Slides from my MPSA 2014 presentation on forecasting turnout

For those of you who missed it, here are the slides from my talk at last weekend’s MPSA conference:

Past + Present = Future: A New Approach to Predicting Voter Turnout

The example shown is more of a toy model than one I’d actually put into practice, but it should give a general sense of the concepts behind the framework I’m proposing. As always, feel free to get in touch with questions.

On the role of gender in vote choice models

Josh Tucker poses an intriguing question over on the Money Cage blog, about why we persist in including gender in models of vote choice. I posted my thoughts as a comment on that page, but decided to repost them here as well (given that they exceeded the length of the original post) to continue the conversation:

Like other demographic variables—race, religion, income, etc.—it can serve as a useful proxy for unobserved issue preferences. Even in the US, where there’s no women’s party (or black party, christian party, or worker’s party, at least not officially), there are certainly issues on which the parties and their candidates differ, where the cleavages at least partially split along gender lines.

For example, running some data from the 2004 NAES, women:

  • supported the assault weapons ban at a rate 10% higher than men,
  • supported increasing the minimum wage at a rate 11% higher, and
  • supported making health insurance more available to children and workers at rates of 7 and 11% higher (respectively).

While in the NAES we have this data directly-measured (of course) and could thus use it on its own in vote models, in many surveys we don’t. Or, when we do, we have such fine-grained measures that aggregation is problematic. In either case, gender serves to capture some of this variation, so it’s useful for keeping vote models simple but meaningful.

Of course, there’s also the path dependency side of the equation; because everyone uses gender, it’s much easier to include it than exclude it. That’s a problem, of course, when we do have preference data, because the correlations between the data often drown out the significance of the issue preferences in regressions.

(Maybe that’s why the issue voting lit is still fairly primitive—social pressure to include demographic confounders makes the burden of proof that issues matter so much higher? Interesting topic for another time.)

Not sure what the status of the lit is on this question, but I imagine somebody’s tackled it before—seeing whether gender matters when everything else is controlled for as well. For what it’s worth, in my most recent paper (presented at APSA, on campaign effects), I found gender to be highly-significant for predicting 2004 presidential vote choice, even after accounting for partisanship, ideology, issue salience, and aggregated issue preferences. Didn’t run it with each issue separately, however, so that might have changed the results.

Now back to dissertation writing.