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Strategies, Political Position, and ElectoralPerformance of Brazilian Political Parties
Hugo Barbosa-FilhoBioComplex Laboratory
Computer SciencesFlorida Institute of Technology
Melbourne, [email protected]
Josemar Faustino, Rafael R. MartinsInformation Systems
Centro Universitario de Ji-ParanaULBRA
Ji-Parana, Brazil{josemar.cruz,rafaelmartins}@ulbra.edu.br
Ronaldo MenezesBioComplex Laboratory
Computer SciencesFlorida Institute of Technology
Melbourne, [email protected]
Abstract—Brazil has a multi-party political system with 30registered parties (as of 2013). However, anyone who knowsa little about politics understands that is nearly impossible tohave 30 dimensions of political positions (e.g. center, left, right,center-left, etc.) with no overlap. Hence, the obvious challengeis to understand this party system and how parties grouptogether. However there is no obvious way to group these partiesbecause the data we normally have come from the parties’ self-assigned positioning. What we see in practice, based on how thealliances are built and how politicians change from one partyto another, is that most of them do not have a well-definedpositional basis. Such phenomenon has been investigated sincethe 90s but always based on how elected politicians migratebetween different parties. Today, we have at our disposal muchmore data that may be used to review political leanings. Inthis paper, we focus on the inter-party movements of candidatesand on the relationship between movements and parties’ ideologyand performance. Results suggest that parties’ performance inelections is strongly correlated with the parties’ strategies forpromoting candidates.
I. INTRODUCTION
Brazil is the largest country in South American and thefirth largest in the world with a population of 193 millionpeople (according to 2012 official estimate) and a thrivingdemocratic system. The political scene is based on a multi-party system with 30 parties (as of 2013) out of which16 have representation in the national senate and 24 in thenational house of representatives. Given this party pluralismit is sometimes hard to classify the parties according to theirpolitical leaning (e.g. left, right) unless we rely exclusivelyon the parties own statements. What can be seen in practice isthat most of them do not have a well-defined ideology and thepositions they stand for may change depending to the politicalcontext [1]. This lack of definition is quite noticeable in theamount of inter-party migration where candidates move fromone party to another from election to election. The generalperception is that politicians change affiliation regardless ofideological positions (individual or collective) [2]. Conversely,the parties themselves rarely stand on the grounds of ideologywhen accepting new members, accepting politicians regardlessof their political views.
This movement of candidates between parties create apolitical dynamics that can be analysed to understand how
parties are related to each other. In this paper we concentrateprimarily on the movement of candidates (which may or notbe elected) from party to party using an approach based onnetwork science [3]. The objective of the paper is twofold: (i)first we look whether the inter-party movement of candidatescan help us identify major political views in Brazil and theparty composition of each of the views. Second, (ii) we verifya possible relation between the structure of the inter-partychanges and the success of a political party from the electoralpoint of view.
II. BACKGROUND
A. Political Parties of Brazil
Brazil is the largest country in South American and the firthlargest in the world (by population) and until recently (circa1985) was ruled by a military dictatorship (with just a fewshort periods of a democratic government). However in 1985a popular movement led to the fall of the dictatorship andthe establishment of a democratic regime where officials aredirectly elected by the people for the legislative (municipal,state, and national) and executive (majors, governors, andpresident).
After the end of the military dictatorship, Brazil multi-party democracy flourished and nowadays (as of March 2013)there are 30 political parties with representation in the nationalcongress (senate and house of representatives). Ideologicallythe Brazilian parties range from the right-wing such as the Pro-gressive Party (Partido Progressista in Portuguese) to the far-left like the United Socialist Worker’s Party (Partido Socialistados Trabalhadores Unificado). For that reason, the BrazilianFederal Senate and the House of Representatives is extremelyheterogeneous and diversified from an ideological perspective.Such diversification requires that even large political partiesmake alliances, given that it is very difficult for a single partyto have majority.
