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Electoral Volatility in Latin America, 1932–2018

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Abstract

This paper examines electoral volatility in Latin America from 1932 to 2018, covering both presidential and lower chamber elections. The paper makes two contributions. First, we present a new, carefully documented dataset about electoral volatility and the vote share of new parties. Scholars interested in both subjects will be able to use the data to explore a wide range of issues. We contribute to the descriptive knowledge about patterns of electoral volatility and the vote share of new parties in Latin America. Second, we contribute to theoretical knowledge about extra-system volatility (the part that results from the emergence of new competitors) and within-system volatility (the part of volatility that stems from aggregate vote transfers among established parties) and to incipient debates about theoretical expectations about differences between extra- and within-system volatility. Poor economic growth, a perception of pervasive corruption, and low levels of partisanship are fertile terrain for new parties (extra-system volatility). Party system polarization and a fragmented party system foster within-system volatility.

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Notes

  1. Cohen et al. (2018) refer to Type A volatility as Party Replacement Volatility and Type B as Stable Party Volatility.

  2. Our lower chamber volatility dataset also includes a calculation for Types A and B volatility.

  3. The concept “genuinely new parties” comes from Sikk (2005), but we operationalize it less restrictively than Sikk. He counted a party as new only if the politicians who formed it did not come from a previously existing party. We do not adopt this criterion. Our criteria for a new party match those of Emanuele and Chiaramonte (2018).

  4. Consistent with our finding, Lago and Torcal (2019) report that endogenous volatility is considerably higher than exogenous volatility.

  5. VB3 and VB20a. The question for VB3 was “For whom did you vote in the last presidential elections in 2005?” VB20a was “If the next presidential elections were Sunday, for whom would you vote.”.

  6. Many changes in party systems result initially from elite decisions to form new parties, merge or split existing ones, or form or dissolve coalitions. Even when changes are elite-initiated, voters make the ultimate decisions that affect electoral volatility.

  7. We used the formula recommended by Paternoster et al. (1998) for conducting the T-tests.

  8. It is not possible to calculate a log from a negative value. To minimize the number of missing observations, we assumed that inflation below 1% per year including deflation has an impact on electoral volatility that is indistinguishable from that of an inflation rate of 1%. We recorded all such cases as having a logged inflation of 0.

  9. The values of the ideological scale range from 1.6 to 19.

  10. For earlier periods in which the ideological scores are unavailable in Baker and Greene’s (2011) dataset, we use the first available data point for the parties in the dataset. For presidential candidates whose ideological scores are unavailable but Baker and Greene (2011) assigned their parties ideological scores in the legislative elections, we use the legislative score. For presidential candidates whose ideological scores are unavailable in both the presidential and legislative elections in Baker and Greene (2011), we rely on Coppedge (1997).

  11. The argument that volatility diminishes over time has been supported by Lupu and Stokes (2010) and Madrid (2005). For fifteen East European democracies, Tavits (2005) showed that volatility increased right after a regime change, but the increasing trend was reversed as democracy aged.

  12. The GEE with AR1 error specification models allow for the fact that errors might be temporally correlated within countries.

  13. The random effects model assumes that unobserved country-specific effects are not correlated with independent variables and control variables. This assumption is violated in most panel data involving countries (Wilson and Butler 2007).

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Acknowledgements

Fernando Bizzarro, Frances Hagopian, Matthew Singer, and two anonymous reviewers offered very helpful comments. We thank María Victoria De Negri for research assistance and comments. The Taiwan Ministry of Science and Technology (grant number: 104-2410-H-004-002-MY2) supported the work of Yen-Pin Su on this paper.

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Authors and Affiliations

  1. Department of Political Science, University of Notre Dame, Notre Dame, IN, 46556, USA

    Scott Mainwaring

  2. Department of Political Science, National Chengchi University, Taipei, Taiwan

    Yen-Pin Su

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Correspondence to Scott Mainwaring.

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Mainwaring, S., Su, YP. Electoral Volatility in Latin America, 1932–2018. St Comp Int Dev 56, 271–296 (2021). https://doi.org/10.1007/s12116-021-09340-x

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