Yet, when given the option in the Chicago suburbs to elect local officials, not many people do. In recent years, local turnout has been under 20% in some elections (earlier posts here and here). There are likely lots of reasons for this.
However, the local control suburbanites like – the ability to influence what happens around their property, the oversight of local schools, pursuing community issues they care about, where their tax money goes – depends on community members voting. How do people get into office? By votes. How are people appointed to boards and commissions? Often by those voted into office. Who decides how to spend local tax dollars? Local officials. And so on.
There is still lots of time to vote today. There are plenty of candidates running in the Chicago region. There is a lot of information available about their platforms and goals. May voters turn out and contribute to the local government and control they say they value.
Citywide, preliminary turnout currently stands at roughly 34.3%, among the lowest turnout rates for a February municipal election in the last 80 years. The total number of ballots cast in this election isn’t final yet because there are still thousands of vote-by-mail ballots en route to the board of election commissioners.
In 2015 and 2019, the return rate for vote-by-mail ballots averaged nearly 80%. Assuming the same return rate this year, the city’s overall voter turnout rate could reach 35%.
Only about 1/3 of registered voters in Chicago cast ballots for mayor, city clerk, city treasurer, City Council and police district councils.
The citywide turnout rate this year was lower than it’s been in the last three municipal elections in 2011, 2015 and 2019. In fact, turnout in 2023 was about 10% lower citywide than it was in 2011.
Notwithstanding the issues of February elections not tied to other state or federal outcomes, I wonder at a few other possible factors involved:
Are the people voting by mail voters who would otherwise not vote or people who would have turned up at a poling place in the past?
Is the motivation of voting in a broader primary with more possible candidates – giving voters more options to find someone who might represent their particular interests – less inviting than having two candidates in the later election and the voters having to choose one or the other?
In a city where leaders tend to be powerful figures, what else might interest voters in selecting these leaders?
For a long time, the phrase suburban voter has been code for white voter. But suburbs are now among the most diverse spaces in American life, and tension is growing over who belongs in suburbia as NPR’s Sandhya Dirks reports.
The primary arena for conflict in this report involves politics:
DIRKS: Last year, white parents and some white folks who weren’t parents screamed at local school board meetings over teaching kids about racism or having diversity and inclusion programs. Most of the places where those fights flared were suburbs, and they were suburbs that are changing, suburbs that have grown more diverse. In some cases, like in Gwinnett County, they are also suburbs where Black people have started to get elected to local seats, like school boards.
KERNODLE: The difference between now and then is that we have power too.
DIRKS: Because as suburbs change, so does the power of the suburban vote.
This tension extends to numerous other areas including neighborhoods, housing, jobs, and schooling.
More broadly, this part of the process of “complex suburbia” where suburbs are changing. Some communities are changing faster than others, with these rates likely tied to social factors and patterns of resources and influence.
Our home mailbox has been filled for weeks with mailers for candidates at the national, state, and local level. What have I learned from all of these mailers? Very little.
Photo by Abstrakt Xxcellence Studios on Pexels.com
However, the one use they may have is for candidates’ names to catch my attention. I consider myself a fairly informed voter yet I cannot keep up with all of the local races. In a state with so many taxing bodies, there are numerous races for the Forest Preserve, County Board, municipal positions, and more. Who has the time to look at all of the positions of those candidates? I will enter the voting booth today with limited knowledge about dozens of names for positions that the average suburbanite has little knowledge about.
Thus, a mailer might catch my eye with a name in a way that another medium might not. All those texts from candidates in recent weeks? Most were automatically marked as spam by my phone and the others I did not look at. Political ads on television or radio? Easy to avoid by switching stations or using streaming services. News broadcasts about candidates? Can click past or avoid reading.
At the least, I took each of those mailers out of the mailbox, looked at them quickly, and then recycled them. Could they have planted a name or idea in my head? Perhaps.
If I am reading this correctly, here are two patterns:
The percent of polling places that are churches can differ quite a bit from state to state. Generally, some of the Northwest and Northeast are less likely to have churches as polling place. The highest percentages are in more “heartland” states with some interesting exceptions (Arizona, Florida).
Which religious groups host the most polling places can differ as well. It would be interesting to see more fine-grained data/ do these patterns of particular traditions hold up across states or is it because certain states have higher concentrations of certain traditions?
I imagine there might be all sorts of additional factors to consider when examining this.
Given the current political sentiments regarding the role or involvement of religious groups in politics, do these figures go up or down significantly in the coming years? And among which groups and locations?
As new research has found, it’s not just that many voters live in neighborhoods with few members of the opposite party; it’s that nearly all American voters live in communities where they are less likely to encounter people with opposing politics than we’d expect. That means, for example, that in a neighborhood where Democrats make up 60 percent of the voters, only 50 percent of a Republican’s nearest neighbors might be Democrats.
Democrats and Republicans are effectively segregated from each other, to varying degrees by place, according to the Harvard researchers Jacob Brown and Ryan Enos. And at least over the past decade, they believe this partisan segregation has been growing more pronounced…
For each individual voter, tied to an address, the researchers looked at their thousand nearest voters, weighting those next door more heavily than those a mile away. Drawn this way, about 25 million voters — urban Democrats especially — live in residential circles where at most only one in 10 encounters is likely to be with someone from the opposite party. Democrats in parts of Columbus, Ohio, and Oklahoma City live this way. So do Republicans in the reddest parts of Birmingham, Ala., and Gillette, Wyo…
These studies together suggest that as places become more politically homogeneous, people there are more likely to conform and to publicly signal their partisanship. Maybe no one says, “I want to move here because of all these Biden yard signs.” But perhaps one neighbor is swayed by the people who put them up, and another neighbor concludes, “This isn’t the place for me.”
