Matching and : Partisan Intensity, Composition & "Potential Outcomes" Red

Matching Red and Blue : Partisan Intensity, Composition & "Potential Outcomes"
Kayla S. Canelo: [email protected]
Chelsea M. Coe: [email protected]
Alexander G. Theodoridis: [email protected]
University of California, Merced
the article. These questions were meant to measure bias, and were
additively compiled to create a single scale.
motivated bias when assimilating this new information.
8.1 ”News” Report
I Existing literature suggests a pronounced asymmetry in intensity
between Democrats and Republicans, with Republicans being more
intense identifiers and engaging in more pronounced partisan behavior.
I Specifically, recent studies have shown that Republicans associate
themselves more strongly with their party (Theodoridis 2015), show
greater motivated bias in assimilation of new information (Theodoridis
2012, 2015a), more consistently associate positive traits with their
candidates and party (Goggin and Theodoridis 2015), and practice
greater avoidance of opposing messages in their ad skipping behavior
(Henderson & Theodoridis 2015).
I
I
I
I
I
I
General Multivariate Matching Method for Achieving Balance in Observational Studies." The Review of
Economics & Statistics 95(3):932-945.
Goggin, Stephen N., and Alexander G. Theodoridis. 2015. "Ownership Mentality? Parties, Issues, and
Traits in the Minds of Voters." Working paper.
Henderson, John A., and Alexander G. Theodoridis. 2015. "Seeing Spots: An Experimental Examination
of Voter Appetite for Partisan and Negative Campaign Ads." Working paper.
Theodoridis, Alexander G. 2015a. "It’s My Party: Partisan Intensity through the Lens of Implicit
Identity." Working paper.
Theodoridis, Alexander G. 2012. Party Identity in Political Cognition PhD Thesis University of
California, Berkeley.
Theodoridis, Alexander G. 2015. "Rooting Interest: Measuring and Manipulating Partisan Bias."
Working paper
Sekhon, Jasjeet S. 2007. "Matching: Algorithms and Software for Multivariate and Propensity Score
Matching with Balance Optimization via Genetic Search." Journal of Statistical Software 55:1-47.
4
4
Political Knowledge
Marital Status
Gender
Figure 7: The Democratic version of the report seen by respondents prior to filling out the
Agree/Disagree grid.
2
2
0
0
Seeing Spots?
Employment
Dem
Rep
Dem
Rep
Dem
Rep
Candidate Party
Dem
Rep
Candidate Party
Education
6
I Using the pre-election portion of the UCM module of the 2012 CCES (N
Age
= 1157), respondents were randomly assigned to watch one of twelve
campaign ads from the 2012 presidential election. Half were from the
Obama campaign and the other half from the Romney campaign. Half
of the ads from each side were negative and half were positive.
0.0
0.2
0.4
0.6
0.8
1.0
6
4
2
0
4
2
0
Democrats
p−value
Republicans
Democrats
Republicans
40
I Subjects were told they could skip the video after watching for five
seconds. Afterwards, subjects were allowed to replay the video, have a
43
link sent to them, or view a similar video.
Ad Seeking Behavior
Before Matching
Before Matching
After Matching
B Presentation
Experimental
I Republicans
seek theirofown
candidate’sStimuli
ads (or avoid the other side’s
After Matching
Democrats
Republicans
Democrats
1.50
1.50
1.25
1.25
Republicans
ads) more consistently than do Democrats.
Voter Registration
Religion
Marital Status
I Research exploring the role of partisan identification on political
I Diamond, Alexis and Jasjeet S. Sekhon. 2013. "Genetic Matching for Estimating Causal Effects: A
6
Income
referred to as an "Intensity Gap" (Theodoridis 2012).
References
6
Republicans
Religion
I This difference in political behavior between the two parties has been
Income
Figure 10: Interface: Here is an example of the way in which the video screen was
presented to respondents.
Matching Red & Blue: Reducing Covariate
Differences
I In order to approach to the experimental benchmark (Diamond &
Sekhon 2013) and to identify the "effect" of party identification on
intensity, we use GenMatch, developed by Sekhon (2007) to match
Democrats and Republicans on key covariates that are integral to
political behavior.
Gender
1.00
1.00
0.75
0.75
0.50
0.50
Employment
Obama
Education
Romney
Obama
Romney
Obama
Romney
Ad Source
(DD−DR)−(RR−RD)
behavior is limited in the sense that we cannot experimentally
manipulate a respondent’s partisan identification. We employ a genetic
optimization algorithm developed by Sekhon (2007) to match Democrats
and Republicans on numerous covariates. This represents a step in the
examination of PID from a potential outcomes perspective by allowing
us to explore whether the observed differences are basic features of
modern partisan affiliation or simply a product of partisan composition
(i.e. the lack of ignorability).
Democrats
Total Ad−Seeking Behaviors
Theory & Expectations
Republicans
Voter Registration
8 Bias
Experiment
I Results demonstrated
that
Republicans showed substantially greater
I We find that, even after balance is substantially improved by matching,
significant differences in intensity levels and biased partisan behavior
persist. Specifically, Republicans remain differentially more inclined to
seek out copartisan campaign ads than Democrats. Republicans in the
matched sample also continue to show more biased processing of new
information than do Democrats.
Democrats
Bias
I Respondents were asked a series of agree/disagree questions related to
After Matching
(DR−DD)−(RD−RR)
framework, we use a genetic optimization matching algorithm
developed by Sehkon (2007) to generate balance and parse out
compositional sources of difference. Matching Democrats and
Republicans on key covariates allows us to begin to conceptualize PID
as a "treatment".
YouGov in 2013 were shown a short "newspaper clipping" about a
Senator who admitted to lying about his opponent’s issue positions. The
party label of the offending Senator was randomly assigned.
Before Matching
Before Matching
After Matching
Age
0.0
0.2
0.4
0.6
p−value
I These covariates include education, age, gender, religious importance,
0.8
1.0
Obama
Romney
Ad Source
(DD−DR)−(RR−RD)
I Using the logic and methods emerging from the potential outcomes
I Participants in a representative online survey (N = 987) fielded via
Bias
differential partisan intensity? Recent literature (highlighted below)
recognizes a persistent "Intensity Gap" between Democrats and
Republicans.
Biased Procesing
Total Ad−Seeking Behaviors
I Is there just something about being a Republican today that generates
Red and Blue Colored Glasses?
(DR−DD)−(RD−RR)
Overview
0.6
0.4
0.2
0.0
0.6
0.4
0.2
0.0
Democrats
Republicans
Democrats
Republicans
marital status, employment status, voter registration, political
knowledge, family income, and propensity score.
I Doing so allows us to make Democrats and Republicans as comparable
as possible to better isolate the "effects" of PID. Using the new matched
datasets, we then re-run the analyses discussed above. This allows us to
conceptualize PID as a "treatment" and better understand its role in
political behavior.
I Certain important variables, such as race, could not be balanced due to a
lack of common support. Others, such as strength of PID and political
interest, were deemed too proximate to underlying intensity.
Conclusions
I The matching methods we employ bring us closer to isolating the "effect" of partisan affiliation. Despite significantly improving balance on
covariates fundamental to political behavior, the "intensity gap" between Republicans and Democrats persists.
I Republicans are still significantly more likely to seek out ads from their own party than are Democrats. Republicans are also more likely to process
new information in a way that is more favorable to their own party than are Democrats. These findings suggest that perhaps there is something,
beyond compositional factors, about being a modern Republican that fosters more intense partisan behavior.