Populist attitudes in Australia: contextualising the demand-side

ABSTRACT

The study of populist attitudes is a burgeoning area of scholarship. While the manner in which populism is measured and the concepts underpinning it are continuously updated, much work remains. In this article we consider the way populist attitudes are associated with key issues in Australian politics and the way these issues shape voting behaviour. We demonstrate that populist attitudes are associated with dissatisfaction with the functioning of liberal democracy, as well as negative attitudes towards Indigenous peoples. However, we find it is not strongly associated with economic anxiety. We argue that measuring populist attitudes in isolation from other national level contextual factors is problematic and can lead to flawed assumptions about the drivers of voting behaviour.

学术界对民粹主义的研究方兴未艾。测量民粹主义的方法、阐述民粹主义的概念方面不断有新的进展，但还有很多工作有待完成。本文研究了民粹主义态度与澳大利亚政治重要话题的结合方式以及这些话题塑造选举行为的方式。民粹主义态度与对自由民主制度运行的不满、与对原住民的负面态度相联系，与经济上的焦虑却无太大关联。笔者认为，将民粹主义脱离其他国家层面的语境因素进行测量是有问题的，会导致对选举行为成因的错误假设。

KEYWORDS:

• Populism
• political attitudes
• voting behaviour
• Australia

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 While there is an earlier Australian literature that explored the political attitudes of voters for Pauline Hanson’s One Nation (Bean 2000; Goot and Watson 2001a, 2001b; Gibson, McAllister, and Swenson 2002), it is important to note that this is not synonymous with populist attitudes. Indeed, this literature did not actually seek to measure populist attitudes or construct a survey item for measuring populism (and this is understandable, given the populist attitudes literature is relatively recent). Instead, it measured the attitudes and drivers of voters for a particular populist radical right party, whereas in this article, we are interested in populist attitudes more generally, which go beyond the cohort of voters for one party.

2 This lacuna is arguably a result of the literature on populist attitudes focusing almost exclusively on Europe. Important exceptions to this include Hawkins, Kaltwasser, and Andreadis (2018); Kaltwasser and Van Hauwaert (2020); and (Kefford, Moffitt, and Werner 2021), but these have not focused on attitudes towards Indigenous peoples.

3 This is a central reason we have not included anti-immigration measures or national identity-related questions in our survey instrument: one of the critiques of the extant literature on populism is that is has been too loose in its definitions and operationalisation, and has tended to conflate populism with other issues – particularly nationalism (see, for example, Bonikowski et al. 2019; De Cleen and Stavrakakis 2017). As such, in the name of conceptual precision, we have chosen to focus only on populist attitudes, and sought to not muddy the waters by introducing such adjacent measures. Moreover, we do not wish to replicate very recent scholarship that has already examined the overlap of populist and nationalist attitudes in Australia, which is precisely what Kefford and Ratcliff (2021) have done in this journal.

4 It is important to note that this is not the trend in all surveys – see, for example, the annual Scanlon Foundation’s ‘Mapping Social Cohesion’ surveys (Markus, 2021).

5 For our purposes here, we have combined the Liberal, National and Liberal National parties as one entity.

6 The two question items we amended from Castanho Silva et al. (2018) were ‘Quite a few people running the government are crooked’ and ‘The will of the people should be the highest principle in this country’s politics’. We changed these to ‘Many of our elected officials are corrupt’ and ‘Politicians need to follow the will of the people’. We argue that the wording we used is less ambiguous to respondents.

7 The sample contained the following numbers of respondents who indicated their vote intention for the 2019 Australian federal election: Labor 329; Liberal 216; LNP 73; Country Liberal 2; Nationals 22; Greens 102; One Nation 56; Other/Independent 130; None 31; 88 did not provide answers. Respondents were asked to respond to our measures using a 4-point Likert scale.

8 Correlation coefficients calculated with pandas native DataFrame.corr() function. We use the Pearson method implementation. Correlation p-values calculated with the scipy.stats.pearsonr() function which also implements the Pearson method. We used partial correlation as the method for controlling for age and gender. Module and function was Pingouin.partial_corr().

Additional information

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors would like to thank the Australian Research Council for their generous support and declare the following funding from the Australian Research Council that has supported this project: Kefford [grant number DE190100210] and Moffitt [grant number DE190101127].

Notes on contributors

Glenn Kefford

Glenn Kefford is a Senior Lecturer in Political Science and Australian Research Council DECRA Fellow in the School of Political Science and International Studies at the University of Queensland. His research focusses on campaigning, elections and political parties. He is the author of Political Parties and Campaigning in Australia: Data Digital & Field, (Palgrave, 2021).

Benjamin Moffitt

Benjamin Moffitt is Senior Lecturer and Australian Research Council DECRA Fellow at the Australian Catholic University, Melbourne. He is the author of Populism (Polity, 2020), Political Meritocracy and Populism: Curse or Cure? (with Mark Chou & Octavia Bryant, Routledge, 2020), and The Global Rise of Populism: Performance, Political Style and Representation (Stanford University Press, 2016), and co-editor of Populism in Global Perspective: A Performative and Discursive Approach (with Pierre Ostiguy and Francisco Panizza, Routledge, 2021).

John Collins

John Collins is a data scientist and PhD candidate at the University of Mannheim with the Mannheimer Zentrum für Europäische Sozialforschung (MZES). He has an honours degree in Sociology from the Australian National University and a Masters in Data Science from James Cook University.

Joshua Marsh

Joshua Marsh is an undergraduate student at the University of Queensland studying Politics, Philosophy, and Economics. He previously represented Australia at MITs Research Science Institute, has been a research intern at the Harvard Centre for Astrophysics and CSIRO’s Data61. While studying computer science at ANU, he was a research assistant in ANU’s Research School of Computer Science where he conducted AI research.