Lots of confounding variables to examine across a lot of locales. But, the underlying patterns are fascinating to consider: do geographic communities, even in an era of reduced neighborly contact and participation in local institutions, influence people’s political belief and behavior? With more focus in recent years on how online and social media behavior influences politics, this connection to geography has the opportunity to reinvigorate conversation about the power of local communities.
I would be interested to see how this plays out among local governments of communities with similar traits. Take a suburb closer to a big city that leans Democrat and a suburb further out that leans Republicans. Are the local decisions made that different? Do local elections look different?
Or, how often are there tipping points across communities and neighborhoods where a majority of voters are of one party or another? The patterns now show some stability but these have changed in the past and could change again in the future. What happens when they do change and does the character of the community change?
Sean Skipworth and Jennifer Lawrence were vying to be the next mayor of Dickinson, but they each ended up with 1,010 votes after a runoff election last month and a recount earlier this week.
According to Texas law, a tie in a race for public office can be resolved by casting lots.
Skipworth became mayor after a ping pong ball with his name was pulled out of a hat during a ceremony Thursday that lasted about 10 minutes, the Galveston County Daily News reported…
Dickinson, located about 40 miles southeast of Houston, has more than 21,000 residents.
Second, Americans often feel better about their local politics – from their community through their representatives in Congress – compared to national politics. Perhaps people want to think better about those from their places or the stakes at the local level are lower (though local disagreements can get heated). The mayor of Dickinson, Texas may not be able to do much in the grand scheme of things but local officials are often non-partisan and say they are about getting things done.
Because this happened at a very local level, there is likely little from this particular solution – casting lots – to apply to the national level. Yet, the spirit and means of local politics may provide regular reminders of what is possible and how politics can be conducted.
The primary for Chicago mayor concluded yesterday and one of the leading stories is the low turnout among the electorate.
There are multiple ways to interpret this data and I would guess some would suggest Chicagoans are not interested in affecting their own fate or argue fourteen mayoral candidates was simply too many. But, here is what I would not want to get lost in the shuffle: voter turnout is low in many American local elections. This is true in some of the biggest cities as well as in small towns and suburbs. And this is in a country that claims to like local government and the ability of residents and community members to be closer to elected officials. While the federal government is large and far away, municipal officials have to address local issues and connect with the needs of their neighbors.
If local government is a feature of civic life many Americans like, higher rates of participation in voting and serving could help ensure its long term viability.
It appears that religiosity affects certain areas more consistently – particularly smoking, voting, happiness, and participation in nonreligious organizations – than others even as these relationships between religiosity and health, well-being, and prosocial behaviors can differ across countries. Of course, why some of these relationships and not others exist, even in the same categories like the example that the more religious do not smoke but religiosity has no impact on obesity or exercise, gets more complicated…
Abstract: The United States spends more than $250 million each year on the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed several years. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may become an increasingly practical supplement to the ACS. Here, we present a method that estimates socioeconomic characteristics of regions spanning 200 US cities by using 50 million images of street scenes gathered with Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22 million automobiles in total (8% of all automobiles in the United States), were used to accurately estimate income, race, education, and voting patterns at the zip code and precinct level. (The average US precinct contains ∼1,000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next presidential election (88% chance); otherwise, it is likely to vote Republican (82%). Our results suggest that automated systems for monitoring demographics may effectively complement labor-intensive approaches, with the potential to measure demographics with fine spatial resolution, in close to real time.
The researchers created an algorithm to identify the brand, model and year of every car sold in the US since 1990.
The types of cars also provided information about the race, income and education levels of a neighborhood, the study said.
Volkswagens and Aston Martins were associated with white neighborhoods while Chryslers, Buicks and Oldsmobiles tended to appear in African-American neighborhoods, the study found.
This study seems to do two things that get at different areas of research:
Linking lifestyle choices to voting behavior as well as other social traits. Researchers and marketers have done this for decades. For example, see this earlier post about media consumption and voting behavior. This hints at the work Bourdieu who suggested class status is defined by cultural tastes and lifestyles in addition to access to resources and power.
Connecting different publicly available big data sets to find connections. Google Street View is available to all and election outcomes are also accessible. All it takes is a method to put these two things together. Here, it was a machine learning algorithm by which different kinds of vehicles could be identified. It would take humans a long time to connect these pieces of data but algorithms, once they correctly are identifying vehicles, can do this very quickly.
Of course, this still leaves us with questions about what to do with it all. The authors seem interested in helping facilitate more efficient national data-gathering efforts. The American Community Survey and the Dicennial Census are both costly efforts. Could machine learning help reduce the effort needed while providing accurate results? At the same time, it is less clear regarding the causal mechanisms behind these findings: do people buy pick-up trucks because they are Republican? How does this choice of a vehicle fit with a larger constellation of behaviors and beliefs? If someone wanted to change voting patterns, could encouraging the purchase of more pick-up trucks or sedans actually change voting patterns (or are these more of correlations)?
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