Spatial Dimensions of the 2010 Australian Greens Vote: The Gay and Lesbian Effect on Voting. Luke John Mansillo Submitted for assessment in October 2013
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Arts (Honours) in Political Science at the School of Politics and International Relations, College of Arts and Social Sciences, the Australian National University.
Word Count: 17,357
Statement of Originality This is to certify that the content of this thesis to the best of my knowledge is my own work. The content of the thesis has not been submitted for any other degree or purpose. I certify that the intellectual content of this thesis is the product of my own work and that all the assistance received in preparing this thesis and sources have been acknowledged.
Luke John Mansillo
Acknowledgements I would like to acknowledge my deepest gratitude to Professor Ian McAllister for his time, supervision, expertise, understanding and patience. Furthermore, the assistance of Jillian Sheppard for her comments and discussions clarifying my own thoughts and expanding on that with supplementary methodological guidance. Ian and Jill, thank you. I would also like to particularly thank Professor James Piscatori, Dr. John Hart, Dr. Benedick Wellings and Dr. Kirill Nourzhanov who in my Bachelor of Arts taught me to identify anomalies. I would like to thank Dr. Juliet Pietsch and Professor Jeffery Karp for giving me the tools to investigate those anomalies. Thanks ought to also go to my friend and once Introduction to Politics tutor, Dr. Mellissa Lovell, for giving me the confidence to investigate the more peculiar ideas of mine attempting to explain those anomalies. The tireless efforts of my high school teachers, particularly in history, deserve a great deal of praise. They allowed a politics junkie who was thought inept by most languorous with NSW curricula to play the game to get into one of the finest undergraduate politics programmes in the world. The pastoral care of those at Burton and Garran Hall, with special mention to Keith Conley and David Segal, deserve praise and thanks. I would like to thank the Honourable Laurie Ferguson M.P., Member for Werriwa, for his input regarding his electorate. Last, I would like to thank my parents who have lovingly invested twenty-two years into me to see the first Mansillo attend a university and submit a thesis. I hope it bears fruit.
Abstract This thesis examines the demographic and geographic trends of the 2010 Australian Greens vote using factor analysis and multiple linear regression analysis of data from the Australian Election Commission and Australian Bureau of Statistics at the electorate and neighbourhood levels. This is the first analysis of Australian voting patterns to investigate gay and lesbian voting behaviours. It reveals sexuality has an impact on the voting patterns at Australian federal elections. The analysis finds gay male communities tend to vote for Labor, and lesbian communities tend to vote for Labor and the Greens. It finds a large gap between gay and lesbian voting patterns. It also finds that generation X is geographically more congruent to the Greens vote than generation Y, contrary to previous individual-level survey literature.
Abbreviations ABS
Australian Bureau of Statistics
AEC
Australian Electoral Commission
AES
Australian Election Study
b
Beta partial coefficient
i.e.
Latin: Id est meaning ‘that is’
NSW
New South Wales
Qld.
Queensland
OECD Organisation for Economic Co-operation and Development R
Pearson’s product-moment correlation coefficients
Resid. Residual RSD
Standardised residual
SA2
Statistical Area 2
Tas.
Tasmania
UK
United Kingdom of Great Britain and Northern Ireland
US
United States of America
Vic.
Victoria
VIF
Variance Inflation Factor
WA
Western Australia
β
Standardised Beta Coefficient
Δb
Difference between two beta coefficients
Contents Introduction ........................................................................................................................................... 1 Chapter 1: Spatial Aspects of Voting....................................................................................................... 2 Chapter 2: Explaining the Australian Greens Vote ............................................................................... 11 Chapter 3: Data, Measure and Methodology ....................................................................................... 29 Chapter 4: Results ................................................................................................................................. 42 Conclusion ............................................................................................................................................. 63 Bibliography .......................................................................................................................................... 66 Appendices............................................................................................................................................ 73
Introduction There are often anecdotes about gay and lesbian politics regarding who each support at the ballot. A gap has emerged in the Australian electoral literature on gay and lesbian voting behaviours. Opportunely, the 2011 Australian census included a same sex couple indicator for the first time, allowing aggregate research into the gay and lesbian population to address this gap. I was drawn to this topic after noticing different Australian Greens voting behaviours in two separate lesbian and gay communities in Sydney. The primary research questions underlying this analysis are: ‘what demographics impact the Australian Greens vote?’ and ‘what is the role of sexuality on the Greens vote?’ This thesis investigates patterns of voter support for the Greens at the 2010 federal election with particular reference to gay and lesbian voting behaviour. Chapter 1 outlines the literature on the neighbourhood effect providing the theory for neighbourhood analysis, Chapter 2 outlines demographic trends of the Greens vote and voting trends of gay and lesbians to construct a set of hypotheses about Greens voting, Chapter 3 outlines the methodology used and Chapter 4 sets out the results of the analysis. I use data from the Australian Bureau of Statistics (ABS) and Australian Electoral Commission (AEC) to build regression models based on demographic trends of Greens voting established in earlier literature at the electorate and neighbourhood levels. From this I find correlations between gay men and the Labor vote, and between lesbians and the Labor and Greens votes. Furthermore, lesbians vary more than gay men from their gender’s general population voting behaviour. In addition, generation X is geographically more correlated with the Greens vote than generation Y, which contradicts the literature on patterns of support at the 2010 election. Outliers of the models are explained by established environmental activism, alternative communities, protest voting and local media, which can have significant effects on the Greens vote. This is the first analysis of gay and lesbian voting behaviour in Australian electoral politics. 1|Page
Chapter 1: Spatial Aspects of Voting Geography has a noticeable effect on electoral outcomes.1 André Siegfried pioneered the study of electoral geography when he compared the 1910 French party vote to the geology of France in his 1913 Tableau Politique de la France de L’ouest.2 Siegfried found chalk areas tended to vote left wing and granite areas tended to vote right wing.3 He inferred geology affected the vote through social stratification of local economies that are impacted by the local economic activities allowed by its geology. Siegfried had most of his findings disproven; however his work ignited the discipline of political geography.4 Works such as Herbert Tingsten’s 1937 Political Behavior and Charles Merriam and Harold Gosnell’s 1924 Non-Voting appeared, drawing on the psychology of the previous decades.5 These were among the seminal texts that were highly influential in establishing the intellectual material which behaviouralists based their models on during the 1950s and 1960s behavioural revolution in political science.6 One of the effects discovered in the behavioural revolution was the neighbourhood effect: the tendency for individuals to vote in a certain way depending on their social and political ecology. The neighbourhood effect is one contextual effect that has continued to appear in aggregate and survey data.7 The effect was first noticed by Kevin Cox in “The Voting Decision in a Spatial Context”.8 William Miller was the first to suggest that the effect could be caused by conversations
1
Agnew, J., ‘Mapping politics: how context counts in electoral geography’, Political Geography, Vol. 15, No. 2, 1996 pp.129-146 ; Dreier, P., Mollenkopf, J., & Swanstorm, T., Place Matters: Metropolitics for the Twenty-First nd Century, University Press of Kansas, Lawrence, Kansas, 2 (ed.), 2005 2 Siegfried, A., Tableau Politique de la France de L’Ouest sous la Troisième République, Librairie Armand Colin, Paris, 1913 3 Therborn, G., ‘How and Why Place Matters’, in Goodin, R. (ed.), The Oxford Handbook of Political Science, Oxford University Press, Oxford, 2011 p.502 4 Judt, T., Socialism in Provence, 1871-1914, New York University Press, New York, 2011 p.239 5 Lipset, S., Lazarfeld, P., Barton, A., & Linz, J., ‘The Psychology of Voting: an analysis of political behavior’, pp.1124-1175 in Lindzey, P. (ed)., Handbook of Social Psychology, Addison-Wesley, Cambridge, MA, 1954 6 Goodin, R., ‘The State of the Discipline’, in Goodin, R. (ed.), The Oxford Handbook of Political Science, Oxford University Press, Oxford, 2011 p.23 7 Pattie, C., & Johnston, R, ‘“People who Talk Together Vote Together”: An exploration of contextual effects in Great Britain’, Annals of the Association of American Geographers, Vol. 90, No. 1, 2000 pp.41-66 8 Ibid. p.41
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whereby individuals are influenced by geographically constrained political information circulating in their social networks. He wrote that “those who speak together vote together”.9 The local context has come to be an important dimension in understanding voting behaviour. Most political scientists accept that voters act on their own individual situations and are influenced by their immediate social and geographical environments.10 There have been multiple explanations for the neighbourhood effect, including local perspective on neighbourhood economic prosperity, localised political issues, the role of campaigning, tactical voting, interregional migration, and the influence of conversations. Unfortunately, as Charles Pattie and Ron Johnston argue, “early interest in the influence of conversations with family, friends and workmates on voters’ decisions has largely evaporated.”11 Cox hypothesised that political cues flow through social networks, or “acquaintance circles”, stimulating responses in the form of partisan opinion and behavioural outcomes. These acquaintance circles are spatially constrained as there is an inverse relationship between distance and “the formation of acquaintanceship”.12 According to Pattie: “If the information reaching an individual through her/his conversations predominately favours one party, then he/she is more likely to vote for that party, irrespective of prior predispositions, than if the information was less biased in that particular direction.”13 Most studies that confirm Cox’s hypothesis do not investigate the neighbourhood effect directly. Instead, most studies assumed the effect’s operation because voting observations were consistent with the hypothesised effect. Party support tended to be greater than anticipated in places where it was expected to be weaker and vice versa. Cox concluded from this inference: “individuals are
9
Miller, W., Electoral Dynamics, Macmillan Press, London, 1977 p.65 Pattie et al. Op.cit. p.41 11 Op. cit. 12 Cox, K., ‘The Voting decision in a Spatial Context’, Progress in Geography, Vol. 1, 1969 p.97 13 Pattie, Op.cit. p.42 10
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somehow affected in their voting behaviour by the information and cues that dominate in their area of residence.”14 Cox identified two avenues of inquiry to put the discipline of political geography “on a firmer foundation”.15 First, more analyses which demonstrate voting patterns are the result of spatially varying cues. Second, the demonstration of the processes whereby an individual is influenced by political information in their local area.16 In Pattie and Johnston’s assessment, most studies of the neighbourhood effect rely on aggregate data only, which infers Miller’s “conversation by conversation” hypothesis and that recent literature on contextual effects have little research that directly addresses “how such effects operate”.17 John Brooks and Charles Prysby’s theory of contextual effects identifies four source factors of potential processes that influence voting behaviour.18 First, personal observations by an individual is an information source factor, as individuals are influenced by events and their interpretation of their situational social milieu such as the local economy, crime, etc. Second, informal interaction with interpersonal communication within the spatially confined community is a factor, as Cox and Miller suggested. However, it remains to be seen to what extent social media has eroded this local dimension.19 Third, organisationally based interaction emerges in formal spatially defined community organisations, such as work places, churches, and labour unions which are local in their structure. They provide a prescribed social setting for meeting and discussion with neighbours. Fourth, the mass media provides locally focused and politically relevant cues on multiple levels: local, regional, national and international.
14
Cox, Op.cit. Op. cit. 16 Pattie, Op. cit. 17 Op.cit. 18 Books, J.W. & Pyrsby, C.L., Political Behavior and the Local Context, Praeger, New York, 1991 p.51 19 nd Errington, W., & Miragliotta, N., Media & Politics: An Introduction, Oxford University Press, Oxford, 2 (ed.), 2012 p.24 15
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One study by Robert Huckfeldt and John Sprague specifically examined the neighbourhood effect through a network analysis. The study detailed the voting decisions and political conversations of the population of South Bend, Indiana without ‘plucking’ citizens from their distinctive social and political setting. Huckfeldt and Sprague, according to their methodological critique, showed that the basic design of voting intention surveys was inherently flawed as they abstracted people from their social settings.20 Instead their study sampled the individuals in the town and asked them to name three people they were inclined to discuss politics with. The researchers then sought interviews with those whom they discussed politics with.21 Huckfeldt and Sprague built up information about the town’s social networks which allowed an analysis of how information flowed through the community. They studied the information flows within family, friend, workplace, and residential circles, as well as the frequencies, discussion intensities, and the formality or informality of the situations where those conversations took place. Huckfeldt and Sprague concluded that the political preferences of discussants have consequences for the voting behaviours of other people they have conversations with. This important contribution to the discipline of political geography showed the mechanism by which “individual voter choice is imbedded within, and influences a social context that emits, sustains and perhaps amplifies some partisan cues while it submerges, attenuates and eliminates others.”22 The choice of discussants is structurally determined by the availability of likeminded people and the social milieu. This, according to Pattie and Johnston, has provided “strong direct, rather than indirect, evidence” for Miller’s statement that “people who talk together vote together”.23 Huckfeldt and Sprague’s study provides sui generis evidence for the impact of conversations in forming public opinion and voting behaviour.
20
Huckfeldt, R. & Sprague, J., Citizens, Politics and Social Communication, Cambridge University Press, Cambridge, 1995 p.103 21 Pattie, Op.cit. p.43 22 Huckfeldt et al., Op.cit. p.189 23 Pattie, Op.cit.; Miller, Op.cit.
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David Butler and Donald Stokes demonstrated the contextual effect of class in Britain, where working class voters were more pro-Conservative in areas where there were more middle class voters (such as coastal resorts), compared to working class voters in mining areas. They found “[w]here the core middle class was dominant, the Conservative vote was higher and the Labour vote was lower than the national cleavage would suggest” and the opposite was true in working class areas.24 Miller held this as evidence for the neighbourhood effect, arguing that a community consensus would build up as people ran into partisan voters and their views were “rubbing off”.25 Economic voting also has a localised neighbourhood effect. David Sanders demonstrated that perceptions of real economic performance can impact voting behaviour, thus one example of a widely accepted contextual effect. Sanders argues that there is “evidence which supports the conclusion that UK voters’ subjective economic perceptions are more important than objective economic realities in determining their political preferences.”26 He demonstrated that when there are controls made for aggregate economic indicators such as unemployment, inflation, changes in disposable income or interest rates, there was little long-term correlation between party support and indicators. Sanders noted that the real economy had little bearing on the popularity of the incumbent Conservative Party; instead voters’ “political preferences are strongly mediated by voters’ economic perceptions”.27 He showed that when the aggregate personal economic expectations rise, the incumbent party’s poll ratings tend to rise and vice versa. In the same way as Sanders demonstrated there is a national aggregate effect, there is a localised effect, as the national economy is made up of many local economies where the effect is repeated on a smaller scale but is geographically constrained.
24
Pattie, Op.cit. p.44 Op.cit. 26 Sanders, D., ‘Economic Performance, Management Competence, and the Outcome of the Next General Election’, Political Studies, Vol. 44, No. 2, 1996, p.204 27 Ibid. 25
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Pattie, Daniel Dorling, and Johnston demonstrated this at a neighbourhood level. They used voter perceptions of regional economies to provide insights into how local economic context affected voter behaviour at the 1992 British general election.28 They found that where local economies are strong and strengthening, support for the governing party is higher; where local economies are weak, the governing party’s support is lower. Additional to that autarkic model of economic voting that looks at economies and polities in isolation, Mark Kayser and Michael Presser demonstrated that there is an international contextual component to economic voting. When one national economy contracts, voters typically punish the incumbent; when many national economies contract, voters are less harsh on incumbents.29 Kayser and Presser provided evidence to support this by using the 16 OECD economies that had two negative quarters of growth from July 2008 to the end of 2009; they found that 13 of the 16 had a contraction in the quarter before their election, and nine of these countries had a swing against the incumbent party. Those swings were proportionate to the domestic decline in the economy. Kayser and Presser rejected the large body of literature that assumed there was parochialism in economic voting with this finding.30 The neighbourhood effect can result from local campaign efforts of political parties and activist groups. Campaigns seek to manipulate local information seeded through local media and communication networks. Munroe Eagles’ Canadian study and Gary Jacobson’s American study demonstrate this.31 Both acknowledge: “How well money transmits information and sways voters depends on the spender and the context. Is the candidate female, an incumbent, a good speaker? Is the constituency wealthy, 28
Pattie, C., Dorling, D., & Johnston, R., ‘The Electoral Geography of Recession: Local Economic Conditions, Public Perceptions and the Economic Vote in the 1992 British General Election’, Transactions of the Institute of British Geographers, Vol. 22, No. 2, 1997 pp.147-161 29 Mayer, M., & Presser, M., ‘Benchmarking across Borders: Electoral Accountability and the Necessity of Comparison’, American Political Science Review, Vol. 106, No. 3, 2012 pp.661-684 30 Ibid. p.680 31 Eagles, M., ‘Money and Votes in Canada: Campaign Spending an Parliamentary Election Outcomes, 1984 and 1988’, Canadian Public Policy – Analyse de Politiques, Vol. 19, No. 4, 1993 p.434
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educated, religious? A female candidate may spend a fortune in a traditional riding and get nowhere, or might spend little in a liberal-minded riding and do quite well. In other words, money alone does not determine election outcomes; there are many other forces that may interact subtly with spending.”32 However, with the decline of party loyalties across the post-industrialised world, Gary Jacobson contends that local factors have accentuated their significance in Congressional elections making candidate characteristics such as the ability to raise and allocate money to a campaign decisive.33 Australia has not been immune to this long-term trend with declining strengths of party attachments since the 1980s.34 The neighbourhood effect explains the tendency for individuals to vote in a direction based on the social ecology of people living in the neighbourhood. Information sourced from personal observation, media, local organisation or conversations affect political cues based on the neighbourhood political sociology. Huckfeldt and Sprague discovered the mechanism by which these political cues are created, confirming Miller’s “people who talk together vote together” hypothesis. It is well established that socioeconomic status influences these cues. Furthermore, perceptions of local economic performance in domestic and international contexts also impacts voting. Campaign finance and local campaign communication also have impacts on local voting behaviours. Critique of Spatial Dimensions of Voting There are two problems with most studies that examine spatial voting behaviour. First, most studies examine the “inferred outcomes” of processes only.35 They affirm various theories of
32
Palda, F., Election Finance Regulation in Canada: A Critical Review, The Fraser Institute, Vancouver, 1991 p.31-32 33 Eagles, Op.cit.; Jacobson, G., ‘Strategic Politicians and the Dynamics of U.S. House Elections, 1946-1986,’ American Political Science Review, Vol. 83, No. 3, pp.779-93 34 McAllister, I., The Australian Voter: 50 years of Change, University of New South Wales Press, Sydney, 2011 p.17; Marsh, I., ‘Policy Convergence between major parties and the representation gap in Australian politics’, in Marsh, I. (ed), Political Parties in Transition?, Federation Press, Sydney, 2006 pp.116-42 35 Pattie, Op.cit. p.45
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contextual effects by the patterns that continue to appear.36 These studies do not uncover the underlying mechanisms at work behind the contextual effects present in elections. However, studies such as Huckfeldt and Sprague offer direct empirical evidence for the neighbourhood effect. This provides a unique explanation for the effects witnessed. Such studies are rare because of the expense of conducting large-scale studies into voting behaviour and interpersonal political communication. A second problem particular to studying the neighbourhood effect is that most aggregate studies use constituencies as their unit of analysis. This unit is too large to apply Cox’s hypothesis.37 Constituencies have a legitimate role of inquiry as they are the units that elections are conducted in. However, due to their scale, they contain multiple neighbourhoods. British electorates have about 70,000 electors and Australian federal electorates about 90,000 electors, whereas a neighbourhood has around 5,000 to 10,000 electors. Using a smaller unit of analysis is more appropriate because no electorate is homogeneous. Political cues have spatially constrained flows through the community which cannot be appropriately measured on such a large scale. The neighbourhood effect on voting has been criticised by Ian McAllister, Donley Studlar, Martin Fitton, and Patrick Dunleavy, among others. First, McAllister and Studlar argue that the “bulk of regional voting can be attributed to social voting compositional effects” and that there is a limited territorial effect. McAllister and Studlar note that Johnston, Pattie and John Allsopp leave out interregional differences; including the urban-rural cleavage and that they had missed “the real basis of the north-south divide”. They argue that voting patterns reflected social composition effects so that “regional voting is [...] functional” not territorial voting.38 However, McAllister and Studlar ignore the fact that social composition is spatially constrained and has a local voting context. Their
36
Johnston, R., ‘The neighbourhood effect won't go away: Observations on the electoral geography of England in the light of Dunleavy's critique’, Geoforum, Vol. 14, No. 2, 1983 pp.161-168 37 Pattie, Op.cit. 38 McAllister, I., & Studlar, D., ‘Regions and Voting in Britain, 1979-87: Territorial Polarization or Artefact?’, American Journal of Political Science, Vol. 36, No. 1, 1992 p.175
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criticism may well apply at a regional level but below that, at the neighbourhood level, the later evidence of Huckfeldt and Sprague establishing the mechanism by which a social context influences an individual’s voting behaviour is stronger than the indirect evidence McAllister and Studlar cite. Furthermore, it does not explain why the neighbourhood effect does not disappear.39 There has been criticism of the neighbourhood effect’s underlying mechanism and outright arguments that it does not exist. Fitton argued that the neighbourhood effect is weak as people tend to avoid talking to their neighbours.40 This does not refute the neighbourhood effect but stresses the tendency for people to speak to others in their neighbourhood who are not their direct neighbours. Similarly, Dunleavy has argued that there was not a neighbourhood effect, but neighbourhoods were formed by class residential segregation through the property market.41 This view has been discredited by the work of Huckfeldt and Sprague, but there is growing evidence for increasing residential segregation forming the voting patterns of neighbourhoods.42 The above literature provides a geographical understanding of a vote that can be applied to the Greens vote. The neighbourhood effect, its various components, and the mechanism that is its cause provides an explanation for variously different geographical voting behaviour. This provides the necessary theory to examine local demographics to learn more about the political sociology of local areas which impacts the voting behaviour of a neighbourhood.
