we have merely redesigned it.” As the Birmingham News proclaimed, it is “undoubtedly the most important book published in this century about the U.S.”
- Now, ten years after it was first published, The New Press is proud to issue a tenth-anniversary edition with a new preface by Michelle Alexander that discusses the impact the book has had and the state of the criminal justice reform movement today.
Interest in the subject has grown sharply, but attention is not the same as causal certainty. Adkins-Jackson and coauthors reported in a 2022 American Journal of Epidemiology article that a PubMed query at the end of 2021 showed more than a 50-fold increase over the preceding five years in citations of papers on “structural racism” and “health.” That is a literature-search and citation metric, not a measure of racism, disparity size, or causal effect.
What should a credible claim make clear?
- Mechanism: What process is proposed, and how is it expected to affect the outcome?
- Measure: Does the measure represent that process, or is it a proxy? What parts of the concept does it leave out?
- Scope: Which population, outcome, geographic level, and historical period does the evidence cover?
- Causal structure: Does the analysis distinguish possible confounders, mediators, and interacting causes rather than treating every related factor as equivalent?
- Robustness: Do different methods or data sources point in a similar direction, and are disagreements explained by differences in definitions or settings?
These questions avoid two opposite errors: treating the existence of a disparity as proof of one cause, and treating uncertainty about one measure or pathway as proof that no systemic process matters. The 2025 reviews emphasize aligning theory with measurement and using multiple methods where they can clarify the question.
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