[gov3009-l] Carpenter on "How Random are Marginal Election Outcomes?"

Hi all, We hope that you can join us for the Applied Statistics Workshop this Wednesday, December 1st when we will be happy to have Dan Carpenter from the Department of Government, presenting on regression discontinuity designs in marginal elections. You will find an abstract below, along with links to two papers. The first is the paper for the talk and the second is supplementary material about how to model these elections using a continuous-time, continuous-space framework in the field of stochastic differential games. As always, we will serve a light lunch and the talk will begin around 12:15p. “How Random are Marginal Election Outcomes? A Critical Examination of Regression Discontinuity Designs for Causal Inference” Dan Carpenter Department of Government, Harvard University CGIS K354 (1737 Cambridge St.) Wednesday, December 1st, 12 noon Abstract: Elections with small margins of victory represent an important form of electoral competition and, increasingly, an opportunity for causal inference. Scholars using regression discontinuity designs (RDD) have interpreted the winners of close elections as randomly separated from the losers, using marginal election results as an experimental assignment of offce-holding to one candidate versus the other. In this paper we suggest that marginal elections may not be as random as RDD analysts suggest. We draw upon the simple intuition that elections that are expected to be close will attract greater campaign expenditures before the election and invite legal challenges and even fraud after the election. We present theoretical models that predict systematic differences between winners and losers, even in elections with the thinnest victory margins. We test predictions of our models on a dataset of all House elections from 1946 to 1990. We demonstrate that candidates whose parties hold structural advantages in their district are systematically more likely to win close elections at a wide range of bandwidths. Our findings call into question the use of close elections for causal inference and demonstrate that marginal elections mask structural advantages that may be troubling normatively. (Co-authored with Justin Grimmer, Eitan Hersh, and Brian Feinstein) Paper: http://www.iq.harvard.edu/events/sites/iq.harvard.edu.events/files/CloseEle… Supplement: http://www.iq.harvard.edu/events/sites/iq.harvard.edu.events/files/Suppleme… Cheers, matt. ~~~~~~~~~~~ Matthew Blackwell PhD Candidate Institute for Quantitative Social Science Department of Government Harvard University url: http://people.fas.harvard.edu/~blackwel/ ~~~~~~~~~~~ Matthew Blackwell PhD Candidate Institute for Quantitative Social Science Department of Government Harvard University url: http://people.fas.harvard.edu/~blackwel/