39
Johnston, 1983, Op.cit. Fitton, M., ‘Neighbourhood and Voting: a Sociometric Examination’, British Journal of Political Science, Vol. 3, No. 4, 1974 pp.445-472 41 Dunleavy, P., ‘The Urban Basis of Political Alignment: Social Class, Domestic Property Ownership, and State Intervention in Consumption Processes’, British Journal of Political Science, Vol. 9, No. 4, 1979 pp.409-443 42 Dreier, et al. Op.cit. 40
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Chapter 2: Explaining the Australian Greens Vote This chapter outlines the institutional barriers to the Greens’ electoral performance. It also examines the question of the Greens being a protest party in the context of the cartel party system, and discusses the demographic trends of Greens voting and gay, lesbian, bisexual and transgendered (LGBT) voting behaviour. From the premises established in the literature, hypotheses will be derived. The Greens have two institutional barriers to improving their vote, namely parental political socialisation and the electoral system for the House of Representatives. In terms of political socialisation, a “phenomenal” amount of views are shared and transferred from parents to children.1 It is generally agreed that a significant amount of parental socialisation embeds the political views of children even by those critical of the theory such as Raewyn Connell.2 However, there is a large number of generation X and their Baby Boomer generation parents who typically identify with the established major parties and raise children with those values. This acts as a barrier to the Greens gaining a greater share of the vote. But there is a significant generational deviation towards the Greens from generation Y, as is shown later. An explanation of generation Y’s divergence in their political socialisation from previous generations is found when assessing generational attitudes to global warming and exposure to a recent secondary school science curriculum. Bruce Tranter used 2007 Australian Election Study (AES) data, controlling for social background and incumbent performance, to determine that age is a highly significant variable between those who believe in global warming and those who do not.3 This translates into higher Greens and Labor support. One sociological explanation for a decline in the power of parental political socialisation in party voting behaviour is provided by Lawrence Saha. In a survey of Australian school students, Saha found that civics curricula affects the political socialisation
1
Sigel, R., Learning about Politics, Random House, New York, 1970 p.104 Connell, R., ‘Political Socialization in the American Family: the Evidence Re-Examined’, Public Opinion Quarterly, Vol. 36, No. 3, 1972 pp.323-333 3 Tranter, B., ‘Political divisions over climate change and environmental issues in Australia’, Environmental Politics, Vol. 20, No. 1, 2011 p.88 Table. 4 ‘Global Warming and Kyoto agreement (odds ratios)’ 2
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and normative values of students, i.e. their political awareness.4 Generation Y has been exposed to a science education involving climate change. Other generations have not gone through such as specific science curriculum. US studies have confirmed a positive correlation between scientific knowledge retention and social activism in students.5 This suggests that generation Y’s more recent science knowledge of global warming and school socialisation should contribute to a significantly higher Greens vote. The second barrier to an increased vote is the majoritarian single member constituency voting system in the House of Representatives. Maurice Duverger’s law holds that “majoritarian vote on one ballot is conducive to a two-party system”.6 This means there is little access to the House of Representatives for minor or third parties, such as the Greens. Similarly, members of parliament have an incumbency advantage.7 While the incumbency advantage is significantly lower than in US Congressional elections, as noted by Albert Cover, there is evidence that high profile frontbench members of parliament have a greater advantage when compared to backbenchers.8 This is a significant disadvantage to the Greens who had no members in the House of Representatives until 2010. A Protest Vote? Is the Greens vote a protest vote? Recent discourse on Australian political socialisation suggests a potential destabilisation in the Australian party system. For example, Aaron Martin and
4
Saha, L., ‘Political Activism and civic education among Australian secondary school students’, Australian Journal of Education, Vol. 44, 2000 p.171 5 Lester, B., Li, M., Lee, O., & Lambert, J., Social Activism in Elementary Science Education: A science, technology, and society approach to teach global warming’, International Journal of Science Education, Vol. 28, No. 4, 2006 pp.315-339 6 Duverger, M., Party Politics and Pressure Groups: A Comparative Introduction, Thomas Y. Crowell, New York, 1972 p.27 7 Eriskson, R., ‘The Advantage of Incumbency in Congressional Elections’, Polity, Vol. 3, No. 3, Spring 1971 pp.395-405 8 Cover, A., ‘One Good Terms Deserves Another: The Advantages of Incumbency in Congressional Elections’, American Journal of Political Science, Vol. 21, No. 3, pp.523-541 August 1977; Horiuchi, Y., & Leigh, A., ‘Estimating Incumbency Advantage: Evidence from Three Natural Experiments’, Visitor Seminar Research Paper, 2 October 2009 http://wwwdocs.fce.unsw.edu.au/economics/news/VisitorSeminar/091014_YusakuHoriuchi.pdf (Accessed: 10 October 2013)
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Juliet Pietsch note that “older generations are being replaced by younger generations with a much stronger propensity to vote for a minor party” therefore the Greens may be developing a natural demographic constituency. 9 Furthermore in their opinion, “the socialisation effects are breaking down.”10 Jamie Walker accused the Greens vote of being a protest vote.11 Nick Turnbull and Ariadne Vromen observed that the claim would require watching the Greens vote over the long-term.12 The protest party debate must therefore be seen within the context of long-term declining partisan support for the major parties. The cartelisation of Australian politics and the growth of postmaterialist values in voting occurred over the last twenty years as the Greens grew in strength. From 1987 to 1990, according to the AES, there was a large decline in the strength of partisan support for the Coalition and Labor. The cumulative proportion of people who describe themselves as having strong partisan support for the major parties was 69% in 1987, 36% in 1990, and 37% in 2010.13 This decline in the strength of partisanship has created an increasingly less stable party system. Don Aitkin placed partisan identity at the centre of the long-term party system stability in his analysis. He argued that the “adoption, by millions of Australians then and since, of relatively unchanging feelings of loyalty to one or other of the Australian parties” was the mechanism of the stability of the Australian system.14 While it is true that party identification has remained high, there have been incremental dealignments from the major parties to minor parties as major parties transition to cartel parties. Catch-all parties attempt to attract people from diverse view points and advocate multiple ideologies, whereas cartel parties act together and have the same basic ideology.
9
Martin, A., & Pietsch, J., ‘Future Shock or Future Stability?: Generational Change and the Australian Party System’, Australian Journal of Politics and History, Vol. 59, No. 2, June 2013 p.220 10 Ibid. 11 Walker, J., ‘The Greens Machine’, The Australian, 27 December 2003 p.11-14 12 Turnbull, N., & Vromen, A., ‘Election 2004: Where do the Greens fit in Election 2004?’, Australian Review of Public Affairs, 17 September 2004 http://www.australianreview.net/digest/2004/09/turnbull_vromen.html (Accessed: 26 September 2013) 13 McAllister, Op.cit., 2011 p.42 Figure 2.3 ‘’Very strong’ Labor and Liberal-National Partisans’, 1967-2010’ 14 nd Aitkin, D., Stability and Change in Australian Politics, Australian National University Press, Canberra, 2 Ed., 1982 p. 1 cited in McAllister, Op.cit., 2011 p.37
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This has bred despondency with some voters. In 1990, there were 5% of people who supported no party and in 1996 this increased to 17%, remaining at a similar level at 14% in 2010.15 The non-major party identification rate increased from about 4% in 1987 to 9% in 2010. Furthermore, in 2010 26% of Australian electors did not identify with the major parties compared with 9% in 1987 and 12% in 1990. The Greens’ rise has occurred in the time that the major parties transformed from catch-all parties into cartel parties. Richard Katz and Peter Mair argue parties such as Labor and the Coalition have “become part of the state apparatus itself”, employing state resources and colluding with each other.16 In the Australian context, the last three decades have seen a convergence of the major parties’ ideologies.17 Murray Goot has used this convergence to suggest one explanation for One Nation’s emergence in the late 1990s, particularly regarding to Asian immigration and neoliberalism.18 This feeling of lack of political representation is borne out in AES survey results (see Table 1). A total of 34% of Greens voters believe there is ‘not much difference between’ Labor and the Coalition compared with 23% and 20% of Labor and Coalition voters respectively. Over the long-term, the proportion of people who believe there is a ‘good deal of difference’ between the major parties remained fairly stable between 1998 and 2010; remaining around 28% having dropped from 44% in 1993.19
15
McAllister, op.cit. p. 39 Figure 2.1 ‘The direction of partisanship, 1967-2010’ Katz, R., Mair, P., ‘Cadre, Catch-all or Cartel? A Rejoinder’, Party Politics, Vol. 2, No. 4, 1995 p.14-15 17 Marsh, I., & Miller, R., Democratic Decline and Renewal: political change in Britain, Australia and New Zealand, Cambridge University Press, Cambridge, 2012 18 Goot, M., ‘The Party System, One Nation and the Cartelisation Thesis’, in Marsh, I. (ed.), Political Parties in Transition?, Federation Press, Sydney, 2006 p.183-190 19 McAllister, I., & Pietsch, J., Trends in Australian Political Opinion: results of the Australian election study, 1987-2010, Australian National Institute of Public Policy and ANU College of Arts and Social Sciences, The Australian National University, Canberra, July 2011 p.15 16
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Table 1: Perceived Party Differences by House of Representatives Vote, 2010 House of Representatives Vote Coalition Labor Greens Other Difference between Labor & Liberal Good Deal 31 26 12 15 Some Difference 47 50 51 25 Not Much Difference 20 23 34 46 No Difference 2 2 4 15 Total 100 100 100 100 (n) (889) (782) (243) (142) n=2057 Rounded to the nearest whole figure. Other includes Informal and those that did not vote. The question was ‘Considering everything the Labor Party and the Liberal Party stand for, would you say there is....’ Source: AES, 2010
In the same way that despondency occurred on the right of Australian politics expressed through One Nation, despondency appeared on the left of Australian politics expressed through the Greens. One Nation has the opposite post-material attitudes to Greens.20 Shaun Wilson has argued that “the emergence of the Greens is yet another by-product of the One-Nationisation of Australian politics that has contributed to sharp cleavages in public opinion on a range of social issues.”21 Major parties in the cartel avoid value issues, giving space to minor parties. Ronald Inglehart’s thesis of post-materialist voting was used by Ian McAllister who argued that major parties have an interest in keeping the debate focused on economic issues rather than divisive social issues as it fractures their supporters and the society at large.22 This period of ideological convergence has allowed minor parties to thrive by exploiting values. One Nation represented a protest party because there was not a long-term social base for the party. McAllister is correct when he acknowledged voting Greens “provides a convenient method of protesting against Labor policies without making the much more substantial commitment
20
Charnock, D., & Ellis, P., ‘Postmaterialism and postmodernization in Australian electoral politics’, Electoral Studies, Vol. 23, 2004 p.48 Figure 1, ‘Idealized Inglehart-style diagram possibly presenting Australian electorate.’ 21 Wilson, S., ‘The Emergence of the Green Electorate in Australia’, Australian Quarterly, Vol. 74, No. 6, November 2002 p.17 22 McAllister, I., Political behaviour: Citizens, Parities and Elites in Australia, Longman Cheshire, Melbourne, 1992
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of voting Liberal.”23 There is truth in that there is a significant protest from Labor to the Greens but there is a swing vote between Labor and the Coalition just as there is a swing vote between Labor and the Greens. Organisationally, the Greens act between a professional electoral party and a social movement.24 This has occurred as the membership become more comfortable with the party seeking electoral gains and having pragmatic policy. The shift from social movement to ‘semiprofessional party’ from 2001 to 2010 suggests that organisationally at least, the Greens are becoming less of a protest party. To suggest that the Greens are primarily a protest party ignores the changing trends of political socialisation and movement to a cartel party system. There is long-term evidence that younger generations have a greater propensity to vote Greens and naturally are replacing older generations as the Greens appear to be transitioning from a protest party and developing a natural demographic constituency. Demographic Trends Greens voters have distinct demographic characteristics, namely that of gender, age, income, education, occupation, religion, ethnicity, marital status and geographic proximity to cities. In addition to Greens voter demographics, a unique American literature on a relevant gay and lesbian voting behaviour is used to assess the LGBT impact on the Greens vote. Hypotheses will be discussed for each demographic trend. Gender For the period 2001 to 2007, gender has not been a significant indicator of political behaviour, according to the AES, as there was little difference between male and female voting in 23
McAllister, Op.cit., p.108 Jackson, S., The Australian Greens: Between Movement and Electoral Professional Party, Ph.D. thesis, Department of Government and International Relations, Faculty of Economics and Business, University of Sydney, July 2011 http://prijipati.library.usyd.edu.au/bitstream/2123/7858/1/sm-jackson-2011-thesis.pdf (Accessed: 14 September 2013) 24
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the case of Labor.25 However, in 1993 there was a 6% gap with men favouring the Labor party; and in the unique election of 2010, with Julia Gillard as Labor leader, there was a spike in the rate of women voting Labor. According to the AES, Greens voters tend to be women. In 2001, 59% of their voters were female, meaning there was an 18% gap between male and female Greens voters.26 The gap has since narrowed but there is still a bias for a greater proportion of women to vote Greens, with 53% of their supporters women in 2010, meaning there was a 6% gap between men and women within the Greens vote (see Table 2). There is also a gendered dimension to environmental activism; women have “[s]ignificantly more general environmental concern than men”. 27 This partially explains the Greens vote gendered bias. Moreover, women “play more prominent roles in grass-roots mobilisation.”28 Tranter has previously noted the gendered divide with environmental groups; however, this does not explain why women are more concerned with post-materialist values.29 While a significant underlying variable contributing to this activism is university education, it does not explain the gap between the genders.30 In particular, Inglehart’s claim that the “cognitively mobilised” have the greatest propensity to participate in environmental politics does not sit well with this gendered gap.31 This leads to the hypothesis that the higher the proportion of women in a neighbourhood, the higher the Greens vote.
25
McAllister, Op.cit. p.115; Figure 5.1, ‘Gender and the vote, 1967-2010’ Wilson, Op. cit. p.21 Table 2, ‘Selected Attitudes and Demographics of Green, Labor and Coalition voters in the Housing House of Representatives, AES 2001’ 27 Tranter, Op.cit. p.80; Zelezney, L., Poh-Pheng, C., & Aldrich, C., ‘New Ways of thinking about environmentalism’, Journal of Social Issues, Vol. 56, No. 3, 2000 pp.444-445 28 Rootes, C., ‘Environmental Movements’ in Snow, D., Soule, S., & Kriesi, H. (eds.), The Blackwell Companion to Social Movements¸ Blackwell, Malden, MA, 2004 p.617 29 Tranter, B., ‘The social bases of environmentalism in Australia’, Australian and New Zealand Journal of Sociology, Vol. 32, No. 2, pp.61-84 30 Tranter, B., ‘Environmentalism and education in Australia’, Environmental Politics, Vol 6, No. 2, pp.123-143 31 Inglehart, R., ‘Values, ideology and cognitive mobilisation in new social movements’ in Dalton, R., & Kuechler, M. (eds.), Challenging the Political Order, Polity Press, Cambridge, pp.43-66 26
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Table 2: Selected Demographics by House of Representatives V ote Percentage House of Representatives Vote Coalition Labor Greens Other Gender Male 55 45 47 42 Female 45 55 53 58 Generation Generation Y 20 25 38 37 Generation X 19 22 23 23 Baby Boomer 34 37 32 25 Depression 27 17 7 15 Highest Qualification No Qualification 31 33 25 29 Trade & Non-Trade 29 28 20 36 Diploma 15 12 10 10 University Degree 24 27 45 22 Religion Catholic 28 27 19 20 Anglican 24 17 13 5 Other Protestant 13 11 6 5 Other 16 15 16 27 No Religion 19 31 46 43 Marital Status Never Married 19 24 31 41 Now Married (incl. de facto) 71 65 61 50 Widowed Divorced or Separated 10 12 8 10 Rural or Urban Country Area 11 5 9 5 Large Town (≥25,000 people) 8 6 7 12 Major City (≥100,000 people) 81 89 84 83 Total (n) (891) (783) (244) (144) Rounded to nearest whole figure. Crosstabs vary between n =2027 and n=2062. Other includes informal votes and did not vote. Source: AES, 2010
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Generation Generations denote a point in historical time for age groups, which makes them useful to discuss party support at historical elections. Angus Campbell has noted “[p]eople of all ages respond to political issues and events in terms of their early socialization and their immediate social and economic circumstances.”32 The socialisation process is important in understanding the formation of the Greens vote, as different generations were socialised under different party systems: mass, catchall, and cartel. Generation has a significant effect on the frequency with which someone identifies with a minority party such as the Greens. Some 37% of generation Y and 27% of generation X identify with minor parties in 2007, compared to 15% of Great Depression generation voters.33 Greens voters, according to the 2010 AES, came mostly from generation Y and the Baby Boomer generation which voted 38% and 32% for the Greens respectively (see Table 2). Depression generation voters were politically socialised with mass party system and mostly retain their support for major parties. Although those aged 18-44 make up a clear majority of the Greens vote, there is a large number of Baby Boomer Greens voters, “hence the Greens are ‘not just a party of youth’”.34 However, generation Y is living at home with their parents for longer periods, compared to previous generations.35 The geographies of generation Y and the geographies of the Baby Boomer generation are highly correlated, in effect cancelling out the expected high vote for the Greens. This leads to the hypothesis that the higher the proportion of generation X voters in an area, the higher the Greens vote in a neighbourhood.
