Interview w/ Jackelyn Hwang: 2020 Addams Award for Best Article

Jackelyn Hwang, an Assistant Professor of Sociology at Stanford University, was the winner of the 2020 Jane Addams Award for best article. Jackelyn’s innovative research agenda examines the relationship between how neighborhoods change and the persistence of neighborhood inequality by race and class in US cities. We reached out to ask her to discuss her research, and we’re including her responses below. Thanks to Jackelyn for participating in our interview series!

What were your main findings?

In the article, “Gentrification without Segregation? Race, Immigration, and Renewal in a Diversifying City,” which was published in City & Community in 2020, I examined how neighborhood ethnoracial compositions affect where gentrification—the socioeconomic upgrading of previously low-income neighborhoods—unfolds across several decades in the City of Seattle. Seattle is an atypical case for studying the relationship between neighborhood ethnoracial composition and gentrification. As an atypical case, it provides an opportunity to test assumptions based on other settings and advance theory on gentrification.

I found that early waves of gentrification during the 1970s and 1980s avoided minority neighborhoods, like in highly segregated cities. In contrast, gentrification since 1990 favored neighborhoods with greater shares of Black residents and avoided neighborhoods with greater shares of Asian residents. By exploring the mechanisms explaining these relationships, the study uncovered that immigrant replenishment is an important mechanism shaping patterns of uneven development and residential selection in cities today.

To analyze the early waves of gentrification, I drew on field survey data collected by geographers Elvin Wyly and Daniel Hammel in 1998 as part of another study. The surveys looked for direct visible indicators of upgrading based on aesthetic changes to the built environment that characterize gentrification. While the observations took place in 1998, they identify areas that began gentrifying during the 1970 and 1980s. In the early waves, I found that the share of all minority groups is negatively associated with gentrification. This is consistent with other research highlighting the racially selective nature of gentrification.

To analyze the recent wave of gentrification, I used data from the 1990 and 2000 U.S. Censuses and 2009-2013 American Community Survey (ACS) 5-year estimates. There is no clear consensus on the best way to operationalize gentrification using Census and ACS data, so I developed a measure that expanded on past approaches and selected variables that were associated with gentrification based on the field surveys. Because census tracts in Seattle are relatively large, I examined census block groups instead, and I considered changes in neighborhoods from either 1990–2013 or from 2000–2013 to allow for both slower and more rapid gentrification. In contrast to the early waves of gentrification and counter to my hypotheses, I found that the shares of Black residents positively predicted recent gentrification, while shares of Asians negatively predicted it.

To better understand these findings, I next assessed several mechanisms with various data sources. First, I examined whether Seattle had unique racial dynamics that drove these findings using survey responses about neighborhood racial preferences and perceptions of disorder from previous studies. Second, I examined if state-driven policies like public housing redevelopment and the locations of new light rail stations drove these findings using geocoded data on Seattle’s public housing sites and light rail stations. Third, I tested if middle-class minorities were driving gentrification in Black neighborhoods by integrating data on poverty and income by race groups from the U.S. Census. None of these findings explained the results. Finally, I tested if immigrant settlement patterns were deterring gentrification in neighborhoods with higher shares of Asians by examining Asian, Latinx, and foreign-born population changes with data from the U.S. Census and ACS. Indeed, increased concentrations of recent immigrants in neighborhoods with greater shares of Asians explained the relationships in the analysis.

Altogether, the study underscores how immigration and points of entry are important factors for understanding uneven development in the contemporary city. They suggest that, in a tight housing market like Seattle, where arriving immigrants move may be limiting where gentrification takes place, shifting pressures to low-cost Black neighborhoods.

What motivated you to study this research topic?

I originally became interested in the topic of gentrification and its uneven development patterns across racial compositions as an undergraduate, when I was conducting research for my senior thesis. My senior thesis project examined the neighborhood names and boundaries that people used to identify their neighborhood in a gentrifying neighborhood undergoing racial change. I was struck by how much race mattered in how gentrifiers defined their neighborhoods and excluded other spaces.

