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Linda Zhao, PhD

Postdoctoral Research Associate

Cornell University

Research Questions

1. How do network evolution and population structure jointly shape social outcomes?​

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  • Key concepts: consolidation (the extent to which different social attributes are correlated in a population), network externalities (when adopting a behavior or practice is more likely given ties to prior adopters), and homophily (the tendency of "birds of a feather to flock together").

  • Findings: Under high consolidation, homophily exacerbates inequality in how behaviors diffuse through social networks. Yet under low consolidation, homophily has the opposite effect.

  • Data and methods: we use agent-based models to simulate populations, network formation, and then social diffusion. 

  • For more information, including any replication materials, media coverage, etc., please click the button below.

 

Zhao, L. and F. Garip. 2021. "Network Diffusion under Homophily and Consolidation as a Mechanism for Social Inequality." Sociological Methods and Research.

2. What can networks and population structure tell us about immigrant integration?

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Zhao, L. “From Superdiversity to Consolidation: Implications of Structural Intersectionality for Interethnic Friendships” R&R at American Journal of Sociology

 

 

 

 

 

Zhao, L. “Integrators and Coordinators: Native-Immigrant Connectors in Classroom Friendships.” under review

  • Key concepts: consolidation (the extent to which different social attributes are correlated in a population) and homophily (the tendency of "birds of a feather to flock together").

  • Findings: Consolidation between socioeconomic status and ethnicity in classrooms predicts greater ethnic homophily in classroom friendships. This suggests that multidimensional diversity could encourage interethnic friendships.

  • Data and methods: I use meta-analyses of exponential random graph models to analyze 11,011 students nested in 503 classroom friendship networks from the CILS4EU survey.

  • Key concepts: integrators (individuals who "bridge" immigrants and natives in a network) and coordinators (individuals who bridge within their own group).

  • Findings: Integrator positions partly reflect high ego-network diversity despite the prevailing native-immigrant segregation in networks. Natives and immigrants are equally likely to be integrators. Among natives, integrators are more open to immigrant cultures than coordinators. Among immigrants, integrators express stronger national identification with their country of residence.

  • Data and methods: I analyze 16,008 students nested in 771 classroom friendship networks in the CILS4EU survey.

 

I use social networks and computational social science to analyze the contextual processes underlying differential outcomes, especially in relation to ethnic and racial inequality, immigrant integration, and policing. Throughout, I ask how compositional differences between social settings (e.g., ethnic diversity or inequality in classrooms, communities, or workplaces) shape how people sort and select into their interpersonal networks, and how such networks in turn influence us and explain divergent social outcomes. 

3. How do police officers’ networks relate to patterns of police misconduct?

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Zhao, L. and A.V. Papachristos. 2020. “Network Position and Police Who Shoot.” The ANNALS of the American Academy of Political and Social Science. 

 

 

 

 

 

 

 

 

 

Zhao, L. and A.V. Papachristos. "Threats to Blue Networks: The Impact of Partner Injuries on Police Misconduct." R&R at American Sociological Review

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Zhao, L. Relational Dynamics of Police Misconduct: Social Influence and Network Evolution. Under Review.

  • Key concepts: shuffling (a phenomena we define as the re-assignment of officers across districts or other units).

  • Findings: Officers who bridge otherwise distinct groups of officers in networks of misconduct are more likely to shoot at civilians. This is strongly related to but not completely explained by organizational-level factors such as shuffling.

  • Data and methods: we re-create the network of police misconduct for the Chicago Police Department using more than 38,442 complaints filed against police officers between 2000 and 2003.

  • For more information, including any replication materials, media coverage, etc., please click the button below.

  • We suggest a networked group threat response that links peer injuries to police misconduct. We find that:

    • Network ties to injured officers predict higher levels of subsequent misconduct.

    • This effect is stronger for officers with stronger ties to the injured officer, but is present even for weaker ties.

    • The increase in misconduct following an injury was over two times larger when officers were injured by Black suspects, compared to when officers were injured by non-Black suspects.

  • Data and methods: we analyze allegations of misconduct, records on injuries, and several types of network ties using large-scale administrative data from the Chicago Police Department.

 
  • Key concepts: police misconduct spreads through social influence between officers, and the co-evolution of networks and misconduct may exacerbate the problem of influence.

  • Findings: First, officers with and without recent allegations of misconduct are subsequently less likely to work together in a capacity that led to an arrest compared to officers that match on recent misconduct status. Second, officers with allegations of misconduct tend to subsequently go through a greater number of ties in the arrest-networks.

  • Data and methods: I re-create annual networks of co-arrests within different units for the Chicago Police Department and model how networks and misconduct both change over time using stochastic actor-oriented models.

4. How can we use longitudinal data to understand the effects of neighborhoods on health?

Zhao, L., Hessel, P., Thomas, J.S. and J. Beckfield. Forthcoming. “Inequality in Place: Effects Exposure to Neighborhood Level Economic Inequality on Mortality.” Demography.

  • Key concepts: dynamic selection (in which A has an effect on B through C, but C can also shape future levels of A).

  • Findings: Recent exposure to higher income inequality in neighborhoods predicts higher mortality risk among individuals at ages 45 or above. We do not observe a cumulative effect.

  • Data and methods: we use the PSID and NCDB to follow 4,774 individuals, where they live, and how unequal their neighborhoods are, for over a decade. We use marginal structural models to account for dynamic selection.