Recent changes in one of the most polarized places in America

More than a quarter of Wisconsin lives in Greater Milwaukee–the four counties of Milwaukee, Waukesha, Ozaukee, and Washington. While these places are inextricably linked economically, they are famously divided politically. Writing in 2014, Craig Gilbert observed that the Milwaukee metro area might be the most politically polarized of all major cities in America. Based on the 2012 election alone, it was certainly among the top few.1

Much political commentary and analysis since 2016 has focused on declining Republican margins2 in suburban areas. Citylab classified every Congressional district by density and found that Democrats in 2018 flipped the House of Representatives due to their suburban gains. They picked up 9 seats in “dense suburban” areas, 13 seats in “sparse suburban” districts, and 5 seats in places with a “rural-suburban mix.”3

Even though no seats flipped in Wisconsin, the state nonetheless saw many political changes. In fact, the median community changed its vote preference by 14% from Obama’s reelection in 2012 to Tony Evers’ election in 2018. The remainder of this post explores how the Trump era has affected partisanship in this most polarized part of Wisconsin.

A note on measurement

To control for the see-saw nature of politics, most of the statistics I present are the “relative margin,” which is the partisan vote minus the statewide lean. First, I calculate the area’s vote margin (% Democrat minus % Republican); then I subtract the statewide margin. For example, the City of Waukesha voted 42% for Evers and 56% for Walker, so its absolute margin is -14%. The state as a whole had a margin of +1%. So Waukesha’s relative margin is -15% because it voted 15% more Republican than the state as a whole.

Overview

Since 1990, the Greater Milwaukee Area combined has voted Democratic in 5 out of 7 Presidential elections and Republican in 7 out of 9 Gubernatorial races. In his first three elections, Scott Walker won the Milwaukee Area by 47,200; 44,900; and 47,900 votes, respectively. In 2018 he lost by 17,600 votes. Since Tony Evers only won statewide by 29,227 votes, you could say Walker’s deteriorating support in the Milwaukee area cost him the election.

So what changed? Let’s first examine the vote in three broad swathes of the Milwaukee Area which collectively hold 85% of the region’s population.

  1. The City of Milwaukee, home to 595,000 people or about 44% of the population.
  2. Milwaukee County’s 18 suburbs, home to 357,000 people–about 23% of the population.
  3. Waukesha County’s 38 suburbs, home to 401,000 residents, or about 30% of the total.

Each of these areas has followed a different trajectory over the last few decades.

Consider the pre-Trump era. From 1990 to 2014, the City of Milwaukee grew steadily more Democratic. By the 2010s, Democrats in Milwaukee were beating their statewide performance by more than 50 points. On the other extreme, Waukesha County grew steadily redder. In the early 1990s, it usually voted around 26 points more Republican than the rest of the state. By the 2012/2014 elections, this had increased to 40. The Milwaukee County suburbs began the ’90s around 7% more Republican than the state, but gradually moved closer to the state average.

The two elections of the Trump era suggest things have changed. In Milwaukee City support for Clinton was even higher relative to the state average than it was for Obama in 2012. Conversely, there was a slight decline in the Democrat’s relative performance between the governor’s race in 2014 and 2018.

The big changes happened in the suburbs. In 2012, the Milwaukee County suburbs voted 4% more Republican than the rest of the state. In 2016, they voted 11% more Democratic. The shift between gubernatorial races was smaller (1% more Republican in 2014 compared with 6% more Democratic in 2018) but similarly abrupt and consequential.

Waukesha County experienced a strikingly similar trend. In 2012 it voted 41 points more Republican than the state overall. In 2016 this fell to 26 points. Likewise, Waukesha voted 40% more Republican than the state in Walker’s 2014 reelection, but this fell to 35% in his 2018 defeat.

Pre-Trump vs Trump-era

To get a better sense of the overall shifts from the pre-Trump to the current Trump era of partisan politics, I generated two statistics. First, I calculated the average adjusted margin of the 2012 and 2014 elections for president and governor (left map). Then I found the same statistic for the following elections in 2016 and 2018 (middle map). The difference between these two numbers shows a durable shift toward the Democratic party across nearly all Milwaukee Area suburbs, but the size of this shift varies considerably (right map).

Whitefish Bay, Fox Point, Bayside, Wauwatosa, and Elm Grove all shifted toward the Democrats by over 20% relative to the rest of the state. A handful of smaller, more far-flung communities actually shifted toward the Republicans. These include Newburg, Kewaskum, Big Bend, and Wayne. Barton, Hartford, Cudahy, South Milwaukee, Addison, and Farmington all experienced less than a 1% shift.