32
Campbell, A., ‘Politics through the Lifecycle’, The Gerontologist, Vol. 11, No. 2, pp.112-117 McAllister, Op.cit. 2011 p.124 Figure 5.6 ‘Minor or non-party identifiers by age, 1967, 1987 & 2007’ 34 Cahill, D., & Brown, S., ‘The Rise and Fall of the Australian Greens: The 2002 Cunningham by-election and its implications’, Australian Journal of Political Science, Vol. 43, No. 2, 2008 p.261 35 Howe, N., & Strauss, W., Millennials Rising: the Next Great Generation, Random House, New York, 2009 p.135 33
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Socioeconomic Status Greens are “disproportionately drawn from” voters with higher personal income levels.36 The 2001 AES showed that 28% of Greens voters are in the top quintile of income, 40% are in the fourth and third quintiles which are proportionate to the population and 36% have incomes in the bottom two quintiles, which is under representative of the population.37 There is a trend for those with higher income levels to vote Green. The Greens vote is not dissimilar to the Greens vote, with 24% of its vote comes from the top quintile, 40% comes from the fourth and third quintiles and 19% from the second quintile (compared with 14% of the Greens vote). The Coalition’s vote is 17% drawn from the bottom quintile, while the Greens draw 18% from the bottom quintile. Greens voters tend to have higher education levels compared to other parties. According to the 2001 AES, 48% of Greens voters had a higher education, compared to 17% of Coalition voters and 22% of Labor voters.38 Education levels have remained high with 45% of Greens voters having a university degree according to the 2010 AES (see Table 2). This is an important divergence from Coalition voters who identify as middle class at the same rate.39 Vromen noted Greens have similar background characteristics to the new social movements which were principally middle class based and university educated.40 Greens voters have many similarities with the “new class” of “knowledge workers” who are made up of the professional middle class and students preparing for professional occupations.41 Barry Bruce-Briggs notes the ‘Knowledge Class’ deviates from the historical trend:
36
McAllister, Op.cit. p.164 Op.cit p.165 Table 6.2 ‘Material Assets and the Vote’ 38 Wilson, Op.cit. 39 Op.cit. 40 Vromen, A., ‘Who are the Australian Greens? Surveying the Membership’, The Australian Sociological Association Conference, 2005, 6-8 December 41 Brint, S., ‘“New-Class” and Cumulative Trend Explanations of the Liberal Political Attitudes of Professionals”, American Journal of Sociology, Vol. 90, No. 1, p.30-71 37
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“The conventional view of the world, buttressed by historical experience, is that the less privileged orders should be the least enthusiastic about the status quo, while the better-off should be conservative.”42 However the knowledge class tend to have liberal values fitting well with Greens’ post-materialist values.43 This interpretation is supported by the high proportion of professional and associate professional jobs (46%) and public sector workers (28%) compared to the rest of the workforce (16%) who support the Greens.44 In a study by Steven Brint, public sector workers were the best indicator for liberal values in his multivariate analysis of the knowledge class.45 The literature on income, education and the knowledge classes leads to the following hypothesis: the higher the socioeconomic status of a neighbourhood, the higher the Greens vote. Religion Greens voters tend to be disproportionally secular. According to the 2001 AES, 43% of respondents who voted Greens were secular; compared to 22% of Labor voters and 17% of Coalition voters.46 This is significant demographic indicator which differentiates Greens voters from middle class Coalition voters. Since 2001, the number of people who say they have no religion grew from 15% to 22%.47 According to the 2010 AES, 46% of Greens voters were secular compared to 31% and 19% for Labor and Coalition voters respectively (see Table 2). The increased rate of secularism among Greens voters is partially a result of the Greens’ large proportion of younger voters. According to McAllister, drawing on AES data, “those aged 34 or less were more than twice as likely to say they
42
Bruce-Briggs, B., ‘A Preface on Purpose and Organization’, p.x in Bruce-Briggs, B. (ed.), The New Class?, Transaction, New Brunswick, New Jersey, 1979 43 Brint, Op.cit. 44 Cahill et al., Op.cit. 45 Brint, Op.cit. p.53 46 Wilson, Op.cit. 47 Anonymous, ‘Reflecting a Nation: Stories from the 2011 census, 2012–2013’, Category No: 2071, Australian Bureau of Statistics, Canberra, 21 June 2012 11:30 AM http://www.abs.gov.au/ausstats/
[email protected]/Lookup/2071.0main+features902012-2013 (Accessed: 10 October 2013)
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had no religion compared to those aged 65 or over.”48 This leads to the hypothesis: the higher the proportion of secularists, the higher the Greens vote. Ethnicity There is limited literature on the Greens vote and ethnicity, however Damien Cahill and Stephen Brown’s analysis of the 2002 Cunningham (NSW) by-election suggest there is a negative correlation with voters from non-English speaking backgrounds.49 There is a strong correlation between polling places within suburbs with high numbers of families from non-English speaking backgrounds and high Labor votes.50 Gianni Zappalà argues that the relationship is a form of clientelism where there is a perception that a party will provide some form of social protection to the community and that is reciprocated with a higher vote.51 Nicholas Economou has argued “voting behaviour is more likely to be determined by the voter’s relationship to the dominant mode of production than by ethnicity.”52 Cahill and Brown provided prima facie evidence for Zappalà’s position, indicating that the polling booth in the suburb with the largest Italian population had the lowest Greens vote.53 Marital Status There are a significant proportion of Greens voters who have never been married, 23% according to the 2001 AES.54 This is compared to 18% Labor of voters who are unwed and 11% of Coalition voters who are unwed. At the 2010 election, 31% Greens voters were unwed (see Table 2). This reflects the younger demographic of Greens voters and the increasing trend to delay marriage and childbearing.55 This suggests that those that have settled down to have a family have lower rates
48
McAllister, Op.cit. p.127 Cahill, Op.cit p.269 50 Op.cit. 51 Zappalà, G., ‘Clientelism, Political Culture and Ethnic Politics in Australia’, Australian Journal of Political Science’, Vol. 33, No. 3, 1998 pp.381-97 52 Economou, N., ‘Influencing Ethnic Voters: Class Interest or Ethnic Loyalty?’, Australian Rationalist, Vol. 38, 1995 pp.29-30 53 Cahill, Op.cit. p.270 54 Wilson, Op.cit. 55 Rowland, D., ‘Historical Trends in Childlessness’, Journal of Family Issues, Vol. 28, No. 10, 2007 pp.1311-1337 49
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of voting Green, resulting in the hypothesis: the higher the proportion of familism, the lifecycle when people have children, the lower the Greens vote. Rural-Urban Divide There is a clearly marked rural-urban dimension to the Greens vote. According to the 2001 AES 61% of Greens voters lived in cities with populations greater than 100,000; contrasted to 54% of Labor voters and 50% of Coalition voters.56 In 2010, 84% of Greens voters lived in cities, 7% of Greens voters lived in large towns and 9% lived in rural areas (see Table 2). There are pockets of high Greens support in rural alternative culture towns, notably in northern New South Wales.57 Simon Jackman noted support higher than 50% of the total vote in some rural polling places in 2010.58 This is notable in the Division of Richmond, namely in Wilsons Creek, Goonengerry and Nimbin, which are west-south-west of Mullumbimby. There is a link between Greens voting and alternative communities. This leads to the hypothesis: where there is a clear alternative culture, there is a higher Greens vote. Alternative Communities It is regularly commented that the Greens vote comes intensely from inner city areas.59 Similar to the rural alternative communities are urban alternative communities in their voting behaviours. Typically in inner city areas that recently have gone under a gentrification process, an alternative culture neighbourhood identity has been created.60 Gordon Douglas has outlined how a Chicago suburb, during its gentrification, drew in “‘hip’ first wave gentrifiers” who formed an
56
Wilson, Op.cit. Cahill, Op.cit. p.262 58 Jackman, S., ‘The Spatial Concentration of the Green Vote’, Research Paper, 13 September 2010 p.8 http://jackman.stanford.edu/papers/download.php?i=5 (Accessed: 13 September 2013) 59 Turnbull et al. Op.cit. 60 Douglas, G., ‘The Edge of the Island: Cultural Ideology and Neighbourhood Identity at the Gentrification Frontier’, Urban Studies, Vol. 49, No. 16, December 2012 pp.3579-3594 57
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“Edgetown identity” which then drew in second wave gentrifiers seeking an “idealised image of bohemian” culture.61 This observation agrees with Janna Michael’s conception that being ‘hip’ is a source of social prestige. Michael expands on Pierre Bourdieu’s social hierarchy conceptualisation in Cultural Capital arguing ‘hipsters’ attempt to “be a mere ideal-type of trendiness failing to convey authenticity”.62 Hence the popular media’s “hipster bashing” writ large in post-industrialised societies. Hipsters are second wave gentrifiers who adopt their neighbourhood alternative cultural identity. These locations provide places for first wave gentrifiers (artisans, musicians etc.), to sell their authentic products and services in the inner city habitus of knowledge class professionals that make up the second wave of gentrifiers.63 This economic relationship tends to be conducted at community fêtes and regular markets such as the Newtown street market in Sydney. Political Activism There is also a link between Greens voting and local activism.64 Cahill and Brown’s Cunningham by-election analysis provides an example. It was proposed that a coal loader off the town of Thirroul would have increased industrial traffic obscuring the local landscape.65 A community action group was established and development did not proceed. However, an environmental consciousness had been established in the community, and a decade later the Greens vote was double that of similar middle class demographics in Cunningham.66 While there is a strong environmental movement and Greens membership link, as evidenced by the 2005 members survey,
61
Ibid. p.3580 Michael, J., ‘It’s really not hip to be a hipster: Negotiating trends an authenticity in the cultural field’, Journal of Consumer Culture, Vol. 0, No. 0, published online 25 June 2013 http://joc.sagepub.com/content/early/2013/06/19/1469540513493206.full.pdf (Accessed: 19 September 2013) p.2 63 Ley, D., ‘Artists, Aetheticisation and the Field of Gentrification’, Urban Studies, Vol. 40, No. 12, 2003 pp.2527-2544 64 Burgmann, V., Power, Profit and Protest: Australian Social Movements and Globalisation, Allen & Unwin, Crows Nest, Sydney, 2003 65 See Mitchell, G, ‘Garden of Illawarra’ p.151 in A History of Wollongong, Hagan, J., & Wells, A. (eds.), University of Wollongong Press, Wollongong, 1997 66 Cahill, Op.cit. p.270 62
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there are other movement links to the Greens; significant connections between the party’s membership include the anti-nuclear movement (57%), refugee moment (51%), Indigenous movement (48%), student movement (44%), labour movement (38%), anti-globalisation movement (36%) and the lesbian, gay, bisexual and transsexual (LGBT) rights movement (19%).67 This leads to the following hypothesis: in geographies where there are/were proposals of industrial development with a clear local impact, there is a higher Greens vote. While the Greens have their social movement origins and retain elements of local activism in their organisation process, the party has moved from a protest party to “an electoral professional party”.68 Stewart Jackson details the organisational and membership changes of the Greens arguing they have become a ‘semi-professional party’ where the membership, who have a great deal of control over the parliamentary party, are comfortable with the party seeking greater electoral success.69 Hence the Greens do not fit the mould of a typical Australian political party in terms of voter support. The evidence above for the liberal values of Greens supporters warrant an investigation of the LGBT vote. Both the links with the LGBT activist movement and the consistently progressive LGBT public policy positions make an investigation of the LGBT Greens vote especially relevant. Furthermore, the strength of Greens voting in urban areas leads to the question: ‘to what extent is the LGBT population affecting the Greens vote?’
The LGBT Vote There is limited literature on Australian LGBT voting patterns, but there is a significant LGBT sociology. This research shows that LGBT identifying people tend to live in spatially clustered areas, meaning that their vote in particular geographies can be powerful. In the US, Mark Hertzog’s 67
Vromen, Op.cit. p.7 Jackson, S., The Australian Greens: Between Movement and Electoral Professional Party, Ph.D. thesis, Department of Government and International Relations, Faculty of Economics and Business, University of Sydney, July 2011 http://prijipati.library.usyd.edu.au/bitstream/2123/7858/1/sm-jackson-2011-thesis.pdf (Accessed: 14 September 2013) 69 Ibid. 68
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Lavender Vote details the lesbian, gay and bisexual vote. This is particularly relevant for the Greens vote because the Greens are the only party that have consistently advocated ‘marriage equality’ legislation for LGBT couples. Hertzog’s work is the only comprehensive text on LGBT voting behaviour in post-industrialised countries. Hertzog establishes that “whatever the number of homosexual or bisexual voters [...] they would not be a significant factor [...] were it not for the unique disabilities imposed on them by the heterosexual majority.”70 While the impositions faced by the LGBT community are not as great as they once were in both the American and Australian contexts, significant curtailments of liberties continue to galvanise the community. Three issues in point mobilise the LGBT community. First, marriage equality is a topical and divisive issue across the electorate as a whole. Second, blood donations is a highly salient topic within the LGBT community because gay men are unable to donate unless they have abstained from sex for more than a year; protected or otherwise.71 Such an imposition is not mandated upon the heterosexual community whose sexual activities constitute 25% of Australian HIV transmissions.72 This discriminatory system of blood donation is not backed up by medical evidence.73 Third, in spite of advances made by the LGBT movement, “gay bashings” and other hate crimes are still a frequent occurrence, along with “anti-gay” politics perpetuated by religious fundamentalists and older voters.74 Within the last year, Coalition Senator Cory Bernardi has exploited this for electoral gain.75
70
Hertzog, M., The Lavender Vote: Lesbians, Gay Men and Bisexual American Electoral Politics, New York University Press, New York, 1996 p.6 71 ‘Frequently Asked Questions’, Australian Red Cross Blood Service website, http://www.donateblood.com.au/faq#faq_300 (Accessed: 20 September 2013) 72 Whereas in 63% of cases were from male homosexual activities; McDonald, A. (ed.), ‘HIV, viral hepatitis and sexually transmissible infections in Australia,’ Annual Surveillance Report, Kirby Institute, UNSW, 2012 p.10 http://www.kirby.unsw.edu.au/sites/default/files/hiv/resources/2012AnnualSurvReport.pdf (Accessed: 15 September 2013) 73 In spite of medical evidence showing HIV transmission rates are made up of significant proportion of heterosexuals and that HIV diagnostic tests have improved significantly so much that the diagnostic window period for testing is now 6 weeks to confirm a positive or negative result. The Australian Red Cross’ insistence on a one year abstention from sex for homosexual men is a baseless barrier preventing them from donating blood on an equal footing which makes it a highly politically charged issue in the LGBT community. see Bourke, S. (ed.), ‘National Management Guidelines for Sexual Transmitted Infections’, Sexual Health Society of Victoria, th Melbourne, 8 Ed., 2008 p.10 74 Hertzog, Op.cit. p.4
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There has been a recent spike in gay hate crimes which has maintained a state of anxiety and feeling of oppression in the LGBT community.76 All three of these cases demonstrate that LGBT issues are politically salient. Identifying as LGBT is a secondary factor in voting according to the framework in Angus Campbell’s The American Voter, as group affiliation is similar to ethnic voting. The LGBT vote only is effective when there is a “hot issue” that relates to the community.77 LGBT issues have in recent federal elections been salient; there is a political connection between LGBT issues and Greens voting. LGBT voting is neglected in the field of electoral behaviour research. Hertzog’s work is the only significant quantitative research on LGBT voting behaviour. It provides a theoretical basis for understanding LGBT voting habits. It is established that LGBT communities cluster in “gay ghettos”, as Martin Levine identified, and typically expand over multiple polling places.78 A particularly significant Australian gay ghetto is around Taylor’s Square in Sydney where within two kilometres live 10% of all male Australian same sex couples, according to the ABS.79 Data suggests that those who live in gay communities are sympathetic to progressive politics. Drawing on 1990 data from the American Voter Research Survey and a CBS exit poll survey, Hertzog found 48% of gay men and lesbians identified as Democrat, compared to 36% of heterosexuals; 33% of LGBT voters were independents, compared to 30% of heterosexuals; and, a total of 19% LGBT voters were Republicans, compared to 34% of heterosexual voters.80 A total of 91% 75
Jabour, B., ‘Cory Bernardi links same-sex marriage to polygamy and bestiality again’, The Guardian, http://www.theguardian.com/world/2013/jun/18/cory-bernardi-same-sex-bestiality (Accessed: 14 September 2013) 76 Sheehan, P., ‘Gay Hate Crime: the shameful crime wave’, Sydney Morning Herald, http://www.smh.com.au/comment/gay-hate-the-shameful-crime-wave-20130303-2fe9w.html (Accessed: 14 September 2013) 77 Hertzog, Op.cit. p.18 78 Levine, M., ‘Gay Ghetto’, pp.182-204 in Levine, M. (ed.), Gay Men: The Sociology of Male Homosexuality, Harper & Row, New York, 1979 79 ‘4102.0 Australian Social Trends’, July 2013, Australian Bureau of Statistics, http://www.abs.gov.au/AUSSTATS/
[email protected]/Lookup/4102.0Main+Features10July+2013 (Accessed: 15 September 2013) 80 Hertzog, Op.cit. p.84; Table 3.2, ‘Comparison of Gay and Lesbian Attitudes with other voters, 1990 US General Election’
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considered themselves to be ideologically liberal or moderate, compared to 66% of heterosexual voters. Only 9% of LGBT voters who considered themselves to be conservatives, compared to 35% of heterosexual voters.81 Post-materialist concerns rated higher than the heterosexual population, with 25% of LGBT voters considering the environment to be the most important issue, compared with 7% of the heterosexual community.82 The strong preference for a liberal ideology, post-materialist concerns and a sense of “unique disabilities” in the American political contest suggests gay and lesbian Australians would be politically progressive. If gay and lesbian Australians are as progressive as their American counterparts there is a strong likelihood they would vote for the Greens or Labor. An investigation of LGBT voting is warranted to gain an understanding of the voting patterns of LGBT Australians who have been neglected in the voting literature to this point. Such an investigation is timely, because of the availability of census data and Australian political science has not asked the question if sexuality impacts the vote. The literature cited above leads to the hypothesis that gay men and lesbians should vote at higher rates for Labor and Green.
81 82
Ibid. Ibid.
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Chapter 3: Data, Measure and Methodology Data Data used in this thesis is taken from the ABS 2011 census and the AEC data packs on the 2010 federal election.1 There are two levels of analyses preformed on demographics and voting behaviour to predict the Greens vote. The first analysis uses Commonwealth electoral divisions as its unit and the second analysis uses the ABS Statistical Area 2 Level (SA2) unit to represent neighbourhoods. The electorate analysis uses all 150 electorates as they existed at the 2010 election, following the 2009 redistribution and all the votes. The neighbourhood analysis uses the 1993 SA2 neighbourhood units with only the ordinary votes lodged at polling places within the SA2 unit. Nationally, 84% of votes were ordinary votes.2 Votes that did not have a geographical location, were pre-polled, cast outside the neighbourhood, or were lodged at an airport’s international terminal have been excluded as the neighbourhood of the vote cannot be reliably ascertained. It is assumed with the neighbourhood analysis that electors voted at polling places near to their residence. There are multiple polling places in each SA2 unit which have been aggregated using Geographic Information Systems (GIS) software. The ABS census has been taken from downloadable data packs with exception of the same sex couples variable; this has been sourced from the ABS census Table Builder website. This is because the census data packs did not include that variable. This data was then standardised by converting it into a percentage.
