Fellow Focus: Steven Alvarado

Author: Kevin Fye

Steven Alvarado is an assistant professor of sociology and faculty fellow at the Klau Institute. His research and teaching centers on how neighborhood disadvantage impinges on well-being across the life course, racial and ethnic inequality in education, the multigenerational structure of inequality, and policies that can potentially alleviate inequality. Using quantitative methods and federally restricted longitudinal data sets, Alvarado accounts for how inequality manifests through the unequal distribution of resources across racial and ethnic groups in schools and neighborhoods. 

Tell us a little about your early academic formation. What led you to a particular interest in the study of race and inequality? 

When I arrived in Berkeley for my first year, I was completely overwhelmed by the towering academic weight of the place. However, I quickly found an intellectual home in the sociology courses I took my freshman year. What hooked me was the fact that there was a tradition of research and inquiry into topics that touched on my lived experience such as immigration, urban poverty, racism, and educational marginalization. From there, I was hooked.


You recently received funding from the Klau Institute to help with a study of neighborhood disadvantage that requires the study of federally restricted data. What are the challenges and benefits of this kind of research?

The challenges are many. First, there is a steep financial cost to accessing these data – about $7,500 per year plus travel costs and time away from home. However, the benefits are also quite significant. To date, I am the only researcher in the world who is able to draw upon these restricted data – from the U.S. Bureau of Labor Statistics – to make discoveries about the social world that would have otherwise gone unnoticed by scholars.


In general terms, have there been any major surprises as you’ve studied the data, or has it largely verified your working assumptions?

I recently published a paper with a former graduate student at Cornell that demonstrated the limited returns to neighborhood mobility for Black Americans. In short, we found that Black Americans who grew up in “good” neighborhoods did not reap any advantage in terms of adult income compared to Black Americans who grew up in “bad” neighborhoods. The working assumption, prior to our paper, had been that moving Black residents out of socioeconomically distressed neighborhoods would lead to long-term economic gains over the life course. However, our research suggests that this is not enough. Structural racism in the labor market is likely to undercut the benefits that come from moving to a higher socioeconomic neighborhood for Black Americans.


The connection you draw between economic opportunity and educational opportunity – and human flourishing in general – might be particularly relevant in light of recent Supreme Court decisions regarding affirmative action. Do you see your work contributing to that conversation?

Absolutely. For the past 60 years, much of the public discussion about affirmative action’s role in college admissions has centered around individual’s physical traits (race/ethnicity in this case) and has even recently spilled over to topics such as legacy admissions and athletic prowess. However, I believe that shifting our thinking about affirmative action to a place-based model may have the power to satisfy almost everyone. Specifically, some recent analyses I have conducted on decades of federally restricted data demonstrates that using neighborhood socioeconomic status, instead of race or ethnicity, can yield about the same amount or racial and ethnic diversity in college admissions as was the case under affirmative action. This, sadly, is due to the fact that our residential landscape is marked by rigid neighborhood segregation by race, ethnicity, and income. This neighborhood approach should satisfy individuals who both lamented and celebrated the end of affirmative action because it produces similar levels of diversity and is not based on the phenotypical attributes of individuals.

Originally published by Kevin Fye at klau.nd.edu on March 18, 2024.