An Empirical Justification for the Use of Racially Distinctive Names to Signal Race in Experiments

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Abstract

Researchers studying discrimination and bias frequently conduct experiments that use racially distinctive names to signal race. The ability of these experiments to speak to racial discrimination depends on the excludability assumption that subjects’ responses to these names are driven by their reaction to the individual’s putative race and not some other factor. We use results from an audit study with a large number of aliases and data from detailed public records to empirically test the excludability assumption undergirding the use of racially distinctive names. The detailed public records allow us to measure the signals about socioeconomic status and political resources that each name used in the study possibly could send. We then reanalyze the audit study to see whether these signals predict legislators’ likelihood of responding. We find no evidence that politicians respond to this other information, thus providing empirical support for the excludability assumption.

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Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments

Jens Hainmueller, Daniel Hopkins, Teppei Yamamoto (2014)

Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show howconjoint analysis, an experimental design yet to be widely applied in political science, enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. In conjoint analysis, respondents score a set of alternatives, where each has randomly varied attributes. Here, we undertake a formal identification analysis to integrate conjoint analysis with the potential outcomes framework for causal inference. We propose a new causal estimand and show that it can be nonparametrically identified and easily estimated from conjoint data using a fully randomized design. The analysis enables us to propose diagnostic checks for the identification assumptions. We then demonstrate the value of these techniques through empirical applications to voter decision making and attitudes toward immigrants.