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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