Abstract
In the final chapter, the main findings of the book are connected to three topics: first, it is shown how the results of the previous analyses can be related to the literature on political (and electoral) accountability. Second, the model based on ideological considerations and valence campaigning is extended to illustrate how it can help to explain the anti-elite rhetoric of parties generally connected to what can be identified as a ‘populist strategy’. Third, and finally, the discussed theoretical points are related to well-established theorizing on parties and party systems, in particular with the cartel party theory. The book concludes by discussing how the possible negative externalities of an extreme valence campaigning generated by a short ideological distance among parties remains an important issue for contemporary democracies.
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Appendices
Appendix: Postscript on the 2016 US Presidential Campaign
According to several observers, the U.S. 2016 Presidential Campaign was one of the most negative, harsh (and surprising, given the final outcome 1 ) in the history of American politics. This judgement finds corroboration when one focuses, in a similar way to what was done in Chap. 4 with respect to the 2014 European Elections, on the posts written in the official Twitter accounts of the two main candidates (Donald Trump : @realDonaldTrump ; Hillary Clinton : @HillaryClinton ). On analysing the tweets published on such accounts during the last two months of the electoral campaign, the following tag-clouds can be produced (see Fig. 5.3). 2
Tag-clouds of the tweets posted in the official Twitter account of Hillary Clinton (left panel) and Donald Trump (right panel) during the last 2 months of the electoral campaign 2016
In the tag-cloud related to the Twitter account of Donald Trump, it can be seen that the words ‘crook’ and ‘corrupt’ appear very often. In particular, ‘crook’ (used by Trump in conjunction with ‘Hillary Clinton’) is the most frequent word used other than proper names or the hashtags of the campaign (1.1 times every 100 words). With respect to the tag-cloud of the Hillary Clinton Twitter account, on the other hand, the word ‘women’, often associated in this case with charges of sexism against Donald Trump, is one of those most frequently used (just less than 1 time every 100 words). In both cases, in other words, non-policy valence issues appear to play a significant role (much more than words related to policy issues, for example). This fact (i.e. a relatively low emphasis on policy that contrasts with a relatively strong emphasis on other issues, mainly non-policy valence issues, used to attack the opposite candidate) has not passed unnoticed also in the popular press: ‘what have we really debated during this long contest [i.e., presidential campaigning]? Not war and peace, capitalism and socialism, surveillance and privacy—but tweets, and pneumonia’. 3
Is the content of the 2016 US Presidential campaign just illustrated inconsistent with the theoretical framework discussed throughout this chapter? The answer would be ‘yes’ if the ideological distance between the two aforementioned candidates had confirmed the recent upward trend towards an increased ideological polarization within the US political system as previously illustrated in Fig. 2.5. This, however, appears not to have been the case. Despite a widespread tendency, especially in the media, to depict Donald Trump as an ‘extremist’ candidate and Hillary Clinton as a ‘moderate’ one, the two candidates appeared much more ideologically proximate to each other than expected. 4
Two pieces of empirical evidences are useful here. First, Pablo Barberá, after having analysed the Twitter networks of each Democratic and Republican main political figures in an attempt to identify how conservative or liberal each of them was, concludes by showing that Donald Trump was the least conservative among the main Republican political figures back in June 2016. 5 Moreover, in this same analysis, Trump is placed quite close to the position of Hillary Clinton (contrary, for example, to Ted Cruz or Ben Carson, just to mention two other Republican presidential primary candidates competing with Donald Trump at that time). 6 Second, in the October 2016 wave of the Institute for the Study of Citizens and Politics study (that is, during the very peak of the presidential campaign), 7 respondents placed Trump on a 7-point ideology scale in a relative centrist position and quite close, on average, to their own. 8
In this sense, and as long as Donald Trump is considered a ‘populist’ candidate (Inglehart and Norris 2016), his position seems to recall a ‘pure’ version of populism that is reduced to an anti-establishment posture without any other strong ideological element, similar to what has characterized populist movements, for example, in Central and Eastern Europe (Učeň 2007; Linde 2012). 9 As a result, his position appears to be rather close to the one adopted by Hillary Clinton. In this scenario, a strong impact of SPATIAL PRESSURE on the incentives to highlight non-policy valence issues in the political confrontation by the two candidates during the 2016 US Presidential Campaign could have been anticipated. And this is precisely what seems to have happened, after all. In sum, had Ted Cruz won the Republican presidential primary rather than Donald Trump, a much stronger presidential ideological campaign would have probably ensued. With another American President, perhaps.
Notes
- 1.
According to the Economist/YouGov Poll of 12–15 November 2016, 66 percent of respondents said they were surprised by Trump’s victory. See: https://d25d2506sfb94s.cloudfront.net/cumulus_uploads/document/e1glb89vb3/econTabReport.pdf.
- 2.
A tag-cloud is a visual representation of text data. In the present case, tags are single words after which I have applied a lemmatization (so that, for example, the ‘crook’ term mentioned below in the text, also includes the term ‘crooked’, and so on). In Fig. 5.3 the importance of each tag is shown with font size and colour.
- 3.
Richard Wolffe The Guardian Monday 3 October 2016 Has 2016 turned into most trivial US election ever? https://www.theguardian.com/commentisfree/2016/oct/03/2017-us-election-trivial-debates.
- 4.
Jacobson (2017) argues for an increased polarization in the 2016 US Elections (if the Presidential and the Congressional Elections are considered together). This does not contradict what is underlined in the main text, however. Jacobson, in fact, refers to a reinforcement in voting behaviour along existing partisan lines (i.e. ‘polarized partisanship’). This, per se, does not necessarily imply something specific in terms of the ideological content of such an alignment (i.e. an increased ideological gap among parties/candidates or otherwise).
- 5.
The method employed by Barberá has been shown to be particularly accurate in computing precise ideological scores of political figures (Barberá 2015).
- 6.
- 7.
- 8.
Respondents also had more uncertainty about exactly where to place Donald Trump. This is not surprising. Authors have often conceived populism as a ‘thin ideology’ (Martinelli 2016; Stanley 2008), i.e. as a rather vague, plastic, and flexible ideology that allows the populist rhetoric to be combined, whenever needed, with a variety of ‘thick’ ideologies, such as nationalism or socialism, that add more specific content to it.
- 9.
Note that saying that a populist party or candidate shows an extremist attitude concerning such-and-such policy does not prove that his/her broad ideology is extreme. As rightly noted by Martelli (2015), it may happen that an extreme right-wing approach to, say, immigration combines with an extreme left-wing demand for income redistribution. Because of this, overall populist moderation may be the result of contrasting populist proposals concerning policy issues, due to the logic of spatial theory in a one-dimensional ideological setting that can somehow mediate between opposite policy stances. This seems to be the case of Donald Trump.
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Curini, L. (2018). What Implications?. In: Corruption, Ideology, and Populism. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-56735-8_5
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