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Party switching and political outcomes: evidence from Brazilian municipalities

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Abstract

Although party switching is a relatively rare event in most countries, the phenomenon is widespread in Brazil. This paper investigates the effects of party switching on political and economic variables between 2001 and 2012 using a database containing more than 4900 Brazilian municipalities. In particular, given that the conventional literature addresses party switching by legislators, this paper focuses on chief executives, specifically mayors, a subject that has not yet been explored in the literature. In this study, party switching is interpreted as a treatment effect: the treated group comprises municipalities whose mayors changed parties during their terms in office. The control group comprises municipalities with no party switching, but with similar likelihoods of being treated, as defined by propensity score matching (PSM) methods. Our results suggest that mayors belonging to the same party as the state governor or the president are less likely to switch parties. Moreover, switching from a different party to the state governor’s party creates the best results for switchers who want to be renominated. Regarding reelection, our panel estimations suggest that the impact of party switching on reelection is positive. Finally, we find no statistically significant effect of party switching on the amounts of transfers received by municipalities from upper government levels.

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Notes

  1. King and Benjamin (1986) recorded 165 switchers in the US House of Representatives between 1789 and 1984, a much smaller number than recorded for any legislature in the Brazilian Chamber of Deputies between 1983 and 2003 (Melo 2003).

  2. Tribunal Superior Eleitoral.

  3. Switching to a new party is considered to be due cause, which opens a loophole for party switching.

  4. As of 2018.

  5. The federal government is an exception because it is composed of two legislative houses, namely, the chamber of deputies and the senate.

  6. “Municipalities Participation Fund.” The fund is composed of 22.5% of all revenues collected by the federal government by means of two taxes: income taxation and taxation of industrialized products. Brazilian states receive 21.5% by means of the “Fundo de Participação dos Estados” or “States Participation Fund.”

  7. Poorer and smaller municipalities receive proportionally more transfers than richer and larger municipalities.

  8. “Imposto sobre a Propriedade de Veículos Automotores” or “Tax on Vehicle Ownership.”. For that state transfer, municipalities receive 50% of the revenues collected by the state government according to the locality in which the automobile is registered.

  9. “Imposto sobre a Circulação de Mercadorias e Serviços” or “Tax on Transactions of Goods and Services.” For that state transfer, a given municipality receives 25% of all revenues collected by the state government locally.

  10. The dataset can be downloaded at http://www.tse.jus.br/partidos/filiacao-partidaria/relacao-de-filiados.

  11. Available at http://www.tse.jus.br/hotSites/pesquisas-eleitorais/index.html.

  12. As of the 2016 municipal elections, the advance notice requirement was reduced from one year to six months. Additionally, the possibility of party switching without loss of office during the 30 days preceding the notification period was instituted.

  13. In addition to not being distributed evenly over time, party switching is not uniform in space either. In some states, the rate of party switching remains high in all elections, notably Amazonas, Bahia, Mato Grosso and Rio de Janeiro. The last is notable for the large number of municipalities that experienced at least one case of switching over our sample period: 77.78%. On the other hand, the states of Minas Gerais, Rio Grande do Sul, Santa Catarina and São Paulo have been affected the least by party switching, with only 4.38% of the municipalities in Rio Grande do Sul being governed by a mayor who switched parties.

  14. Currently DEM (Democratas).

  15. The sum of the percentages for each election does not necessarily add up to 100. For example, all centrist and leftist mayors may belong to parties forming the presidential coalition, so the sum of the percentages of centrist and leftist mayors aligned with the presidential coalition adds up 200.

  16. Proportion of the local population living in urban areas.

  17. Proportion of people over 65 years old.

  18. Available at http://www.sidra.ibge.gov.br/bda/tabela/protabl.asp?c=200&z=cd&o=13&i=P.

  19. Municipalities whose mayors changed parties more than once during their first terms of office are excluded from our database because they represent cases of “double treatment” and represent a very limited fraction of our observations.

  20. Mostly small parties.

  21. Approximately 45% of municipalities are administered by mayors belonging to a centrist party, 32% by mayors belonging to a rightist party, 21% by mayors belonging to a leftist party and 2% by mayors belonging to the group “Other parties.”