1
For ABS data see http://www.abs.gov.au/websitedbs/censushome.nsf/home/data (Accessed: 21 September 2013); For AEC data on the 2010 election see http://results.aec.gov.au/15508/Website/Default.htm (Accessed: 23 September 2013) 2 ‘House of Representatives: vote by state’, Virtual Tally Room website, Australian Electoral Commission (AEC), 29 September 2010 http://results.aec.gov.au/15508/Website/HouseVotesCountedByState-15508.htm (Accessed: 14 October 2013)
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Measure This study is based on factor analysis and multiple regression analysis of voting behaviour and demographics. The measures that are included in the factor analyses and regression analyses are standardised proportional demographics and factor scores that are representative of underlying concepts. The factor scores are used in the regressions and hence the factor scores constitute a measure. Variables such as gender and female same sex couples are measured as percentage proportions. This thesis acknowledges that generations and lifecycles are difficult to disentangle from aggregate data.3 This thesis groups ages into generations as used by the AES.4 It is difficult to empirically distinguish generation from lifecycles; however a factor score is used to measure the familism lifecycle point. Familism is the stage in a lifecycle when a couple has children. Factor scales contribute a conceptual measure for variables that do not exist within the census. They are made up of proportional data to construct a hypothetical scale. An example would be socioeconomic status which combines rates of high social status occupations, incomes, and education. Table 3 contains the descriptive statistics for the conceptual variables and Table 4 contains the variables used in the factor analysis to construct hypothetical conceptual scales. Both tables have electorate and neighbourhood level data. In the methodology section, Table 10 contains the descriptive statistics of the Greens, Labor and Coalition votes which are the dependent variables in the regressions.
3
Pilcher, J., ‘Manheim’s Sociology of Generations: an undervalued legacy’, Journal of Sociology, Vol. 45, No. 3, 1994 pp.481-495 4 This thesis uses generations defined within the AES run by Ian McAllister, Juliet Pietsch and Clive Bean: the Depression Generation was born before 1946 and were 65 years or old at the 2010 election; the Baby Boomer Generation was born between 1946 and 1964 and were aged between 46 and 64 at the 2010 election; Generation X was born between 1965 and 1976 and were between 33 and 45 at the 2010 election; and, Generation Y was born between 1977 and 1994 and they were aged between 16 and 32 at the 2010 election. For the purposes of this thesis, Generation Y is measured from those who were aged 19 and over at the 2011 census as they were eligible to vote for the 2010 election. For an outline of the generations in the AES see Martin, A., & Pietsch, J., ‘Future Shock or Future Stability?: Generation Change and the Australian Party System’, Australian Journal of Political Science, Vol. 59, No. 2, June 2013, pp.212-221
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Table 3: Descriptive Statistics of Concept Representative Variables Variable
Mean
Standard Deviation
Electorate Level Age 19-34
21.60
5.26
Age 35-44
14.18
1.34
0.81
1.98
Male Same Sex Couples Female Same Sex Couples No Religion
0.69
0.72
22.38
5.83
Female
50.61
0.94
Neighbourhood Level Age 35-44
14.07
2.16
Definition
Concept
Percentage of population aged 19-34 Percentage of population aged 35-44 Percentage of couples that are gay Percentage of couples that are lesbian Percentage of population who are secular Percentage of population who are female
Generation Y Generation X Gay couples & communities Lesbians couples & communities Secularism Gender
Percentage of population Generation X aged 35-44 Male Same Sex 0.30 0.82 Percentage of couples that Gay couples & Couples are gay communities Female Same Sex 0.28 0.35 Percentage of couples that Lesbian couples & Couples are lesbian communities No Religion 23.02 6.90 Percentage of population Secularism who are secular Female 50.22 2.28 Percentage of population Gender who are female Figures rounded to two decimal places Electorate level n=150; Neighbourhood level n=1993. Source: ABS, census 2011
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Table 4: Descriptive Statistics of Variables in Factor Analysis Variable
Mean
Standard Deviation
Definition
Electorate Level Mortgages Occupation: Information Technology Media & Telecommunications Occupation: Finance & Insurance Occupation: Professional Scientific & Technical Income: ≥$2000 per week Income: $1500-999 per week
35.40 0.82
6.62 0.57
Percentage of dwellings with a mortgage Percentage of population who work in information technology, media & telecommunications
1.73 3.36
1.15 2.12
4.97 5.18
3.45 1.64
Mortgage: $3000-999 per month
3.37
1.82
Mortgage: ≥$4000 per month
2.24
1.89
Employed Full Time Parents aged 35-9 with 2 children
29.47 1.22
3.90 0.19
Parents aged 30-4 with 2 children
0.79
0.18
Postgraduate Degree Graduate Diploma or Graduate Certificate Bachelor’s Degree
2.89 1.38
2.34 0.70
10.80
5.50
Percentage of population who work in finance & insurance Percentage of population who work in professional, scientific & technical services Percentage of population with incomes over $2000 Percentage of population with incomes between $15001999 Percentage of dwellings with mortgage repayments between $3000-3999 per month Percentage of dwellings with mortgage repayments equal to or greater than $4000 per month Percentage of population employed full time Percentage of households with parents aged 35-39 with 2 children Percentage of households with parents aged 30-34 with 2 children Percentage of population with a postgraduate degree Percentage of population with a graduate diploma or graduate certificate Percentage of population with a bachelor’s degree
31.15 0.72
10.8 0.58
1.46 3.11
1.16 2.30
Neighbourhood Level Mortgages Occupation: Information Technology Media & Telecommunications Occupation: Finance & Insurance Occupation: Professional Scientific & Technical Income: ≥$2000 per week Income: $1500-999 per week
Percentage of dwellings with a mortgage Percentage of population who work in information technology, media & telecommunications
Percentage of population who work in finance & insurance Percentage of population who work in professional, scientific & technical services 4.88 4.14 Percentage of population with incomes over $2000 5.18 2.29 Percentage of population with incomes between $15001999 Mortgage: $3000-999 per month 1.87 2.35 Percentage of dwellings with mortgage repayments between $3000-3999 per month Mortgage: ≥$4000 per month 2.88 2.00 Percentage of dwellings with mortgage repayments equal to or greater than $4000 per month Employed Full Time 29.53 5.80 Percentage of population employed full time Parents aged 35-9 with 2 children 1.19 0.36 Percentage of households with parents aged between 3539 with 2 children Parents aged 30-4 with 2 children 0.78 0.27 Percentage of households with parents aged between 3034 with 2 children Postgraduate Degree 2.65 2.66 Percentage of population with a postgraduate degree Graduate Diploma or Graduate 1.37 0.90 Percentage of population with a graduate diploma or Certificate graduate certificate Bachelor’s Degree 10.18 6.11 Percentage of population with a bachelor’s degree Figures rounded to two decimal places. Electorate level n=150; Neighbourhood level n=1993. The factor analysis produces 2 factor scales that conceptually represent socioeconomic & familism scales. Source: ABS, census 2011
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Factor analysis is a mathematical technique that reduces variables into a set of linear components. Factor analysis obtains a mathematically optimal estimation in a model where observed indicators are taken as imperfect and proxy indicators for an underlying hypothetical trait. The factor analysis uses observed data to construct a scale of an unobserved variable. These scales measure underlying conceptual traits. The factor scale produced is one measure which this thesis will use in its regression analyses, along with representative variables. The results are based on principal component factor analysis. Factors with an eignenvalue less than one are omitted. If the model is properly speculated, it provides an optimal estimate of underlying traits. If the model is approximately speculated, it provides a robust estimate of a trait. For example, a hypothetical trait is rurality. A scale for rurality can be devised by factor analysing variables such as the proportion of people who work in agriculture and fisheries and the proportion of dwellings with more than four vehicles. A value score is given for each electorate. These scales have no natural unit, but for convenience I have scaled the factor scores from 0 to 100.5 Factor analysis is useful as there is less random measurement error from a single item. Combining additional items reduces the error and thus its bias. Moreover, a single item itself may not capture an underlying concept; there is a real risk using a single item may lead an analysis astray. This thesis endeavours to explain rather than predict the Greens vote. This is done by conducting a series of multiple linear regression analyses with factors for which the Greens voting and LGBT voting literature suggest positive correlations. The dependent variable is the percentage Greens vote. The Labor and Coalition votes are also included to benchmark the results of the Greens vote. The following tables are the factor correlations, factor loadings and their descriptive statistics for both the electorate and the neighbourhood analyses.
5
McAllister, I., & Kelley, J., ‘Contextual Characteristics of Australian Federal Electorates’, Australian and New Zealand Journal of Sociology, Vol. 19, 1983 p.120
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Table 5: Factorisation correlations (Electorate Level) Ecological correlations between selected socioeconomic variables 1 Employed Full Time
1
2
4
5
6
7
0.59
*
3 Graduate Diploma or Graduate Certificate
0.52
0.82
9
10
11
12
13
* 0.85 0.67 0.7
* 0.54 0.87
* 0.71
*
0.62
0.71
0.48
0.74
*
0.58
0.72
0.56
0.80
0.92
*
0.79
0.89
0.56
0.83
0.86
0.85
*
Bachelor’s Degree 0.62 0.97 0.87 * Total Mortgages 0.08 -0.32 -0.27 -0.31 * Parents aged 30-34 with 2 children -0.17 -0.65 -0.71 -0.70 0.70 * Parents aged 35-39 with 2 children 0.14 -0.08 -0.08 -0.07 0.77 0.56 * Income: $1500-999 per week 0.84 0.74 0.70 0.76 -0.06 -0.38 0.14 Income: over $2000 per week 0.70 0.80 0.71 0.84 -0.25 -0.59 -0.03 Mortgage:$3000-9999 per month 0.58 0.50 0.33 0.50 0.34 0.01 0.57 Mortgage: $4000 or more per month 0.55 0.77 0.56 0.78 -0.10 -0.49 0.15 Occupation: Information Technology, Media 12 0.59 0.86 0.64 0.85 -0.21 -0.57 -0.03 & Telecommunications 13 Occupation: Finance & Insurance 0.57 0.83 0.56 0.84 -0.14 -0.47 0.08 Occupation: Professional, Scientific & 14 0.69 0.93 0.82 0.97 -0.23 -0.66 -0.03 Technical Services The values are Pearson’s product-moment correlation coefficients (R). Figures rounded to 2 decimal places. Source: ABS, 2011 census
8
*
2 Postgraduate Degree
4 5 6 7 8 9 10 11
3
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Table 6: Factorisation correlations (Neighbourhood Level) Ecological correlations between selected 1 2 3 4 5 6 7 socioeconomic variables 1 Employed Full Time * 2 Postgraduate Degree 0.43 * 3 Graduate Diploma or Graduate Certificate 0.41 0.82 * 4 Bachelor’s Degree 0.50 0.95 0.85 * 5 Total Mortgages 0.28 -0.1 -0.03 -0.07 * 6 Parents aged 30-34 with 2 children 0.11 -0.43 -0.41 -0.43 0.58 * 7 Parents aged 35-39 with 2 children 0.27 -0.03 0.09 0.04 0.7 0.57 * 8 Income: $1500-999 per week 0.79 0.66 0.69 0.71 0.21 0.07 0.30 9 Income: over $2000 per week 0.60 0.71 0.65 0.75 -0.05 -0.31 0.10 10 Mortgage:$3000-9999 per month 0.48 0.38 0.35 0.42 0.59 0.22 0.60 11 Mortgage: $4000 or more per month 0.37 0.6 0.49 0.64 0.20 -0.24 0.26 12 Occupation: Information Technology, Media & 0.48 0.75 0.60 0.78 0.07 -0.28 0.08 Telecommunications 13 Occupation: Finance & Insurance 0.46 0.68 0.47 0.74 0.15 -0.18 0.19 14 Occupation: Professional, Scientific & Technical 0.57 0.88 0.79 0.94 0.04 -0.38 0.09 Services The values are Pearson’s product-moment correlation coefficients (R). Figures rounded to 2 decimal places. Source: ABS, 2011 census
8
9
10
11
12
* 0.8 0.63 0.55 0.55
* 0.47 0.75 0.56
* 0.67 0.41
* 0.55
*
0.49 0.73
0.57 0.79
0.47 0.50
0.62 0.70
0.85 0.82
13
* 0.80
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Table 7: Factor Loadings of Socioeconomic & Other Variables Variable
Factor Loadings (Varimax rotation) Factor I Factor II
Electorate Level Occupation: Professional, Scientific & Technical Services 0.97 -0.15 Bachelor’s Degree 0.95 -0.23 Postgraduate Degree 0.93 -0.22 Income: over $2000 per week 0.91 -0.10 Mortgage: $4000 or more per month 0.88 0.07 Occupation: Information Technology, Media & Telecommunication 0.87 -0.13 Income: $1500-999 per week 0.87 0.12 Occupation: Finance & Insurance 0.87 -0.02 Graduate Diploma or Graduate Certificate 0.79 -0.27 Employed Full Time 0.76 0.23 Mortgage: $3000-999 per week 0.69 0.58 Parents aged 35-9 with 2 children 0.10 0.90 Total Mortgages -0.14 0.90 Parents aged 30-4 with 2 children -0.57 0.75 Neighbourhood Level Occupation: Professional, Scientific & Technical Services 0.97 -0.01 Bachelor’s Degree 0.96 -0.11 Postgraduate Degree 0.91 -0.16 Income: over $2000 per week 0.85 0.03 Occupation: Information Technology, Media & Telecommunication 0.83 0.01 Graduate Diploma or Graduate Certificate 0.83 -0.10 Income: $1500-999 per week 0.80 0.31 Occupation: Finance & Insurance 0.79 0.12 Mortgage: $4000 or more per month 0.75 0.32 Employed Full Time 0.61 0.39 Mortgage: $3000-999 per week 0.54 0.69 Parents aged 35-9 with 2 children 0.10 0.86 Total Mortgages 0.02 0.88 Parents aged 30-4 with 2 children -0.40 0.77 Electorate level n=150; Neighbourhood level n=1993; Varimax rotated factor loadings from a principal components factor analysis. The initial eignenvalues for the first four factors at the electorate level are 8.68, 2.73, 0.76 & 0.55, respectively. The initial eignenvalues for the first four factors at the neighbourhood level are 7.53, 2.86, 0.90 & 0.74, respectively. Factor I is socioeconomic status; Factor II is Familism. Figures rounded to 2 decimal places. Source: ABS, census 2011
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Table 8: Descriptive Statistics for Factor Scales Scale Electorate Level Socioeconomic Status
Mean
Standard Deviation
29.58
22.63
Familism
44.21
17.64
Neighbourhood Level Socioeconomic Status
36.89
11.13
Concept Economic & sociological combined status Stage in a lifecycle where a couple has children
Economic & sociological combined status Familism 26.28 17.70 Stage in a lifecycle where a couple has children Electorate level n=150; Neighbourhood level n=1993. Figures rounded to 2 decimal places. Source: ABS, census 2011; see Tables 4-7
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Methods Multiple linear regression analysis is used to predict the vote of a party (see Appendix 1). The correlations between the independent variables are used to determine their significance on the vote. The outliers of the predicted vote at the electorate level will be examined to identify trends. A secondary analysis is conducted at the neighbourhood level to investigate the voting behaviours in LGBT areas as the electorate analysis did not produce significant results; furthermore, the literature indicated analyses of the neighbourhood effect would be more useful with a smaller unit.1 A smaller unit of analysis with a voter population of around 10,000 is required to better test the hypotheses. Multiple linear regression models the relationship between a dependent variable and multiple explanatory independent variables to predict a single scalar variable.2 The equation for multiple linear regression is:
The aim of these analyses is to predict the party vote of the Greens, Labor and Coalition. The regression is carried out using the following method. There are two factorised scales: socioeconomic status and familism. The socioeconomic scale combines education, income and occupational variables. The familism scale uses the proportion of dwellings with mortgages and proportion of parents aged 30-39 with two children. This was selected because the median age at which women have their first child is 30 years.3 Some individual variables represent concepts. The proportion of people who have no religion is used for the demographic concept of religion, as it is the most representative of the Greens vote in the literature. The proportion of people who are female is used to represent gender, as it is likewise the most representative. 1
Pattie, C., & Johnston, R, ‘“People who Talk Together Vote Together”: An exploration of contextual effects in Great Britain’, Annals of the Association of American Geographers, Vol. 90, No. 1, 2000 p.45 2 Keller, G., Statistics for Management and Economics, South-Western, Mason, Ohio, 2012 p.693 3 ‘Record number of women over 40 having babies’, Media Release, Category number: 3301, Australian Bureau of Statistics, Canberra, 11:30am (AEST) 25 October 2012 http://www.abs.gov.au/ausstats/
[email protected]/mediareleasesbytitle/8668A9A0D4B0156CCA25792F0016186A?Op enDocument (Accessed: 25 September 2013)
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The 2011 age groups of 19-34 and 35-44 years are used as they are representative of generation Y and generation X respectively at the 2010 election.4 Each is used separately to test generational effects on the Greens vote at the electorate level. In the neighbourhood analysis, only 35-44 years is used to represent generation X as it is the most significant of the two generations, and significant results were found at the electorate level. The proportion of same sex couples is taken as a proxy indicator for gay and lesbians as they typically cluster in gay ghettos.5 Female same sex couples and male same sex couples will be used separately to test the effect of sexuality on each gender on the Greens vote. Twelve regression equations at the electorate level and six regression equations at the neighbourhood level are estimated to predict the Greens vote and benchmark the results with other parties’ votes. Table 9 outlines the analyses.
4
Ages in the 2011 census for the appropriate 2010 generational ages at the time of the 2010 election Levine, M., ‘Gay Ghetto’, pp.182-204 in Levine, M. (ed.), Gay Men: The Sociology of Male Homosexuality, Harper & Row, New York, 1979 5
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Table 9: Regressions by Tested Independent Variables Regression number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Generation X X Y Y X X Y Y X X Y Y X X X X X X
LGBT Couples Gay Lesbian Gay Lesbian Gay Lesbian Gay Lesbian Gay Lesbian Gay Lesbian Gay Lesbian Gay Lesbian Gay Lesbian
Dependent Variable Greens Vote Greens Vote Greens Vote Greens Vote Labor Vote Labor Vote Labor Vote Labor Vote Coalition Vote Coalition Vote Coalition Vote Coalition Vote Greens Vote Greens Vote Labor Vote Labor Vote Coalition Vote Coalition Vote
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These analyses aim to test whether a higher proportion of generation X or generation Y voters produces a higher Greens vote geographically, and whether a higher proportion of lesbians, or gay men produce a higher Greens vote, the statistical significance and correlations of independent variables are recorded; namely, the p-value results, beta partial and standard coefficients, partial correlation and partial correlations squared. To test the remaining hypotheses, the outliers in predicting of the Greens vote that are greater than 1.5 standard deviations from the mean are compared to other demographics and a brief qualitative survey of the local politics, culture and history is completed. The results of the socioeconomic status, familism, religion and gender correlations are studied at the neighbourhood level. At the neighbourhood level, lesbians and gay men are used to predict the party votes along with the socioeconomic status, gender, familism and religion. The descriptive statistics of the dependent variables are produced in Table 10.
Table 10: Descriptive Statistics of Party Votes New students Mean Standard Deviation Electorate Level Greens 43.08 12.24 Labor 37.98 11.06 Coalition 11.77 4.81 Neighbourhood Level Greens 44.49 13.44 Labor 37.51 12.56 Coalition 11.52 6.05 Electorate level n=150; Neighbourhood level n=1993. Figures rounded to 2 decimal places. Source: AEC, 2010 House of Representatives
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Chapter 4: Results This chapter outlines the results of the analyses examining the relationship between the Greens vote and sexuality, gender, generation, lifecycles, religion, and socioeconomic status. Specifically, lifecycles examines familism, the stage of life when a couple has children, and religion examines the aspect of secularism. Electorate and neighbourhood level analyses are performed. The natural unit for measuring voting is the electorate, as that is the unit elections are conducted in and the neighbourhood effect is best measured with neighbourhood size units. Both are used to outline demographic trends in Greens voting. The outliers from the electorate level analyses are discussed at the end of the chapter.
