13 October 2015

More robust estimates

Political psychologists have long held that over-simplified “rational” models of voters do not help accurately predict their actual behavior. What most behavioural researchers have found is the decision-making (e.g., voting) often boils down to emotional, unconscious factors. So, in attempting to build up our voting agents, we will need to at least:
  • include multiple issue perspectives, not just a simple evaluation of “left-right”;
  • include data for non-policy factors that could determine voting; and
  • not prescribe values to our agents beyond what we can empirically derive.

Given that we are unable to peek into voters’ minds (and remember: we are trying to avoid using polls[1]), we need data for (or proxies for) factors that might influence someone’s vote. So, we gathered (or created) and joined detailed data for the 2006, 2008, and 2011 Canadian federal elections (as well as the 2015 election, which will be used for predictions).

In a new paper, we discuss what influence multiple factors, such as “leader likeability”, incumbency, “star” status, demographics and policy platforms, may have on voting outcomes, and use these results to predict the upcoming federal election in Toronto ridings.

At a high-level, we find that:
  • Almost all variables are statistically significant.
  • Being either a star candidate or an incumbent can boost a candidates share of the vote by 21%, but being both an incumbent and a star candidate does not give a candidate an incremental increase. The two effects are equivalent to belonging to a party (21%).
  • Leader likeability is associated with a 0.3% change in the proportion of votes received by a candidate. So, a leader that essentially polls the same as their party yields their Toronto-based candidates about 14 points.
  • The relationships between age, gender, family income, and the proportion of votes vary widely across the parties (as expected). For example, family income tends to increase support for Conservatives (0.005/$10,000) while decreasing for the other two major parties by roughly the same magnitude.
  • Policy matters, but only slightly, and only economic and environmental issues overall.
With our empirical results, we can turn to predicting the 2015 federal election in Toronto ridings.
It turns out that our Toronto-wide results are fairly in line with recent Toronto-specific polling results (weighted by age and sample size) – though we’ll see how right we all are come election day – which means that there may some inherent truth in the coefficients we have found.

Given that we haven’t used polls or included localized details or party platforms, these results are surprisingly good. The seeming shift from Liberal to Conservative is something that we’ll need to look into further. It is likely highlighting an issue with our data: namely, that we only have three years of detailed federal elections data, and these elections have seen some of the best showings for the Conservatives (and their predecessors) in Ontario since the end of the second world war (the exceptions being in the late 1950s with Diefenbaker, 1979 with Joe Clark, and 1984 with Brian Mulroney), with some of the worst for the Liberals over the same time frame. That is, we are not picking up a (cyclical) reversion to the mean in our variables, but might investigate the cycle itself.

Nonetheless, given we set out to understand (both theoretically and empirically) how to predict an election while significantly limiting the use of polls, and it appears that we are at least on the right track.
[1] This is true for a number of reasons: first, we want to be able to simulate elections, and therefore would not always have access to polls; second, we are trying to do something fundamentally different by observing behaviour instead of asking people questions, which often leads to lying (e.g., social desirability biases: see the “Bradley effect”); third, while polls in aggregate are generally good at predicting outcomes, individual polls are highly volatile.

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