Paper proposals are being invited for a Summer Conference on Election Science, Reform, and Administration, hosted by Reed College and Portland State University, and co-sponsored by the Early Voting Information Center at Reed College and the Election Data and Science Lab at MIT. The conference will be held in Portland, OR from July 27-28, 2017.
The goals of the conference are, first, to provide a forum for scholars in political science, public administration, law, computer science, statistics, and other fields who are working to develop rigorous empirical approaches to the study of how laws and administrative procedures affect the quality of elections in the United States; and, second, to build scientific capacity by identifying major questions in the field, fostering collaboration, and connecting senior and junior scholars.
The conference is designed to facilitate close attention to the papers presented, including extensive feedback and discussion. Therefore, papers should represent new work, with early drafts of papers encouraged.
We hope that a wide variety of topics will be addressed at the conference. We are particularly interested in new and innovative projects that address long standing questions about the impact of election reforms on registration and turnout at both the state and federal level; how the voter experience has improved or eroded during the two recent waves of election reform; and the research design and methodological challenges in election science. The following is a list provides a few sample ideas, but should not be considered exhaustive:
- How new or changed election laws affect the size and makeup of the pool of registered voters and the federal, state, and/or local electorates;
- Professionalization (or the lack thereof) and the quality of election administration;
- Evaluating the impact of voting centers, consolidated precincts, and convenience voting;
- How election reform has differentially impacted historically disadvantaged segments of the electorate;
- The analytical and methodological tools needed to work with voter registration and voter history files, and challenges in making causal inferences when working with these files;
- New methods for connecting other behavioral records (e.g. survey data) or geospatial data with voter history and voter turnout data
Airfare, lodging, and conference meals will be covered for paper presenters and discussants. Other scholars are welcome to attend if they can cover conference costs (details to be announced within a month).
Lonna Atkeson, University of New Mexico, and Bernard Fraga, Indiana University, will serve as program co-chairs, and Paul Gronke, Reed College and Phil Keisling, Center for Public Service at PSU, will act as conference organizers and hosts.
Paper proposals of no more than 250 words should be submitted by April 15, 2017. Submit proposals at http://bit.ly/PDXelection – we expect to announce decisions by May 1. Any questions can be sent to firstname.lastname@example.org, email@example.com, or firstname.lastname@example.org.
Scholars wishing to attend without presenting a paper should also contact Emily Hebbron (email@example.com) by May 1st. Further details about the conference will posted on the conference Web site soon thereafter.
Please feel free to re-distribute this announcement to relevant individuals and e-mail lists. We look forward to reading your paper proposals!
Lonna Atkeson, Bernard Fraga, and Paul Gronke
From the email:
The Election Law Program (ELP) is a joint project of William & Mary Law School and the National Center for State Courts. ELP develops resources to assist judges in understanding the unique challenges election litigation presents. ELP is seeking a full-time Program Manager to help oversee and execute Program projects.
The Election Law Program Manager will be responsible for implementing a grant to expand ELP tools and resources, including the eBenchbook project. The Program Manager will be responsible for conducting legal research, coordinating with state election law experts and officials, and supervising student research. The Program Manager will also participate in strategic planning processes and oversee implementation of ELP projects.
Bachelor’s degree required; JD strongly preferred
Demonstrated legal research experience; non-profit experience a plus.
Demonstrated strong communication and organizational skills, with excellent attention to detail.
Demonstrated ability to work independently and to manage multiple tasks.
Demonstrated experience in project management.
Previous experience supervising others.
Background in election law/election administration preferred
Strong graphic design, computer and web skills a plus.
More information about this position is available here and here. Please contact Rebecca Green at firstname.lastname@example.org with questions.
Secretary of State Dennis Richardson continues to try to make a mark, this time by creating a task force to study the feasibility of an independent redistricting commission in the state. Richardson has penned an op-ed about the effort published by Pamplin Media.
More information as this process moves forward.
Always love reposting that image!
(This is a guest posting from Nick Solomon, Reed College senior in Mathematics)
One of our first assignments in our Election Sciences course was to take a look at the Oregon Motor Voter data and try and tease out any patterns we could find in it.
I’ve always been interested in geographic statistics, so I decided to examine Oregon counties. This can be especially valuable because geography tends to to be a good proxy for making inferences about demographic variables we might not have access to, like income, race, or education level (none of these are accessible via the Oregon statewide voter registration file).
The figure displays party of registration among citizens registered via OMV. It’s important to remember when looking at the graphic that the OMV process initially categorizes all citizens as “NAV” (non-affiliated voters), and citizens must return a postcard designating a party. As of January 2017, as shown on the left, 78% of registrants did not return the card, and only 11% decided to select a party.