Sexuality Four analyses were performed to predict the Greens vote at the electorate level and two use neighbourhood data which showed two gendered trends for positive voting relationships between Greens and lesbian communities. First, lesbians predicted the Greens vote better than gay men and second, gay males deviate more from their gender on Greens voting than lesbians. The results were inconclusive for sexuality at the electorate level as there was an insignificant relationship between Greens voting and both gay and lesbian communities at the electorate level (see Tables 12-15). Only a weak correlation was found between Greens voting and gay communities (b=0.2*) in one analysis. This is compared to lesbians and the Greens vote which scored at a higher level of significance than gay men (b=1.7***) in electorate analyses. There is low statistical significance for the LGBT indicator at the electorate level because the data has a large distribution range and is highly skewed as gay and lesbian couples tend to concentrate in a few electorates (see Table 11).
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Table 11: Electorates with the Most & Least Gay and Lesbians Identifying Populations Electorate
Gay Couples
Lesbian Couples
Total LGBT Couples
Largest LGBT population Sydney, NSW 20.98 6.06 27.04 Melbourne, Vic. 6.79 3.46 10.25 Wentworth, NSW 8.12 1.61 9.73 Grayndler, NSW 4.79 4.79 9.58 Melbourne Ports, Vic. 5.71 1.96 7.68 Brisbane, Qld. 4.02 1.84 5.86 Batman, Vic. 1.43 2.74 4.17 Higgins, Vic. 3.11 0.94 4.05 Griffith, Qld. 1.84 1.82 3.66 Perth, WA 2.13 1.52 3.65 Smallest LGBT Population Calwell, Vic. 0.20 0.22 0.42 Werriwa, NSW 0.17 0.24 0.42 Maranoa, Qld. 0.13 0.28 0.41 Mitchell, NSW 0.17 0.23 0.39 Menzies, Vic. 0.23 0.15 0.38 Scullin, Vic 0.17 0.18 0.36 Blaxland, NSW 0.18 0.15 0.33 Aston, Vic 0.10 0.22 0.31 McMahon, NSW 0.13 0.13 0.25 Fowler, NSW 0.14 0.11 0.25 Figures rounded to 2 decimal places. Presented as a percentage of all couples. See Appendix 2. Source: ABS, census 2011
One problem with the electorate level analysis is that the results are not significant. This was previously highlighted by Pattie and Johnston. Aggregate-level studies using electorates as the unit of analysis are too large to produce significant results for Cox’s hypothesis, “people who talk together vote together”.1 Electorates include geographies of people who do not interact with each other. Gay and lesbian communities form clusters in neighbourhoods that are not homogeneous in any electorate. Gay and lesbian neighbourhoods are captured by much larger units, making electorate level analysis less reliable. 1
Pattie, C., & Johnston, R., ‘”People who Talk Together Vote Together”: An exploration of the conceptual effects of voting in Great Britain’, The Annals of the Association of American Geographers, Vol. 90, No. 1, 2000 p.45
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From Taylor’s Square to Hyde Park, Oxford Street separates the country’s largest gay community between the electorates of Sydney and Wentworth, each with different demographics and voting behaviours. In the south of the electorate of Sydney, there is a great deal of gentrification, drawing in knowledge class workers and Australia’s largest public housing estate, the Northcote Estates, home to around 8,000 people of low socioeconomic status. Contrast Sydney with Wentworth, an electorate which contains higher income levels in suburbs including Point Piper, Vaucluse, Paddington, Double Bay, and Bellevue Hill. When taken at the aggregate level these dissimilar demographics, and their associated voting patterns, while not exhibiting the voting patterns of gay men, do reflect those of lesbian Australians. To find the voting effect of gay men and lesbians, it is essential to look at smaller units of analysis, i.e. neighbourhoods. The lack of significant results on the gay and lesbian vote at the electorate level, and the literature on the neighbourhood effect, warrants a neighbourhood level investigation. The neighbourhood analyses find highly significant positive correlations between both the Greens vote and the Labor vote with both lesbian and gay voters. There were highly significant negative correlations between the Coalition vote and both gay and lesbian voters. The size of the correlation was different between each of the party’s votes and gay and lesbian populations. The lesbian vote will be analysed first, followed by the gay vote. There was a high correlation between lesbians and the Greens vote (b=3.55***). For every 1% increase in the proportion of lesbians in a neighbourhood, there was 3.55% increase in the Greens vote (see Tables 16 & 17). The relationship between lesbians and the Labor vote (b=7.66***) was double the relationship between lesbians and the Greens vote. For every 1% increase in the proportion of lesbians in a neighbourhood, there was a 7.66% increase in the Labor vote. Conversely, there was a highly negative relationship between lesbians and the Coalition vote (b=-11.92***). For every 1% increase in the proportion of lesbian couples in a neighbourhood, there was an 11.92% decrease in the Coalition vote. The partial correlation between the lesbian and the Greens vote was
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its 0.25 and squared value was 0.06. This means that lesbians explains 6% of the Greens vote when controlling for other variables, meaning it has significant explanatory power. This agrees with the AES data on gendered voting and Hertzog’s data of American LGBT voting patterns.2 The AES showed that 53% of Greens voters were female as were 55% of Labor voters, and 45% of Coalition voters (see Table 2). Lesbians typically fit this data with a general preference for the Labor vote which has the highest positive correlation, followed by the Greens. Lesbians had a highly negative relationship with the Coalition vote. I suspect the lesbian vote is being split between Labor and the Greens in antagonism to the Coalition. Lesbians deviated significantly from their gender’s general voting behaviour. Females and the Greens vote shows a positive correlation (b=0.24***); the difference between it and lesbians and the Greens is Δb=3.11, indicating lesbians significantly deviate from the general female population on the Greens vote. Likewise, females and the Labor vote had a strong correlation (b=0.87***); the difference between it and lesbians and Labor voting is Δb=6.79. This suggests lesbian voting patterns deviates by a factor of two from the general female voting pattern for the Labor than the Greens. Lesbians deviated most from their gender on the Coalition vote. Females and the Coalition had a negative correlation (b=-0.97***). The difference between the general female population and lesbians was remarkable (Δb=10.95), indicating the deviation came from the Coalition and was distributed over Labor and the Greens. Labor received the majority of this deviation (Δb=6.79) whereas the Greens received half that (Δb=3.11). Sexuality is having an effect on the voting behaviour of lesbian Australians. Gay men had a weaker Greens vote than lesbians. Gay men and the Greens vote had a positive correlation (b=0.47***). For every 1% increase in the proportion of gay men in a neighbourhood, there is a 0.47% increase in the Greens vote. Gay men and the Labor vote had a
2
Hertzog, M., The Lavender Vote: Lesbians, Gay Men and Bisexual American Electoral Politics, New York University Press , New York, 1996 p.84
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positive correlation (b=1.74***), three times that of the Greens. For every 1% increase in the proportion of gay men in a neighbourhood, there is a 1.74% increase in the Labor vote. The most negative relationship is between gay men and the Coalition vote (b=-2.55***). For every 1% increase in the proportion of gay men in a neighbourhood, there is a 2.55% decrease in the Coalition vote. Lesbians deviate four times more to vote Greens than gay men and are twice as much to vote for Labor as gay men. The negative relationship between lesbians and the Coalition vote is twice that of gay men. The partial correlation between gay men and the Greens vote was 0.08 and its squared value was 0.01. This means that gay couples explain 1% of the Greens vote when controlling for the other variables, therefore it has a low explanatory power. Lesbians are better indicators of the Greens, Labor and Coalition votes than gay men. Gay men have also deviated significantly from their gender’s general voting behaviour. Males and the Greens vote have a negative relationship (b=-0.32***); the difference between male voting patterns and gay men and Greens voting is Δb=0.79, indicating there was a significant deviation of gay men towards voting Green, which is greater than the general male population and the Greens vote. Likewise, there a negative relationship between males and the Labor vote (b=1.06***); the difference between it, and gay men and the Labor vote is Δb=2.8. This means there is a larger deviation from the general male voting pattern for Labor than Greens, indicating gay men typically prefer Labor. Gay men have deviated most from their gender on the Coalition vote. Males and the Coalition vote have a positive relationship (b=1.27***). The difference between gay men and the general male population is Δb=3.82, indicating the deviation came from the Coalition and is distributed over Labor and the Greens. The Labor vote received the majority of this deviation (Δb=2.8) compared to the Greens (Δb=0.79). Sexuality is also having an effect on the voting behaviour of gay men. The effect of sexuality appears to be greater on lesbians than on gay men. The deviation of lesbians voting for the Greens from females (Δb=3.11) is higher than the deviation of gay men voting 46 | P a g e
for the Greens from males (Δb=0.79). The deviation of gay men from males voting for Labor (Δb=2.8) is lower than lesbians from females (Δb=6.79). The deviation of lesbians voting for the Coalition from females (Δb=11.95) is greater than gay men from males (Δb=3.82). Gay men and lesbians tend to vote more for Labor than the Greens. However, both have a significant positive effect on the Greens vote. The strength of the lesbians’ effect is four times greater than gay men on the Greens vote. Both lesbians and gay men have positive effects on the Labor vote; but the lesbian effect is twice as large as that of gay men. This agrees with Hertzog’s literature on the LGBT vote, where 81% of LGBT Americans identified as Democrat or as an independent, compared to 19% as Republicans.3 In addition, 91% of American gay men and lesbians consider themselves to be ideologically liberal or moderate, compared to 9% who considered themselves conservatives. Australian gay men and lesbians are averse to voting for the Coalition just as American LGBT voters are adverse to voting for the Republican Party. The gay and lesbian preference for liberal and moderate ideology appears to make Labor and Greens natural parties for the Australian LGBT vote. One factor that may be relevant to LGBT voting behaviour in the 2010 election is Tony Abbott’s arguably insensitive remarks toward gay and lesbian Australians on both Channel Nine’s Sixty Minutes programme in March 2010 and on the ABC’s Q&A programme in the final week of the election campaign. During the Sixty Minutes interview, when asked about how he felt about homosexuality, Abbott responded: “probably feel a bit threatened ... as most people do”.4 There was an immediate response in the LGBT media with Samesame.com.au – Australia’s largest online LGBT news provider – covering the Opposition Leader’s comments.5 On the Q&A programme, he
3
Hertzog, Op.cit. p.84; Table 3.2, ‘Comparison of Gay and Lesbian Attitudes with other voters, 1990 US General Election’ 4 Anonymous, ‘Abbott still ‘threatened’ by homosexuality’, ABC Online, Australian Broadcasting Corporation, 2 March 2010, 1:37 PM www.abc.net.au/new/2010-03-09/abbott-still-streatened-by-homosexuality/356168 (Accessed: 12 October 2013) 5 Taylor, C., ‘Tony Abbott “Threatened” by Gays’, Samesame.com.au, 8 March 2010 www.samesame.com.au/news/local/5160/Tony-Abbott-Threatened-By-Gays.htm (Accessed: 12 October 2013)
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reiterated his position that gay Australians can have dignity without the equal right to marriage.6 However, this ignores an emotionally charged issued in the LGBT community because it is seen as a “unique disabilit[y] imposed on them by the heterosexual majority”, which the Coalition seeks to maintain.7 These results demonstrate that sexuality plays a role in the voting behaviour of Australians of both genders. Gay and lesbian voters tend not to vote for the Coalition. This thesis has confirmed the initial hypothesis that gay men and lesbians largely vote for Labor and the Greens. The lesbian deviation towards the Greens is four times higher than the gay vote for the Greens; while the lesbian vote for Labor is twice as high as the gay vote for Labor. Lesbians tend to vote for Labor but significantly vote for the Greens, while gay men tend to vote for Labor. Lesbians deviate most from their gender’s general voting behaviour to vote Labor followed by the Greens. The lesbian deviation towards voting Greens is about four times the size of gay men. This suggests sexuality plays a larger role in influencing the voting patterns of females than males. This thesis has demonstrated that there is a unique gay and lesbian vote in Australia, and gay men and lesbians behave in a distinct way.
Gender Gender has a significant effect on the Greens and other parties’ votes. Gender analyses show a positive relationship between the Greens vote and females and a negative relationship between males and the Greens vote. The same relationship is observed for the Labor vote. There is a negative correlation between females and the Coalition vote. At the neighbourhood level, there is a positive correlation between females and the Greens vote (b=0.24***)(see Table 17). This means that for every 1% increase in the proportion of females, there
6
‘Tony Abbott Joins Q&A’, Transcript, Q&A, Australian Broadcasting Corporation, 16 August 2010 http://www.abc.net.au/tv/qanda/tst/s2978032.htm (Accessed: 12 October 2010) 7 Hertzog, M., The Lavender Vote: Lesbians, Gay Men and Bisexual American Electoral Politics, New York University Press, New York, 1996 p.6
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is a 0.24% increase in the Greens vote. Labor and has a positive correlation (b=0.87***), indicating for every 1% increase in the proportion of females in a neighbourhood, there is a 0.87% in the Labor vote. Females and the Coalition are negatively correlated (b=-0.97***), indicating that for every 1% increase in the proportion of females in a neighbourhood there is a 0.97% decrease in the Coalition vote. This is consistent with AES data on gender and voting. At the 2010 election, a greater proportion of the Greens and Labor vote came from women than men. Furthermore, Labor had a higher proportion of female voters (55%) than the Greens (53%)(see Table 2). This analysis provides additional evidence that women vote Labor and the Greens more than the Coalition, and have a slight preference for Labor over the Greens. Females and the Greens had a partial correlation of 0.12 and squared partial correlation of 0.02, meaning 2% of the Greens vote can be explained by gender when controlling for the other independent variables. Gender has only a relatively small influence on the Greens vote compared to other factors such as secularism, which is discussed later. The positive correlation between females and Labor may be due to the 2010 election’s unique circumstances. For the first time, there was a female leader of a major party at an election. This may explain why some men deviated from the Labor party to the Coalition, re-establishing a gender gap in Labor support which disappeared at the 2001 election as indicated by longitudinal data from the AES.8 This research has demonstrated that at the 2010 election, men tended to vote for the Coalition and women tended to vote for the Greens and Labor. The results support the hypotheses that there is a higher tendency for women to vote for the Greens than men.
8
McAllister, I., The Australian Voter, UNSW Press, Sydney, 2012 p.115 Figure 5.1 ‘Gender and the Vote, 19672010’
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Generation Do high generation X populations vote Greens more than geographies with high generation Y populations? The analyses confirm that electorates with a higher generation X proportion have higher Greens vote (see Tables 12-15). Generation X and the Greens vote had a positive relationship (b=1.46***). For every 1% increase in generation X, there is a 1.46% increase in the Greens vote. Whereas generation Y and the Greens vote had a negative relationship (b=-0.22***). For every 1% increase in generation Y there is a 0.22% increase in the Greens vote. This result contradicts the 2010 AES data, where 38% of Greens voters were generation Y; a further 23% were generation X, 32% were Baby Boomers and 7% were Depression generation. This result reveals more about the geography of voting and political economy than the actual voting behaviours of these generations, which the AES has reliably demonstrated using individual-level surveys. One possible explanation for this geographic difference is that the vote of the Depression generation is cancelling out some of generation Y’s vote. An established trend is generation Y remains at home with their parents more than previous generations.9 Those suburbs contain large Depression generation populations who purchased homes in the early post-war era in rapidly expanding suburbs as people moved from what are now the inner suburbs of metropolitan areas. These suburbs continued to expand, allowing a hollowing out of inner suburban wealth and creating low socioeconomic areas in the 1960s and 1970s, which were rejuvenated in the late 1980s and early 1990s. Gentrification followed when generation X moved into these inner city areas. Baby Boomers born before 1955 gave birth to generation X children who could take advantage of inner city property prices. Baby Boomers who were born after that period had generation X and generation Y children who could not take advantage of cheap inner city property prices. This is coupled with an increase in the income to median housing price ratio in the 1967-2010 period that 9
Howe, N., & Strauss, W., Millennials Rising: the Next Great Generation, Random House, New York, 2009 p.135
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from 1:3.2, to 1:10.8, making it more difficult for generation Y to leave the nest by 2010 compared to previous generations.10 These historical circumstances have led to a situation in which generation Y is living in close proximity to the Depression generation which would explain a lower Greens relationship than generation X. Further research should be done to confirm such a theory. Both generations Y and X have positive relationships to the Greens vote; however there is no clear explanation for the higher geographic vote for the Greens by generation X, which is not congruent with the AES data. Generation X is geographically more prominent in Greens voting neighbourhoods than generation Y.
Familism There is a significant negative relationship between familism and Greens voting (b=-0.10***) and a significant positive relationship between familism and Coalition voting (b=0.18***)(see Tables 16 & 17). There is no significant relationship between familism and the Labor vote at the neighbourhood level. This reflects the AES finding that Greens voters are predominately unmarried, which would suggest most Greens voters have not reached the familism stage. A total of 31% of Greens voters were unmarried compared to 19% of Coalition voters (see Table 2). It also indicates that those who are not at the familism stage have less immediate economic needs from raising a family and are prone to post-materialist voting. Familism creates an attitude that values the exclusive incentives of the family group over general society, according to Adela Garzón.11 There is a negative relationship between the Greens vote and familism and the reverse is true of the Coalition vote.
Religion There was a positive relationship between Greens voting and secularism (b=0.38***), indicating for every 1% increase in the proportion of secularists in a neighbourhood, there is a 0.38% 10
Mansillo, L., ‘Australian housing is too expensive. So why can't we talk about it?’, The Guardian, 24 July 2013 http://www.theguardian.com/commentisfree/2013/jul/24/australia-housing-shortage-debt (Accessed: 12 October 2013) 11 Garzón, A., ‘Cultural change and familism’, Psicothema, Vol. 12, 2000 p.46
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increase in the Greens vote (see Tables 12-17). By contrast, for every 1% increase in secularism in a neighbourhood, there is a 0.17% decrease in the Labor vote. There were mixed results on secularism and the Coalition vote; one analysis suggested no significant relationship and one suggested a negative highly significant relationship (b=-0.18***). This means for every 1% increase in secularism there is a 0.18% decrease in the Coalition vote. Secularism alone can explain a great deal of the Greens vote. The partial correlation was 0.535 and its squared value is 0.29 meaning secularism, when controlling for the other variables, explains 29% of the Greens vote. Compare this to gender, where the male/female gender gap explains 2% of the Greens vote. This strongly reflects data on Greens voting from the 2010 AES where 46% Greens voters were secular (see Table 2). These results confirm the hypothesis that a higher Greens vote is likely to be found in electorates with high proportions of secular populations. Secularism is highly correlated with Greens voting (=0.38***), with lesser correlations to the other major parties’ votes. Contextually, secularism stands out from other demographics, but gay men have a similar relationship to the secularism (b=0.47***), which is low compared to lesbians (b=3.35***). The secularism effect is so great that it can be used as a demographic litmus test for Greens voting, unlike LGBT voting which has a highly skewed distribution.
Socioeconomic Status Socioeconomic status has a positive relationship on the Greens (b=0.09***) and Coalition votes (b=0.21***)(see Tables 12-17). This indicates the relationship between socioeconomic status and the Greens vote is half that of the Coalition vote. This is in contrast with Labor’s strong negative relationship to socioeconomic status (b=-0.22***).
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This result is congruent with previous literature, which established the Greens are a midway point between the Coalition and Labor votes on the socioeconomic status.12 These results confirm McAllister’s comment that the Greens are an “intermediate position for the two parties” in terms of socioeconomic status.13 The Greens (b=0.09***) are much closer to the Coalition (b=0.21***) than Labor (b=-0.22***) on the socioeconomic scale. Therefore, this thesis confirms that the Greens have a positive correlation with higher socioeconomic neighbourhoods. This thesis reveals sexuality is an important factor of voting behaviour at Australian federal elections; geographically, generation Y is not congruent with a high Greens vote as suggested in survey data, and agrees with previous literature on gender, lifecycles, socioeconomic status and religion. Most notably, the analysis reveals sexuality plays an important role in Australian voting behaviour for both genders. Gay and lesbian voters tend not to vote for the Coalition. Gay men and lesbians largely vote for Labor and the Greens. The lesbian deviation towards the Greens is four times higher than the gay deviation; while the lesbian vote for Labor is twice as high as the gay vote for Labor. Lesbians and gay men both tend to vote for Labor, but lesbians significantly vote for the Greens. Sexuality plays a larger role in influencing the voting habits of lesbians than gay men. Geographically, generation X has a higher correlation with the Greens vote than generation Y. Gender affects voting patterns as women tend to vote Greens more than men. Familism and the Greens vote is negatively correlated as those voters tend to hold more pressing economic concerns for their direct family than the society as a whole. The Greens vote sits at a midpoint between the Coalition and Labor but closer to the Coalition. Secularism is highly correlated with Greens voting.
