civil rights

This paper develops and provides software for a class of spatial hierarchical (multilevel) models for binary outcomes, motivated by situations where outcome dependence arises both from diffusion among lower-level units and from spatially structured unobserved group effects. The authors propose two model variants—one that treats unobserved group effects as spatially independent and another that treats them as spatially dependent—and use a Bayesian estimation approach. They evaluate performance with Monte Carlo simulations and illustrate the approach by analyzing causes of civil rights protests in the United States in the 1960s.

This paper provides the first causal estimates of how presidential judicial appointments shape outcomes in federal district court civil rights cases. Using an original dataset of about 70,000 cases and nearly 200 judges, the analysis examines whether assignment to a Democratic- or Republican-appointed judge changes the likelihood of settlement, dismissal, or other outcomes.