The 2007 federal election in Australia saw voters throw out of office the Howard Coalition Government, which had been in power for more than a decade, and elect the Rudd Labor Government. That represents a fundamental change in Australia's socio-political landscape. This paper provides an analysis of voter support for parties focusing on the disaggregated spatial level of local polling booths. Relationships between votes for political parties for the House of Representatives and the demographic and socio-economic characteristics of populations living in polling booth catchments across all the electorates in Australia are modelled to identify key demographic and socio-political dimensions underlying voter support for political parties.
At the November 2007 federal election for the House of Representatives, voters handed the Labor Party, led by Kevin Rudd, a resounding victory, throwing out of office the John Howard-led Coalition (Liberal-National) Government which had been in office since 1996. The two-party preferred vote of 52.6 per cent for Labor as against 47.4 per cent for the Coalition gave the new Rudd Labor Government 83 seats in the House. Between the 2004 and the 2007 elections the swing in voter support from the Coalition to Labor was about five per cent for the primary vote and 5.3 per cent for the two-party preferred vote. The Liberals lost 20 seats, including that of the Prime Minister, and the Nationals lost two seats. Labor had a gain of 22 seats.
This was a decisive victory for Labor which had lost government to the Coalition at the 1996 election with a similarly large swing to the Coalition of 6.17 per cent for the primary vote, a swing which saw the Labor Party consigned to a long period of time in opposition. Between winning government in 1996 and losing it in 2007, the Coalition government had experienced four successive electoral victories, with the 2001 and 2004 election victories being decisive, particularly the 2004 victory in which the Coalition had gained a swing of 3.69 per cent for the primary vote.
Thus the 2007 election outcome represented a fundamental change in Australia's political landscape, ending more than a decade of Liberal-National Party ascendancy as the 'John Howard battlers', the working-class living in the suburbs of the big cities and in the regional centres deserted the Coalition and returned to Labor. It has been said that much of the swing was due to Labor capturing what has been referred to as the 'working families'.
In this paper we discuss some of results of modelling to identify the demographic and socio-economic dimensions that might explain spatial variations in the level of voter support for political parties at the 2007 federal election for the House of Representatives.
Researchers at the University of Queensland (1) have been using Geographic Information Systems (GIS) technology and spatial statistical modelling tools to analyse voter support for political parties at the last three federal elections in Australia. They have done this in order to map the spatial patterns of voter behaviour at the disaggregated level of local polling booths and to develop typologies of socio-political landscapes. This has been done by identifying those demographic and socio-economic characteristics of the populations that live in polling booth catchments which might explain geographic variations in the level of voter support for political parties at elections for candidates standing for a seat in the House of Representatives. The research uses Australian Electoral Commission data on voting for political parties at the level of polling booths and interfaces those data with Census of Population and Households data at the Census Collectors' District (CCD) level of scale on the demographic and socio-economic characteristics of people living in polling booth catchments. The researchers have developed online GIS-enabled databases (2) (see
The data used for the modelling discussed in this paper are the primary votes cast for candidates standing for the House of Representatives at the 2007 federal election at the highly spatially disaggregated level of 7,439 polling booths across Australia. Those polling booth locations were geocoded, and the voting data were then integrated in a GIS with 48 demographic and socio-economic data variables (see list in Table 1). These were derived from the 2006 census for aggregations of CCDs that form polling booth catchments, thus generating a 7,439 x 48 socio-political spatial data matrix for analysis.
Table 1: Variables derived from the 2006 census representing the demographic and socioeconomic characteristics of polling booth catchments
Age and sex
per cent population males (MALES)
per cent population age 0-17 years children and youth (YOUTH)
per cent population age 18-22 years first voters (FIRST)
per cent population age 23-34 years (GENY)
per cent population age 35-44 years (GENX)
per cent population age 45-59 years boomer (BOOMERS)
per cent population age 60-74 years (Post Depression Wartime Generation) (WW2GEM)
per cent population age 75+ years (Pre Depression Generation) (DEPGEN)
Family and household structure
per cent single person households (SINGLES)
per cent couple without children households (COUPLES)
per cent one parent family households (ONEPARENT)
per cent couples with children households (COUPCHILD)
per cent households that are home owners (HOMEOWN)
per cent households that are home purchasers (MORTGAGEES)
per cent households that are private renters (RENTERS)
per cent households that are public housing tenants (PUBHOUS)
per cent indigenous persons (INDIG)
per cent born overseas (IMMIG) per cent bom in UK (UK)
per cent bom in Southern and Eastern Europe (SEEUROPE)
per cent bom in Middle East (MIDEAST)
per cent bom in Asia (ASIA)
per cent Catholic (CATH)
per cent Anglican (ANG)
per cent Pentecostal (PENT)
per cent other Christian (OTHCHRIST)
per cent Islamic (ISLAM)
per cent other non-Christian religion (ONCHREL)
per cent with no religion (NORELIG)
per cent of population not at the same address five years ago (MOBILE)
per cent dwellings (not population) using Internet (INTERNET)
Engagement in work
Labour force participation rate (INWORK)
Unemployment rate (UNEMPLOY)
Industry of work
per cent employed in Extractive Industries (EXTRACT)
per cent employed in Transformative Industries (TRANSFORM)
per cent employed in Distributive Services (DISTRIB)
per cent employed in Producer/Business Services (BUSSERV)
per cent employed in Social Services (SOCSERV)
per cent employed in Administrative & support services (ADSS)
per cent employed in Personal Services (PERSERV)
Occupation' (Robert Reich's categories)
per cent employed as routine production workers (ROUTPROD)
per cent employed as in-person service workers (INPERS)
per cent employed as symbolic analyst (SYMBA)
per cent persons age 15 years and over with a degree or higher qualification (DEGREE)
per cent persons age 15 and over with a certificate, diploma or advanced diploma (CERTDIP)
Low income category--per cent households in the
lowest quintile for household weekly income (less than $650) (LOWINC)
Middle income category--per cent households in
the middle three quintiles for household weekly
income ($650-$ 1,999) (MIDINC)
High income category--per cent
households in the highest quintile
for household weekly income ($2,000+) (HIGHINC)
Notes: (1) The occupation categories relate to those proposed by Robert Reich, The Work of Nations, Vintage Books, New York, 1991. Broad occupations in the 2006 Census of Population and Housing are grouped to approximate the Reich categories.
(2) Uses mean gross household income per week in 2006 dollars (Household Expenditure Survey, Australia: Summary of Results, 2003-04, Australian Bureau of Statistics, Catalogue no. 6530.0 as a reference to derive quintile groups).
A number of statistical modelling tools have been used to analyse the relationships between the spatial variations in the level of voter support for political parties across polling booths and the demographic and socio-economic characteristics of populations living in polling booth catchments. These tools include simple and multiple regression analysis, multiple discriminant analysis, and cluster analysis. In this way it is possible to identify key social dimensions which differentiate between clusters of groups of polling booths that display specified levels of voter support for a political party and to generate maps that represent socio-political landscapes across the cities, towns and regions of Australia. The modelling results discussed in this paper enable the predictors of spatial variations in voter support for political parties at the 2007 federal election to be identified. They also enable us to plot the position of political parties against two key dimensions in what we term a sociopolitical space and to show how those positions have changed over the last three federal elections.
PREDICTING LOCAL PATTERNS OF SUPPORT FOR POLITICAL PARTIES
The approach: using discriminant analysis
Discriminant analysis (4) is used to analyse the relationship between the patterns of voter support for political parties at the level of the polling booth and the demographic and socio-economic characteristics of local populations living in polling booth catchment areas across Australia. This...