12
Wilson, S., ‘The Emergence of the Green Electorate in Australia’, Australian Quarterly, Vol. 74, No. 6, November 2002 p.21 13 McAllister, Op.cit. p.167
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Table 12: Party vote coefficients of independent v ariables: Gay Communities & Generation X, Electorate Level Coalition β
Labor β
Greens b β 0.17 0.16
Variable b b Male same sex couples -0.74 0.84 0.15 -0.12 (%) Generation X (%) -5.77 -0.12*** 4.7 0.57*** 1.46 0.31*** Secular (%) 0.07 0.035 -0.28 -0.15* 0.33 0.04*** Female (%) -1.67 -0.13 2.88 0.25** 1.12 0.29*** Socioeconomic Scale 0.30 0.55*** -0.29 -0.59*** 0.04 0.02* Familism Scale 0.14 0.2* 0 0.01 -0.06 0.02** * p=≤.05, ** p=≤.01, *** p=≤.001; for a unit of change in the independent variable affects the dependent variable by a factor indicated by the beta coefficient(b). The standardised beta (β) has a mean of zero and standard deviation of one. Figures rounded to 2 decimal places. The inversion of Female (%) is Male (%). Coalition R2=0.189 Labor R2=0.216 Greens R2=0.654 The VIF scores are respectively from male same sex couples to familism: 1.89, 3.02, 1.17, 1.33, 2.47 & 1.83. Regressions I, V & IX. Source: Analysis, ABS census 2011 & AEC 2010
Table 13: Party vote coefficients of independent variables: Lesbian Communities & Generation X, Electorate Level Coalition Labor Greens Variable b β b β b β Female same sex couples (%) -5.77 -0.34*** 4.36 0.28** 1.39 0.21** Generation X (%) -4.36 -0.48*** 3.79 0.46*** 1.13 0.31*** Secular (%) 0.18 0.09 -0.35 -0.18* 0.30 0.37*** Female (%) -1.37 -0.11 2.59 0.22** 1.05 0.28*** Socioeconomic Scale 0.29 0.53*** 0.27 -0.56*** 0.04 0.02* Familism Scale 0.06 0.08 0.05 0.09 -0.04 -0.14* * p=≤.05, ** p=≤.01, *** p=≤.001; for a unit of change in the independent variable affects the dependent variable by a factor indicated by the beta coefficient(b). The standardised beta (β) has a mean of zero and standard deviation of one. Figures rounded to 2 decimal places. The inversion of Female (%) is Male (%). Coalition R2=0.237 Labor R2=0.244 Greens R2=0.673 The VIF scores are respectively from female same sex couples to familism: 2.05, 3.46, 1.23, 1.31, 2.34 & 1.98. Regressions II, VI & X. Source: Analysis, ABS census 2011 & AEC 2010
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Table 14: Party vote coefficients of independent variables: Gay Communities & Generation Y, Electorate Level Coalition b β -0.12 -0.02
Labor β 0.02
Greens b β 0.20 0.18*
Variable b Male same sex 0.10 couples (%) Generation Y (%) -1.69 -0.73*** 1.63 0.78*** 0.22 0.25** Secular (%) -0.14 -0.07 -0.08 -0.04 0.37 0.45*** Female (%) -1.94 -0.15 3.48 0.30*** 0.89 0.17** Socioeconomic Scale 0.31 0.57*** -0.33 -0.66*** 0.06 0.28*** Familism Scale -0.08 -0.11 0.05 0.30*** -0.01 -0.02 * p=≤.05, ** p=≤.01, *** p=.≤001; for a unit of change in the independent variable affects the dependent variable by a factor indicated by the beta coefficient(b). The standardised beta (β) has a mean of zero and standard deviation of one. Figures rounded to 2 decimal places. The inversion of Female (%) is Male (%). Coalition R2=0.287 Labor R2=0.374 Greens R2=0.626 The VIF scores are respectively from male same sex couples to familism: 2, 2.28, 1.19, 1.27, 2.12 & 2.28. Regressions III, VII, & XI. Source: Analysis, ABS census 2011 & AEC 2010
Table 15: Party vote coefficients of independent variables: Lesbian Communities & Generation Y, Electorate Level Coalition Labor Greens Variable b β b β b β Female same sex -4.32 -0.25* 2.3 0.15 1.7 0.25*** couples (%) Generation Y (%) -1.41 -0.61*** 1.29 0.71*** 0.14 0.15 Secular (%) -0.01 0.00 -0.15 -0.08 0.32 0.39*** Female (%) -1.88 -0.15 3.44 0.29*** 0.84 0.16** Socioeconomic Scale 0.32 0.58*** -0.33 -0.67*** 0.06 0.28*** Familism Scale -0.12 -0.17* 0.21 0.33*** 0.01 0.02 * p=≤.05, ** p=≤.01, *** p=.≤001; for a unit of change in the independent variable affects the dependent variable by a factor indicated by the beta coefficient(b). The standardised beta (β) has a mean of zero and standard deviation of one. Figures rounded to 2 decimal places. The inversion of Female (%) is Male (%). Coalition R2=0.319 Labor R2=0.384 Greens R2=0.654 The VIF scores are respectively from female same sex couples to familism: 2.06, 2.5, 1.29, 1.25, 2.01, & 1.22. Regressions IV, VIII & XII. Source: Analysis, ABS census 2011 & AEC 2010
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Table 16: Party vote coefficients of independent variables: Gay Communities & Generation X, Neighbourhood Level Coalition b β -2.55 -0.16***
Labor β
Greens b β 0.11*** 0.47 0.06***
Variable b Male same sex couples 1.74 (%) Generation X (%) -2.35 -0.38*** 1.87 0.32*** 0.63 0.22*** Secular (%) -0.18 -0.10*** -0.10 -0.06* 0.41 0.47*** Female (%) -1.27 -0.21*** 1.06 0.19*** 0.32 0.12*** Socioeconomic Scale 0.21 0.28*** -0.22 -0.30*** 0.09 0.28*** Familism Scale 0.24 0.20*** -0.08 -0.07* -0.12 -0.22*** * p=≤.05, ** p=≤.01, *** p=.≤001; for a unit of change in the independent variable affects the dependent variable by a factor indicated by the beta coefficient(b). The standardised beta (β) has a mean of zero and standard deviation of one. Figures rounded to 2 decimal places. The inversion of Female (%) is Male (%). Coalition R2=0.112 Labor R2=0.09 Greens R2=0.593 The VIF scores are respectively from male same sex couples to familism: 2, 2.28, 1.19, 1.27, 2.12 & 1.25. Regressions XIII, XV & XVII. Source: Analysis, ABS census 2011 & AEC 2010
Table 17: Party vote coefficients of independent variables: Lesbian Communities & Generation X, Neighbourhood Level Coalition b β -11.92 -0.31***
Labor β 0.21***
Greens b β 3.35 0.19***
Variable b Female same sex 7.66 couples (%) Generation X (%) -1.83 -0.29*** 1.55 0.27*** 0.45 0.16*** Secular (%) -0.08 -0.04 -0.17 -0.09*** 0.38 0.44*** Female (%) -0.97 -0.16*** 0.87 0.16*** 0.24 0.09*** Socioeconomic Scale 0.21 0.28*** -0.21 -0.30*** 0.09 0.27*** Familism Scale 0.18 0.15*** -0.04 -0.04 -0.10 -0.18*** * p=≤.05, ** p=≤.01, *** p=.≤001; for a unit of change in the independent variable affects the dependent variable by a factor indicated by the beta coefficient(b). The standardised beta (β) has a mean of zero and standard deviation of one. Figures rounded to 2 decimal places. The inversion of Female (%) is Male (%). Coalition R2=0.161 Labor R2=0.112 Greens R2=0.615 The VIF scores are respectively from female same sex couples to familism: 2.06, 2.5, 1.29, 1.25, 2.01 & 1.22. Regressions XIV, XVI & XVIII. Source: Analysis, ABS census 2011 & AEC 2010
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Outliers Outliers from the models predicting the Greens vote have been grouped into underpredicted and over-predicted categories. They can be explained through a number of local factors namely, the presence of environmental activism, alternative cultures, high university student populations, a hostile local media campaign, candidate incumbency effects, and protest voting (see Table 18). Environmental activism The electorate of Lyons (Tas.) illustrates a case where proposed industrial development helps produce local politics conducive to Greens politics. The rural electorate encompassed the site of the proposed Wesley Vale pulp mill which was to be developed in a farming area during the late 1980s. This resulted in a high degree of political activism which eventually saw Christine Milne enter the Tasmanian parliament in 1989 and the development did not eventuate. In the 2010 election, the proposed Tamar Valley pulp mill galvanised Greens support.14 There was highly divisive politics over the issue as many farmers from the 1980s felt a sense of betrayal that the conservative parties would allow chlorine-bleaching to occur in close proximity to farming water ways. 15 Voters may have been reminded of this at the 2010 election. There has been a dealignment of rural voters from conservative parties, transferring support to the Greens in Tasmania.16 As an aside, both Lyons and Franklin suggest a high Greens vote is a regional trend in Tasmania compared to the country as a whole. The national Greens vote was 11.7%, whereas in Tasmania it was 16.8%.17
14
Maiden, S., ‘Pulp Mill Decision Announced’, Australian Broadcasting Corporation (ABC), 5 January 2009 http://www.abc.net.au/local/audio/2009/01/05/2459504.htm (Accessed: 1 October 2013) 15 Milne, C., ‘Green Politics’, The Companion to Tasmanian History, Centre for Tasmanian Historical Studies, University of Tasmania Library, 2006 http://www.utas.edu.au/library/companion_to_tasmanian_history/G/Green%20Politics.htm (Accessed: 1 October 2013) 16 Crabb, A., Kitchen Cabinet, Australian Broadcasting Corporation, 14 November 2010 17 Anonymous, ‘ House of Representatives – First Preference by Party’ ,Virtual Tally Room, Australian Electoral Commission, 29 September 2010 http://results.aec.gov.au/15508/Website/HouseStateFirstPrefsByParty15508-NAT.htm (Accessed: 14 October 2013); Anonymous, ‘House of Representative – First Preference by Party – Tas’, Virtual Tally Room, Australian Electoral Commission, 9 September 2010
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Alternative cultures Electorates with alternative cultures, such as Melbourne (Vic.) and Grayndler (NSW), have distinctive neighbourhood identities, namely the inner city “Melbourne” identity and the “Newtown” identity, may have increase higher Greens vote. The inner city areas within Melbourne and that surrounding Newtown constitute areas where neighbourhood alternative culture exists, as Gordon Douglas described.18 Both areas have experienced substantial gentrification processes. Melbourne has the Rose Street Artists’ Market and Grayndler had the Newtown King Street Markets, which allows authentic products by artists and artisans to be sold to second wave knowledge class gentrifiers. There is also a rural alternative culture in Richmond (NSW) with a “tree change” culture which appears to increase the Greens vote. Richmond, like Melbourne and Grayndler, has its own street market, the Nimbin Markets. Nimbin has a strong cannabis counterculture and hosts the annual ‘Mardi Grass’ festival to draw attention to drug law reform.19 Near Bryon Bay, there is an influx of generation X families from metropolitan areas driven by the growing housing shortages in Sydney and Melbourne. These voters have had a “values shift”, and are interested more in quality of life than money.20 It would appear second wave gentrifiers in urban alternative neighbourhood identifying communities and tree change generation X migrants have more post-materialist concerns than the general population, which explain why these electorates are outliers.
http://results.aec.gov.au/15508/Website/HouseStateFirstPrefsByParty-15508-TAS.htm (Accessed: 14 October 2013) 18 Douglas, G., ‘The Edge of the Island: Cultural Ideology and Neighbourhood Identity at the Gentrification Frontier’, Urban Studies, Vol. 49, No 16, December 2012 pp.3579-3594 19 Tavener, R., ‘On the Hippy Trail in Nimbin’, The Telegraph, London, 2 July 2013 http://www.telegraph.co.uk/expat/expatlife/10139154/On-the-hippy-trail-in-Nimbin.html (Accessed: 1 October 2013) 20 Weekes, P., ‘In Pursuit of a Richer Lifestyle’, Sydney Morning Herald, 25 August 2010 http://www.smh.com.au/money/planning/in-pursuit-of-a-richer-lifestyle-20100825-13r1y.html (Accessed: 1 October 2013)
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Students In the electorate of Batman (Vic.) the gentrification process as well as the presence of high student populations has a profound influence on the vote. In Batman 3.7% of the population is aged 24 or under and studying full time at a university with cheap accommodation around Carlton and Fitzroy, close to the University of Melbourne, the Royal Melbourne Institute of Technology and La Trobe University. The mean proportion of full time university students under the age of 25 in an electorate is 2.2%. The connection between students and Greens voting is unsurprising because students are exposed to higher education, and Greens voters tend to have an increased rate of higher education. Local media and protest There is an indication that the Greens benefited from protest votes. Large protest votes against Labor, and a hostile local media campaign in Werriwa (NSW) provides a case where media interaction may have exacerbated the protest vote. Werriwa had a number of effects working against the Labor candidate, Laurie Ferguson. The Greens benefited greatly from the strong protest vote as there were only three parties contesting the election: Labor, Liberal and Greens. The Labor protest vote had nowhere to go except the Greens. McAllister’s suggests voting that Greens “provides a convenient method of protesting against Labor [...] without making the much more substantial commitment of voting Liberal” is particularly relevant in Werriwa.21 Ferguson attributed the increased vote to a local press campaign against himself, which he was labelled an ‘outsider’, following the controversy where he was moved into Werriwa after Reid was abolished.22 This was acknowledged by the Campbelltown-Macarthur Advertiser in 2012, commenting on the Liberals’ plan to import an ‘outsider’ for the 2013 election.23 Gillard struck a factional deal and moved then member, Chris Hayes, into Fowler, allowing Ferguson to run in 21
McAllister, Op.cit., p.108 Telephone conversation with Laurie Ferguson, 28 September 2013 23 Pleffer, A., ‘‘Outsider’ aims for Werriwa’, Campbelltown-Macarthur Advertiser, 4 August 2012 http://www.macarthuradvertiser.com.au/story/245057/outsider-aims-for-werriwa/ (Accessed: 14 October 2013) 22
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Werriwa. 24 The mass media appears to have provoked a large protest vote towards the Greens. This was particularly damaging in Werriwa since people with low interest levels in politics and low education levels are more susceptible to influence.25 The circumstances of the media campaign against Labor, and the limited number of candidates on the ballot concentrated the protest vote, fostering a higher Greens vote. The high protest vote against the incumbent party, in a highly safe Labor seat with low education levels responding to a negative media campaign created a perfect storm in Werriwa for an outer metropolitan electorate to deviate from its usual voting behaviour. In contrast to these under predicted votes, the presence of high ethnic populations, high profile incumbent candidates and high gay male population reduces the Greens vote. Ethnicity The electorate of Bennelong (NSW) appears to have had their vote under-represented by the high ethnic Korean and Vietnamese population in the electorate. Ethnic voters tend to vote for Labor in a clientele relationship as Zappalà pointed out in Cunningham with the Italian community.26 There appears to have been candidate effects in addition to the high ethnic population that negatively affected the Greens vote. Candidate effect There were large over-predictions in seats where high profile candidates were endorsed. For example, in the electorate of Lalor (Vic.), which was Gillard’s seat, there was a significant swing to the prime minister. Australia does not have an incumbency effect similar to Cover’s ‘sophomore
24
see Coorey, P., ‘Victory for Gillard as safe spot found for Ferguson’, Sydney Morning Herald, 18 November 2009 http://www.smh.com.au/national/victory-for-gillard-as-safe-spot-found-for-ferguson-20091117ikf1.html (Accessed: 1 October 2013) 25 McCoombs, M., & Shaw, D., ‘The Agenda Setting Function of Mass Media’, Pulbic Opinion Quarterly, Vol. 36, No. 2, 1972 p.186. 26 Zappalà Op.cit.
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surge’ in the US Congress, as Horiuchi Yusaka and Andrew Leigh make clear.27 However those with high profiles have high name recognition and an effect may be seen for a select few high profile candidates. Sexuality effect The electorate of Sydney has Australia’s highest population of gay men, which is affecting the vote. Gay men and the Labor vote have a strong correlation (b=1.74***). Sexuality would be expected to have a large impact on the Labor vote as gay men compromise 21% of all in Sydney; this is followed by the electorate is Wentworth (NSW) where gay men compromise 8% of couples (see Table 11).28 The gay vote for Labor is far greater than the vote for the Greens as gay men vote at three times the rate for Labor than the Greens (b=0.47***). The gay vote may explain the low Greens vote in Sydney. The Greens voting tends to be prominent where there are high numbers of university students, alternative cultures, and environmental activism which is congruent with the literature. Greens voting is lower than expected in electorates with high ethnic populations or candidate effects. There is also a negative effect of gay men on the Greens vote. These findings from outliers are complementary to findings that indicate that gay men and lesbians vote Labor and Greens.
