About

Jennie E. Brand is Professor of Sociology at the University of California, Los Angeles (UCLA), and Professor of Statistics and Data Science (by courtesy). She is Co-Director of the Center for Social Statistics (CSS) at UCLA. She studies social stratification and inequality, mobility, social demography, education, and methods for causal inference.

Research topics

• Machine Learning
• Artificial Intelligence
• Computer Science
• Medicine
• Political Science
• Mathematics
• Social Science
• Sociology
• Statistics
• Engineering
• Geography
• Demography
• Demographic economics
• Economics
• Economic growth
• Econometrics

Selected publications

• Replication code for: "Causal inference with a continuous treatment: Addressing positivity constraints, nonlinearity, and effect heterogeneity"

  Harvard Dataverse · 2026-05-14

  datasetOpen accessSenior author

  Causal inference approaches often emphasize binary treatments. But in many applications, the underlying constructs are continuous. In the potential outcomes framework, a continuous treatment can take on numerous values, each corresponding to a potential outcome that may be realized. In this setting, common estimands may be intractable due to a common issue in social research, particularly research on social inequality: the exposure is highly stratified by confounders. We show how to avoid drawing inferences about counterfactuals where data are unlikely to exist by carefully selecting the causal estimand. We adopt an additive shift estimand that adds a small, fixed amount to each unit's income. Our approach is preferable to population-average dose-response curves in settings where some treatment values rarely occur in some subgroups. We also show how to estimate and summarize patterns of nonlinearity and effect heterogeneity with continuous treatments. As a motivating example, we consider the causal effect of parental income on college attendance, a setting in which the exposure is highly stratified by confounders (e.g., parental education). Our approach applies to a wide range of possible treatment conditions in sociology.

  PublisherDOI

• Trapped in Declining Occupations: Barriers to Worker Mobility in a Changing Economy

  2026-01-07

  article

  The U.S. has undergone substantial changes in jobs, occupations, and mobility over the last two decades. Using administrative data from the U.S. Occupational Outlook Handbook (2000–2020), we examine how immediate and projected occupational restructuring affects workers’ mobility. In an update to prior research, we find that workers in both growing and declining occupations experience greater mobility than those in stable occupations. However, the direction of movement varies. Workers in declining occupations often move laterally into other declining occupations, with nearly 60% experiencing downward mobility. In contrast, growing occupations offer better prospects for upward mobility, particularly for workers transitioning from declining to growing occupations, where almost 50% enter higher-paying occupations. Yet, such moves to emerging jobs are relatively rare, accounting for only 5% of all occupational movements. These results highlighthow recent shifts in the occupational structure exacerbate existing disadvantages for workers facing declining job opportunities.

  PublisherDOI

• Causal Machine Learning: A Deductive–Inductive Framework for Sociological Research

  KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie · 2026-02-19 · 3 citations

  articleOpen accessSenior author

  Abstract Causal explanation is central to sociological research, shaping both theoretical development and empirical inquiry. This paper argues that causal machine learning—which integrates deductive identification strategies with inductive estimation techniques—offers an analytical approach for modeling complex, nonlinear social processes within the potential outcomes framework. We argue that causal machine learning operates through an iterative feedback loop: Theoretical assumptions guide flexible estimation, which inductively uncovers complex heterogeneities and nonlinearities, and these discoveries subsequently refine and expand sociological knowledge. Drawing on a systematic review of recent sociological research (2014–2024), we highlight how causal machine learning is advancing work in three key areas: causal effect heterogeneity, causal mediation analysis, and time-varying causal inference. These developments expand the methodological tool kit available to sociologists and strengthen the discipline’s ability to test, refine, and extend theories of social explanation. We conclude by outlining emerging directions, including high-dimensional causal inference and generative artificial intelligence, that are opening new methodological frontiers in causal machine learning for sociology.

