Postdoctoral Fellow and
Assistant Professor (beginning Fall 2025)
Department of Government
The University of Texas at Austin
mauslen@austin.utexas.edu
My research focuses on democratic representation, the media, and public opinion's role in policymaking, with an emphasis on state and local politics. I am particularly interested in understanding how actors such as local news organizations and political parties enhance or weaken the links between the public and elected representatives. I also study political methodology, in particular methods for public opinion estimation.
I received my PhD in Political Science from Columbia University. I also have an MPP from the John F. Kennedy School of Government at Harvard University and a BA from Indiana University in Journalism and Political Science. Previously, I worked as a journalist covering state and local politics for the Tampa Bay Times, Miami Herald, and Indianapolis Star.
– Best Graduate Student Poster, 2022 State Politics and Policy Conference
The development of multilevel regression and poststratification (MRP) has allowed scholars to more accurately estimate subnational public opinion using national polls. However, MRP generally recovers less accurate estimates from polls whose respondents are selected using cluster sampling – also called area-probability sampling. This is in part because cluster-sampled polls rely on a complex form of random sampling focused on national representativeness that may result in small or unrepresentative subsamples in subnational geographies. This has limited MRP’s usefulness in subnational opinion estimation in several contexts, including historical polls in the US, where cluster-sampling was common into the 1980s, and large academic studies in many countries today. In this paper, I propose two approaches to improve estimation from MRP with cluster-sampled polls. The first is pooling data from multiple surveys to produce a larger sample of clusters. The second is clustered MRP (CMRP), which extends MRP by modeling opinion using the geographic information included in a survey’s cluster-sampling procedure. Using simulations, I show that both methods improve upon traditional MRP, and I validate them using historical polls in the US.
– Christopher Z. Mooney Best Dissertation Prize, APSA State Politics and Policy Section
Electoral accountability is central to theories of representation in democracies, and it is widely believed that the news media play a critical role. This paper examines whether and how the media contribute to accountability. Drawing on an extensive archive of local newspaper transcripts, media market and circulation data, state legislative roll-call votes, and measures of district-level public opinion on five policy areas, I find that media coverage is associated with greater policy responsiveness in state legislatures. Defying the seminal theories of electoral accountability, however, I find no evidence that the media affects what the public knows about state politics or how they behave in state legislative elections. Rather, I conjecture that local news affects representation via a more direct, elite-focused “watchdog” mechanism—by informing legislators about public opinion or increasing the perceived costs that politicians face when deciding to cast an unpopular vote.
– Christopher Z. Mooney Best Dissertation Prize, APSA State Politics and Policy Section
Dramatic, decades-long declines in local news has raised alarm bells about democratic accountability in cities and towns where legacy local news outlets are often the only sources of information for the public. But even in its heyday, local news almost never covered most municipal governments. Using text analysis on a large archive of stories published in U.S. newspapers, I identify the cities and towns where local politics is most frequently covered by the press. I show that although decisions about where to prioritize coverage of local politics is broadly consistent with news organizations’ profit incentives, there are striking disparities in access to information about municipal governments. The local press is much more likely to cover politics in larger cities and those with more white and wealthy residents. In cities and towns that the press covers more frequently, local governments also spend more on popular and visible public goods, such as policing, parks, housing, and public transportation. This suggests that increasing financial pressures on news outlets will have negative implications for local public goods provision that may exacerbate existing inequalities in American democracy.
Measuring public opinion at subnational geographies is critical to many theories in political science. Multilevel regression and post-stratification (MRP) is a popular tool for doing so, although existing work is limited to measuring opinion on a single survey question. We provide a framework for estimating the joint distribution of opinion on multiple questions ("Multivariate MRP"). To do so, we derive a novel method for variational inference in multinomial logistic regression with many random effects. This requires performing variational inference with high-dimensional fixed effects, but we show that this can be done at a low computational cost. We validate this procedure by estimating public opinion by party in the United States and show that existing methods can be improved considerably by adding contextual covariates on the prior levels of party identification. Substantively, we show how the output of multivariate MRP can be used to study representation across multiple policy issues simultaneously.