Pollsters Keep Putting Voters in Boxes. Voters Keep Breaking Them.
Summary
Recent Democratic primary elections in Wisconsin and Michigan showed large differences between poll predictions and actual results, revealing challenges in predicting voter behavior. While new demographic categories can improve data accuracy, they do not guarantee better predictions about who will vote or the choices voters will make.Key Facts
- In Wisconsin’s Democratic primary for governor, David Crowley won by about half a percent despite polls showing Francesca Hong leading by 20 points.
- In Michigan’s Senate primary, Abdul El-Sayed won by a much larger margin than polls predicted.
- Pollsters use categories like age, race, education, and party affiliation to model elections but still find it hard to predict who will actually vote.
- Open primaries, where any voter can choose any party’s primary, make it harder to predict voters.
- California lawmakers are considering SB 1387, a bill to add a separate category for Jewish ancestry in state demographic data.
- The new Jewish category would be optional and confidential, and it aims to better capture identity in government data.
- Better demographic categories help measure things like discrimination or health differences but don’t make voter behavior more predictable.
- Polling errors highlight limits in using demographic boxes to forecast election outcomes.
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