I am a sociologist focusing on social inequality, social networks, and family demography. I apply and develop quantitative and computational methods to analyze how individuals’ dynamic selection into and interactions within various social and natural environments influence social stratification. In this program, I examine how friendship, family, political, and climate dynamics shape individual life chances and aggregate patterns of inequality. Methodologically, I specialize in modeling complex dependencies in longitudinal, multilevel, spatial, and network data.
Friendship dynamics as drivers of social inequality
Adolescent friendship networks are characterized by low interaction across socioeconomic and racial lines. In contrast to the relatively well-established determinants of racial segregation, the mechanisms underlying socioeconomic segregation are less understood.
In my first dissertation chapter, I examine the determinants of socioeconomic segregation in friendship networks in high schools, drawing on sociological literature that highlights how structural barriers and boundary-making among groups with intersecting attributes drive segregation dynamics. I integrate these perspectives using data from the Add Health study to disentangle the relative impact of students’ residential locations, course and extracurricular selections, and intersecting preferences on socioeconomic segregation. The results from this analysis make three substantive contributions.
First, parents’ decisions on residential location and school enrollment produce compositional differences between schools that account for about half of the total segregation. The other half is attributable to students’ friendship choices within schools. Therefore, existing desegregation efforts (e.g., busing, zoning) are insufficient to fully integrate friendship networks as they only address between-school segregation. Second, within schools, SES-stratified course selections and racial homophily lie behind much of what prior research identifies as socioeconomic homophily. Therefore, SES-integrated friendship networks will be difficult to achieve without addressing tracking practices and racial homophily within schools. Third, remaining socioeconomic homophily is characterized by unilateral closure among high-SES students rather than mutual avoidance between socioeconomic groups. Therefore, interventions should focus on the friending behavior of students at the top of the SES distribution.
This paper has received three best paper awards and an R&R at the American Sociological Review. During my doctoral studies at Cornell University, I secured a three-year, $240,000 grant from the National Science Foundation to develop this project. As part of this project, I also co-authored a review paper synthesizing the state of the art in causal network analysis. The paper, published in the Annual Review of Sociology, is a comprehensive resource for researchers seeking to advance from descriptive to causal analyses with social network data.
Family dynamics as drivers of social inequality
Like friendship dynamics, family dynamics can also influence socioeconomic outcomes. A second line of my research examines their aggregate consequences for social inequality. Specifically, I investigate the “motherhood penalty”—a prominent concept in sociology and demography that describes the decline in earnings women experience after giving birth. While existing studies show that the motherhood penalty on women’s earnings varies across economic strata, the implications of these heterogeneous effects for earnings inequality remain unclear.
In my second dissertation chapter, I address this gap using Census data to analyze how variations in the motherhood penalty across earnings levels over the past four decades have affected earnings inequality within and between socioeconomic strata. I propose a new decomposition method to accomplish this methodologically. The approach extends the classic variance decomposition (Western and Bloome 2009) to a causal framework, enabling researchers to measure treatment effects on inequality statistics and decompose these effects into within-group and between-group components. Unlike methods that focus solely on mean differences between groups, my approach also accounts for variability within groups, offering a more comprehensive measurement of treatment effects on inequality.
Results from this study show that changes in the motherhood penalty since the 1980s have reduced total inequality in household earnings despite increasing earnings inequality between economic strata. This is because between-group effects are small and the decline in the motherhood penalty during this period has reduced earnings inequality within economic strata. These patterns remain hidden if only mean differences between groups are examined.
My method has broader applications beyond the motherhood penalty and contributes to a longstanding tradition of decomposition methods in demography. I am preparing a manuscript for submission to a methodology journal in which I formally develop the model, validate its performance through simulations, and provide an R package for implementation.
Political dynamics as drivers of social inequality
Research on inequality rarely translates into the implementation of equitable policies. A third area of my research explores the political barriers hindering progress toward social equity. One such barrier is government instability. Nations plagued by frequent cabinet collapses become unstable and rudderless, leading to social unrest, declining trust in institutions, and increasing inequality.
In my third dissertation chapter, which won the McGinnis Best Paper Award from the Cornell Department of Sociology, I examine how the stability of coalition governments in parliamentary democracies is influenced by the interplay of political parties that comprise them.
This research involves a complex multilevel problem where units at a lower level (political parties) influence outcomes at a higher level (government stability). While multilevel models exist for analyzing how lower-level outcomes (e.g., student achievement) are influenced by higher-level units (e.g., schools), we lack methods for studying micro-macro links where the conventional multilevel setup is reversed. I develop such a model by incorporating an aggregation function into the regression equation to model how parties and their interrelationships collectively shape governments.
I use my model to examine the relationship between parties’ financial dependence on member contributions and the risk of coalition government breakdowns involving those parties. The results indicate that the more parties’ financial resources comprise member contributions, the higher the risk of collapse for cabinets that include these parties. The aggregation analysis further shows that this effect varies by parties’ relative parliamentary seat share. Therefore, government stability in parliamentary democracies is affected by the internal structure of political parties and their clout within parliament.
My method has broader applications beyond coalition research. It provides an empirical-statistical approach to studying micro-macro links and contributes to sociological methodology for multilevel analyses. I am preparing a manuscript for submission to a methodology journal in which I formally develop the model, validate its performance through simulations, and provide an R package for its implementation.
Climate dynamics as drivers of social inequality
Climate change is increasingly recognized as not just a biophysical and technological challenge but also a complex social issue, with both social causes and consequences. Emerging sociological research on the topic indicates, for example, that extreme events—like floods, fires, heatwaves, and droughts—worsen inequalities by disproportionately affecting vulnerable communities and countries.
During my postdoc at Princeton University, I will expand my research on drivers of inequality to include climate dynamics as a fourth pillar. In collaboration with Filiz Garip (Sociology, Princeton University), I will analyze the climate-inequality nexus through a network lens. Specifically, I will use high-resolution weather data to track the evolution of microclimates across 170 communities in Mexico since the 1980s. This will allow me to create bipartite networks linking communities to different types of extreme events and address the following key questions: How have networks of communities that experience similar weather events evolved over time? Do communities with similar microclimates experience different levels of vulnerability to extreme events, and what are the resulting patterns of inequality within and between these communities? And do US immigration and economic policies moderate the relationship between extreme events and inequality within and between communities?
As part of this research line, I have collaborated with the OECD International Programme for Action on Climate (IPAC) to develop an R Shiny application to monitor countries’ climate actions and policies. This app, which was presented at COP27, can be accessed here. More information can be found here.