Conceptualizing Structural Racism with IPUMS Time Use Data

by

By Solvejg Wastvedt and Yash Singh

A major challenge for researchers studying racism is measuring the consequences of this macro-level phenomenon in people’s everyday lives. For the purposes of this post we define structural racism as a system of racialized advantage and disadvantage underlying and structuring multiple domains of life, including housing, interactions with government, education, the criminal legal system, and more. 1 2 3 4 5 Structural racism does not refer to individual prejudices or discriminatory beliefs. Rather, it is a macro-level phenomenon: a principle upon which structures in our society are built. When researchers study racism as a structural force, its impacts in any one area affect and reinforce impacts in every other area.

The consequences of structural racism for individuals are well-documented; for example, research shows structural racism produces poorer health outcomes for racialized groups. 6 However, research often focuses on a single aspect of structural racism, such as police brutality, and struggles to capture the full impact of this multidimensional phenomenon. Measures used to assess impacts of structural racism typically differ by domain, and while robust research documents these impacts, it can also be difficult to capture the mechanisms through which they occur.

Time — specifically time use — is a type of data that can unite studies of structural racism across domains and offer a glimpse into how this macro-level force acts on individuals.

Time is a form of capital: we only have so much of it, and time spent in one place is not spent elsewhere. For example, an individual living in a segregated neighborhood because of residential discrimination may be required to drive long distances for work or health care. This reduces time available for all other activities, including exercise. The resulting lack of time may lead, in turn, to negative health outcomes. While simply an illustration of one possible pathway linking the macro and micro levels, this example shows how time use data can capture impacts across multiple domains and, ultimately, on daily life.

IPUMS Time Use provides data from the American Time Use Survey (ATUS), a nationally representative U.S. time diary survey. In this post, we highlight measures from the ATUS corresponding to two aspects of structural racism: residential segregation and discrimination in government services. Researchers interested in other aspects can create and select variables from IPUMS ATUS data to match their areas of interest.

Residential segregation

Housing plays a key role in our lives. Schooling, employment opportunities, health, and a wide range of economic outcomes are directly affected by residence. Structural racism within housing is well documented; discriminatory practices such as redlining, racial covenants, and racialized zoning laws in tandem with mob violence and persecution deeply embedded residential segregation in the American system. Lack of investments in infrastructure, poor delivery of services, limited employment opportunities, punitive policing etc., borne from residential segregation, have locked in the Black disadvantage. This disadvantage translates to an onerous time tax. For instance, since Black neighborhoods are systematically over policed, Blacks spend more time interacting with law enforcement and the judicial system. These interactions can range from being questioned by police or stopped for a traffic violation to appearing as a witness in court or meeting with a parole officer. Time being spent on such interactions trades off with time available for other, more generative activities. The sprawling effects of housing on a person’s daily lives make these time taxes especially damaging.

IPUMS ATUS provides detailed time use data on a wide range of activities that can yield insights into residential segregation and people’s daily lives. For example, researchers can study the interaction between housing and travel times by utilizing IPUMS ATUS activity code Traveling (180000), which aggregates activity specific travel time data. Within the broader category of Traveling, activity codes such as Travel Related to Work (180500), Consumer Purchases (180700), using Professional and Personal Care services (180800) provide detailed time use data that can be used to better understand how residential segregation interacts with employment, access to goods and services, healthcare access, and a range of other activities. Even within these activity codes, ATUS data provides further detail. For instance, Travel related to Professional and Personal Care services includes detailed codes for time spent on travel related to childcare services (180801), legal services (180803), and medical services (180804) among others. IPUMS ATUS offers a tool for users to create custom time use variables to aggregate specific activity codes to allow researchers flexibility to define measurements that capture aspects of residential segregation of interest to them.

Government services

Federal and state governmental assistance programs in the United States have a long history of racialized discrimination. Some of this discrimination is overt: New Deal-era housing and welfare programs, for example, explicitly barred or limited benefits for Black Americans.5 7 8 However, even seemingly race-neutral policies, such as work requirements for government assistance, are often built upon racist stereotypes and have racially disparate impacts. 9 For example, Black individuals are more likely than non-Hispanic whites to live in states with stringent work requirements and sanctions for financial assistance programs. Within some states, families of color are punished at higher rates than non-Hispanic white families for violating these requirements. 10 11

Time use data can highlight unique aspects of these types of disparities in bureaucratic processes. As anyone who has waited on hold with a government office can attest, bureaucratic hurdles eat up time, 12 and disparities in areas such as work requirements mean racialized groups face a greater time burden. IPUMS ATUS activity codes such as Government Services utilization (100000) help researchers quantify how much time people are spending engaging with bureaucracy. Codes in this group capture time spent on waiting (100304), security procedures (100400), using social services (100102), and more. Detailed activity codes related to time spent on travel, including travel related to using government services (181001), may also be of interest.

