Strategies, Political Position, and Electoral ...

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Strategies, Political Position, and Electoral Performance of Brazilian Political Parties Hugo Barbosa-Filho

Josemar Faustino, Rafael R. Martins

Ronaldo Menezes

BioComplex Laboratory Computer Sciences Florida Institute of Technology Melbourne, USA [email protected]

Information Systems Centro Universit´ario de Ji-Paran´a ULBRA Ji-Paran´a, Brazil {josemar.cruz,rafaelmartins}@ulbra.edu.br

BioComplex Laboratory Computer Sciences Florida Institute of Technology Melbourne, USA [email protected]

Abstract—Brazil has a multi-party political system with 30 registered parties (as of 2013). However, anyone who knows a little about politics understands that is nearly impossible to have 30 dimensions of political positions (e.g. center, left, right, center-left, etc.) with no overlap. Hence, the obvious challenge is to understand this party system and how parties group together. However there is no obvious way to group these parties because the data we normally have come from the parties’ selfassigned positioning. What we see in practice, based on how the alliances are built and how politicians change from one party to another, is that most of them do not have a well-defined positional basis. Such phenomenon has been investigated since the 90s but always based on how elected politicians migrate between different parties. Today, we have at our disposal much more data that may be used to review political leanings. In this paper, we focus on the inter-party movements of candidates and on the relationship between movements and parties’ ideology and performance. Results suggest that parties’ performance in elections is strongly correlated with the parties’ strategies for promoting candidates.

I. I NTRODUCTION Brazil is the largest country in South American and the firth largest in the world with a population of 193 million people (according to 2012 official estimate) and a thriving democratic system. The political scene is based on a multiparty system with 30 parties (as of 2013) out of which 16 have representation in the national senate and 24 in the national house of representatives. Given this party pluralism it is sometimes hard to classify the parties according to their political leaning (e.g. left, right) unless we rely exclusively on the parties own statements. What can be seen in practice is that most of them do not have a well-defined ideology and the positions they stand for may change depending to the political context [1]. This lack of definition is quite noticeable in the amount of inter-party migration where candidates move from one party to another from election to election. The general perception is that politicians change affiliation regardless of ideological positions (individual or collective) [2]. Conversely, the parties themselves rarely stand on the grounds of ideology when accepting new members, accepting politicians regardless of their political views. This movement of candidates between parties create a political dynamics that can be analysed to understand how

parties are related to each other. In this paper we concentrate primarily on the movement of candidates (which may or not be elected) from party to party using an approach based on network science [3]. The objective of the paper is twofold: (i) first we look whether the inter-party movement of candidates can help us identify major political views in Brazil and the party composition of each of the views. Second, (ii) we verify a possible relation between the structure of the inter-party changes and the success of a political party from the electoral point of view. II. BACKGROUND A. Political Parties of Brazil Brazil is the largest country in South American and the firth largest in the world (by population) and until recently (circa 1985) was ruled by a military dictatorship (with just a few short periods of a democratic government). However in 1985 a popular movement led to the fall of the dictatorship and the establishment of a democratic regime where officials are directly elected by the people for the legislative (municipal, state, and national) and executive (majors, governors, and president). After the end of the military dictatorship, Brazil multiparty democracy flourished and nowadays (as of March 2013) there are 30 political parties with representation in the national congress (senate and house of representatives). Ideologically the Brazilian parties range from the right-wing such as the Progressive Party (Partido Progressista in Portuguese) to the farleft like the United Socialist Worker’s Party (Partido Socialista dos Trabalhadores Unificado). For that reason, the Brazilian Federal Senate and the House of Representatives is extremely heterogeneous and diversified from an ideological perspective. Such diversification requires that even large political parties make alliances, given that it is very difficult for a single party to have majority. This large number of parties also makes it difficult to identify similar parties and maybe group them according to their ideological point of view. Most people in Brazil (and justifiably) are not capable of differentiating the leanings of many of the parties. It is easy to understand why: while in 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 can be confusing to the average citizen. Moreover, it is hard to imagine that the 30 current choices represent a system in which the 30 parties represent 30 different ideologies different from each other. The practice of alliances in Brazil is common and probably influenced by candidates inter-party movement. Moreover the fluidity in candidate movement should lead to a disintegration of the 30 dimensions into fewer dimensions that combine like-minded parties. This hypothesis is not totally new and has investigated since the 90s but always from traditional approaches based on how politicians (elected officials) migrate among different parties [2]. Furthermore, the previous studies are mostly based on migration of elected politicians from the Federal House of Representatives and Federal Senate. In this paper we will focus on the inter-party movements of candidates (not only elected politicians) at different types of elections (municipal, state and national) and the relationship between the movement with the parties’ leaning in the political spectrum and its performance in elections. We believe this paper sheds light on the organization of the political scene in Brazil and helps us understand if data supports how parties become successful. B. Network Sciences The study of how pieces of data relate to each other is not new and has been studied since Euler’s introduced the concept of graphs in the year 1735. Since then, we have seen graph theory move from a field in discrete mathematics into a larger field of study where graphs are used as frameworks to study real-world phenomena. Since the work of Barab´asi and Albert [4], researchers have turned their attention not on mining the data itself but rather organizing the data in a network which captures relationships between pieces of data and, only then, mining the network structure and hence the relations between pieces of data. The network may reveal information that could not possibly be seen from mining the raw pieces of data. The use of networks as a framework for the understanding of natural phenomena is nowadays called Network Science (aka Complex Networks). Complex networks are represented as graphs and therefore can be said to be directed or undirected. The field of complex networks provides plenty of algorithms and metrics aiming at identifying patterns in the structure of these networks [5], [6] as well as important individuals and relationships within the networks [7], [8]. Among these measures, the most basic is the node degree which represents the number of edges that are adjacent to a particular node. The total number of edges incoming to a node is called the node in-degree while the number of outgoing edges is the node out-degree. In this work, we centered our statistical analyses in following metrics: Weighted in-degree: The Weighted in-degree Win (u) of a