The county by county totals are fascinating. OMV voters constitute the highest percentage of registered voters in Malheur county. Many readers may recognize the name–the Malheur National Wildlife Refuge was the site of a 41 day standoff between law enforcement and a small group of occupiers.
Malheur is located in the farthest southeast corner of the state. It’s rural, relatively poor, and much more Republican than the rest of the state. John McCain received 69% of the vote in Malheur in 2008.
In an upcoming blog post, another student will be posting a map of this county by county visualization, and it’s apparent that a number of rural counties have high percentages of OMV registrants.
At the recommendation of a few experts who looked at the graphic I decided to examine the percentage of OMV voters by county versus the total number of registered voters. This lets us get a sense of whether Malheur is an outlier caused by a very small sample size making the percentage value overly sensitive or if this is a number that we can trust.
Here, the total number of voters is plotted on a log scale, as many counties have smaller numbers of voters, while the Portland metro area has many more.
The log scale allows us to get a better sense of any relationship between number of voters and percent registered by OMV without the few large numbers dominating the plot.
This graphic shows that there are quite a few counties of similar size to Malheur, and some that are even smaller. Furthermore, we see that Malheur is not very far from other counties of its size.
Finally, to my eye, there seems to be no meaningful relationship between these two variables, so I find myself concluding that Malheur county, along with Umatilla and Morrow and Curry and Coos are experiencing a much greater benefit in access to voter registration than some larger, more urban counties.
For those interested, these graphics were made with R and ggplot2. I’ll be posting on my personal blog with more details about how I made them.
Eventually, I hope to learn more I was also curious about hoe OMV might be affecting party turnout at the polls. Keep tuned for future updates!
Over the next few weeks, I hope to be featuring on this blog postings from students who are in a new course being offered at Reed College: Data Sciences / Election Sciences.
The course is a collaborative effort with Andrew Bray, a statistics professor at Reed College, and is partially supported by a Student Digital Research grant from the Andrew Mellon Foundation (more information about the grant and the projects it has supported at Reed is contained at this website.)
What better question to have these young scientists answer than the impact of Oregon’s innovative automatic registration system, aptly named “Oregon Motor Voter”? We hope to go beyond the reports provided by the Oregon Secretary of State to understand not just who is being registered via OMV, but who votes as a result of the law, and how the electorate has changed, and may change in the future, as a consequence of this reform.
Step one is going to be a set of pretty data visualizations to whet the appetite. Expect more very soon!
A number of Northeast states are considering adding or expanding early voting, according to a story in The Hill.
I hope that administrators and legislators in the states make sure they make a decision based on comprehensive and accurate information and not rely on anecdote.
Most importantly, early voting has a complicated relationship to overall voter turnout. Most studies show a small but positive relationship, though one prominent study reports a negative relationship. If you put in more early voting locations, more citizens vote early (but it’s not clear if more voters overall cast a ballot).
Jan Leighley and Jonathan Nagler put it best in a recent blog posting (in the context of voter registration laws): higher turnout depends mostly on parties and candidates, not on changes to voting laws.
The point? New Hampshire Secretary of State Bill Gardner is quoted in the story and his statement reflects many common misconceptions about early voting:
“We’re seeing turnout nationally go down in each of the last three elections even as more and more states rush to make it easier to vote by having early voting,”
Misconception 1: there has been no “rush” to add early voting options since 2008. The rate of states adding early voting provisions has slowed substantially as we get down the final 13 holdouts (according to the National Conference of State Legislatures, 37 states plus DC offered some form of early voting in 2016, compared to 36 plus DC in 2012, and 34 in 2008).
Misconception 2: turnout has not declined for the last three cycles. Final totals in 2016 appear to be slightly up from 2012 and about 2% lower than 2008.
Misconception 3: national turnout is the best way to understand the impact of state and local laws. National totals disguise enormous variation in turnout between and within states, competitiveness in statewide races, and differences in rules and laws. There is also some scattered evidence that early voting benefits some subpopulations more than others, and this can be overlooked in national and even statewide totals.
The second point in the article is harder to address: the costs of early voting. Michael McDonald suggests that there is resistance to early voting in the Northeast because most of these states administer elections at the township level. McDonald is right to highlight the importance of providing sufficient funding to jurisdictions to conduct elections, regardless of what options are offered (budgets were the most common point of discussion at a recent NCSL gathering).
All I’d add here is that we don’t have a clear sense of how much early voting costs, and whether cost savings can be obtained by strategically reallocating resources between early voting and election day voting (though mis-forecasts of voting turnout can turn disastrous).