So what causes some suburbs to be more Democratic than others, and why have some changed greatly in the Trump era while others haven’t? To explore those questions, I created two regression models. The first model takes the margin in 2018 governor’s race as its dependent variable. The second model takes the pre-Trump to Trump era shift described above. In each case I test the influence of the following independent variables: % Black or African-American, % Latinx (of any race), % with a graduate degree, median income (divided by 10,000), median age, and the percent of workers commuting to the City of Milwaukee. In both models the unit of analysis is suburbs in Milwaukee, Waukesha, Ozaukee, and Washington counties. Each suburb is weighted to its total population, so large communities count more than small ones.

All of these variables have a significant, independent effect on the 2018 vote margin. Collectively, the model explains 93% of the variance–a remarkably high figure for this sort of analysis. Perhaps the most surprising result is the strong correlation between the portion of workers commuting to Milwaukee and improved Democratic performance. For every additional 1% of workers commuting to the central city, Tony Evers improved his margin of victory by about 0.93%.

Fewer variables correlate strongly with the partisan shift of a given suburb from the pre-Trump elections of 2012-14 to the Trump-era elections of 2016-18. Income and age don’t have a significant relationship one way or the other. The share of workers commuting to Milwaukee actually has a small negative coefficient. Conversely, larger Black and Latinx shares of the population do positively correlate with a larger shift toward the Democrats. The biggest predictor of a pro-Democratic shift is, by far, high levels of education. For every additional 1% of the population with a graduate degree, the average suburb shifted by 0.8% toward the Democrats from 2012/14 to 2016/18.

Regression Results
Dependent variable:
2018 margin Shift
(1) (2)
% Black 0.876*** 0.198**
(0.217) (0.091)
% Latinx 0.755*** 0.230**
(0.252) (0.105)
% w/Grad degree 1.798*** 0.825***
(0.158) (0.066)
Median income/10,000 -0.061*** -0.004
(0.007) (0.003)
Median age -0.007*** -0.0005
(0.002) (0.001)
% of workers commuting to Milwaukee 0.931*** -0.089**
(0.089) (0.037)
Constant 0.025 0.032
(0.088) (0.037)
Observations 90 90
R2 0.935 0.796
Adjusted R2 0.930 0.781
Residual Std. Error (df = 83) 6.791 2.841
F Statistic (df = 6; 83) 199.096*** 53.832***
Note: p<0.1; p<0.05; p<0.01

Statewide consequences

Tony Evers’ 2018 victory is only the second time since (at least) 1990 that a Democratic candidate for governor carried the 4-county Milwaukee Area. The last time it happened was 2006 when Jim Doyle won reelection. In that year, Doyle won the state handily by 7.4%, but he only eked out a victory in Greater Milwaukee by 1,578 votes.

Things were much different twelve years later. Evers’ performance in the Milwaukee Area (a 2.4% lead) exceeded his statewide victory of 1.1%. Continued Democratic strength in the central city combined with eroding Republican support in the suburbs was more than enough to overcome growing GOP margins in the more rural regions of the state.

  1. Craig Gilbert, “The Red and the Blue: Political Polarization Through the Prism of Metropolitan Milwaukee”, Marquette Lawyer, Fall 2014.↩
  2. By “margin”, I mean (Party1 %) – (Party2 %)↩
  3. David Montgomery, “Suburban Voters Gave Democrats Their House Majority”, Citylab.com, 7 Nov 2018, https://www.citylab.com/equity/2018/11/house-races-election-results-democrats-suburbs-blue-wave/575287/↩


 

Continue ReadingRecent changes in one of the most polarized places in America

Wisconsin 2018: a shift toward the Democrats, but not a uniform one

In a recent article for the Milwaukee Journal Sentinel, Craig Gilbert described how Scott Walker’s 2018 election loss was the result of declining support across all kinds of populous villages and cities in Wisconsin.[1] Walker averaged a 10% decline in places with at least 30,000 people, a 9% decline in places with 10,000 to 30,000, a 6% decline in places with 5,000 to 10,000, and a 3% decline in places with 2,000 to 5,000 residents.

Things improved for Walker in Wisconsin’s numerous small communities. His performance fell by just 0.6% in municipalities with 1,000 to 2,000, and he actually improved over 2014 in communities with less than 1,000 residents.

The overall trend is shown in the graph below.

Even though Walker beat his 2014 performance in over 40% of Wisconsin communities, these places only represent 16% of the state’s adult citizens.