This large number of parties also makes it difficult toidentify similar parties and maybe group them according totheir ideological point of view. Most people in Brazil (andjustifiably) are not capable of differentiating the leanings ofmany of the parties. It is easy to understand why: whilein countries such as the USA the political scene presents
people with a clear dichotomy (conservative and liberal),Brazil presents the people with a plethora of choices that canbe confusing to the average citizen. Moreover, it is hard toimagine that the 30 current choices represent a system in whichthe 30 parties represent 30 different ideologies different fromeach other.
The practice of alliances in Brazil is common and probablyinfluenced by candidates inter-party movement. Moreover thefluidity in candidate movement should lead to a disintegrationof the 30 dimensions into fewer dimensions that combinelike-minded parties. This hypothesis is not totally new andhas investigated since the 90s but always from traditionalapproaches based on how politicians (elected officials) migrateamong different parties [2]. Furthermore, the previous studiesare mostly based on migration of elected politicians fromthe Federal House of Representatives and Federal Senate. Inthis paper we will focus on the inter-party movements ofcandidates (not only elected politicians) at different types ofelections (municipal, state and national) and the relationshipbetween the movement with the parties’ leaning in the politicalspectrum and its performance in elections.
We believe this paper sheds light on the organization ofthe political scene in Brazil and helps us understand if datasupports how parties become successful.
B. Network Sciences
The study of how pieces of data relate to each other is notnew and has been studied since Euler’s introduced the conceptof graphs in the year 1735. Since then, we have seen graphtheory move from a field in discrete mathematics into a largerfield of study where graphs are used as frameworks to studyreal-world phenomena. Since the work of Barabasi and Albert[4], researchers have turned their attention not on mining thedata itself but rather organizing the data in a network whichcaptures relationships between pieces of data and, only then,mining the network structure and hence the relations betweenpieces of data. The network may reveal information that couldnot possibly be seen from mining the raw pieces of data.The use of networks as a framework for the understanding ofnatural phenomena is nowadays called Network Science (akaComplex Networks).
Complex networks are represented as graphs and thereforecan be said to be directed or undirected. The field of complexnetworks provides plenty of algorithms and metrics aiming atidentifying patterns in the structure of these networks [5], [6]as well as important individuals and relationships within thenetworks [7], [8]. Among these measures, the most basic isthe node degree which represents the number of edges thatare adjacent to a particular node. The total number of edgesincoming to a node is called the node in-degree while thenumber of outgoing edges is the node out-degree.
In this work, we centered our statistical analyses in follow-ing metrics:
Weighted in-degree: The Weighted in-degree Win(u) of a
node u is defined as
Win(u) =∑
(v,u)∈E
w(v, u),
where E is the set of edges and w(v, u) is the weight ofthe edge from v to u. This measure corresponds to thesum of all weights of the incoming edges to a node u.
Weighted out-degree: The Weighted out-degree Wout(u) of anode u is defined as
Wout(u) =∑
(u,v)∈E
w(u, v),
where E is the set of edges and w(u, v) is the weight ofthe edge from u to v. This measure corresponds to thesum of all weights of the outgoing edges from a node u.
III. METHODOLOGY
In this paper we want to verify how parties are groupedaccording to their leanings. For this we chose to look at partycomposition based on the movement of candidates betweenthese parties. We argue that despite the possible positionsparties say they stand for (the self-declared leaning), theircomposition is more accurate. We also see if we can finda pattern from these movements and their relation to partysuccess in elections.
First we built a network of political parties from looking atthe candidacies of politicians that run for office in different po-litical parties in a defined period. In other words, a relationshipbetween two parties pi and pj exists if and only if a candidate cran in one election for the party pi and in a subsequent electionfor the party pj such that i 6= j. The relationship’s weight isthe number of candidates involved in such change. Note alsothat if a candidate changes from pi to pj and changes againto pi in a subsequent election, such movements will accountfor the relationships’ weights of both wij and wji. Figure 1shows a simple example of how a network is generated fromthe movement of candidates.