  PublisherOA PDFDOI

• AI-Accelerated Occupational Decline and the Mobility Trap

  AEA Papers and Proceedings · 2026-05-01

  article

  Economic transformation challenges workers beyond job creation or loss by reshaping their ability to move across occupations. This paper examines whether AI-driven occupational restructuring, accelerated after 2018 by transformer-based technologies, creates a “mobility trap” for workers in declining, high-AI-exposed occupations. Using BLS administrative data and CPS worker transitions, we analyze who moves, where they move, and with what outcomes. We find that while these workers are more mobile, they are 5.2 times more likely to move into another declining occupation than a growing one. Nearly 70 percent experience downward or lateral mobility, suggesting that AI-driven restructuring entrenches disadvantage rather than enabling upward mobility.

  PublisherDOI

• Trapped in Declining Occupations: Barriers to Worker Mobility in a Changing Economy

  SocArXiv (OSF Preprints) · 2026-01-07

  preprint1st authorCorresponding

  The U.S. has undergone substantial changes in jobs, occupations, and mobility over the last two decades. Using administrative data from the U.S. Occupational Outlook Handbook (2000–2020), we examine how immediate and projected occupational restructuring affects workers’ mobility. In an update to prior research, we find that workers in both growing and declining occupations experience greater mobility than those in stable occupations. However, the direction of movement varies. Workers in declining occupations often move laterally into other declining occupations, with nearly 60% experiencing downward mobility. In contrast, growing occupations offer better prospects for upward mobility, particularly for workers transitioning from declining to growing occupations, where almost 50% enter higher-paying occupations. Yet, such moves to emerging jobs are relatively rare, accounting for only 5% of all occupational movements. These results highlight how recent shifts in the occupational structure exacerbate existing disadvantages for workers facing declining job opportunities.

  Publisher

• Trapped in declining occupations: Barriers to worker mobility in a changing economy

  Science Advances · 2026-03-06 · 1 citations

  articleOpen access

  The US has undergone substantial changes in jobs, occupations, and mobility over the past two decades. Using administrative data from the US Occupational Outlook Handbook (2000 to 2020), we examine how immediate and projected occupational restructuring affects workers' mobility. In an update to prior research, we find that workers in both growing and declining occupations experience greater mobility than those in stable occupations. However, the direction of movement varies. Workers in declining occupations often move laterally into other declining occupations, with nearly 60% experiencing downward mobility. In contrast, growing occupations offer better prospects for upward mobility, particularly for workers transitioning from declining to growing occupations, where almost 50% enter higher-paying occupations. However, these moves to emerging jobs are relatively rare, accounting for only 5% of all occupational movements. These results highlight how recent shifts in the occupational structure exacerbate existing disadvantages for workers facing declining job opportunities.

  PublisherDOI

• Differential Effects of Completing College in Reducing COVID-19 Job Loss by Race and Skin Color

  2025-06-23

  preprintSenior author

  The COVID-19 pandemic led to unprecedented labor market disruptions, disproportionately affectinglow-wage workers and exacerbating existing social and racial inequalities (Coats et al. 2022;Cortes and Forsythe 2023; Fazzari and Needler 2021; Parker, Minkin, and Bennett 2020; Sáenzand Sparks 2020). While large-scale crises, including pandemics, have sometimes disrupted entrenchedeconomic hierarchies and reduced inequality by shaking the foundations of power andprivilege (Scheidel 2017), the COVID-19 crisis stands in contrast to this pattern, with inequalitypatterns more in line with recent economic recessions (Couch and Fairlie 2010; Couch, R. Fairlie,and Xu 2018; Couch, R. W. Fairlie, and Xu 2020; Hoynes 1999). Workers in low-wage sectors, suchas hospitality, retail, and other service industries, who are disproportionately from racial minoritygroups and less likely to hold a college degree, faced high levels of job insecurity. In contrast, workersin high-skilled occupations, particularly those able to work remotely, were better positioned toweather the economic storm, highlighting the role of educational attainment as a key factor in mitigatinglabor market disruptions (Acemoglu 2002; Angelucci, Manuela, Marco Angrisani, Daniel MBennett, Arie Kapteyn, and Simone G Schaner. 2020; Grusky et al. 2021; Montenovo et al. 2022).The sudden economic downturn and unprecedented labor market conditions triggered by COVID-19 provide a unique backdrop to reassess the utility of college degrees under crisis conditions andexplore whether the effects of having college degrees vary across different subsets of workers.