node u is defined as Win (u) =

X

w(v, u),

(v,u)∈E

where E is the set of edges and w(v, u) is the weight of the edge from v to u. This measure corresponds to the sum of all weights of the incoming edges to a node u. Weighted out-degree: The Weighted out-degree Wout (u) of a node u is defined as X w(u, v), Wout (u) = (u,v)∈E

where E is the set of edges and w(u, v) is the weight of the edge from u to v. This measure corresponds to the sum of all weights of the outgoing edges from a node u. III. M ETHODOLOGY In this paper we want to verify how parties are grouped according to their leanings. For this we chose to look at party composition based on the movement of candidates between these parties. We argue that despite the possible positions parties say they stand for (the self-declared leaning), their composition is more accurate. We also see if we can find a pattern from these movements and their relation to party success in elections. First we built a network of political parties from looking at the candidacies of politicians that run for office in different political parties in a defined period. In other words, a relationship between two parties pi and pj exists if and only if a candidate c ran in one election for the party pi and in a subsequent election for the party pj such that i 6= j. The relationship’s weight is the number of candidates involved in such change. Note also that if a candidate changes from pi to pj and changes again to pi in a subsequent election, such movements will account for the relationships’ weights of both wij and wji . Figure 1 shows a simple example of how a network is generated from the movement of candidates. PARTIES

CANDIDATES

2

Figure 1: Movement of candidates between parties (left) yields a network of parties (right). To build the network we used a dataset of more then 2 million candidacies in Brazil for all political positions (legislative and executive) from 1998 to 2010. The raw data is provided by the Brazilian Electoral Justice and is freely available on-line 1 . The network analyzed here is directed, 1 http://www.tse.jus.br

weighted, and temporal [9]. The full network for the entire period has 37 nodes (number of parties) and 1,151 weighted edges corresponding to the total number of 334,814 inter-party movements. In this paper we focus on the nodes’ degree because this metric can capture the connectedness of a parties. Such connectedness can be interpreted as an estimator for the party’s heterogeneity and/or attractiveness to each other. A party with a high weighted in-degree is one that received a significant number of candidates from other parties. On the other hand, parties with low weighted in-degree can be seen as less attractive or permissive, thus absorbing fewer candidates from fewer parties. Moreover, in addition to the weighted in-degree and outdegree we used the number of candidates as an independent variable. For each election, we divided the candidacies in three groups, namely: • First-time candidates (Cf ) are those who are running for a political position for the first time in the considered time period • Migrant candidates (Cm ) are those who ran for a different party in a past election; • Non-migrant candidates (Cn ) are candidates that have ran for the same party during the period analyzed. We applied a set of statistical analysis to investigate the parties’ strategies on elections and see whether the network of political parties can be used to estimate their performance in the elections. In the context of this paper, we define the performance of a party in one election as the number of votes it received in that particular election-year. In Brazil, there are two different types of elections: majoritarian in which the candidates who receive the most votes are elected and the proportional elections where the parties or alliances 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 as follows: National: The elections in which the candidates are the same for all the country. The presidential election is the only one that is national. State: Those in which the states and the Federal District, Brazil’s capital will vote and elect their governors, representatives for the national congress and representatives for the state congress. Municipality: The elections in which the city mayors and aldermen are chosen.