2
PARTIES CANDIDATES
Figure 1: Movement of candidates between parties (left) yieldsa network of parties (right).
To build the network we used a dataset of more then2 million candidacies in Brazil for all political positions(legislative and executive) from 1998 to 2010. The raw datais provided by the Brazilian Electoral Justice and is freelyavailable on-line 1. The network analyzed here is directed,
1http://www.tse.jus.br
weighted, and temporal [9]. The full network for the entireperiod has 37 nodes (number of parties) and 1,151 weightededges corresponding to the total number of 334,814 inter-partymovements.
In this paper we focus on the nodes’ degree becausethis metric can capture the connectedness of a parties. Suchconnectedness can be interpreted as an estimator for the party’sheterogeneity and/or attractiveness to each other. A party witha high weighted in-degree is one that received a significantnumber of candidates from other parties. On the other hand,parties with low weighted in-degree can be seen as lessattractive or permissive, thus absorbing fewer candidates fromfewer parties.
Moreover, in addition to the weighted in-degree and out-degree we used the number of candidates as an independentvariable. For each election, we divided the candidacies in threegroups, namely:• First-time candidates (Cf ) are those who are running for
a political position for the first time in the consideredtime period
• Migrant candidates (Cm) are those who ran for a differentparty in a past election;
• Non-migrant candidates (Cn) are candidates that have ranfor the same party during the period analyzed.
We applied a set of statistical analysis to investigate theparties’ strategies on elections and see whether the networkof political parties can be used to estimate their performancein the elections. In the context of this paper, we define theperformance of a party in one election as the number of votesit received in that particular election-year.
In Brazil, there are two different types of elections: ma-joritarian in which the candidates who receive the most votesare elected and the proportional elections where the parties oralliances that receive the most votes will elect more candidates.Table I shows the election methods for all political positions.
Elections in Brazil can also be classified by jurisdiction asfollows:National: The elections in which the candidates are the same
for all the country. The presidential election is the onlyone that is national.
State: Those in which the states and the Federal District,Brazil’s capital will vote and elect their governors, rep-resentatives for the national congress and representativesfor the state congress.
Municipality: The elections in which the city mayors andaldermen are chosen.
Table I: Election methods in Brazil per position.
Election Method Candidacy Jurisdiction
President Majoritarian CountrySenators Majoritarian StateFederal Representatives Proportional StateState Governors Majoritarian StateState Representatives Proportional StateCity Mayors Majoritarian MunicipalityCity Aldermen Proportional Municipality
IV. EXPERIMENTAL RESULTS
The analyses carried out in this section provides a betterunderstanding of the organization of the multi-party system inBrazil, their strategies, and the dynamics of party movement.The focus here is using a network science framework tounveil the complex mechanisms that drives the complex andintertwined Brazilian multi-party system.
A. Ideological Diversity via Community Analysis
Here we want to verify whether the inter-parties movementsare, to some extent, influenced by ideological factors. To assesssuch influence, first we need to examine the existence ofcommunity structures in the network. If the movements aredriven by some general factors (e.g. ideological positions), it isexpected that such network will be organized in communities.In the context of this work, communities represent the exis-tence of clusters of parties where candidate movement amongparties in the cluster is more likely to happen than movementbetween parties from different clusters. Thus, the presence ofcommunities in the network suggest that movements follow apattern.
The second step in this analysis deals with testing thehypothesis that the communities in the network show, to someextent, ideological patterns. It means that parties with similarpositions are expected to fall in the same community whereasthose with clear ideological differences are expected to be indifferent communities.
For each network, we applied the leading eigenvectormethod [10] in order to measure the networks’ modularity. Atotal of six election-specific networks were analyzed, each onebased the movements of candidates for a particular election.The presence of communities in the networks (Figure 2) showsthat in a macroscopic scale, the candidates’ movements haveindeed structural patterns.
Table II: Parties’ ideological positions according to Migueland Machado [11].