An uneven Democratic wave

I divide the state’s communities into 6 categories based on their shift between the 2012 and 2016 presidential elections.[2]

  1. FLIP BLUE: 5 communities turned blue in 2016 (pop. 17,000).
  2. FLIP RED: 543 communities turned red (pop. 847,000).
  3. TRUMP ENTHUSIASTIC: 977 communities voted for Romney and Trump, and gave Trump an even larger victory (pop. 1,631,000)
  4. TRUMP SKEPTICAL: 85 communities voted for Romney and Trump, but gave Trump a narrower victory (pop. 700,000).
  5. CLINTON ENTHUSIASTIC: 46 communities voted for Obama and Clinton, and gave Clinton an even larger victory (pop. 582,000).
  6. CLINTON SKEPTICAL: 213 communities voted for Obama and Clinton, but gave Clinton a narrower victory (pop. 1,937,000).

Clinton Enthusiastic places include Madison and some of the mostly-wealthy Madison and Milwaukee suburbs. Clinton Skeptical areas include the more peripheral Madison-area suburbs as well as some of the traditional northwestern Democratic strongholds. The only two places of any size which flipped blue are River Falls and Hudson–both located in the St. Paul suburbs.

Communities which flipped red are strewn across the western half of the state with concentrations in the southwestern Driftless Area as well as the northwestern Lake Superior coastal counties of Douglas, Bayfield, and Ashland. Trump Enthusiastic areas cover most of the remaining rural northern half of the state. Trump Skeptical areas are predominantly located outside of Milwaukee in suburban Waukesha and Ozaukee counties.

2018 was a Democratic wave year, and Evers improved over Mary Burke’s margin in every type of community. However, the 2012-2016 shifts described above still had enduring consequences for the 2018 gubernatorial race.

Summarizing the entire vote in each category reveals that Walker won the vote in communities which flipped red in 2016 while Evers narrowly won in places which flipped blue. But the largest and most notable shifts relative to 2014 occurred in Clinton Enthusiastic and Trump Skeptical places, which shifted 13% and 12% toward the Democrats, respectively. These categories represent the two partisan poles of the state. Evers won Clinton Enthusiastic places by 47%; he lost Trump Skeptical places by 29%. But the trend in each place was nearly identical–a double-digit swing toward the Democrats.

In other words, the areas which shifted the most away from the Republican candidate in 2016 were the most Republican parts of the state. Communities which were the most supportive of the pre-Trump Republican Party were the least satisfied with Trump. At least to some extent, that dissatisfaction carried over to 2018. Likewise, support for the Democrats only intensified in communities which were already enthusiastic about Clinton.

2018 governor’s vote trends by category

MCD count Population % of Pop. Evers’ margin Clinton’s margin Burke’s margin Shift from 2016 Shift from 2014
Clinton Enthusiastic 46 582086 10.2 47.2 45.8 34.4 1.5 12.8
Clinton Skeptical 213 1937002 33.9 29.8 25.6 23.2 4.1 6.6
Flip Blue 5 17447 0.3 0.2 1.2 -9.0 -1.1 9.2
Flip Red 543 847386 14.8 -6.0 -11.8 -7.2 5.7 1.1
Trump Enthusiastic 977 1630848 28.5 -27.8 -29.3 -31.1 1.6 3.3
Trump Skeptical 85 700210 12.3 -29.2 -22.8 -41.1 -6.4 11.9

Most of Wisconsin’s wards (52%) experienced flip-flopping trends between the last two races for president and governor. They voted more for Trump than for Romney, but supported Evers more than Burke. Twenty-nine percent of wards shifted in a Republican direction each time. Nineteen percent of wards moved toward both Clinton and Evers. Virtually nowhere moved Democratic in presidential voting and Republican in gubernatorial races.

These divisions have a strong geographic component. Imagine a diagonal line stretching across the state from Green Bay to where the Wisconsin River meets the Mississippi. Trump/Walker trending places are strongly concentrated north of that line.

Clinton/Evers places, by contrast, are mostly south of that line. They include Madison and some suburbs, Milwaukee’s suburbs (but not the city itself), and a few communities in the Fox Valley. A handful of more rural population centers in the northern and western parts of the state are also trending Democratic. Most notably, Democrats have been gaining ground consistently in the Wisconsin suburbs of St. Paul.

The flip-floppers are spread across the state. They make up most of the populous south-eastern half of the state apart from the Clinton/Evers communities.

In another post-election column, Craig Gilbert observed that despite the partisan changes taking place around Wisconsin, “the state persists as a partisan battleground because all those regional shifts over the past two decades have somehow canceled each other out.”[3] Judging by the past two gubernatorial and presidential election cycles, Wisconsin can currently be divided into three general regions. Republican-trenders, Democratic-trenders, and a sizeable third group which moves whither the political winds blow.

[1] https://www.jsonline.com/story/news/blogs/wisconsin-voter/2018/12/22/loss-support-broad-set-cities-suburbs-walkers-undoing/2386626002/

[2] 42 minor civil divisions have missing data and are excluded from the analysis.

