Distinguished Professor, Department of Government
Department of Mathematics & Statistics,
Founding Director, Center for Data Science
Member, Center for Neuroscience and Behavior

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Modeling Latent Information in Voting Data with Dirichlet Process Priors

  • Traunmuller, Richard, Andreas Murr, and Jeff Gill. “Modeling Latent Information in Voting Data with Dirichlet Process Priors”. Political Analysis 23, no. 1 (2015): 1-20

    We apply a specialized Bayesian method that helps us deal with the methodological challenge of unobservedheterogeneity among immigrant voters. Our approach is based on \emphgeneralized linear mixed Dirichlet models (GLMDM) whererandom effects are specified semiparametrically using a Dirichlet process mixture prior that has been shown to account forunobserved grouping in the data. Such models are drawn from Bayesian nonparametrics to help overcome objections handling latenteffects with strongly informed prior distributions. Using 2009 German voting data of immigrants, we show that for difficultproblems of missing key covariates and unexplained heterogeneity this approach provides (1) overall improved model fit, (2)smaller standard errors on average, and (3) less bias from omitted variables. As a result, the GLMDM changed our substantiveunderstanding of the factors affecting immigrants’ turnout and vote choice. Once we account for unobserved heterogeneity amongimmigrant voters, whether a voter belongs to the first immigrant generation or not is much less important than the extantliterature suggests.  When looking at vote choice we also found that an immigrant’s degree of structural integration does notaffect the vote in favor of the CDU/CSU, a party which is traditionally associated with restrictive immigration policy.

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