The takeaway is that states considering adding early voting options should consider them mostly on the grounds of voter convenience, on how well the options can be adapted to the conditions faced by local jurisdictions, and only lastly on how they may increase overall turnout.
From today’s Oregonian. I don’t even remember giving that quote!
Nice job by Betsey Hammond. More on OMV in the upcoming weeks, watch this space!
The Hawaii Elections Commission is meeting this week to discuss problems with mail-in ballots in recent elections, and discuss an initiative to move to all by-mail elections in the state (this will require action by the legislature).
It’s hard to get details from this short news story, but one thing seems clear: the state needs to require local officials to notify voters when their ballots are invalidated due to missing or non-matching signatures.
Those provisions are in place in Oregon (Statutory reference 254.431 Special procedure for ballots challenged due to failure to sign return envelope or nonmatching signature; public record limitation), and Washington, and Colorado.
It may not have been endorsed yet as a “best practice” by an appropriate committee of election experts, but a follow up if the signature verification fails certainly is standard practice in the three states that conduct elections fully by mail.
Nate Silver’s posted a quick analysis today that purports to show that “education, not income, predicted who would vote for Trump.” This is a pretty important finding, if true, because it stands in contrast to a lot of post-election analysis that claims that Democratic abandonment of the white working class played a large role in Clinton’s defeat.
It would also be an impressive finding before the major academic surveys, such as the National Election Study and the Cooperative Congressional Election Study are released, since they are the gold standard in terms of helping us understand how individual demographic and attitudes predict vote choice.
Silver takes a smart cut at the income and education relationship by partitioning up counties by their median income and percent college degree, and then comparing the 2016 Clinton vote to the 2012 Obama vote. Here he can show in some of the educated counties, Clinton did remarkably well.
He acknowledges that income and education are highly correlated, however, so he takes a different cut at the data, looking at a set of counties with relatively high educational levels and only moderate income levels.
Silver describes one of these counties, Ingham County, MI, where he grew up, as
…home to Michigan State University and the state capital of Lansing, along with a lot of auto manufacturing jobs (though fewer than there used to be). The university and government jobs attract an educated workforce, but there aren’t a lot of rich people in Ingham County.
Clinton, he notes, did quite well there, even though incomes aren’t that high. In most places that fit this description, according to Silver, Clinton did quite well. This is evidence, he argues, that education, not income, was the driving force in the 2016 election.
Anyone who is familiar with higher education should immediately recognize this list. If you don’t, you will in a moment.
Silver notes that “many of the counties on the list are home to major colleges or universities, although there are some exceptions.” He notes Davidson County, TN and Buncombe County, NC as not “really college towns.”
Not exactly. Below I list the major universities in each of these counties. Silver hit the jackpot with his list: every single one is home to a large university, in most cases, a huge university.
It’s true that college students as a percentage of total residents is pretty small in Davidson, TN and Buncombe, NC, but I’d be pretty skeptical to generalize anything from the homes of country music, bluegrass music–what Silver calls “cultural havens” (Missoula MT and New Hanover NC fit into that category as well).
But the rest? Almost all are college counties. Most are home to huge institutions that are the dominant cultural and economic force in those counties. Of course they have a combination of high education levels and modest income levels. That’s life as a student and employee of a university! It’s no wonder that Clinton did relatively better in those counties.
This list may be indicative of the kind of cultural divide that Silver speculates about at the end of the essay. I’m far less certain that this reveals anything systematic about the relationship of income and education and vote choice more broadly.
I posted on my Reed College introductory politics class “Moodle”. I shared this on Facebook and getting a lot of requests to share more broadly. Any questions about the class readings and other references below, please email email@example.com.
I’ve spent the day trying to absorb and understand the election results, and I thought it might help to provide a list of resources where I am going to try to reason through this. I certainly don’t mind, and I’m sure Chris would not mind, if people want to talk, or rant, or celebrate, or protest.
We are not suggesting that you should be dispassionate or apolitical about the election outcome. I handle unexpected political changes by doing by best to deconstruct it and understand it. That’s my makeup. It need not be yours. Do what you will with below.
1) The 50,000 Foot Look:
I still think the best place to look and reflect is at a site that allows you to drill down to the county level, and compare vote changes from 2012. I prefer the NY Times, but I list a number of other sites below. Click through at these sites to see the various maps.
