Abstract
This paper revises the conceptualization of party system institutionalization, as defined in Mainwaring and Scully’s (1995) groundbreaking work. Regarding the rules of aggregation involved in conceptual structure, my argument resonates with Goertz and Mahoney’s (2012) identification of two different “cultures” in contemporary social science. They understand one culture as building what Goertz calls “family resemblance” concepts, usually measured through a latent variable approach. The other culture understands concepts as structured in terms of “necessary and sufficient” elements, which can then be measured using an ontological approach. I claim that party system institutionalization has an implicit “family resemblance” structure and show that conceptual structure to be empirically and theoretically inadequate. In its current form, the concept of party system institutionalization also suffers from deficiencies when it comes to indicator validity and aggregation, as per (Munck and Verkulien, Comp Polit Stud 35(1):5–34, 2002) criteria. Problems of validity are caused by analysts’ frequent reliance on a single indicator for operationalizing the concept. Problems of aggregation and conceptual structure arise from inconsistencies between the implicit theoretical assumption that party system institutionalization is conceptually linear and non-linear patterns that are not only theoretically plausible but also empirically observable in a large set of cases. Therefore, this paper advocates revising the concept and the way it is currently applied in the comparative party system literature. Such revision will permit better understanding of both the characteristics and dynamic evolution of party systems.
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Notes
For a more thorough review of this literature, see Rosenblatt (2013).
The linear correlation is .24, but it is not statistically significant (sig. = .23, obs = 27). Furthermore, the Cronbach’s alpha scalability coefficient for this pair of indicators is low (.55), indicating that the components do not scale well on a one-dimensional index. In more technical statistical terms, they do not relate (and measure) linearly.
To be sure, scholars can and do often control for electoral system characteristics when comparing electoral volatility across systems.
See also Luna and Altman (2011) for an extensive argument along these lines and illustration with the Chilean case.
Usually, this assumption is based on a reevaluation of the Venezuelan party system, which collapsed a few years later, despite having qualified as “institutionalized” in 1995. In light of this case, excessive institutionalization was later theorized as a possible, if theoretically exceptional, hindrance to representation (Crisp 2000; Mainwaring 1999; Morgan 2012).
Candidates relying on new technologies might now be better able to develop programmatic campaigns even in the absence of stable parties. However, at least theoretically, such candidate-centered programmatic links would not generate the kind of accountability over time usually attributed to programmatically structured party systems. Such accountability remains key for enabling responsible party government (Aldrich 1995; Kitschelt et al. 2010).
Morgan, Hartlyn, and Espinal (2008) elaborate a similar narrative for the Dominican Republic, reporting increased stability (“institutionalization”) with declining ideological polarization and increasing clientelism.
See Luna and Altman (2011) for a less positive appraisal of the Chilean case.
Once again, it should be recalled that Mainwaring and Scully’s original treatment did not necessarily consider programmatic rootedness, so this analysis is not pursued as a retrospective judgment but rather as an analytical exercise.
I thank Scott Mainwaring for generously sharing this dataset.
To be sure, an estimation based on the comparison of (maximum-likelihood) pseudo-R squares across models (and cases) is not without perils (see, e.g., Long and Freese 2006). For that reason, I rely below on a different operationalization based on a more straightforward coding of expert surveys. The operationalization based on pseudo-R squares provides the opportunity to analyze programmatic rootedness across time (for the 1990s and 2000s). Moreover, it is similar to Mainwaring and Torcal’s (2006), though the incorporation of the substantive component seeks to enhance robustness. For each wave and country, the dependent variable is defined as the party for which each respondent declares to have voted in the previous election. All parties obtaining more than 5 % of survey respondents in a given country sample and wave were included in each model. (In general, sample size was about 1,000 cases.) The 5 % threshold seeks to restrict the analysis to parties for which a sufficient number of observations is available in each national survey to improve reliability. The online appendix provides detailed documentation on the operationalization I chose, which is largely consistent with that of Mainwaring and Torcal (2006). Table A-2 in the online appendix displays the waves available for each country and the minimum and maximum values obtained for each case on the indicators of programmatic structuring discussed in this section. The number of parties considered for each measurement, which does not significantly correlate to the obtained pseudo-R squares, is also displayed.
In both instances, the number of shared cases is 26. Discrepancies between the two indexes are not only caused by the addition of the substantive component to my proposed measure but also by coding decisions regarding relevant political parties in each case (i.e., my inclusion of all parties obtaining at least 5 % of survey responses).
Although the two measures of programmatic structure are only modestly correlated (.26, sig = .05, obs = 54), the structure of their relationship to electoral volatility is essentially equivalent. Similarly, regional configurations regarding the combination of electoral volatility and programmatic structure are also closely related across the two measurements of programmatic structure. For the sake of transparency, the relationship between the pseudo-R square indicators of programmatic structure and electoral volatility for both the 1990s and 2000s is included in the appendix.
I would like to thank Herbert Kitschelt for granting access to this dataset at an early stage of the research project. I also thank Yi-Ting Wang for providing assistance in working with the database. The survey item corresponding to programmatic strategies is E2.
Membership in the set was calibrated. To calibrate, I relied on the direct method presented in Ragin (2008), which was applied using the fuzzy.ado module in Stata 12. The direct method relies on the researcher’s identification of three anchors: a minimum threshold for set membership (below which cases are definitely considered not to be members of the set), a crossover point (above which cases are more likely than not to be members of the set), and a maximum threshold (above which cases are definitely considered set members). Although specific criteria could be used to calibrate each variable, for the sake of transparency, I decided to set independent variable anchors using three percentiles: minimum threshold = 25 %, crossover point = 75 %, and maximum threshold = 90 %. The appendix includes the list of countries and their degree of membership in the set of cases in which programmatic mobilization is high. Naturally, the calibrated version is significantly and strongly correlated with the original 1–4 scale (.84, sig = .000, obs = 88).
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Acknowledgments
I thank Agustina Giraudy, Hillel Soifer, Gary Goertz, Mariano Torcal, Omar Sánchez, Elaine Thomas, David Altman, Jana Morgan, Fernando Rosenblatt and four anonymous reviewers for useful feedback on earlier drafts. I received generous funding from the following research projects: FONDECYT numbers 1090605 and 1110565; and MILENIO # NS 100014.
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Luna, J.P. Party System Institutionalization: Do We Need a New Concept?. St Comp Int Dev 49, 403–425 (2014). https://doi.org/10.1007/s12116-014-9171-1
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DOI: https://doi.org/10.1007/s12116-014-9171-1