I was just highlighting the need to be careful with stocks and flow comparisons. If we’re measuring the impact of an election, we need to use the swing rather than the stock, and then need to be careful that what we’re measuring as a variable is useful.

In an ideal world we could use panel data – but with more elections than census’ and with the boundaries changing significantly in some places across the intervening years, it makes it generally impossible without spending vast quantities of time reorganising old census data.

Another thing, in this sort of regression, I don’t think there is any problem with including stocks and flows in the same regression. For example, the stock of people with a university education (just using this as an example) may affect the swing if such people altered their view on the relative merits of the two parties. The flow could also matter because the change in the proportion with a university degree might reflect changes in economic conditions between the two elections.

http://blogs.crikey.com.au/pollytics/files/2008/12/commentswa1.png

Controlling for WA has no bearing on the effect in the seats where the effect is actually at play (the metro seats).

I might have a squiz over the next few weeks at more census data in relation to this age cohort growth and the ALP swing – but with the census data by electorate, collinearity quickly becomes a bit of a pain in the arse most times. Have to be a bit careful with what you throw together in the same regression.

On OVB – I wasn’t saying that everything can be controlled for – but you have census data available that allows quite a lot of things to be controlled for in the regression besides the two variables you have chosen. I’d like to see how the results stand up when you include additional educational and demographic variables…..

A fair whack of demographic assumptions that seem to be made about a number of seats arent really holding up well anymore. WIth just the 0-4 yr age growth, we might expect places like Lyndsay to keep on being veritable baby making factories – but the young families that moved to Lyndsay and produced armies of sproglets have since become a bit older. So while they still have a reasonably high proportion of 0-4yrs, the replacemeent rate of that proportion is tanking as these families slow down on the breeding but arent replaced by families that are at the begining of their reproduction curve.

A lot of the seats we used to think of as being filled with “young families” dont have families in them that are so young anymore. Same with the older Australians – there’s a few interesting patterns there as well that might need to make us have a second look at some assumptions about demographics at the next election. That relationship between 65+ growth and the ALP swing in metro seats is an interesting one because of a large number of 55-64 year olds in the country and how some of them are quite clustered in a number of seats.

If you haven’t played around with the relatively new Census CDATA online, it’s worth a few hours to get used to it. It’s a handy little tool and has convenient categories like state and commonwealth electoral divisions.

http://www.abs.gov.au/CDataOnline

OVB is the ultimately unavoidable problem with any regression work using electorate level data describing vote share or a change in vote share. By aggregating human behaviour like this, we can never explain (and would never really bother trying!) to explain everything. Most things (apart from State as a variable) only explain small amounts of variation in the swing.

I probably need to write a FAQ about the things I do here including all this.The purpose here wasnt to identify a causative phenomenon – (whenever it is I spell that out clearly, like the informal vote models we did a while back) but to highlight how some unknown thing or group of unkown things that are correlating with that age growth pattern, had a collective impact on the ALP swing by electorate at the last election.

More seriously tho’ David Richards you are bit out of touch here. People are spawning much later than ever before especially in those aspirational outer metro seats like Lindsay. For example my bro in law has by my reckoning voted in 7 federal general elections and has just become a daddy for the first time.

Anyway, I plugged the Melbourne data alone into the trusty XLStats application and both the correlation and R-squared amounted to two-fifths of sweet Jack diddly shit. Then I put the Sydney data in and got a scatterplot you ski down – correlation -0.66, R-squared 0.45. What do we make of that, I wonder? Sydney also didn’t have the patterns of 0-4 growth I would have expected: Lindsay, Werriwa, Mitchell and Berowra were in the negative.

Also, it seems to me that the most significant variable in explaining an electorate’s swing is usually the state it’s in, so I’ve looked at the overall 0-4 growth rate for each state. Head and shoulders above the rest on 3.6% is WA (not on my account, I hasten to add), and we all know what happened there in terms of relative swings. There probably aren’t enough seats to skew your results too much, but I still thought it might be of interest. Elsewhere: NSW 0.8%, Vic 2.0%, Qld 1.9%, SA 1.5%.