Welfare programs place burdens on citizens to document their vulnerability through means-tested regulations in the United States, but theories of the welfare state do not necessarily account for mismatches between residents’ eligibility and their legibility to state infrastructure. Focusing on housing instability during the COVID-19 pandemic, we explain how Chicago residents who were eligible for emergency rental assistance programs (ERAPs) were unable to render their vulnerability and survival strategies legible to formal bureaucratic systems. This meant that despite the extensive federal funding allocated to state and municipal ERAPs during the pandemic, many people who were behind on rent did not even apply for support. Based on 76 in-depth interviews with low-income renters and 25 interviews with people working with these programs in Chicago, we document three mismatches between renters’ survival strategies and the requirements of formal bureaucratic systems of categorization. First, we illustrate how people who informally leased apartments in Chicago struggled to properly document their housing instability and the administrative burdens they faced in doing so. Second, because of acute housing precarity and fear of eviction, some renters prioritized their rent over other needs and then could not translate their vulnerability into ERAP eligibility. Third, we explain how undocumented Chicagoans often avoided ERAPs because of the perceived risks associated with becoming legible to the state. Being unable or unwilling to access aid created a cascade of other precarious conditions.

Molecular sequence variation at a locus informs about the evolutionary history of the sample and past population size dynamics. The Kingman coalescent is used in a generative model of molecular sequence variation to infer evolutionary parameters. However, it is well understood that inference under this model does not scale well with sample size. Here, we build on recent work based on a lower resolution coalescent process, the Tajima coalescent, to model longitudinal samples. While the Kingman coalescent models the ancestry of labeled individuals, we model the ancestry of individuals labeled by their sampling time. We propose a new inference scheme for the reconstruction of effective population size trajectories based on this model and the infinite-sites mutation model. Modeling of longitudinal samples is necessary for applications (e.g., ancient DNA and RNA from rapidly evolving pathogens like viruses) and statistically desirable (variance reduction and parameter identifiability). We propose an efficient algorithm to calculate the likelihood and employ a Bayesian nonparametric procedure to infer the population size trajectory. We provide a new MCMC sampler to explore the space of heterochronous Tajima’s genealogies and model parameters. We compare our procedure with state-of-the-art methodologies in simulations and an application to ancient bison DNA sequences. Supplementary materials for this article are available online including a standardized description of the materials available for reproducing the work.