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Alexis C. Dennis: Assessing Racial Differences in Socioeconomic Status across the Life Course


November 6, 2019

 

Alexis Dennis
Alexis C. Dennis

Alexis C. Dennis, a predoctoral Trainee at CPC and a doctoral candidate in sociology, received a poster award at the 2019 Interdisciplinary Association for Population Health Science (IAPHS) Conference for her poster, “Racial/Ethnic Differences in the Socioeconomic Correlates of Depression Among U.S. Born Young Adults.” Her research used AddHeath data to assess racial differences in socioeconomic status across the life course.

We recently interviewed Alexis to learn more about her research.

I’d like to start by asking you to describe what conventional operationalization of SES means, and why those measures may not fully reflect the resources most salient to the mental health of young adults at different phases of the life course.

Dennis: A large proportion of the literature to date that has investigated the relationship between SES and health has operationalized SES by using one or more measures of educational attainment, income, and/or occupation. Choosing some form of one or more of these three measures seems to be the conventional or “go to” choice for health scholars. This is what I mean by conventional operationalization.

Prior scholarship has also found that different SES indicators (e.g., education, income, occupation) are related to health in different ways. This means that different SES indicators are not necessarily interchangeable when using them to assess health. It also means that there may be aspects of the relationship between access to material resources and mental health that we are not capturing by relying on the same three measures to assess SES. For example, what else might we learn about the relationship between access to material resources and mental health by including other SES indicators in our models, such as indicators that capture processes related to the intergenerational transmission of wealth, or indicators related to material deprivation?

Moreover, while the conventional SES measures may be useful indicators of one’s ability to access material resources among adults, I questioned whether those conventional measures were the best way to capture access to material resources at younger ages.  For example, what does a measure of “educational attainment” really tell us about the health of someone who is currently in high school but will go on to earn a master’s degree?  What is “total household income” really capturing about the health of a full time college student?  While some studies utilize parental SES characteristics for respondents who are children or adolescents, are parental SES characteristics the most appropriate SES measures for individuals who are transitioning to adulthood?  I wondered whether we could learn something new about the relationship between SES and mental health by measuring SES differently at different points across the life course.

Finally, the sociology of race literature documents that African Americans don’t necessarily receive the same health returns or “protective effects” of high SES for health as whites. We also know that historically, African Americans have experienced economic, political, and social barriers to accessing material resources as well as barriers to upward socioeconomic mobility. As such, I wondered whether incorporating measures beyond educational attainment, income, and/or occupation could be important for better understanding the patterns of mental health among different racial groups.

You looked at whether the relationship between socioeconomic status and depressive symptoms is consistent across racial groups when using life course appropriate measures of SES. What did you find?

Dennis: I found that the relationship between SES and depressive symptoms is not consistent across racial groups when using life course appropriate measures of SES.  I also found that for both blacks and whites, the measures of SES that were related to mental health were different at different stages in the life course. Finally, I found that at some stages of the life course, unconventional measures of SES (e.g., parental work characteristics, reverse intergenerational wealth transfers) were associated with depressive symptoms.

Did anything surprise you in the results?

Dennis: Prior literature documents that among whites, wealth generally flows from parents to children. In contrast, African Americans are more likely to have to provide financial support for parents and other kin. As such, I was surprised to find that among white young adults, having to provide financial support to parents was associated with increased depressive symptoms. Among African Americans, there was no significant relationship between having to provide financial support to parents and depressive symptoms, but the trend was protective against depressive symptoms.

What are your next steps?

Dennis: I used the Add Health Wave V early release subsample data for the current iteration of this project. As such, I am looking forward to re-running these models once the full Wave V sample is released.  I also plan to extend this analysis for one of my dissertation chapters.

What else are you thinking about these days in terms of your research?

Dennis: I am broadly interested in better understanding racial/ethnic disparities in mental health across the life course and am currently working on my dissertation proposal. In addition to understanding how disparate access to material resources contributes to racial/ethnic disparities in mental health, I’m also interested in better understanding how coping resources are socially structured, and how the unequal distribution of these resources contributes to racial/ethnic disparities in mental health.