IV. E XPERIMENTAL R ESULTS The analyses carried out in this section provides a better understanding of the organization of the multi-party system in Brazil, their strategies, and the dynamics of party movement. The focus here is using a network science framework to unveil the complex mechanisms that drives the complex and intertwined Brazilian multi-party system. A. Ideological Diversity via Community Analysis Here we want to verify whether the inter-parties movements are, to some extent, influenced by ideological factors. To assess such influence, first we need to examine the existence of community structures in the network. If the movements are driven by some general factors (e.g. ideological positions), it is expected that such network will be organized in communities. In the context of this work, communities represent the existence of clusters of parties where candidate movement among parties in the cluster is more likely to happen than movement between parties from different clusters. Thus, the presence of communities in the network suggest that movements follow a pattern. The second step in this analysis deals with testing the hypothesis that the communities in the network show, to some extent, ideological patterns. It means that parties with similar positions are expected to fall in the same community whereas those with clear ideological differences are expected to be in different communities. For each network, we applied the leading eigenvector method [10] in order to measure the networks’ modularity. A total of six election-specific networks were analyzed, each one based the movements of candidates for a particular election. The presence of communities in the networks (Figure 2) shows that in a macroscopic scale, the candidates’ movements have indeed structural patterns. Table II: Parties’ ideological positions according to Miguel and Machado [11]. Left

Centre

Right

PT, PCB, PSTU, PCdoB, PCO, PDT, PHS, PMN, PPS, PSB, PSTU, PV

PMDB, PSDB

PP, PPB, PDF, PFL, PDC, PL, PSDC, PSC, PGT, PRP, PSL, PTdoB, PRONA, PST

PPR, PRN, PTB, PSP, PAN, PSD,

Table I: Election methods in Brazil per position. Election President Senators Federal Representatives State Governors State Representatives City Mayors City Aldermen

Method

Candidacy Jurisdiction

Majoritarian Majoritarian Proportional Majoritarian Proportional Majoritarian Proportional

Country State State State State Municipality Municipality

To test the correlation between communities and ideological positions, we measured the ideological homogeneity among parties within communities. Communities were assigned to the predominant position of parties within them (according to Table II). For each party with a position different from the predominant of the community, it accounted as a miss. The proportion between the sum of all misses in a network and the total number of parties in it is the measure of ideological

2008

PFL

PST

PSD

PRTB

PSDC

PSL

PHS

PSDB

PV

PMN

PPS

PMDB

PSB

PT

PDT

PCB

PPB

PP

PFL

PL

PSDB

PTN

PSDC

PMDB

PMN

PTB

● ● ● ● ● ● ● ● ● ● ● ●

PSC



PHS

PRP

PCO

PRTB

PRB

PRONA

PTC

PP

PTN

PSL

PRP

PTdoB

PV

PST

PPS

PSOL

PCO

PCdoB

PCB

DEM

PSTU

PR

PSC

PSDB

PTB

PSDC

PMDB

PMN

PRTB

PSD

PRB

PHS

PRONA

PT

PDT

PAN

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● PTC



PSB



PGT

PSDB

PRONA

PTB

PMN

PRTB

PDT

PSB

PCO

PSOL

PT

PCdoB

PCB

PSTU

PP

PSD

DEM

PSDC

PSC

PR

PST

PTN

PAN

PMDB

PHS

PTC

PRP

PRB

PTdoB

PSL

PSL

PAN

PGT

PDT

PSD

PV

PST

PTdoB

PPS

PT

PSB

PSOL

PSTU

PCB

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

2006

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● PV

● ● ● ● ● ● ● ● ● ● ● ● ● ●



2004

PPS



PCdoB

PL

PFL

PTB

PSC

PSDB

PSDC

PHS

PMN

PRTB

PTN

PRP

PSD

PST

PTdoB

PTC

PCO

PRONA

PSL

PDT

PPS

PP

PMDB

PV

PAN

PGT

PSB

PSTU

PT

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● PCB



PGT



2002

PCdoB

2000



PSTU

PCdoB

PTB

PL

PTN

PGT

● ● ● ● ● ● ● PSC

PRP



PCO

PTdoB

PAN

PRN

● ● ● ● ● PRONA

PFL

PPB

PL

PTB

PGT

PST

PSC

PSDC

PTN

PSD

PSDB

PRN

PAN

PRP

PTdoB

PHS

PMN

PRTB

PMDB

PV

PRONA

PSL

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● PDT



PPS

PSTU

PT

PCdoB

● ● PSB



PCB



2010

Figure 2: Networks of inter-parties movements for the elections in the period from 2000 to 2010. The nodes’ colors correspond to their communities while sizes are proportional to nodes’ degrees. Within each community the parties are ordered from left to right in terms of political leaning. The number of parties may vary from one year to another due to parties’ creation, merging or extinction. The high density makes it difficult to identify the communities from the visualization.