Left Centre Right
PT, PCB, PSTU,PCdoB, PCO, PDT,PHS, PMN, PPS,PSB, PSTU, PV
PMDB, PSDB PP, PPB, PPR,PDF, PFL, PRN,PDC, PL, PTB,PSDC, PSC, PSP,PGT, PRP, PAN,PSL, PTdoB, PSD,PRONA, PST
To test the correlation between communities and ideologicalpositions, we measured the ideological homogeneity amongparties within communities. Communities were assigned tothe predominant position of parties within them (accordingto Table II). For each party with a position different from thepredominant of the community, it accounted as a miss. Theproportion between the sum of all misses in a network andthe total number of parties in it is the measure of ideological
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PC
doB
PS
TU PT
PS
B
PC
B
PD
T
PP
S
PV
PS
L
PR
ON
A
PM
DB
PH
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doB
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PS
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PC
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PAN
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P
PT
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TB
PM
N
PH
S
PS
DB
PS
DC
PS
C
PT
B PL
PF
L
2004
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PS
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PC
doB
PC
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PS
C PL
PF
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PP
2006
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M PP
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2008
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doB
PC
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doB
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P
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PP
2010
Figure 2: Networks of inter-parties movements for the elections in the period from 2000 to 2010. The nodes’ colors correspondto their communities while sizes are proportional to nodes’ degrees. Within each community the parties are ordered from left toright in terms of political leaning. The number of parties may vary from one year to another due to parties’ creation, mergingor extinction. The high density makes it difficult to identify the communities from the visualization.
influence on candidates movements. Formally, the position πof a community C given by πC is defined as:
πC = argmaxπ
∑p∈C
F (p, π)
where
F (p, π) =
{1 if πp = π0 otherwise ,
and πp is the position of party p. Since the two partiesin the center of the ideological spectrum can oscillate fromleft to right, depending on local political idiosyncrasies aspointed out by Miguel and Machado [11], they will alwaysbe considered as correct classifications. Regardless of the lowmodularity, the communities detected in the network suggestthat inter-party migrations are in some sense coherent from anideological perspective. Surprisingly, due to the high densityof the networks and the lack of strong ideological roots, for
most of the political parties in Brazil [1], such good matchingrate (Tab. III) was unexpected.
Table III: Communities and the matching rate to the politicalideological position described in Table II. Given the networksare quasi-cliques, modularity values are very low.
Network Communities Modularity Matching Rate
2000 2 0.05 90%2002 3 0.03 93%2004 2 0.01 80%2006 2 0.01 81%2008 2 0.00 81%2010 2 0.01 69%
B. Strategies and Electoral Performance
The second part of this analysis aims to verify whether theinter-party movements can lead to political strategies from
the parties’ perspective and whether such strategies reflectinto electoral performance. For this analysis we grouped theelections by the scope of the candidacies. The years of 2000,2004 and 2008 had State and National elections while 2002,2006 and 2010 had Municipal elections.
Due to the Brazilian electoral system that is based D’hondtformula [12] as a method for allocating seats in party-listproportional representation, the existence of large districts,and the high number of candidates allowed to run in eachparty or coalition (150% of the vacancies for parties and 200%for coalitions), noncompetitive candidates become extremelyimportant [13]. In this context, those candidates may play animportant role and for this reason they are carefully chosenby political parties and coalitions as part of their strategies.
The hypothesis tested in here is a possible correlationbetween the number of first-time candidates running for a partyin a particular election and the total number of votes a partyreceives.
In addition to the number of first-time candidates, anothervariable that showed a strong impact to the parties’ perfor-mance on municipal elections was the weighted in-degree.Figure 3 is the log-log plot of the performance by the weightedin-degree. From the plot we can see that a strong correlationfor the variables exists but with a different intercept for theyear of 2004. Nevertheless, the model with the log of theweighted in-degree and the log of the number of first-timecandidates shows good results. Rewriting the log-log modelwe end up with the equation (1).
