Pinpointing the Powerful: Covoting Network Centrality as a Measure of Political Influence

Date01 August 2016
Published date01 August 2016
AuthorNils Ringe,Steven L. Wilson
University of Wisconsin-Madison
Pinpointing the Powerful:
Covoting Network Centrality as a
Measure of Political Influence
This article introduces centrality in covoting networks as a measure of influence.
Based on a simple cueing dynamic, it conceptualizes those lawmakers as most central—
and thus as having the greatest signaling influence—who impact the greatest number of
colleagues’ voting decisions. A formal proof and an agent-based simulation show that
cue-providers are always more central than followers; hence, we can use real-world
voting data to identify the most influential legislators. To confirm the measure’s con-
struct validity, we predict covoting centrality in the European Parliament and find those
factors that are expected to impact legislators’ influence to predict their centrality.
Political actors are not created equal. Some are more inf‌luential
than others, be it due to the formal positions of power they hold, positive
personal traits like skills, experience, expertise, ambition, and determina-
tion, or less positive traits, like a lack of scruples. Yet, measuring political
power and inf‌luence is exceedingly diff‌icult. In this article, we introduce
a new measure of inf‌luence that draws from the study of power in social
networks: political actors’ centrality in covoting networks. Specif‌ically,
we use legislators’ voting patterns to measure their social connectedness
and, on this basis, examine the structural positions of individual law-
makers to determine their relative inf‌luence (Bailey and Sinclair 2008).
The basis for this conceptualization of voting as a relational activity is a
simple cueing dynamic in which some lawmakers (cue-receivers) follow
the lead of select colleagues (cue-providers) when voting in a legislature.
Those legislators whose cues inf‌luence the greatest number of colleagues’
voting decisions are most central in the legislative covoting network and
thus have the greatest signaling inf‌luence.
We maintain that cue-providers should always be more central
than their followers in covoting networks, which allows us to identify
those legislators who are most inf‌luential in the sense that their signals
affect the votes of the greatest number of colleagues. We make this case,
DOI: 10.1111/lsq.12129
C2016 Washington University in St. Louis
f‌irst, by presenting a formal proof to show that cue-providers are always
more central than cue-followers. Second, we construct an agent-based
model that simulates legislatures of various sizes and parameterizations
to demonstrate the proof’s robustness to a variety of real-world scenar-
ios. In the empirical section, we calculate the covoting centrality scores
of members of the European Parliament (EP) and identify the factors
that determine them. Our analysis suggests the construct validity of our
measure (i.e., covoting centrality measures what we purport it measures)
in that factors that can be expected to impact individual legislators’
signaling inf‌luence are indeed predictive of the centrality scores of mem-
bers of the EP (MEPs).
The main innovation of this article lies in the use of social network
analysis to gain insights into legislators’ voting behavior. Social network
analysis focuses on the examination of social relationships (or ties)
between individuals (or nodes) and how these connections affect social
and political interactions, processes, and outcomes. The basic unit of
analysis is thus pairs (or dyads) of individual actors. Social network anal-
ysis also allows for the mapping of entire network structures, which are
composed, in the case at hand, of all dyadic covoting ties between a pop-
ulation of lawmakers. Legislative voting is more often than not
conceived of as an individual-level activity, and key to many analyses of
voting behavior is the assumption that voting decisions are independent
of each other.
However, in this article we think of voting, and voting
data, in broader terms, by considering how we can garner information
about individual-level inf‌luence by investigating the covoting network
structure as a whole. Treating covoting data as a relational measure
(where the nodes are individual legislators and the ties the extent to
which dyads of lawmakers both vote yea, both vote nay, or both abstain)
and using them to identify inf‌luential legislators constitutes a novel
approach to the study of legislative inf‌luence.
Importantly, the approach travels easily to other legislative arenas,
because roll-call votes are often readily available and because our model
is both parsimonious and general—most importantly, it does not make
any restrictive assumptions a priori about why legislators follow cues or
who serves as cue-provider or cue-receiver.
It is also less costly than
engaging in survey-based research because all that is needed to deter-
mine lawmakers’ signaling inf‌luence is a suff‌icient number of roll-call
votes. Our measure does not make assumptions about legislative inf‌lu-
ence by considering the formal positions of power legislators hold (such
as party or committee leader), their seniority, or their fund-raising prow-
ess; instead, it lets the data tell us who wields power by inf‌luencing the
votes of the greatest number of fellow legislators.
740 Nils Ringe and Steven L. Wilson

To continue reading

Request your trial

VLEX uses login cookies to provide you with a better browsing experience. If you click on 'Accept' or continue browsing this site we consider that you accept our cookie policy. ACCEPT