Income-based inequality in post-disaster migration is lower in high resilience areas : evidence from US internal migration

Residential relocation following environmental disasters is an increasingly necessary climate change adaptation measure. However, relocation is among the costliest individual-level adaptation measures, meaning that it may be cost prohibitive for disadvantaged groups. As climate change continues to worsen, it is important to better understand how existing socioeconomic inequalities affect climate migration and how they may be offset. In this study we use network regression models to look at how internal migration patterns in the United States vary by disaster-related property damage, household income, and local-level disaster resilience. Our results show that post-disaster migration patterns vary considerably by the income level of sending and receiving counties, which suggests that income-based inequality impacts both individuals' access to relocation and the ability of disaster-afflicted areas to rebuild. We further find evidence that income-based inequality in post-disaster outmigration is attenuated in areas with higher disaster resilience, not due to increased relocation out of poorer areas but instead because there is decreased relocation from richer ones. This finding suggests that, as climate adaptation measures, relocation and resilience-building are substitutes, with the implication that resilience incentivizes in situ adaptation, which can be a long term drain on individual wellbeing and climate adaptation resources.Peer reviewe

Shared E-scooter Adoption and Mode Substitution Patterns

This thesis explores the adoption and mode substitution patterns of e-scooters using survey data from four metropolitan areas in the southern United States, obtained from Fall 2019 to Spring 2020. For adoption patterns, we find a positive correlation between the use of ridehailing services and being an e-scooter user, as well as observed higher multimodality for e-scooter users compared to non-users (N =2,914). E-scooters are found to be used by people with lower income, higher racial diversity, and certain disabilities. For substitution patterns, we examine heterogeneity in trip attributes, substitution patterns, and rider characteristics in a sample of e-scooter rides (N=295). With a latent-class cluster analysis, we identify three distinctive classes of e-scooter rides and associated users. The off-to-nightlife class (39.9%) captures many rides for social and recreational trips at night, many of which substitute for private vehicles, ridehailing, or taxis. Many users associated with this class are college-educated and middle-aged with middle-to-high household income, convenient access to cars, and positive attitudes toward density, technology, and environmental policies. The weekend-fun class (31.9%) includes many trips made “just for fun” by users, many of which would not have been made otherwise. Riders taking this type of trip rarely use e-scooters, live in the least dense suburbs with auto-oriented lifestyles, and are more likely to be female, older (relative to the other classes), well-educated, and wealthy. The commutes class (28.2%) tends to involve short rides during weekday daytime for work/school-related trips, most of which would replace active modes. Most commutes users are low-income young students with diverse racial backgrounds and limited access to cars. These tend to reside in the densest neighborhoods and are the most multimodal in the sample. For each class, we discuss behavioral mechanisms and policy options for sustainable transportation. In brief, this thesis fills important literature gaps by identifying heterogeneous e-scooter rides and users, incorporating attitudes, and focusing on the southern U.S.M.S

Spread of tweets in climate discussions: A case study of the 2019 Nobel Peace Prize announcement

Peer reviewe

Generative Dynamics of Supreme Court Citations : Analysis with a New Statistical Network Model

The significance and influence of U.S. Supreme Court majority opinions derive in large part from opinions' roles as precedents for future opinions. A growing body of literature seeks to understand what drives the use of opinions as precedents through the study of Supreme Court case citation patterns. We raise two limitations of existing work on Supreme Court citations. First, dyadic citations are typically aggregated to the case level before they are analyzed. Second, citations are treated as if they arise independently. We present a methodology for studying citations between Supreme Court opinions at the dyadic level, as a network, that overcomes these limitations. This methodology - the citation exponential random graph model, for which we provide user-friendly software - enables researchers to account for the effects of case characteristics and complex forms of network dependence in citation formation. We then analyze a network that includes all Supreme Court cases decided between 1950 and 2015. We find evidence for dependence processes, including reciprocity, transitivity, and popularity. The dependence effects are as substantively and statistically significant as the effects of exogenous covariates, indicating that models of Supreme Court citations should incorporate both the effects of case characteristics and the structure of past citations.Peer reviewe

Generative Dynamics of Supreme Court Citations : Analysis with a New Statistical Network Model

The significance and influence of U.S. Supreme Court majority opinions derive in large part from opinions' roles as precedents for future opinions. A growing body of literature seeks to understand what drives the use of opinions as precedents through the study of Supreme Court case citation patterns. We raise two limitations of existing work on Supreme Court citations. First, dyadic citations are typically aggregated to the case level before they are analyzed. Second, citations are treated as if they arise independently. We present a methodology for studying citations between Supreme Court opinions at the dyadic level, as a network, that overcomes these limitations. This methodology - the citation exponential random graph model, for which we provide user-friendly software - enables researchers to account for the effects of case characteristics and complex forms of network dependence in citation formation. We then analyze a network that includes all Supreme Court cases decided between 1950 and 2015. We find evidence for dependence processes, including reciprocity, transitivity, and popularity. The dependence effects are as substantively and statistically significant as the effects of exogenous covariates, indicating that models of Supreme Court citations should incorporate both the effects of case characteristics and the structure of past citations.Peer reviewe