  22. The results are very similar when the two dummies measuring political alignment between the local mayor and the president as well as that of the local mayor and the state governor are replaced by dummies measuring political alignment between the mayor’s party and parties belonging to the federal coalition and the mayor’s party and parties belonging to the state government coalition—see Table A2 in the online appendix. However, alignment with the president’s and state governor’s party seems to exert stronger negative effects on party switching than the broader alignment with the parties belonging to the federal and state government coalitions.

  23. The exception is the case of the logit estimation for 2007, for which the coefficient remains negative but is not statistically significant.

  24. In addition, Tables A3, A4 and A5, presented in the online appendix of this paper, indicate that, after matching, the covariates of the two groups reveal means that are not statistically different.

  25. Conditional logit estimations include only those observations for which the dependent variable varies at least once during the sample period, meaning that municipalities in which all mayors switched parties or remained in the original party were excluded from the estimations. That empirical choice explains why the number of observations in column (4) is not equal to the sum of observations in columns (1) to (3).

  26. The remaining cases of party switching are (i) mayors who switch from a different party from the governor's party to another different one and (ii) mayors who switch from the same party as the state governor's party to a different one.

  27. Additionally, the coefficient on Other switchers is statistically significant and positive in 2012.

  28. The exception is the coefficient on Other switchers, which is statistically significant in 2012.

  29. The exception is the negative and statistically significant coefficient for Other switchers in 2008.

  30. The hypothesis test rejects the null that these two coefficients are statistically equal.

  31. Although a mayor may have switched in any month of 2003, we assume that the observed effect relates to the entire year, given that the dependent variable (transfers) is measured on an annual rather than a monthly, basis. The results (available upon request) are qualitatively the same when only the last year of the local mayoral term is considered as the after-treatment period.

  32. For example, if a mayor switched parties in 2003, Switcher equals 1 in all years of that mayor's 2001–2004 term.

  33. The only exception is the case of discretionary transfers from the federal government during the 2005–2008 local mayoral term, where β is negative and statistically significant at the 10% level.

  34. The exception is the case of state non-discretionary current transfers during the 2009–2012 local mayoral term: the estimated coefficient is negative and statistically significant.

  35. Here, the only exception relates to federal discretionary transfers during the 2005–2008 term: βos is negative and statistically significant.

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Acknowledgements

The authors acknowledge comments from Ana Luiza Gouvêa Abras, Andras Gregor, Enlinson Mattos, Luiz Guilherme Scorzafave, Marcos Nakaguma, Marta Arretche, Paolo Ricci, participants at the 2018 European Public Choice Society Meeting and two anonymous referees. The authors also thank CNPq, Fapesp and Capes for financial support.

Funding

CNPq Research Grant 130658/2016-2 (Henrique Augusto Campos Ferndez Hott).

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  1. FEA/USP, São Paulo, SP, Brazil

    Henrique Augusto Campos Fernandez Hott

  2. FEA-RP/USP, Ribeirão Preto, SP, Brazil

    Sergio Naruhiko Sakurai

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  1. Henrique Augusto Campos Fernandez Hott
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Correspondence to Henrique Augusto Campos Fernandez Hott.

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Appendix

Appendix

See Figs. 7, 8, 9, 10.

Fig. 7
figure 7

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Histogram of propensity score, by year—control and treatment groups. a 2003, b 2007, c 2011

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Fig. 8
figure 8

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Intergovernmental transfers—R$ per capita—2001–2004 term. a Federal Transfers, b State Transfers.

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Fig. 9
figure 9

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Intergovernmental transfers—R$ per capita—2005–2008 term. a Federal Transfers, b State Transfers.

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Fig. 10
figure 10

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Intergovernmental transfers—R$ per capita—2009–2012 term. a Federal Transfers, b State Transfers.

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Hott, H.A.C.F., Sakurai, S.N. Party switching and political outcomes: evidence from Brazilian municipalities. Public Choice 187, 403–438 (2021). https://doi.org/10.1007/s11127-020-00786-6

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