Conclusion

Structural racism has destructive consequences for individuals across multiple domains and in intersecting and mutually-reinforcing ways. Researchers studying these impacts can gain insights using measures, such as time use, that cross domain boundaries.

In addition to the IPUMS ATUS activity codes highlighted here, other IPUMS variables can add nuance to the study of structural racism and time. For example, geographic variables including state, county, and metro area illuminate how the impacts of structural racism differ by location. IPUMS Time Use also offers measures of activity location, others present, secondary activities occurring at the same time (such as eating or drinking), and self-reports of well-being during the activity.

As with any measure, researchers should be aware of certain caveats regarding these data. Time use captures duration and type of activity, but for the most part other details are limited. For example, two people may spend equal amounts of time on grocery shopping, but do they have equal access to quality food? Data on how time is spent cannot provide the answer.

Time use data also does not distinguish among the many reasons a respondent might not report any time spent on an activity. For example, in order to spend time accessing government benefits, a person must first choose to apply for assistance. We may uncover time use disparities among those who successfully engage with programs, but those disparities may fail to account for populations who, because of structural discrimination, do not find it worthwhile to apply for government services in the first place.

Further, ATUS data represent activities spanning a 24-hour period for each respondent. While in the aggregate these time diaries yield a representative picture of population-level time use, the days about which some individuals report may be atypical and not representative of their usual patterns.

In summary, although time use data requires careful consideration of context, it can illuminate a key mechanism through which structural racism harms individuals. By considering the supplemental variables mentioned throughout this post and pairing them with data from IPUMS ATUS, researchers can better understand structural racism and the work needed to dismantle it.

Post Views: 884
  1. Bonilla-Silva, E. (1997). Rethinking Racism: Toward a Structural Interpretation. American Sociological Review, 63(3), 465-480. https://doi.org/10.2307/2657316
  2. Reskin, B. (2012). The Race Discrimination System. Annu. Rev. Sociol, 38, 17–35. https://doi.org/10.1146/annurev-soc-071811-145508
  3. Hicken, M. T., Kravitz-Wirtz, N., Durkee, M., & Jackson, J. S. (2018). Racial inequities in health: Framing future research. Soc Sci Med, 199, 11-18. https://doi.org/10.1016/j.socscimed.2017.12.027
  4. Sewell, A. A. (2016). The Racism-Race Reification Process: A Mesolevel Political Economic Framework for Understanding Racial Health Disparities. Sociology of Race and Ethnicity, 2(4), 402-432. https://doi.org/10.1177/2332649215626936
  5. Bailey, Z.D., Feldman, J.M., & Bassett, M.T. (2021, February 25). How Structural Racism Works-Racist Policies as a Root Cause of U.S. Racial Health Inequities. The New England Journal of Medicine, 384:768-773. 10.1056/NEJMms2025396
  6. See, for example: Alang, S., McAlpine, D., McCreedy, E., Hardeman, R. (2017). Police Brutality and Black Health: Setting the Agenda for Public Health Scholars. Am J Public Health, 107(5), 662-665. https://doi.org/10.2105/AJPH.2017.303691; Williams, D. R., Collins, C. (2001). Racial residential segregation: a fundamental cause of racial disparities in health. Public Health Rep., 116(5), 404-416. https://doi.org/10.1093/phr/116.5.404
  7. Rothstein, R. (2017). The Color of Law: A Forgotten History of How Our Government Segregated America. Liveright Publishing Corporation.
  8. Piven, F. (2003). The Culture of Work Enforcement: Race, Gender, and U.S. Welfare Policy. In Schwarzenbach S. & Smith P. (Eds.), Women and the U.S. Constitution: History, Interpretation, and Practice (pp. 93-107). New York: Columbia University Press. http://www.jstor.org/stable/10.7312/schw12892.11
  9. IMinoff, E. (2020). The Racist Roots of Work Requirements. Center for the Study of Social Policy. https://cssp.org/wp-content/uploads/2020/02/Racist-Roots-of-Work-Requirements-CSSP-1.pdf
  10. McDaniel, M., Woods, T., Pratt, E., & Simms, M. C. (2017). Identifying Racial and Ethnic Disparities in Human Services. Urban Institute. https://www.urban.org/sites/default/files/publication/94986/identifying-racial-and-ethnic-disparities-in-human-services_1.pdf
  11. Hahn, H., Aron, L., Lou, C., Pratt, E., & Okoli, A. (2017). Why Does Cash Welfare Depend on Where You Live? Urban Institute. https://www.urban.org/sites/default/files/publication/90761/tanf_cash_welfare_0.pdf
  12. Lowrey, A. (2021). The Time Tax. The Atlantic. https://www.theatlantic.com/politics/archive/2021/07/how-government-learned-waste-your-time-tax/619568/