This article presents a conceptual clarification of asymmetric hypotheses and a discussion of methodologies available to test them. Despite the existence of a litany of theories that posit asymmetric hypotheses, most empirical studies fail to capture their core insight: boundaries separating zones of data from areas that lack data are substantively interesting. We discuss existing set-theoretic and large-N approaches to the study of asymmetric hypotheses, introduce new ones from the literatures on stochastic frontier and data envelopment analysis, evaluate their relative merits, and give three examples of how asymmetric hypotheses can be studied with this suite of tools.
Authors’ note: The authors are grateful to Jose Fortou, Christopher Gelpi, Oul Han, Marcus Kurtz, Adam Lauretig, Richard McAlexander, William Minozzi, Anna Meyerrose, Jason Morgan, Adam Ramey, Gregory Smith, Avery White, and the participants in our panel at the 2015 American Political Science Association conference for feedback and comments on previous drafts. Replication materials are available at doi:10.7910/DVN/9NXGHP. These techniques are implemented in the open-source software asymmetric, which can be downloaded at https://github.com/asrosenberg/asymmetric-package. Further examples and resources can be found at https://www.asrosenberg.com/asymmetric-package/.
Contributing Editor: Jonathan N. Katz
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