The best interactive maps in my opinion at the NY Times: http://www.nytimes.com/elections/results/president
Great mix of maps and exit polls at BBC: http://www.bbc.com/news/election-us-2016-37889032
USA Today does a better job displaying change in support http://www.usatoday.com/…/intera…/how-the-election-unfolded/
CNN has a different look and feel, not my choice but has very nice individual state results http://www.cnn.com/election/results
2) This election is a game changer and this election is a realignment
Most of the evidence is that this election reinforced the existing divisions between the two parties. What was surprising to many observers was that more Republicans did not abandon their party standard bearer, given a lack of endorsements and many leaders distancing themselves from Trump. If you are able to ignore that for a moment, Trump’s support coalition looks nearly identical to Romney’s. Clinton underperformed Obama, especially among African Americans and Latinos. That’s the election in a nutshell.
Larry Bartels at the Monkey Cage examines election 2016 https://www.washingtonpost.com/…/2016-was-an-ordinary-elec…/
3) What about race, ethnicity, gender? Didn’t the horrible things Trump said make a difference?
You know from our class that voters decide based on a wide variety of things–partisanship most importantly, then issues (mostly the economy), and then finally candidate characteristics. It has never been the case that candidate characteristics are the most important consideration. And it is often the case that attitudes about particular “single issues” can overwhelm everything else. While the things Trump said may matter a lot to you, you can’t expect that those same things matter to other people, who may believe in very different things and have very different life experiences. We won’t be able to answer this question in detail for a few months, but I suspect we are going to find not that many Trump voters did not completely ignore the things he said, but they heavily discounted them because of other concerns. And for another big chunk, race and ethnicity in particular get bound up with fear and discontent. That, unfortunately, is very common in the human condition.
This graphic from the NY Times summarizes Trump and Clinton support, compared to elections back to 2004, among key demographics. You may want to look at this first before following up on the links below.http://www.nytimes.com/…/…/elections/exit-poll-analysis.html
3a) On Gender: Clinton simply did not benefit much from her gender, at least that’s what the evidence indicates. Gender identity is very different from racial solidarity, so expecting the gender effect in 2016 to function like the race effect in 2012 and 2008 was probably wishful thinking, no matter how much gender identity may matter to you.
Michael Tesler at the Monkey Cage, with extensive citations to past work on the comparative weakness of gender identity.https://www.washingtonpost.com/…/why-the-gender-gap-doomed…/
3b) On Ethnicity (primarily Latinos): Evidence is far more mixed. The finding you are seeing in the press is that Trump received 29% of the Latino vote, which exceeds Romney’s margin by 9%. However, others are disputing this finding, critiquing the way the exit polls are conducted. This one will be debated for a while.
Matt Barreto of UCLA and Latino Decisions (and older brother of a recent Reed alum in political science) runs down why he thinks the exit polls overestimate Trump support among Latinos http://www.latinodecisions.com/…/the-rundown-on-latino-vot…/ (UPDATE: Nice article at the Monkey Cage.)
3c) On African American support: Clinton did not do as well as Obama among African Americans. If the 88% number holds, that’s down 5% from 2012. But what appears to have been more damaging is lower turnout overall, and this really hurt in states like Florida, Michigan, North Carolina, and Pennsylvania.
Politico story on the number of African Americans in Florida who voted early in 2012 and did not in 2016, citing the work of political scientist Daniel Smith of University of Florida. http://www.politico.com/…/clinton-campaign-struggles-in-get…
Analysis of the exit poll data by political scientists Stanley Feldman and Melissa Herrman http://www.cbsnews.com/…/cbs-news-exit-polls-how-donald-tr…/
4) What about the polls and the forecasts? Does this indicate that polling and statistical forecasting is junk?
It may not surprise that my answer is “no.” There was a systematic miss for the polls, and consequently the forecasts, and the misses were all in red states. If the models were junk, they would have missed in the blue states as well. That means there was something going on in the red states that was missed by the political observers and political scientists who obviously need to scrutinize what they are doing. But your own fundamentals based forecasts predicted Clinton’s vote almost precisely, as did at least two of the forecasts in PS. Something is seriously amiss about Trump support, but there’s no evidence (yet) that there is something seriously amiss about the fundamental underpinnings of election science.
Andrew Gelman does a nice job showing the consistent miss in red states http://andrewgelman.com/…/polls-just-fine-blue-states-blew…/
Gelman shows how a comparatively small yet systematic 2% shift in support toward Trump appears to explain virtually all the “misses.”http://andrewgelman.com/…/11/09/explanations-shocking-2-sh…/
See also Nate Silver http://fivethirtyeight.com/…/what-a-difference-2-percentag…/
Silver reminds us (as you read in his book) that there is a tendency among people to refuse to acknowledge the meaning of uncertainty and probability. He’s to blame as much as anyone for producing these seemingly precise forecasts, but he’s not to blame for reporters and citizens not understanding uncertainty.