How do we understand and measure structural racism?

In order to develop a research agenda for better understanding and addressing structural racism, it is important to consider historical and contemporary policies and other structural factors that affect the health and well-being of individuals, families, and communities. Strategies for measuring these structural factors are key to enhancing our understanding as well.

Studying Race and Structural Racism Responsibly

  • The use of narrative and metaphor is important for better understanding the complexities of race and lived experiences of racism.
  • The study of structural racism is complex given that race is continually being made and remade over time by custom, law, and scholarship.
  • Thinking about racism only as a form of prejudice makes it impossible to understand the drivers of structural racism; instead, a theoretical understanding of the racialized social system would be beneficial.
  • The key path forward in structural racism research centers on building interdisciplinary frameworks that integrate scholarship from the arts, humanities, social sciences, and population health.

To learn more about studying race and structural racism responsibly, see Chapter 1 of the workshop proceedings.

The Measurement and Modeling of Structural Racism

  • Experimental design is a valuable approach to better understand existing racial stereotypes and their social consequences.
  • Quasi-experimental approaches are useful in the study of structural racism in that they allow for estimates of causal parameters that policy makers can interpret. For example, the current use of lethal force among the police is driven primarily by governmental and institutional decisions rather than by intractable structural factors.
  • The history and historical contexts of structural racism operate as a fundamental cause of disease; therefore, including historical indicators in analyses is essential. Historical data at both the spatial/community and individual levels have the potential to advance research in health and social equity.
  • Understudied Indigenous populations that have been historically marginalized, under-resourced, systematically excluded, and erased are reclaiming their data sovereignty by decolonizing data.
  • Algorithms can be trained to perform better than humans and reduce bias in health care, but only if these algorithms learn from nature (i.e., patient experiences and health outcomes) and prioritize patient experience.
  • The complexity of people’s experience of structural racism can be better understood with the use of diverse mixed methods (quantitative and qualitative) to explicate a phenomenon about which there is limited information, to support existing evidence, or to dispute existing evidence.
  • Connections to land and place are central to Indigenous frameworks for health and healing.
  • Although longitudinal social surveys are well-suited for exploring the mechanisms that produce racialized inequities in health and other outcomes, their use also involves theoretical, epistemological, and methodological challenges.
  • Novel data not only present new opportunities to measure state violence but also reveal the limits of official statistics. Building infrastructure for unofficial data collection can enable valuable research on structural racism.

To learn more about the measurement and modeling of structural racism, see Chapter 2 of the workshop proceedings.

Data Infrastructure Needs in Harnessing Data for Research in Structural Racism

  • Power is a fundamental driver of health and equity. Collective power and political will are necessary for aligning critical data and research tools to improve methods and measurement in structural racism research.
  • Large panel datasets such as the National Longitudinal Study of Youth could be used to study structural racism by providing useful indicators of cumulative disadvantage and revealing how racism moves across institutions.
  • A sustainable infrastructure for research on structural racism, health, and aging includes funding for longitudinal design and core support for multiple types of data. It also includes multidisciplinary research teams with proven competence and expertise in health equity research and incentives to focus research frameworks on structural and policy change.
  • A nationally, publicly available data repository for use by researchers, community members, and policy makers will enable a better understanding of how structural racism is operating and its impact on population health and well-being.

To learn more about data infrastructure needs in harnessing data for research in structural racism, see Chapter 3 of the workshop proceedings.


These key takeaways from the workshop were identified in the final session in response to the workshop’s guiding questions:

How can insights be applied regarding the conceptualization, measurement, and modeling of structural racism to inform decisions about:


What new measures of structural racism or data linkages could be used in ongoing or future studies to advance aging research?


What mechanisms or data linkages could be used in studies that link structural racism to disparities in health and well-being over time and place?


What study designs could be used to consider how structural factors operate to shape health over the life course?

  • Structural racism is a complex research topic
    • The notion of “unpacking the other” is essential for structural racism research (for example: National Institute on Aging-funded study of Vietnamese Americans, the trauma of war, and implications for cognitive functioning).
    • Structural racism research is complex because of different manifestations in subpopulations, as well as different mechanisms across time periods and geographic locations.
    • It is helpful for researchers to explore race as a fluid, contingent, and socially constructed condition (rather than a fixed independent variable).
    • Consider that structural racism is not only a historical process but also a dynamic process with evolving form and function.
    • Since structural racism is relational, modeling could be both unidirectional and bidirectional (race is both “acted out and acted upon”).
    • It is helpful to avoid creating new silos by developing measurement and modeling approaches that avoid replicating efforts.
    • Understanding racism and inequality requires not only data collection and visualization but also an understanding of place and space.
    • It is helpful to capture the benefits of scholars approaching problems in different ways while still having a coordinated way forward.
    • Research could be motivated by specific problematic policies or policy interventions.
  • The role of gatekeepers (such as journal editors, funders, and reviewers)
    • There are issues in data availability and data integration – barriers often created by disciplinary norms and gatekeepers.
    • Reviewers should have the appropriate expertise to recognize the complex nature of structural racism research.
    • Academic institutions can reduce barriers to innovative research by developing a grant program that would allow people to earn credits for research not normally recognized in the tenure process.
  • Theoretical and empirical research frameworks
    • Research frameworks that integrate the complexity of structural racism and incentivize theory-making and multimethod interdisciplinary approaches are useful in improving our understanding of structural racism.
    • It is important to define what “interdisciplinary” means in the context of specific research.
    • There are opportunities for innovation in methodology, such as coordinating different types of theoretical and historical assumptions about uncertainty and context.
    • Consider what structural racism is before determining how to operationalize it in qualitative and quantitative research.
    • A life course perspective helps to contextualize data (a 30-year-old Black man has a different experience of racism from a 60-year-old Black man).
    • It is helpful to read the work of legal scholars who have studied race to better conceptualize models for structural racism research in public health.
  • Prioritize the voice of marginalized scholars, especially those from the humanities
    • The humanities is a space that offers key insights where oppressed people have been encouraged to tell their stories.
    • Think carefully about who and what are included in the evidence base for studies to avoid furthering racial inequalities.
    • White scholars who have access to the rooms where decisions are being made should use their privilege to ensure that research and research teams are diverse, inclusive, and equitable.
  • Data sources and methods
    • It is important to consider sources of error in data and to think critically about uncertainty and data-generating processes.
    • Structural racism research is not just about studying explicitly racialized people because the data-generation process is itself structurally racist.
    • Because limitations in data and methods have political implications that can prevent the realization of true equity and justice, descriptive evidence could be leveraged to move the field forward in new ways.

To learn more about the key takeaways, see Chapter 4  of the proceedings.