When does regression discontinuity design work? Evidence from random election outcomes
Hyytinen, A., Meriläinen, J., Saarimaa, T., Toivanen, O., & Tukiainen, J. (2018). When does regression discontinuity design work? Evidence from random election outcomes. Quantitative Economics, 9(2), 1019-1051. https://doi.org/10.3982/QE864
Published inQuantitative Economics
© 2018 The Authors
We use elections data in which a large number of ties in vote counts betweencandidates are resolved via a lottery to study the personal incumbency advantage. We benchmark non-experimental regression discontinuity design (RDD) estimates against the estimate produced by this experiment that takes place exactlyat the cutoff. The experimental estimate suggests that there is no personal incumbency advantage. In contrast, conventional local polynomial RDD estimates suggest a moderate and statistically significant effect. Bias-corrected RDD estimatesthat apply robust inference are, however, in line with the experimental estimate.Therefore, state-of-the-art implementation of RDD can meet the replication standard in the context of close elections.
Publication in research information system
MetadataShow full item record
- Kauppakorkeakoulu 
Showing items with similar title or keywords.
Kulju, S.; Riegger, L.; Koltay, P.; Mattila, Keijo; Hyväluoma, J. (Elsevier; Academic Press, 2018)Hypothesis While multiphase flows, particularly droplet dynamics, are ordinary in nature as well as in industrial processes, their mathematical and computational modelling continue to pose challenging research tasks - ...
Tsatsishvili, Valeri; Cong, Fengyu; Ristaniemi, Tapani; Toiviainen, Petri; Alluri, Vinoo; Brattico, Elvira; Nandi, Asoke (IEEE, 2014)In contrast to block and event-related designs for fMRI experiments, it becomes much more difficult to extract events of interest in the complex continuous stimulus for finding corresponding blood-oxygen-level ...
Melegati, Jorge; Wang, Xiaofeng; Abrahamsson, Pekka (IEEE, 2019)Recent studies have proposed the use of experiments to guide software development in order to build features that the user really wants. Some authors argue that this approach represents a new way to develop software that ...
Nordhausen, Klaus; Oja, Hannu; Tyler, David E. (Elsevier, 2022)Many linear dimension reduction methods proposed in the literature can be formulated using an appropriate pair of scatter matrices. The eigen-decomposition of one scatter matrix with respect to another is then often used ...
Tikka, Santtu; Karvanen, Juha (Elsevier, 2019)Identification of causal effects is one of the most fundamental tasks of causal inference. We consider an identifiability problem where some experimental and observational data are available but neither data alone is ...