27
Cover, A., ‘One Good Terms Deserves Another: The Advantages of Incumbency in Congressional Elections’, American Journal of Political Science, Vol. 21, No. 3, pp.523-541 August 1977; Horiuchi, Y., & Leigh, A., ‘Estimating Incumbency Advantage: Evidence from Three Natural Experiments’, Visitor Seminar Research Paper, 2 October 2009 http://wwwdocs.fce.unsw.edu.au/economics/news/VisitorSeminar/091014_YusakuHoriuchi.pdf (Accessed: 10 October 2013) 28 Akersten, M., ‘census results reveal where gay couples are’, Samesame.com.au, 30 July 2013 http://www.samesame.com.au/news/local/10055/census-results-reveal-where-gay-couples-are.htm (Accessed: 12 October 2013)
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Table 18: Outliers predicting Greens vote by more than 1.5 standard deviations I II III IV Electorate RSD Resid. RSD Resid. RSD Resid. RSD Resid. Melbourne, Vic. 4.46 12.86 4.86 13.64 4.81 14.45 5.19 14.99 Franklin, Tas. 2.65 7.65 2.68 7.53 2.60 7.82 2.63 7.58 Werriwa, NSW 2.32 6.69 2.29 6.42 2.05 6.14 2.09 6.03 Lyons, Tas. 2.32 6.69 2.38 6.68 2.42 7.26 2.43 7.00 Wills, Vic. 1.83 5.28 1.71 4.78 2.20 6.60 1.98 5.72 Cunningham, NSW 1.72 4.96 1.61 4.52 Batman, Vic. 1.62 4.65 2.55 7.64 1.79 5.16 Grayndler, NSW 2.00 6.02 Fairfax, Qld. 1.58 4.56 1.71 4.81 1.90 5.71 1.95 5.64 Richmond, NSW 1.62 4.85 Calwell, Vic. 1.57 4.41 1.51 4.53 1.66 4.80 Ryan, Qld. 1.53 4.42 1.56 4.36 Brand, WA 1.52 4.38 Petrie, Qld. -1.52 -4.57 Lalor, Vic. -1.55 -4.46 Leichhardt, Qld. -1.66 -4.78 -1.50 -4.22 Gippsland, Vic. -1.57 -4.71 -1.58 -4.55 Warringah, NSW -1.72 -4.96 Wentworth, NSW -1.88 -5.41 Sydney, NSW -1.96 -5.67 North Sydney, NSW -2.04 -5.87 -1.60 -4.49 Melbourne Ports, -1.88 -5.66 -2.10 -6.08 Vic. Bennelong, NSW -2.16 -6.24 -2.10 -5.89 -2.40 -7.22 -2.25 -6.50 Residuals (Resid.) are rounded to 2 decimal places. Residuals are the difference between the predicted vote (%) and actual vote (%). Standardised residuals (RSD) are used to compare cases because difference residuals have difference valences. Standardised Residuals are measured in standard deviations from the predicted vote. Source: Analysis, ABS census 2011 & AEC 2010
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Conclusion This thesis predicted the Greens vote using ABS census and AEC voting data to produce a geospatial analysis of the neighbourhood Greens vote. Results agree with existing literature. The results confirm Greens voting is affected by gender: more females than males vote for the Greens. It confirms that in terms of socioeconomic status the Greens are an intermediate point between Labor and the Coalition, but closer to the Coalition. It finds that there is a negative relationship between Greens voting and familism, the stage in a lifecycle where couples have children. It confirms that Greens voting is highly correlated with secularism. It also finds that in geographies with environmental activism, alternative communities or protest votes against an incumbent, there is a high Greens vote. One finding departs from the previous literature. The results showed a stronger geographic correlation between the Greens vote with the generation X than generation Y. This contradicts individual-level survey data which reveals generation Y vote Greens more than generation X. One explanation for this involving Australia’s housing political economy and changing urban structure was provided. Further research is required to better explain this finding. This study finds one innovative finding, that sexuality has a significant influence on the vote at federal elections. It finds that sexuality has a greater effect on lesbians than gay men. Lesbians deviate more from their gender’s general voting behaviour than gay men. Lesbians tend to vote for Labor at twice the rate they vote for the Greens. Gay men vote Labor at four times the rate than they vote Green. Both gay men and lesbians have strong negative correlations with the Coalition vote. Lesbians deviate from their gender’s general voting pattern to vote for the Greens at four times the rate than gay men. While the lesbian vote for Labor was twice that of gay men.
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One caveat to these results that must be acknowledged is the ecological fallacy, although Garry King has demonstrated ecological studies are highly reliable.1 Gay and lesbian communities are not segregated from heterosexual communities. For example, gay and lesbian voters may vote more or less for the Greens or any other party than what is suggested by the neighbourhood area-level findings. This could be similar to British election spatial analyses where far-right parties do well in constituencies which have high ethnic populations; this not because ethnic voters vote for far-right parties but as their presence as a visible minority prompts a far-right response in other voters. Further research, using individual survey analysis of the LGBT population could assist in revealing whether the ecological data in this thesis is true; or if there is a directional difference between the neighbourhood area-level correlations and the individual-level correlation for LGBT voting patterns. Further individual-level research into LGBT voting could exclude the potential for a community classic ecological fallacy. Another caveat to these results is that census records rely on self-reporting and are susceptible to over and under reporting of gay and lesbian couples. This is the first spatial investigation of gay and lesbian voting patterns at Australian federal elections. Previously, sexuality has been a neglected factor in Australian political science largely due to the absence of data. The gap in literature ought to be addressed as there is empirical evidence for a previously neglected effect in voters’ behaviour: sexuality. This variable has the potential to affect the outcome of elections in future due to the concentration of gay ghettos in marginal Labor-Greens electorates such as Sydney and Grayndler at the 2010 election. This thesis intends to partially fill the gap in Australian political science with a spatial area-level investigation of Greens voting behaviour. The gender gap between gay and lesbian voters is particularly noteworthy. One explanation for this gap could be that the values of lesbians are different from the values of gay men. For example, lesbians may value companionship more than gay men. A case which illustrates this point is the geospatial network application Grindr, which has increased the average number of partners 1
King, G., A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data, Princeton University Press, Princeton, New Jersey, 1997
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gay men have.2 Gay men in Sydney and Melbourne are the fourth and sixth most frequent users of the application worldwide.3 Australian gay men may have less of a desire for long-term commitment as their sexual behaviour indicates. Gay men tend to have “frequent relationship dissolutions” compared to lesbians, who value intimacy more in their relationships.4 Gay men may have less of an interest in voting Greens on LGBT public policy issues such as gay and lesbian marriage equality. Moreover, men tend to have less of an interest in post-materialist concerns than women making them less susceptible to vote Greens.5 This is not the only difference between gay men and lesbians in their politics. Lesbians have a stereotype of being more politically active than gay men. However, there is no empirical data upon which to draw conclusions. Explaining this large gap in voting behaviour between lesbians and gay men will require a thorough investigation of LGBT political sociology.
2
Rendina, J., Jimenez, R., Grov, C., Ventuneac, A., & Parsons, J., ‘Patterns of Lifetime and Recent HIV Testing Among Men Who Have Sex with Men in New York City Who Use Grindr’, AIDS and Behaviour, Research Paper, published online: 8 August 2013 3 Akersen, M., ‘2 Aussie cities in Grindr top 10’, Samesame.com.au, 1 July 2011 http://www.samesame.com.au/news/local/7004/2-Aussie-cities-in-Grindr-top-10.htm (Accessed: 15 October 2013) 4 Kurdek, L., ‘Relationship Outcomes and Their Predictors: Longitudinal Evidence from Heterosexual Married, Gay Cohabiting, and Lesbian Cohabiting Couples’, Journal of Marriage and Family, Vol. 60, No. 3, August 1998 pp.553-568 5 Tranter, B., ‘Political divisions over climate change and environmental issues in Australia’, Environmental Politics, Vol. 20, No. 1, 2011 p.80; Zelezney, L., Poh-Pheng, C., & Aldrich, C., ‘New Ways of thinking about environmentalism’, Journal of Social Issues, Vol. 56, No. 3, 2000 pp.444-445
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Sanders, David, ‘Economic Performance, Management Competence, and the Outcome of the Next General Election’, Political Studies, Vol. 44, No. 2, 1996 pp.203-231 Tranter, Bruce, ‘Environmentalism and education in Australia’, Environmental Politics, Vol 6, No. 2, pp.123-143 Tranter, Bruce, ‘Political divisions over climate change and environmental issues in Australia’, Environmental Politics, Vol. 20, No. 1, 2011 Tranter, Bruce, ‘The social bases of environmentalism in Australia’, Australian and New Zealand Journal of Sociology, Vol. 32, No. 2, pp.61-84 Wilson, Shaun, ‘The Emergence of the Green Electorate in Australia’, Australian Quarterly, Vol. 74, No. 6, November 2002 pp. 17-22 Zappalà, Gianni., ‘Clientelism, Political Culture and Ethnic Politics in Australia’, Australian Journal of Political Science, Vol. 33, No. 3, 1998 pp.381-97 Zelezney, Lynnette, Poh-Pheng, Chua, & Aldrich, Christina, ‘New Ways of thinking about environmentalism’, Journal of Social Issues, Vol. 56, No. 3, 2000 pp.443-457
Theses Jackson, Stewart, ‘The Australian Greens: Between Movement and Electoral Professional Party’, Ph.D. thesis, Department of Government and International Relations, Faculty of Economics and Business, University of Sydney, July 2011 http://prijipati.library.usyd.edu.au/bitstream/2123/7858/1/sm-jackson-2011-thesis.pdf (Accessed: 14 September 2013)
Research Papers Horiuchi, Yusaku, & Leigh, Andrew, ‘Estimating Incumbency Advantage: Evidence from Three Natural Experiments’, UNSW Visitor Seminar Research Paper, 2 October 2009 http://wwwdocs.fce.unsw.edu.au/economics/news/VisitorSeminar/091014_YusakuHoriuchi .pdf (Accessed: 10 October 2013) Jackman, Simon, ‘The Spatial Concentration of the Green Vote’, Research Paper, 13 September 2010 http://jackman.stanford.edu/papers/download.php?i=5 (Accessed: 13 September 2013) Vromen, Ariadne, ‘Who are the Australian Greens? Surveying the Membership’, The Australian Sociological Association Conference, 2005, 6-8 December Redinia, H. Jonathon, Jimenez, Ruben, Grov, Christian, Ventuneax, Ana, & Parsons, Jeffrey, ‘Patterns of Lifetime and Recent HIV Testing Amoung Men Who Have Sex with Men in New York City Who Use Grindr’, research paper, AIDS and Behavior, published online August 2013
Online Sources Akersen, Matt, ‘2 Aussie cities in Grindr top 10’, Samesame.com.au, 1 July 2011 http://www.samesame.com.au/news/local/7004/2-Aussie-cities-in-Grindr-top10.htm (Accessed: 15 October 2013) Akersten, Matt, ‘census results reveal where gay couples are’, Samesame.com.au, 30 July 2013 http://www.samesame.com.au/news/local/10055/census-results-reveal-where-gaycouples-are.htm (Accessed: 12 October 2013)
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Anonymous, ‘Frequently Asked Questions’, Australian Red Cross Blood Service website, http://www.donateblood.com.au/faq#faq_300 (Accessed: 20 September 2013) Anonymous, ‘House of Representatives: vote by state’, Virtual Tally Room website, Australian Electoral Commission (AEC), 29 September 2010 http://results.aec.gov.au/15508/Website/HouseVotesCountedByState-15508.htm (Accessed: 14 October 2013) Anonymous, ‘Record number of women over 40 having babies’, Media Release, Category number: 3301, Australian Bureau of Statistics, Canberra, 11:30am (AEST) 25 October 2012 http://www.abs.gov.au/ausstats/
[email protected]/mediareleasesbytitle/8668A9A0D4B0156CCA257 92F0016186A?OpenDocument (Accessed: 25 September 2013) Anonymous, ‘House of Representative: NSW – Cunningham’, 2010 Virtual Talley Room, House of Representatives vote by polling places, Australian Electoral Commission (AEC) http://results.aec.gov.au/15508/Website/HouseDivisionPollingPlaces-15508-114.htm (Accessed: 1 October 2013) Anonymous, ‘4102.0 Australian Social Trends’, July 2013, Australian Bureau of Statistics, http://www.abs.gov.au/AUSSTATS/
[email protected]/Lookup/4102.0Main+Features10July+2013 (Accessed: 15 September 2013) Anonymous, ‘Reflecting a Nation: Stories from the 2011 census, 2012–2013’, Category No: 2071, Australian Bureau of Statistics, Canberra, 21 June 2012 11:30 AM (AEST) http://www.abs.gov.au/ausstats/
[email protected]/Lookup/2071.0main+features902012-2013 (Accessed: 10 October 2013) Coorey, Phillip., ‘Victory for Gillard as safe spot found for Ferguson’, Sydney Morning Herald, 18 November 2009 http://www.smh.com.au/national/victory-for-gillard-as-safe-spot-foundfor-ferguson-20091117-ikf1.html (Accessed: 1 October 2013) Jabour, Bridie, ‘Cory Bernardi links same-sex marriage to polygamy and bestiality again’, The Guardian, http://www.theguardian.com/world/2013/jun/18/cory-bernardi-same-sexbestiality (Accessed: 14 September 2013) Maiden, Samantha, ‘Pulp Mill Decision Announced’, ABC News Online, Australian Broadcasting Corporation (ABC), 5 January 2009 http://www.abc.net.au/local/audio/2009/01/05/2459504.htm (Accessed: 1 October 2013) Milne, C., ‘Green Politics’, The Companion to Tasmanian History, Centre for Tasmanian Historical Studies, University of Tasmania Library, 2006 Mansillo, Luke, ‘Australian housing is too expensive. So why can't we talk about it?’, The Guardian, 24 July 2013 http://www.theguardian.com/commentisfree/2013/jul/24/australiahousing-shortage-debt (Accessed: 12 October 2013) Pleffer, Alexandra, ‘’Outsider’ aims for Werriwa’, Campbelltown-Macarthur Advertiser, 4 August 2012 http://www.macarthuradvertiser.com.au/story/245057/outsider-aims-forwerriwa/ (Accessed: 14 October 2013) http://www.utas.edu.au/library/companion_to_tasmanian_history/G/Green%20Politics.htm (Accessed: 1 October 2013) Sheehan, Paul, ‘Gay Hate Crime: the shameful crime wave’, Sydney Morning Herald, http://www.smh.com.au/comment/gay-hate-the-shameful-crime-wave-201303032fe9w.html (Accessed: 14 September 2013)
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Tavener, Roger, ‘On the Hippy Trail in Nimbin’, The Telegraph, London, 2 July 2013 http://www.telegraph.co.uk/expat/expatlife/10139154/On-the-hippy-trail-in-Nimbin.html (Accessed: 1 October 2013) Turnbull, Nicholas, & Vromen, Ariadne, ‘Election 2004: Where do the Greens fit in Election 2004?’, Australian Review of Public Affairs, 17 September 2004 http://www.australianreview.net/digest/2004/09/turnbull_vromen.html (Accessed: 26 September 2013) Weekes, Peter, ‘In Pursuit of a Richer Lifestyle’, Sydney Morning Herald, 25 August 2010 http://www.smh.com.au/money/planning/in-pursuit-of-a-richer-lifestyle-2010082513r1y.html (Accessed: 1 October 2013)
Newspapers Walker, Jamie, ‘The Greens Machine’, The Australian, 27 December 2003 pp.11-14
Audio Visual Material Crabb, Annabel, Kitchen Cabinet, Australian Broadcasting Corporation, 14 November 2010
Data sources 2010 House of Representatives data pack, Australian Electoral Commission, 29 September 2010 http://results.aec.gov.au/15508/Website/Default.htm (Accessed: 23 September 2013) 2011 census data pack, Australian Bureau of Statistics, 27 March 2013 http://www.abs.gov.au/websitedbs/censushome.nsf/home/data (Accessed: 21 September 2013) McAllister, Ian, Bean, Clive, Gibson, Rachel, & Pietsch, Juliet, The Australian Election Study, 2010 http://aes.anu.edu.au/ (Accessed: 21 October 2013)
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Appendices Appendix 1: Actual & Predicted Greens Vote Electorate Actual Vote I Adelaide, SA Aston, Vic.
II
III
IV
Pred.
Resid.
Pred.
Resid.
Pred.
Resid.
Pred.
Resid.
13.69
14.75
-1.06
15.23
-1.54
16.39
-2.70
16.39
-2.70
9.67
11.75
-2.08
11.50
-1.83
12.20
-2.53
11.79
-2.12
Ballarat, Vic.
11.34
13.25
-1.91
13.45
-2.11
13.14
-1.80
13.39
-2.05
Banks, NSW
9.61
11.43
-1.82
11.27
-1.66
12.39
-2.78
11.88
-2.27
Barker, SA
9.13
10.28
-1.15
10.04
-0.91
10.02
-0.89
9.86
-0.73
Barton, NSW
10.85
10.79
0.06
10.76
0.09
9.73
1.12
9.99
0.86
Bass, Tas.
15.58
12.60
2.98
12.15
3.43
12.89
2.69
12.20
3.38
Batman, Vic.
23.48
18.82
4.66
20.25
3.23
15.84
7.64
18.32
5.16
Bendigo, Vic.
12.29
12.18
0.11
12.19
0.10
12.60
-0.31
12.49
-0.20
Bennelong, NSW
7.95
14.19
-6.24
13.84
-5.89
15.17
-7.22
14.45
-6.50
Berowra, NSW
11.39
10.69
0.70
10.99
0.40
11.96
-0.57
12.18
-0.79
Blair, Qld.
11.06
9.62
1.44
9.74
1.32
9.74
1.32
9.80
1.26
6.26
4.99
1.27
4.78
1.48
5.32
0.94
4.89
1.37
Bonner, Qld.
11.18
12.50
-1.32
12.47
-1.29
11.77
-0.59
11.95
-0.77
Boothby, SA
13.24
14.52
-1.28
14.47
-1.23
15.41
-2.17
15.09
-1.85
Bowman, Qld.
9.97
10.27
-0.30
10.44
-0.47
10.65
-0.68
10.79
-0.82
Braddon, Tas.
11.96
11.90
0.06
11.42
0.54
11.84
0.12
11.27
0.69
Bradfield, NSW
16.34
14.73
1.61
14.41
1.93
14.95
1.39
14.69
1.65
Brand, WA
14.74
12.43
2.31
12.32
2.42
12.83
1.91
12.53
2.21
Brisbane, Qld.
21.28
18.55
2.73
18.64
2.64
19.33
1.95
19.10
2.18
9.41
8.20
1.21
7.84
1.57
9.02
0.39
8.30
1.11
Calare, NSW
6.00
6.18
-0.18
6.46
-0.46
6.66
-0.66
6.92
-0.92
Calwell, Vic.
11.86
7.68
4.18
7.45
4.41
7.33
4.53
7.06
4.80
Canberra, ACT
18.56
14.25
4.31
14.51
4.05
15.48
3.08
15.50
3.06
Canning, WA
8.29
10.35
-2.06
10.61
-2.32
11.90
-3.61
11.78
-3.49
Blaxland, NSW
Bruce, Vic.
Capricornia, Qld.
5.52
7.40
-1.88
7.41
-1.89
7.84
-2.32
7.78
-2.26
11.52
13.71
-2.19
13.48
-1.96
14.15
-2.63
13.71
-2.19
Charlton, NSW
8.83
8.63
0.20
9.33
-0.50
9.58
-0.75
10.17
-1.34
Chifley, NSW
8.43
6.13
2.30
6.31
2.12
6.49
1.94
6.53
1.90
Chisholm, Vic.
11.87
14.62
-2.75
14.10
-2.23
16.44
-4.57
15.16
-3.29
7.73
11.15
-3.42
11.13
-3.40
11.21
-3.48
11.22
-3.49
Corangamite, Vic.
11.43
12.93
-1.50
12.91
-1.48
12.53
-1.10
12.68
-1.25
Corio, Vic.
12.53
12.19
0.34
11.80
0.73
11.73
0.80
11.34
1.19
Cowan, WA
12.53
10.72
1.81
10.74
1.79
11.54
0.99
11.31
1.22
9.09
8.98
0.11
9.49
-0.40
8.92
0.17
9.65
-0.56
Cunningham, NSW
15.12
10.16
4.96
10.60
4.52
11.49
3.63
11.63
3.49
Curtin, WA
17.72
15.70
2.02
15.29
2.43
16.94
0.78
16.12
1.60
Casey, Vic.
Cook, NSW
Cowper, NSW
Dawson, Qld.
7.72
8.39
-0.67
8.30
-0.58
8.44
-0.72
8.36
-0.64
Deakin, Vic.
12.92
16.22
-3.30
15.39
-2.47
14.98
-2.06
14.30
-1.38
Denison, Tas.
18.98
15.39
3.59
15.54
3.44
16.45
2.53
16.26
2.72
Dickson, Qld.
10.91
12.51
-1.60
12.58
-1.67
11.56
-0.65
11.94
-1.03
73 | P a g e
Dobell, NSW
8.61
9.43
-0.82
9.80
-1.19
9.34
-0.73
9.83
-1.22
Dunkley, Vic.
11.69
14.62
-2.93
14.22
-2.53
13.92
-2.23
13.61
-1.92
Durack, WA
9.25
9.50
-0.25
8.54
0.71
7.63
1.62
7.10
2.15
Eden-Monaro, NSW
9.72
10.11
-0.39
10.28
-0.56
9.55
0.17
10.05
-0.33
Fadden, Qld.
9.33
12.24
-2.91
12.04
-2.71
11.58
-2.25
11.50
-2.17
Fairfax, Qld.
18.00
13.44
4.56
13.19
4.81
12.29
5.71
12.36
5.64
Farrer, NSW
5.88
7.76
-1.88
7.67
-1.79
8.25
-2.37
8.06
-2.18
Fisher, Qld.
