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

Matt Blackwell mblackwell at iq.harvard.edu
Mon Nov 29 10:33:21 EST 2010


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/CloseElections4.pdf
Supplement: http://www.iq.harvard.edu/events/sites/iq.harvard.edu.events/files/Supplemental.pdf

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/


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