I’m just about to start a long series of posts - 150 seats long to be exact - that runs a quick eye over the demographic profile of each Australian electorate between now and the next election. Yet, with literally hundreds of demographic variables available for use in each profile, only some of them are valuable as far as making a significant impact on the way an electorate will vote.
So the big question is - which variables are worth using?
To do this, we’ll use the seat of Adelaide as the example since it’s first alphabetically. So each profile will contain the historical TPP vote since 1996 as well as the primary vote results at the 2007 election:
| Party | LP | ALP | GRN | FFP | OTH |
| Primary 07 |
38.43 | 48.26 | 9.75 | 2.03 | 1.53 |
We’ll also add the age profile of the electorate (telling us the proportion of the electorate aged within a certain range) as well as an income profile (where we divide the number of people earning a given income range that live in the electorate by the number of the total population aged 15 and over in the electorate to give us a percentage) - and we’ll use the 2006 census data:
But what else is valuable info?
Education levels? Ancestry? Proportion of the electorate receiving the Family Tax Benefit? We can add a great many things, but they all take a fair whack of time to organise - so rather than include every possible thing (and turn myself into some socially isolated geek with no life in the process) , which ones would be important? Which variables and stats would you like to see included, and why?
UPDATE 1:
Trevor Cook makes an excellent point in comments:
I’d like to see union membership included to see if it says something about the impact of workchoices and the ACTU campaign.
I know a not insignificant number of union honchos read this blog upon occasion - would there be any possibility of the ACTU sharing basic electorate level membership data with us?
UPDATE 2:
Jason and Oz asked about occupational groups. How’s this?
UPDATE 3:
I’ve had a bit of a squiz at the census data on the topic of volunteer work, so for Adelaide we’d get:
But looking at a dozen random seats, there doesnt seem to be enough variation in the levels of volunteering in most seats to make this metric particularly useful - which is a pity, as what this kind of thing is trying to measure is handy, it’s just that the metric itself doesnt appear to be very good at doing it.
UPDATE 4:
I think we need to include housing and tenure - so we’ll measure the proportion of the electorate that owns their home, that is purchasing their home and that is renting their home. We’ll also throw into the mix the proportion of the electorate that lived in the same address between 2001-2006, the proportion of the electorate that moved locally between 2001-2006 (moved within the same Statistical Local Area), and the proportion of the electorate that lived outside of the local area in 2001.
For the seat of Adelaide, it’s interesting that they have a higher proportion of renters than SA and Australia as a whole, as well as have a higher proportion of people that recently moved into the seat from a non-local area. When we get onto the QLD seats, you’ll be surprised at how high the percentage of non-local migration gets in places.
UPDATE 5:
A few more - Occupation (to compliment the industry profile), Centerlink Recipients and finally, Family Composition and Ancestry:
13 Comments
I’d like to see union membership included to see if it says something about the impact of workchoices and the ACTU campaign
That would be interesting indeed Trevor. I wonder if the unions would be willing to share basic electorate level total membership data? I’ll ask around and see.
Possum - perhaps this is too obvious but I think alongside union membership, some of the ABS data on membership of some of the basic occupational groups would be relevant to understanding how/why people are affected by various policy areas.
Occupational groups, seconded.
How about ethnicity, or what percentage are immigrants?
Poss,
George from the OZ provided a good workout in ‘07. Try some stuff from him (can’t find the links).
Would be good to include some volunteering/community service metrics (apparently how many choirs there are is a good mesaure of a community’s vitality).
Another would be “distance to travel to work (plus transport mode)”.
And another would be along the lines of what type of studies the post-secondary students are undertaking.
Finally, there may be something of interest in the “what the people want” forum.
Regards,
Paul
Poss I’d suggest at least the following, which are significant for various forms of actvity in my field:
- age, sex, work status, average education level, % renting/own house/paying mortgage
Some statistics don’t mean much - eg car ownership doesn’t always include company cars. Average income is very messy - average wage & salary doesn’t include all the transfer payments or investment income. Plus it assumes people truthfully declare all income, which they don’t, even to ABS, let alone the taxman. Likewise % owning shares doesn’t discriminate betwene millionaires and those witha few hundred Tesltra shares.
An age profile would be useful - % old, % young - rather than just an average age woudl be useful. % over 70 is a very good indicator of health care needs.
How about people from a Non-English Speaking Background?
I’m sure you’ll see a high correlation between the proportion of NESBs in an electorate and the Labor TPP vote…
Being a resident of the electorate - the correlation between the higher than the rest of SA and Australia graphs with regard to the 20-34 age group and the upper income brackets would be due to the recent construction of high density CBD and environs yuppie apartment buildings (replacing now superfluous office buildings). Also, the electorate includes some other high income enclaves such as North Adelaide.
NESB and first and second generation migrant numbers as a proportion of the electorate would be handy, I’ll go have a squiz and chase that up.
Paul, I think the Census has a data set on something like “voluntary work for an organisation”. I’ll run that through some regressions and see what pops out - I’ll post the details a little later.
Re occupational groups - quality - looks great.
Hi Possum, interesting stuff. I’d group variables by type, and then see what is available in the census:
- demographic variables (poportions of different age, family type, age of children, etc)
- household means (average income, distribution of income, proportion dual income, iporportion in receipt of government income support, direct equity holdings, in receipt of property income, proportion self funded retirees, etc)
- household balance sheets (avarage housing wealth, financial wealth, leverage, proportion with mortgage, repayment burden, etc)
- geographic variables (remoteness, distance from CBD (for metro), coastal)
- regional economic variables (economic diversity, occupational proportions, industry proportions, educational proportions, employment/population growth since last census, in-migration/out-migration, change in unemployment rate since last census, change in average income since last census, etc)
- ethno-cultural variables (proportions of various ethnic groups, proportion NESB, proportion ATSIC, etc)
- tenure variables (proportion renting, paying off mortgage, proportion of housing stock public)
- attitudinal variables (proportion engaged in voluntary activity, proportion regularly attend church, proportion athiest, proportion members of union, etc)
As much as possible use dummy variable categories, rather than averages to capture possible non-linearities (ie, split income into quintiles, age into cohorts, etc).
Use information on changes since the last election as well as proportions at time of last census.
To choose the variables, you are best off running some simple regressions. As you say, there is so much information, you cannot tell beforehand, which are the most relevent for voting behaviour.
Before running the regressions, put together a covariance matrix so that it is very clear where the potential for multi-collinearity lies.
Move from general to specific so that you let the data tell you which variables are important, rather than pre-empting it too much.
Hopefully you will spit out 15-20 significant variables that you can then use to profile each electorate.
Sorry - got on to this a bit late - I have been having some computer-free days. I suggest splitting ‘overseas’ born between UK/South Africa and the rest of the world. Also worth having a look at parental country of birth. Median rent may also be a good one as it seems to be a bit of a proxy for median house value. Education level is also worth a look.