Electorates that swung to the government more likely to have higher unemployment, lower income and fewer migrants

The eight charts that help explain why the Coalition won the 2019 Australian election

Electorates that swung harder to the Liberal and National parties are more likely to have higher unemployment, lower income, lower levels of education and fewer migrants, according to a Guardian Australia analysis.

Conversely, electorates that swung to Labor are more likely to have higher levels of education, more young people, more people in work or study, and more people over the age of 80.

And, perhaps surprisingly, electorates with larger numbers of people receiving franking credit refunds or making use of negative gearing on properties were less likely to swing to the Coalition.

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While we don’t yet know why more people voted for the Coalition over Labor, looking at the characteristics of electorates that swung towards the government can give us some ideas as to what sections of the population were more inclined to vote for them (or vote for minor parties in the first instance, and then preference the major parties highly).

Using data from the 2016 census and figures on negative gearing and franking credits from the Australian Taxation Office, I looked at the correlation between a range of demographic and socioeconomic figures with two-party-preferred swing. That is, as swing to the Coalition increases or decreases, how does something like median income increase or decrease?

It’s important to emphasise what this type of analysis doesn’t tell us. Despite the correlations, this analysis doesn’t show that recent migrants voted for Labor, or people on lower incomes voted for the Coalition. It just shows which aspects of an electorate were associated with a swing either way. We won’t know what actually influenced voter decisions until election-related surveys like the Australian Electoral Study are published.

Included with each chart, and in the text, is a measurement of how strong the association between the swing and the variable is. This measure is known as the “r value”, and varies between -1 and 1. A value closer to -1 or 1 indicates stronger correlation, while a lower value like 0.2, indicates a weaker correlation.

Excluded from this analysis are seats that didn’t have a contest primarily between Labor and Coalition candidates, as it skews the two-party-preferred measure given by the Australian Electoral Commission.

Income and tax

As median weekly income decreases, the swing to the Coalition increases:

This means that richer electorates were more likely to swing to Labor, and poorer electorates were more likely to swing to the Coalition. The relationship between income and swing was weaker than some of the other variables I looked at (r value = -0.34).

It’s also worth noting that several of the richest electorates, including Wentworth and Warringah, were excluded from this as they had contests between independents and Liberals.

The ATO publishes statistics that include the number of people receiving franking credits by postcode, and the number of people with a “net rent loss”, which indicates those negatively gearing property investments.

I used these figures to get a rate for the number of people receiving franking credits for every 100 people in the electorate, and for the number of people negatively gearing a property for every 100 people in the electorate (see the notes section below for more information about this).

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This is of obvious interest as the Coalition and industry lobby groups ran a substantial scare campaign against Labor’s policies to remove franking credit refunds for people who paid no tax and to abolish negative gearing on existing properties from January 2020.

It is worth noting that the ATO’s figure of people receiving franking credit refunds is not exactly the same group of people that Labor’s policy would target, as Labor’s policy only proposed to end the cash refunds for people who hadn’t paid tax, and the ATO data includes all people receiving franking credits.

There was a weak, negative correlation with both the franking credit and negative gearing figures with swing to the Coalition. That is, as the rate of people receiving franking credits goes up, and the rate of people negatively gearing properties goes up, so does the swing towards Labor. This is the opposite of what you’d expect if the Coalition’s campaign had influenced these groups to vote against Labor.

Work and education

One of the strongest correlations between swing and the data I considered was with education and employment status.

The percentage of people who had completed year 12 in an electorate was moderately correlated with a swing towards Labor. That is, electorates with higher education rates were more likely to swing towards Labor, and electorates with lower rates of education were more likely to swing towards the Coalition.

There is a similar trend with the census figures for “engagement in employment, education or training” which classifies a person’s participation in work (ie employment, underemployment or unemployment) or study.

Electorates with a higher proportion of the population in full-time work or study were more likely to swing to Labor:

And, conversely, electorates with higher unemployment or lower participation in education were more likely to swing to the Coalition:

Age and immigration

The only two age groups that showed a correlation with swing were the percentage of 18 to 34 year olds in an electorate, and the percentage of people aged over 80 in an electorate.

The correlation in both cases was not as strong as for other factors, and both showed a tendency to swing towards Labor. That is, electorates with higher proportions of younger people were more likely to swing towards Labor, as were electorates with a higher proportion of people aged 80 and over.

The number of recent migrants in an electorate was also a good indicator for a swing to Labor. Both the proportion of people who were recent migrants, and the proportion of people who were born overseas were positively correlated with a swing to Labor.

Geographic trends

It has been reported widely that Queensland swung hard for the Coalition, and Victoria swung for Labor, which is immediately obvious when looking at the swing by state.

Within this overall trend, there are some interesting geographic divides.

Sydney shows a city divided between east and west, with the suburbs east of the Red Rooster line swinging for Labor, and those to the west swinging for the Coalition:

Facebook Twitter Pinterest Two-party-preferred swing by electorate, showing the Sydney region. Illustration: Nick Evershed/The Guardian

Regional New South Wales electorates were more likely to swing towards the Coalition.

In Victoria, it’s a different story, with a number of regional electorates swinging to Labor.

Facebook Twitter Pinterest Two-party-preferred swing by electorate, showing Victoria. Illustration: Nick Evershed/The Guardian

In Melbourne, the Coalition swing came in the outer ring suburbs.

In Brisbane, two inner-city electorates and the division of Ryan in the western suburbs were the only ones that swung towards Labor.

Facebook Twitter Pinterest Two-party-preferred swing by electorate, showing Brisbane and surrounds. Illustration: Nick Evershed/The Guardian

Looking across the east coast, some of the biggest swings to the Coalition came in coalmining regions, such as the electorates of Capricornia and Hunter (and both these seats had high primary votes for One Nation).

Because of this, I had a look at the correlation between the number of coalmines in an electorate, and swing to the Coalition, and seats with more mines do tend to have a higher swing in that direction:

Notes and data

Census data for current electoral divisions is from here. The ATO data on franking credits is reported by postcode in the ATO’s tax statistics publication. I converted the figures to electorates using a postcode-to-electorate correspondence, and then adjusted to a per-population figure using the 2016 census population number.

Figures on negative gearing were generated in the same way by the Parliamentary Library. Coalmine numbers are from 2015 (and some mines have since closed).

Correlation results and data are here.

Update

I have added some more information about the strength of correlation, and how it should be interpreted.