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This paper presents the results of a conjoint survey experiment in which Swiss citizens were asked to choose among parliamentary candidates with different class profiles determined by occupation, education and income. Existing survey-experimental literature on this topic suggests that respondents are indifferent to the class profiles of candidates or biased against candidates with high-status occupations and high incomes. We find that respondents are biased against upper middle-class candidates as well as routine working-class candidates. While the bias against upper middle-class candidates is primarily a bias among working-class individuals, the bias against routine working-class candidates is most pronounced among middle-class individuals. Our supplementary analysis of observational data confirms the bias against routine working-class candidates, but not the bias against upper middle-class candidates.

Multilevel regression with post-stratification (MrP) has quickly become the gold standard for small area estimation. While the first MrP models did not include contextlevel information, current applications almost always make use of such data. When using MrP, researchers are faced with three problems: how to select features, how to specify the functional form, and how to regularize the model parameters. These problems are especially important with regard to features included at the context level. We propose a systematic approach to estimating MrP models that addresses these issues by employing a number of machine learning techniques. We illustrate our approach based on 89 items from public opinion surveys in the US and demonstrate that our approach outperforms a standard MrP model, in which the choice of contextlevel variables has been informed by a rich tradition of public opinion research.

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