influence on candidates movements. Formally, the position π of a community C given by πC is defined as: πC = arg max π

where

 F (p, π) =

1 0

X

Table III: Communities and the matching rate to the political ideological position described in Table II. Given the networks are quasi-cliques, modularity values are very low.

F (p, π)

p∈C

if πp = π otherwise

most of the political parties in Brazil [1], such good matching rate (Tab. III) was unexpected.

,

and πp is the position of party p. Since the two parties in the center of the ideological spectrum can oscillate from left to right, depending on local political idiosyncrasies as pointed out by Miguel and Machado [11], they will always be considered as correct classifications. Regardless of the low modularity, the communities detected in the network suggest that inter-party migrations are in some sense coherent from an ideological perspective. Surprisingly, due to the high density of the networks and the lack of strong ideological roots, for

Network

Communities

Modularity

Matching Rate

2000 2002 2004 2006 2008 2010

2 3 2 2 2 2

0.05 0.03 0.01 0.01 0.00 0.01

90% 93% 80% 81% 81% 69%

B. Strategies and Electoral Performance The second part of this analysis aims to verify whether the inter-party movements can lead to political strategies from

Prediction plot

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Figure 4: Prediction plot for the municipal elections with the prediction line. The x axis is the log of the performance and the y is the log of the predicted performance where each point in the graph corresponds to a party in a state and national election.

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Figure 5: Party performance correlated to the number of nonmigrant candidates.

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log(Votes)

the parties’ perspective and whether such strategies reflect into electoral performance. For this analysis we grouped the elections 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’hondt formula [12] as a method for allocating seats in party-list proportional representation, the existence of large districts, and the high number of candidates allowed to run in each party or coalition (150% of the vacancies for parties and 200% for coalitions), noncompetitive candidates become extremely important [13]. In this context, those candidates may play an important role and for this reason they are carefully chosen by political parties and coalitions as part of their strategies. The hypothesis tested in here is a possible correlation between the number of first-time candidates running for a party in a particular election and the total number of votes a party receives. In addition to the number of first-time candidates, another variable that showed a strong impact to the parties’ performance on municipal elections was the weighted in-degree. Figure 3 is the log-log plot of the performance by the weighted in-degree. From the plot we can see that a strong correlation for the variables exists but with a different intercept for the year of 2004. Nevertheless, the model with the log of the weighted in-degree and the log of the number of first-time candidates shows good results. Rewriting the log-log model we end up with the equation (1).

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2000 2004 2008

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log(Weighted In−Degree)

Figure 3: Performance by weighted in-degree for 2000, 2004 and 2008, years that had municipal elections.

α β Vˆm = Win Cf

(1)

where Win is the weighted in-degree of a party and Cf is the number of first time candidates. In the model, α = 1.03±0.32 and β = 0.11 ± 0.044. The log-log model has an adjusted R2 = 0.94, F − value = 647.3 for 82 degrees of freedom. Figure 4 shows the correlation between the predicted and the actual response values.

For the state and national elections it is possible to see in Figure 5 a strong correlation between the performance of a party and the number of non-migrant candidates in a given election. The high F-statistic combined with a very low pvalue shows that both variables are indeed highly correlated. Such correlation is not as strong as the one observed for the municipal elections but from the R2 value we can believe that the linear model below is a good fit for the relation. Vˆs = Cnβ e−α

(2)

where Cn is the number of non-migrant candidates. In the model, α = 1.296 ± 0.22 and β = 1.9225 ± 0.22 with an adjusted R2 = 0.79, an F − value = 237.01 for 82 degrees of freedom and a p − value > 0.000. Figure 6 shows the correlation between the predicted and the actual response values. Again, the linear model for the state/national elections

is less significant than the one for municipal elections. Prediction plot ●

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A. Future Works



The network of political parties deserves further investigation regarding several aspects such as the existence of overlapping communities, influence to each other, to name a few. Also, the different behavior on the 2004 election deserves further investigation to determine whether changes on the electoral regulations in 2002 and 2003 aiming to foster the partisan loyalty affected the candidates’ movements. Finally, a natural next step in this work is to validate the proposed models against the results of the 2014 elections.