For my first research project in graduate school, with my graduate advisor Robert Sampson, we expanded on this idea and examined how racial composition affects the pace of gentrification in Chicago neighborhoods. We chose Chicago because we had other data available to us for testing different pathways predicting our outcome. We also drew on the field surveys of gentrification mentioned above, which were conducted in 1995 in Chicago, and Google Street View imagery, which was a new source of data at the time for observing neighborhoods. One of the main findings from the study was that gentrification took place at a much slower pace and even declined in neighborhoods that began with greater shares of Black residents. We also found that gentrification from the 1970s and 1980s, based on the field surveys, had a negative correlation with the share of Black and Latinx residents.

The findings contrasted depictions of gentrification as synonymous with the racial transformation of predominantly minority neighborhoods by upper-class white residents. On the other hand, our study, along with some others, depicted gentrification as a racially selective process that avoids minority neighborhoods. Most studies that conclude the latter are quantitative in nature and based on either broad national trends or focus on highly segregated cities.

I was curious if the same trends that we found in Chicago would unfold in a less segregated city. I also became interested in the role of immigrants in gentrification based on my first dissertation chapter, which examined the relationship between immigrants and early waves of gentrification. With low segregation levels along standard metrics (e.g., dissimilarity index) and high immigration levels, Seattle was a perfect case study. Seattle is a majority-white city with low segregation levels, growing ethnoracial diversity, and widespread gentrification. Because places with low segregation levels have more diverse neighborhoods and race and class are less strongly tied, gentrifiers’ preferences and neighborhood selection patterns are likely distinct from highly segregated cities.

What surprises did you find as you conducted your study?

I was most surprised about the opposite directions of the relationships between gentrification and the share of Black residents and share of Asian residents. Not only were the opposite directions surprising, but the actual directions for each group were also surprising. Based on a long line of research on racial stratification, I would have expected the results to reflect a racial hierarchy consistent with general trends of racial stratification, favoring white over Asian neighborhoods, Asian over Latinx neighborhoods, and Latinx over Black neighborhoods, or reflect the socioeconomic order of ethnoracial groups in Seattle, reversing the ordering of Latinx and Asian neighborhoods. This surprising finding led me down a path of trying to understand and explain the finding. I reviewed so much more literature and collected so much more data in this process. Ultimately, this journey led to this article’s mai contribution, but it was a long-winded path to get there.

Another surprise to me was that there was very little variation in the pace of gentrification across Seattle neighborhoods that were gentrifying according to the field surveys in 1998. I collected data using Google Street View imagery in Seattle, as I did in Chicago, to analyze the predictors of variation in the pace of gentrification. This part of the study did not make it into the article, but I think it’s worth mentioning. Parts of studies can take a lot of time and effort and may sometimes yield minimal insights. While it was frustrating at the time, in retrospect, it helped refine my focus and analysis.

How do you plan to build on this work in the future?

I’m engaged in a couple projects that build from this work. First, this study inspired me to look at this topic beyond Chicago and Seattle. With Hesu Yoon, a graduate student at Stanford, we’re working on a national-level analysis across large metropolitan areas that examines the relationships between recent immigrants and gentrification during the 1990s and 2000s and how this varies by neighborhood racial composition. Second, the findings from this study suggest that property owners in some neighborhoods may play an important role in creating barriers to entry for gentrifiers while they may be facilitators in others. With Nima Dahir, another graduate student at Stanford, we’re assembling a dataset of property ownership in San Francisco going back to 1990 and examining trends in transactions in ownership by race/ethnicity, type (e.g., corporations, individuals), and tenure to better understand the role of specific actors in shaping neighborhood trajectories. I’m also interested in studying the housing preferences of recent immigrants, especially as immigration continues to play an increasingly important role in shaping contemporary housing market dynamics.

One comment

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.