Neil Diamond writes:
Last week there was a federal election in Australia. Contrary to expectations and to opinion polls, the Government (a coalition between the Liberal (actually conservative) and National parties, referred to as LNP or the Coalition) was returned with an increased majority defeating the Australian Labor Party (ALP or Labor, no “u”).
Voting in Australia is a bit different since we have compulsory voting, that is you get fined if you don’t vote, and we have preferential voting. Allocation of preferences is difficult and sometimes based on what happened last election and the pollsters all do it differently.
Attached is a graph of the two party preferred vote over the last three years given by Kevin Bonham, one of the most highly regarded poll analysts in Australia. Note that in Australia Red means Labor and Blue means Liberal. The stars correspond to what actually happened at the election.
Since the election there has been much analysis of what went wrong with the polls. I’m attaching two links—one by a Nobel Laureate, Professor Brian Schmidt of the Australian National University, who pointed out that the published polls had a much lower variability than was expected, and another (very long) post from Kevin Bonham which looks at what has happened and suggests among other things that the polls “may have been oversampling voters who are politically engaged or highly educated (often the same thing).”
Diamond also links to this news article where Adrian Beaumont writes:
The Electoral Commission’s two party preferred projection is . . . the Coalition wins by 51.5-48.5 . . . Polls throughout the campaign gave Labor between 51 and 52% of the two party preferred vote. The final Newspoll had a Labor lead of 51.5-48.5 [in the other direction as what happened; thus the polls were off by 3 percentage points] . . . I [Beaumont] believe the poll failure was caused in part by “herding”: polls were artificially too close to each other, afraid to give results that may have seemed like outliers.
While this was a failure for the polls, it was also a failure of the betting markets, which many people believe are more accurate than the polls. . . . the Betfair odds . . . implying that the Coalition had only an 8% chance of winning. . . . It is long past time that the “betting markets know best” wisdom was dumped. . . .
Another reason for the poll failure may be that pollsters had too many educated people in their samples. Australian pollsters ask for age and gender of those they survey, but not for education levels. Perhaps pollsters would have been more accurate had they attempted to stratify by education to match the ABS Census statistics. People with higher levels of education are probably more likely to respond to surveys than those with lower levels.
Compulsory voting in Australia may actually have contributed to this problem. In voluntary voting systems, the more educated people are also more likely to vote. . . .
If there is not a large difference between the attitudes of those with a high level of education, and those without, pollsters will be fine. . . . If there is a big difference, as occurred with Trump, Brexit, and now it appears the [Australian] federal election, pollsters can miss badly. If you sort the seats by two party swing, those seats that swung to Labor tended to be highly educated seats in the cities, while those that swung biggest to the Coalition were regional electorates. . . .
I’m surprised to hear that Australian polls don’t adjust for education levels. Is that really true? In the U.S., it’s been standard for decades to adjust for education (see for example here). In future, I recommend that Australian pollsters go Carmelo Anthony.