[3] https://www.jsonline.com/story/news/blogs/wisconsin-voter/2018/11/30/wisconsin-undergoes-political-shifts-while-somehow-staying-purple/2160683002/

[4] Ward analysis is conducted using the LTSB’s disaggregated ward files.

Continue ReadingWisconsin 2018: a shift toward the Democrats, but not a uniform one

The Milwaukee Area’s Future Workforce

This post is part 3 of a 3-part series based on data originally presented at the first Milwaukee Area Project conference. Part 1, overviews trends in population, employment, and wages since 1990. Part 2, on commuting and migrating in the Milwaukee area is available here.

The Milwaukee region’s economy has undergone major shifts in the past quarter century. In the graph above, nearly all non-farm jobs in Milwaukee, Waukesha, Washington, and Ozaukee counties are grouped into one of the nine displayed “supersectors”. This data is gathered by the Bureau of Labor Statistics as part of its Quarterly Census of Employment and Wages (QCEW). The QCEW is a particularly good measure of labor trends because it is not simply a survey; it includes all businesses that participate in the federally mandated state unemployment insurance systems.

Education and health is now the largest supersector—having enjoyed decades of nearly unbroken growth. Recessions have had little effect on its growth so far. Trade and transportation is second. This supersector closely mirrors the fortunes of the manufacturing industry, albeit with less extreme declines. Manufacturing jobs fell dramatically during the 2000s and again during the Great Recession. Since then it has remained largely stable. During the recover, Professional and businesses services surpassed it in share of total employment. These jobs are more effected by downturns in the business cycle than those in education and health, but they tend to recover more quickly than those in manufacturing or trade.

Government employment (including federal, state, and local) has trended slightly down in recent years. The growing sector of leisure and hospitality is close to surpassing it. Construction has yet to recover fully from the 2008 collapse of the housing industry. Even in the best of times, however, it constitutes a relatively small portion of the region’s economy.

Grouping many kinds of jobs into a handful of supersectors is useful for understanding some kinds of broad economic changes. But these categories also include broadly disparate jobs in terms of wages and experience required. Above are the specific jobs likely to grow the fastest in Milwaukee county by 2024. These estimates were created by the Wisconsin Department of Workforce Development in 2014. Below is the same chart recreated for Waukesha, Ozaukee, and Washington counties.

Both Milwaukee and the WOW counties are expected to add some high paying jobs. The ranks of registered nurses, computer systems analysts, and certain sales representatives are all expected to grow considerably. An average employee in each of these industries makes over $50,000 a year. However, even more jobs will likely be added at the lower-paying end of the spectrum. Due to the aging nature of our population, many of these will be in the caring professions—personal care aides, home health aides, nursing assistants, etc. None of these jobs pay well. An average employee will do well to make $25,000 annually. Food service workers will likely make even less.

As discussed elsewhere, the Milwaukee area fails to attract many new migrants. Not counting international immigration, we have a net outflow of movers. Filling the needs of our labor market requires making good use of our own homegrown workforce. This means businesses across the region will likely look to Milwaukee to fill both low and high-paying jobs. Regarding the latter—the region’s largest colleges and universities are all located in Milwaukee. One of Milwaukee county’s most significant growing industries in the 21st century is higher education which has added close to 3,000 jobs since 2000.

Milwaukee county will also likely provide much of the region’s future lower-wage laborers, if only because Milwaukee is where most of the future workforce lives. The population pyramids below show the age distributions of the populations of Milwaukee city and Waukesha county. The most populous four bars in Milwaukee are all under age 30. By comparison, the largest four bars in Waukesha county are ages 45 to 65. Waukesha’s workforce is aging out just as the bulk of Milwaukee’s population is entering its prime working years.

A recent Marquette Law School Poll of the Milwaukee Area asked respondents about their satisfaction with their community and plans for the future. Overall, wealthy people were likely to say, “I’m happy here and will probably stay for the next five years.” This view was shared by 69 percent of those earning at least $75,000. Only 4 percent answered, “I’m unhappy here and will probably move in the next five years.” By comparison, only 37 percent of respondents from households earning fewer than $40,000 a year reported being happy and intending to stay. Twenty-one percent were unhappy and intended to leave “their community” within the next five years.

The Milwaukee area’s economy continues to shift—in line with national trends—away from manufacturing and trades and toward a more service-based economy. Some of these jobs, such as nurses and high-tech workers, pay well. A few solidly middle-class jobs such as customer service representatives and advanced computer-based manufacturing continue to exhibit strong growth as well. Nonetheless, many of these new service-sector jobs pay poorly, and these low wages likely contribute to the desire of so many low-income area residents to leave their communities. Building a stable, prosperous future for the area will require not just making the region attractive to newcomers, but also improving the quality of life for people who already live here.

Continue ReadingThe Milwaukee Area’s Future Workforce