  PublisherDOI

• :<i>The Accidental Equalizer: How Luck Determines Pay After College</i>

  American Journal of Sociology · 2025-05-01

  article1st authorCorresponding
  PublisherDOI

• What We Have (Recently) Learned: RC28’s Contributions Over the Last Two Decades

  2025-09-08 · 1 citations

  articleOpen access1st authorCorresponding

  In this article, we provide an updated complement to Hout and DiPrete’s (2006) influential review of RC28 scholarship, offering an analysis of contributions from the RC28 community since the early 2000s. Drawing on topic models of conference presentations, highly cited articles and books presented at RC28 conferences and written by RC28 board members, RSSM edited volumes, a qualitative survey of RC28 members, and plenary session feedback from two RC28 conferences, we identify 21 recent key contributions to social stratification and mobility research. We show thatRC28 scholars have increasingly emphasized educational, income and wealth, and gender inequality, while placing less focus on social class. Some of the most highly cited research over the last two decades include studies on the consequences of income inequality, differential economic returns to college, family background effects on children’s achievement, gender gaps in educational achievement, persistent inequality in educational attainment, changes inintergenerational mobility patterns, and the importance of wealth inequality. The community’s methodological focus has shifted from log-linear models to methods for causal inference. Despite improvements in participation from Asia, the field would benefit from broader global inclusion. We conclude that to remain relevant and impactful, RC28 must not only continue producing highquality research but also actively engage with policymakers, practitioners, and the public to ensure that its insights help shape more equitable societies.

  PublisherOA PDFDOI

• Gender and racial diversity socialization in science

  Nature Computational Science · 2025-04-17

  articleOpen access

  Scientific collaboration networks are a form of unequally distributed social capital that shapes both researcher job placement and long-term research productivity and prominence. However, the role of collaboration networks in shaping the gender and racial diversity of the scientific workforce remains unclear. Here we propose a computational null model to investigate the degree to which early-career scientific collaborators with representationally diverse cohorts of scholars are associated with forming or participating in more diverse research groups as established researchers. When testing this hypothesis using two large-scale, longitudinal datasets on scientific collaborations, we find that the gender and racial diversity in a researcher's early-career collaboration environment is strongly associated with the diversity of their collaborators in their established period. This diversity-association effect is particularly prominent for men. Coupled with gender and racial homophily between advisors and advisees, collaborator diversity represents a generational effect that partly explains why changes in representation within the scientific workforce tend to happen very slowly.

  PublisherOA PDFDOI

Recent grants

• California Center for Population Research Training Program

  NIH · $4.1M · 2001–2027

• Developing computational tools for nonparametric summaries of income mobility

  NSF · $69k · 2021–2022

Frequent coauthors

• Yu Xie

  Peking University

  17 shared

• Ravaris Moore

  Loyola Marymount University

  12 shared

• Xi Song

  University of Pennsylvania

  11 shared

• John Robert Warren

  7 shared

• Sarah A. Burgard

  University of Michigan–Ann Arbor

  6 shared

• Ian Lundberg

  Cornell University

  6 shared

• Andrew Halpern-Manners

  Indiana University Bloomington

  5 shared

• Nanum Jeon

  4 shared

Labs

• Sociology Undergraduate Advisory BoardPI

Education

• Ph.D., Sociology

  University of Wisconsin Madison

  2004

Awards & honors

• ASA Methodology Leo Goodman Mid-Career Award
• ASA Inequality, Poverty, and Mobility William Julius Wilson…
• Numerous distinguished book and article awards