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−8 −6 −4 −2 0
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log(Weighted In−Degree)
log(
Vot
es)
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200020042008
Figure 3: Performance by weighted in-degree for 2000, 2004and 2008, years that had municipal elections.
Vm =WαinC
βf (1)
where Win is the weighted in-degree of a party and Cf is thenumber of first time candidates. In the model, α = 1.03±0.32and β = 0.11 ± 0.044. The log-log model has an adjustedR2 = 0.94, F − value = 647.3 for 82 degrees of freedom.Figure 4 shows the correlation between the predicted and theactual response values.
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Figure 4: Prediction plot for the municipal elections with theprediction line. The x axis is the log of the performance andthe y is the log of the predicted performance where each pointin the graph corresponds to a party in a state and nationalelection.
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2 4 6 8
1012
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log(Non−migrant Candidates)
log(
Vot
es)
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MunicipalityState/Country
Figure 5: Party performance correlated to the number of non-migrant candidates.
For the state and national elections it is possible to see inFigure 5 a strong correlation between the performance of aparty and the number of non-migrant candidates in a givenelection. The high F-statistic combined with a very low p-value shows that both variables are indeed highly correlated.Such correlation is not as strong as the one observed for themunicipal elections but from the R2 value we can believe thatthe linear model below is a good fit for the relation.
Vs = Cβne−α (2)
where Cn is the number of non-migrant candidates. In themodel, α = 1.296 ± 0.22 and β = 1.9225 ± 0.22 withan adjusted R2 = 0.79, an F − value = 237.01 for 82degrees of freedom and a p− value > 0.000. Figure 6 showsthe correlation between the predicted and the actual responsevalues. Again, the linear model for the state/national elections
is less significant than the one for municipal elections.
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Figure 6: Prediction plot for the state and national electionswith the prediction line.
V. DISCUSSION
The community analysis presented earlier provides someinteresting conclusions. First, the communities identified inthe network can be at some extent mapped to the ideologicalposition of the parties. This result is somehow surprising. Thepopular belief in Brazil that political parties no longer haveideologies does not hold for several parties. It is particularlytrue for the parties identified more in the left or in the rightof the ideological spectrum. On the other hand, we could notidentify a strong community of parties that are more on thecenter of the spectrum. This may be explained by the fact thatseveral parties in the center have interest that are out of theleft/right spectrum such as the religious parties (e.g., PTC andPSC).
The dataset that we built for this study was able to capturethe different strategies adopted by parties and candidatesaiming to improve their performances on electoral results. Ourresults have shown that municipal and state/national electionshave different correlations of first-time, migrant and non-migrant candidacies with the electoral outcome. For munic-ipal elections, the relevance of first-time candidates is quiterevealing. Such phenomenon can be explained by a possiblepreference that voters may have for new/unknown candidates.In some sense, such preference may represent the peoples’desire for political change. The hypothesis for this behaviorhaving appeared only in municipal elections is that the votersmay be more prone to give the vote for first-time candidatesthat are running for positions such as aldermen and mayors.On the other hand, for positions such as state governors andsenators, voters appear to vote for those candidates who aremore experienced and well-known.
Another surprising result was the strong correlation betweenthe candidates’ coherence to their ideological positions and thecollective performance on elections. The state and nationalresults show that the parties that favors candidates with astronger identity with the party itself, tend to have a better
performance. This can be interpreted as the preference ofvoters for candidates that are more coherent and with strongerideological beliefs.
A. Future Works
The network of political parties deserves further inves-tigation regarding several aspects such as the existence ofoverlapping communities, influence to each other, to name afew. Also, the different behavior on the 2004 election deservesfurther investigation to determine whether changes on theelectoral regulations in 2002 and 2003 aiming to foster thepartisan loyalty affected the candidates’ movements. Finally,a natural next step in this work is to validate the proposedmodels against the results of the 2014 elections.
ACKNOWLEDGEMENTS
Work partially supported by Conselho Nacional de Desen-volvimento Cientıfico e Tecnologico - CNPq under grants233190/2012-0 and 238474/2012-7.
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