15.84
12.46
3.38
12.52
3.32
11.69
4.15
12.00
3.84
Flinders, Vic.
11.50
12.48
-0.98
12.47
-0.97
12.29
-0.79
12.40
-0.90
Flynn, Qld.
3.96
7.92
-3.96
7.71
-3.75
7.92
-3.96
7.75
-3.79
Forde, Qld.
12.22
10.91
1.31
11.06
1.16
11.47
0.75
11.40
0.82
Forrest, WA
13.48
12.64
0.84
12.35
1.13
12.04
1.44
11.89
1.59
Fowler, NSW
6.69
5.65
1.04
5.44
1.25
5.32
1.37
5.09
1.60
Franklin, Tas.
20.87
13.22
7.65
13.34
7.53
13.05
7.82
13.29
7.58
Fraser, ACT
19.84
15.98
3.86
16.78
3.06
18.42
1.42
18.58
1.26
Fremantle, WA
17.65
14.90
2.75
14.74
2.91
14.29
3.36
14.23
3.42
Gellibrand, Vic.
15.35
16.11
-0.76
16.32
-0.97
13.73
1.62
14.64
0.71
Gilmore, NSW
9.57
7.48
2.09
7.99
1.58
8.06
1.51
8.63
0.94
Gippsland, Vic.
6.57
10.19
-3.62
10.30
-3.73
11.28
-4.71
11.12
-4.55
Goldstein, Vic.
16.21
17.58
-1.37
16.86
-0.65
15.41
0.80
15.30
0.91
Gorton, Vic.
10.15
7.89
2.26
7.65
2.50
7.07
3.08
6.92
3.23
Grayndler, NSW
25.90
23.67
2.23
27.03
-1.13
19.88
6.02
25.09
0.81
Greenway, NSW
6.01
7.53
-1.52
8.05
-2.04
8.20
-2.19
8.63
-2.62
Grey, SA
7.77
9.64
-1.87
9.25
-1.48
9.82
-2.05
9.37
-1.60
Griffith, Qld.
15.39
17.76
-2.37
18.22
-2.83
17.19
-1.80
17.79
-2.40
Groom, Qld.
7.30
8.41
-1.11
8.39
-1.09
8.49
-1.19
8.44
-1.14
Hasluck, WA
12.78
10.88
1.90
10.89
1.89
11.93
0.85
11.60
1.18
Herbert, Qld.
8.85
10.75
-1.90
10.95
-2.10
11.62
-2.77
11.50
-2.65
Higgins, Vic.
17.90
19.21
-1.31
18.38
-0.48
19.87
-1.97
18.64
-0.74
Hindmarsh, SA
12.07
12.89
-0.82
12.99
-0.92
13.32
-1.25
13.27
-1.20
Hinkler, Qld.
5.71
9.02
-3.31
9.08
-3.37
8.81
-3.10
8.99
-3.28
Holt, Vic.
9.15
9.57
-0.42
9.38
-0.23
9.27
-0.12
9.04
0.11
Hotham, Vic.
10.19
12.13
-1.94
11.72
-1.53
11.19
-1.00
10.94
-0.75
Hughes, NSW
6.29
7.01
-0.72
7.54
-1.25
8.29
-2.00
8.63
-2.34
Hume, NSW
7.65
7.40
0.25
7.76
-0.11
6.63
1.02
7.43
0.22
Hunter, NSW
8.92
7.33
1.59
7.51
1.41
7.48
1.44
7.67
1.25
Indi, Vic.
9.45
10.02
-0.57
9.87
-0.42
10.10
-0.65
9.97
-0.52
Isaacs, Vic.
10.93
12.99
-2.06
12.54
-1.61
11.47
-0.54
11.33
-0.40
Jagajaga, Vic.
14.95
15.82
-0.87
15.37
-0.42
15.31
-0.36
14.94
0.01
Kennedy, Qld.
4.49
7.55
-3.06
7.21
-2.72
6.83
-2.34
6.72
-2.23
Kingsford Smith, NSW
12.05
14.27
-2.22
14.23
-2.18
14.37
-2.32
14.20
-2.15
Kingston, SA
12.27
14.10
-1.83
13.90
-1.63
14.90
-2.63
14.35
-2.08
Kooyong, Vic.
18.48
16.62
1.86
16.05
2.43
18.38
0.10
17.21
1.27
La Trobe, Vic.
12.28
14.90
-2.62
14.43
-2.15
13.82
-1.54
13.57
-1.29
Lalor, Vic.
6.83
11.29
-4.46
11.01
-4.18
11.00
-4.17
10.62
-3.79
Leichhardt, Qld.
9.06
13.84
-4.78
13.28
-4.22
11.16
-2.10
11.23
-2.17
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Lilley, Qld. Lindsay, NSW Lingiari, NT
12.17
13.84
-1.67
14.13
-1.96
12.68
-0.51
13.33
-1.16
4.74
7.90
-3.16
8.40
-3.66
8.77
-4.03
9.05
-4.31
12.59
11.15
1.44
10.69
1.90
9.32
3.27
9.22
3.37
Longman, Qld.
9.12
10.52
-1.40
10.64
-1.52
10.56
-1.44
10.68
-1.56
Lyne, NSW
4.29
7.92
-3.63
8.08
-3.79
7.68
-3.39
8.06
-3.77
Lyons, Tas.
16.75
10.06
6.69
10.07
6.68
9.49
7.26
9.75
7.00
Macarthur, NSW
5.56
7.34
-1.78
7.89
-2.33
8.11
-2.55
8.54
-2.98
Mackellar, NSW
16.77
13.79
2.98
13.60
3.17
12.76
4.01
12.99
3.78
Macquarie, NSW
14.09
11.51
2.58
12.72
1.37
11.21
2.88
12.85
1.24
Makin, SA
10.09
12.01
-1.92
11.86
-1.77
12.39
-2.30
12.06
-1.97
Mallee, Vic.
7.86
8.81
-0.95
8.50
-0.64
8.70
-0.84
8.40
-0.54
Maranoa, Qld.
5.15
6.43
-1.28
6.28
-1.13
5.83
-0.68
5.89
-0.74
Maribyrnong, Vic.
11.85
10.72
1.13
10.22
1.63
9.65
2.20
9.30
2.55
Mayo, SA
16.97
13.89
3.08
13.96
3.01
13.98
2.99
14.12
2.85
McEwen, Vic.
11.84
13.25
-1.41
13.02
-1.18
12.47
-0.63
12.44
-0.60
McMahon, NSW
8.05
4.66
3.39
4.83
3.22
5.70
2.35
5.56
2.49
McMillan, Vic.
9.72
11.72
-2.00
11.79
-2.07
12.26
-2.54
12.18
-2.46
McPherson, Qld.
12.40
12.53
-0.13
12.24
0.16
12.06
0.34
11.80
0.60
Melbourne, Vic.
36.17
23.31
12.86
22.53
13.64
21.72
14.45
21.18
14.99
Melbourne Ports, Vic.
20.66
22.65
-1.99
24.12
-3.46
26.32
-5.66
26.74
-6.08
Menzies, Vic.
10.75
12.47
-1.72
11.95
-1.20
12.69
-1.94
12.09
-1.34
Mitchell, NSW
7.60
10.26
-2.66
10.55
-2.95
10.25
-2.65
10.75
-3.15
Moncrieff, Qld.
11.56
11.80
-0.24
11.62
-0.06
12.40
-0.84
11.90
-0.34
Moore, WA
13.57
12.46
1.11
12.41
1.16
12.95
0.62
12.80
0.77
Moreton, Qld.
15.89
12.97
2.92
13.05
2.84
14.30
1.59
13.88
2.01
6.09
8.85
-2.76
8.49
-2.40
8.23
-2.14
8.02
-1.93
Murray, Vic. New England, NSW
3.57
6.62
-3.05
6.97
-3.40
7.05
-3.48
7.38
-3.81
Newcastle, NSW
15.47
11.09
4.38
12.02
3.45
12.68
2.79
13.28
2.19
North Sydney, NSW
15.53
21.40
-5.87
20.02
-4.49
19.53
-4.00
18.44
-2.91
8.86
11.09
-2.23
10.68
-1.82
10.54
-1.68
10.26
-1.40
Oxley, Qld.
11.79
11.32
0.47
11.22
0.57
10.65
1.14
10.66
1.13
Page, NSW
8.58
8.24
0.34
8.73
-0.15
8.49
0.09
9.08
-0.50
Parkes, NSW
5.60
5.60
0.00
5.65
-0.05
5.10
0.50
5.38
0.22
Parramatta, NSW
7.96
9.33
-1.37
9.06
-1.10
10.40
-2.44
9.63
-1.67
O'Connor, WA
Paterson, NSW
5.99
7.44
-1.45
7.70
-1.71
7.65
-1.66
8.00
-2.01
Pearce, WA
13.24
12.69
0.55
12.73
0.51
12.01
1.23
12.29
0.95
Perth, WA
16.15
13.60
2.55
14.21
1.94
14.57
1.58
14.97
1.18
Petrie, Qld.
9.10
12.37
-3.27
12.22
-3.12
11.27
-2.17
11.37
-2.27
Port Adelaide, SA
15.11
12.21
2.90
12.29
2.82
11.74
3.37
11.90
3.21
Rankin, Qld.
11.20
10.18
1.02
10.23
0.97
11.12
0.08
10.78
0.42
Reid, NSW
11.18
11.54
-0.36
11.40
-0.22
12.56
-1.38
12.00
-0.82
Richmond, NSW
16.15
12.74
3.41
13.05
3.10
11.30
4.85
12.19
3.96
Riverina, NSW
4.50
5.44
-0.94
5.72
-1.22
5.99
-1.49
6.19
-1.69
Robertson, NSW
8.99
10.28
-1.29
10.61
-1.62
10.30
-1.31
10.75
-1.76
Ryan, Qld.
18.96
14.54
4.42
14.60
4.36
17.15
1.81
16.41
2.55
Scullin, Vic.
8.44
7.82
0.62
7.53
0.91
7.74
0.70
7.35
1.09
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Shortland, NSW
10.33
9.07
1.26
9.30
1.03
9.20
1.13
9.50
0.83
Solomon, NT
13.29
12.27
1.02
12.14
1.15
12.66
0.63
12.35
0.94
Stirling, WA
12.90
12.54
0.36
12.26
0.64
13.07
-0.17
12.49
0.41
Sturt, SA
10.01
13.29
-3.28
12.90
-2.89
13.14
-3.13
12.68
-2.67
Swan, WA
11.81
11.73
0.08
11.92
-0.11
14.24
-2.43
13.59
-1.78
Sydney, NSW
23.75
25.72
-1.97
27.95
-4.20
26.90
-3.15
29.42
-5.67
Tangney, WA
13.49
10.98
2.51
10.95
2.54
12.75
0.74
12.25
1.24
Throsby, NSW
11.93
7.97
3.96
8.20
3.73
8.38
3.55
8.58
3.35
Wakefield, SA
11.30
12.20
-0.90
11.94
-0.64
12.99
-1.69
12.36
-1.06
Wannon, Vic.
6.03
9.57
-3.54
9.40
-3.37
9.34
-3.31
9.27
-3.24
16.34
21.30
-4.96
19.95
-3.61
17.73
-1.39
17.21
-0.87
9.62
6.90
2.72
6.69
2.93
7.30
2.32
6.86
2.76
Wentworth, NSW
17.44
22.85
-5.41
21.29
-3.85
20.15
-2.71
19.15
-1.71
Werriwa, NSW
12.70
6.01
6.69
6.28
6.42
6.56
6.14
6.67
6.03
Wide Bay, Qld.
11.00
10.49
0.51
10.49
0.51
9.75
1.25
10.08
0.92
Wills, Vic.
20.60
15.32
5.28
15.82
4.78
14.00
6.60
14.88
5.72
Wright, Qld.
11.95
10.76
1.19
10.50
1.45
8.93
3.02
9.23
2.72
Warringah, NSW Watson, NSW
Source: Analysis, ABS census 2011 & AEC 2010
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Appendix 2: Gay and Lesbian Identifying Couples in Each Electorate Electorate Sydney, NSW Melbourne, Vic. Wentworth, NSW Grayndler, NSW Melbourne Ports, Vic. Brisbane, Qld. Batman, Vic. Higgins, Vic. Griffith, Qld. Perth, WA Gellibrand, Vic. Wills, Vic. Adelaide, SA Fraser, ACT Kingsford Smith, NSW Newcastle, NSW Macquarie, NSW Denison, Tas. Lilley, Qld. Leichhardt, Qld. North Sydney, NSW Moncrieff, Qld. Swan, WA Richmond, NSW Barton, NSW Ballarat, Vic. Moreton, Qld Port Adelaide, SA Reid, NSW Canberra, ACT Solomon, NT Ryan, Qld. Hindmarsh, SA Curtin, WA Warringah, NSW Kooyong, Vic. Page, NSW Cowper, NSW Franklin, Tas. Robertson, NSW Stirling, WA Herbert, Qld. Bendigo, Vic. Cunningham, NSW Eden-Monaro, NSW McPherson, Qld. Lingiari, NT Wide Bay, Qld. Fairfax, Qld. Mayo, SA Bonner, Qld. Blair, Qld. Petrie, Qld.
Gay Couples 20.98 6.79 8.12 4.79 5.71 4.02 1.43 3.11 1.84 2.13 1.96 1.28 1.44 1.19 1.30 0.97 0.83 1.14 1.02 1.22 1.47 1.16 0.96 0.75 1.01 0.75 0.80 0.59 0.78 0.67 0.64 0.73 0.54 0.79 0.78 0.86 0.51 0.42 0.49 0.57 0.67 0.45 0.51 0.44 0.47 0.67 0.36 0.49 0.55 0.43 0.48 0.41 0.48
Lesbian Couples Total LGBT Couples 6.06 3.46 1.61 4.79 1.96 1.84 2.74 0.94 1.82 1.52 1.61 1.67 1.39 1.40 1.04 1.34 1.42 1.08 1.15 0.84 0.52 0.74 0.91 1.05 0.74 0.93 0.85 0.88 0.66 0.76 0.78 0.67 0.84 0.59 0.54 0.44 0.77 0.85 0.75 0.67 0.54 0.73 0.67 0.71 0.68 0.46 0.77 0.63 0.55 0.66 0.60 0.66 0.58
27.04 10.25 9.73 9.58 7.68 5.86 4.17 4.05 3.66 3.65 3.57 2.94 2.83 2.58 2.34 2.30 2.25 2.22 2.17 2.06 1.99 1.90 1.87 1.80 1.75 1.69 1.65 1.47 1.44 1.43 1.42 1.40 1.39 1.38 1.32 1.31 1.28 1.27 1.24 1.24 1.21 1.19 1.18 1.15 1.15 1.13 1.12 1.12 1.10 1.08 1.08 1.07 1.06
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Hume, NSW Dobell, NSW Boothby, SA Fisher, Qld. Fremantle, WA Goldstein, Vic. Lalor, Vic. Gilmore, NSW Charlton, NSW Lyne, NSW Dunkley, Vic. Lyons, Tas. Corangamite, Vic. Hotham, Vic. Fadden, Qld. Maribyrnong, Vic. Oxley, Qld. Sturt, SA Jagajaga, Vic. Flinders, Vic. Chisholm, Vic. Longman, Qld. Dickson, Qld. Lindsay, NSW Pearce, WA McMillan, Vic. Forde, Qld. Calare, NSW McEwen, Vic. Shortland, NSW Bass, Tas. Bennelong, NSW Banks, NSW Parramatta, NSW Wakefield, SA La Trobe, Vic. Corio, Vic. New England, NSW Rankin, Qld. Paterson, NSW Canning, WA Brand, WA Throsby, NSW Kingston, SA Isaacs, Vic. Hasluck, WA Hinkler, Qld. Deakin, Vic. Wright, Qld. Gippsland, Vic. Indi, Vic. Hunter, NSW Makin, SA Cook, NSW Bowman, Qld. Mackellar, NSW Groom, Qld.
0.45 0.40 0.44 0.35 0.35 0.50 0.56 0.38 0.29 0.50 0.47 0.38 0.32 0.48 0.48 0.52 0.37 0.49 0.41 0.34 0.48 0.33 0.32 0.31 0.31 0.26 0.30 0.39 0.39 0.32 0.46 0.42 0.44 0.40 0.32 0.38 0.35 0.25 0.28 0.32 0.26 0.29 0.34 0.25 0.32 0.30 0.24 0.37 0.36 0.20 0.32 0.25 0.27 0.33 0.27 0.25 0.29
0.60 0.64 0.59 0.68 0.67 0.52 0.44 0.62 0.71 0.49 0.52 0.59 0.65 0.49 0.48 0.43 0.57 0.45 0.53 0.60 0.45 0.59 0.58 0.57 0.57 0.60 0.56 0.47 0.45 0.51 0.37 0.41 0.38 0.41 0.48 0.42 0.45 0.53 0.50 0.46 0.51 0.49 0.42 0.51 0.43 0.45 0.51 0.37 0.38 0.53 0.38 0.45 0.42 0.36 0.40 0.41 0.37
1.05 1.04 1.03 1.03 1.02 1.02 1.00 1.00 1.00 0.99 0.99 0.98 0.97 0.96 0.96 0.95 0.95 0.94 0.94 0.93 0.93 0.91 0.91 0.88 0.88 0.86 0.86 0.85 0.84 0.83 0.83 0.82 0.82 0.80 0.80 0.80 0.80 0.78 0.78 0.78 0.78 0.78 0.76 0.76 0.76 0.75 0.75 0.74 0.74 0.73 0.71 0.70 0.70 0.68 0.68 0.66 0.66
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Macarthur, NSW Dawson, Qld. Durack, WA O'Connor, WA Casey, Vic. Forrest, WA Greenway, NSW Watson, NSW Capricornia, Qld. Wannon, Vic. Farrer, NSW Bradfield, NSW Riverina, NSW Barker, SA Mallee, Vic. Kennedy, Qld. Parkes, NSW Braddon, Tas. Berowra, NSW Moore, WA Hughes, NSW Chifley, NSW Cowan, WA Grey, SA Murray, Vic. Flynn, Qld. Gorton, Vic. Holt, Vic. Bruce, Vic. Tangney, WA Calwell, Vic. Werriwa, NSW Maranoa, Qld. Mitchell, NSW Menzies, Vic. Scullin, Vic. Blaxland, NSW Aston, Vic. McMahon, NSW Fowler, NSW
0.20 0.27 0.24 0.21 0.23 0.20 0.23 0.32 0.23 0.20 0.31 0.34 0.16 0.17 0.29 0.25 0.17 0.24 0.22 0.17 0.20 0.15 0.22 0.16 0.20 0.18 0.25 0.18 0.24 0.20 0.20 0.17 0.13 0.17 0.23 0.17 0.18 0.10 0.13 0.14
0.45 0.39 0.41 0.44 0.41 0.43 0.39 0.30 0.39 0.41 0.29 0.25 0.42 0.41 0.28 0.32 0.38 0.28 0.29 0.34 0.30 0.33 0.24 0.30 0.26 0.28 0.20 0.27 0.20 0.23 0.22 0.24 0.28 0.23 0.15 0.18 0.15 0.22 0.13 0.11
0.66 0.66 0.65 0.64 0.64 0.63 0.63 0.62 0.62 0.61 0.60 0.59 0.58 0.58 0.57 0.56 0.55 0.52 0.51 0.51 0.50 0.48 0.46 0.46 0.46 0.45 0.45 0.45 0.44 0.42 0.42 0.42 0.41 0.39 0.38 0.36 0.33 0.31 0.25 0.25
Figures rounded to 2 decimal places. Presented as a percentage of all couples. Source: ABS, census 2011
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