Silver Spring enters Waukesha County. Photo by Carl Baehr.

Silver Spring enters Waukesha County. Photo by Carl Baehr.

After an election—particularly a close one—a wide variety of trends can be claimed to explain the results. One approach is to look at where each candidate received the most votes. From that viewpoint, Dane and Milwaukee counties performed excellently for Joe Biden, while small town and rural counties turned out for Donald Trump.

As can be seen from the figure below, both candidates received more votes in 2020 than in 2016. The total vote count grew by more than 16%.

Vote totals for 2016 and 2020

Vote totals for 2016 and 2020

Comparing the 2020 and 2016 elections, Biden managed to flip about 43,000 votes compared to 2016. The next graph shows the Democrat’s margin in both years. In 2016, Hillary Clinton was short a bit more the 22,000 votes; in 2020, Biden won by a bit more than 20,000 votes. But when you look at where those 43,000 votes came from, it’s a change in Republican areas, not Democratic strongholds.

Democratic Margin

Democratic Margin

Wisconsin is largely a state of small cities and towns and rural areas. The next graph shows the two-party vote of Wisconsin’s 20 largest cities. After the two largest cities—Milwaukee and Madison—populations and voters plummet abruptly. Notably, six of the largest cities—Waukesha, Wauwatosa, West Allis, New Berlin, Brookfield, and Greenfield—are Milwaukee suburbs.

Largest cities by total 2020 vote

Largest cities by total 2020 vote

Wisconsin’s election administration competes with Michigan to have the most locally run elections. Almost 2,000 cities, villages, and towns—most very small—are responsible for administering elections.

To get a sense of where Biden got his victory, I grouped the municipalities into three groups as shown in the graph below.

  1. The five biggest cities, Milwaukee, Madison, Green Bay, Kenosha, and Racine. These cities worked together to improve their election systems, supported by a grant from the Center for Tech and Civic Life (CTCL). As I reported earlier, the belief among many on the right that the grants were aimed at increasing Biden’s vote total is not supported by the evidence.
  2. The next 15 largest cities, including six Milwaukee suburbs.
  3. Everywhere else, which contributed almost three quarters of the vote total.
The Wisconsin vote in 2020

The Wisconsin vote in 2020

The next graph shows the proportion of the three groups voting for the Democratic candidate in 2016 (shown in yellow) and 2020 (in green). 49.6% of the Wisconsin electorate voted for Clinton in 2016, compared to 50.3% for Biden in 2020. The shift was sufficient to win the state. Where did this shift come from?

It did not come from the five largest cities. Both Clinton and Biden won 76.7% of those cities’ voters. Instead, Biden’s owes his victory to the other two groups.

Percent of Vote for Democrat

Percent of Vote for Democrat

The next graph compares the percentage of support for Clinton in 2016 (on the horizontal axis) for the twenty largest cities in Wisconsin to that for Biden in the cities four years later (on the vertical axis).

The solid line is the diagonal. In cities above the diagonal, Biden did better than did Clinton. All but five of the 20 cities saw a shift towards Biden. This was particularly true of cities that voted for Trump. All six Milwaukee suburbs shifted towards Biden, as did all the cities giving Trump a majority in 2016.

That said, the shifts were minor. This is supported by the coefficient of determination (R2) of 0.9698, indicating that 97% of the variation is explained by the trend. In other words, if asked to predict the vote in 2020 the best answer would look to the vote in 2016.

The trend, shown as a dotted line on the graph closely matches the diagonal on the right of the graph—cities that Clinton won by a large margin. On the left—’Trump country”—the two lines diverge. On average, Biden gets a slightly larger share of the vote than Clinton in cities that Trump won in 2016. The change was small but it was enough to put Biden over the top.

Wisconsin's Largest Cities (2020 vs. 2016)

Wisconsin’s Largest Cities (2020 vs. 2016)

This analysis supports several conclusions:

  1. It further undermines Trump’s claims that he lost because of fraud. The pattern in the above graph would be very difficult to produce by fraud. If a group intent of affecting the election result discovered a vulnerability to election administration, it is very unlikely that all the nearly 2,000 Wisconsin election districts would equally share that vulnerability. Thus, rather than a series of small changes in 2020 voting compared to 2016, we would see a small number of dramatic changes.

For example, suppose there was some truth behind the accusations that Dominican machines were able to change Trump votes to votes for Biden. In that case, the increasing Biden totals would be concentrated on municipalities using Dominion machines.

  1. If there were fraud to help Biden, one would expect it to be concentrated in Democratic district. Yet Trump lost because his votes in Republican municipalities did not grow as much as Biden’s.
  2. If there were any truth behind right-wing claims that the motivation behind the CTCL grants was to help Biden, the donors did not get their money’s worth based on the election result. The simpler conclusion is that the aim was to improve election administration in meeting the challenge of the pandemic.
  3. Is Trump country responsible for Biden’s victory? Without the overwhelming support from voters in Wisconsin’s largest cities, especially Madison and Milwaukee, Biden would have lost. If, however, the question to ask who is responsible for converting Clinton’s 20,000 vote loss into Biden’s 20,000 vote win, it turns out that the Big Five played no part. Biden won because a smaller proportion of voters in smaller cities and towns supported Donald Trump.
  4. This analysis shows the challenge for Democrats. Especially with extreme gerrymandering, people considering running for the Legislature may look at the long odds of winning and decide it’s too difficult. How does the party develop a strong local presence under such circumstances?



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