About

Michael Auslen is an Assistant Professor in the Department of Government at The University of Texas at Austin. His research focuses on democratic representation, the media, and the role of public opinion in policymaking, with a particular emphasis on state and local politics. He investigates how actors such as local news organizations and political parties influence the connections between the public and elected representatives. Additionally, he studies political methodology, especially methods for estimating public opinion. Auslen earned his PhD in Political Science from Columbia University, holds a Master of Public Policy from the John F. Kennedy School of Government at Harvard University, and obtained a BA in Journalism and Political Science from Indiana University. Prior to his academic career, he worked as a journalist covering state and local politics for the Tampa Bay Times, Miami Herald, and Indianapolis Star.

Research topics

• Political Science
• Computer Science
• Sociology
• Law
• Political economy
• Economics
• Statistics
• Development economics
• Mathematics
• Public administration
• Public economics
• Regional science
• Chemistry
• Demographic economics
• Demography
• Geography

Selected publications

• Multivariate MRP

  ArXiv.org · 2025-07-04

  preprintOpen accessSenior author

  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.

  PublisherOA PDF

• Divided by Income? Policy Preferences of the Rich and Poor Within the Democratic and Republican Parties

  Political Behavior · 2024 · 2 citations

  1st authorCorresponding
  • Political Science
  • Sociology
  • Political Science
  PublisherDOI

• The Culture War and Partisan Polarization: State Political Parties, 1960–2018

  Studies in American Political Development · 2024 · 4 citations

  Senior authorCorresponding
  • Political Science
  • Political Science
  • Political economy

  Abstract Partisan polarization on “culture war” issues has become a defining feature of contemporary American politics. This was not always the case; for the first two-thirds of the twentieth century, social issues such as abortion and LGBTQ rights played no role in politics. Where and when did the partisan divide begin? Did the initiative come from state or national parties? Was there a critical moment, or was position change incremental? We have constructed an original database of nearly 2,000 state party platforms from 1960 to 2018. These platforms allow us to trace position-taking on these issues and generate estimates of platform ideology. By the time national parties took positions, we show, they lagged state-level position-taking. Contrary to long-held assumptions, we show that state party system polarization did not occur around any critical moment but rather was incremental.

  PublisherOA PDFDOI

• Improving Subnational Opinion Estimation from Cluster-Sampled Polls

  State Politics & Policy Quarterly · 2024 · 1 citations

  1st authorCorresponding
  • Political Science
  • Computer Science
  • Political Science

  Abstract 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

  PublisherOA PDFDOI

• Improving Subnational Opinion Estimation from Cluster-Sampled Polls

  2023-02-19

  preprintOpen access1st authorCorresponding

  The development of Multilevel Regression and Poststratification (MRP) has allowed scholars to more accurately estimate subnational public opinion using national polls. However, MRP generally fails to recover reliable 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 United States, where cluster-sampling was common into the 1980s, and large academic studies in many countries today. In this paper, I test 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 United States.

  PublisherOA PDFDOI

Frequent coauthors

• Justin Phillips

  2 shared

• Matthew Carr

  1 shared

• Gerald Gamm

  1 shared

Labs

• The Texas Politics ProjectPI

  The Texas Politics Project conducts regular, non-partisan, statewide polls of registered voters in Texas, and makes the results and data available for public use.