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performance. This can be interpreted as the preference of voters for candidates that are more coherent and with stronger ideological beliefs.

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Figure 6: Prediction plot for the state and national elections with the prediction line.

V. D ISCUSSION The community analysis presented earlier provides some interesting conclusions. First, the communities identified in the network can be at some extent mapped to the ideological position of the parties. This result is somehow surprising. The popular belief in Brazil that political parties no longer have ideologies does not hold for several parties. It is particularly true for the parties identified more in the left or in the right of the ideological spectrum. On the other hand, we could not identify a strong community of parties that are more on the center of the spectrum. This may be explained by the fact that several parties in the center have interest that are out of the left/right spectrum such as the religious parties (e.g., PTC and PSC). The dataset that we built for this study was able to capture the different strategies adopted by parties and candidates aiming to improve their performances on electoral results. Our results have shown that municipal and state/national elections have different correlations of first-time, migrant and nonmigrant candidacies with the electoral outcome. For municipal elections, the relevance of first-time candidates is quite revealing. Such phenomenon can be explained by a possible preference 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 behavior having appeared only in municipal elections is that the voters may be more prone to give the vote for first-time candidates that are running for positions such as aldermen and mayors. On the other hand, for positions such as state governors and senators, voters appear to vote for those candidates who are more experienced and well-known. Another surprising result was the strong correlation between the candidates’ coherence to their ideological positions and the collective performance on elections. The state and national results show that the parties that favors candidates with a stronger identity with the party itself, tend to have a better

ACKNOWLEDGEMENTS Work partially supported by Conselho Nacional de Desenvolvimento Cient´ıfico e Tecnol´ogico - CNPq under grants 233190/2012-0 and 238474/2012-7. 2 R EFERENCES [1] C. Zucco, “Ideology or what? legislative behavior in multiparty presidential settings,” The Journal of Politics, vol. 71, no. 3, pp. 1076–1092, 2009. [2] C. R. Melo, Retirando as cadeiras do lugar: migrac¸a˜ o partid´aria na cˆamara dos deputados, 1985-2002. Editora UFMG, 2004, (In portuguese). [3] A.-L. Barab´asi, “Network science,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 371, no. 1987, Jan. 2013. [4] A.-L. Barab´asi and R. Albert, “Emergence of scaling in random networks,” science, vol. 286, no. 5439, pp. 509–512, 1999. [5] G. Palla, I. Der´enyi, I. Farkas, and T. Vicsek, “Uncovering the overlapping community structure of complex networks in nature and society,” Nature, vol. 435, no. 7043, pp. 814–818, 2005. [6] I. Der´enyi, G. Palla, and T. Vicsek, “Clique percolation in random networks,” Physical review letters, vol. 94, no. 16, p. 160202, 2005. [7] L. Page, S. Brin, R. Motwani, and T. Winograd, “The pagerank citation ranking: bringing order to the web.” Stanford University, Tech. Rep., 1999. [8] M. E. Newman, “A measure of betweenness centrality based on random walks,” Social networks, vol. 27, no. 1, pp. 39–54, 2005. [9] P. Holme and J. Saram¨aki, “Temporal networks,” Physics reports, vol. 519, no. 3, pp. 97–125, 2012. [10] M. E. J. Newman, “Finding community structure in networks using the eigenvectors of matrices,” Phys. Rev. E, vol. 74, p. 036104, Sep 2006. [Online]. Available: http://link.aps.org/doi/10.1103/PhysRevE.74.036104 [11] L. F. Miguel and C. Machado, “Um equil´ıbrio delicado: a dinˆamica das coligac¸o˜ es do pt em eleic¸o˜ es municipais (2000 e 2004),” Dados, vol. 50, no. 4, pp. 757–793, 2007, (In portuguese). [12] U. Schwingenschl¨ogl and F. Pukelsheim, “Seat biases in proportional representation systems with thresholds,” Social Choice and Welfare, vol. 27, no. 1, pp. 189–193, 2006. [13] F. Cireno and C. Lubambo, “Estrat´egias eleitorais e o papel dos munic´ıpios na eleic¸a˜ o para cˆamara legislativa no Brasil em 2006. 12,” Fundac¸a˜ o Joaquim Nabuco – Minist´erio da Educac¸a˜ o, Recife, Brazil, Tech. Rep., 2009, (In portuguese).